The 100 most-cited articles in Applied Sciences, 2023

In recent years, the rise of advanced artificial intelligence technologies has had a profound impact on many fields, including education and research. One such technology is ChatGPT, a powerful large language model developed by OpenAI. This technology offers exciting opportunities for students and educators, including personalized feedback, increased accessibility, interactive conversations, lesson preparation, evaluation, and new ways to teach complex concepts. However, ChatGPT poses different threats to the traditional education and research system, including the possibility of cheating on online exams, human-like text generation, diminished critical thinking skills, and difficulties in evaluating information generated by ChatGPT. This study explores the potential opportunities and threats that ChatGPT poses to overall education from the perspective of students and educators. Furthermore, for programming learning, we explore how ChatGPT helps students improve their programming skills. To demonstrate this, we conducted different coding-related experiments with ChatGPT, including…

The world’s primary modes of transportation are facing two major problems: rising oil costs and increasing carbon emissions. As a result, electric vehicles (EVs) are gaining popularity as they are independent of oil and do not produce greenhouse gases. However, despite their benefits, several operational issues still need to be addressed for EV adoption to become widespread. This research delves into the evolution of EVs over time and highlights their benefits, including reducing carbon emissions and air pollution. It also explores the challenges and difficulties faced in their adoption, such as the high cost of infrastructure, scarcity of charging stations, limited range or range anxiety, and the performance of batteries. To overcome these challenges, potential solutions include enhancing the charging infrastructure, increasing the number of charging stations, using battery swapping techniques, and improving battery technology to address range anxiety and reduce charging times. Governments can incentivize consumers to purchase EVs through tax credits or subsidies and invest in…

The use of artificial intelligence (AI) is becoming more prevalent across industries such as healthcare, finance, and transportation. Artificial intelligence is based on the analysis of large datasets and requires a continuous supply of high-quality data. However, using data for AI is not without challenges. This paper comprehensively reviews and critically examines the challenges of using data for AI, including data quality, data volume, privacy and security, bias and fairness, interpretability and explainability, ethical concerns, and technical expertise and skills. This paper examines these challenges in detail and offers recommendations on how companies and organizations can address them. By understanding and addressing these challenges, organizations can harness the power of AI to make smarter decisions and gain competitive advantage in the digital age. It is expected, since this review article provides and discusses various strategies for data challenges for AI over the last decade, that it will be very helpful to the scientific research community to create new and novel ideas…

Transformers are models that implement a mechanism of self-attention, individually weighting the importance of each part of the input data. Their use in image classification tasks is still somewhat limited since researchers have so far chosen Convolutional Neural Networks for image classification and transformers were more targeted to Natural Language Processing (NLP) tasks. Therefore, this paper presents a literature review that shows the differences between Vision Transformers (ViT) and Convolutional Neural Networks. The state of the art that used the two architectures for image classification was reviewed and an attempt was made to understand what factors may influence the performance of the two deep learning architectures based on the datasets used, image size, number of target classes (for the classification problems), hardware, and evaluated architectures and top results. The objective of this work is to identify which of the architectures is the best for image classification and under what conditions. This paper also describes the importance of the Multi-Head Attention…

International students face unique challenges in pursuing higher education in a foreign country. To address these challenges and enhance their academic experience, higher education institutions are increasingly exploring the use of artificial intelligence (AI) applications. This research essay aims to investigate the impact of AI on the education of international students. Instead of a traditional literature review, it employs a research approach to examine the potential applications of AI and discuss associated concerns. The research paper explores various AI applications, such as personalized learning experiences, adaptive testing, predictive analytics, and chatbots for learning and research. By analyzing the role of AI in education for international students, this research paper sheds light on how AI can improve learning efficiency and provide customized educational support. Additionally, it identifies significant risks and limitations, including privacy concerns, cultural differences, language proficiency, and ethical implications, which must be effectively addressed. The…

Fourier-transform infrared (FTIR) spectroscopy is a fast and simple technique for functional group identification. This work provides a review and insight into the application and interpretation of FTIR spectroscopy for cementitious binders that comprise ordinary Portland cement, alkaline-activated binders, geopolymers, and material characterization for civil engineering material applications. This technique can be used to identify different compounds and a moiety of bond vibrations in inorganic molecules such as Si-O, -OH, H-O-H (water), C-O (carbonate or carbonation), aluminosilicate (Si-O-T, where T is Al or Si), and S-O (sulfate or gypsum) found in hydrated cement, alkaline binders, and geopolymers. The prominent bands include those representing carbonation (CO32− 1390–1475 cm−1), calcium carbonate (871, 1792–2516 cm−1), hydroxylation and water molecules (1607, 3400–3650 cm−1), strength skeletal framework compositions or Al-Si substitutions, silicate organization (C-A-S-H, N-A-S-H, or C-S-H (950–1055 cm−1), and sulfate (600–680, 1080–1100 cm−1). Some of the factors that could…

Artificial intelligence (AI) technologies are used in many dimensions of our lives, including education. Motivated by the increasing use of AI technologies and the current state of the art, this study examines research on AI from the perspective of online distance education. Following a systematic review protocol and using data mining and analytics approaches, the study examines a total of 276 publications. Accordingly, time trend analysis increases steadily with a peak in recent years, and China, India, and the United States are the leading countries in research on AI in online learning and distance education. Computer science and engineering are the research areas that make the most of the contribution, followed by social sciences. t-SNE analysis reveals three dominant clusters showing thematic tendencies, which are as follows: (1) how AI technologies are used in online teaching and learning processes, (2) how algorithms are used for the recognition, identification, and prediction of students’ behaviors, and (3) adaptive and personalized learning empowered through artificial…

Machine learning applications are having a great impact on the global economy by transforming the data processing method and decision making. Agriculture is one of the fields where the impact is significant, considering the global crisis for food supply. This research investigates the potential benefits of integrating machine learning algorithms in modern agriculture. The main focus of these algorithms is to help optimize crop production and reduce waste through informed decisions regarding planting, watering, and harvesting crops. This paper includes a discussion on the current state of machine learning in agriculture, highlighting key challenges and opportunities, and presents experimental results that demonstrate the impact of changing labels on the accuracy of data analysis algorithms. The findings recommend that by analyzing wide-ranging data collected from farms, incorporating online IoT sensor data that were obtained in a real-time manner, farmers can make more informed verdicts about factors that affect crop growth. Eventually, integrating these technologies can transform…

Sentiment analysis is a critical subfield of natural language processing that focuses on categorizing text into three primary sentiments: positive, negative, and neutral. With the proliferation of online platforms where individuals can openly express their opinions and perspectives, it has become increasingly crucial for organizations to comprehend the underlying sentiments behind these opinions to make informed decisions. By comprehending the sentiments behind customers’ opinions and attitudes towards products and services, companies can improve customer satisfaction, increase brand reputation, and ultimately increase revenue. Additionally, sentiment analysis can be applied to political analysis to understand public opinion toward political parties, candidates, and policies. Sentiment analysis can also be used in the financial industry to analyze news articles and social media posts to predict stock prices and identify potential investment opportunities. This paper offers an overview of the latest advancements in sentiment analysis, including preprocessing techniques, feature…

Wearable technology is increasingly vital for improving sports performance through real-time data analysis and tracking. Both professional and amateur athletes rely on wearable sensors to enhance training efficiency and competition outcomes. However, further research is needed to fully understand and optimize their potential in sports. This comprehensive review explores the measurement and monitoring of athletic performance, injury prevention, rehabilitation, and overall performance optimization using body wearable sensors. By analyzing wearables’ structure, research articles across various sports, and commercial sensors, the review provides a thorough analysis of wearable sensors in sports. Its findings benefit athletes, coaches, healthcare professionals, conditioners, managers, and researchers, offering a detailed summary of wearable technology in sports. The review is expected to contribute to future advancements in wearable sensors and biometric data analysis, ultimately improving sports performance. Limitations such as privacy concerns, accuracy issues, and costs are…

Seniors who live alone at home are at risk of falling and injuring themselves and, thus, may need a mobile robot that monitors and recognizes their poses automatically. Even though deep learning methods are actively evolving in this area, they have limitations in estimating poses that are absent or rare in training datasets. For a lightweight approach, an off-the-shelf 2D pose estimation method, a more sophisticated humanoid model, and a fast optimization method are combined to estimate joint angles for 3D pose estimation. As a novel idea, the depth ambiguity problem of 3D pose estimation is solved by adding a loss function deviation of the center of mass from the center of the supporting feet and penalty functions concerning appropriate joint angle rotation range. To verify the proposed pose estimation method, six daily poses were estimated with a mean joint coordinate difference of 0.097 m and an average angle difference per joint of 10.017 degrees. In addition, to confirm practicality, videos of exercise activities and a scene of a person falling were filmed, and the joint angle…

Weather and climate prediction have been crucial in human history for enabling effective agricultural planning, safeguarding against natural disasters, and facilitating strategic decision-making in various sectors. In this context, the need for accurate and timely forecasting is highly significant. Machine learning has the potential to improve the accuracy and speed of weather and climate prediction. This comprehensive survey assesses the efficacy of Machine Learning (ML) and Machine Learning-Enhanced (ML-Enhanced) methodologies in short-term weather forecasting and medium-to-long-term climate prediction. Acknowledging the historical importance of weather and climate prediction as crucial tools affecting human life and societal functions, the paper scrutinizes over 20 methods, highlighting six that appear to be at the forefront of ML applications in meteorological forecasting. While ML shows promising results in short-term weather prediction, its role remains supplementary in medium-to-long-term climate forecasting due to the complexity of climate conditions and limited data…

Regular inspection and monitoring of buildings and infrastructure, that is collectively called the built environment in this paper, is critical. The built environment includes commercial and residential buildings, roads, bridges, tunnels, and pipelines. Automation and robotics can aid in reducing errors and increasing the efficiency of inspection tasks. As a result, robotic inspection and monitoring of the built environment has become a significant research topic in recent years. This review paper presents an in-depth qualitative content analysis of 269 papers on the use of robots for the inspection and monitoring of buildings and infrastructure. The review found nine different types of robotic systems, with unmanned aerial vehicles (UAVs) being the most common, followed by unmanned ground vehicles (UGVs). The study also found five different applications of robots in inspection and monitoring, namely, maintenance inspection, construction quality inspection, construction progress monitoring, as-built modeling, and safety inspection. Common research areas investigated by researchers…

The rise of Industry 4.0, which employs emerging powerful and intelligent technologies and represents the digital transformation of manufacturing, has a significant impact on society, industry, and other production sectors. The industrial scene is witnessing ever-increasing pressure to improve its agility and versatility to accommodate the highly modularized, customized, and dynamic demands of production. One of the key concepts within Industry 4.0 is the smart factory, which represents a manufacturing/production system with interconnected processes and operations via cyber-physical systems, the Internet of Things, and state-of-the-art digital technologies. This paper outlines the design of a smart cyber-physical system that complies with the innovative smart factory framework for Industry 4.0 and implements the core industrial, computing, information, and communication technologies of the smart factory. It discusses how to combine the key components (pillars) of a smart factory to create an intelligent manufacturing system. As a demonstration of a simplified smart factory model, a…

Ceramic matrix materials have attracted great attention from researchers and industry due to their material properties. When used in engineering systems, and especially in aero-engine applications, they can result in reduced weight, higher temperature capability, and/or reduced cooling needs, each of which increases efficiency. This is where high-temperature ceramics have made considerable progress, and ceramic matrix composites (CMCs) are in the foreground. CMCs are classified into non-oxide and oxide-based ones. Both families have material types that have a high potential for use in high-temperature propulsion applications. The oxide materials discussed will focus on alumina and aluminosilicate/mullite base material families, whereas for non-oxides, carbon, silicon carbide, titanium carbide, and tungsten carbide CMC material families will be discussed and analyzed. Typical oxide-based ones are composed of an oxide fiber and oxide matrix (Ox-Ox). Some of the most common oxide subcategories are alumina, beryllia, ceria, and zirconia ceramics. On the other hand, the largest number of…

Scanning electron microscopy (SEM) is a powerful tool in the domains of materials science, mining, and geology owing to its enormous potential to provide unique insight into micro and nanoscale worlds. This comprehensive review discusses the background development of SEM, basic SEM operation, including specimen preparation and image processing, and the fundamental theoretical calculations underlying SEM operation. It provides a foundational understanding for engineers and scientists who have never had a chance to dig in depth into SEM, contributing to their understanding of the workings and development of this robust analytical technique. The present review covers how SEM serves as a crucial tool in mineral characterization, with specific discussion on the workings and research fronts of SEM-EDX, SEM-AM, SEM-MLA, and QEMSCAN. With automation gaining pace in the development of all spheres of technology, understanding the uncertainties in SEM measurements is very important. The constraints in mineral phase identification by EDS spectra and sample preparation are conferred. In the end,…

This research aims to explore the potential impact of the ChatGPT on b-learning methodologies in engineering education, specifically in mathematics. The study focuses on how the use of these artificial intelligence tools can affect the acquisition of critical thinking, problem-solving, and group work skills among students. The research also analyzes the students’ perception of the reliability, usefulness, and importance of these tools in academia. The study collected data through a survey of 110 students enrolled in a Mathematics I course in BEng Aerospace Engineering where a blended methodology, including flipped teaching, escape room gamification, problem-solving, and laboratory sessions and exams with a computer algebraic system were used. The data collected were analyzed using statistical methods and tests for significance. Results indicate students have quickly adopted ChatGPT tool, exhibiting high confidence in their responses (3.4/5) and general usage in the learning process (3.61/5), alongside a positive evaluation. However, concerns arose regarding the potential impact on…

Industrialization and population growth have significantly increased the demand for lightweight, high-strength materials for construction and automotive applications, ultimately increasing the demand for eco-friendly materials. Due to its environmental acceptability, technological feasibility, and economic viability, natural fiber-reinforced composite exhibits many potential engineering applications. However, the production and recycling of natural fibers are expensive. Researchers are now comparing natural fiber-reinforced composites with synthetic composites to determine the best materials, especially for construction and automotive engineering applications. This review paper focuses on natural fiber reinforced epoxy composites’ physical, mechanical, and thermal characteristics. These properties are critical for the effective design and use of composite materials such as construction and automotive applications. This review begins with a background of epoxy and natural fibers. The physical and chemical treatment for natural fiber composites to improve their properties is also…

In the Industry 5.0 era, companies are leveraging the potential of cutting-edge technologies such as artificial intelligence for more efficient and green human-centric production. In a similar approach, project management would benefit from artificial intelligence in order to achieve project goals by improving project performance, and consequently, reaching higher sustainable success. In this context, this paper examines the role of artificial intelligence in emerging project management through a systematic literature review; the applications of AI techniques in the project management performance domains are presented. The results show that the number of influential publications on artificial intelligence-enabled project management has increased significantly over the last decade. The findings indicate that artificial intelligence, predominantly machine learning, can be considerably useful in the management of construction and IT projects; it is notably encouraging for enhancing the planning, measurement, and uncertainty performance domains by providing promising forecasting and…

Electric vehicles (EVs) are universally recognized as an incredibly effective method of lowering gas emissions and dependence on oil for transportation. Electricity, rather than more traditional fuels like gasoline or diesel, is used as the main source of energy to recharge the batteries in EVs. Future oil demand should decline as a result of the predicted rise in the number of EVs on the road. The charging infrastructure is considered as a key element of EV technology where the recent research is mostly focused. A strong charging infrastructure that serves both urban and rural areas, especially those with an unstable or nonexistent electrical supply, is essential in promoting the global adoption of EVs. Followed by different EV structures such as fuel-cell- and battery-integrated EVs, the charging infrastructures are thoroughly reviewed in three modes, specifically—off-grid (standalone), grid-connected, and hybrid modes (capable of both standalone and grid-connected operations). It will be interesting for the readers to understand in detail several energy-source-based charging…

Object detection remains a pivotal aspect of remote sensing image analysis, and recent strides in Earth observation technology coupled with convolutional neural networks (CNNs) have propelled the field forward. Despite advancements, challenges persist, especially in detecting objects across diverse scales and pinpointing small-sized targets. This paper introduces YOLO-SE, a novel YOLOv8-based network that innovatively addresses these challenges. First, the introduction of a lightweight convolution SEConv in lieu of standard convolutions reduces the network’s parameter count, thereby expediting the detection process. To tackle multi-scale object detection, the paper proposes the SEF module, an enhancement based on SEConv. Second, an ingenious Efficient Multi-Scale Attention (EMA) mechanism is integrated into the network, forming the SPPFE module. This addition augments the network’s feature extraction capabilities, adeptly handling challenges in multi-scale object detection. Furthermore, a dedicated prediction head for tiny object detection is incorporated, and the original detection…

Forecasting the behavior of the stock market is a classic but difficult topic, one that has attracted the interest of both economists and computer scientists. Over the course of the last couple of decades, researchers have investigated linear models as well as models that are based on machine learning (ML), deep learning (DL), reinforcement learning (RL), and deep reinforcement learning (DRL) in order to create an accurate predictive model. Machine learning algorithms can now extract high-level financial market data patterns. Investors are using deep learning models to anticipate and evaluate stock and foreign exchange markets due to the advantage of artificial intelligence. Recent years have seen a proliferation of the deep reinforcement learning algorithm’s application in algorithmic trading. DRL agents, which combine price prediction and trading signal production, have been used to construct several completely automated trading systems or strategies. Our objective is to enable interested researchers to stay current and easily imitate earlier findings. In this paper, we have…

Urban Resilience (UR) enables cities and communities to optimally withstand disruptions and recover to their pre-disruption state. There is an increasing number of interdisciplinary studies focusing on conceptual frameworks and/or tools seeking to enable more efficient decision-making processes that lead to higher levels of UR. This paper presents a systematic review of 68 Scopus-indexed journal papers published between 2011 and 2022 that focus on UR. The papers covered in this study fit three categories: literature reviews, conceptual models, and analytical models. The results of the review show that the major areas of discussion in UR publications include climate change, disaster risk assessment and management, Geographic Information Systems (GIS), urban and transportation infrastructure, decision making and disaster management, community and disaster resilience, and green infrastructure and sustainable development. The main research gaps identified include: a lack of a common resilience definition and multidisciplinary analysis, a need for a unified scalable and adoptable UR…

Bioactive plant chemicals are considered to be rich and useful for creating nanomaterials. The current work investigated the biosynthesis of silver nanoparticles (AgNPs) using ethanolic flaxseed extract as an efficient reducing factor. The production of AgNPs was verified by color-shifting observation of the mixture of silver nitrate (AgNO3) from yellow to a reddish suspension after the addition of the extract and by evaluating it by UV–visible inspection. Additionally, FTIR spectrum was used to support the identification of functional groups. The morphology and structure of AgNPs were assessed using scanning electron microscopy (SEM), and X-ray diffraction (XRD) examinations, which revealed spherical AgNPs with a diameter of 46.98 ± 12.45 nm and a crystalline structure. The zeta potential (ZP) and dynamic light scattering (DLS) measurements of AgNPs revealed values of −44.5 mV and 231.8 nm, respectively, suggesting appropriate physical stability. The antibacterial activity of AgNPs was investigated against Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus, and…

Deep learning (DL) has been introduced in automatic heart-abnormality classification using ECG signals, while its application in practical medical procedures is limited. A systematic review is performed from perspectives of the ECG database, preprocessing, DL methodology, evaluation paradigm, performance metric, and code availability to identify research trends, challenges, and opportunities for DL-based ECG arrhythmia classification. Specifically, 368 studies meeting the eligibility criteria are included. A total of 223 (61%) studies use MIT-BIH Arrhythmia Database to design DL models. A total of 138 (38%) studies considered removing noise or artifacts in ECG signals, and 102 (28%) studies performed data augmentation to extend the minority arrhythmia categories. Convolutional neural networks are the dominant models (58.7%, 216) used in the reviewed studies while growing studies have integrated multiple DL structures in recent years. A total of 319 (86.7%) and 38 (10.3%) studies explicitly mention their evaluation paradigms, i.e., intra- and inter-patient paradigms, respectively,…

The potential for an intelligent transportation system (ITS) has been made possible by the growth of the Internet of things (IoT) and artificial intelligence (AI), resulting in the integration of IoT and ITS—known as the Internet of vehicles (IoV). To achieve the goal of automatic driving and efficient mobility, IoV is now combined with modern communication technologies (such as 5G) to achieve intelligent connected vehicles (ICVs). However, IoV is challenged with security risks in the following five (5) domains: ICV security, intelligent device security, service platform security, V2X communication security, and data security. Numerous AI models have been developed to mitigate the impact of intrusion threats on ICVs. On the other hand, the rise in explainable AI (XAI) results from the requirement to inject confidence, transparency, and repeatability into the development of AI for the security of ICV and to provide a safe ITS. As a result, the scope of this review covered the XAI models used in ICV intrusion detection systems (IDSs), their taxonomies, and outstanding research…

Every year, close to 8 million tons of waste crab, shrimp and lobster shells are produced globally, as well as 10 million tons of waste oyster, clam, scallop and mussel shells. The disposed shells are frequently dumped at sea or sent to landfill, where they modify soils, waters and marine ecosystems. Waste shells are a major by-product, which should become a new raw material to be used to the best of their potential. There are a number of applications for waste shells in many fields, such as agriculture, medicine, chemical production, construction, environmental protection, cosmetic industry, food and feed industry, and a plethora of other (often niche) applications, which are being developed by the day. This review provides a broad picture of crustacean and mollusc shell waste management and reutilization possibilities, reviewing well established, current, and potential strategies, particularly from the standpoint of sustainability challenges and energy demand.

Imbalanced Data (ID) is a problem that deters Machine Learning (ML) models from achieving satisfactory results. ID is the occurrence of a situation where the quantity of the samples belonging to one class outnumbers that of the other by a wide margin, making such models’ learning process biased towards the majority class. In recent years, to address this issue, several solutions have been put forward, which opt for either synthetically generating new data for the minority class or reducing the number of majority classes to balance the data. Hence, in this paper, we investigate the effectiveness of methods based on Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) mixed with a variety of well-known imbalanced data solutions meaning oversampling and undersampling. Then, we propose a CNN-based model in combination with SMOTE to effectively handle imbalanced data. To evaluate our methods, we have used KEEL, breast cancer, and Z-Alizadeh Sani datasets. In order to achieve reliable results, we conducted our experiments 100 times with randomly shuffled data…

The world’s population has reached 8 billion and is projected to reach 9.7 billion by 2050, increasing the demand for food production. Artificial intelligence (AI) technologies that optimize resources and increase productivity are vital in an environment that has tensions in the supply chain and increasingly frequent weather events. This study performed a systemic review of the literature using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology on artificial intelligence technologies applied to agriculture. It retrieved 906 relevant studies from five electronic databases and selected 176 studies for bibliometric analysis. The quality appraisal step selected 17 studies for the analysis of the benefits, challenges, and trends of AI technologies used in agriculture. This work showed an evolution in the area with increased publications over the last five years, with more than 20 different AI techniques applied in the 176 studies analyzed, with machine learning, convolutional neural networks, IoT, big data, robotics, and computer vision being the…

Digital twins are virtual replicas of physical objects or systems. This new technology is increasingly being adopted in industry to improve the monitoring and efficiency of products and organizations. In healthcare, digital human twins (DHTs) represent virtual copies of patients, including tissues, organs, and physiological processes. Their application has the potential to transform patient care in the direction of increasingly personalized data-driven medicine. The use of DHTs can be integrated with digital twins of healthcare institutions to improve organizational management processes and resource allocation. By modeling the complex multi-omics interactions between genetic and environmental factors, DHTs help monitor disease progression and optimize treatment plans. Through digital simulation, DHT models enable the selection of the most appropriate molecular therapy and accurate 3D representation for precision surgical planning, together with augmented reality tools. Furthermore, they allow for the development of tailored early diagnosis protocols and new targeted drugs.…

The recent advancements in security approaches have significantly increased the ability to identify and mitigate any type of threat or attack in any network infrastructure, such as a software-defined network (SDN), and protect the internet security architecture against a variety of threats or attacks. Machine learning (ML) and deep learning (DL) are among the most popular techniques for preventing distributed denial-of-service (DDoS) attacks on any kind of network. The objective of this systematic review is to identify, evaluate, and discuss new efforts on ML/DL-based DDoS attack detection strategies in SDN networks. To reach our objective, we conducted a systematic review in which we looked for publications that used ML/DL approaches to identify DDoS attacks in SDN networks between 2018 and the beginning of November 2022. To search the contemporary literature, we have extensively utilized a number of digital libraries (including IEEE, ACM, Springer, and other digital libraries) and one academic search engine (Google Scholar). We have analyzed the relevant studies and categorized…

We are currently witnessing an unprecedented era of digital transformation in sports, driven by the revolutions in Artificial Intelligence (AI), Virtual Reality (VR), Augmented Reality (AR), and Data Visualization (DV). These technologies hold the promise of redefining sports performance analysis, automating data collection, creating immersive training environments, and enhancing decision-making processes. Traditionally, performance analysis in sports relied on manual data collection, subjective observations, and standard statistical models. These methods, while effective, had limitations in terms of time and subjectivity. However, recent advances in technology have ushered in a new era of objective and real-time performance analysis. AI has revolutionized sports analysis by streamlining data collection, processing vast datasets, and automating information synthesis. VR introduces highly realistic training environments, allowing athletes to train and refine their skills in controlled settings. AR overlays digital information onto the real sports environment, providing real-time…

Attacks on networks are currently the most pressing issue confronting modern society. Network risks affect all networks, from small to large. An intrusion detection system must be present for detecting and mitigating hostile attacks inside networks. Machine Learning and Deep Learning are currently used in several sectors, particularly the security of information, to design efficient intrusion detection systems. These systems can quickly and accurately identify threats. However, because malicious threats emerge and evolve regularly, networks need an advanced security solution. Hence, building an intrusion detection system that is both effective and intelligent is one of the most cognizant research issues. There are several public datasets available for research on intrusion detection. Because of the complexity of attacks and the continually evolving detection of an attack method, publicly available intrusion databases must be updated frequently. A convolutional recurrent neural network is employed in this study to construct a deep-learning-based hybrid intrusion detection system that…

The Intrusion Detection System (IDS) is an effective tool utilized in cybersecurity systems to detect and identify intrusion attacks. With the increasing volume of data generation, the possibility of various forms of intrusion attacks also increases. Feature selection is crucial and often necessary to enhance performance. The structure of the dataset can impact the efficiency of the machine learning model. Furthermore, data imbalance can pose a problem, but sampling approaches can help mitigate it. This research aims to explore machine learning (ML) approaches for IDS, specifically focusing on datasets, machine algorithms, and metrics. Three datasets were utilized in this study: KDD 99, UNSW-NB15, and CSE-CIC-IDS 2018. Various machine learning algorithms were chosen and examined to assess IDS performance. The primary objective was to provide a taxonomy for interconnected intrusion detection systems and supervised machine learning algorithms. The selection of datasets is crucial to ensure the suitability of the model construction for IDS usage. The evaluation was conducted for both…

This article provides a comprehensive overview of the fairness issues in artificial intelligence (AI) systems, delving into its background, definition, and development process. The article explores the fairness problem in AI through practical applications and current advances and focuses on bias analysis and fairness training as key research directions. The paper explains in detail the concept, implementation, characteristics, and use cases of each method. The paper explores strategies to reduce bias and improve fairness in AI systems, reviews challenges and solutions to real-world AI fairness applications, and proposes future research directions. In addition, this study provides an in-depth comparative analysis of the various approaches, utilizing cutting-edge research information to elucidate their different characteristics, strengths, and weaknesses. The results of the comparison provide guidance for future research. The paper concludes with an overview of existing challenges in practical applications and suggests priorities and solutions for future research. The conclusions…

Blockchain can be realized as a distributed and decentralized database, also known as a “distributed ledger,” that is shared among the nodes of a computer network. Blockchain is a form of democratized and distributed database for storing information electronically in a digital format. Under the framework of Industry 4.0, the digitization and digitalization of manufacturing and production systems and networks have been focused, thus Big Data sets are a necessity for any manufacturing activity. Big Data sets are becoming a useful resource as well as a byproduct of the activities/processes taking place. However, there is an imminent risk of cyberattacks. The contribution of blockchain technology to intelligent manufacturing can be summarized as (i) data validity protection, (ii) inter- and intra-organizational communication organization, and (iii) efficiency improvement of manufacturing processes. Furthermore, the need for increased cybersecurity is magnified as the world is heading towards a super smart and intelligent societal model, also known as “Society 5.0,” and the industrial…

Nanotechnology is a discipline of science and engineering that emphasizes developing, modifying, characterizing, and using nanoscale components in a variety of applications. Owing to their multiple advantages, including adhesion strength, surface hardness, long-term and extra-high-temperature corrosion resistance, improvement of interfacial behavior, etc., nanocoatings are efficiently utilized to minimize the influence of a corrosive environment. Additionally, nanocoatings are often applied in thinner and finer concentrations, allowing for greater versatility in instrumentation and reduced operating and maintenance costs. The exemplary physical coverage of the coated substrate is facilitated by the fine dimensions of nanomaterials and the significant density of their grounded boundaries. For instance, fabricated self-healing eco-sustainable corrosion inhibitors including PAC/CuONPs, PAC/Fe3O4NPs, and PAC/NiONPs, with uniform distributions and particulate sizes of 23, 10, and 43 nm, correspondingly, were effective in producing PAC/MONPs nanocomposites which exhibited IE% of 93.2,…

This study examines Industry 4.0-based technologies, focusing on the barriers to their implementation in European small- and medium-sized enterprises (SMEs). The purpose of this research was to determine the most significant obstacles that prevent SMEs from implementing smart manufacturing, as well as to identify the most important components of such an operationalization and to evaluate whether only large businesses have access to technological opportunities given the financial complexities of such an adoption. The study is premised on the notion that, in the setting of cyber-physical production systems, the gap between massive corporations and SMEs may result in significant disadvantages for the latter, leading to their market exclusion by the former. The research aim was achieved by secondary data analysis, where previously gathered data were assessed and analyzed. The need to investigate this topic originates from the fact that SMEs require more research than large corporations, which are typically the focus of mainstream debates. The findings validated Industry 4.0′s critical…

This paper offers a comprehensive overview of machine learning (ML) methodologies and algorithms, highlighting their practical applications in the critical domain of water resource management. Environmental issues, such as climate change and ecosystem destruction, pose significant threats to humanity and the planet. Addressing these challenges necessitates sustainable resource management and increased efficiency. Artificial intelligence (AI) and ML technologies present promising solutions in this regard. By harnessing AI and ML, we can collect and analyze vast amounts of data from diverse sources, such as remote sensing, smart sensors, and social media. This enables real-time monitoring and decision making in water resource management. AI applications, including irrigation optimization, water quality monitoring, flood forecasting, and water demand forecasting, enhance agricultural practices, water distribution models, and decision making in desalination plants. Furthermore, AI facilitates data integration, supports decision-making processes, and enhances overall water management…

The capabilities of autonomous mobile robotic systems have been steadily improving due to recent advancements in computer science, engineering, and related disciplines such as cognitive science. In controlled environments, robots have achieved relatively high levels of autonomy. In more unstructured environments, however, the development of fully autonomous mobile robots remains challenging due to the complexity of understanding these environments. Many autonomous mobile robots use classical, learning-based or hybrid approaches for navigation. More recent learning-based methods may replace the complete navigation pipeline or selected stages of the classical approach. For effective deployment, autonomous robots must understand their external environments at a sophisticated level according to their intended applications. Therefore, in addition to robot perception, scene analysis and higher-level scene understanding (e.g., traversable/non-traversable, rough or smooth terrain, etc.) are required for autonomous robot navigation in unstructured outdoor environments. This paper provides a…

Three-dimensional scanning and 3D printing have become increasingly important tools in the field of cultural heritage. Three-dimensional scanning is used to create detailed digital models of cultural heritage sites, artifacts, and monuments, which can be used for research, restoration, and virtual display. Three-dimensional printing, on the other hand, allows for the creation of physical copies of cultural heritage objects, which can be used for education, exhibition, and preservation. The use of these technologies has many advantages, including the ability to document and preserve cultural heritage sites, artifacts, and monuments in a non-invasive manner, as well as the ability to create digital and physical replicas that can be used for education and exhibition purposes. However, there are also challenges, such as the need for specialized equipment and expertise, as well as concerns about the preservation of the original objects. Despite these challenges, 3D scanning and 3D printing have proven to be valuable tools in the field of cultural heritage preservation and their use is…

Although solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs) have been successfully used as drug delivery systems for about 30 years, the usage of these nanoparticles as food-grade nanovehicles for nutraceuticals or bioactive compounds has been, relatively speaking, scarcely investigated. With fast-increasing interest in the incorporation of a wide range of bioactives in food formulations, as well as health awareness of consumers, there has been a renewed urge for the development of food-compatible SLNs and/or NLCs as nanovehicles for improving water dispersibility, stability, bioavailability, and bioactivities of many lipophilic nutraceuticals or poorly soluble bioactives. In this review, the development of food-grade SLNs and NLCs, as well as their utilization as nanosized delivery systems for lipophilic or hydrophobic nutraceuticals, was comprehensively reviewed. First, the structural composition and preparation methods of food-grade SLNs and NLCs were simply summarized. Next, some key issues about the usage of such nanoparticles as oral nanovehicles, e.g.,…

Sentiment analysis has become an important area of research in natural language processing. This technique has a wide range of applications, such as comprehending user preferences in ecommerce feedback portals, politics, and in governance. However, accurate sentiment analysis requires robust text representation techniques that can convert words into precise vectors that represent the input text. There are two categories of text representation techniques: lexicon-based techniques and machine learning-based techniques. From research, both techniques have limitations. For instance, pre-trained word embeddings, such as Word2Vec, Glove, and bidirectional encoder representations from transformers (BERT), generate vectors by considering word distances, similarities, and occurrences ignoring other aspects such as word sentiment orientation. Aiming at such limitations, this paper presents a sentiment classification model (named LeBERT) combining sentiment lexicon, N-grams, BERT, and CNN. In the model, sentiment lexicon, N-grams, and BERT are used to vectorize words selected from a section of…

Earth slopes’ stability analysis is a key task in geotechnical engineering that provides a detailed view of the slope conditions used to implement appropriate stabilizations. In the stability analysis process, calculating the safety factor (F.S) plays an essential part in the stability assessment, which guarantees operations’ success. Providing accurate and reliable F.S can be used to improve the stability analysis procedure as well as stabilizations. In this regard, researchers used computational intelligent methodologies to reach highly accurate F.S calculations. The presented study focused on the F.S estimation process and attempted to provide a comparative analysis based on computational intelligence and machine learning methods. In this regard, the well-known multilayer perceptron (MLP), decision tree (DT), support vector machines (SVM), and random forest (RF) learning algorithms were used to predict/calculate F.S for the earth slopes. These machine learning classifiers have a strong capability predict the F.S under certain conditions for slope failures and uncertainties. These…

Although the effectiveness of machine learning (ML) for machine diagnosis has been widely established, the interpretation of the diagnosis outcomes is still an open issue. Machine learning models behave as black boxes; therefore, the contribution given by each of the selected features to the diagnosis is not transparent to the user. This work is aimed at investigating the capabilities of the SHapley Additive exPlanation (SHAP) to identify the most important features for fault detection and classification in condition monitoring programs for rotating machinery. The authors analyse the case of medium-sized bearings of industrial interest. Namely, vibration data were collected for different health states from the test rig for industrial bearings available at the Mechanical Engineering Laboratory of Politecnico di Torino. The Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) diagnosis models are explained by means of the SHAP. Accuracies higher than 98.5% are achieved for both the models using the SHAP as a criterion for feature selection. It is found that the skewness and the…

Global environmental pollutants are becoming intense because of the increasing human population, urbanisation, and industrialisation. Human health and the ecosystem are affected by soil and water contamination. Therefore, creating strategies is essential to tackle this persistent issue. In the process, the health and environmental risk associated with these pollutants can be signifi-cantly reduced. Previously, traditional remediation techniques have been employed in combating these environmental pollutants, proving ineffective. Mycoremediation, which uses fungi or their compounds to remediate environmental pollutants, has shown to be a cost-efficient, environmen-tally friendly, and effective method of environmental remediation that includes organic, inorganic, and emerging contaminants (antibiotics, pharmaceuticals). This review provides an overview of various mycoremediation approaches through fungi for biosorption, precipitation, biotransfor-mation, and sequestration of environmental pollutants. In addition, the removal of metals, persis-tent organic pollutants, and other emerging…

The Folin-Ciocalteu assay is a widely used method for measuring the total phenolic content (TPC) in honey, but it can be affected by the presence of reducing sugars in honey, which can lead to interference and an over-estimation of its TPC. To optimize the Folin-Ciocalteu assay for honey analysis, the effect of pH on the assay was investigated. A number of pH scenarios were tested using different concentrations of Na2CO3 (0.00%, 0.75%, 0.94%, and 7.50%) in order to minimize reducing sugar interference and maximize the reaction of phenolics in the assay. The modified TPC method was then validated in accordance with current International Council on Harmonisation (ICH) guidelines. The findings of this study demonstrate that the traditional Folin-Ciocalteu assay (using 7.50% aqueous Na2CO3 solution, pH 10.8) leads to a significant overestimation of the TPC of honey due to the interference of reducing sugars. However, a pH of 7.9, achieved by using a 0.75% aqueous Na2CO3 solution, provides suitable conditions to account for most of the phenolic compounds without interference from…

Deep learning (DL) has made significant strides in medical imaging. This review article presents an in-depth analysis of DL applications in medical imaging, focusing on the challenges, methods, and future perspectives. We discuss the impact of DL on the diagnosis and treatment of diseases and how it has revolutionized the medical imaging field. Furthermore, we examine the most recent DL techniques, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), and their applications in medical imaging. Lastly, we provide insights into the future of DL in medical imaging, highlighting its potential advancements and challenges.

Breast cancer causes hundreds of women’s deaths each year. The manual detection of breast cancer is time-consuming, complicated, and prone to inaccuracy. For Breast Cancer (BC) detection, several imaging methods are explored. However, sometimes misidentification leads to unnecessary treatment and diagnosis. Therefore, accurate detection of BC can save many people from unnecessary surgery and biopsy. Due to recent developments in the industry, deep learning’s (DL) performance in processing medical images has significantly improved. Deep Learning techniques successfully identify BC from ultrasound images due to their superior prediction ability. Transfer learning reuses knowledge representations from public models built on large-scale datasets. However, sometimes Transfer Learning leads to the problem of overfitting. The key idea of this research is to propose an efficient and robust deep-learning model for breast cancer detection and classification. Therefore, this paper presents a novel DeepBraestCancerNet DL model for breast cancer detection and classification. The proposed…

The continuous temperature rise has raised global concerns about CO2 emissions. As the country with the largest CO2 emissions, China is facing the challenge of achieving large CO2 emission reductions (or even net-zero CO2 emissions) in a short period. With the strong support and encouragement of the Chinese government, technological breakthroughs and practical applications of carbon capture, utilization, and storage (CCUS) are being aggressively pursued, and some outstanding accomplishments have been realized. Based on the numerous information from a wide variety of sources including publications and news reports only available in Chinese, this paper highlights the latest CCUS progress in China after 2019 by providing an overview of known technologies and typical projects, aiming to provide theoretical and practical guidance for achieving net-zero CO2 emissions in the future.

Because of the complexity, nonlinearity, and volatility, stock market forecasting is either highly difficult or yields very unsatisfactory outcomes when utilizing traditional time series or machine learning techniques. To cope with this problem and improve the complex stock market’s prediction accuracy, we propose a new hybrid novel method that is based on a new version of EMD and a deep learning technique known as long-short memory (LSTM) network. The forecasting precision of the proposed hybrid ensemble method is evaluated using the KSE-100 index of the Pakistan Stock Exchange. Using a new version of EMD that uses the Akima spline interpolation technique instead of cubic spline interpolation, the noisy stock data are first divided into multiple components technically known as intrinsic mode functions (IMFs) varying from high to low frequency and a single monotone residue. The highly correlated sub-components are then used to build the LSTM network. By comparing the proposed hybrid model with a single LSTM and other ensemble models such as the support vector machine (SVM), Random…

People with autistic spectrum disorders (ASDs) have difficulty recognizing and engaging with others. The symptoms of ASD may occur in a wide range of situations. There are numerous different types of functions for people with an ASD. Although it may be possible to reduce the symptoms of ASD and enhance the quality of life with appropriate treatment and support, there is no cure. Developing expert systems for identifying ASD based on the facial landmarks of children is the main contribution for improvements in the healthcare system in Saudi Arabia for detecting ASD at an early stage. However, deep learning algorithms have provided outstanding performances in a variety of pattern-recognition studies. The use of techniques based on convolutional neural networks (CNNs) has been proposed by several scholars to use in investigations of ASD. At present, there is no diagnostic test available for ASD, making this diagnosis challenging. Clinicians focus on a patient’s behavior and developmental history. Therefore, using the facial landmarks of children has become very important for detecting…

The concept of smart and connected healthcare has emerged in response to the growing demand for the improvement of healthcare systems and the increasing prevalence of chronic diseases. Looking towards the future, smart healthcare holds great potential to transform the healthcare industry by providing more efficient, personalized, and accessible healthcare services. This paper delves into the concept of intelligent, interconnected, and customized healthcare systems within the Internet of Medical Things (IoMT) framework. It explores the utilization of cutting-edge technologies, including the IoMT, in conjunction with big data, cloud computing, artificial intelligence, and blockchain to provide healthcare services that are not only more efficient but also more convenient and personalized. It draws on existing literature, bibliometric data, and global marketing analysis to gain a deeper understanding of these technologies and their impact on the healthcare system. We have explored several upcoming features of the Healthcare 5.0 paradigm, which represents the next evolution in healthcare…

Cloud computing (CC) is becoming an essential technology worldwide. This approach represents a revolution in data storage and collaborative services. Nevertheless, security issues have grown with the move to CC, including intrusion detection systems (IDSs). Intruders have developed advanced tools that trick the traditional IDS. This study attempts to contribute toward solving this problem and reducing its harmful effects by boosting IDS performance and efficiency in a cloud environment. We build two models based on deep neural networks (DNNs) for this study: the first model is built on a multi-layer perceptron (MLP) with backpropagation (BP), and the other is trained by MLP with particle swarm optimization (PSO). We use these models to deal with binary and multi-class classification on the updated cybersecurity CSE-CIC-IDS2018 dataset. This study aims to improve the accuracy of detecting intrusion attacks for IDSs in a cloud environment and to enhance other performance metrics. In this study, we document all aspects of our experiments in depth. The results show that the best…

This article presents the history of zinc, its production and demand. The quantity of zinc production, both primary zinc from ores and concentrates, and secondary zinc from scrap and zinc-rich waste, was discussed. A comprehensive economic analysis covers zinc prices in the years 1960–2021. The basic methods of obtaining zinc from ores, including pyrometallurgical (Imperial Smelting Process ISP, Kivcet, Ausmelt) and hydrometallurgical (roasting–leaching–electrowinning RLE, atmospheric direct leaching ADL, Engitec Zinc Extraction EZINEX, zinc pressure leach) and their short characteristics, are presented. The global zinc market and the main areas of its application were analyzed. Technologies used for the recovery of zinc from scrap are discussed along with their characteristics. Galvanized steel is the main source of secondary zinc, both in the galvanizing process and in the remelting of galvanized steel. It can be easily recycled with other scrap steel in the electric arc furnace (EAF) for steel production. Currently, with high volatility in the price of zinc, as well as its…

Science and engineering research studies are currently concentrating on synthesizing, designing, producing, and consuming ecologically benign chemical species to replace harmful chemicals. This is due to the increasing demands of conservation knowledge and strict ecological regulations. Numerous environmentally friendly substitutes produced from natural resources, including biopolymers, plant extracts, chemical pharmaceuticals (drugs), and so on, are now frequently used as inhibitors to replace dangerous corrosion inhibitors. Many compounds have been extensively used. A range of methods, including physisorption, chemisorption, barrier protection, thin-film growth, and electrochemical procedures, will be used to provide corrosion resistance. The various kinds of corrosion inhibitors (CIs), the mechanisms underlying inhibition, and the evaluation procedures have all been covered in-depth. This review provides an overview of the relevant literature in which researchers and scientists used different types of CIs, the effect of CIs on metals, and information about designs and mechanisms…

This review study examines the relationship between radon and geology, including its origins, transmission, and accumulation, as well as its impact on human health and mitigation strategies. The decay of uranium and thorium produces radon, a radioactive gas. Its concentration in the environment fluctuates based on local geology, soil permeability, climate, and terrain, as well as regional, seasonal, and daily meteorological conditions. Exposure to radon indoors is associated with an increased risk of lung cancer, making it a significant public health concern. Measuring radon concentrations in indoor environments is essential for identifying high-risk areas and devising effective mitigation strategies, such as ventilation, fissure sealing, and exhaust installation. The need for greater knowledge of regional and seasonal fluctuations in radon concentration, as well as the development of more effective and cost-effective measuring and mitigation strategies, has implications for future research. To influence public health policies and practices, more research on the long-term health…

An economy based on renewable energy sources is the hallmark of responsible companies. Climate policy and energy crisis commitments have led to a search for alternative ways to produce energy. Bioenergy is considered the most consistent renewable energy source due to its economic and environmental benefits. Biogas and biomethane are promising forms of renewable energy derived from widely available evergreen raw materials. Agricultural, animal, industrial and food wastes are excellent substrates used to produce clean and sustainable energy in a circular economy context. Their conversion into biogas and biomethane through the anaerobic digestion (AD) process is an efficient solution to the treatment of waste of different origins. The production and use of biomethane favor important environmental advantages, such as the reduction in greenhouse gas emissions compared with those deriving from the use of conventional fossil fuels. This review would like to highlight modern trends and approaches to evaluate processes and strategies to control biogas and biomethane production. In…

The primary goal of this research is to present a novel computational intelligence approach of the AI-based Levenberg–Marquardt scheme under the influence of backpropagated neural network (LMS-BPNN) for optimizing MHD ternary hybrid nanofluid using Casson fluid over a porous shrinking sheet in the existence of thermal radiation (Rd) effects. The governing partial differential equations (PDEs) showing the Casson ternary hybrid nanofluid are converted into a system of ordinary differential equations (ODEs) with suitable transformations. The numerical data is constructed as a reference with bvp4c (MATLAB built-in function used to solve a system of ODEs) by varying Casson fluid parameters (β), magnetic field (M), porosity (S), nanoparticle concentrations (ϕ1=ϕ2=ϕ3), and thermal radiation (Rd) effects across all LMS-BPNN scenarios. The numerical data-sheet is divided into 80% of training, 10% of testing, and 10% of validation for LMS-BPNN are used to analyze the estimated solution and its assessment with a numerical solution using bvp4c is discussed. The efficiency and consistency of…

Buildings consume a significant amount of energy throughout their lifecycle; Thus, sustainable energy management is crucial for all buildings, and controlling energy consumption has become increasingly important for achieving sustainable construction. Digital twin (DT) technology, which lies at the core of Industry 4.0, has gained widespread adoption in various fields, including building energy analysis. With the ability to monitor, optimize, and predict building energy consumption in real time. DT technology has enabled sustainable building energy management and cost reduction. This paper provides a comprehensive review of the development and application of DT technology in building energy. Specifically, it discusses the background of building information modeling (BIM) and DT technology and their application in energy optimization in buildings. Additionally, this article reviews the application of DT technology in building energy management, indoor environmental monitoring, and building energy efficiency evaluation. It also examines the benefits and challenges of implementing DT…

Due to the harm forest fires cause to the environment and the economy as they occur more frequently around the world, early fire prediction and detection are necessary. To anticipate and discover forest fires, several technologies and techniques were put forth. To forecast the likelihood of forest fires and evaluate the risk of forest fire-induced damage, artificial intelligence techniques are a crucial enabling technology. In current times, there has been a lot of interest in machine learning techniques. The machine learning methods that are used to identify and forecast forest fires are reviewed in this article. Selecting the best forecasting model is a constant gamble because each ML algorithm has advantages and disadvantages. Our main goal is to discover the research gaps and recent studies that use machine learning techniques to study forest fires. By choosing the best ML techniques based on particular forest characteristics, the current research results boost prediction power.

Speech emotion recognition (SER) is the process of predicting human emotions from audio signals using artificial intelligence (AI) techniques. SER technologies have a wide range of applications in areas such as psychology, medicine, education, and entertainment. Extracting relevant features from audio signals is a crucial task in the SER process to correctly identify emotions. Several studies on SER have employed short-time features such as Mel frequency cepstral coefficients (MFCCs), due to their efficiency in capturing the periodic nature of audio signals. However, these features are limited in their ability to correctly identify emotion representations. To solve this issue, this research combined MFCCs and time-domain features (MFCCT) to enhance the performance of SER systems. The proposed hybrid features were given to a convolutional neural network (CNN) to build the SER model. The hybrid MFCCT features together with CNN outperformed both MFCCs and time-domain (t-domain) features on the Emo-DB, SAVEE, and RAVDESS datasets by achieving an accuracy of 97%, 93%, and 92%…

The ideal biomaterials substitute for bone repair should possess the following characteristics: provide osteogenic, osteoinductive and osteoconductive properties; stimulate the neo-angiogenesis process; absence of antigenic, teratogenic or carcinogenic reactions; avoid the systemic toxicity complications; assure satisfactory support and stability from mechanical properties point of view; hydrophilic nature of the surface properties and good interface with human bone; good handling in clinical condition and ability to be easy sterilized; and able to be supplied in sufficient quantities with reduced costs. Despite years of effort, the perfect bone reconstruction material has not yet been developed; further effort is required to make this objective feasible. The aim of this article is to provide a contemporary and comprehensive overview of the grafting materials that can be applied for the treatment of bone defects by the clinicians from orthopedics surgery, neurosurgery and dentistry, discussing their properties, advantages and disadvantages, and illuminating present and future…

Characterizing the electric energy curve can improve the energy efficiency of existing buildings without any structural change and is the basis for controlling and optimizing building performance. Artificial Intelligence (AI) techniques show much potential due to their accuracy and malleability in the field of pattern recognition, and using these models it is possible to adjust the building services in real time. Thus, the objective of this paper is to determine the AI technique that best forecasts electrical loads. The suggested techniques are random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost), multilayer perceptron (MLP), long short-term memory (LSTM), and temporal convolutional network (Conv-1D). The conducted research applies a methodology that considers the bias and variance of the models, enhancing the robustness of the most suitable AI techniques for modeling and forecasting the electricity consumption in buildings. These techniques are evaluated in a single-family dwelling located in the United States. The performance comparison is…

People’s emotions play an important part in our daily life and can not only reflect psychological and physical states, but also play a vital role in people’s communication, cognition and decision-making. Variations in people’s emotions induced by external conditions are accompanied by variations in physiological signals that can be measured and identified. People’s psychological signals are mainly measured with electroencephalograms (EEGs), electrodermal activity (EDA), electrocardiograms (ECGs), electromyography (EMG), pulse waves, etc. EEG signals are a comprehensive embodiment of the operation of numerous neurons in the cerebral cortex and can immediately express brain activity. EDA measures the electrical features of skin through skin conductance response, skin potential, skin conductance level or skin potential response. ECG technology uses an electrocardiograph to record changes in electrical activity in each cardiac cycle of the heart from the body surface. EMG is a technique that uses electronic instruments to evaluate and record the electrical activity of muscles, which is…

The advancement in 3D printing techniques has raised the hope to use additively manufactured parts as final products in various industries. However, due to the layer-by-layer nature of AM parts, they are highly susceptible to failure when they are subjected to fatigue loading. This review provides a detailed account of the influence of 3D printing parameters on the fatigue properties of parts manufactured by fused filament fabrication (FFF). Existing standards for fatigue testing of polymers and their limitation for 3D-printed parts are discussed. In addition, the cyclic behaviour of polymers is reviewed, and the impact of 3D printing parameters on the mechanical behaviour of FFF parts under tensile, compressive, flexural, and bending fatigue is investigated according to the published results in the literature. Finally, a summary of the works undertaken and suggestions for future research are provided. The influence of 3D printing parameters on the fatigue performance of prints can be different from that seen in the case of static loading and strongly depends on the fatigue loading…

Electronic commerce has a strong connection with recommendation processes. There are various forms of recommendations, ranging from virtual assistants to online suggestions made in real time. Different algorithms and technologies are utilized for each form, and the choice of technique is dependent on the task at hand. For instance, artificial intelligence may utilize deep learning or machine learning techniques. The type of data also plays a role in determining the techniques used. Predictive modeling is applied to textual data, while image data requires image processing followed by AI algorithms for prediction. This study aimed to investigate the extent to which artificial intelligence is utilized in recommender systems for electronic commerce, as well as the current and future trends in the field. This was achieved through a systematic literature review of scientific articles from the past decade, using WosViewer for data collection and the Bibliometrix R package for analysis. The findings demonstrate that artificial intelligence works in conjunction with other technologies, such…

The structurally diverse bioactive compounds found in marine organisms represent valuable resources for the food and pharmaceutical industries. The marine ecosystem encompasses over half of the world’s biota, providing an extensive range of bioactive compounds that can be extracted from various marine life forms, including marine microorganisms (such as bacteria, cyanobacteria, and actinobacteria), algae (both macroalgae and microalgae), invertebrates (including sponges, mollusks, echinoderms, and crustaceans), and, most importantly, fish. Many of these organisms thrive in extreme marine environments, leading to the production of complex molecules with unique biological functions. Consequently, marine biomolecules, such as lipids (especially polyunsaturated fatty acids), proteins/peptides, polysaccharides, carotenoids, phenolics, and saponins, exhibit a wide range of biological properties and can serve as valuable components in nutraceuticals and functional foods. Nevertheless, most of these biomolecules are susceptible to oxidation and degradation; encapsulation-based technologies…

This paper provides a comprehensive review of the research progress, current state-of-the-art, and future research directions of energy storage systems. With the widespread adoption of renewable energy sources such as wind and solar power, the discourse around energy storage is primarily focused on three main aspects: battery storage technology, electricity-to-gas technology for increasing renewable energy consumption, and optimal configuration technology. The paper employs a visualization tool (CiteSpace) to analyze the existing works of literature and conducts an in-depth examination of the energy storage research hotspots in areas such as electrochemical energy storage, hydrogen storage, and optimal system configuration. It presents a detailed overview of common energy storage models and configuration methods. Based on the reviewed articles, the future development of energy storage will be more oriented toward the study of power characteristics and frequency characteristics, with more focus on the stability effects brought by transient shocks. This review article compiles and…

A smart city is where existing facilities and services are enhanced by digital technology to benefit people and companies. The most critical infrastructures in this city are interconnected. Increased data exchange across municipal domains aims to manage the essential assets, leading to more automation in city governance and optimization of the dynamic offered services. However, no clear guideline or standard exists for modeling these data flows. As a result, operators, municipalities, policymakers, manufacturers, solution providers, and vendors are forced to accept systems with limited scalability and varying needs. Nonetheless, it is critical to raise awareness about smart-city cybersecurity and implement suitable measures to safeguard citizens’ privacy and security because cyber threats seem to be well-organized, diverse, and sophisticated. This study aims to present an overview of cyber threats, attacks, and countermeasures on the primary domains of smart cities (smart government, smart mobility, smart environment, smart living, smart healthcare, smart economy, and smart people).…

This paper proposes a novel hybrid model for sentiment analysis. The model leverages the strengths of both the Transformer model, represented by the Robustly Optimized BERT Pretraining Approach (RoBERTa), and the Recurrent Neural Network, represented by Gated Recurrent Units (GRU). The RoBERTa model provides the capability to project the texts into a discriminative embedding space through its attention mechanism, while the GRU model captures the long-range dependencies of the embedding and addresses the vanishing gradients problem. To overcome the challenge of imbalanced datasets in sentiment analysis, this paper also proposes the use of data augmentation with word embeddings by over-sampling the minority classes. This enhances the representation capacity of the model, making it more robust and accurate in handling the sentiment classification task. The proposed RoBERTa-GRU model was evaluated on three widely used sentiment analysis datasets: IMDb, Sentiment140, and Twitter US Airline Sentiment. The results show that the model achieved an accuracy of 94.63% on IMDb, 89.59% on…

As the volume of data generated by information systems continues to increase, machine learning (ML) techniques have become essential for the extraction of meaningful insights. However, the sheer volume of data often causes these techniques to become sluggish. To overcome this, feature selection is a vital step in the pre-processing of data. In this paper, we introduce a novel K-nearest neighborhood (KNN)-based wrapper system for feature selection that leverages the iterative improvement ability of the weighted superposition attraction (WSA). We evaluate the performance of WSA against seven well-known metaheuristic algorithms, i.e., differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), flower pollination algorithm (FPA), symbiotic organisms search (SOS), marine predators’ algorithm (MPA) and manta ray foraging optimization (MRFO). Our extensive numerical experiments demonstrate that WSA is highly effective for feature selection, achieving a decrease of up to 99% in the number of features for large datasets without sacrificing classification accuracy.…

In the process of image feature extraction, noise in the image will greatly affect the accuracy of edge detection. In this paper, the image is filtered to remove noise before edge detection by using the algorithm of wavelet transformation. Different wavelet functions are used to decompose image. Based on the experimental results, the best denoising wavelet function is selected. Canny algorithm is used to detect the edge of the denoised image, and the result of edge detection is evaluated according to the Pratt quality factor. It is proved that wavelet transform can improve the edge detection results.

As the need for clean and sustainable energy sources grows rapidly, green hydrogen and ammonia have become promising sources of low-carbon energy and important key players in the transition to green energy. However, production and storage problems make it hard to use them widely. The goal of this review paper is to give a complete overview of the latest technology for the manufacture and storage of hydrogen and ammonia. This paper deals with hydrogen and ammonia synthesis and storage. It examines the most recent technological breakthroughs in areas such as electrolysis, reforming, C-ZEROS, HYSATA, DAE, sulfide, and SRBW, as well as novel storage techniques, such as solid-state storage, plasma kinetics, and POWERPASTE. This article examines the history of ammonia production and discusses some of the newer and more sustainable techniques for producing ammonia, such as electrochemical and biological approaches. This study also looks at how artificial intelligence (AI) and additive manufacturing (AM) could be used to revolutionize the way green hydrogen and ammonia are produced, with an…

There are several industrial processes in which heavy metals are used, including but not limited to chrome plating and tanning. Amongst the most toxic heavy metals to human health are arsenic, cadmium, chromium, lead, copper, nickel, and mercury. The aforementioned toxic metals possess the ability to cause contamination upon their release into the environment. Humans and aquatic and terrestrial animals are at risk from heavy metals in water and soil. Heavy metal toxicity has the potential to result in several health complications, such as renal and hepatic impairment, dermatological afflictions, cognitive lethargy, and potentially oncogenic manifestations. The removal of heavy metals from wastewater and soil can be accomplished using a variety of conventional methods, such as membrane filtration, reverse osmosis, chemical reduction, and adsorption. These methods have several disadvantages, such as generating an abundance of secondary pollutants, and entail significantly higher costs in comparison to biological methods. Conversely, eco-friendly techniques based on microbes have…

In recent decades, the urgency to protect and upgrade cultural heritage structures (CHS) has become of primary importance due to their unique value and potential areas of impact (economic, social, cultural, and environmental). Structural health monitoring (SHM) and the management of CHS are emerging as decisive safeguard measures aimed at assessing the actual state of the conservation and integrity of the structure. Moreover, the data collected from SHM are essential to plan cost-effective and sustainable maintenance solutions, in compliance with the basic preservation principles for historic buildings, such as minimum intervention. It is evident that, compared to new buildings, the application of SHM to CHS is even more challenging because of the uniqueness of each monitored structure and the need to respect its architectural and historical value. This paper aims to present a state-of-the-art evaluation of the current traditional and innovative SHM techniques adopted for CHS and to identify future research trends. First, a general introduction regarding the use of monitoring…

Chatbots have become increasingly popular in recent years, but they also present security risks and vulnerabilities that need to be addressed. This systematic literature review examines the existing research relating to information security in chatbots, identifying the potential threats, proposed solutions, and future directions for research. The review finds that chatbots face various security threats, including malicious input, user profiling, contextual attacks, and data breaches, and that solutions such as blockchain technology, end-to-end encryption, and organizational controls can be used to mitigate these concerns. The review also highlights the importance of maintaining user trust and addressing privacy concerns for the successful adoption and continued use of chatbots. A taxonomy developed in this review provides a useful framework for categorizing the articles and their findings. The review concludes by identifying future research directions that include developing more sophisticated authentication and authorization mechanisms, exploring the use of privacy-enhancing…

Cyber threats are attempts to secure unauthorized access to, change, or delete private information, to demand money from victims, or to disrupt business. Cybercrime includes everything from identity theft, malware threats, email and online fraud, to bank fraud. Businesses and individuals use this method to guard their data centers and other digital systems. The lack of scalability, sluggish response times, and inability to spot advanced and insider threats are among some of the problems with conventional approaches to network security. These flaws highlight the need for research to build more efficient and all-encompassing security methods to guard against the expanding variety of network attacks. Cybercriminals use AI and data poisoning, as well as model theft strategies to automate their attacks. A cyber security technique based on artificial intelligence is presented in this study for financial sector management (CS-FSM). In order to map and prevent unexpected risks from devouring a business, artificial intelligence is one of the best technologies. Using the proposed technique,…

Sign language recognition (SLR) is one of the crucial applications of the hand gesture recognition and computer vision research domain. There are many researchers who have been working to develop a hand gesture-based SLR application for English, Turkey, Arabic, and other sign languages. However, few studies have been conducted on Korean sign language classification because few KSL datasets are publicly available. In addition, the existing Korean sign language recognition work still faces challenges in being conducted efficiently because light illumination and background complexity are the major problems in this field. In the last decade, researchers successfully applied a vision-based transformer for recognizing sign language by extracting long-range dependency within the image. Moreover, there is a significant gap between the CNN and transformer in terms of the performance and efficiency of the model. In addition, we have not found a combination of CNN and transformer-based Korean sign language recognition models yet. To overcome the challenges, we proposed a convolution and…

Maintenance of production equipment has a key role in ensuring business continuity and productivity. Determining the implementation time and the appropriate selection of the scope of maintenance activities are necessary not only for the operation of industrial equipment but also for effective planning of the demand for own maintenance resources (spare parts, people, finances). A number of studies have been conducted in the last decade and many attempts have been made to use artificial intelligence (AI) techniques to model and manage maintenance. The aim of the article is to discuss the possibility of using AI methods and techniques to anticipate possible failures and respond to them in advance by carrying out maintenance activities in an appropriate and timely manner. The indirect aim of these studies is to achieve more effective management of maintenance activities. The main method applied is computational analysis and simulation based on the real industrial data set. The main results show that the effective use of preventive maintenance requires large amounts of reliable annotated…

Artificial intelligence (AI) is a contemporary, information-driven innovative technology. Prosthetic dentistry, also known as prosthodontics, is the restoration and reconstruction of missing teeth utilizing implants for permanent and removable prostheses. It enhances healthy soft and hard tissues, promoting oral health. This study examined the use of artificial intelligence in prosthodontics to diagnose abnormalities and create patient-specific prostheses. Two researchers searched Google Scholar, Scopus, PubMed/MEDLINE, EBSCO host, Science Direct, and Web of Science (MEDLINE, WOS, and KJD). Articles on AI in English were reviewed. We also collected the following broad article aspects: research and control groups, assessment methodology, outcomes, and quality rankings. This methodological study examined AI use in prosthodontics using the latest scientific findings. The findings were statistically evaluated using ANOVA. Titles and abstracts revealed 172 AI-related dentistry studies, which were analyzed in this research. Thirty-eight papers were eliminated. According to the evaluation,…

Phishing is an online threat where an attacker impersonates an authentic and trustworthy organization to obtain sensitive information from a victim. One example of such is trolling, which has long been considered a problem. However, recent advances in phishing detection, such as machine learning-based methods, have assisted in combatting these attacks. Therefore, this paper develops and compares four models for investigating the efficiency of using machine learning to detect phishing domains. It also compares the most accurate model of the four with existing solutions in the literature. These models were developed using artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and random forest (RF) techniques. Moreover, the uniform resource locator’s (URL’s) UCI phishing domains dataset is used as a benchmark to evaluate the models. Our findings show that the model based on the random forest technique is the most accurate of the other four techniques and outperforms other solutions in the literature.

In the era of technological transformation, mobility and transportation systems are becoming more intelligent and greener. Thanks to powerful technologies and tools, electric and autonomous vehicles are spreading worldwide, substituting internal combustion engine vehicles and revolutionizing the way to drive. In this context, this paper is an extended version of the paper “Digital Twin in Intelligent Transportation Systems: a Review published in 2020 7th International Conference on Control, Decision and Information Technologies (CoDIT)”. The aim of this paper is to provide a comprehensive review of the literature from the last five years on the use of digital twin (DT) technology for Intelligent Transportation Systems (ITSs), focusing on electric and autonomous vehicles. In particular, with respect to the previous work, the focus has been expanded to include DT integration with other cutting-edge technologies, such as the Internet of Things (IoT), Big Data, artificial intelligence (AI), machine learning (ML), and 5G for ITS. Moreover, this paper presents a broad perspective on…

Healthcare comprises the largest revenue and data boom markets. Sharing knowledge about healthcare is crucial for research that can help healthcare providers and patients. Several cloud-based applications have been suggested for data sharing in healthcare. However, the trustworthiness of third-party cloud providers remains unclear. The third-party dependency problem was resolved using blockchain technology. The primary objective of this growth was to replace the distributed system with a centralized one. Therefore, security is a critical requirement for protecting health records. Efforts have been made to implement blockchain technology to improve the security of this sensitive material. However, existing methods depend primarily on information obtained from medical examinations. Furthermore, they are ineffective for sharing continuously produced data streams from sensors and other monitoring devices. We propose a trustworthy access control system that uses smart contracts to achieve greater security while sharing electronic health records among various patients and healthcare…

Groundwater level (GWL) refers to the depth of the water table or the level of water below the Earth’s surface in underground formations. It is an important factor in managing and sustaining the groundwater resources that are used for drinking water, irrigation, and other purposes. Groundwater level prediction is a critical aspect of water resource management and requires accurate and efficient modelling techniques. This study reviews the most commonly used conventional numerical, machine learning, and deep learning models for predicting GWL. Significant advancements have been made in terms of prediction efficiency over the last two decades. However, while researchers have primarily focused on predicting monthly, weekly, daily, and hourly GWL, water managers and strategists require multi-year GWL simulations to take effective steps towards ensuring the sustainable supply of groundwater. In this paper, we consider a collection of state-of-the-art theories to develop and design a novel methodology and improve modelling efficiency in this field of evaluation. We examined 109 research…

Facing the problem of increasing waste, scientists, foundations, and companies around the globe resulted in ideas and invented technologies to slow down the process. Sources of waste range from industrial waste (e.g., construction and demolition materials, hazardous wastes, ashes) to municipal solid waste (e.g., food wastes, paper, cardboard, plastics, textiles). Modern solutions do not focus only on technological aspects of waste management but also on sociological ones. Thanks to the Internet and social media platforms, scientists can influence ecological consciousness and awareness on a much larger scale. This research was conducted using the search keywords related to modern technologies for waste management. This paper presents a spectrum of selected modern solutions that changed in recent years and how they have impacted waste management. It also discusses challenges and future directions of waste management in the context of the circular economy. The use of modern solutions in waste management allows to achieve selected goals of sustainable development.

Collecting time-series receive signal strength (RSS) observations and averaging them is a common method for dealing with RSS fluctuation. However, outliers in the time-series observations affect the averaging process, making this method less efficient. The Z-score method based on the median absolute deviation (MAD) scale estimator has been used to detect outliers, but it is only efficient with symmetrically distributed observations. Experimental analysis has shown that time-series RSS observations can have a symmetric or asymmetric distribution depending on the nature of the environment in which the measurement was taken. Hence, the use of the Z-score method with the MAD scale estimator will not be efficient. In this paper, the Sn scale estimator is proposed as an alternative to MAD to be used with the Z-score method in detecting outliers in time-series RSS observations. Performance comparison using an online RSS dataset shows that the Z-score with MAD and Sn as scale estimators falsely detected about 50% and 13%, respectively, of the RSS observations as outliers. Furthermore, the…

In the current era, blockchain has approximately 30 consensus algorithms. This architecturally distributed database stores data in an encrypted form with multiple checks, including elliptical curve cryptography (ECC) and Merkle hash tree. Additionally, many researchers aim to implement a public key infrastructure (PKI) cryptography mechanism to boost the security of blockchain-based data management. However, the issue is that many of these are required for advanced cryptographic protocols. For all consensus protocols, security features are required to be discussed because these consensus algorithms have recently been attacked by address resolution protocols (ARP), distributed denial of service attacks (DDoS), and sharding attacks in a permission-less blockchain. The existence of a byzantine adversary is perilous, and is involved in these ongoing attacks. Considering the above issues, we conducted an informative survey based on the consensus protocol attack on blockchain through the latest published article from IEEE, Springer, Elsevier, ACM, Willy, Hindawi, and other publishers. We…

Nowadays, manufacturing plants are required to be flexible to respond quickly to customer demands, adapting production and processes without affecting their efficiency. In this context, Industrial Robots (IRs) are a primary resource for modern factories due to their versatility which allows the execution of flexible, reconfigurable, and zero-defect manufacturing tasks. Even so, the control and programming of the commercially available IRs are limiting factors for their effective implementation, especially for dynamic production environments or when complex applications are required. These issues have stimulated the development of new technologies that support more efficient methods for robot control and programming. The goal of this research is to identify and evaluate the main approaches proposed in scientific papers and by the robotics industry in the last decades. After a critical review of the standard IR control schematic, the paper discusses the available control alternatives and summarizes their characteristics, range of applications, and remaining limitations.

X-ray diffraction (XRD) is a proven, powerful technique for determining the phase composition, structure, and microstructural features of crystalline materials. The use of machine learning (ML) techniques applied to crystalline materials research has increased significantly over the last decade. This review presents a survey of the scientific literature on applications of ML to XRD data analysis. Publications suitable for inclusion in this review were identified using the “machine learning X-ray diffraction” search term, keeping only English-language publications in which ML was employed to analyze XRD data specifically. The selected publications covered a wide range of applications, including XRD classification and phase identification, lattice and quantitative phase analyses, and detection of defects and substituents, as well as microstructural material characterization. Current trends in the field suggest that future efforts pertaining to the application of ML techniques to XRD data analysis will address shortcomings of ML approaches related to data quality and availability,…

Metaverse is the buzz technology of the moment raising attention both from academia and industry. Many stakeholders are considering an extension of their existing applications into the metaverse environment for more usability. The healthcare industry is gradually making use of the metaverse to improve quality of service and enhance living conditions. In this paper, we focus on the potential of digital anti-aging healthcare in the metaverse environment. We show how we can use metaverse environment to enhance healthcare service quality and increase the life expectancy of patients through more confident processes, such as chronic disease management, fitness, and mental health control, in the metaverse. The convergence of artificial intelligence (AI), blockchain (BC), Internet of Things (IoT), immersive technologies, and digital twin in the metaverse environment presents new scopes for the healthcare industry. By leveraging these technologies, healthcare providers can improve patient outcomes, reduce healthcare costs, and create new healthcare experiences for a better life, thus…

Abstract As living standards improve, modern products need to meet increasingly diversified and personalized user requirements. Traditional product design methods fall short due to their strong subjectivity, limited survey scope, lack of real-time data, and poor visual display. However, recent progress in big data and artificial intelligence (AI) are bringing a transformative big data and AI driven product design methodology with significant impact on many industries. Big data in the product lifecycle contains valuable information, such as customer preferences, market demands, product evaluation, and visual display: online product reviews reflect customer evaluations and requirements, while product images contain shape, color, and texture information that can inspire designers to quickly generate initial design schemes or even new product images. This survey provides a comprehensive review on big data and AI-driven product design, focusing on how big data of various modalities can be processed, analyzed, and exploited in aiding product design using AI algorithms. It identifies the…

In the world, brain tumor (BT) is considered the major cause of death related to cancer, which requires early and accurate detection for patient survival. In the early detection of BT, computer-aided diagnosis (CAD) plays a significant role, the medical experts receive a second opinion through CAD during image examination. Several researchers proposed different methods based on traditional machine learning (TML) and deep learning (DL). The TML requires hand-crafted features engineering, which is a time-consuming process to select an optimal features extractor and requires domain experts to have enough knowledge of optimal features selection. The DL methods outperform the TML due to the end-to-end automatic, high-level, and robust feature extraction mechanism. In BT classification, the deep learning methods have a great potential to capture local features by convolution operation, but the ability of global features extraction to keep Long-range dependencies is relatively weak. A self-attention mechanism in Vision Transformer (ViT) has the ability to model long-range dependencies…

Various deep learning techniques have recently been developed in many fields due to the rapid advancement of technology and computing power. These techniques have been widely applied in finance for stock market prediction, portfolio optimization, risk management, and trading strategies. Forecasting stock indices with noisy data is a complex and challenging task, but it plays an important role in the appropriate timing of buying or selling stocks, which is one of the most popular and valuable areas in finance. In this work, we propose novel hybrid models for forecasting the one-time-step and multi-time-step close prices of DAX, DOW, and S&P500 indices by utilizing recurrent neural network (RNN)–based models; convolutional neural network-long short-term memory (CNN-LSTM), gated recurrent unit (GRU)-CNN, and ensemble models. We propose the averaging of the high and low prices of stock market indices as a novel feature. The experimental results confirmed that our models outperformed the traditional machine-learning models in 48.1% and 40.7% of the cases in terms of the mean squared…

Immersive Virtual Reality (iVR) is a new technology, the novelty effect of which can reduce the enjoyment of iVR experiences and, especially, learning achievements when presented in the classroom; an effect that the interactive tutorial proposed in this research can help overcome. Its increasingly complex levels are designed on the basis of Mayer’s Cognitive Theory of Multimedia Learning, so that users can quickly gain familiarity with the iVR environment. The tutorial was included in an iVR learning experience for its validation with 65 users. It was a success, according to the user satisfaction and tutorial usability survey. First, it gained very high ratings for satisfaction, engagement, and immersion. Second, high skill rates suggested that it helped users to gain familiarity with controllers. Finally, a medium-high value for flow pointed to major concerns related to skill and challenges with this sort of iVR experience. A few cases of cybersickness also arose. The survey showed that only intense cybersickness levels significantly limited performance and enjoyment; low levels…

An Augmented Reality (AR) system is a technology that overlays digital information, such as images, sounds, or text, onto a user’s view of the real world, providing an enriched and interactive experience of the surrounding environment. It has evolved into a potent instrument for improving human perception and decision-making across various domains, including industrial, automotive, healthcare, and urban planning. This systematic literature review aims to offer a comprehensive understanding of AR technology, its limitations, and implementation challenges in the most significant areas of application in engineering and beyond. The review will explore the state-of-the-art AR techniques, their potential use cases, and the barriers to widespread adoption, while also identifying future research directions and opportunities for innovation in the rapidly evolving field of augmented reality. This study works as a compilation of the existing technologies in the subject, especially useful for beginners in AR or as a starting point for developers who seek to innovate or implement new…

Cracks seriously endanger the safe and stable operation of dams. It is important to detect surface cracks in a timely and accurate manner to ensure the safety and serviceability of a dam. The above-water crack detection technology of dams has been widely studied, but due to the complex underwater environment, above-water crack detection technology on dam surfaces cannot be directly applied to underwater crack detection. To adapt to the underwater detection environment and improve the efficiency and accuracy of underwater crack detection, many methods have been proposed for underwater crack detection, including sensor detection and image detection. This paper presents a systematic overview of the development and application practices of existing underwater crack detection technologies for concrete dams, focusing on methods that use underwater robots as underwater mobile carriers to acquire images that are combined with digital image processing algorithms to identify, locate, and quantify underwater cracks in dams. This method has been widely used for underwater crack detection on dam…

While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, ’Four-Know’ (Know-what, Know-why, Know-when, Know-how) and ’Four-Level’ (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised…

This review aims to address the current data on edible packaging systems used in food production. The growing global population, changes in the climate and dietary patterns, and the increasing need for environmental protection, have created an increasing demand for waste-free food production. The need for durable and sustainable packaging materials has become significant in order to avoid food waste and environmental pollution. Edible packaging has emerged as a promising solution to extend the shelf life of food products and reduce dependence on petroleum-based resources. This review analyzes the history, production methods, barrier properties, types, and additives of edible packaging systems. The review highlights the advantages and importance of edible packaging materials and describes how they can improve sustainability measures. The market value of edible packaging materials is expanding. Further research on and developments in edible food packaging materials are needed to increase sustainable, eco-friendly packaging practices that are significant for environmental protection…

Artificial Intelligence (AI) in the automotive industry allows car manufacturers to produce intelligent and autonomous vehicles through the integration of AI-powered Advanced Driver Assistance Systems (ADAS) and/or Automated Driving Systems (ADS) such as the Traffic Sign Recognition (TSR) system. Existing TSR solutions focus on some categories of signs they recognise. For this reason, a TSR approach encompassing more road sign categories like Warning, Regulatory, Obligatory, and Priority signs is proposed to build an intelligent and real-time system able to analyse, detect, and classify traffic signs into their correct categories. The proposed approach is based on an overview of different Traffic Sign Detection (TSD) and Traffic Sign Classification (TSC) methods, aiming to choose the best ones in terms of accuracy and processing time. Hence, the proposed methodology combines the Haar cascade technique with a deep CNN model classifier. The developed TSC model is trained on the GTSRB dataset and then tested on various categories of road signs. The achieved testing accuracy rate…