The 100 most-cited articles in Scientific Reports, 2024

Research on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not familiar with statistics, it might be difficult to understand how to evaluate the performance of ML models and compare them with each other. Here, we introduce the most common evaluation metrics used for the typical supervised ML tasks including binary, multi-class, and multi-label classification, regression, image segmentation, object detection, and information retrieval. We explain how to choose a suitable statistical test for comparing models, how to obtain enough values of the metric for testing, and how to perform the test and interpret its results. We also present a few practical examples about comparing convolutional neural networks used to classify X-rays with different lung infections and detect cancer tumors in positron emission tomography images.

Personalized interventions are deemed vital given the intricate characteristics, advancement, inherent genetic composition, and diversity of cardiovascular diseases (CVDs). The appropriate utilization of artificial intelligence (AI) and machine learning (ML) methodologies can yield novel understandings of CVDs, enabling improved personalized treatments through predictive analysis and deep phenotyping. In this study, we proposed and employed a novel approach combining traditional statistics and a nexus of cutting-edge AI/ML techniques to identify significant biomarkers for our predictive engine by analyzing the complete transcriptome of CVD patients. After robust gene expression data pre-processing, we utilized three statistical tests (Pearson correlation, Chi-square test, and ANOVA) to assess the differences in transcriptomic expression and clinical characteristics between healthy individuals and CVD patients. Next, the recursive feature elimination classifier assigned rankings to transcriptomic features based on their relation to the case-control variable. The top ten percent of…

The novelty of this article lies in introducing a novel stochastic technique named the Hippopotamus Optimization (HO) algorithm. The HO is conceived by drawing inspiration from the inherent behaviors observed in hippopotamuses, showcasing an innovative approach in metaheuristic methodology. The HO is conceptually defined using a trinary-phase model that incorporates their position updating in rivers or ponds, defensive strategies against predators, and evasion methods, which are mathematically formulated. It attained the top rank in 115 out of 161 benchmark functions in finding optimal value, encompassing unimodal and high-dimensional multimodal functions, fixed-dimensional multimodal functions, as well as the CEC 2019 test suite and CEC 2014 test suite dimensions of 10, 30, 50, and 100 and Zigzag Pattern benchmark functions, this suggests that the HO demonstrates a noteworthy proficiency in both exploitation and exploration. Moreover, it effectively balances exploration and exploitation, supporting the search process. In light of the results from addressing four distinct…

Matching the language or content of a message to the psychological profile of its recipient (known as "personalized persuasion") is widely considered to be one of the most effective messaging strategies. We demonstrate that the rapid advances in large language models (LLMs), like ChatGPT, could accelerate this influence by making personalized persuasion scalable. Across four studies (consisting of seven sub-studies; total N = 1788), we show that personalized messages crafted by ChatGPT exhibit significantly more influence than non-personalized messages. This was true across different domains of persuasion (e.g., marketing of consumer products, political appeals for climate action), psychological profiles (e.g., personality traits, political ideology, moral foundations), and when only providing the LLM with a single, short prompt naming or describing the targeted psychological dimension. Thus, our findings are among the first to demonstrate the potential for LLMs to automate, and thereby scale, the use of personalized persuasion in ways that enhance its effectiveness and efficiency.…

Abstract Artificial intelligence (AI) has become an integral part of many contemporary technologies, such as social media platforms, smart devices, and global logistics systems. At the same time, research on the public acceptance of AI shows that many people feel quite apprehensive about the potential of such technologies—an observation that has been connected to both demographic and sociocultural user variables (e.g., age, previous media exposure). Yet, due to divergent and often ad-hoc measurements of AI-related attitudes, the current body of evidence remains inconclusive. Likewise, it is still unclear if attitudes towards AI are also affected by users’ personality traits. In response to these research gaps, we offer a two-fold contribution. First, we present a novel, psychologically informed questionnaire (ATTARI-12) that captures attitudes towards AI as a single construct, independent of specific contexts or applications. Having observed good reliability and validity for our new measure across two studies ( N 1 = 490; N 2 = 150), we examine several personality traits—the Big…

Heavy metal ions can be introduced into the water through several point and non-point sources including leather industry, coal mining, agriculture activity and domestic waste. Regrettably, these toxic heavy metals may pose a threat to both humans and animals, particularly when they infiltrate water and soil. Heavy metal poisoning can lead to many health complications, such as liver and renal dysfunction, dermatological difficulties, and potentially even malignancies. To mitigate the risk of heavy metal ion exposure to humans and animals, it is imperative to extract them from places that have been polluted. Several conventional methods such as ion exchange, reverse osmosis, ultrafiltration, membrane filtration and chemical precipitation have been used for the removal of heavy metal ions. However, these methods have high operation costs and generate secondary pollutants during water treatment. Biosorption is an alternative approach to eliminating heavy metals from water that involves employing eco-friendly and cost-effective biomass. This review is focused on the heavy metal ions…

Abstract Artificial intelligence models, like ChatGPT, have the potential to revolutionize higher education when implemented properly. This study aimed to investigate the factors influencing university students’ attitudes and usage of ChatGPT in Arab countries. The survey instrument “TAME-ChatGPT” was administered to 2240 participants from Iraq, Kuwait, Egypt, Lebanon, and Jordan. Of those, 46.8% heard of ChatGPT, and 52.6% used it before the study. The results indicated that a positive attitude and usage of ChatGPT were determined by factors like ease of use, positive attitude towards technology, social influence, perceived usefulness, behavioral/cognitive influences, low perceived risks, and low anxiety. Confirmatory factor analysis indicated the adequacy of the “TAME-ChatGPT” constructs. Multivariate analysis demonstrated that the attitude towards ChatGPT usage was significantly influenced by country of residence, age, university type, and recent academic performance. This study validated “TAME-ChatGPT” as a useful tool for assessing ChatGPT adoption among university students.…

As the burgeoning field of Artificial Intelligence (AI) continues to permeate the fabric of healthcare, particularly in the realms of patient surveillance and telemedicine, a transformative era beckons. This manuscript endeavors to unravel the intricacies of recent AI advancements and their profound implications for reconceptualizing the delivery of medical care. Through the introduction of innovative instruments such as virtual assistant chatbots, wearable monitoring devices, predictive analytic models, personalized treatment regimens, and automated appointment systems, AI is not only amplifying the quality of care but also empowering patients and fostering a more interactive dynamic between the patient and the healthcare provider. Yet, this progressive infiltration of AI into the healthcare sphere grapples with a plethora of challenges hitherto unseen. The exigent issues of data security and privacy, the specter of algorithmic bias, the requisite adaptability of regulatory frameworks, and the matter of patient acceptance and trust in AI solutions demand immediate and thoughtful…

Artificial intelligence-powered deep learning methods are being used to diagnose brain tumors with high accuracy, owing to their ability to process large amounts of data. Magnetic resonance imaging stands as the gold standard for brain tumor diagnosis using machine vision, surpassing computed tomography, ultrasound, and X-ray imaging in its effectiveness. Despite this, brain tumor diagnosis remains a challenging endeavour due to the intricate structure of the brain. This study delves into the potential of deep transfer learning architectures to elevate the accuracy of brain tumor diagnosis. Transfer learning is a machine learning technique that allows us to repurpose pre-trained models on new tasks. This can be particularly useful for medical imaging tasks, where labelled data is often scarce. Four distinct transfer learning architectures were assessed in this study: ResNet152, VGG19, DenseNet169, and MobileNetv3. The models were trained and validated on a dataset from benchmark database: Kaggle. Five-fold cross validation was adopted for training and testing. To enhance the balance…

The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model's superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the…

Climate change affects plant growth, food production, ecosystems, sustainable socio-economic development, and human health. The different artificial intelligence models are proposed to simulate climate parameters of Jinan city in China, include artificial neural network (ANN), recurrent NN (RNN), long short-term memory neural network (LSTM), deep convolutional NN (CNN), and CNN-LSTM. These models are used to forecast six climatic factors on a monthly ahead. The climate data for 72 years (1 January 1951-31 December 2022) used in this study include monthly average atmospheric temperature, extreme minimum atmospheric temperature, extreme maximum atmospheric temperature, precipitation, average relative humidity, and sunlight hours. The time series of 12 month delayed data are used as input signals to the models. The efficiency of the proposed models are examined utilizing diverse evaluation criteria namely mean absolute error, root mean square error (RMSE), and correlation coefficient (R). The modeling result inherits that the proposed hybrid CNN-LSTM model achieves a greater accuracy…

Current human population growth along Earth's coasts is on a collision path with anticipated consequences of increasing natural and anthropogenic induced coastal hazards. Using recently-available ambient, dasymetric data, we developed methods to estimate annual continental and global coastal populations from (2000-2018) measured horizontally from the shoreline inward. We found: (1) large concentrations of population in relatively small bands and regions along the coast (~ 2 billion within 50 km and ~ 1 billion within 10 km); (2) higher growth rates of coastal population than inland population (an addition of 463 million within 50 km and 233 million within 10 km); (3) strong influence of distance from the coast to predict population distribution; and (4) that macro population patterns and growth could be expressed and modeled as a power function at continental and global levels. Findings point to emerging macro population patterns along the coast as contributing to increasing anthropogenic effects on Earth systems and increasing human risks associated with sea-level rise, land…

The emergence of publicly accessible artificial intelligence (AI) large language models such as ChatGPT has given rise to global conversations on the implications of AI capabilities. Emergent research on AI has challenged the assumption that creative potential is a uniquely human trait thus, there seems to be a disconnect between human perception versus what AI is objectively capable of creating. Here, we aimed to assess the creative potential of humans in comparison to AI. In the present study, human participants (N = 151) and GPT-4 provided responses for the Alternative Uses Task, Consequences Task, and Divergent Associations Task. We found that AI was robustly more creative along each divergent thinking measurement in comparison to the human counterparts. Specifically, when controlling for fluency of responses, AI was more original and elaborate. The present findings suggest that the current state of AI language models demonstrate higher creative potential than human respondents.

Sentiment analysis is an essential task in natural language processing that involves identifying a text's polarity, whether it expresses positive, negative, or neutral sentiments. With the growth of social media and the Internet, sentiment analysis has become increasingly important in various fields, such as marketing, politics, and customer service. However, sentiment analysis becomes challenging when dealing with foreign languages, particularly without labelled data for training models. In this study, we propose an ensemble model of transformers and a large language model (LLM) that leverages sentiment analysis of foreign languages by translating them into a base language, English. We used four languages, Arabic, Chinese, French, and Italian, and translated them using two neural machine translation models: LibreTranslate and Google Translate. Sentences were then analyzed for sentiment using an ensemble of pre-trained sentiment analysis models: Twitter-Roberta-Base-Sentiment-Latest, bert-base-multilingual-uncased-sentiment, and GPT-3, which is an LLM from OpenAI. Our experimental…

Large language models (LLMs) have the potential to transform our lives and work through the content they generate, known as AI-Generated Content (AIGC). To harness this transformation, we need to understand the limitations of LLMs. Here, we investigate the bias of AIGC produced by seven representative LLMs, including ChatGPT and LLaMA. We collect news articles from The New York Times and Reuters, both known for their dedication to provide unbiased news. We then apply each examined LLM to generate news content with headlines of these news articles as prompts, and evaluate the gender and racial biases of the AIGC produced by the LLM by comparing the AIGC and the original news articles. We further analyze the gender bias of each LLM under biased prompts by adding gender-biased messages to prompts constructed from these news headlines. Our study reveals that the AIGC produced by each examined LLM demonstrates substantial gender and racial biases. Moreover, the AIGC generated by each LLM exhibits notable discrimination against females and individuals of the Black race. Among the LLMs,…

Alzheimer's disease (AD) is a debilitating neurodegenerative disorder that requires accurate diagnosis for effective management and treatment. In this article, we propose an architecture for a convolutional neural network (CNN) that utilizes magnetic resonance imaging (MRI) data from the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset to categorize AD. The network employs two separate CNN models, each with distinct filter sizes and pooling layers, which are concatenated in a classification layer. The multi-class problem is addressed across three, four, and five categories. The proposed CNN architecture achieves exceptional accuracies of 99.43%, 99.57%, and 99.13%, respectively. These high accuracies demonstrate the efficacy of the network in capturing and discerning relevant features from MRI images, enabling precise classification of AD subtypes and stages. The network architecture leverages the hierarchical nature of convolutional layers, pooling layers, and fully connected layers to extract both local and global patterns from the data, facilitating accurate…

In 2023 Amazonia experienced both historical drought and warm conditions. On October 26th 2023 the water levels at the port of Manaus reached its lowest record since 1902 (12.70 m). In this region, October monthly maximum and minimum temperature anomalies also surpassed previous record values registered in 2015 (+ 3 °C above the normal considering the 1981-2020 average). Here we show that this historical dry and warm situation in Amazonia is associated with two main atmospheric mechanisms: (i) the November 2022-February 2023 southern anomaly of vertical integrated moisture flux (VIMF), related to VIMF divergence and extreme rainfall deficit over southwestern Amazonia, and (ii) the June-August 2023 downward motion over northern Amazonia related to extreme rainfall deficit and warm conditions over this region. Anomalies of both atmospheric mechanisms reached record values during this event. The first mechanism is significantly correlated to negative sea surface temperature (SST) anomalies in the equatorial Pacific (November-February La Niña events). The second mechanism is…

The pollution of soil and aquatic systems by inorganic and organic chemicals has become a global concern. Economical, eco-friendly, and sustainable solutions are direly required to alleviate the deleterious effects of these chemicals to ensure human well-being and environmental sustainability. In recent decades, biochar has emerged as an efficient material encompassing huge potential to decontaminate a wide range of pollutants from soil and aquatic systems. However, the application of raw biochars for pollutant remediation is confronting a major challenge of not getting the desired decontamination results due to its specific properties. Thus, multiple functionalizing/modification techniques have been introduced to alter the physicochemical and molecular attributes of biochars to increase their efficacy in environmental remediation. This review provides a comprehensive overview of the latest advancements in developing multiple functionalized/modified biochars via biological and other physiochemical techniques. Related mechanisms and further applications of multiple modified biochar…

Brain-computer interface (BCI) technology bridges the direct communication between the brain and machines, unlocking new possibilities for human interaction and rehabilitation. EEG-based motor imagery (MI) plays a pivotal role in BCI, enabling the translation of thought into actionable commands for interactive and assistive technologies. However, the constrained decoding performance of brain signals poses a limitation to the broader application and development of BCI systems. In this study, we introduce a convolutional Transformer network (CTNet) designed for EEG-based MI classification. Firstly, CTNet employs a convolutional module analogous to EEGNet, dedicated to extracting local and spatial features from EEG time series. Subsequently, it incorporates a Transformer encoder module, leveraging a multi-head attention mechanism to discern the global dependencies of EEG's high-level features. Finally, a straightforward classifier module comprising fully connected layers is followed to categorize EEG signals. In subject-specific evaluations, CTNet achieved remarkable decoding…

Abstract The main aim of this study is to determine the physical and chemical properties of biochar synthesized from different materials (straw rice, sawdust, sugar cane, and tree leaves) at different pyrolysis temperatures (400, 600, and 800 °C). The physical and chemical properties such as moisture content, water holding capacity, bulk density, and porosity; and pH, electrical conductivity (EC), organic matter, organic carbon, total nitrogen, potassium, phosphorus, calcium, magnesium, sodium, and sulfur were determined, respectively. The results show that the biochar yield decreased with increasing pyrolysis temperature, and the values of the analyzed properties varied depending on the type of biochar and pyrolysis temperature. The moisture content ranged from 1.11 to 4.18%, and the water holding capacity ranged from 12.9 to 27.6 g water g −1 dry sample. The highest value of bulk density (211.9 kg m −3 ) was obtained from sawdust at a pyrolysis temperature of 800 °C. The porosity values ranged from 45.9 to 63.7%. The highest values of pH and EC (10.4 and 3.46 dS m −1 ) were…

Abstract Water pollution, arising from the presence of toxic dyes and chemicals, is a global challenge, urging the need for eco-friendly solutions in water decontamination. This study focused on the synthesis of copper oxide nanoparticles (CuO NPs), zinc oxide nanoparticles (ZnO NPs), and a bimetallic CuO-ZnO nanocomposite (CZ NC) through an environmentally friendly method employing Tragia involucrata L. leaf extract. Comprehensive analysis of structural and optical properties involved using various analytical techniques such as XRD, FT-IR, XPS, UV-DRS, PL, FE-SEM, EDAX, TEM, SAED, zeta potential, TGA, and BET. In comparison to pristine CuO and ZnO NPs, the CZ-NC demonstrated notably enhanced photocatalytic activity in the degradation of Rhodamine B dye (RhB). The optimum conditions for RhB degradation were found to be a pH of 9 and a catalyst dosage of 1 mg/mL for a concentration of 10 ppm. Under these conditions, CuO NPs, ZnO NPs, and CZ-NC demonstrated high efficiencies of 78%, 83%, and 96.1% respectively over 105 min. Through LC-HRMS, the identification of degradation products…

Health is very important for human life. In particular, the health of the brain, which is the executive of the vital resource, is very important. Diagnosis for human health is provided by magnetic resonance imaging (MRI) devices, which help health decision makers in critical organs such as brain health. Images from these devices are a source of big data for artificial intelligence. This big data enables high performance in image processing classification problems, which is a subfield of artificial intelligence. In this study, we aim to classify brain tumors such as glioma, meningioma, and pituitary tumor from brain MR images. Convolutional Neural Network (CNN) and CNN-based inception-V3, EfficientNetB4, VGG19, transfer learning methods were used for classification. F-score, recall, imprinting and accuracy were used to evaluate these models. The best accuracy result was obtained with VGG16 with 98%, while the F-score value of the same transfer learning model was 97%, the Area Under the Curve (AUC) value was 99%, the recall value was 98%, and the precision value was 98%. CNN…

The future of the food system on the planet is increasingly facing uncertainties that are attributable to population growth and a surge in demand for nutritious food. Traditional agricultural practices are poised to place strain on production, as well as natural resources and ecosystem services provided, particularly under a changing climate. Given their remarkable attributes, including a low environmental footprint, high food conversion ratio, rapid growth and nutritional values, edible insects can play a vital role in the global food system. Nonetheless, substantial knowledge gaps persist regarding their diversity, global distribution, and shared characteristics across regions, potentially impeding effective scaling and access to edible insects. Therefore, we compiled and analysed the fragmented database on edible insects and identified potential drivers that elucidate insect consumption, globally, focusing on promoting a sustainable food system. We collated data from various sources, including the literature for a list of edible insect species, the Global Biodiversity Information…

In the current global context of environmental degradation and resource constraints, the pursuit of sustainable development has become an imperative. One avenue that holds promise for achieving this objective is the application of digital technologies, which have the potential to decouple economic growth from its carbon footprint. However, it is crucial to ensure that these technologies are designed and governed in a prudent manner, with a strong alignment to environmental priorities. This study focuses on exploring the potential roles of blockchain and artificial intelligence (AI) in supply chain coordination and impact mitigation. Furthermore, they have the capacity to incentivize recycling and circular business models, as well as facilitate carbon accounting and offsetting. To fully realize these benefits, it is essential to deploy these technologies within inclusive collaborative frameworks that take into consideration social and ecological considerations. The study also offers policy recommendations that highlight key leverage points for digital innovation, enabling countries…

The early diagnosis of Alzheimer's disease (AD) presents a significant challenge due to the subtle biomarker changes often overlooked. Machine learning (ML) models offer a promising tool for identifying individuals at risk of AD. However, current research tends to prioritize ML accuracy while neglecting the crucial aspect of model explainability. The diverse nature of AD data and the limited dataset size introduce additional challenges, primarily related to high dimensionality. In this study, we leveraged a dataset obtained from the National Alzheimer's Coordinating Center, comprising 169,408 records and 1024 features. After applying various steps to reduce the feature space. Notably, support vector machine (SVM) models trained on the selected features exhibited high performance when tested on an external dataset. SVM achieved a high F1 score of 98.9% for binary classification (distinguishing between NC and AD) and 90.7% for multiclass classification. Furthermore, SVM was able to predict AD progression over a 4-year period, with F1 scores reached 88% for binary task and 72.8% for…

Internet of Things (IoT) integration in healthcare improves patient care while also making healthcare delivery systems more effective and economical. To fully realize the advantages of IoT in healthcare, it is imperative to overcome issues with data security, interoperability, and ethical considerations. IoT sensors periodically measure the health-related data of the patients and share it with a server for further evaluation. At the server, different machine learning algorithms are applied which help in early diagnosis of diseases and issue alerts in case vital signs are out of the normal range. Different cyber attacks can be launched on IoT devices which can result in compromised security and privacy of applications such as health care. In this paper, we utilize the publicly available Canadian Institute for Cybersecurity (CIC) IoT dataset to model machine learning techniques for efficient detection of anomalous network traffic. The dataset consists of 33 types of IoT attacks which are divided into 7 main categories. In the current study, the dataset is pre-processed, and a balanced…

This comprehensive review investigates the growing adoption of electric vehicles (EVs) as a practical solution for environmental concerns associated with fossil fuel usage in mobility. The increasing demand for EVs underscores the critical importance of establishing efficient, fast-charging infrastructure, especially from the standpoint of the electrical power grid. The review systematically examines the planning strategies and considerations for deploying electric vehicle fast charging stations. It emphasizes their unique dual role as loads and storage units, intricately linked to diverse road and user constraints. Furthermore, the review underscores the significant opportunity surrounding these stations for the integration of distributed renewable energy sources. It thoroughly explores the challenges and opportunities intrinsic to the planning and localization process, providing insights into the complexities associated with these multifaceted stations. Renewable resources, including wind and solar energy, are investigated for their potential in powering these charging stations,…

In the field of engineering systems-particularly in underground drilling and green stormwater management-real-time predictions are vital for enhancing operational performance, ensuring safety, and increasing efficiency. Addressing this niche, our study introduces a novel LSTM-transformer hybrid architecture, uniquely specialized for multi-task real-time predictions. Building on advancements in attention mechanisms and sequence modeling, our model integrates the core strengths of LSTM and Transformer architectures, offering a superior alternative to traditional predictive models. Further enriched with online learning, our architecture dynamically adapts to variable operational conditions and continuously incorporates new field data. Utilizing knowledge distillation techniques, we efficiently transfer insights from larger, pretrained networks, thereby achieving high predictive accuracy without sacrificing computational resources. Rigorous experiments on sector-specific engineering datasets validate the robustness and effectiveness of our approach. Notably, our model exhibits clear…

Abstract Graphitic carbon nitride (g-C3N4), an organic semiconductor-based photocatalyst was reported to have several beneficial properties to be used in wastewater treatment applications. However, g-C3N4, in its bulk form has poor photocatalytic degradation efficiency due to its inherent limitations such as poor specific surface area and fast electron-hole pair recombination rate. In this study, we have tuned the physiochemical properties of bulk g-C3N4 by direct thermal exfoliation and examined their photocatalytic degradation efficiency against abundant textile dyes such as methylene blue (MB), methyl orange (MO), and rhodamine B (RhB). The degradation efficiencies for MB, MO, and RhB dyes are 92 ± 0.18%, 93 ± 0.31%, and 95 ± 0.4% respectively in 60 min of UV light irradiation. The degradation efficiency increases with an increase in the exfoliation temperature. The prepared catalysts were characterized using FTIR, XRD, FE-SEM, EDAX, BET, and UV-DRS, to confirm their material structure, phase, surface morphology, elemental composition, specific surface area and optical properties…

Abstract Photocatalysts have developed into a successful strategy for degrading synthetic and organic toxins, such as chemicals and dyes, in wastewater. In this study, graphene oxide was reduced at different temperatures and used for degrading indigo carmine and neutral red dyes. The wide surface areas, strong adsorption sites, and oxygen functionalities of reduced graphene oxide (rGO) at 250 °C (rGO-250) produced more photocatalytic degradation efficiency and adsorption percentage. The catalyst dosage, initial dye concentration, solution pH and recyclability were all used to optimize the photocatalytic activity of rGO-250. This research presents a capable nano-adsorbent photocatalyst for the efficient degradation of organic dyes. GO and rGOs were also investigated for carbon dioxide (CO2) absorption properties. Results showed that rGO-250 has better CO2 adsorption properties than other rGOs. Overall, it was observed that rGO-250 has better photocatalytic and CO2 adsorption capabilities compared to graphene oxide reduced at different temperatures.

Abstract With the recent success of lipid nanoparticle (LNP) based SARS-CoV-2 mRNA vaccines, the potential for RNA therapeutics has gained widespread attention. LNPs are promising non-viral delivery vectors to protect and deliver delicate RNA therapeutics, which are ineffective and susceptible to degradation alone. While food and drug administration (FDA) approved formulations have shown significant promise, benchmark lipid formulations still require optimization and improvement. In addition, the translatability of these formulations for several different RNA cargo sizes has not been compared under the same conditions. Herein we analyze “gold standard” lipid formulations for encapsulation efficiency of various non-specific RNA cargo lengths representing antisense oligonucleotides (ASO), small interfering RNA (siRNA), RNA aptamers, and messenger RNA (mRNA), with lengths of 10 bases, 21 base pairs, 96 bases, 996 bases, and 1929 bases, respectively. We evaluate encapsulation efficiency as the percentage of input RNA encapsulated in the final LNP product (EE input %), which shows…

Depression has been reported as one of the most prevalent psychiatric illnesses globally. This study aimed to obtain information on the global burden of depression and its associated spatiotemporal variation, by exploring the correlation between the global burden of depression and the social development index (SDI) and associated risk factors. Using data from the Global Burden of Disease study from 1990 to 2019, we described the prevalence and burden of disease in 204 countries across 21 regions, including sex and age differences and the relationship between the global disease burden and SDI. The age-standardized rate and estimated annual percentage change were used to assess the global burden of depression. Individuals with documented depression globally ranged from 182,183,358 in 1990 to 290,185,742 in 2019, representing an increase of 0.59%. More patients experienced major depressive disorder than dysthymia. The incidence and disability-adjusted life years of depression were the highest in the 60-64 age group and much higher in females than in males, with this trend occurring…

Abstract In the present study, ZnO nanoparticles have been synthesized using an aqueous extract of shilajit. The nanoparticles were characterized using different techniques such as UV (ultraviolet–visible spectrophotometer), FTIR (Fourier transform infrared), XRD (X-ray diffraction), particle size analysis, SEM (scanning electron microscope) and EDAX (Energy-dispersive X-ray) analysis. The UV absorption peak at 422.40 nm was observed for ZnO nanoparticles. SEM analysis showed the shape of nanoparticles to be spherical, FTIR spectrum confirmed the presence of zinc atoms, particle size analysis showed the nanoparticle size, EDAX confirmed the purity of ZnO nanoparticles whereas XRD pattern similar to that of JCPDS card for ZnO confirmed the presence of pure ZnO nanoparticles. The in vitro anticancer activity of ZnO nanoparticles against the HeLa cell line showed the IC 50 value of 38.60 μg/mL compared to reference standard cisplatin. This finding confirms that ZnO nanoparticles from shilajit extract have potent cytotoxic effect on human cervical cancer cell lines.

The pooling layer in convolutional neural networks plays a crucial role in reducing spatial dimensions, and improving computational efficiency. However, standard pooling operations such as max pooling or average pooling are not suitable for all applications and data types. Therefore, developing custom pooling layers that can adaptively learn and extract relevant features from specific datasets is of great significance. In this paper, we propose a novel approach to design and implement customizable pooling layers to enhance feature extraction capabilities in CNNs. The proposed T-Max-Avg pooling layer incorporates a threshold parameter T, which selects the K highest interacting pixels as specified, allowing it to control whether the output features of the input data are based on the maximum values or weighted averages. By learning the optimal pooling strategy during training, our custom pooling layer can effectively capture and represent discriminative information in the input data, thereby improving classification performance. Experimental results show that the proposed T-Max-Avg…

In this paper, we propose hybrid consensus algorithms that combine machine learning (ML) techniques to address the challenges and vulnerabilities in blockchain networks. Consensus Protocols make ensuring agreement among the applicants in the distributed systems difficult. However, existing mechanisms are more vulnerable to cyber-attacks. Previous studies extensively explore the influence of cyber attacks and highlight the necessity for effective preventive measures. This research presents the integration of ML techniques with the proposed hybrid consensus algorithms and advantages over predicting cyber-attacks, anomaly detection, and feature extraction. Our hybrid approaches leverage and optimize the proposed consensus protocols' security, trust, and robustness. However, this research also explores the various ML techniques with hybrid consensus algorithms, such as Delegated Proof of Stake Work (DPoSW), Proof of Stake and Work (PoSW), Proof of CASBFT (PoCASBFT), Delegated Byzantine Proof of Stake (DBPoS) for security enhancement and intelligent decision making in consensus…

Abstract Graphene oxide (GO) is a monolayer of oxidized graphene which is a convenient and potential candidate in a wide range of fields of applications like electronics, photonics, optoelectronics, energy storage, catalysis, chemical sensors, and many others. GO is often composed of various oxygen-containing groups such as hydroxyl, carboxyl, and epoxy. One appealing method for achieving graphene-like behavior with sp 2 hybridized carbon is the reduction of GO i.e. formation of reduced graphene oxide (RGO). A stepwise reduction GO to form a family of RGO, containing various quantities of oxygen-related defects was carried out. Herein, the defects related chemical and physical properties of GO and the RGO family were studied and reported in an effort to understand how the properties of RGO vary with the reduction rate. Although there are several reports on various features and applications of GO and RGO but a systematic investigation of the variation of the physical and chemical properties in RGO with the varying quantities of oxygeneous defects is imperative for the engineered…

In recent years, major advances in artificial intelligence (AI) have led to the development of powerful AI systems for use in the field of nutrition in order to enhance personalized dietary recommendations and improve overall health and well-being. However, the lack of guidelines from nutritional experts has raised questions on the accuracy and trustworthiness of the nutritional advice provided by such AI systems. This paper aims to address this issue by introducing a novel AI-based nutrition recommendation method that leverages the speed and explainability of a deep generative network and the use of novel sophisticated loss functions to align the network with established nutritional guidelines. The use of a variational autoencoder to robustly model the anthropometric measurements and medical condition of users in a descriptive latent space, as well as the use of an optimizer to adjust meal quantities based on users' energy requirements enable the proposed method to generate highly accurate, nutritious and personalized weekly meal plans. Coupled with the ability of ChatGPT to…

Industrial advancements and utilization of large amount of fossil fuels, vehicle pollution, and other calamities increases the Air Quality Index (AQI) of major cities in a drastic manner. Major cities AQI analysis is essential so that the government can take proper preventive, proactive measures to reduce air pollution. This research incorporates artificial intelligence in AQI prediction based on air pollution data. An optimized machine learning model which combines Grey Wolf Optimization (GWO) with the Decision Tree (DT) algorithm for accurate prediction of AQI in major cities of India. Air quality data available in the Kaggle repository is used for experimentation, and major cities like Delhi, Hyderabad, Kolkata, Bangalore, Visakhapatnam, and Chennai are considered for analysis. The proposed model performance is experimentally verified through metrics like R-Square, RMSE, MSE, MAE, and accuracy. Existing machine learning models, like k-nearest Neighbor, Random Forest regressor, and Support vector regressor, are compared with the proposed model. The proposed model attains better…

The consumption of water constitutes the physical health of most of the living species and hence management of its purity and quality is extremely essential as contaminated water has to potential to create adverse health and environmental consequences. This creates the dire necessity to measure, control and monitor the quality of water. The primary contaminant present in water is Total Dissolved Solids (TDS), which is hard to filter out. There are various substances apart from mere solids such as potassium, sodium, chlorides, lead, nitrate, cadmium, arsenic and other pollutants. The proposed work aims to provide the automation of water quality estimation through Artificial Intelligence and uses Explainable Artificial Intelligence (XAI) for the explanation of the most significant parameters contributing towards the potability of water and the estimation of the impurities. XAI has the transparency and justifiability as a white-box model since the Machine Learning (ML) model is black-box and unable to describe the reasoning behind the ML classification. The proposed work uses various…

Abstract Plant breeding is gaining importance as a sustainable tool to address the challenges posed by a growing global population and enhance food security. Advanced high-throughput omics technologies are utilized to accelerate crop improvement and develop resilient varieties with higher yield performance. These technologies generate vast genetic data, which can be exploited to manipulate key plant characteristics for crop improvement. The integration of big data and AI in plant breeding has the potential to revolutionize the field and increase food security. By using branching data (phenotype) of 1918 soybean accessions and 42k SNP polymorphic data (genotype), this study systematically compared 11 non-linear regression AI models, including four deep learning models (DBN regression, ANN regression, Autoencoders regression, and MLP regression) and seven machine learning models (e.g., SVR, XGBoost regression, Random Forest regression, LightGBM regression, GPS regression, Decision Tree regression, and Polynomial regression). After being evaluated by four valuation metrics: R2…

This study aimed to explore the interplay between physical activity, self-efficacy, stress self-management and mental health among adolescents. The study gathered data from an online survey conducted among 400 Chinese middle school students (mean age = 13.74 years). The collected data were analyzed using SPSS 27.0 and PROCESS 4.1. The findings indicated a positive and significant relationship between physical activity, self-efficacy, stress self-management, and mental health. Notably, the association between physical activity and mental health was entirely mediated by self-efficacy and stress self-management. Moreover, self-efficacy and stress self-management exhibited a chain mediation effect on the relationship between physical activity and mental health. It is suggested that interventions focusing on physical activity should prioritize strategies for enhancing students' self-efficacy and stress self-management skills as integral components of promoting adolescents' mental health. Future research should delve into identifying specific types of physical activities that have a…

Recent studies have indicated potential health risks associated with microplastics (MPs) exposure, including alterations in blood coagulation homeostasis. This cross-sectional study aimed to quantitatively examine MPs in human blood and assess their association with coagulation markers. We recruited 36 healthy adults, collected whole blood samples, and analyzed MPs using Fourier-transform infrared (µ-FTIR) spectroscopy. Lifestyle factors related to MP exposure were assessed, such as the use of plastic food containers. Coagulation and inflammatory markers in blood samples were analyzed, including C-reactive protein, prothrombin time, activated partial prothrombin time (aPTT), antithrombin III, platelet count, erythrocyte sedimentation rate, and fibrinogen. MPs were detected in 88.9% of the participants, with a mean concentration of 4.2 MPs/mL. The predominant types of plastics identified were polystyrene and polypropylene. MPs were significantly higher in participants with a greater use of plastic food containers. A high MP load in the blood (≥ 3 MPs/mL) was significantly correlated…

The emergence of generative AI technologies has led to an increasing number of people collaborating with AI to produce creative works. Across two experimental studies, in which we carefully designed and programmed state-of-the-art human-AI interfaces, we examine how the design of generative AI systems influences human creativity (poetry writing). First, we find that people were most creative when writing a poem on their own, compared to first receiving a poem generated by an AI system and using sophisticated tools to edit it (Study 1). Following this, we demonstrate that this creativity deficit dissipates when people co-create with-not edit-AI and establish creative self-efficacy as an important mechanism in this process (Study 2). Thus, our findings indicate that people must occupy the role of a co-creator, not an editor, to reap the benefits of generative AI in the production of creative works.

Abstract Developing stable and highly efficient metal oxide photocatalysts remains a significant challenge in managing organic pollutants. In this study, zinc oxide nanoparticles (ZnO NPs) were successfully synthesized using various plant extracts, pomegranate (P.M), beetroot roots (B.S), and seder, along with a chemical process. The produced ZnO NPs were characterized using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), ultraviolet–visible spectroscopy (UV–Vis), Field Emission Scanning Electron Microscope (FESEM), High-Resolution Transmission Electron Microscopy (HRTEM), and Surface Area. For all prepared samples, the results indicated that the composition of the plant extract affects several characteristics of the produced particles, such as their photocatalytic properties, energy bandgap (E g ), particle size, and the ratio of the two intensity (0 0 2) and (1 0 0) crystalline planes. The particle size of the produced NPs varies between 20 and 30 nm. To examine NPs' photocatalytic activity in the presence of UV light, Methyl Orange (MO) was utilized. The…

A Wireless Sensor Network (WSN) aided by the Internet of Things (IoT) is a collaborative system of WSN systems and IoT networks are work to exchange, gather, and handle data. The primary objective of this collaboration is to enhance data analysis and automation to facilitate improved decision-making. Securing IoT with the assistance of WSN necessitates the implementation of protective measures to confirm the safety and reliability of the interconnected WSN and IoT components. This research significantly advances the current state of the art in IoT and WSN security by synergistically harnessing the potential of machine learning and the Firefly Algorithm. The contributions of this work are twofold: firstly, the proposed FA-ML technique exhibits an exceptional capability to enhance intrusion detection accuracy within the WSN-IoT landscape. Secondly, the amalgamation of the Firefly Algorithm and machine learning introduces a novel dimension to the domain of security-oriented optimization techniques. The implications of this research resonate across various sectors, ranging from critical…

One of the critical issues in medical data analysis is accurately predicting a patient's risk of heart disease, which is vital for early intervention and reducing mortality rates. Early detection allows for timely treatment and continuous monitoring by healthcare providers, which is essential but often limited by the inability of medical professionals to provide constant patient supervision. Early detection of cardiac problems and continuous patient monitoring by physicians can help reduce death rates. Doctors cannot constantly have contact with patients, and heart disease detection is not always accurate. By offering a more solid foundation for prediction and decision-making based on data provided by healthcare sectors worldwide, machine learning (ML) could help physicians with the prediction and detection of HD. This study aims to use different feature selection strategies to produce an accurate ML algorithm for early heart disease prediction. We have chosen features using chi-square, ANOVA, and mutual information methods. The three feature groups chosen were SF-1, SF-2, and SF-3.…

Abstract Multi-drug resistant (MDR) bacteria are considered a serious public health threat. Also, increasing rate of resistance to anticancer drugs, as well as their toxicity, is another point of concern. Therefore, the new antibacterial and anticancer agents are always needed. The synthesizing silver nanoparticles (AgNPs) using medicinal plants, is an effective approach for developing novel antibacterial and anticancer agents. Rubus discolor , a native species of the Caucasus region, produces leaves that are typically discarded as a by-product of raspberry production. The present study has focused on optimizing the green synthesis of AgNPs using R. discolor leaves extract through response surface methodology. The optimal values for AgNPs synthesis were an AgNO 3 concentration of 7.11 mM, a time of 17.83 h, a temperature of 56.51 °C, and an extract percentage of 29.22. The production of AgNPs was confirmed using UV–visible spectroscopy (λ max at 456.01 nm). TEM analysis revealed well-dispersed AgNPs (an average size of 37 nm). The XRD analysis confirmed the crystalline structure.…

Due to global climate change, crops are certainly confronted with a lot of abiotic and biotic stress factors during their growth that cause a serious threat to their development and overall productivity. Among different abiotic stresses, salt and drought are considered the most devastating stressors with serious impact on crop's yield stability. Here, the current study aimed to elucidate how melatonin works in regulating plant biomass, oxidative stress, antioxidant defense system, as well as the expression of genes related to salt and drought stress in rice plants. Eight groups of rice plants (3 replicates, 5 plants each) underwent varied treatments: control, melatonin, salt, drought, salt + drought, salt + melatonin, drought + melatonin, and salt + drought + melatonin. Melatonin (100 µM) was alternately applied a week before stress exposure; salt stress received 100 mM NaCl every 3 days for 3 weeks, and drought stress involved 10% PEG. Young leaves were randomly sampled from each group. The results showed that melatonin treatment markedly reduces salt and drought stress damage by…

Cervical cancer, the second most prevalent cancer affecting women, arises from abnormal cell growth in the cervix, a crucial anatomical structure within the uterus. The significance of early detection cannot be overstated, prompting the use of various screening methods such as Pap smears, colposcopy, and Human Papillomavirus (HPV) testing to identify potential risks and initiate timely intervention. These screening procedures encompass visual inspections, Pap smears, colposcopies, biopsies, and HPV-DNA testing, each demanding the specialized knowledge and skills of experienced physicians and pathologists due to the inherently subjective nature of cancer diagnosis. In response to the imperative for efficient and intelligent screening, this article introduces a groundbreaking methodology that leverages pre-trained deep neural network models, including Alexnet, Resnet-101, Resnet-152, and InceptionV3, for feature extraction. The fine-tuning of these models is accompanied by the integration of diverse machine learning algorithms, with ResNet152 showcasing exceptional performance,…

Abstract This study assessed the environmental and health risks associated with heavy metals in the water resources of Egypt's northwestern desert. The current approaches included the Spearman correlation matrix, principal component analysis, and cluster analysis to identify pollution sources and quality-controlling factors. Various indices (HPI, MI, HQ, HI, and CR) were applied to evaluate environmental and human health risks. Additionally, the Monte Carlo method was employed for probabilistic carcinogenic and non-carcinogenic risk assessment via oral and dermal exposure routes in adults and children. Notably, all water resources exhibited high pollution risks with HPI and MI values exceeding permissible limits (HPI > 100 and MI > 6), respectively. Furthermore, HI oral values indicated significant non-carcinogenic risks to both adults and children, while dermal contact posed a high risk to 19.4% of samples for adults and 77.6% of samples for children (HI > 1). Most water samples exhibited CR values exceeding 1 × 10 –4 for Cd, Cr, and Pb, suggesting vulnerability to carcinogenic…

This large community-based cohort study investigates the impact of glucagon-like peptide-1 receptor agonists (GLP-1 RAs), specifically Liraglutide and Semaglutide, on the risk of developing psychiatric conditions such as depression, anxiety, and suicidal behaviors in patients with obesity. Utilizing post-marketing data, this research compares patients prescribed GLP-1 RAs (cases) with those not taking these medications (controls). The analysis spanned data from January 1, 2015, to December 31, 2023. To minimize selection bias, we employed 1:1 propensity score matching to account for demographic factors such as age, sex, race, and comorbidities. After matching, the study included 162,253 case and control patients. This study showed a significant association between GLP-1 RA treatment and an 98% increased risk of any psychiatric disorders. Notably, patients on GLP-1 RAs exhibited a 195% higher risk of major depression, a 108% increased risk for anxiety, and a 106% elevated risk for suicidal behavior. These findings underscore the critical need for physicians to thoroughly assess…

Abstract The qualitative and quantitative assessment of groundwater is one of the important aspects for determining the suitability of potable water. Therefore, the present study has been performed to evaluate the groundwater quality for Achhnera block in the city of Taj, Agra, India, where groundwater is an important water resource. The groundwater samples, 50 in number were collected and analyzed for major ions along with some important trace element. This study has further investigated for the applicability of groundwater quality index (GWQI), and the principal component analysis (PCA) to mark out the major geochemical solutes responsible for origin and release of geochemical solutes into the groundwater. The results confirm that, majority of the collected groundwater samples were alkaline in nature. The variation of concentration of anions in collected groundwater samples were varied in the sequence as, HCO 3− > Cl − > SO4 2− > F − while in contrast the sequence of cations in the groundwater as Na > Ca > Mg > K. The Piper diagram demonstrated the major hydro chemical facies…

Abstract The treatment of methylene blue (MB) dye wastewater through the adsorption process has been a subject of extensive research. However, a comprehensive understanding of the thermodynamic aspects of dye solution adsorption is lacking. Previous studies have primarily focused on enhancing the adsorption capacity of methylene blue dye. This study aimed to develop an environmentally friendly and cost-effective method for treating methylene blue dye wastewater and to gain insights into the thermodynamics and kinetics of the adsorption process for optimization. An adsorbent with selective methylene blue dye adsorption capabilities was synthesized using rice straw as the precursor. Experimental studies were conducted to investigate the adsorption isotherms and models under various process conditions, aiming to bridge gaps in previous research and enhance the understanding of adsorption mechanisms. Several adsorption isotherm models, including Langmuir, Temkin, Freundlich, and Langmuir–Freundlich, were applied to theoretically describe the adsorption mechanism. Equilibrium…

Pneumonia is a widespread and acute respiratory infection that impacts people of all ages. Early detection and treatment of pneumonia are essential for avoiding complications and enhancing clinical results. We can reduce mortality, improve healthcare efficiency, and contribute to the global battle against a disease that has plagued humanity for centuries by devising and deploying effective detection methods. Detecting pneumonia is not only a medical necessity but also a humanitarian imperative and a technological frontier. Chest X-rays are a frequently used imaging modality for diagnosing pneumonia. This paper examines in detail a cutting-edge method for detecting pneumonia implemented on the Vision Transformer (ViT) architecture on a public dataset of chest X-rays available on Kaggle. To acquire global context and spatial relationships from chest X-ray images, the proposed framework deploys the ViT model, which integrates self-attention mechanisms and transformer architecture. According to our experimentation with the proposed Vision Transformer-based framework, it achieves a…

High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of…

This paper investigated the use of language models and deep learning techniques for automating disease prediction from symptoms. Specifically, we explored the use of two Medical Concept Normalization-Bidirectional Encoder Representations from Transformers (MCN-BERT) models and a Bidirectional Long Short-Term Memory (BiLSTM) model, each optimized with a different hyperparameter optimization method, to predict diseases from symptom descriptions. In this paper, we utilized two distinct dataset called Dataset-1, and Dataset-2. Dataset-1 consists of 1,200 data points, with each point representing a unique combination of disease labels and symptom descriptions. While, Dataset-2 is designed to identify Adverse Drug Reactions (ADRs) from Twitter data, comprising 23,516 rows categorized as ADR (1) or Non-ADR (0) tweets. The results indicate that the MCN-BERT model optimized with AdamP achieved 99.58% accuracy for Dataset-1 and 96.15% accuracy for Dataset-2. The MCN-BERT model optimized with AdamW performed well with 98.33% accuracy for Dataset-1 and 95.15% for Dataset-2, while the BiLSTM…

Abstract Concrete compressive strength (CS) is a crucial performance parameter in concrete structure design. Reliable strength prediction reduces costs and time in design and prevents material waste from extensive mixture trials. Machine learning techniques solve structural engineering challenges such as CS prediction. This study used Machine Learning (ML) models to enhance the prediction of CS, analyzing 1030 experimental CS data ranging from 2.33 to 82.60 MPa from previous research databases. The ML models included both non-ensemble and ensemble types. The non-ensemble models were regression-based, evolutionary, neural network, and fuzzy-inference-system. Meanwhile, the ensemble models consisted of adaptive boosting, random forest, and gradient boosting. There were eight input parameters: cement, blast-furnace-slag, aggregates (coarse and fine), fly ash, water, superplasticizer, and curing days, with the CS as the output. Comprehensive performance evaluations include visual and quantitative methods and k-fold cross-validation to assess the study’s reliability and accuracy. A…

Parkinson's Disease (PD) is a prevalent neurological condition characterized by motor and cognitive impairments, typically manifesting around the age of 50 and presenting symptoms such as gait difficulties and speech impairments. Although a cure remains elusive, symptom management through medication is possible. Timely detection is pivotal for effective disease management. In this study, we leverage Machine Learning (ML) and Deep Learning (DL) techniques, specifically K-Nearest Neighbor (KNN) and Feed-forward Neural Network (FNN) models, to differentiate between individuals with PD and healthy individuals based on voice signal characteristics. Our dataset, sourced from the University of California at Irvine (UCI), comprises 195 voice recordings collected from 31 patients. To optimize model performance, we employ various strategies including Synthetic Minority Over-sampling Technique (SMOTE) for addressing class imbalance, Feature Selection to identify the most relevant features, and hyperparameter tuning using RandomizedSearchCV. Our experimentation reveals that the FNN and KSVM…

Abstract Lead (Pb) is toxic to the development and growth of rice plants. Nanoparticles (NPs) have been considered one of the efficient remediation techniques to mitigate Pb stress in plants. Therefore, a study was carried out to examine the underlying mechanism of iron (Fe) and silicon (Si) nanoparticle-induced Pb toxicity alleviation in rice seedlings. Si–NPs (2.5 mM) and Fe-NPs (25 mg L −1 ) were applied alone and in combination to rice plants grown without (control; no Pb stress) and with (100 µM) Pb concentration. Our results revealed that Pb toxicity severely affected all rice growth-related traits, such as inhibited root fresh weight (42%), shoot length (24%), and chlorophyll b contents (26%). Moreover, a substantial amount of Pb was translocated to the above-ground parts of plants, which caused a disturbance in the antioxidative enzyme activities. However, the synergetic use of Fe- and Si–NPs reduced the Pb contents in the upper part of plants by 27%. It reduced the lethal impact of Pb on roots and shoots growth parameters by increasing shoot length (40%), shoot fresh weight…

Cardiovascular diseases (CVDs) continue to be the leading cause of more than 17 million mortalities worldwide. The early detection of heart failure with high accuracy is crucial for clinical trials and therapy. Patients will be categorized into various types of heart disease based on characteristics like blood pressure, cholesterol levels, heart rate, and other characteristics. With the use of an automatic system, we can provide early diagnoses for those who are prone to heart failure by analyzing their characteristics. In this work, we deploy a novel self-attention-based transformer model, that combines self-attention mechanisms and transformer networks to predict CVD risk. The self-attention layers capture contextual information and generate representations that effectively model complex patterns in the data. Self-attention mechanisms provide interpretability by giving each component of the input sequence a certain amount of attention weight. This includes adjusting the input and output layers, incorporating more layers, and modifying the attention processes to collect relevant…

As AI-generated text continues to evolve, distinguishing it from human-authored content has become increasingly difficult. This study examined whether non-expert readers could reliably differentiate between AI-generated poems and those written by well-known human poets. We conducted two experiments with non-expert poetry readers and found that participants performed below chance levels in identifying AI-generated poems (46.6% accuracy, χ 2 (1, N = 16,340) = 75.13, p < 0.0001). Notably, participants were more likely to judge AI-generated poems as human-authored than actual human-authored poems (χ 2 (2, N = 16,340) = 247.04, p < 0.0001). We found that AI-generated poems were rated more favorably in qualities such as rhythm and beauty, and that this contributed to their mistaken identification as human-authored. Our findings suggest that participants employed shared yet flawed heuristics to differentiate AI from human poetry: the simplicity of AI-generated poems may be easier for non-experts to understand, leading them to prefer AI-generated poetry and misinterpret the complexity of…

Abstract Despite the widespread use of titanium dioxide (TiO 2 ) in photocatalytic applications, its inherent limitations, such as low efficiency under visible light and rapid recombination of electron-hole pairs, hinder its effectiveness in environmental remediation. This study presents a comparative investigation of TiO 2 -based composites, including TiO 2 /ZrO 2 , ZnO, Ta 2 O 3 , SnO, Fe 2 O 3 , and CuO, aiming to assess their potential for enhancing photocatalytic applications. Photocatalysis holds promise in environmental remediation, water purification, and energy conversion, with TiO 2 being a prominent photocatalyst. To improve efficiency and broaden applicability, various metal oxide composites have been explored. Composites were synthesized and characterized using techniques such as XRD, SEM, TEM, and zeta potential analysis to evaluate their structural and morphological properties. Photocatalytic performance was assessed by degrading herbicide Imazapyr under UV illumination. Results revealed that, the photo-activity of all prepared composites were more effective than the…

Chronic kidney disease (CKD) is a major worldwide health problem, affecting a large proportion of the world's population and leading to higher morbidity and death rates. The early stages of CKD sometimes present without visible symptoms, causing patients to be unaware. Early detection and treatments are critical in reducing complications and improving the overall quality of life for people afflicted. In this work, we investigate the use of an explainable artificial intelligence (XAI)-based strategy, leveraging clinical characteristics, to predict CKD. This study collected clinical data from 491 patients, comprising 56 with CKD and 435 without CKD, encompassing clinical, laboratory, and demographic variables. To develop the predictive model, five machine learning (ML) methods, namely logistic regression (LR), random forest (RF), decision tree (DT), Naïve Bayes (NB), and extreme gradient boosting (XGBoost), were employed. The optimal model was selected based on accuracy and area under the curve (AUC). Additionally, the SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable…

Abstract This study assesses the relationships between vegetation dynamics and climatic variations in Pakistan from 2000 to 2023. Employing high-resolution Landsat data for Normalized Difference Vegetation Index (NDVI) assessments, integrated with climate variables from CHIRPS and ERA5 datasets, our approach leverages Google Earth Engine (GEE) for efficient processing. It combines statistical methodologies, including linear regression, Mann–Kendall trend tests, Sen's slope estimator, partial correlation, and cross wavelet transform analyses. The findings highlight significant spatial and temporal variations in NDVI, with an annual increase averaging 0.00197 per year (p < 0.0001). This positive trend is coupled with an increase in precipitation by 0.4801 mm/year (p = 0.0016). In contrast, our analysis recorded a slight decrease in temperature (− 0.01011 °C/year, p < 0.05) and a reduction in solar radiation (− 0.27526 W/m 2 /year, p < 0.05). Notably, cross-wavelet transform analysis underscored significant coherence between NDVI and climatic factors, revealing periods of synchronized…

The emerging expanding scope of the Internet of Things (IoT) necessitates robust intrusion detection systems (IDS) to mitigate security risks effectively. However, existing approaches often struggle with adaptability to emerging threats and fail to account for IoT-specific complexities. To address these challenges, this study proposes a novel approach by hybridizing convolutional neural network (CNN) and gated recurrent unit (GRU) architectures tailored for IoT intrusion detection. This hybrid model excels in capturing intricate features and learning relational aspects crucial in IoT security. Moreover, we integrate the feature-weighted synthetic minority oversampling technique (FW-SMOTE) to handle imbalanced datasets, which commonly afflict intrusion detection tasks. Validation using the IoTID20 dataset, designed to emulate IoT environments, yields exceptional results with 99.60% accuracy in attack detection, surpassing existing benchmarks. Additionally, evaluation on the network domain dataset, UNSW-NB15, demonstrates robust performance with 99.16% accuracy, highlighting the…

Crop diseases can significantly affect various aspects of crop cultivation, including crop yield, quality, production costs, and crop loss. The utilization of modern technologies such as image analysis via machine learning techniques enables early and precise detection of crop diseases, hence empowering farmers to effectively manage and avoid the occurrence of crop diseases. The proposed methodology involves the use of modified MobileNetV3Large model deployed on edge device for real-time monitoring of grape leaf disease while reducing computational memory demands and ensuring satisfactory classification performance. To enhance applicability of MobileNetV3Large, custom layers consisting of two dense layers were added, each followed by a dropout layer, helped mitigate overfitting and ensured that the model remains efficient. Comparisons among other models showed that the proposed model outperformed those with an average train and test accuracy of 99.66% and 99.42%, with a precision, recall, and F1 score of approximately 99.42%. The model was deployed on an edge device (Nvidia Jetson…

Waste from construction and demolition (also known as CDW) is one of the most harmful environmental issues. This study's primary goal is to produce new mortar and brick materials from recycled concrete powder (RCP) and recycled brick powder (RBP), two of the most popular CDW. Geopolymeric mortar and brick samples were produced by passing RCP and RBP through sieve No. 50 (with sand filler if necessary) and combining them with an alkaline solution made of water glass (WG) and NaOH. In this study, the mixture was then cured for three days at 80 °C in an oven. The effects of filler, RBP amount, WG amount, and the concentration of NaOH alkaline solution on the samples' strength were examined. Additionally, XRF and SEM/XRD tests were performed to verify the materials' composition and microstructure. The mechanical strength of the samples showed an increase with the increase of RCP values, so the brick sample with filler showed the highest compressive strength, measuring 59.53 MPa. The study's samples exhibited strong mechanical properties. Additionally, all of the bricks' water absorption…

High-precision and high-efficiency Semantic segmentation of high-resolution remote sensing images is a challenge. Existing models typically require a significant amount of training data to achieve good classification results and have numerous training parameters. A novel model called MST-DeepLabv3+ was suggested in this paper for remote sensing image classification. It's based on the DeepLabv3+ and can produce better results with fewer train parameters. MST-DeepLabv3+ made three improvements: (1) Reducing the number of model parameters by substituting MobileNetV2 for the Xception in the DeepLabv3+'s backbone network. (2) Adding the attention mechanism module SENet to increase the precision of semantic segmentation. (3) Increasing Transfer Learning to enhance the model's capacity to recognize features, and raise the segmentation accuracy. MST-DeepLabv3+ was tested on international society for photogrammetry and remote sensing (ISPRS) dataset, Gaofen image dataset (GID), and practically applied to the Taikang cultivated land dataset. On the ISPRS dataset, the mean intersection over…

Hypothyroidism is characterized by thyroid hormone deficiency and has adverse effects on both pregnancy and fetal health. Chat Generative Pre-trained Transformer (ChatGPT) is a large language model trained with a very large database from many sources. Our study was aimed to evaluate the reliability and readability of ChatGPT-4 answers about hypothyroidism in pregnancy. A total of 19 questions were created in line with the recommendations in the latest guideline of the American Thyroid Association (ATA) on hypothyroidism in pregnancy and were asked to ChatGPT-4. The reliability and quality of the responses were scored by two independent researchers using the global quality scale (GQS) and modified DISCERN tools. The readability of ChatGPT was assessed used Flesch Reading Ease (FRE) Score, Flesch-Kincaid grade level (FKGL), Gunning Fog Index (GFI), Coleman-Liau Index (CLI), and Simple Measure of Gobbledygook (SMOG) tools. No misleading information was found in any of the answers. The mean mDISCERN score of the responses was 30.26 ± 3.14; the median GQS score was 4 (2-4). In terms of…

Heterozygosity-rich regions (HRR) are genomic regions of high heterozygosity, which may harbor loci related to key functional traits such as immune response, survival rate, fertility, and other fitness traits. This study considered 30 Italian and 19 worldwide goat breeds genotyped with the Illumina GoatSNP50k BeadChip. The aim of the work was to study inter-breed relationships and HRR patterns using Sliding Window (SW) and Consecutive Runs (CR) detection methods. Genetic relationships highlighted a clear separation between non-European and European breeds, as well as the north-south geographic cline within the latter. The Pearson correlation coefficients between the descriptive HRR parameters obtained with the SW and CR methods were higher than 0.9. A total of 166 HRR islands were detected. CHI1, CHI11, CHI12 and CHI18 were the chromosomes harboring the highest number of HRR islands. The genes annotated in the islands were linked to various factors such as productive, reproductive, immune, and environmental adaptation mechanisms. Notably, the Montecristo feral goat showed the…

Abstract Biochar is an eco-friendly carbon material whose properties allow it to be used as a sorbent for wastewater treatment or soil remediation. The paper presents the results of research related to the pyrolysis process of apple waste after supercritical CO 2 extraction with the simultaneous use of physical activation. The research assessed the influence of the temperature of the pyrolysis process and steam activation on the structural properties of the obtained biochar, i.e. specific surface, porous structure, and presence of functional groups. The results obtained confirmed that lower temperature pyrolysis produces biochar characterised by the presence of functional groups and ordered structure. On the other hand, high temperature pyrolysis with simultaneous steam activation determines microporosity and high values of the specific surface area. Taking into consideration pollutant sorption mechanisms (physical and chemical sorption), the obtained biochar materials can be used as sorbents in water and wastewater treatment.

The Mediterranean region has long been identified as a climate change hotspot. However, within the Mediterranean, there are smaller sub-areas that exhibit a higher risk of climate change and extremes. Previous research has often focused on indices based on mean climate values, yet extremes are typically more impactful on humans and ecosystems. This study aims to identify the most vulnerable sub-areas of the Mediterranean as climate change hotspots using two indices: the newly introduced Mediterranean Hotspot Index (MED-HOT) and the well-defined Regional Climate Change Index (RCCI). The MED-HOT focuses on extreme high maximum and minimum temperatures, rainfall, and drought, while RCCI assesses changes in mean climate conditions. By combining these indices, we provide an identification of Mediterranean hotspots, capturing both mean climate shifts and extremes. The spatiotemporal variation of both indices across the Mediterranean region is presented and the 20 subregions are categorized into distinct groups. The results reveal that the southeastern Mediterranean is at high risk…

Cancer is one of the most concerning public health issues and breast cancer is one of the most common cancers in the world. The immune cells within the tumor microenvironment regulate cancer development. In this study, single immune cell data sets were used to identify marker gene sets for exhausted CD8 + T cells (CD8Tex) in breast cancer. Machine learning methods were used to cluster subtypes and establish the prognostic models with breast cancer bulk data using the gene sets to evaluate the impacts of CD8Tex. We analyzed breast cancer overexpressing and survival-associated marker genes and identified CD8Tex hub genes in the protein-protein-interaction network. The relevance of the hub genes for CD8 + T-cells in breast cancer was evaluated. The clinical associations of the hub genes were analyzed using bulk sequencing data and spatial sequencing data. The pan-cancer expression, survival, and immune association of the hub genes were analyzed. We identified biomarker gene sets for CD8Tex in breast cancer. CD8Tex-based subtyping systems and prognostic models performed well in the…

Rapid placement of electric vehicle charging stations (EVCSs) is essential for the transportation industry in response to the growing electric vehicle (EV) fleet. The widespread usage of EVs is an essential strategy for reducing greenhouse gas emissions from traditional vehicles. The focus of this study is the challenge of smoothly integrating Plug-in EV Charging Stations (PEVCS) into distribution networks, especially when distributed photovoltaic (PV) systems are involved. A hybrid Genetic Algorithm and Simulated Annealing method (GA-SAA) are used in the research to strategically find the optimal locations for PEVCS in order to overcome this integration difficulty. This paper investigates PV system situations, presenting the problem as a multicriteria task with two primary objectives: reducing power losses and maintaining acceptable voltage levels. By optimizing the placement of EVCS and balancing their integration with distributed generation, this approach enhances the sustainability and reliability of distribution networks.

Previous research has found that parenting style influences academic resilience. Nonetheless, few studies have focused on the mechanism underlying the relationship between parenting style and academic resilience. This study aims to examine the relationship between adolescents' parenting style and academic resilience, drawing upon the framework of Social Cognitive Theory. Specifically, it wants to explore the mediating roles of self-efficacy and academic motivation in this relationship. The participants were 518 students chosen at random from educational institutions in the Chinese provinces of Zhejiang, Shanghai, and Jiangsu. Social Cognitive Theory was the theoretical foundation for the study, and the Parental Authority Questionnaire was used to measure parenting style. Out of the respondents, 55.5% were male and 45.5% female. The student allocation in the study sample was as follows: 62.34% undergraduate, 28.22% master's, and 9.44% PhD. More than 60% of participants were over 25 years old. Moreover, the findings revealed that parenting style was directly and positively related to…

Activated carbon (AC) and ZnO@AC composite derived from wood sawdust were prepared to be utilized as adsorbents for methyl red (MR) and methyl orange (MO) anionic dyes from the aqueous solutions. The maximum adsorption capacity of the AC and ZnO@AC composite toward both dyes was achieved in the strong acidic medium (pH = 3), and under stirring for 60 min. The kinetic studies revealed that the adsorption of MR and MO dyes onto the AC and ZnO@AC composite fitted well with the pseudo-second-order model. Furthermore, the intraparticle diffusion and Elovich kinetic models confirmed the adsorption is controlled by external surfaces, and the adsorption is chemisorption process. The isotherm results indicated that the MR and MO dye adsorption occurred via monolayer adsorption, and the estimated maximum adsorption capacities of both dyes onto the ZnO@AC composite were higher than those achieved by AC. Thermodynamic analysis suggested that the adsorption is endothermic and spontaneous. The mechanism for MR, and MO dyes adsorption onto the AC and ZnO@AC composite is proposed to be controlled…

Recent advances in deep learning and imaging technologies have revolutionized automated medical image analysis, especially in diagnosing Alzheimer's disease through neuroimaging. Despite the availability of various imaging modalities for the same patient, the development of multi-modal models leveraging these modalities remains underexplored. This paper addresses this gap by proposing and evaluating classification models using 2D and 3D MRI images and amyloid PET scans in uni-modal and multi-modal frameworks. Our findings demonstrate that models using volumetric data learn more effective representations than those using only 2D images. Furthermore, integrating multiple modalities enhances model performance over single-modality approaches significantly. We achieved state-of-the-art performance on the OASIS-3 cohort. Additionally, explainability analyses with Grad-CAM indicate that our model focuses on crucial AD-related regions for its predictions, underscoring its potential to aid in understanding the disease's causes.

This paper discusses the simultaneous management of active and reactive power of a flexible renewable energy-based virtual power plant placed in a smart distribution system, based on the economic, operational, and voltage security objectives of the distribution system operator. The formulated problem aims to specify the minimum weighted sum of energy cost, energy loss, and voltage security index, considering the optimal power flow model, voltage security formulation, and the operating model of the virtual power plant. The virtual unit includes renewable sources, like wind systems, photovoltaic, and bio-waste units. Flexibility resources include electric vehicle parking lot and price-based demand response. In the mentioned scheme, parameters of load, renewable sources, electric vehicles, and energy prices are uncertain. This paper utilizes the Unscented Transformation method for modeling uncertainties. Fuzzy decision-making is utilized to extract a compromised solution. The suggested approach innovatively considers the simultaneous management of active and reactive power of a virtual…

Diabetic Retinopathy (DR) stands as a significant global cause of vision impairment, underscoring the critical importance of early detection in mitigating its impact. Addressing this challenge head-on, this study introduces an innovative deep learning framework tailored for DR diagnosis. The proposed framework utilizes the EfficientNetB0 architecture to classify diabetic retinopathy severity levels from retinal images. By harnessing advanced techniques in computer vision and machine learning, the proposed model aims to deliver precise and dependable DR diagnoses. Continuous testing and experimentation shows to the efficiency of the architecture, showcasing promising outcomes that could help in the transformation of both diagnosing and treatment of DR. This framework takes help from the EfficientNet Machine Learning algorithms and employing advanced CNN layering techniques. The dataset utilized in this study is titled 'Diagnosis of Diabetic Retinopathy' and is sourced from Kaggle. It consists of 35,108 retinal images, classified into five categories: No Diabetic Retinopathy (DR),…

Parkinson's is the second most common neurodegenerative disease, affecting nearly 8.5M people and steadily increasing. In this research, Multimodal Deep Learning is investigated for the Prodromal stage detection of Parkinson's Disease (PD), combining different 3D architectures with the novel Excitation Network (EN) and supported by Explainable Artificial Intelligence (XAI) techniques. Utilizing data from the Parkinson's Progression Markers Initiative, this study introduces a joint co-learning approach for multimodal fusion, enabling end-to-end training of deep neural networks and facilitating the learning of complementary information from both imaging and clinical modalities. DenseNet with EN outperformed other models, showing a substantial increase in accuracy when supplemented with clinical data. XAI methods, such as Integrated Gradients for ResNet and DenseNet, and Attention Heatmaps for Vision Transformer (ViT), revealed that DenseNet focused on brain regions believed to be critical to prodromal pathophysiology, including the right temporal and left pre-frontal areas. Similarly,…

Abstract Due to the uncertainty of weather conditions and the nonlinearity of high-dimensional data, as well as the need for a continuous and stable power supply to the power system, traditional regression analysis and time series forecasting methods are no longer able to meet the high accuracy requirements of today's PV power forecasting. To significantly improve the prediction accuracy of short-term PV output power, this paper proposes a short-term PV power forecasting method based on a hybrid model of temporal convolutional networks and gated recurrent units with an efficient channel attention network (TCN-ECANet-GRU) using the generated data of an Australian PV power station as the research object. First, temporal convolutional networks (TCNs) are used as spatial feature extraction layers, and an efficient channel attention network (ECANet) is embedded to enhance the feature capture capability of the convolutional network. Then, the GRU is used to extract the timing information for the final prediction. Finally, based on the experimental validation, the TCN-ECANet-GRU method…

Magnesium oxide nanoparticles (MgO NPs) represent an interesting inorganic material widely utilized across various fields including sensing, antimicrobial applications, optical coatings, water purification, fuel additives, absorbents, and catalysis, owing to their exceptional broad energy band gap, surface affinity, and strong chemical and thermal durability. In this investigation, MgO NPs were successfully synthesized through a green approach employing fruit extract from the gingerbread tree (Hyphaene thebaica). Analysis via scanning electron microscopy (SEM) and transmission electron microscopy (TEM) confirmed their agglomerated quasi-spherical shape with a size range of 20–60 nm. The X-ray diffraction (XRD) pattern exhibited prominent peaks at planes (200) and (220), indicating the high crystallinity of MgO NPs with a crystallite size of 32.6 ± 5 nm while Energy-dispersive X-ray spectroscopy (EDS) analysis highlighted the composition comprises 40.47% Magnesium and 48.64% Oxygen by weight. Fourier transform infrared spectroscopy (FT-IR) revealed characteristic Mg-O bonds through…

This study explores integrating blockchain technology into the Internet of Medical Things (IoMT) to address security and privacy challenges. Blockchain's transparency, confidentiality, and decentralization offer significant potential benefits in the healthcare domain. The research examines various blockchain components, layers, and protocols, highlighting their role in IoMT. It also explores IoMT applications, security challenges, and methods for integrating blockchain to enhance security. Blockchain integration can be vital in securing and managing this data while preserving patient privacy. It also opens up new possibilities in healthcare, medical research, and data management. The results provide a practical approach to handling a large amount of data from IoMT devices. This strategy makes effective use of data resource fragmentation and encryption techniques. It is essential to have well-defined standards and norms, especially in the healthcare sector, where upholding safety and protecting the confidentiality of information are critical. These results illustrate that it is…

Dengue is rapidly expanding its transmission area across Brazil and much of South America. In this study, data-mining techniques were used to identify climatic and demographic indicators that could explain the recent (2014-2020) and simultaneous trends of expansion and exacerbation of the incidence in some regions of Brazil. The previous circulation of the virus (dengue incidence rates between 2007 and 2013), urbanization, and the occurrence of temperature anomalies for a prolonged period were the main factors that led to increased incidence of dengue in the central region of Brazil. Regions with high altitudes, which previously acted as a barrier for dengue transmission, became areas of high incidence rates. The algorithm that was developed during this study can be utilized to assess future climate scenarios and plan preventive actions.

The study investigates the impact of Phase Change Material (PCM) and nano Phase Change Materials (NPCM) on solar still performance. PCM and a blend of NPCM are placed within 12 copper tubes submerged in 1 mm of water to enhance productivity. Thermal performance is assessed across four major scenarios with a fixed water level of 1 mm in the basin. These scenarios include the conventional still, equipped with 12 empty copper rods and 142 g of PCM in each tube, as well as stills with NPCM Samples 1 and 2. Sample 1 contains 0.75% nanoparticle concentration plus 142 g of PCM in the first 6 tubes, while Sample 2 features 2% nanoparticle concentration plus 142 g of PCM in the subsequent 6 tubes. Aluminum oxide (Al2O3) nanoparticles ranging in size from 20 to 30 nm are utilized, with paraffin wax (PW) serving as the latent heat storage (LHS) medium due to its 62 °C melting temperature. The experiments are conducted under the local weather conditions of Vaddeswaram, Vijayawada, India (Latitude-80.6480 °E, Longitude-16.5062 °N). A differential scanning calorimeter (DSC) is utilized to examine…

During the manufacturing process of printed circuit boards (PCBs), quality defects can occur, which can affect the performance and reliability of PCBs. Existing deep learning-based PCB defect detection methods are difficult to simultaneously achieve the goals of high detection accuracy, fast detection speed, and small number of parameters. Therefore, this paper proposes a PCB defect detection algorithm based on CDI-YOLO. Firstly, the coordinate attention mechanism (CA) is introduced to improve the backbone and neck network of YOLOv7-tiny, enhance the feature extraction capability of the model, and thus improve the accuracy of model detection. Secondly, DSConv is used to replace part of the common convolution in YOLOv7-tiny to achieve lower computing costs and faster detection speed. Finally, Inner-CIoU is used as the bounding box regression loss function of CDI-YOLO to speed up the bounding box regression process. The experimental results show that the method achieves 98.3% mAP on the PCB defect dataset, the detection speed is 128 frames per second (FPS), the parameters is 5.8 M,…

Technology plays a pivotal role in shaping cultural identities and practices across the globe. This paper explores the complex relationship between technology and cultural transformation, recognizing both its opportunities and challenges in diverse cultural contexts. We employ key theoretical frameworks, including cultural diffusion, technological determinism and the digital divide, to examine how technological advancements contribute to cultural development. Our findings reveal that technology acts as a catalyst for cultural exchange, innovation and adaptation, enabling unprecedented global communication and the exchange of ideas. For example, the rapid spread of the internet and mobile technologies has facilitated cross-cultural interactions, leading to the blending of cultural values and practices on a global scale. However, the research also highlights the challenges posed by technology, such as the digital divide, which exacerbates inequalities in access to digital resources, and the risk of cultural homogenization, where dominant global cultures overshadow local identities.…

LLMs can accomplish specialized medical knowledge tasks, however, equitable access is hindered by the extensive fine-tuning, specialized medical data requirement, and limited access to proprietary models. Open-source (OS) medical LLMs show performance improvements and provide the transparency and compliance required in healthcare. We present OpenMedLM, a prompting platform delivering state-of-the-art (SOTA) performance for OS LLMs on medical benchmarks. We evaluated OS foundation LLMs (7B-70B) on medical benchmarks (MedQA, MedMCQA, PubMedQA, MMLU medical-subset) and selected Yi34B for developing OpenMedLM. Prompting strategies included zero-shot, few-shot, chain-of-thought, and ensemble/self-consistency voting. OpenMedLM delivered OS SOTA results on three medical LLM benchmarks, surpassing previous best-performing OS models that leveraged costly and extensive fine-tuning. OpenMedLM displays the first results to date demonstrating the ability of OS foundation models to optimize performance, absent specialized fine-tuning. The model achieved 72.6% accuracy on MedQA, outperforming the…

Abstract This investigation reported a one-step green synthesis of nickel oxide nanoparticles (NiO NPs) using aloe vera leaves extract solution for their application in a supercapacitor. This method used aloe vera leaves as a reducing agent, which is very simple and cost-effective. The synthesized NPs were thoroughly characterized using various techniques. The X-ray diffraction analysis unequivocally confirmed the crystalline nature; field emission scanning electron microscopy and transmission electron microscopy images showed different shapes and forms of an agglomerated cluster of synthesized NPs. The absorption spectra were recorded from UV visible spectroscopy, while Fourier transform infrared spectroscopy provided insights into the functional groups present. Electrochemical assessments were carried out via cyclic voltammetry, galvanostatic charging-discharging and electrochemical impedance spectroscopy. These experiments were performed using a 2 M KOH electrolyte within a 1.0 V potential window. Impressively, the single electrode displayed a remarkable specific capacitance of…

Artificial intelligence (AI) provides considerable opportunities to assist human work. However, one crucial challenge of human-AI collaboration is that many AI algorithms operate in a black-box manner where the way how the AI makes predictions remains opaque. This makes it difficult for humans to validate a prediction made by AI against their own domain knowledge. For this reason, we hypothesize that augmenting humans with explainable AI improves task performance in human-AI collaboration. To test this hypothesis, we implement explainable AI in the form of visual heatmaps in inspection tasks conducted by domain experts. Visual heatmaps have the advantage that they are easy to understand and help to localize relevant parts of an image. We then compare participants that were either supported by (a) black-box AI or (b) explainable AI, where the latter supports them to follow AI predictions when the AI is accurate or overrule the AI when the AI predictions are wrong. We conducted two preregistered experiments with representative, real-world visual inspection tasks from manufacturing and…

Abstract Rivers are the natural drainage system, transporting anthropogenic wastes and pollution, including microplastics (plastic < 5 mm). In a riverine system, microplastics can enter from different sources, and have spatial variance in concentration, physical and chemical properties, and imposed risk to the ecosystem. This pilot study presents an examination of microplastics in water and sediment samples using a single sample collection from the rural Raquette River, NY to evaluate a hypothesis that distinct locations of the river, such as downstream of a wastewater treatment plant, upstream of a hydro-dam, and river confluence, may be locations of higher microplastics concentration. In general, our results revealed the presence of high microplastic concentrations downstream of the wastewater treatment plant (in sediments), upstream of the hydro dam (both water and sediment), and in the river confluence (water sample), compared to other study sites. Moreover, the risk assessment indicates that even in a rural river with most of its drainage basin comprising forested and…

Using plant extracts in the green synthesis of nanoparticles has become an environmentally acceptable approach. In our study, copper oxide nanoparticles (CuO NPs) were synthesized using ethanolic extracts of Azadirachta indica and Simmondsia chinensis. CuO NP formation was confirmed by the change in color and by UV‒visible spectroscopy (CuO NPs peaked at a wavelength of 344 nm). TEM images confirmed the semispherical shape of the CuO NPs, with particle sizes ranging from 30.9 to 10.7 nm. The antibacterial activity of these NPs was evaluated by using the agar diffusion method against clinical isolates, including methicillin-resistant Staphylococcus aureus (MRSA), Escherichia coli, Pseudomonas aeruginosa, Acinetobacter spp., Klebsiella pneumoniae, and Stenotrophomonas maltophilia. The minimum inhibitory concentration (MIC) of CuO NPs ranged from 62.5 to 125 µg/ml. In contrast, the antioxidant activity and antibiofilm activity of CuO NPs ranged from 31.1 to 92.2% at 125-500 µg/ml and 62.2-95%, respectively, at 125 -62.5 µg/ml. Our results confirmed that CuO NPs had IC50s of 383.41 ±…

Abstract The growing interest in using plant extracts for the biogenic synthesis of zinc oxide nanoparticles (ZnO NPs) stems from their facile, eco-friendly, and biologically safe approach instead of chemical routes. For the first time, ZnO NPs were successfully biosynthesized using Rhus coriaria fruit aqueous extract as a reducing and capping agent. Characterization revealed that the biosynthesized ZnO NPs possessed a maximum absorbance of approximately 359 nm and closely resembled the hexagonal ZnO wurtzite crystalline structure, with an average crystalline size of 16.69 nm. The transmission electron microscope (TEM) showed the presence of spherical and hexagonal morphologies, with an average grain size of 20.51 ± 3.90 nm. Moreover, the elemental composition of the synthesized ZnO NPs was assessed via energy-dispersive X-ray spectrometry (EDX), and the presence of phytocompounds on their surface was subsequently verified through FT-IR analysis. The ζ-potential of ZnO NPs was recorded at − 19.9 ± 0.1663 mV. Regarding anti-cancer properties, ZnO NPs were found to possess potent…

Road damage detection is an crucial task to ensure road safety. To tackle the issues of poor performance on multi-scale pavement distresses and high costs in detection task, this paper presents an improved lightweight road damage detection algorithm based on YOLOv8n, named YOLOv8-PD (pavement distress). Firstly, a BOT module that can extract global information of road damage images is proposed to adapt to the large-span features of crack objects. Secondly, the introduction of the large separable kernel attention (LKSA) mechanism enhances the detection accuracy of the algorithm. Then, a C2fGhost block is constructed in the neck network to strengthen the feature extraction of complex road damages while reducing the computational load. Furthermore, we introduced lightweight shared convolution detection head (LSCD-Head) to improve feature expressiveness and reduce the number of parameters. Finally, extensive experiments on the RDD2022 dataset yield a model with parametric and computational quantities of 2.3M and 6.1 GFLOPs, which are only 74.1% and 74.3% of the baseline, and the mAP…

Wearable sensors are widely used in medical applications and human-computer interaction because of their portability and powerful privacy. Human activity identification based on sensor data plays a vital role in these fields. Therefore, it is important to improve the recognition performance of different types of actions. Aiming at the problems of insufficient time-varying feature extraction and gradient explosion caused by too many network layers, a time convolution network recognition model with attention mechanism (TCN-Attention-HAR) was proposed. The model effectively recognizes and emphasizes the key feature information. The ability of extracting temporal features from TCN (temporal convolution network) is improved by using the appropriate size of the receiver domain. In addition, attention mechanisms are used to assign higher weights to important information, enabling models to learn and identify human activities more effectively. The performance of the Open Data Set (WISDM, PAMAP2 and USC-HAD) is improved by 1.13%, 1.83% and 0.51%, respectively, compared with other advanced…

Virtual machine (VM) integration methods have effectively proven an optimized load balancing in cloud data centers. The main challenge with VM integration methods is the trade-off among cost effectiveness, quality of service, performance, optimal resource utilization and compliance with service level agreement violations. Deep Learning methods are widely used in existing research on cloud load balancing. However, there is still a problem with acquiring noisy multilayered fluctuations in workload due to the limited resource-level provisioning. The long short-term memory (LSTM) model plays a vital role in the prediction of server load and workload provisioning. This research presents a hybrid model using deep learning with Particle Swarm Intelligence and Genetic Algorithm ("DPSO-GA") for dynamic workload provisioning in cloud computing. The proposed model works in two phases. The first phase utilizes a hybrid PSO-GA approach to address the prediction challenge by combining the benefits of these two methods in fine-tuning the Hyperparameters. In the second phase, CNN-LSTM is utilized.…

Lung cancer is an important global health problem, and it is defined by abnormal growth of the cells in the tissues of the lung, mostly leading to significant morbidity and mortality. Its timely identification and correct staging are very important for proper therapy and prognosis. Different computational methods have been used to enhance the precision of lung cancer classification, among which optimization algorithms such as Greylag Goose Optimization (GGO) are employed. These algorithms have the purpose of improving the performance of machine learning models that are presented with a large amount of complex data, selecting the most important features. As per lung cancer classification, data preparation is one of the most important steps, which contains the operations of scaling, normalization, and handling gap factor to ensure reasonable and reliable input data. In this domain, the use of GGO includes refining feature selection, which mainly focuses on enhancing the classification accuracy compared to other binary format optimization algorithms, like bSC, bMVO, bPSO, bWOA, bGWO,…

Novel methods are required to enhance lung cancer detection, which has overtaken other cancer-related causes of death as the major cause of cancer-related mortality. Radiologists have long-standing methods for locating lung nodules in patients with lung cancer, such as computed tomography (CT) scans. Radiologists must manually review a significant amount of CT scan pictures, which makes the process time-consuming and prone to human error. Computer-aided diagnosis (CAD) systems have been created to help radiologists with their evaluations in order to overcome these difficulties. These systems make use of cutting-edge deep learning architectures. These CAD systems are designed to improve lung nodule diagnosis efficiency and accuracy. In this study, a bespoke convolutional neural network (CNN) with a dual attention mechanism was created, which was especially crafted to concentrate on the most important elements in images of lung nodules. The CNN model extracts informative features from the images, while the attention module incorporates both channel attention and spatial attention…

Accumulating research showed that ENC1 plays a critical role in maintaining the physiological functions. However, little is known about its role in predicting prognosis and immunotherapy response across cancers. In our results, compared to normal tissues, most cancer tissues exhibit increased ENC1 expression. We found that the most common type of genetic variation was gene mutation. In addition, a positive correlation was found between CNV and ENC1 expression. Moreover, the overexpression of ENC1 was positively correlated with poor clinical outcomes. The GSEA results showed that ENC1 is closely correlated with tumor-promoting biological functions in most cancers. ENC1 is also closely negatively associated with the infiltration levels of T cells, activated NK cells, and B cells. Most immunomodulators are positively associated with ENC1. Further, we verified that inhibition of ENC1 expression suppressed the proliferation and migration of breast cancer, pancreatic cancer and glioma cells. In conclusion, our study demonstrated that ENC1 plays a protumorigenic role in most cancers.…

Abstract A library of novel bis-Schiff base derivatives based on thiobarbituric acid has been effectively synthesized by multi-step reactions as part of our ongoing pursuit of novel anti-diabetic agents. All these derivatives were subjected to in vitro α-glucosidase inhibitory potential testing after structural confirmation by modern spectroscopic techniques. Among them, compound 8 (IC50 = 0.10 ± 0.05 µM), and 9 (IC50 = 0.13 ± 0.03 µM) exhibited promising inhibitory activity better than the standard drug acarbose (IC50 = 0.27 ± 0.04 µM). Similarly, derivatives (5, 6, 7, 10 and 4) showed significant to good inhibitory activity in the range of IC50 values from 0.32 ± 0.03 to 0.52 ± 0.02 µM. These derivatives were docked with the target protein to elucidate their binding affinities and key interactions, providing additional insights into their inhibitory mechanisms. The chemical nature of these compounds were reveal by performing the density functional theory (DFT) calculation using hybrid B3LYP functional with 6-311++G(d,p) basis set. The presence of intramolecular H-bonding was…