The 100 most-cited articles in Applied Sciences, 2024

Integrating Artificial Intelligence (AI) in healthcare represents a transformative shift with substantial potential for enhancing patient care. This paper critically examines this integration, confronting significant ethical, legal, and technological challenges, particularly in patient privacy, decision-making autonomy, and data integrity. A structured exploration of these issues focuses on Differential Privacy as a critical method for preserving patient confidentiality in AI-driven healthcare systems. We analyze the balance between privacy preservation and the practical utility of healthcare data, emphasizing the effectiveness of encryption, Differential Privacy, and mixed-model approaches. The paper navigates the complex ethical and legal frameworks essential for AI integration in healthcare. We comprehensively examine patient rights and the nuances of informed consent, along with the challenges of harmonizing advanced technologies like blockchain with the General Data Protection Regulation (GDPR). The issue of algorithmic bias in healthcare is also explored, underscoring the…

Predictive maintenance (PdM) is a policy applying data and analytics to predict when one of the components in a real system has been destroyed, and some anomalies appear so that maintenance can be performed before a breakdown takes place. Using cutting-edge technologies like data analytics and artificial intelligence (AI) enhances the performance and accuracy of predictive maintenance systems and increases their autonomy and adaptability in complex and dynamic working environments. This paper reviews the recent developments in AI-based PdM, focusing on key components, trustworthiness, and future trends. The state-of-the-art (SOTA) techniques, challenges, and opportunities associated with AI-based PdM are first analyzed. The integration of AI technologies into PdM in real-world applications, the human–robot interaction, the ethical issues emerging from using AI, and the testing and validation abilities of the developed policies are later discussed. This study exhibits the potential working areas for future research, such as digital twin, metaverse, generative AI, collaborative robots…

Natural language Processing (NLP) has significantly transformed in the last decade, especially in the field of Language Modeling. Large Language Models (LLMs) have achieved SOTA performances on Natural Language Understanding (NLU) and Natural Language Generation (NLG) tasks by learning language representation in self-supervised ways. This paper provides a comprehensive survey to capture the progression of advances in Language Models. In this paper, we examine the different aspects of Language Models, which started with a few million parameters but have reached the size of a trillion in a very short time. We also look at how these LLMs transitioned from task-specific to task-independent to task-and-language-independent architectures. This paper extensively discusses different pre-training objectives, benchmarks, and transfer learning methods used in LLMs. It also examines different fine-tuning and In-Context learning techniques used in downstream tasks. It also explores how LLMs can perform well across many domains and datasets if sufficiently trained on a large and diverse dataset.…

State space models (SSM) with selection mechanisms and hardware-aware architectures, namely Mamba, have recently shown significant potential in long-sequence modeling. Since the complexity of transformers’ self-attention mechanism is quadratic with image size, as well as increasing computational demands, researchers are currently exploring how to adapt Mamba for computer vision tasks. This paper is the first comprehensive survey that aims to provide an in-depth analysis of Mamba models within the domain of computer vision. It begins by exploring the foundational concepts contributing to Mamba’s success, including the SSM framework, selection mechanisms, and hardware-aware design. Then, we review these vision Mamba models by categorizing them into foundational models and those enhanced with techniques including convolution, recurrence, and attention to improve their sophistication. Furthermore, we investigate the widespread applications of Mamba in vision tasks, which include their use as a backbone in various levels of vision processing. This encompasses general visual tasks,…

This systematic literature review employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to investigate recent applications of explainable AI (XAI) over the past three years. From an initial pool of 664 articles identified through the Web of Science database, 512 peer-reviewed journal articles met the inclusion criteria—namely, being recent, high-quality XAI application articles published in English—and were analyzed in detail. Both qualitative and quantitative statistical techniques were used to analyze the identified articles: qualitatively by summarizing the characteristics of the included studies based on predefined codes, and quantitatively through statistical analysis of the data. These articles were categorized according to their application domains, techniques, and evaluation methods. Health-related applications were particularly prevalent, with a strong focus on cancer diagnosis, COVID-19 management, and medical imaging. Other significant areas of application included environmental and agricultural management, industrial…

Emulsions have garnered significant attention within a variety of industries, including pharmaceuticals, food production, and cosmetics. The importance of emulsions across these sectors is attributed to their versatility and unique properties, such as increased interfacial area and the ability to deliver compounds insoluble in water or to mask the flavor of unpalatable ingredients. A comprehensive and precise assessment of the physicochemical properties, structural features, and stability of emulsions is an indispensable phase in the pursuit of new formulations and the improvement of manufacturing protocols. The characterization of emulsions encompasses an array of methodologies designed to determine their attributes, such as droplet size, distribution, concentration, surface charge, and others. In this review, we explore the techniques most frequently used to characterize emulsions and critically assess the significance each method holds in understanding the behavior and predicting the stability of emulsions. We elucidate the basic principles of these methods while emphasizing what…

Mental health disorders are a leading cause of disability worldwide, and there is a global shortage of mental health professionals. AI chatbots have emerged as a potential solution, offering accessible and scalable mental health interventions. This study aimed to conduct a scoping review to evaluate the effectiveness and feasibility of AI chatbots in treating mental health conditions. A literature search was conducted across multiple databases, including MEDLINE, Scopus, and PsycNet, as well as using AI-powered tools like Microsoft Copilot and Consensus. Relevant studies on AI chatbot interventions for mental health were selected based on predefined inclusion and exclusion criteria. Data extraction and quality assessment were performed independently by multiple reviewers. The search yielded 15 eligible studies covering various application areas, such as mental health support during COVID-19, interventions for specific conditions (e.g., depression, anxiety, substance use disorders), preventive care, health promotion, and usability assessments. AI chatbots demonstrated potential…

This review comprehensively examines the burgeoning field of intelligent techniques to enhance power systems’ stability, control, and protection. As global energy demands increase and renewable energy sources become more integrated, maintaining the stability and reliability of both conventional power systems and smart grids is crucial. Traditional methods are increasingly insufficient for handling today’s power grids’ complex, dynamic nature. This paper discusses the adoption of advanced intelligence methods, including artificial intelligence (AI), deep learning (DL), machine learning (ML), metaheuristic optimization algorithms, and other AI techniques such as fuzzy logic, reinforcement learning, and model predictive control to address these challenges. It underscores the critical importance of power system stability and the new challenges of integrating diverse energy sources. The paper reviews various intelligent methods used in power system analysis, emphasizing their roles in predictive maintenance, fault detection, real-time control, and monitoring. It details extensive…

As large language models (LLMs) continue to advance, evaluating their comprehensive capabilities becomes significant for their application in various fields. This research study comprehensively evaluates the language, vision, speech, and multimodal capabilities of GPT-4o. The study employs standardized exam questions, reasoning tasks, and translation assessments to assess the model’s language capability. Additionally, GPT-4o’s vision and speech capabilities are tested through image classification and object-recognition tasks, as well as accent classification. The multimodal evaluation assesses the model’s performance in integrating visual and linguistic data. Our findings reveal that GPT-4o demonstrates high accuracy and efficiency across multiple domains in language and reasoning capabilities, excelling in tasks that require few-shot learning. GPT-4o also provides notable improvements in multimodal tasks compared to its predecessors. However, the model shows variability and faces limitations in handling complex and ambiguous inputs, particularly in audio and vision capabilities.…

In recent years, there has been a significant increase in research on hydrogen due to the urgent need to move away from carbon-intensive energy sources. This transition highlights the critical role of hydrogen storage technology, where hydrogen tanks are crucial for achieving cleaner energy solutions. This paper aims to provide a general overview of hydrogen treatment from a mechanical viewpoint, and to create a comprehensive review that integrates the concepts of hydrogen safety and storage. This study explores the potential of hydrogen applications as a clean energy alternative and their role in various sectors, including industry, automotive, aerospace, and marine fields. The review also discusses design technologies, safety measures, material improvements, social impacts, and the regulatory landscape of hydrogen storage tanks and safety technology. This work provides a historical literature review up to 2014 and a systematic literature review from 2014 to the present to fill the gap between hydrogen storage and safety. In particular, a fundamental feature of this work is…

In recent years, the use of magnetic nanoparticles (MNPs) in biomedical applications has gained more and more attention. Their unusual properties make them ideal candidates for the advancement of diagnosis, therapy, and imaging applications. This review addresses the use of MNPs in the field of biomedicine encompassing their synthesis, biofunctionalization, and unique physicochemical properties that make them ideal candidates for such applications. The synthesis of magnetic nanoparticles involves a range of techniques that allow for control over particle size, shape, and surface modifications. The most commonly used synthesis techniques that play a crucial role in tailoring the magnetic properties of nanoparticles are summarized in this review. Nevertheless, the main characterization techniques that can be employed after a successful synthesis procedure are also included together with a short description of their biomedical applications. As the field of magnetic nanoparticles in biomedical applications is rapidly evolving, this review aims to serve as a valuable resource, especially…

This research presents the Cross-Cultural Intelligent Language Learning System (CILS), a novel approach integrating Artificial Intelligence (AI) into language education to enhance cross-cultural communication. CILS utilizes advanced AI technologies to provide adaptive, personalized learning experiences that cater to the unique linguistic and cultural backgrounds of each learner. By dynamically adjusting content and methodology, CILS significantly improves linguistic proficiency and cultural understanding, essential for effective global interactions. The implementation of CILS in platforms such as Busuu and HelloTalk has demonstrated marked improvements in engagement and communication skills among learners. Empirical studies validate the system's effectiveness in real-world settings, showing enhanced learner performance and increased intercultural competence. Additionally, the Technology Acceptance Model (TAM) applied confirms that the usability and perceived usefulness of AI-driven systems strongly influence learner acceptance and sustained use. This study not only underscores the…

This study compares various F1-score variants—micro, macro, and weighted—to assess their performance in evaluating text-based emotion classification. Lexicon distillation is employed using the multilabel emotion-annotated datasets XED and GoEmotions. The aim of this paper is to understand when each F1-score variant is better suited for evaluating text-based multilabel emotion classification. Unigram lexicons were derived from the annotated GoEmotions and XED datasets through a binary classification approach. The distilled lexicons were then applied to the GoEmotions and XED annotated datasets to calculate their emotional content, and the results were compared. The findings highlight the behavior of each F1-score variant under different class distributions, emphasizing the importance of appropriate metric selection for reliable model performance evaluation in imbalanced multilabel datasets. Additionally, this study also investigates the effect of the aggregation of negative emotions into broader categories on said F1 metrics. The contribution of this study is to provide insights into…

Recent advancements in Large Language Models (LLMs) like ChatGPT and Copilot have led to their integration into various educational domains, including software development education. Regular use of LLMs in the learning process is still not well-researched; thus, this paper intends to fill this gap. The paper explores the nuanced impact of informal LLM usage on undergraduate students’ learning outcomes in software development education, focusing on React applications. We carefully designed an experiment involving thirty-two participants over ten weeks where we examined unrestricted but not specifically encouraged LLM use and their correlation with student performance. Our results reveal a significant negative correlation between increased LLM reliance for critical thinking-intensive tasks such as code generation and debugging and lower final grades. Furthermore, a downward trend in final grades is observed with increased average LLM use across all tasks. However, the correlation between the use of LLMs for seeking additional explanations and final grades was not as strong, indicating…

Artificial intelligence (AI) and machine learning (ML) can assist in the effective development of the power system by improving reliability and resilience. The rapid advancement of AI and ML is fundamentally transforming energy management systems (EMSs) across diverse industries, including areas such as prediction, fault detection, electricity markets, buildings, and electric vehicles (EVs). Consequently, to form a complete resource for cognitive energy management techniques, this review paper integrates findings from more than 200 scientific papers (45 reviews and more than 155 research studies) addressing the utilization of AI and ML in EMSs and its influence on the energy sector. The paper additionally investigates the essential features of smart grids, big data, and their integration with EMS, emphasizing their capacity to improve efficiency and reliability. Despite these advances, there are still additional challenges that remain, such as concerns regarding the privacy of data, challenges with integrating different systems, and issues related to scalability. The paper finishes…

The discovery of antibiotics and pesticides has greatly contributed to the social and economic development of human society but, due to the long-term irrational application, it has led to drug-resistant microorganisms, environmental damage, and other hazards, so the selection of alternative natural, safe, and non-hazardous bioactive substances is an effective solution for this problem. D-limonene is a bioactive compound widely present in various plant essential oils, exhibiting excellent broad-spectrum bioactivity and promising prospects for development and clinical application. This review provides a detailed overview of the biological activities of D-limonene, emphasizing its antimicrobial, anthelmintic, insecticidal, and medicinal potential. While nanoencapsulation technology shows promise in improving the physicochemical properties of D-limonene and enhancing its practical applications, it is also crucial to comprehensively evaluate the potential side effects of D-limonene before use.

This comprehensive review explores the applications and challenges of Digital Twin (DT) technology in smart grids. As power grid systems rapidly evolve to meet the increasing energy demands and the new requirements of renewable source integration, DTs offer promising solutions to enhance the monitoring, control, and optimization of these systems. In this paper, we examine the concept of DTs in the context of smart grids, and their requirements, challenges, and integration with the Internet of Things (IoT) and Artificial Intelligence (AI). We also discuss different applications in asset management, system operation, and disaster response. This paper analyzes current challenges, including data management, interoperability, cost, and ethical considerations. Through case studies from various sectors in Canada, we illustrate the real-world implementation and impact of DTs. Finally, we discuss emerging trends and future directions, highlighting the potential of DTs to revolutionize smart grid networks and contribute to more efficient, reliable, and sustainable power systems.

The exponential growth of network intrusions necessitates the development of advanced artificial intelligence (AI) techniques for intrusion detection systems (IDSs). However, the reliance on AI for IDSs presents several challenges, including the performance variability of different AI models and the opacity of their decision-making processes, hindering comprehension by human security analysts. In response, we propose an end-to-end explainable AI (XAI) framework tailored to enhance the interpretability of AI models in network intrusion detection tasks. Our framework commences with benchmarking seven black-box AI models across three real-world network intrusion datasets, each characterized by distinct features and challenges. Subsequently, we leverage various XAI models to generate both local and global explanations, shedding light on the underlying rationale behind the AI models’ decisions. Furthermore, we employ feature extraction techniques to discern crucial model-specific and intrusion-specific features, aiding in understanding the discriminative factors influencing the detection…

Digital Twin (DT) technology has experienced substantial advancements and extensive adoption across various industries, aiming to enhance operational efficiency and effectiveness. Defined as virtual replicas of physical objects, systems, or processes, Digital Twins enable real-time simulation, monitoring, and analysis of real-world behavior. This comprehensive review delves into the evolution of DT technology, tracing its journey from conceptual origins to contemporary technological implementations. The review provides detailed definitions, a classification of different types of Digital Twins, and a comparative analysis of their architectures. Furthermore, it investigates the application of DT technology in diverse sectors, with a particular emphasis on medicine and manufacturing, exemplified by use cases such as personalized medicine. Moreover, the review highlights emerging trends and future directions in DT technology, underscoring the transformative potential of integrating artificial intelligence and machine learning to augment DT capabilities. This analysis not only elucidates…

In recent years, electric vehicles (EVs) have become increasingly popular, bringing about fundamental shifts in transportation to reduce greenhouse effects and accelerate progress toward decarbonization. The role of EVs has also experienced a paradigm shift for future energy networks as an active player in the form of vehicle-to-grid, grid-to-vehicle, and vehicle-to-vehicle technologies. EVs spend a significant part of the day parked and have a remarkable potential to contribute to energy sustainability as backup power units. In this way, EVs can be connected to the grid as stationary power units, providing a range of services to the power grid to increase its reliability and resilience. The available systems show that EVs can be used as alternative energy sources for various network systems like smart grids, microgrids, and virtual power plants besides transportation. While the grid–EV connection offers various contributions, it also has some limitations and effects. In this context, the current study highlights the power system impacts and key contributions of EVs connected to…

Biotechnological processes are essential for developing economies that aim to stand out in future markets. The use of bioreactors is one of the most important unit operations of biotechnological processes, and real-time monitoring of bioreactors is essential to ensure precise bioprocess control. This review presents different types of bioreactors, sensors, and applications in other sectors. Bioreactors, controlled systems for cultivating microorganisms and cells, are essential tools in various fields, from scientific research to industrial production. The use of a variety of sensors is critical for accurate, real-time monitoring, early problem detection, reproducibility, cost reduction, and increased efficiency. These benefits are being realized in numerous applications, including biofuel production, bioremediation and leaching processes, tissue engineering, and drug manufacturing. Innovations in bioreactor technology are expanding opportunities for a more sustainable and healthier future. By developing new types of bioreactors, integrating advanced sensors, and exploring promising…

In the era of Industry 4.0, 3D printing, or additive manufacturing (AM), has revolutionized product design and manufacturing across various sectors. This review explores the evolution of 3D printing technology and its impact on industrial innovation, highlighting advancements in aeronautics, the automotive industry, and biomedicine. Various AM processes, such as binder jetting, direct energy deposition, and powder bed fusion, and materials like metals, polymers, ceramics, and composites, are discussed. Innovations like high-speed sintering, continuous liquid interface production, and bioprinting demonstrate ongoing advancements. The potential of 3D printing in personalized medical applications is emphasized due to its flexibility in geometry and materials. Despite progress, challenges like standardization, material quality, recycling, sustainability, and economic feasibility hinder widespread adoption. Overcoming these challenges is crucial for optimizing 3D printing technologies, ensuring high-quality, efficient, and affordable production. The review also addresses the future…

This systematic literature review explores the intersection of AI-driven digital twins and IoT in creating a sustainable building environment. A comprehensive analysis of 125 papers focuses on four major themes. First, digital twins are examined in construction, facility management, and their role in fostering sustainability and smart cities. The integration of IoT and AI with digital twins and energy optimization for zero-energy buildings is discussed. Second, the application of AI and automation in manufacturing, particularly in Industry 4.0 and cyber-physical systems, is evaluated. Third, emerging technologies in urban development, including blockchain, cybersecurity, and EEG-driven systems for sustainable buildings, are highlighted. The study underscores the role of data-driven approaches in flood resilience and urban digital ecosystems. This review contributes to sustainability by identifying how digital technologies and AI can optimize energy use and enhance resilience in both urban and industrial contexts.

The importance of biomaterials lies in their fundamental roles in medical applications such as tissue engineering, drug delivery, implantable devices, and radiological phantoms, with their interactions with biological systems being critically important. In recent years, advancements in deep learning (DL), artificial intelligence (AI), machine learning (ML), supervised learning (SL), unsupervised learning (UL), and reinforcement learning (RL) have significantly transformed the field of biomaterials. These technologies have introduced new possibilities for the design, optimization, and predictive modeling of biomaterials. This review explores the applications of DL and AI in biomaterial development, emphasizing their roles in optimizing material properties, advancing innovative design processes, and accurately predicting material behaviors. We examine the integration of DL in enhancing the performance and functional attributes of biomaterials, explore AI-driven methodologies for the creation of novel biomaterials, and assess the capabilities of ML in predicting biomaterial responses…

This study introduces a sophisticated intrusion detection system (IDS) that has been specifically developed for internet of things (IoT) networks. By utilizing the capabilities of long short-term memory (LSTM), a deep learning model renowned for its proficiency in modeling sequential data, our intrusion detection system (IDS) effectively discerns between regular network traffic and potential malicious attacks. In order to tackle the issue of imbalanced data, which is a prevalent concern in the development of intrusion detection systems (IDSs), we have integrated the synthetic minority over-sampling technique (SMOTE) into our approach. This incorporation allows our model to accurately identify infrequent incursion patterns. The rebalancing of the dataset is accomplished by SMOTE through the generation of synthetic samples belonging to the minority class. Various strategies, such as the utilization of generative adversarial networks (GANs), have been put forth in order to tackle the issue of data imbalance. However, SMOTE (synthetic minority over-sampling technique) presents some…

This comprehensive review paper explores the intricate integration of biosensors with multi-hop Internet of Things (IoT) systems, representing a paradigm shift in healthcare through real-time remote patient monitoring. The strategic deployment of biosensors in different locations in medical facilities, intricately connected to multiple microcontrollers, serves as a cornerstone in the establishment of robust multi-hop IoT networks. This paper highlights the role of this multi-hop IoT network, which efficiently facilitates the seamless transmission of vital health data to a centralized server. Crucially, the utilization of cloud connectivity emerges as a linchpin in this integration, providing a secure and scalable platform for remote patient monitoring. This cloud-based approach not only improves the accessibility of critical health information but also transcends physical limitations, allowing healthcare providers to monitor patients in real-time from any location. This paper highlights the transformative potential of this integration in overcoming traditional healthcare limitations…

This systematic review critically examines the diverse applications of Blockchain technology in the food supply chain and identifies areas where its potential remains underutilized. By analysing 60 Blockchain-based frameworks, the study highlights the most frequently employed drivers such as transparency, traceability, and security within food supply chains. Additionally, underexplored applications such as food donation and redistribution, supply chain financing, animal welfare, food waste management, and data analysis are identified, revealing opportunities for further innovation. The research employed NVivo 14 to analyze the extent of Blockchain’s implementation in various food supply chain drivers, and the findings informed the development of a more diverse framework for Blockchain integration. Key insights demonstrate Blockchain’s transformative potential, particularly in enhancing data integrity, trust, and operational efficiency through its immutable ledger and smart contracts, which streamline transactions, cut administrative costs, and reduce fraud. In terms of…

Polluting species released in industrial-colored effluents contaminate water, degrading its quality and persisting in the aquatic environment; therefore, it must be treated for safe discharge or onsite reuse/recycling to ensure a fresh water supply. This review has the principal goal of facilitating understanding of some important issues concerning wastewater (WW) treatment systems, mainly based on a coagulation–flocculation step, as follows: (i) the significance of and facilities offered by specialized treatment processes, including the coagulation–flocculation step as a single or associated step (i.e., coagulation–flocculation followed by sedimentation/filtration or air flotation); (ii) the characteristics of industrial-colored WW, especially WW from the textile industry, which can be reduced via the coagulation–flocculation step; (iii) primary and secondary groups of hybrid materials and their characteristics when used as coagulants–flocculants; (iv) the influence of different process operating variables and treatment regimens on the efficiency of the studied treatment step; and…

Electric vehicle (EV) fast charging systems are rapidly evolving to meet the demands of a growing electric mobility landscape. This paper provides a comprehensive overview of various fast charging techniques, advanced infrastructure, control strategies, and emerging challenges and future trends in EV fast charging. It discusses various fast charging techniques, including inductive charging, ultra-fast charging (UFC), DC fast charging (DCFC), Tesla Superchargers, bidirectional charging integration, and battery swapping, analysing their advantages and limitations. Advanced infrastructure for DC fast charging is explored, covering charging standards, connector types, communication protocols, power levels, and charging modes control strategies. Electric vehicle battery chargers are categorized into on-board and off-board systems, with detailed functionalities provided. The status of DC fast charging station DC-DC converters classification is presented, emphasizing their role in optimizing charging efficiency. Control strategies for EV systems are analysed, focusing on effective charging…

Herein, we examined the transformative potential of artificial intelligence (AI) and machine learning (ML) as new fronts in addressing some of the pertinent challenges posed by food integrity to human and animal health. In recent times, AI and ML, along with other Industry 4.0 technologies such as big data, blockchain, virtual reality, and the internet of things (IoT), have found profound applications within nearly all dimensions of the food industry with a key focus on enhancing food safety and quality and improving the resilience of the food supply chain. This paper provides an accessible scrutiny of these technologies (in particular, AI and ML) in relation to food integrity and gives a summary of their current advancements and applications within the field. Key areas of emphasis include the application of AI and ML in quality control and inspection, food fraud detection, process control, risk assessments, prediction, and management, and supply chain traceability, amongst other critical issues addressed. Based on the literature reviewed herein, the utilization of AI and ML in the…

An electronic nose, designed to replicate human olfaction, captures distinctive ‘fingerprint’ data from mixed gases or odors. Comprising a gas sensing system and an information processing unit, electronic noses have evolved significantly since their inception in the 1980s. They have transitioned from bulky, costly, and energy-intensive devices to today’s streamlined, economical models with minimal power requirements. This paper presents a comprehensive and systematic review of the electronic nose technology domain, with a special focus on advancements over the last five years. It highlights emerging applications, innovative methodologies, and potential future directions that have not been extensively covered in previous reviews. The review explores the application of electronic noses across diverse fields such as food analysis, environmental monitoring, and medical diagnostics, including new domains like veterinary pathology and pest detection. This work aims to underline the adaptability of electronic noses and contribute to their continued development and application in various…

Photo-fermentation is an efficient hydrogen production pathway in which purple non-sulfur bacteria (PNSB) play an active role and produce hydrogen as a part of their metabolism under optimal conditions. These bacteria work under the influence of light to advance their metabolism and use various substrates, such as simple sugars and volatile fatty acids, to produce hydrogen. This article presents a comparative review of several bacterial strains that have been efficiently used to produce hydrogen by photo-fermentation under different optimized conditions, including the substrate, its concentration, type and capacity of the bioreactor, light sources and intensities, and process conditions to achieve the maximum biohydrogen production rate. The analysis showed that the Rhodopseudomonas palustris is the main bacterium used for hydrogen production, with a maximum hydrogen production rate of 3.2 mM/h using 27.8 mM of glucose in a 165 mL serum bottle and 3.23 mM/h using 50 mM of glycerol at pH 7, followed by Rhodobacter sphaeroides, which gave a hydrogen production rate as high as 8.7…

The gut microbiota plays a crucial role in maintaining human health and influencing disease states. Recent advancements in artificial intelligence (AI) have opened new avenues for exploring the intricate functionalities of the gut microbiota. This article aims to provide an overview of the current state-of-the-art applications of AI in microbiome analysis, with examples related to metabolomics, transcriptomics, proteomics, and genomics. It also offers a perspective on the use of such AI solutions in probiotic interventions for various clinical settings. This comprehensive understanding can lead to the development of targeted therapies that modulate the gut microbiota to improve health outcomes. This article explores the innovative application of AI in understanding the complex interactions within the gut microbiota. By leveraging AI, researchers aim to uncover the microbiota’s role in human health and disease, particularly focusing on CIDs and probiotic interventions.

This study introduces the Dynamic Feedback-Driven Learning Optimization Framework (DFDLOF), an innovative machine learning-based approach to personalize educational pathways. DFDLOF leverages advanced algorithms for in-depth analysis of learner data, enabling the dynamic customization of educational content and methodologies. Its application in diverse online platforms, including Khan Academy and Coursera, underscores its adaptability and scalability. DFDLOF enhances learner engagement and outcomes by providing tailored learning experiences and real-time feedback. Key insights reveal its strengths in personalized education, particularly in dynamically adapting learning paths and the importance of real-time feedback. Challenges identified include integration complexities and data privacy concerns. Future research directions emphasize the need for longitudinal impact studies and broader demographic applications. This study contributes significantly to the evolving domain of adaptive learning technologies, demonstrating the transformative potential of machine learning in personalizing…

With the deepening of research on Large Language Models (LLMs), significant progress has been made in recent years on the development of Large Multimodal Models (LMMs), which are gradually moving toward Artificial General Intelligence. This paper aims to summarize the recent progress from LLMs to LMMs in a comprehensive and unified way. First, we start with LLMs and outline various conceptual frameworks and key techniques. Then, we focus on the architectural components, training strategies, fine-tuning guidance, and prompt engineering of LMMs, and present a taxonomy of the latest vision–language LMMs. Finally, we provide a summary of both LLMs and LMMs from a unified perspective, make an analysis of the development status of large-scale models in the view of globalization, and offer potential research directions for large-scale models.

In recent years, nanomaterials have gained special attention for removing contaminants from wastewater. Nanoparticles (NPs), such as carbon-based materials and metal oxides, exhibit exceptional adsorption capacity and antimicrobial properties for wastewater treatment. Their unique properties, including reactivity, high surface area, and tunable surface functionalities, make them highly effective adsorbents. They can remove contaminants such as organics, inorganics, pharmaceuticals, medicine, and dyes by adsorption mechanisms. In this review, the effectiveness of different types of carbon-based NPs, including carbon nanotubes (CNTs), graphene-based nanoparticles (GNPs), carbon quantum dots (CQDs), carbon nanofibers (CNFs), and carbon nanospheres (CNSs), and metal oxides, including copper oxide (CuO), zinc oxide (ZnO), iron oxide (Fe2O3), titanium oxide (TiO2), and silver oxide (Ag2O), in the removal of different contaminants from wastewater has been comprehensively evaluated. In addition, their synthesis methods, such as physical, chemical, and biological, have been described. Based…

In the last two decades, plant taxonomy has bloomed, following the development of a novel technique, namely DNA Barcoding. DNA barcodes are standardized sequences, ideally unique, coding or non-coding, either from the genome of the organism or from its organelles, that are used to identify/classify an organismal group; in short the method includes amplification of the DNA barcode, sequencing and comparison with a reference database containing the relevant sequences from different species. In plants, the use a universal DNA barcode, such as COI which is used in animals, has not been achieved so far. Several DNA barcodes from single loci as well as combinations of barcodes have been successfully used in various settings; additionally, as next generation sequencing becomes affordable, very long sequences- super barcodes, such as the chloroplast genome are being used successfully especially to distinguish between close species. In this review we summarize the work done on DNA barcoding in plants and discuss different approaches used so far.

The integration of Artificial Intelligence (AI) and smart technologies into safety management is a pivotal aspect of the Fourth Industrial Revolution or Industry 4.0. This study conducts a systematic literature review to identify and analyze how AI and smart technologies enhance safety management across various sectors within the Safety 4.0 paradigm. Focusing on peer-reviewed journal articles that explicitly mention “Smart”, “AI”, or “Artificial Intelligence” in their titles, the research examines key safety management factors, such as accident prevention, risk management, real-time monitoring, and ethical implementation, across sectors, including construction, industrial safety, disaster and public safety, transport and logistics, energy and power, health, smart home and living, and other diverse industries. AI-driven solutions, such as predictive analytics, machine learning algorithms, IoT sensor integration, and digital twin models, are shown to proactively identify and mitigate potential hazards, optimize energy consumption, and enhance operational efficiency. For instance, in…

Bias could be defined as the tendency to be in favor or against a person or a group, thus promoting unfairness. In computer science, bias is called algorithmic or artificial intelligence (i.e., AI) and can be described as the tendency to showcase recurrent errors in a computer system, which result in “unfair” outcomes. Bias in the “outside world” and algorithmic bias are interconnected since many types of algorithmic bias originate from external factors. The enormous variety of different types of AI biases that have been identified in diverse domains highlights the need for classifying the said types of AI bias and providing a detailed overview of ways to identify and mitigate them. The different types of algorithmic bias that exist could be divided into categories based on the origin of the bias, since bias can occur during the different stages of the Machine Learning (i.e., ML) lifecycle. This manuscript is a literature study that provides a detailed survey regarding the different categories of bias and the corresponding approaches that have been proposed to identify and mitigate…

Educational institutions are increasingly focused on supporting students who may be facing academic challenges, aiming to enhance their educational outcomes through targeted interventions. Within this framework, leveraging advanced deep learning techniques to develop recommendation systems becomes essential. These systems are designed to identify students at risk of underperforming by analyzing patterns in their historical academic data, thereby facilitating personalized support strategies. This research introduces an innovative deep learning model tailored for pinpointing students in need of academic assistance. Utilizing a Gated Recurrent Neural Network (GRU) architecture, the model is rich with features such as a dense layer, max-pooling layer, and the ADAM optimization method used to optimize performance. The effectiveness of this model was tested using a comprehensive dataset containing 15,165 records of student assessments collected across several academic institutions. A comparative analysis with existing educational recommendation models, like Recurrent Neural Network (RNN),…

This study explores the intricate relationships between perceived value, customer satisfaction, trust, and loyalty in the context of the dynamic online entertainment platform industry. As the entertainment landscape has evolved from traditional formats to digital and interactive experiences, businesses face intense competition and the need to innovate to attract and retain users. This study introduces a comprehensive research model that defines perceived value in three dimensions: utilitarian, hedonic, and social. It also investigates the roles of customer satisfaction and trust as mediators in the connection between perceived value and loyalty. A survey of entertainment platform users reveals that enhancing utilitarian and hedonic values can increase customer satisfaction and that all three perceived value dimensions positively influence trust. Customer satisfaction partially mediates the relationship between utilitarian value and loyalty and fully mediates the relationship between hedonic value and loyalty; however, trust does not act as a mediator in this context. The theoretical…

Nowadays, drug delivery has an important role in medical therapy. The use of biopolymers in developing drug delivery systems (DDSs) is increasingly attracting attention due to their remarkable and numerous advantages, in contrast to conventional polymers. Biopolymers have many advantages (biodegradability, biocompatibility, renewability, affordability, and availability), which are extremely important for developing materials with applications in the biomedical field. Additionally, biopolymers are appropriate when they improve functioning and have a number of positive effects on human life. Therefore, this review presents the most used biopolymers for biomedical applications, especially in drug delivery. In addition, by combining different biopolymers DDSs with tailored functional properties (e.g., physical properties, biodegradability) can be developed. This review summarizes and provides data on the progress of research on biopolymers (chitosan, alginate, starch, cellulose, albumin, silk fibroin, collagen, and gelatin) used in DDSs, their preparation, and mechanism of action.

Maritime transportation is crucial for global trade but faces significant risks and operational challenges. Ensuring safety is essential for protecting lives, the environment, and economic stability. This review explores the role of artificial intelligence (AI) in enhancing maritime safety and risk management. Key AI applications include risk analysis, crew resource management, hazardous material handling, predictive maintenance, and navigation systems. AI systems identify potential hazards, provide real-time decision support, monitor hazardous materials, predict equipment failures, and optimize shipping routes. Case studies, such as Wärtsilä’s Fleet Operations Solution and ABB Ability™ Marine Pilot Vision, illustrate the benefits of AI in improving safety and efficiency. Despite these advancements, integrating AI poses challenges related to infrastructure compatibility, data quality, and regulatory issues. Addressing these is essential for successful AI implementation. This review highlights AI’s potential to transform maritime safety, emphasizing the need for innovation,…

This review presents the chemical composition, bioactive properties, and diverse applications of Chlorella vulgaris, a green microalga widely recognized for its exceptional nutritional value and therapeutic potential. The study emphasizes the presence of key nutrients, including high-quality proteins, essential vitamins, minerals, and an array of bioactive compounds such as carotenoids, chlorophyll, and polysaccharides. These compounds have been shown to exhibit a wide spectrum of biological activities, including potent antioxidant, anti-inflammatory, immunomodulatory, antiviral, anticancer, antidiabetic, lipid-lowering, and detoxifying effects. The review explores the multifaceted applications of C. vulgaris in various sectors, including its growing role as a functional food ingredient, a nutraceutical supplement in animal feed, and a promising therapeutic agent for combatting chronic diseases. This paper also highlights its potential for enhancing immune responses, mitigating oxidative stress, promoting detoxification of heavy metals, and improving overall health outcomes.…

Renewable energies present an opportunity to enhance energy security, reduce dependence on imports, and lower greenhouse gas emissions. Natural gas, viewed as a transitional fuel from coal to renewables, lacks reliable environmental sustainability and does not contribute to EU energy independence. Recently, biomethane has been gaining attention as an alternative to natural gas. Obtained from purified or “upgraded” biogas, it offers environmental and economic advantages. Several developed technologies, including absorption, adsorption, membrane separation, and cryogenic separation, are commercially available. However, those are energy- and resource-intensive. In this context, this review aims to examine the recent advancements in biogas upgrading, particularly in physical, chemical, and biological pathways. It focuses on CO2 removal and/or conversion to methane, offering an updated overview for future studies. The technologies are classified based on the separation method (by phase addition, by solid agent, by phase creation, and by biological process), and an analysis of each…

Physical activity analysis assessment has been a concern throughout human history. The intersection of technological growth with sports has given rise to a burgeoning field known as sports engineering. In the 19th century, the advent of chrono-photography and pioneering marked the inception of sports performance analysis. In recent years, the noticeable developments achieved in wearable low-power electronics with wireless high interconnection capability, as a part of modern technologies, have aided us in studying sports parameters such as motor behavior, biomechanics, equipment design, and materials science, playing an essential role in the understanding of sports dynamics. This study aims to review over 250 published articles since 2018, focusing on utilizing and validating these emergent technologies in sports and clinical aspects. It is predicted that one of the next steps in sports technology and engineering development will be using algorithms based on artificial intelligence to analyze the measurements obtained by multi-sensor systems (sensor fusion) to monitor biometric and…

Dynamic human activity recognition (HAR) is a domain of study that is currently receiving considerable attention within the fields of computer vision and pattern recognition. The growing need for artificial-intelligence (AI)-driven systems to evaluate human behaviour and bolster security underscores the timeliness of this research. Despite the strides made by numerous researchers in developing dynamic HAR frameworks utilizing diverse pre-trained architectures for feature extraction and classification, persisting challenges include suboptimal performance accuracy and the computational intricacies inherent in existing systems. These challenges arise due to the vast video-based datasets and the inherent similarity in the data. To address these challenges, we propose an innovative, dynamic HAR technique employing a deep-learning-based, deep bidirectional long short-term memory (Deep BiLSTM) model facilitated by a pre-trained transfer-learning-based feature-extraction approach. Our approach begins with the utilization of Convolutional Neural Network (CNN) models, specifically…

The integration of edge computing with IoT (EC-IoT) systems provides significant improvements in addressing security and privacy challenges in IoT networks. This paper examines the combination of EC-IoT and artificial intelligence (AI), highlighting practical strategies to improve data and network security. The published literature has suggested decentralized and reliable trust measurement mechanisms and security frameworks designed explicitly for IoT-enabled systems. Therefore, this paper reviews the latest attack models threatening EC-IoT systems and their impacts on IoT networks. It also examines AI-based methods to counter these security threats and evaluates their effectiveness in real-world scenarios. Finally, this survey aims to guide future research by stressing the need for scalable, adaptable, and robust security solutions to address evolving threats in EC-IoT environments, focusing on the integration of AI to enhance the privacy, security, and efficiency of IoT systems while tackling the challenges of scalability and resource limitations.

Freeze drying (FD) is a technique used to preserve the bioactive content and flavor of samples. Foods with a short shelf life due to nature have been preserved via FD in recent years. The success of the drying technique is thanks to the high retention ability of the bioactive compounds and the flavor of the sample. However, the high cost and energy consumption of FD limit its usage in several fields. Freezing, primary drying, and secondary drying, which take place in the same device, are the stages of FD. In addition, several parameters, including pretreatment, size, temperature, sample species, time, and pressure, affect the FD process. These parameters are controlled to increase the effectiveness of FD on the samples. Moreover, FD has been compared with other drying techniques including hot air drying, microwave drying, vacuum drying, and solar drying to determine its protective capability. It has also been applied to the valorization of waste products, which is a common problem worldwide. For valorization, microencapsulation and powder production have been achieved using FD. In…

The size of power grids and a complex technological infrastructure with higher levels of automation, connectivity, and remote access make it necessary to be able to detect anomalies of various kinds using optimal and intelligent methods. This paper is a review of studies related to the detection of anomalies in smart grids using AI. Digital repositories were explored considering publications between the years 2011 and 2023. Iterative searches were carried out to consider studies with different approaches, propose experiments, and help identify the most applied methods. Seven objects of study related to anomalies in SG were identified: attacks on data integrity, unusual measurements and consumptions, intrusions, network infrastructure, electrical data, identification of cyber-attacks, and use of detection devices. The issues relating to cybersecurity prove to be widely studied, especially to prevent intrusions, fraud, data falsification, and uncontrolled changes in the network model. There is a clear trend towards the conformation of anomaly detection frameworks or hybrid solutions.…

Signals play a fundamental role in science, technology, and communication by conveying information through varying patterns, amplitudes, and frequencies. This paper introduces innovative methodologies for processing electromyographic (EMG) signals to develop artificial intelligence systems capable of decoding muscle activity for controlling arm movements. The study investigates advanced signal processing techniques and machine learning classification algorithms using the GRABMyo dataset, aiming to enhance prosthetic control systems and rehabilitation technologies. A comprehensive analysis was conducted on signal processing techniques, including signal filtering and discrete wavelet transform (DWT), alongside a composite feature set comprising Mean Absolute Value (MAV), Waveform Length (WL), Zero Crossing (ZC), Slope Sign Changes (SSC), Root Mean Square (RMS), Enhanced Waveform Length (EWL), and Enhanced Mean Absolute Value (EMAV). These features, refined through Linear Discriminant Analysis (LDA) for dimensionality reduction, were classified using Support Vector Machine (SVM)…

Cloud computing services have become indispensable to people’s lives. Many of their activities are performed through cloud services, from small companies to large enterprises and individuals to government agencies. It has enabled clients to use companies’ services on demand at the lowest cost anywhere, anytime, over the Internet. Despite these advantages, cloud networks are vulnerable to many types of attacks. However, as the adoption of cloud services accelerates, the risks associated with these services have also increased. For this reason, solutions have been implemented to improve cloud security, such as monitoring networks, the backbone of the cloud infrastructure, and detecting and classifying cyberattacks. Therefore, an intrusion detection system (IDS) is one of the essential defenses for detecting attacks in the cloud computing network. Current IDSs encounter some challenges in handling and simultaneously analyzing the large scale of traffic found in the cloud environment, and this affects the accuracy of cyberattack detection. Therefore, this research proposes a deep…

The highway construction industry carries substantial safety risks for workers, necessitating thorough accident analyses to implement effective preventive measures. Current research lacks comprehensive investigations into safety incidents, relying heavily on conventional statistical methods and overlooking valuable textual information in publicly available databases. This study leverages a state-of-the-art large language model (LLM), specifically OpenAI’s GPT-3.5 model. The primary focus is to enhance text-based incident analysis that is sourced from OSHA’s Severe Injury Reports (SIR) database. By incorporating novel natural language processing (NLP) techniques, dimensionality reduction, clustering algorithms, and LLM prompting of incident narratives, the study aims to develop an approach to the analysis of major accident causes in highway construction. The resulting cluster analysis, coupled with LLM summarization and cause identification, reveals the major accident types, such as heat-related and struck-by injuries, as well as commonalities between incidents. This research…

Currently, the process of tracking moving objects and determining their indoor location is considered to be one of the most attractive applications that have begun to see widespread use, especially after the adoption of this technology in some smartphone applications. The great developments in electronics and communications systems have provided the basis for tracking and location systems inside buildings, so-called indoor positioning systems (IPSs). The ultra-wideband (UWB) technology is one of the important emerging solutions for IPSs. This radio communications technology provides important characteristics that distinguish it from other solutions, such as secure and robust communications, wide bandwidth, high data rate, and low transmission power. In this paper, we review the implementation of the most important real-time indoor positioning and tracking systems that use ultra-wideband technology for tracking and localizing moving objects. This paper reviews the newest in-market UWB modules and solutions, discussing several types of algorithms that are used by the real-time…

The central aim of adhesive dentistry is to improve the compatibility between current adhesives and a range of substrates by employing diverse application techniques. Therefore, the overarching objective of this review is to offer a comprehensive analysis of dentin bonding systems, starting with an introduction to adhesion and a detailed overview of enamel and dentin structures, their histology, and the impact of dentin structure on resin–dentin bonding. It covers the mechanisms of resin–dentin bonding, including resin adhesive application, bonding mechanisms, and factors influencing efficacy. Further, this review explores the composition of resin adhesive systems, including acidic components, cross-linking monomers, solvents, and other critical elements. It also examines various adhesive strategies—etch-and-rinse, self-etch, and universal adhesives—highlighting their applications and advantages. The review extends to clinical applications of dental adhesion, including direct restorations, indirect restorations, and immediate dentin sealing (IDS), demonstrating the practical…

Transparency in AI models is essential for promoting human–AI collaboration and ensuring regulatory compliance. However, interpreting these models is a complex process influenced by various methods and datasets. This study presents a comprehensive overview of foundational interpretation techniques, meticulously referencing the original authors and emphasizing their pivotal contributions. Recognizing the seminal work of these pioneers is imperative for contextualizing the evolutionary trajectory of interpretation in the field of AI. Furthermore, this research offers a retrospective analysis of interpretation techniques, critically evaluating their inherent strengths and limitations. We categorize these techniques into model-based, representation-based, post hoc, and hybrid methods, delving into their diverse applications. Furthermore, we analyze publication trends over time to see how the adoption of advanced computational methods within various categories of interpretation techniques has shaped the development of AI interpretability over time. This analysis highlights a notable…

Resveratrol (RSV), a naturally occurring phytoalexin, is the most important stilbenoid synthesized by plants, as a defense mechanism, in response to microbial aggression, toxins or ultraviolet radiation. RSV came to the attention of researchers both as a potential chemopreventive agent and a possible explanation for the low incidence of cardiovascular disease (CVD) in French people with a high-fat diet. RSV is mainly administered as a food supplement, its properties being evaluated in vitro or in vivo, on various experimental models. RSV modulates signaling pathways that limit the spread of tumor cells, protects nerve cells from damage, is useful in the prevention of diabetes, and generally acts as an anti-aging natural compound. It was highlighted that RSV could ameliorate the consequences of an unhealthy lifestyle caused by an exaggerated caloric intake. This paper reviews the evidence supporting the beneficial effect of RSV for various pathological conditions, e.g., neoplastic diseases, neurodegeneration, metabolic syndrome, diabetes, obesity, CVDs, immune diseases, bacterial,…

The impact of marine sediment pollution is crucial for the health of the seas, particularly in densely populated coastal areas worldwide. This study assesses the concentration and distribution of heavy metals in the marine sediments of the main regions of the Mediterranean Sea. The results underscore high concentrations of mercury (Hg), nickel (Ni), and copper (Cu), whereas chromium (Cr), zinc (Zn), cadmium (Cd), barium (Ba), and vanadium (V) exhibit moderate values. To assess the heavy metal results, sediment quality guidelines and pollution indices (Igeo and Geochemical Signal Type-GST) were employed, revealing a consistent trend of decreasing concentrations from the coastal zone to the open sea. Principal Component Analysis (PCA) emphasizes the significant roles of Cu, Zn, Ba, and Cr in sediment chemistry. The study suggests that the distribution patterns of heavy metals are linked to wastewater discharges in coastal areas, requiring effective management strategies to ensure the health of the Mediterranean Sea.

The dynamic expressions of emotion convey both the emotional and functional states of an individual’s interactions. Recognizing the emotional states helps us understand human feelings and thoughts. Systems and frameworks designed to recognize human emotional states automatically can use various affective signals as inputs, such as visual, vocal and physiological signals. However, emotion recognition via a single modality can be affected by various sources of noise that are specific to that modality and the fact that different emotion states may be indistinguishable. This review examines the current state of multimodal emotion recognition methods that integrate visual, vocal or physiological modalities for practical emotion computing. Recent empirical evidence on deep learning methods used for fine-grained recognition is reviewed, with discussions on the robustness issues of such methods. This review elaborates on the profound learning challenges and solutions required for a high-quality emotion recognition system, emphasizing the benefits of dynamic expression analysis, which aids…

This study aims to evaluate the utilization and effectiveness of artificial intelligence (AI) applications in managing symptoms of anxiety and depression. The primary objectives are to identify current AI tools, analyze their practicality and efficacy, and assess their potential benefits and risks. A comprehensive literature review was conducted using databases such as ScienceDirect, Google Scholar, PubMed, and ResearchGate, focusing on publications from the last five years. The search utilized keywords including "artificial intelligence," "applications," "mental health," "anxiety," "LLMs" and "depression". Various AI tools, including chatbots, mobile applications, wearables, virtual reality settings, and large language models (LLMs), were examined and categorized based on their functions in mental health care. The findings indicate that AI applications, including LLMs, show significant promise in symptom management, offering accessible and personalized interventions that can complement traditional mental health treatments. Tools such as AI-driven chatbots, mobile apps, and LLMs…

Magnetorheological (MR) dampers have significantly advanced automotive suspension systems by providing adaptable damping characteristics in response to varying road conditions and driving dynamics. This review offers a comprehensive analysis of the evolution and integration of MR dampers in semi-active suspension systems. Semi-active systems present an optimal balance by integrating the simplicity inherent in passive systems with the adaptability characteristic of active systems, while mitigating the substantial energy consumption. The fundamental principles of MR technology, the design of MR dampers, and the diverse control strategies employed to optimize suspension performance were examined. The classical, modern, and intelligent control methods, along with the related research, were emphasized. Based on the above-mentioned methods, the benefits of MR semi-active control were highlighted, while the challenges and future research directions in MR damper technology were also addressed. Through a synthesis of recent research findings and practical applications, this paper underscores…

The gut microbiota, recognised for its vital functions in host health, operates as an endocrine organ, exerting systemic effects beyond the gastrointestinal tract. This “virtual organ” produces hormones that influence distal organs, including the brain. With its diverse microbial composition, the gut microbiota surpasses the biochemical complexity of traditional endocrine organs, generating neurotransmitters like GABA, dopamine, and serotonin. Despite challenges in culturing gut bacteria, advances in research methodologies have elucidated their role in behaviour, metabolism, appetite, and insulin resistance. As microbial endocrinology continues to evolve, further exploration of the intricate connections between hormones and the microbiome are anticipated, highlighting hormones’ pivotal role in the dynamic host–microbiota relationship.

Hyperspectral imaging (HSI) is one of the non-destructive quality assessment methods providing both spatial and spectral information. HSI in food quality and safety can detect the presence of contaminants, adulterants, and quality attributes, such as moisture, ripeness, and microbial spoilage, in a non-destructive manner by analyzing spectral signatures of food components in a wide range of wavelengths with speed and accuracy. However, analyzing HSI data can be quite complicated and time consuming, in addition to needing some special expertise. Artificial intelligence (AI) has shown immense promise in HSI for the assessment of food quality because it is so powerful at coping with irrelevant information, extracting key features, and building calibration models. This review has shown various machine learning (ML) approaches applied to HSI for quality and safety control of foods. It covers the basic concepts of HSI, advanced preprocessing methods, and strategies for wavelength selection and machine learning methods. The application of HSI to AI increases the speed with which food…

Land-area classification (LAC) research offers a promising avenue to address the intricacies of urban planning, agricultural zoning, and environmental monitoring, with a specific focus on urban areas and their complex land usage patterns. The potential of LAC research is significantly propelled by advancements in high-resolution satellite imagery and machine learning strategies, particularly the use of convolutional neural networks (CNNs). Accurate LAC is paramount for informed urban development and effective land management. Traditional remote-sensing methods encounter limitations in precisely classifying dynamic and complex urban land areas. Therefore, in this study, we investigated the application of transfer learning with Inception-v3 and DenseNet121 architectures to establish a reliable LAC system for identifying urban land use classes. Leveraging transfer learning with these models provided distinct advantages, as it allows the LAC system to benefit from pre-trained features on large datasets, enhancing model generalization and performance compared to starting from scratch.…

Artificial intelligence technologies based on deep convolutional neural networks and large language models have made significant breakthroughs in many tasks, such as image recognition, target detection, semantic segmentation, and natural language processing, but also face a conflict between the high computational capacity of the algorithms and limited deployment resources. Quantization, which converts floating-point neural networks into low-bit-width integer networks, is an important and essential technique for efficient deployment and cost reduction in edge computing. This paper analyzes various existing quantization methods, showcases the deployment accuracy of advanced techniques, and discusses the future challenges and trends in this domain.

Microencapsulation of polyphenols is an innovative approach in food technology by which to protect these bioactive compounds from degradation and increase their bioavailability. Polyphenols, naturally occurring in plants, exhibit potent antioxidant, anti-inflammatory and anticancer properties, which make them valuable functional ingredients in foods. However, their susceptibility to external factors, such as light, temperature and pH, presents a significant challenge for their incorporation into food products. Microencapsulation, based on various techniques and carriers such as polysaccharides, proteins and lipids, allows polyphenols to be stabilized and released in a controlled manner in the digestive system. This article reviews the different microencapsulation techniques, carrier properties, and the applications of microencapsulated polyphenols in food products, including bakery, dairy and functional beverages. Additionally, the article discusses the benefits and challenges associated with this technology, highlighting its potential to improve the stability, nutritional value and…

HR decision-making is changing as a result of artificial intelligence (AI), especially in the areas of hiring, onboarding, and retention. This study examines the use of AI tools throughout the lifecycle of an employee, emphasizing how they enhance the effectiveness, customization, and scalability of HR procedures. These solutions streamline employee setup, learning, and documentation. They range from AI-driven applicant tracking systems (ATSs) for applicant selection to AI-powered platforms for automated onboarding and individualized training. Predictive analytics also helps retention and performance monitoring plans, which lowers turnover, but issues such as bias, data privacy, and ethical problems must be carefully considered. This paper addresses the limitations and future directions of AI while examining its disruptive potential in HR.

The rapid advancement of artificial intelligence (AI), coupled with the global rollout of 4G and 5G networks, has fundamentally transformed the Big Data landscape, redefining data management and analysis methodologies. The ability to manage and analyze such vast and varied datasets has exceeded the capacity of any individual or organization. This study introduces an enhanced framework that expands upon the traditional four Vs of Big Data—volume, velocity, volatility, and veracity—by incorporating six additional dimensions: value, validity, visualization, variability, volatility, and vulnerability. This comprehensive framework offers a novel and straightforward approach to understanding and addressing the complexities of Big Data in the AI era. This article further explores the use of ‘Big D’, an AI-driven, RAG-based Big Data analytical bot powered by the ChatGPT-4o model (ChatGPT version 4.0). This article’s innovation represents a significant advance in the field, accelerating and deepening the extraction and analysis of insights from large-scale datasets. This will enable us to…

Big Data and blockchain technology are coming together to revolutionize how decisions are made in a decentralized way across various industries. This review looks at how these technologies, along with distributed systems, can improve data security, transparency, and real-time processing, making decision-making more efficient and informed. The integration enhances data security with unchangeable records, increases transparency and traceability, and supports real-time data analysis. However, there are challenges to overcome, including scalability, data privacy, interoperability, regulatory compliance, and high costs. By examining case studies such as Estonia's healthcare system, IBM and Walmart's Food Trust, and the Brooklyn Microgrid project, we explore the practical applications and benefits of combining Big Data with blockchain. Despite these hurdles, the review finds that the ongoing advancements and innovative solutions in these technologies offer significant promise. They are set to drive the adoption and effectiveness of decentralized decision-making, ultimately leading to…

In response to the challenge of handling large-scale 3D point cloud data, downsampling is a common approach, yet it often leads to the problem of feature loss. We present a dynamic downsampling algorithm for 3D point cloud maps based on an improved voxel filtering approach. The algorithm consists of two modules, namely, dynamic downsampling and point cloud edge extraction. The former adapts voxel downsampling according to the features of the point cloud, while the latter preserves edge information within the 3D point cloud map. Comparative experiments with voxel downsampling, grid downsampling, clustering-based downsampling, random downsampling, uniform downsampling, and farthest-point downsampling were conducted. The proposed algorithm exhibited favorable downsampling simplification results, with a processing time of 0.01289 s and a simplification rate of 91.89%. Additionally, it demonstrated faster downsampling speed and showcased improved overall performance. This enhancement not only benefits productivity but also highlights the system’s efficiency and effectiveness.

The Fourth Industrial Revolution combined with the advent of artificial intelligence brought significant changes to humans’ daily lives. Extended research in the field has aided in both documenting and presenting these changes, giving a more general picture of this new era. This work reviews the application field of the scientific research literature on the presence of machine vision in the Fourth Industrial Revolution and the changes it brought to each sector to which it contributed, determining the exact extent of its influence. Accordingly, an attempt is made to present an overview of its use in the Fifth Industrial Revolution to identify and present the changes between the two consequent periods. This work uses the PRISMA methodology and follows the form of a Scoping Review using sources from Scopus and Google Scholar. Most publications reveal the emergence of machine vision in almost every field of human life with significant influence and performance results. Undoubtedly, this review highlights the great influence and offer of machine vision in many sectors, establishing its…

Underwater object detection plays a significant role in marine ecosystem research and marine species conservation. The improvement of related technologies holds practical significance. Although existing object-detection algorithms have achieved an excellent performance on land, they are not satisfactory in underwater scenarios due to two limitations: the underwater objects are often small, densely distributed, and prone to occlusion characteristics, and underwater embedded devices have limited storage and computational capabilities. In this paper, we propose a high-precision, lightweight underwater detector specifically optimizing for underwater scenarios based on the You Only Look Once Version 8 (YOLOv8) model. Firstly, we replace the Darknet-53 backbone of YOLOv8s with FasterNet-T0, reducing model parameters by 22.52%, FLOPS by 23.59%, and model size by 22.73%, achieving model lightweighting. Secondly, we add a Prediction Head for Small Objects, increase the number of channels for high-resolution feature map detection heads, and decrease the number of channels for low-resolution…

The agricultural sector is undergoing a transformative paradigm shift with the integration of advanced technologies, particularly artificial intelligence (AI), to enhance data analysis techniques and streamline decision-making processes. This paper delves into the integration of advanced technologies in agriculture, focusing specifically on optimizing data analysis through artificial intelligence (AI) to strengthen decision-making processes in farming. We present a novel AI-powered model that leverages historical agricultural datasets, utilizing a comprehensive array of established machine learning algorithms to enhance the prediction and classification of agricultural data. This work provides tailored algorithm recommendations, bypassing the need to deploy and fine-tune numerous algorithms. We approximate the accuracy of suitable algorithms, highlighting those with the highest precision, thus saving time by leveraging pre-trained AI models on historical agricultural data. Our method involves three phases: collecting diverse agricultural datasets, applying multiple classifiers, and…

This literature review explores the pivotal role of brain–computer interface (BCI) technology, coupled with electroencephalogram (EEG) technology, in advancing rehabilitation for individuals with damaged muscles and motor systems. This study provides a comprehensive overview of recent developments in BCI and motor control for rehabilitation, emphasizing the integration of user-friendly technological support and robotic prosthetics powered by brain activity. This review critically examines the latest strides in BCI technology and its application in motor skill recovery. Special attention is given to prevalent EEG devices adaptable for BCI-driven rehabilitation. The study surveys significant contributions in the realm of machine learning-based and deep learning-based rehabilitation evaluation. The integration of BCI with EEG technology demonstrates promising outcomes for enhancing motor skills in rehabilitation. The study identifies key EEG devices suitable for BCI applications, discusses advancements in machine learning approaches for rehabilitation assessment, and highlights the…

Since the emergence of ChatGPT, research on large language models (LLMs) has actively progressed across various fields. LLMs, pre-trained on vast text datasets, have exhibited exceptional abilities in understanding natural language and planning tasks. These abilities of LLMs are promising in robotics. In general, traditional supervised learning-based robot intelligence systems have a significant lack of adaptability to dynamically changing environments. However, LLMs help a robot intelligence system to improve its generalization ability in dynamic and complex real-world environments. Indeed, findings from ongoing robotics studies indicate that LLMs can significantly improve robots’ behavior planning and execution capabilities. Additionally, vision-language models (VLMs), trained on extensive visual and linguistic data for the vision question answering (VQA) problem, excel at integrating computer vision with natural language processing. VLMs can comprehend visual contexts and execute actions through natural language. They also provide descriptions of scenes in natural language.…

The maritime industry, responsible for moving approximately 90% of the world’s goods, significantly contributes to environmental pollution, accounting for around 2.5% of global greenhouse gas emissions. This review explores the integration of artificial intelligence (AI) in promoting sustainability within the maritime sector, focusing on shipping and port operations. By addressing emissions, optimizing energy use, and enhancing operational efficiency, AI offers transformative potential for reducing the industry’s environmental impact. This review highlights the application of AI in fuel optimization, predictive maintenance, route planning, and smart energy management, alongside its role in autonomous shipping and logistics management. Case studies from Maersk Line and the Port of Rotterdam illustrate successful AI implementations, demonstrating significant improvements in fuel efficiency, emission reduction, and environmental monitoring. Despite challenges such as high implementation costs, data privacy concerns, and regulatory complexities, the prospects for AI in the maritime…

Large language models (LLMs), designed to provide helpful and safe responses, often rely on alignment techniques to align with user intent and social guidelines. Unfortunately, this alignment can be exploited by malicious actors seeking to manipulate an LLM’s outputs for unintended purposes. In this paper, we introduce a novel approach that employs a genetic algorithm (GA) to manipulate LLMs when model architecture and parameters are inaccessible. The GA attack works by optimizing a universal adversarial prompt that—when combined with a user’s query—disrupts the attacked model’s alignment, resulting in unintended and potentially harmful outputs. Our novel approach systematically reveals a model’s limitations and vulnerabilities by uncovering instances where its responses deviate from expected behavior. Through extensive experiments, we demonstrate the efficacy of our technique, thus contributing to the ongoing discussion on responsible AI development by providing a diagnostic tool for evaluating and enhancing alignment of LLMs with human intent. To our knowledge, this is the first…

Digital society strategies in healthcare include the rapid development of digital twins (DTs) for patients and human organs in medical research and the use of artificial intelligence (AI) in clinical practice to develop effective treatments in a cheaper, quicker, and more effective manner. This is facilitated by the availability of large historical datasets from previous clinical trials and other real-world data sources (e.g., patient biometrics collected from wearable devices). DTs can use AI models to create predictions of future health outcomes for an individual patient in the form of an AI-generated digital twin to support the rapid assessment of in silico intervention strategies. DTs are gaining the ability to update in real time in relation to their corresponding physical patients and connect to multiple diagnostic and therapeutic devices. Support for this form of personalized medicine is necessary due to the complex technological challenges, regulatory perspectives, and complex issues of security and trust in this approach. The challenge is also to combine different datasets…

With increasing electronic medical data and the development of artificial intelligence, Clinical Decision Support Systems (CDSSs) assist clinicians in diagnosis and prescription. Traditional knowledge-based CDSSs follow an accumulated medical knowledgebase and a predefined rule system, which clarifies the decision-making process; however, maintenance cost issues exist in the medical data quality control and standardization process. Non-knowledge-based CDSSs utilize vast amounts of data and algorithms to effectively decide; however, the deep learning black-box problem causes unreliable results. EXplainable Artificial Intelligence (XAI)-based CDSS provides a valid rationale and explainable results. It ensures trustworthiness and transparency by showing the recommendation and prediction results process through explainable techniques. However, existing systems have limitations, such as the scope of data utilization and the lack of explanatory power of AI models. This study proposes a new XAI-based CDSS framework to address these issues; introduce resources, datasets, and models that can…

Vehicle-to-grid (V2G) systems play a key role in the integration of electric vehicles (EVs) into smart grids by enabling bidirectional energy flows between EVs and the grid. Optimizing V2G operations poses significant challenges due to the dynamic nature of energy demand, grid constraints, and user preferences. This paper addresses the optimization challenges in V2G systems and explores the use of artificial intelligence (AI) methods to tackle these challenges. The paper provides a comprehensive analysis of existing work on optimization in V2G systems and identifies gaps where AI-driven algorithms, machine learning, metaheuristic extensions, and agile optimization concepts can be applied. Case studies and examples demonstrate the efficacy of AI-driven algorithms in optimizing V2G operations, leading to improved grid stability, cost optimization, and user satisfaction. Furthermore, agile optimization concepts are introduced to enhance flexibility and responsiveness in V2G optimization. The paper concludes with a discussion on the challenges and future directions for integrating…

Microgrids usually employ distributed energy resources such as wind turbines, solar photovoltaic modules, etc. When multiple distributed generation resources with different features are used in microgrids, managing these resources becomes an important problem. The generated power of solar photovoltaic modules and wind turbines used in microgrids is constantly changing with solar irradiation and wind speed. Due to this impermanent and uncertain nature of renewable energy resources, generally, energy storage systems are employed in microgrid systems. To control the distributed energy resources and energy storage units and sustain the supply and demand balance within the microgrid and provide sustainable and reliable energy to the loads, energy management systems are used. Many methods are used to realize and optimize energy management in microgrids. This review article provides a comparative and critical analysis of the energy management systems used in microgrids. The energy management system can be tailored for different purposes, which are also discussed in detail. Additionally,…

Large Language Models (LLMs), based on their highly developed ability to comprehend and generate human-like text, promise to revolutionize all aspects of society. These LLMs facilitate complex language understanding, translation, content generation, and problem-solving, enabled by vast historical data processing and fine-tuning. Throughout the past year, with the initial release of ChatGPT to the public, many papers have appeared on how to exploit LLMs for the ways we operate and interact with intelligent transportation systems. In this study, we review more than 130 papers on the subject and group them according to their major contributions into the following five categories: autonomous driving, safety, tourism, traffic, and others. Based on the aggregated proposals and findings in the extant literature, this paper concludes with a set of challenges and research recommendations, hopefully contributing to guide research in this young, yet extremely active research domain.

Aquaculture is rapidly becoming one of the pivotal sectors in the farming economy, driven by the increasing demand for high-quality animal protein at an affordable cost, especially with the escalating human population. However, the expansion of high-density fish populations also brings forth a challenge—the rapid transmission and spread of infectious disease agents among them. To combat this, vaccination is emerging as a reliable and standardized method for providing immunity against viral and bacterial outbreaks. The ideal vaccine is expected to be safe, effective, economical, and easily administered. The fish vaccination industry continually publishes new information on fish immunology and vaccinology, contributing to the improvement in vaccine formulation and efficacy. This review aims to offer insights into the current status of bacterial, viral, and parasitic diseases, discuss existing vaccinations, and address potential industry-threatening diseases like infectious edwardsiellosis, motile aeromonas septicemia (MAS), Tilapia Lake Virus (TiLV) disease, infectious salmon anemia…

As an emerging teaching method, online learning is becoming increasingly popular among learners. However, one of the major drawbacks of this learning style is the lack of effective communication and feedback, which can lead to a higher risk of students failing or dropping out. In response to this challenge, this paper proposes a student performance prediction model based on multidimensional time-series data analysis by considering multidimensional data such as students’ learning behaviors, assessment scores, and demographic information, which is able to extract the characteristics of students’ learning behaviors and capture the connection between multiple characteristics to better explore the impact of multiple factors on students’ performance. The model proposed in this paper helps teachers to individualize education for students at different levels of proficiency and identifies at-risk students as early as possible to help teachers intervene in a timely manner. In experiments on the Open University Learning Analytics Dataset (OULAD), the model achieved 74% accuracy and 73% F1…

Phishing is one of the most widely observed types of internet cyber-attack, through which hundreds of clients using different internet services are targeted every day through different replicated websites. The phishing attacker spreads messages containing false URL links through emails, social media platforms, or messages, targeting people to steal sensitive data like credentials. Attackers generate phishing URLs that resemble those of legitimate websites to gain these confidential data. Hence, there is a need to prevent the siphoning of data through the duplication of trustworthy websites and raise public awareness of such practices. For this purpose, many machine learning and deep learning models have been employed to detect and prevent phishing attacks, but due to the ever-evolving nature of these attacks, many systems fail to provide accurate results. In this study, we propose a deep learning-based system using a 1D convolutional neural network to detect phishing URLs. The experimental work was performed using datasets from Phish-Tank, UNB, and Alexa, which successfully…

Medical devices play a crucial role in human health. These are instruments, machines or even software programs used to diagnose, treat, monitor or prevent health conditions. They are designed to help improve patients’ quality of life and range from simple items, such as thermometers, to more advanced technologies, such as pacemakers. In order to guarantee the safety and efficacy of medical devices intended for use on patients, the establishment of appropriate regulatory frameworks is crucial to ascertain whether devices function as intended, comply with safety standards and offer benefits that outweigh the associated risks. Depending on the country, different regulatory agencies are responsible for the evaluation of these products. The regulatory landscape for medical devices varies significantly across major markets, including the European Union, the United States of America and Japan, reflecting diverse approaches aimed at ensuring the safety and efficacy of medical technologies. However, these regulatory differences can contribute to a “medical device lag,” where disparities in…

Fungi play an important and multifaceted role in the production and processing of food, influencing various stages from cultivation to consumption. This paper explores the complex relationship between fungi and food systems, highlighting their diverse contributions. Firstly, fungi serve as essential agents in food cultivation, aiding in the breakdown of organic matter and the recycling of nutrients, and promoting plant growth through symbiotic relationships. Moreover, fungi such as yeasts and molds are integral to fermentation processes, yielding a wide array of fermented foods and beverages with unique flavors and textures. Additionally, fungi are indispensable in the creation of enzymes and bioactive compounds utilized in food processing, enhancing the nutritional value, shelf life, and safety. However, certain fungal species pose significant challenges as food spoilage agents and mycotoxin producers, necessitating stringent quality control measures. Understanding the intricate interplay between fungi and food systems is essential for optimizing food production, ensuring food…

This review emphasizes the recent ongoing shift in the wood coating industry towards bio-based resources and circular economy principles, promoting eco-friendly alternatives. In addressing wood’s vulnerabilities, this study investigates the use of natural compounds and biopolymers to enhance wood coatings. These materials contribute to protective matrices that safeguard wood surfaces against diverse challenges. Essential oils, vegetable oils, and bio-based polymers are explored for their potential in crafting eco-friendly and durable coating matrices. Furthermore, this review covers efforts to counter weathering and biological decay through the application of various natural compounds and extracts. It evaluates the effectiveness of different bio-based alternatives to traditional chemical preservatives and highlights promising candidates. This review also delves into the incorporation of sustainable pigments and dyes into wood coatings to enhance both protective and aesthetic qualities. Innovative pigments are able to provide visually appealing solutions in line with sustainability…

Grayscale image processing is a key research area in the field of computer vision and image analysis, where image quality and visualization effects may be seriously damaged by high-density salt and pepper noise. A traditional median filter for noise removal may result in poor detail reservation performance under strong noise and the judgment performance of different noise characteristics has strong dependence and rather weak robustness. In order to reduce the effects of high-density salt and pepper noise on image quality when processing high-noise grayscale images, an improved two-dimensional maximum Shannon entropy median filter (TSETMF) is proposed for the adaptive selection of a threshold to enhance the filter performance while stably and effectively retaining the details of the images. The framework of the proposed improved TSETMF algorithm is designed in detail. The noise in images is filtered by means of automatically partitioning a window size, the threshold value of which is adaptively calculated using two-dimensional maximum Shannon entropy. The theoretical model is…

Federated learning, as a novel distributed machine learning mode, enables the training of machine learning models on multiple devices while ensuring data privacy. However, the existence of single-point-of-failure bottlenecks, malicious threats, scalability of federated learning implementation, and lack of incentive mechanisms have seriously hindered the development of federated learning technology. In recent years, as a distributed ledger, blockchain has the characteristics of decentralization, tamper-proof, transparency, security, etc., which can solve the issues encountered in the above-mentioned federated learning. Particularly, the integration of federated learning and blockchain leads to a new paradigm, called blockchain-based federated learning (BFL), which has been successfully applied in many application scenarios. This paper aims to provide a comprehensive review of recent efforts on blockchain-based federated learning. More concretely, we propose and design a taxonomy of blockchain-based federated learning models, along with providing a comprehensive summary of the state…

The integrative potential of LPBF-printed parts for various innovative applications depends upon the robustness and infallibility of the part quality. Eliminating or sufficiently reducing factors contributing to the formation of defects is an integral step to achieving satisfiable part quality. Significant research efforts have been conducted to understand and quantify the triggers and origins of LPBF defects by investigating the material properties and process parameters for LPBF-printed geometries using various sensing technologies and techniques. Frequently, combinations of sensing techniques are applied to deepen the understanding of the investigated phenomena. The main objectives of this review are to cover the roles of selective sensing technologies by (1) providing a summary of LPBF metal print defects and their corresponding causes, (2) informing readers of the vast number and types of technologies and methodologies available to detect defects in LPBF-printed parts, and (3) equipping readers with publications geared towards defect detection using combinations of sensing…

This study examines Retrieval-Augmented Generation (RAG) in large language models (LLMs) and their significant application for undertaking systematic literature reviews (SLRs). RAG-based LLMs can potentially automate tasks like data extraction, summarization, and trend identification. However, while LLMs are exceptionally proficient in generating human-like text and interpreting complex linguistic nuances, their dependence on static, pre-trained knowledge can result in inaccuracies and hallucinations. RAG mitigates these limitations by integrating LLMs’ generative capabilities with the precision of real-time information retrieval. We review in detail the three key processes of the RAG framework—retrieval, augmentation, and generation. We then discuss applications of RAG-based LLMs to SLR automation and highlight future research topics, including integration of domain-specific LLMs, multimodal data processing and generation, and utilization of multiple retrieval sources. We propose a framework of RAG-based LLMs for automating SRLs, which covers four stages of SLR process: literature…

The research problem of this systematic review was whether green 6G networks can integrate energy-efficient Industrial Internet of Things (IIoT) in terms of distributed artificial intelligence, green 6G pervasive edge computing communication networks and big-data-based intelligent decision algorithms. We show that sensor data fusion can be carried out in energy-efficient IoT smart industrial urban environments by cooperative perception and inference tasks. Our analyses debate on 6G wireless communication, vehicular IoT intelligent and autonomous networks, and energy-efficient algorithm and green computing technologies in smart industrial equipment and manufacturing environments. Mobile edge and cloud computing task processing capabilities of decentralized network control and power grid system monitoring were thereby analyzed. Our results and contributions clarify that sustainable energy efficiency and green power generation together with IoT decision support and smart environmental systems operate efficiently in distributed artificial intelligence 6G pervasive edge computing…

With the proliferation of the Internet, network complexities for both commercial and state organizations have significantly increased, leading to more sophisticated and harder-to-detect network attacks. This evolution poses substantial challenges for intrusion detection systems, threatening the cybersecurity of organizations and national infrastructure alike. Although numerous deep learning techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and graph neural networks (GNNs) have been applied to detect various network attacks, they face limitations due to the lack of standardized input data, affecting model accuracy and performance. This paper proposes a novel preprocessing method for flow data from network intrusion detection systems (NIDSs), enhancing the efficacy of a graph neural network model in malicious flow detection. Our approach initializes graph nodes with data derived from flow features and constructs graph edges through the analysis of IP relationships within the system. Additionally, we propose a new graph model based on the…

In recent times, the significance of advancing road transportation technologies has notably increased. This is mainly driven by the escalating need for road transportation systems that are not only safe but also environmentally sustainable. Moreover, enhancing fuel efficiency in road vehicles (i.e., automobiles) holds the potential to contribute significantly to the reduction of a country’s economic vulnerability (i.e., improved energy security), by reducing the reliance on energy imports. While research reports and scientific journals provide valuable information on automobile fuel consumption, it is essential to continually update our understanding of the factors that influence it. This document aims to review and describe the various factors, both direct and indirect, that play a role in an automotive’s overall energy efficiency and fuel consumption.

The interest in natural deep eutectic solvents (NADESs) in green technology as an alternative to organic solvents has grown over the past decades. In this work, for the first time, the effect of dilution with water on the physicochemical and antimicrobial properties of lactic acid-based NADES with choline chloride (NADES1), sorbitol (NADES2), and glucose (NADES3) was systematically studied. According to FTIR data, after the dilution of NADESs with water, the strong hydrogen bonds weakened, however, were not destroyed after dilution of up to 40% water. The dilution of NADES with water resulted in a linear decrease in density and refractive index and in a linear increase in pH. The equations for the prediction of NADES density, pH, and refractive index as a function of water content were calculated. The viscosity decreased by half after adding approximately 10% water. The initial viscosity of NADES2 and NADES3 was significantly different. However, after adding 20% of the water, the viscosity was almost the same. The most pronounced decrease in surface tension (by 46.7%) was found for…

The integration of wind and solar energy with green hydrogen technologies represents an innovative approach toward achieving sustainable energy solutions. This review examines state-of-the-art strategies for synthesizing renewable energy sources, aimed at improving the efficiency of hydrogen (H2) generation, storage, and utilization. The complementary characteristics of solar and wind energy, where solar power typically peaks during daylight hours while wind energy becomes more accessible at night or during overcast conditions, facilitate more reliable and stable hydrogen production. Quantitatively, hybrid systems can realize a reduction in the levelized cost of hydrogen (LCOH) ranging from EUR 3.5 to EUR 8.9 per kilogram, thereby maximizing the use of renewable resources but also minimizing the overall H2 production and infrastructure costs. Furthermore, advancements such as enhanced electrolysis technologies, with overall efficiencies rising from 6% in 2008 to over 20% in the near future, illustrate significant progress in this domain. The review also addresses operational…

The integration of artificial intelligence (AI) in healthcare management marks a significant advance in technological innovation, promising transformative effects on healthcare processes, patient care, and the efficacy of emergency responses. The scientific novelty of the study lies in its integrated approach, combining systematic review and predictive algorithms to provide a comprehensive understanding of AI’s role in improving healthcare management across different contexts. Covering the period between 2019 and 2023, which includes the global challenges posed by the COVID-19 pandemic, this research investigates the operational, strategic, and emergency response implications of AI adoption in the healthcare sector. It further examines how the impact of AI varies across temporal and geographical contexts. The study addresses two main research objectives: to explore how AI influences healthcare management in operational, strategic, and emergency response domains, and to identify variations in the impact of AI on healthcare management based on temporal and geographical contexts.…

The You Only Look Once (YOLO) series of object detection models is widely recognized for its efficiency and real-time performance, particularly under the challenging conditions of underwater environments, characterized by insufficient lighting and visual disturbances. By modifying the YOLOv9s model, this study aims to improve the accuracy and real-time capabilities of underwater object detection, resulting in the introduction of the YOLOv9s-UI detection model. The proposed model incorporates the Dual Dynamic Token Mixer (D-Mixer) module from TransXNet to improve feature extraction capabilities. Additionally, it integrates a feature fusion network design from the LocalMamba network, employing channel and spatial attention mechanisms. These attention modules effectively guide the feature fusion process, significantly enhancing detection accuracy while maintaining the model’s compact size of only 9.3 M. Experimental evaluation on the UCPR2019 underwater object dataset shows that the YOLOv9s-UI model has higher accuracy and recall than the existing YOLOv9s model, as well as excellent…

Industry 4.0, which was proposed ten years ago to address both the industry’s strengths and faults, has finally been replaced by Industry 5.0. It seeks to put human welfare at the core of manufacturing systems, achieving societal goals beyond employment and growth to firmly provide wealth for the long-term advancement of all of humanity. The purpose of this research is to examine the risks involved in the adoption of Industry 5.0’s architecture. The paper discusses the significance of Industry 5.0 and the advanced technology needed for this industrial revolution, followed by a detailed discussion of Industry 5.0’s human-centric strategy. The comprehensive literature review has resulted in the identification of risks and their mitigation strategies in Industry 5.0 architecture. A taxonomy with respect to different categories of risks has also been proposed. This study classifies Industry 5.0 system assets, identifies platform-independent risks, and develops countermeasures to protect against potential threats, irrespective of the business or domain.