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Tumors in the brain can be life-threatening, making early and precise detection crucial for effective treatment and improved patient outcomes. Deep learning (DL) techniques have shown significant potential in automating the early diagnosis of brain tumors by analyzing magnetic resonance imaging (MRI), offering a more efficient and accurate approach to classification. Deep convolutional neural networks (DCNNs), which are a sub-field of DL, have the potential to analyze rapidly and accurately MRI data and, as such, assist human radiologists, facilitating quicker diagnoses and earlier treatment initiation. This study presents an ensemble of three high-performing DCNN models, i.e., DenseNet169, EfficientNetB0, and ResNet50, for accurate classification of brain tumors and non-tumor MRI samples. Our proposed ensemble model demonstrates significant improvements over various evaluation parameters compared to individual state-of-the-art (SOTA) DCNN models. We implemented ten SOTA DCNN models, i.e., EfficientNetB0, ResNet50, DenseNet169, DenseNet121, SqueezeNet, ResNet34, ResNet18, VGG16, VGG19, and LeNet5, and provided a detailed performance comparison. We evaluated these models using two learning rates (LRs) of 0.001 and 0.0001 and two batch sizes (BSs) of 64 and 128 and identified the optimal hyperparameters for each model. Our findings indicate that the ensemble approach outperforms individual models, having 92% accuracy, 90% precision, 92% recall, and an F1 score of 91% at a 64 BS and 0.0001 LR. This study not only highlights the superior performance of the ensemble technique but also offers a comprehensive comparison with the latest research. [ABSTRACT FROM AUTHOR]

Modern agriculture is characterized by the use of smart technology and precision agriculture to monitor crops in real time. The technologies enhance total yields by identifying requirements based on environmental conditions. Plant phenotyping is used in solving problems of basic science and allows scientists to characterize crops and select the best genotypes for breeding, hence eliminating manual and laborious methods. Additionally, plant phenotyping is useful in solving problems such as identifying subtle differences or complex quantitative trait locus (QTL) mapping which are impossible to solve using conventional methods. This review article examines the latest developments in image analysis for plant phenotyping using AI, 2D, and 3D image reconstruction techniques by limiting literature from 2020. The article collects data from 84 current studies and showcases novel applications of plant phenotyping in image analysis using various technologies. AI algorithms are showcased in predicting issues expected during the growth cycles of lettuce plants, predicting yields of soybeans in different climates and growth conditions, and identifying high-yielding genotypes to improve yields. The use of high throughput analysis techniques also facilitates monitoring crop canopies for different genotypes, root phenotyping, and late-time harvesting of crops and weeds. The high throughput image analysis methods are also combined with AI to guide phenotyping applications, leading to higher accuracy than cases that consider either method. Finally, 3D reconstruction and a combination with AI are showcased to undertake different operations in applications involving automated robotic harvesting. Future research directions are showcased where the uptake of smartphone-based AI phenotyping and the use of time series and ML methods are recommended. [ABSTRACT FROM AUTHOR]

Before vaccine development during the COVID-19 pandemic, Non-Pharmaceutical Interventions (NPIs) were the only solutions to mitigate COVID-19 infections. Governments continued to use them even after starting vaccine administration. In this research, we review different big data analytics models that assess and optimize the effectiveness of NPIs. These models are categorized into three big data analytics groups: descriptive, which measures the infection rate changes caused by NPIs; predictive, which predicts the future of the pandemic by implementing several NPIs; and data-driven prescriptive, which suggests optimal control policies. We further analyze each method's basic assumptions, limitations, and applicability during different pandemic phases and under different scenarios. This review of COVID-19 NPI evaluation methods will be beneficial for decision-makers to know which model to select for policy-making in possible future pandemics, which are more likely recently due to globalization. Finally, we suggest some future research directions. [ABSTRACT FROM AUTHOR]

Modern agriculture is characterized by the use of smart technology and precision agriculture to monitor crops in real time. The technologies enhance total yields by identifying requirements based on environmental conditions. Plant phenotyping is used in solving problems of basic science and allows scientists to characterize crops and select the best genotypes for breeding, hence eliminating manual and laborious methods. Additionally, plant phenotyping is useful in solving problems such as identifying subtle differences or complex quantitative trait locus (QTL) mapping which are impossible to solve using conventional methods. This review article examines the latest developments in image analysis for plant phenotyping using AI, 2D, and 3D image reconstruction techniques by limiting literature from 2020. The article collects data from 84 current studies and showcases novel applications of plant phenotyping in image analysis using various technologies. AI algorithms are showcased in predicting issues expected during the growth cycles of lettuce plants, predicting yields of soybeans in different climates and growth conditions, and identifying high-yielding genotypes to improve yields. The use of high throughput analysis techniques also facilitates monitoring crop canopies for different genotypes, root phenotyping, and late-time harvesting of crops and weeds. The high throughput image analysis methods are also combined with AI to guide phenotyping applications, leading to higher accuracy than cases that consider either method. Finally, 3D reconstruction and a combination with AI are showcased to undertake different operations in applications involving automated robotic harvesting. Future research directions are showcased where the uptake of smartphone-based AI phenotyping and the use of time series and ML methods are recommended.

The rapid identification of structural damage in pavements remains a challenging issue. This paper proposes an automatic identification method that combines nondestructive testing, the ground penetrating radar (GPR), and object detection (OD) models for pavement structural assessment. Radar wave image data were collected from a 40 km single-lane track road and processed to create the training data set of 2,000 labeled images with two types of structural distress. Four OD models, including Faster R-CNN, YOLOv5, YOLOv8, and DETR, were adapted and trained on the data set to recognize structural damage in the images, and their prediction performances were compared. The results demonstrated that YOLOv8 was the state-of-the-art (SOTA) model, which achieved precise identification of the location and type of pavement structural damage with a mean average precision (mAP) of 98.5%. This study contributes to the improvement of latest OD models in the non-destructive inspection, facilitating the development of real-time automated pavement assessment and maintenance decision systems. Practical Applications: This research addresses a common problem in civil engineering: how to quickly detect all hidden damage in pavement structures over long stretches of highway projects. Although there has been previous research in this area, finding a more reliable solution has proven to be a challenging goal. This study presents a method that utilizes the geological radar, a nondestructive testing technology, meaning it will not harm the road, along with artificial intelligence (AI) computer models to identify damage under the pavement surface. Think of it as teaching a computer to recognize cracks or looseness in the radar images of the road. Four different computer models were trained using 2,000 road images with known damage to teach them how to identify different damage features. The results were impressive—the computer could accurately identify the location and type of damage with a high level of confidence. In fact, the best model achieved a 98.5% score measured by mean average precision, which indicates its effectiveness. This research has significant practical applications as it streamlines the process of inspecting roads for structural problems, making it faster and more efficient. It can help with real-time evaluations and decisions about road maintenance. [ABSTRACT FROM AUTHOR]

Solid composites used in material extrusion additive manufacturing have experienced considerable expansion over the past 5 years, incorporating functional properties into 3D‐printed objects. This paper presents a systematic review that aims to: (I) analyze the current state of development in the field; (II) quantify and categorize the adopted polymeric matrices, reinforcing materials, feedstock shapes, characterization strategies, and extrusion mechanisms; and (III) identify the applications, limitations, and trends for future research. The PRISMA statement was followed and the databases Scopus, Web of Science, and PubMed were consulted. Among the 116 included studies, the use of customized filaments surpassed the commercially available ones, with particulate matter being the most common form of filler when melt‐mixed with the polymer. Polyamide is the most widely adopted matrix (30.3%), followed by PLA (22.0%) and ABS (17.4%). In terms of reinforcements, carbon fiber (32.4%), glass fiber (12.5%), and ceramics (12.5%) compose the podium as the most frequently used, with nanofillers receiving increasing attention. In the conducted analysis, no standardized protocols were identified that clearly encompassed the entire process from feedstock formulation to technical prototype fabrication. At last, recycling, exploration of biodegradable materials, and 4D printing were identified as the main opportunities for future research. Highlights: Reinforced polymers enable the enhancement of properties for 3D‐printed parts.Composite feedstock is usually formulated and mixed before printing.3D printers with filament‐ or screw‐based extrusion mechanisms have been used.Mechanical and morphological analyses are common in material characterization.No identified applications were employed in real‐world scenarios. [ABSTRACT FROM AUTHOR]

In recent times, Islamic Wealth Management (IWM) has garnered increasing attention for its potential impacts on sustainable development goals, yet its role in achieving these goals remains under-researched. This study employs an advanced analytical approach to explore the relationship between IWM and SDGs, aiming to illuminate emerging trends and address key issues. Utilizing a comprehensive methodology, the research synthesizes insights from a variety of scholarly articles, journals, and research papers over a specified timeframe. By critically analyzing existing knowledge, the study seeks to understand how IWM contributes to or challenges sustainable development. Key areas of focus include wealth distribution mechanisms, ethical investment practices, and the alignment of Islamic financial principles with SDGs. Additionally, it examines practical aspects, such as the implementation of Islamic financial instruments and the role of Islamic institutions in promoting sustainable socio-economic growth. The study also addresses contemporary challenges faced by IWM, providing a nuanced understanding of its current state and potential future paths. By highlighting both successes and obstacles, this research enriches the discourse on Islamic financial practices and the evolving landscape of global Islamic wealth management. Ultimately, the study identifies a significant interplay between IWM and sustainable development, focusing on wealth distribution, ethical investments, and practical implementation. [ABSTRACT FROM AUTHOR]

We present a simulation tool that utilizes advanced modeling techniques to quantify the customer-relevant features of different battery electric vehicles (BEV) concepts. By incorporating comprehensive longitudinal and lateral dynamics analyses, the tool provides an in-depth understanding of passenger vehicle concepts in various driving conditions. Furthermore, the tool emphasizes evaluating comfort, presenting a holistic view concluding into the overall driving experience. Metrics considering the vehicle’s packaging are employed to assess the comfort aspects of the concepts. Implementing this simulation tool achieves a more systematic and reliable approach to evaluating BEV concepts. The application based on the parameters of 18 state-of-the-art BEV proved its compliance in the presented categories with the results of physical tests performed by reputable magazines with great experience and comprehensice test protocols. Its academic foundation ensures the utilization of state-of-the-art methodologies and the integration of the latest research findings in vehicle dynamics and comfort. This simulation tool offers automotive engineers, researchers, and industry stakeholders a valuable resource for objectively assessing and benchmarking the performance and comfort aspects of BEV concepts. With its ability to provide accurate evaluations and reasoning for weaknesses, this tool has the potential to significantly contribute to the advancement and optimization of future electric vehicle designs by assessing concepts in early development stages before physical prototypes are available, allowing for cost-efficient modifications.

PurposeTo compare the diagnostic accuracy and explainability of a Vision Transformer deep learning technique, Data-efficient image Transformer (DeiT), and ResNet-50, trained on fundus photographs from the Ocular Hypertension Treatment Study (OHTS) to detect primary open-angle glaucoma (POAG) and identify the salient areas of the photographs most important for each model's decision-making process.DesignEvaluation of a diagnostic technology.Subjects participants and controlsOverall 66 715 photographs from 1636 OHTS participants and an additional 5 external datasets of 16 137 photographs of healthy and glaucoma eyes.MethodsData-efficient image Transformer models were trained to detect 5 ground-truth OHTS POAG classifications: OHTS end point committee POAG determinations because of disc changes (model 1), visual field (VF) changes (model 2), or either disc or VF changes (model 3) and Reading Center determinations based on disc (model 4) and VFs (model 5). The best-performing DeiT models were compared with ResNet-50 models on OHTS and 5 external datasets.Main outcome measuresDiagnostic performance was compared using areas under the receiver operating characteristic curve (AUROC) and sensitivities at fixed specificities. The explainability of the DeiT and ResNet-50 models was compared by evaluating the attention maps derived directly from DeiT to 3 gradient-weighted class activation map strategies.ResultsCompared with our best-performing ResNet-50 models, the DeiT models demonstrated similar performance on the OHTS test sets for all 5 ground-truth POAG labels; AUROC ranged from 0.82 (model 5) to 0.91 (model 1). Data-efficient image Transformer AUROC was consistently higher than ResNet-50 on the 5 external datasets. For example, AUROC for the main OHTS end point (model 3) was between 0.08 and 0.20 higher in the DeiT than ResNet-50 models. The saliency maps from the DeiT highlight localized areas of the neuroretinal rim, suggesting important rim features for classification. The same maps in the ResNet-50 models show a more diffuse, generalized distribution around the optic disc.ConclusionsVision Transformers have the potential to improve generalizability and explainability in deep learning models, detecting eye disease and possibly other medical conditions that rely on imaging for clinical diagnosis and management.

ABSTRACTThe evolution of manufacturing systems toward Industry 4.0 and 5.0 paradigms has pushed the diffusion of Machine Learning (ML) in this field. As the number of articles using ML to support manufacturing functions is expanding tremendously, the main objective of this review article is to provide a comprehensive and updated overview of these applications. 114 journal articles have been collected, analysed, and classified in terms of supervision approaches, function, ML algorithm, data inputs and outputs, and application domain. The findings show the fragmentation of the field and that most of the ML-based systems address limited objectives. Some inputs and outputs of the analysed support tools are shared across the reviewed contributions, and their possible combinations have been outlined. The advantages, limitations, and research opportunities of ML support in manufacturing are discussed. The paper outlines that the excessive specialization of the reviewed applications could be overcome by increasing the diffusion of transfer learning in the manufacturing domain.

In recent years, companies have adapted their business models towards a digital format whereby their internal organization, development, production and logistics processes have become part of a connected ecosystem. This adaptation process, known as digitalization, has largely driven organizational change in companies. Specifically in cooperatives, which are collective associations of professionals interested in common economic objectives, the new technology adoption has been configured as a solid strategy where digital transformation processes should be appropriately executed. In this context, the present study aims to identify the development of the characteristics of the digitalization process in the scientific literature and to link it to the cooperatives sector. To this end, a bibliometric study of the studies published in the period from 2000 to 2022 is developed. In this endeavor, processes such as bibliographic coupling, word co-occurrence, and co-citation analysis are computed and analyzed. The results reveal a map of relationships and key elements in the studied field of research, and where particularly prominent are areas directly linked to cooperatives, such as the circular economy, sustainability, or innovation. The results of this study contribute to the construction of a general overview of the publications developed to date in relation to digitalization and its state-of-the-art, as well as its future link to cooperatives. For the development process of digitalization in cooperatives, a total of five phases are identified, described, and discussed. Likewise, a total of 17 future research questions and 3 research propositions are discussed for subsequent research. Finally, theoretical and practical implications of the study are presented. [ABSTRACT FROM AUTHOR]

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Pedestrian crossings are an essential part of the urban landscape, providing safe passage for pedestrians to cross busy streets. While some are regulated by timed signals and are marked with signs and lights, others are simply marked on the road and do not have additional infrastructure. Nevertheless, the markings undergo wear and tear due to traffic, weather, and road maintenance activities. If pedestrian crossing markings are excessively worn, drivers may not be able to see them, which creates road safety issues. This paper presents a study of computer vision techniques that can be used to identify and classify pedestrian crossings. It first introduces the related concepts. Then, it surveys related work and categorizes existing solutions, highlighting their key features, strengths, and limitations. The most promising techniques are identified and described: Convolutional Neural Networks, Histogram of Oriented Gradients, Maximally Stable Extremal Regions, Canny Edge, and thresholding methods. Their performance is evaluated and compared on a custom dataset developed for this work. Insights on open issues and research opportunities in the field are also provided. It is shown that managers responsible for road safety, in the context of a smart city, can benefit from computer vision approaches to automate the process of determining the wear and tear of pedestrian crossings. [ABSTRACT FROM AUTHOR]

Sustainability in power electronics is a recent research topic. It takes place among current actions to grasp design choices that enable eco-design and circular economy in the domain. This paper shows the results and analysis of a literature review at the intersection of power electronics and sustainability without considering the reliability study of the power electronics systems. The first part explains the scope of the study. The second part shows a bibliometric analysis of the collected publications that underlines a pioneering position at the European level. The third part details the state-of-the-art and its analysis over four investigation topics which are: tools and methods, indicators, circularity and materials. This paper and the work behind are the results of collaboration at the French national level, as part of the workgroup CEPPS (Convertisseurs Electronique de Puissance Plus Soutenables—More Sustainable Power Electronics Converters) supported by the CNRS (Centre National de la Recherche Scientifique—French National Centre for Scientific Research) research group SEEDS (Systèmes d'énergie électrique dans leurs dimensions sociétales—Societal dimensions of electrical energy systems). [ABSTRACT FROM AUTHOR]

Deep learning architectures have brought about new heights in computer vision, with the most common approach being the Convolutional Neural Network (CNN). Through CNN, tasks previously deemed unattainable, including facial recognition, autonomous driving systems, and sophisticated medical diagnostics, among others can now be achieved. Convolutional layers, non-linear processing units, and subsampling layers are used in conjunction throughout the several learning phases that make up CNN’s structure. Generally, 2D and 3D CNNs have been used to achieve impressive results across numerous areas, and several survey papers have been published to review their state-of-the-art applications. However, they are unsuitable in some domain-specific areas where temporal dynamics and dependencies must be captured. Examples of such domains are time series prediction and signal identification, which necessitates the use of one-dimensional signals. Recently, 1D-CNN has evolved and has been used to develop various state-of-the-art models that cut across numerous research fields. However, there has been no survey paper detailing the evolution and advancements in the applications of the 1D-CNN to several computer vision tasks. In addressing this gap, this paper provides the first exhaustive survey to examine the historical development of 1D-CNNs and elucidate their structural intricacies and architectural frameworks. It also highlights recent advancements in their applications across more than twelve distinct domains. Furthermore, this paper provides an overview of the significant challenges impacting the current state-of-the-art 1D-CNN training and deployment while highlighting potential directions for future research exploration. By carrying out this survey, researchers across several fields can have a comprehensive understanding of the evolution, structural intricacies, and recent advancements in the applications of 1D-CNNs across various computer vision tasks. This paper also equip researchers with the knowledge needed to address the significant challenges faced in the current state-of-the-art 1D-CNN hurdles.

Tumors in the brain can be life-threatening, making early and precise detection crucial for effective treatment and improved patient outcomes. Deep learning (DL) techniques have shown significant potential in automating the early diagnosis of brain tumors by analyzing magnetic resonance imaging (MRI), offering a more efficient and accurate approach to classification. Deep convolutional neural networks (DCNNs), which are a sub-field of DL, have the potential to analyze rapidly and accurately MRI data and, as such, assist human radiologists, facilitating quicker diagnoses and earlier treatment initiation. This study presents an ensemble of three high-performing DCNN models, i.e., DenseNet169, EfficientNetB0, and ResNet50, for accurate classification of brain tumors and non-tumor MRI samples. Our proposed ensemble model demonstrates significant improvements over various evaluation parameters compared to individual state-of-the-art (SOTA) DCNN models. We implemented ten SOTA DCNN models, i.e., EfficientNetB0, ResNet50, DenseNet169, DenseNet121, SqueezeNet, ResNet34, ResNet18, VGG16, VGG19, and LeNet5, and provided a detailed performance comparison. We evaluated these models using two learning rates (LRs) of 0.001 and 0.0001 and two batch sizes (BSs) of 64 and 128 and identified the optimal hyperparameters for each model. Our findings indicate that the ensemble approach outperforms individual models, having 92% accuracy, 90% precision, 92% recall, and an F1 score of 91% at a 64 BS and 0.0001 LR. This study not only highlights the superior performance of the ensemble technique but also offers a comprehensive comparison with the latest research.

Systematic literature reviews can provide an objective global overview of background research on hydrological questions. This study presents a methodology to ensure rigor, traceability, and replicability in evaluating state-of-the-art hydrological topics. The proposed methodology involved systematic, objective, and explicit steps. Strategies with defined selection criteria were used to search the relevant literature comprehensively and accurately on hydrological uncertainty as a case study. The most pertinent documents were filtered to build a critical state-of-the-art synthesis for evaluating their quality and relevance. This methodology allows systematic literature analysis to provide an objective summary of the evidence, with a structured procedure for state-of-the-art reviews, which promotes transparency in the search, unlike typical review papers that lack reproducible methodologies that may lead to obtaining a potentially subjective reference selection. Consequently, the proposed methodology improves reference reliability and study reproducibility while generalizing the methodology. This procedure has proven effective and practical for building state-of-the-art research in the study case (State-of-the-art Uncertainty in Hydrological forecasting from 2017 to 2023) and can be considered a relevant tool in hydrology and other STEM disciplines, providing a rigorous and transparent approach that enhances evidence quality and reliability. [ABSTRACT FROM AUTHOR]

Cyber security has become a very critical concern that needs the attention of researchers, academicians, and organizations to confidentially ensure the protection and security of information systems. Due to the increasing demand for digitalization, every individual and organization faces continually shifting cyber threats. This article provides an overview of the state of the art in cyber security, challenges, and tactics, current conditions, and global trends of cyber security. To stay ahead of the curve in cyber security, we conducted a systematic review to uncover the latest trends, challenges, and state-of-the-art in cyber security. Moreover, we address the future direction of cyber security, presenting the possible strategies and approaches to addressing the increasing cyber security threat landscapes, the emerging trends, and innovations like Artificial Intelligence (AI) and machine learning (ML) to detect and automate cyber threat responses. Additionally, this article underlines the importance of ongoing adoption along with collaboration among stakeholders in the cyber ecosystem. [ABSTRACT FROM AUTHOR]

Timely maintenance of road pavements is crucial to ensure optimal performance. The accurate prediction of trends in pavement defects enables more efficient allocation of funds, leading to a safer, higher-quality road network. This article systematically reviews machine learning (ML) models for predicting the international roughness index (IRI), specifically focusing on flexible pavements, offering a comprehensive synthesis of the state-of-the-art. The study's objective was to assess the effectiveness of various ML techniques in predicting IRI for flexible pavements. Among the evaluated ML models, tree ensembles and boosted trees are identified as the most effective, particularly in managing data related to traffic, pavement structure, and climatic conditions, which are essential for training these models. Our analysis reveals that traffic data are present in 89% of the studies, while pavement structure and climatic factors are featured in 78%. However, maintenance and rehabilitation history appears less frequently, included in 33% of the studies. This research underscores the need for high-quality, standardized datasets, and highlights the importance of model interpretability and computational efficiency. Addressing data consistency, model interpretability, and replicability across studies are crucial for leveraging ML's full potential in fine-tuning IRI predictions. Future research directions include developing more interpretable, computationally efficient, and less complex models to maximize the impact of this research field in road infrastructure management. [ABSTRACT FROM AUTHOR]

Cyber security has become a very critical concern that needs the attention of researchers, academicians, and organizations to confidentially ensure the protection and security of information systems. Due to the increasing demand for digitalization, every individual and organization faces continually shifting cyber threats. This article provides an overview of the state of the art in cyber security, challenges, and tactics, current conditions, and global trends of cyber security. To stay ahead of the curve in cyber security, we conducted a systematic review to uncover the latest trends, challenges, and state-of-the-art in cyber security. Moreover, we address the future direction of cyber security, presenting the possible strategies and approaches to addressing the increasing cyber security threat landscapes, the emerging trends, and innovations like Artificial Intelligence (AI) and machine learning (ML) to detect and automate cyber threat responses. Additionally, this article underlines the importance of ongoing adoption along with collaboration among stakeholders in the cyber ecosystem.

Fires are increasingly recognized as one of the major hazards causing damage to bridges and disruption in their operational continuity. As a transportation hub, cable-supported bridges are particularly critical to social sustainability and economic development. However, ensuring the resilience of cable-supported bridges in unexpected fire events has arisen as a challenge in recent years due to the lack of available standards and guidance for practitioners. In this context, this paper presents an up-to-date review of the fire resilience enhancement methods for cable-supported bridges and proposes research needs for impending decades. Representative fire incidents and the concerns of bridge managers are analyzed to define the critical fire scenarios that need to be considered in future fire-resistant designs. The fire performance of cable-supported bridges whose cables, girders, and pylons are exposed to fires are analyzed respectively. To enhance the fire resilience of cable-supported bridges, this paper compiles probabilistic methods, including fire risk evaluation and mitigation based on identified vulnerable bridges, and deterministic methods, including active and passive fire protection measures. This paper reveals that guidance on the fire resilience of cable-supported bridges should be further improved with regard to aspects including the fire performance of cable systems, the ability of cable-supported bridges to provide continued service in fires, risk- and cost–benefit-based fire resistance design, multi-level fire damage determination, and emergency response strategies. [ABSTRACT FROM AUTHOR]

Event sequence data is a special type of time-dependent data that captures information about the order in which discrete events occur. The time-dimension is one of the factors that makes event sequence data hard to understand. Other factors that contribute to this complexity are the dimensionality in terms of the amount of events and attributes, the frequency of events in terms of consistency and density, varying durations, and parallel occurrences. When end-users need to compare event sequences, all these characteristics need to be considered and justified. In this state-of-the-art report we review visualization techniques for the event sequence comparison task. We focus specifically on comparison, in contrast to general event sequence visualization, and review how different aspects, such as the data attributes or granularity, affect the comparison. We define a taxonomy based on five dimensions: Scale, Comparison Type, Size, Data, and Visualization & Interaction. Based on these dimensions, we provide an overview of the literature of the past 17 years. This overview is accompanied with an analysis of the strengths and weaknesses of the proposed techniques. Furthermore, we identify current gaps and research challenges in the literature. [Display omitted] [ABSTRACT FROM AUTHOR]

This article examines how the research into Lean Supply Chain Management (LSCM) has evolved over the period 1996–2018 with the identification of the main issues explored and future trends. A performance bibliometric analysis has been conducted through a study of published documents, citations, journal impact factor, h-index, most cited papers and authors, and data on the geographic distribution of publications. More relevantly, a science mapping analysis has been performed with SciMAT bibliometric analysis software using co-word networks in a longitudinal overview. The large number of studies of LSCM published has enabled a conceptual evolution map to be constructed of the way that this research has developed. The results have enabled us to identify four thematic areas in the LSCM field: Methods, Key factors, Internal efficiency, and Performance. [ABSTRACT FROM AUTHOR]

Pedestrian crossings are an essential part of the urban landscape, providing safe passage for pedestrians to cross busy streets. While some are regulated by timed signals and are marked with signs and lights, others are simply marked on the road and do not have additional infrastructure. Nevertheless, the markings undergo wear and tear due to traffic, weather, and road maintenance activities. If pedestrian crossing markings are excessively worn, drivers may not be able to see them, which creates road safety issues. This paper presents a study of computer vision techniques that can be used to identify and classify pedestrian crossings. It first introduces the related concepts. Then, it surveys related work and categorizes existing solutions, highlighting their key features, strengths, and limitations. The most promising techniques are identified and described: Convolutional Neural Networks, Histogram of Oriented Gradients, Maximally Stable Extremal Regions, Canny Edge, and thresholding methods. Their performance is evaluated and compared on a custom dataset developed for this work. Insights on open issues and research opportunities in the field are also provided. It is shown that managers responsible for road safety, in the context of a smart city, can benefit from computer vision approaches to automate the process of determining the wear and tear of pedestrian crossings.

This paper provides a literature review on zero defect manufacturing based on the content analysis performed for 280 research articles published from 1987 to 2018 in a variety of academic journals and conference proceedings. The review summarises the state-of-the-art, highlights shortcomings and further directions in research. Accordingly, we investigated how zero defect manufacturing was implemented and evaluated the main research patterns in the sample by analysing key factors. Based on the extensive review of the zero defect manufacturing literature, we identified and highlighted four distinctive strategies based on overarching themes for zero defect manufacturing, i.e. detection, repair, prediction, and prevention. Evaluation of current research and descriptive analysis highlighted six major shortcomings of current research in zero defect manufacturing: (i) focus on a single strategy instead of a holistic approach for global optima; (ii) certain industries are under-researched; (iii) full potential of industry-academia collaboration is not achieved; (iv) not enough focus on the beginning of manufacturing lifecycle; (v) cost–benefit comparative analysis is not evident; (vi) standard and clear definition of terms are missing. Finally, we presented four further directions in which an advance of the topic would stimulate scholarly and practical needs: (i) shift from local to global solutions; (ii) investigate pros and cons; (iii) role of people and human activities in manufacturing; (iv) new business models for zero defect manufacturing. [ABSTRACT FROM AUTHOR]

Background In the near future, the rapid adoption of electric vehicles is inevitable, driven by environmental concerns and climate change awareness. However, this progressive trend also brings forth safety concerns and hazards, notably regarding the risk of EV fires, which have garnered significant media attention. This necessitates the need to study for comprehensive fire risk assessment strategies aimed at preventing and mitigating such incidents. Methods This study presents a framework for assessing fire risks in EVs using Fault Tree Analysis (FTA). By integrating disparate data sources into a unified dataset, the proposed methodology offers a holistic approach to understanding potential hazards. The study embarked on a comprehensive exploration of EV fire causes through qualitative FTA. Results Through this approach, the work discerned five major causes: human factors, vehicle factors, management factors, external factors, and unknown factors. Using a meticulous weighted average approach, the annual EV fire frequency for each country was deduced, revealing an average annual EV fire rate of 2.44 × 10-4 fires per registered EV. This metric provides a significant benchmark, reflecting both the probability and inherent risk of such incidents. However, uncertainties in data quality and reporting discrepancies highlight the imperative of continued research. Conclusions As EV adoption surges, this study underscores the importance of comprehensive, data-driven insights for proactive risk management, emphasizing the necessity for vigilant and adaptive strategies. The findings emphasize the pivotal role of this assessment in shaping response strategies, particularly for first responders dealing with EV fires. In essence, this research not only elevates the understanding of EV fire risks but also offer a foundation for future safety measures and policies in the domain.

The continuing Machine Learning (ML) revolution indubitably has had a significant positive impact on the analysis of downlinked satellite data. Other aspects of the Earth Observation industry, despite being less susceptible to widespread application of Machine Learning, are also following this trend. These applications, actual use cases, possible prospects and difficulties, as well as anticipated research gaps, are the focus of this review of Machine Learning applied to Earth Observation Operations. A wide range of topics are covered, including mission planning, fault diagnosis, fault prognosis and fault repair, optimization of telecommunications, enhanced GNC, on-board image processing, and the use of Machine Learning models on platforms with constrained compute and power capabilities, as well as recommendations in the respective areas of research. The review tackles all on-board and off-board applications of machine learning to Earth Observation with one notable exception: it omits all post-processing of payload data on the ground, a topic that has been studied extensively by past authors. In addition, this review article discusses the standardization of Machine Learning (i.e., Guidelines and Roadmaps), as well as the challenges and recommendations in Earth Observation operations for the purpose of building better space missions. [ABSTRACT FROM AUTHOR]

Malware has emerged as a cyber security threat that continuously changes to target computer systems, smart devices, and extensive networks with the development of information technologies. As a result, malware detection has always been a major worry and a difficult issue, owing to shortcomings in performance accuracy, analysis type, and malware detection approaches that fail to identify unexpected malware attacks. This paper seeks to conduct a thorough systematic literature review (SLR) and offer a taxonomy of machine learning methods for malware detection that considers these problems by analyzing 77 chosen research works related to malware detection using machine learning algorithm. The research investigates malware and machine learning in the context of cybersecurity, including malware detection taxonomy and machine learning algorithm classification into numerous categories. Furthermore, the taxonomy was used to evaluate the most recent machine learning algorithm and analysis. The paper also examines the obstacles and associated concerns encountered in malware detection and potential remedies. Finally, to address the related issues that would motivate researchers in their future work, an empirical study was utilized to assess the performance of several machine learning algorithms.

The decline curve analysis (DCA) technique is the simplest, fastest, least computationally demanding, and least data-required reservoir forecasting method. Assuming that the decline rate of the initial production data will continue in the future, the estimated ultimate recovery (EUR) can be determined at the end of the well/reservoir lifetime based on the declining mode. Many empirical DCA models have been developed to match different types of reservoirs as the decline rate varies from one well/reservoir to another. In addition to the uncertainties related to each DCA model's performance, structure, and reliability, any of them can be used to estimate one deterministic value of the EUR, which, therefore, might be misleading with a bias of over- and/or under-estimation. To reduce the uncertainties related to the DCA, the EUR could be assumed to be within a certain range, with different levels of confidence. Probabilistic decline curve analysis (pDCA) is the method used to generate these confidence intervals (CIs), and many pDCA approaches have been introduced to reduce the uncertainties that come with the deterministic DCA. The selected probabilistic type of analysis (i.e., frequentist or Bayesian), the used DCA model(s), the type and the number of wells, the sampling technique of the data or the model's parameters, and the parameters themselves undergo a probability distribution, and these are the main differences among all of these approaches and the factors that determine how each approach can quantify the uncertainties and mitigate them. In this work, the Bayesian and frequentist approaches are deeply discussed. In addition, the uncertainties of DCA are briefly discussed. Further, the bases of the different probabilistic analyses are explained. After that, 15 pDCA approaches are reviewed and summarized, and the differences among them are stated. The study concludes that Bayesian analysis is generally more effective than frequentist analysis, though with narrower CIs. However, the choice of DCA model and sampling algorithm can also affect the bounds of the CIs and the calculation of the EUR. Moreover, the pDCA approach is recommended for quantifying uncertainties in DCA, with narrower CIs that indicate greater effectiveness. However, the computational time and the number of iterations in sampling are also considered critical factors. That is why various assumptions and modifications have been made in the pDCA approaches, including the assumption of a certain probability distribution for the sampled parameters to improve their reliability of reserve estimation. The motivation behind this research was to present a full state-of-the-art review of the pDCA and the latest developments in this area of research. [ABSTRACT FROM AUTHOR]

The article focuses on the practice research in social work which is an evolving phenomenon. Topics include examines it is both influenced by changes in social work practice and shaped by developments in social work research and an agreed definition of practice research in social work has proved somewhat elusive, though Fisher et al.'s entry in the Oxford Bibliographies in Social Work is helpful in this regard.

Developing precise soft computing methods for groundwater management, which includes quality and quantity, is crucial for improving water resources planning and management. In the past 20 years, significant progress has been made in groundwater management using hybrid machine learning (ML) models as artificial intelligence (AI). Although various review articles have reported advances in this field, existing literature must cover groundwater management using hybrid ML. This review article aims to understand the current state-of-the-art hybrid ML models used for groundwater management and the achievements made in this domain. It includes the most cited hybrid ML models employed for groundwater management from 2009 to 2022. It summarises the reviewed papers, highlighting their strengths and weaknesses, the performance criteria employed, and the most highly cited models identified. It is worth noting that the accuracy was significantly enhanced, resulting in a substantial improvement and demonstrating a robust outcome. Additionally, this article outlines recommendations for future research directions to enhance the accuracy of groundwater management, including prediction models and enhance related knowledge. [ABSTRACT FROM AUTHOR]

Knowledge of the feeding habits of primate species is important for an understanding of their natural history and ecological interactions between their groups, and their interactions with their environments. Capuchin monkeys (Sapajus spp.) present significant diet flexibility and are a good model for investigating diet diversity between species. We undertook a systematic literature review of publications on the diet of free-living Sapajus spp. groups by using the Web of Science platform. We performed scientometric analyses of the objectives and hypotheses of the reviewed works, identified knowledge gaps, and evaluated the composition of each group's diet. Our results indicate geographic and taxonomic biases among the 59 published studies that we examined. The studies were focused on Sapajusnigritus, Sapajuslibidinosus, and Sapajusapella, and undertaken in long-term study sites. Foraging and behavioral aspects of how food is processed were recurring themes. Capuchin monkeys increase their consumption of anthropogenic food according to its supply. Despite the similar objectives of these studies, standardized data collection protocols were not employed. Although Sapajus spp. are widely distributed and are used for cognitive studies, basic aspects of their natural history, such as their diet, remain unknown. We emphasize the importance of undertaking studies on this genus to fill the identified knowledge gaps, and propose that investigations should be carried out on the effects of dietary changes on individuals and groups. We stress that, as the Neotropical region is one of those most affected by anthropogenic impacts, opportunities to study these primates in their natural environments are diminishing daily. [ABSTRACT FROM AUTHOR]

Literature in a discipline generally represents the advancement of knowledge and a richer understanding of associated phenomena, factors, and context. However, an unplanned growth in literature can impede advancement by introducing the possibility of complex, competing, and occasionally counterintuitive arguments in the ongoing scholarly dialogue. The exponential growth of literature in small business and entrepreneurship research in recent years has made salient just such risk. Thus, there is an urgent need for developing a clear understanding of advancements and state of knowledge stock in the discipline to inform and guide future researchers aiming to advance the field. The Journal of Small Business Management's first special issue on review articles intends to contribute to the advancement of small business and entrepreneurship literature by informing researchers on the current state of research. [ABSTRACT FROM AUTHOR]

The environmental impacts of activities such as raw material extraction, construction of infrastructure, and demolition, place construction as one of the sectors that exert the highest pressures on the environment, society, and economy. Some of the major environmental impacts for which the construction industry is responsible are mineral resource depletion, greenhouse gas emissions, and waste generation. Among the different strategies that exist to decrease such impacts, recycling demolition waste into recycled concrete aggregates has been considered a promising alternative. As such, at present, the literature dealing with the impact assessment of recycled aggregate concrete structures is very extensive. Therefore, the objective of this article is to present a critical view of the state‐of‐the‐art in terms of sustainability assessment of recycled aggregate concrete structures, taking a holistic perspective by considering environmental, social, and economic impacts. [ABSTRACT FROM AUTHOR]