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Disasters disrupt the normal functioning of society, leading to significant financial and human losses. Effective disaster management relies heavily on robust logistics, which ensures efficient supply and support chains. A key strategy for maintaining operational continuity in healthcare systems during disruptions is to improve the resilience of supply chains and adapt to unpredictable events. The COVID-19 pandemic highlighted the need for adaptable healthcare supply chains, exemplified by factories pivoting to produce essential personal protective equipment. Despite the critical importance of quantitative models in healthcare supply chain management, their application has a noticeable gap. Artificial Intelligence (AI) has emerged as a transformative tool to address these complexities, offering solutions for diagnostics, chronic disease management, and logistics optimisation. AI technologies enhance patient care and improve healthcare logistics, proving invaluable in disaster scenarios. This special issue aims to explore innovative AI-based approaches to tackle the challenges faced by healthcare supply chains, especially in the context of recent disruptions like the COVID-19 pandemic, which exacerbated shortages of essential medicines and increased patient demand. We are inviting papers that focus on integrating AI methods to enhance the efficiency and effectiveness of healthcare supply chains. This Editorial summarises these studies, emphasising possibilities for future research pathways. [ABSTRACT FROM AUTHOR]

Computational humor recognition is considered to be one of the hardest tasks in natural language processing (NLP) since humor is such a particularly complex emotion. There are very few recent studies that offer an analysis of certain aspects of computational humor. However, there has been no attempt to study the empirical evidence on computational humor recognition in a systematic way. The aim of this research is to examine computational humor detection from three aspects: datasets, features and algorithms. Therefore, a Systematic Literature Review (SLR) was carried out to present in detail the computational techniques for humor identification under these aspects. After posing some research questions, a total of 106 primary papers were identified as relevant to the objectives of these questions and further detailed analysis was conducted. The study revealed that there are a great number of publicly available annotated humor datasets with many different types of humor instances. Twenty-one (21) humor features have been carefully studied, and research evidence of their use in humor computational detection is presented. Additionally, a classification of the humor detection approaches was performed, and the results are presented. Finally, the challenges of applying these techniques to humor recognition as well as promising future research directions are discussed. [ABSTRACT FROM AUTHOR]

The aim of this study is to estimate the solution of Eyring–Powell nanofluid model (EPNFM) with Darcy Forchheimer slip flow involving bioconvection and nonlinear thermal radiation by employing stupendous knacks of neural networks-based Bayesian computational intelligence (NNBCI). A dataset for the designed NNBCI is generated with Adam numerical procedure for sundry variations of EPNFM by use of several variants including slip constant, Schmidt number, mixed convection parameter, Prandtl number, and bioconvection Lewis parameter. Numerical computations of various physical parameters of interest on EPNFM are estimated with artificial intelligence-based NNBCI and compared with reference data values generated with Adam's numerical procedure. The accuracy, efficacy, and convergence of the proposed NNBCI to successfully solve the EPNFM are endorsed through M.S.E, statistical instance distribution studies of error-histograms, and assessment of regression metric. The proposed dataset exhibits a close alignment with the reference dataset based on error analysis from level E − 1 1 to E − 0 5 authenticates the precision of the designed procedure NNBCI for solving EPNFMs. The executive and novel physical importance of parameters governing the flow, such as nanofluid velocity, temperature, and concentration profiles, are discussed. The observations imply that the presence of the slip constant, mixed convection parameter and Lewis number influences the velocity of the nanofluid. However, it is observed that temperature of the nanofluid declines for higher values of Prandl number while the concentration of nanofluid improves with increasing values of Schmidt number. [ABSTRACT FROM AUTHOR]

The performance of nanofluids is largely determined by their thermophysical properties. Optimizing these properties can significantly enhance nanofluid performance. This study introduces a hybrid strategy based on computational intelligence to determine the optimal conditions for ternary hybrid nanofluids. The goal is to minimize dynamic viscosity and maximize thermal conductivity by varying the volume fraction, temperature, and nanomaterial mixing ratio. The proposed strategy integrates machine learning, multi-objective optimization, and multi-criteria decision-making. Three machine learning techniques—GMDH-type neural network, gene expression programming, and combinatorial algorithm—are applied to model dynamic viscosity and thermal conductivity as functions of the input variables. Then, the high-performing models provide the foundation for optimization using the well-established multi-objective particle swarm optimization algorithm. Finally, the decision-making technique TOPSIS is employed to identify the most desirable points from the Pareto front, based on various design scenarios. To validate the proposed strategy, a ternary hybrid nanofluid composed of graphene oxide (GO), iron oxide (Fe₃O₄), and titanium dioxide (TiO₂) was employed as a case study. The results demonstrated that the combinatorial approach excelled in accurately modeling (R = 0.99964–0.99993). The optimization process revealed that optimal VFs span a broad range across all mixing ratios, while optimal temperatures were consistently near the maximum value (65 °C). The decision-making outcomes indicated that the mixing ratio was consistent across all design scenarios, with the volume fraction serving as the key differentiating factor. [ABSTRACT FROM AUTHOR]

In the Kolmogorov Theory of Consciousness, algorithmic agents utilize inferred compressive models to track coarse-grained data produced by simplified world models, capturing regularities that structure subjective experience and guide action planning. Here, we study the dynamical aspects of this framework by examining how the requirement of tracking natural data drives the structural and dynamical properties of the agent. We first formalize the notion of a generative model using the language of symmetry from group theory, specifically employing Lie pseudogroups to describe the continuous transformations that characterize invariance in natural data. Then, adopting a generic neural network as a proxy for the agent dynamical system and drawing parallels to Noether's theorem in physics, we demonstrate that data tracking forces the agent to mirror the symmetry properties of the generative world model. This dual constraint on the agent's constitutive parameters and dynamical repertoire enforces a hierarchical organization consistent with the manifold hypothesis in the neural network. Our findings bridge perspectives from algorithmic information theory (Kolmogorov complexity, compressive modeling), symmetry (group theory), and dynamics (conservation laws, reduced manifolds), offering insights into the neural correlates of agenthood and structured experience in natural systems, as well as the design of artificial intelligence and computational models of the brain. [ABSTRACT FROM AUTHOR]

Computational workflows are a common class of application on supercomputers, yet the loosely coupled and heterogeneous nature of workflows often fails to take full advantage of their capabilities. We created Colmena to leverage the massive parallelism of a supercomputer by using Artificial Intelligence (AI) to learn from and adapt a workflow as it executes. Colmena allows scientists to define how their application should respond to events (e.g., task completion) as a series of cooperative agents. In this paper, we describe the design of Colmena, the challenges we overcame while deploying applications on exascale systems, and the science workflows we have enhanced through interweaving AI. The scaling challenges we discuss include developing steering strategies that maximize node utilization, introducing data fabrics that reduce communication overhead of data-intensive tasks, and implementing workflow tasks that cache costly operations between invocations. These innovations coupled with a variety of application patterns accessible through our agent-based steering model have enabled science advances in chemistry, biophysics, and materials science using different types of AI. Our vision is that Colmena will spur creative solutions that harness AI across many domains of scientific computing. [ABSTRACT FROM AUTHOR]

This article introduces RPLMSED, a relational prompt-based pre-trained language model for social event detection (SED), addressing challenges in Graph Neural Networks (GNN)-based methods. It proposes a pairwise message modeling strategy with multi-relational sequences, a prompt-based learning mechanism, and a clustering constraint to optimize message representation, demonstrating state-of-the-art performance across various scenarios, including offline, online, and low-resource settings.

This article reviews the literature surrounding innovative computational tools for waste management within smart cities. With the rise of urbanization and the increasing challenges of waste management, innovative technologies play a pivotal role in optimizing waste collection, sorting, recycling, and disposal processes. Leveraging computational tools such as artificial intelligence, Internet of Things, and big data analytics, smart waste management systems enable real-time monitoring, predictive modeling, and optimization of waste-related operations. These tools empower authorities to enhance resource efficiency, minimize environmental impact, and improve the overall quality of urban living. Through a comprehensive review of recent research and practical implementations, this article highlights the key features, benefits, and challenges associated with the development of cutting-edge computational tools for waste management. Emerging trends and opportunities for research and development in this rapidly evolving field are identified, emphasizing the importance of integrating technological innovations for building sustainable and resilient waste management in smart cities. [ABSTRACT FROM AUTHOR]

Intelligent controllers represent a class of advanced control systems designed to adapt, learn, and make decisions based on changing environments and dynamic conditions. These controllers leverage artificial intelligence and computational techniques to enhance traditional control methods, providing a level of adaptability and sophistication that is particularly valuable in complex and uncertain systems. In this work for the first time, the Proportional–integral–derivative (PID) controller the intelligent controller is used to speed up vibration attenuation of a sandwich smart structure subjected to external excitation. The sandwich smart structure is made of a composite core and sensor and actuator patches. Using shear deformation theory, Hamilton's principle, and compatibility equations, the governing equations of the sandwich smart structure under external excitations are obtained. After that using numerical solver and time-dependent solution procedure, the equations are solved. After obtaining the datasets via mathematical modeling, this kind of dataset is used as the input and output data to train, validate, and test the machine learning method to use it with low computational cost. After that, using this kind of machine learning method, future researchers can use it to estimate a control vibration of the sandwich structure after mechanical excitation. The results suggest some applicable recommendations to speed up the vibration attenuation of a sandwich smart structure subjected to external excitation. [ABSTRACT FROM AUTHOR]

AbstractBackgroundPurposeMethodsResultsConclusion\nIMPLICATIONS FOR REHABILITATIONExisting image description methods when used as Assistive Technologies often fall short in meeting the needs of blind or low vision (BLV) individuals. They tend to either compress all visual elements into brief captions, create disjointed sentences for each image region, or provide extensive descriptions.To address these limitations, we introduce VIIDA, a procedure aimed at the Visually Impaired which implements an Image Description Approach, focusing on webinar scenes. We also propose InViDe, an Inclusive Visual Description metric, a novel approach for evaluating image descriptions targeting BLV people.We reviewed existing methods and developed VIIDA by integrating a multimodal Visual Question Answering model with Natural Language Processing (NLP) filters. A scene graph-based algorithm was then applied to structure final paragraphs. By employing NLP tools, InViDe conducts a multicriteria analysis based on accessibility standards and guidelines.Experiments statistically demonstrate that VIIDA generates descriptions closely aligned with image content as well as human-written linguistic features, and that suit BLV needs. InViDe offers valuable insights into the behaviour of the compared methods – among them, state-of-the-art methods based on Large Language Models – across diverse criteria.VIIDA and InViDe emerge as efficient Assistive Technologies, combining Artificial Intelligence models and computational/mathematical techniques to generate and evaluate image descriptions for the visually impaired with low computational costs. This work is anticipated to inspire further research and application development in the domain of Assistive Technologies. Our codes are publicly available at: https://github.com/daniellf/VIIDA-and-InViDe.Development of low-cost computational approaches for generating and automatically evaluating image descriptions based on accessibility standards and rules for people with visual impairments as Assistive Technology, thus increasing inclusion and reducing accessibility limitations.Extraction of semantic visual information and modelling of textual descriptions of images using current Computer Vision and Natural Language Processing models and techniques.The synthetic images generated from the paragraphs produced by our approaches closely resemble the original images in terms of semantic similarity and statistical distribution of features.As this work is one of the few studies in the area and is characterised by flexibility and interpretability, researchers can use the approaches presented here to produce new or improve existing Assistive Technologies for the visually impaired.Development of low-cost computational approaches for generating and automatically evaluating image descriptions based on accessibility standards and rules for people with visual impairments as Assistive Technology, thus increasing inclusion and reducing accessibility limitations.Extraction of semantic visual information and modelling of textual descriptions of images using current Computer Vision and Natural Language Processing models and techniques.The synthetic images generated from the paragraphs produced by our approaches closely resemble the original images in terms of semantic similarity and statistical distribution of features.As this work is one of the few studies in the area and is characterised by flexibility and interpretability, researchers can use the approaches presented here to produce new or improve existing Assistive Technologies for the visually impaired. [ABSTRACT FROM AUTHOR]

AbstractThis study investigates the thermal buckling behavior and structural optimization of multi-hybrid nanocomposite-reinforced concrete annular sector plates supported by auxetic foundations. Utilizing the Carrera unified formulation (CUF), a versatile higher-order theory is applied to capture the complex thermo-mechanical interactions in these advanced materials. The multi-hybrid nanocomposites are engineered by combining conventional reinforcement with nanoscale fillers to enhance stiffness, and thermal stability. The CUF framework facilitates the accurate representation of material behavior, geometric, and boundary conditions (BCs), allowing for a comprehensive analysis of the system’s thermal buckling response. An innovative artificial intelligence (AI) algorithm is employed to validate and further refine the results obtained from the analytical models. This AI-based approach ensures robust verification and provides deeper insights into the parameter sensitivities affecting the structural performance. Additionally, the optimization process focuses on minimizing weight and maximizing thermal resistance while adhering to design constraints imposed by the auxetic foundation, known for its unique negative Poisson’s ratio properties. The findings reveal the pivotal role of nanocomposite configurations and auxetic foundation properties in determining the critical buckling temperature and structural integrity. The integration of CUF with AI offers a powerful methodology for exploring complex design spaces, leading to enhanced performance and innovative solutions in engineering applications. This research bridges advanced material science, structural mechanics, and computational intelligence, paving the way for optimized designs in next-generation construction and aerospace systems. [ABSTRACT FROM AUTHOR]

Forest fires are a recurring problem in areas of natural forests and reforestation, causing the loss of large areas of vegetation, resulting in economic and environmental damage. The reduction of fire occurrences must be based on prevention, detection, and combat, with rapid detection being a decisive factor in firefighting. The objective of this study is to present an integrated approach using operational research and geographic information system (GIS) technology to optimize the spatial coverage of conventional observation towers in areas at risk of forest fires. The study area is the Vale Nature Reserve (VNR), located in the north part of Espírito Santo, Brazil. The locations of forest fire risk (FFR) that were used as input in the mathematical programming model were defined, and then visibility analyses were performed by varying the height of the fire towers under eight scenarios. The mathematical model efficiently positioned the fire towers, allowing for optimized planning based on resource limitations and efficiency sensitivity analyses by increasing the number of towers in the scenarios. The proposed methodology provides a simple and robust analysis tool for the decision maker by allowing numerous combinations to be evaluated in a short time. [ABSTRACT FROM AUTHOR]

This research work aims to explain the model and assessment of a differential mathematical system of the magneto-bioconvection of the Williamson nanofluid model (MBWNFM) by capitalising on the strength of the stochastic technique through computational intelligence of Bayesian regularisation back-propagated neural networks (CIBRB-NNs). This facilitates a more accurate, reliable and proficient computation of the dynamics. A reference dataset is built using the Adams technique in the Mathematica software to depict multiple situations and account for numerous influential parameters of the MBWNFM. The reference data results are split into 70% for training and 30% for validation and testing methods. This approach aims to enhance the accuracy of the approximated results and enable them to be compared with established solutions. The demonstration of the accuracy and efficiency of the created CIBRB-NNs involves a comparison of the results obtained from the dataset using the Adams approach, by adjusting several influential parameters which include magnetic parameter (M ), bioconvection Lewis Number ( L b ), thermal diffusivity (α ) and thermal Biot number (γ ). The stability and accuracy of CIBRB-NNs are validated using various methodologies, including the analysis of fitness curves depicting mean square error, regression studies, evaluation of error using histogram plots and measurement of absolute errors. The excellent measures of performance in terms of MSE are achieved at levels 4.50e-12, 6.73e-13, 1.07e-13, 7.08e-13, 4.77e-13 and 1.70e-13 against 82, 150, 98, 83, 170 and 189 epochs. The error analysis of the proposed and reference datasets shows that CIBRB-NNS is authentic and precise, ranging from e-09 to e-04 for all scenarios. [ABSTRACT FROM AUTHOR]

The objectives of this work are (i) to classify soybean cultivars under different irrigation managements using hyperspectral data, looking for the best machine-learning algorithm for the classification and the input that improves the performance of the models. The experiment was implemented in the 2023/24 harvest in the experimental area of the Federal University of Mato Grosso do Sul, Câmpus Chapadão do Sul, Mato Grosso do Sul, and it was conducted in a strip scheme with seven cultivars subjected to irrigated and rainfed management. Sixty days after crop emergence, three leaves per plot were collected for evaluation by the hyperspectral sensor. The spectral data was then separated into 28 bands to reduce dimensionality. In this way, two databases were generated: one with all the spectral information provided by the sensor (WL) and one with the 28 spectral bands (SB). Each database was subjected to different machine-learning models to ascertain the improved accuracy of the models in distinguishing the different eucalyptus species. The models tested were artificial neural networks (ANN), decision trees (DT), linear regression (LR), M5P algorithm, random forest (RF), and support vector machine (SVM). The results demonstrate the effectiveness of machine-learning models in differentiating soybean management under rainfed and irrigated conditions, highlighting the advantage of hyperspectral data (WL) over selected spectral bands (SB). Models such as the support vector machine (SVM) showed the best levels of accuracy when using the entire available spectrum. On the other hand, artificial neural networks (ANN) performed well with spectral band data, demonstrating their ability to work with smaller data sets without compromising the classification. [ABSTRACT FROM AUTHOR]

The metabolic pathway known as gluconeogenesis, which produces glucose from non-carbohydrate substrates, is essential for maintaining balanced blood sugar levels while fasting. It's extremely important to anticipate gluconeogenesis rates accurately to recognize metabolic disorders and create efficient treatment strategies. The implementation of deep learning and machine learning methods to forecast complex biological processes has been gaining popularity in recent years. The recognition of both the regulation of the pathway and possible therapeutic applications of proteins depends on accurate identification associated with their gluconeogenesis patterns. This article analyzes the uses of machine learning and deep learning models, to predict gluconeogenesis efficiency. The study also discusses the challenges that come with restricted data availability and model interpretability, as well as possible applications in personalized healthcare, metabolic disease treatment, and the discovery of drugs. The predictor utilizes statistics moments on the structures of gluconeogenesis and their enzymes, while Random Forest is utilized as a classifier to ensure the accuracy of this model in identifying the best outcomes. The method was validated utilizing the independent test, self-consistency, 10k fold cross-validations, and jackknife test which achieved 92.33 %, 91.87%, 87.88%, and 87.02%. An accurate prediction of gluconeogenesis has significant implications for understanding metabolic disorders and developing targeted therapies. This study contributes to the rising field of predictive biology by mixing algorithms for deep learning, and machine learning, with metabolic pathways. • Utilizing advanced machine learning methods it is possible to predict gluconeogenesis protein. • Gluconeogenesis is essential for metabolic health and helps find therapeutic targets and metabolic illnesses. • Data limitations and model interpretation present challenges. • The method was validated utilizing the independent test, self-consistency, 10fold cross-validations, and jackknife test which achieved 92.33 %, 91.87 %, 87.88 %, and 87.02 %. [ABSTRACT FROM AUTHOR]

Purpose: Ensuring accessible urban infrastructure remains a challenge to inclusive societies and equal participation of people with disabilities in economic, cultural & social life and is thus a stunting factor in economic development. This paper proposes using an Artificial Intelligence-based model for evaluating accessibility in urban infrastructure towards identifying & predicting problematic areas in the existing or future built environment. The objective is to describe a reliable and extensible model capable of detecting mobility-problematic areas, evaluating the quality of urban infrastructure, proposing alternative routes and creating the base of a holistic detection and evaluation digital tool for better urban planning and efficient application of European Social Policies. Methodology: The research identifies obstacle and difficulty components useful within a Digital AI system via structured interviews performed with members of 2 key organizations in social development and inclusion in Eastern Macedonia and Thrace, Greece. Findings: The set of obstacles and difficulties is aggregated in a vector of solvable difficulties suitable for an AI system. Additionally, we propose methodologies for collecting and comparing data from predefined pilot routes between people with disabilities and the general population to build an initial training dataset for a continuous decision-making and evaluation AI system. Originality: Research originality is derived from combining Artificial Intelligence with the sector of computational evaluation of material infrastructure, as perceived by humans with disabilities, and as a tool of increased economic activity. It additionally defines key obstacles perceived by PwDs that are sufficiently measurable and subsequently solvable by AI. [ABSTRACT FROM AUTHOR]

In recent years, the intensification of the urban heat island (UHI) effect has become a significant concern as urbanization accelerates. This survey comprehensively explores the current status of surface UHI research, emphasizing the role of land use and land cover changes (LULC) in urban environments. We conducted a systematic review of 8260 journal articles from the Web of Science database, employing bibliometric analysis and keyword co-occurrence analysis using CiteSpace to identify research hotspots and trends. Our investigation reveals that vegetation cover and land use types are the two most critical factors influencing UHI intensity. We analyze various computational intelligence techniques, including machine learning algorithms, cellular automata, and artificial neural networks, used for simulating urban expansion and predicting UHI effects. The study also examines numerical modeling methods, including the Weather Research and Forecasting (WRF) model, while examining the application of Computational Fluid Dynamics (CFD) in urban microclimate research. Furthermore, we evaluate potential mitigation strategies, considering urban planning approaches, green infrastructure solutions, and the use of high-albedo materials. This comprehensive survey not only highlights the critical relationship between land use dynamics and UHIs but also provides a direction for future research in computational intelligence-driven urban climate studies. [ABSTRACT FROM AUTHOR]

The present study introduces an integrated software approach that provides an automated product design toolkit for customized products like knives, incorporating topology optimization (TO) and numerical simulations in order to streamline engineering workflows during the product development procedure. The modeling framework combines state-of-the-art technologies into a single platform, enabling the design and the optimization of mechanical structures with minimal human intervention. In particular, the proposed solution leverages artificial intelligence (AI), shape optimization methods, and computational tools in order to iteratively optimize material utilization as well as the design of products based on certain criteria. By embedding simulation within the design optimization loop, the developed software module ensures that performance constraints are respected throughout the design process. The case studies are concentrated in designing knives, demonstrating the platform's ability to reduce design time, enhance product performance and provide rapid iterations of structurally optimized geometries. Finally, it should be noted that this research showcases the potential of integrated modeling technologies towards the transformation of traditional design paradigms, in this way contributing to faster, more reliable and efficient product development in various engineering industries through the training and deployment of AI models in these scientific fields. [ABSTRACT FROM AUTHOR]

Speech emotion recognition (SER) is an emerging technology that utilizes speech sounds to identify a speaker's emotional state. Computational intelligence is receiving increasing attention from academics, health, and social media applications. This research was conducted to identify emotional states in verbal communication. We applied a publicly available dataset called RAVDEES. The data augmentation process involved adding noise, applying time stretching, shifting, and pitch, and extracting the features zero cross rate (ZCR), chroma shift, Mel-Frequency Cepstral Coefficients (MFCC), and a spectrogram. In addition, we used many pretrained deep learning models, such as VGG16, ResNet50, Xception, InceptionV3, and DenseNet121. Out of all of the deep learning models, Xception yielded superior outcomes. Furthermore, we improved performance by changing the Xception model to include hyperparameters and additional layers. We used a variety of performance evaluation parameters to test the proposed model. These included F1-score, accuracy, misclassification rate (MCR), precision, sensitivity, specificity, negative predictive value, false negative rate, false positive rate, false discovery rate, false omission rate, and false discovery rate. The model that we suggested demonstrated an overall accuracy of 98%, with an MCR of 2%. Additionally, it attained precision, sensitivity, and specificity values of 91.99%, 91.78%, and 98.68%, respectively. Additional models attained an F1-score of 91.83%. Our suggested model demonstrated superiority compared to other cutting-edge techniques [ABSTRACT FROM AUTHOR]

The 15-minute city concept, designed to ensure that all essential services and amenities are accessible within a 15 min walk or bike ride from home, presents a transformative vision for urban living. This paper explores the concept of a 15-minute city and its implications, along with its main features and pillars. Furthermore, it elaborates on how the integration of artificial intelligence (AI) and computational tools can be utilized in optimizing the 15-minute city model. We reveal how AI-driven algorithms, machine learning techniques, and advanced data analytics can enhance urban planning, improve accessibility, and foster social integration. Our paper focuses on the practical applications of these technologies in creating pedestrian-friendly neighborhoods, optimizing public transport coordination, and enhancing the quality of life for urban residents. By executing some of these computational models, we demonstrate the potential of AI and computational tools to realize the vision of the 15-minute city, making urban spaces more inclusive, resilient, and adaptive to the evolving needs of their inhabitants. [ABSTRACT FROM AUTHOR]

When discussing the Education 4.0 concept and the role of technology-based learning systems along with creativity, it is interesting to explore how these are reflected as educational innovations in the case of design education. This study aims to provide an overview of interactive technologies used in product design education and examine their integration into the learning process. A literature search was conducted, analyzing scientific papers to review relevant articles. The findings highlight several categories of technologies utilized in design education, including virtual and augmented reality, robotics, interactive embedded systems, immersive technologies, and computational intelligence systems. These technologies are primarily integrated as supportive tools throughout different stages of the design process within learning environments. This study suggests that integrating such technologies alongside pedagogical methods positively impacts education, offering numerous opportunities for further research and innovation. In conclusion, this review contributes to ongoing research in technological advancements and innovations in design education, offering insights into the diverse applications of interactive technologies in enhancing learning environments. [ABSTRACT FROM AUTHOR]

In recent years, substantial advancements have been achieved in understanding the diversity of the human virome and its intricate roles in human health and diseases. Despite this progress, the field of human virome research remains nascent, primarily hindered by the lack of effective methods, particularly in the domain of computational tools. This perspective systematically outlines ten computational challenges spanning various types of virome studies. These challenges arise due to the vast diversity of viromes, the absence of a universal marker gene in viral genomes, the low abundance of virus populations, the remote or minimal homology of viral proteins to known proteins, and the highly dynamic and heterogeneous nature of viromes. For each computational challenge, we discuss the underlying reasons, current research progress, and potential solutions. The resolution of these challenges necessitates ongoing collaboration among computational scientists, virologists, and multidisciplinary experts. In essence, this perspective serves as a comprehensive guide for directing computational efforts in human virome studies. • This perspective systematically outlines ten computational challenges spanning various types of virome studies. • We emphasized that computational methodologies for virome sequencing data analysis are still in early stages of development. • This perspective may serve as a comprehensive guide for directing computational efforts in human virome studies. [ABSTRACT FROM AUTHOR]

Buildings are one of the most important goals of human transformation of the Earth's surface. Therefore, building extraction (BE), such as in urban resource management and planning, is a task that is meaningful to actual production and life. Computational intelligence techniques based on convolutional neural networks (CNNs) and Transformers have begun to be of interest in BE, and have made some progress. However, the BE methods based on CNNs are limited by the difficulty in capturing global long-range relationships, while Transformer-based methods are often not detailed enough for pixel-level annotation tasks because they focus on global information. To conquer the limitations, a multi-scale Transformer (MSTrans) is proposed for BE from high-resolution remote sensing images. In the proposed MSTrans, we develop a plug-and-play multi-scale Transformer (MST) module based on atrous spatial pyramid pooling (ASPP). The MST module can effectively capture tokens of different scales through the Transformer encoder and Transformer decoder. This can enhance multi-scale feature extraction of buildings, thereby improving the BE performance. Experiments on three real and challenging BE datasets verify the effectiveness of the proposed MSTrans. While the proposed approach may not achieve the highest Precision and Recall accuracies compared with the seven benchmark methods, it improves the overall metrics F1 and mIoU by 0.4% and 1.67%, respectively. [ABSTRACT FROM AUTHOR]

Abstract This narrative review explores the intersection of computational intelligence (CI) techniques and the Sustainable Development Goals (SDGs) in the context of female cancer patients. With the increasing prevalence of cancer among women worldwide, there is a pressing need to integrate advanced computational methods to enhance diagnosis, treatment, and management. This review highlights various CI methods, including artificial intelligence, machine learning and data science, and examines their contributions to achieving specific SDGs like health and well-being (SDG 3), gender parity (SDG 5), and reduced disparity (SDG 10). Additionally, the review considers the impact of CI on other relevant SDGs, such as poverty eradication (SDG 1), quality education (SDG 4), economic growth and decent work (SDG 8), innovation and infrastructure (SDG 9), and global partnerships (SDG 17). The paper discusses the current state of CI applications in female cancer care, identifies challenges, and proposes future directions for research and practice.

Intelligent abstractive text summarization of scholarly publications refers to machine-generated summaries that capture the essential ideas of an article while maintaining semantic coherence and grammatical accuracy. As information continues to grow at an overwhelming rate, text summarization has emerged as a critical area of research. In the past, summarization of scientific publications predominantly relied on extractive methods. These approaches involve selecting key sentences or phrases directly from the original document to create a summary or generate a suitable title. Although extractive methods preserve the original wording, they often lack the ability to produce a coherent, concise, and fluent summary, especially when dealing with complex or lengthy texts. In contrast, abstractive summarization represents a more sophisticated approach. Rather than extracting content from the source, abstractive models generate summaries using new language, often incorporating words and phrases not found in the original text. This allows for more natural, human-like summaries that better capture the key ideas in a fluid and cohesive manner. This study introduces two advanced models for generating titles from the abstracts of scientific articles. The first model employs a Gated Recurrent Unit (GRU) encoder coupled with a greedy-search decoder, while the second utilizes a Transformer model, known for its capacity to handle long-range dependencies in text. The findings demonstrate that both models outperform the baseline Long Short-Term Memory (LSTM) model in terms of efficiency and fluency. Specifically, the GRU model achieved a ROUGE-1 score of 0.2336, and the Transformer model scored 0.2881, significantly higher than the baseline LSTM model, which reported a ROUGE-1 score of 0.1033. These results underscore the potential of abstractive models to enhance the quality and accuracy of summarization in academic and scholarly contexts, offering more intuitive and meaningful summaries.

Abstract Background The global health system remains determined to leverage on every workable opportunity, including artificial intelligence (AI) to provide care that is consistent with patients’ needs. Unfortunately, while AI models generally return high accuracy within the trials in which they are trained, their ability to predict and recommend the best course of care for prospective patients is left to chance. Purpose This review maps evidence between January 1, 2010 to December 31, 2023, on the perceived threats posed by the usage of AI tools in healthcare on patients’ rights and safety. Methods We deployed the guidelines of Tricco et al. to conduct a comprehensive search of current literature from Nature, PubMed, Scopus, ScienceDirect, Dimensions AI, Web of Science, Ebsco Host, ProQuest, JStore, Semantic Scholar, Taylor & Francis, Emeralds, World Health Organisation, and Google Scholar. In all, 80 peer reviewed articles qualified and were included in this study. Results We report that there is a real chance of unpredictable errors, inadequate policy and regulatory regime in the use of AI technologies in healthcare. Moreover, medical paternalism, increased healthcare cost and disparities in insurance coverage, data security and privacy concerns, and bias and discriminatory services are imminent in the use of AI tools in healthcare. Conclusions Our findings have some critical implications for achieving the Sustainable Development Goals (SDGs) 3.8, 11.7, and 16. We recommend that national governments should lead in the roll-out of AI tools in their healthcare systems. Also, other key actors in the healthcare industry should contribute to developing policies on the use of AI in healthcare systems.

In urban networks, periodic peak traffic congestion often occurs during the day, namely in the morning and afternoon hours. Due to spatial constraints and the inability to increase capacity through physical road expansion, modern traffic management increasingly relies on Intelligent Transport Systems (ITS) solutions. One such solution is the integration of automatic licence plate recognition, an expert system and microsimulation tools aimed at optimising the network performance of signalised intersections within a network. Based on real-time and historical data on individual vehicle trajectories, the system predicts the route of each vehicle through the observed segment of the traffic network, determines the network load and proposes optimal signal plans. This paper provides an overview of conducted research related to the optimisation of signal plans utilising expert systems. Mathematical models for capacity and load determination, as well as computational intelligence-based systems used for signalised intersection management strategies, are described. Finally, the paper proposes a basic framework and guidelines related to the suggested system, highlighting open questions and potential challenges in its development.

This research aims to establish a systematic framework for parametric identification of materials undergoing high temperatures and high strain rates. While advanced testing equipment, such as the Gleeble physical simulator, can produce controlled measurements of specimens under various conditions, significant challenges remain in determining the parameters of constitutive relations. Temperature gradients inevitably arise during Gleeble testing, leading to nonuniform strain distribution caused by complex thermal–mechanical coupling. Although finite element analysis of Gleeble testing can be performed, such simulations are computationally expensive, making brute-force optimization to minimize the difference between experimental data and finite element simulation across the parametric space infeasible. Furthermore, since the related constitutive relations are semi-empirical in nature, the ground truth of the constitutive parameters is generally unknown. In this context, a single-objective optimization based on a number of testing conditions may yield biased results or become trapped in local minima. In this research, we employ finite element analysis simulating Gleeble operation as the foundation, leveraging a suite of computational intelligence tools to address these challenges. We first develop a multi-response Gaussian process surrogate model, trained using a relatively small amount of finite element data, to rapidly emulate the forward analysis. We then implement a multi-objective optimization approach using simulated annealing to individually minimize the differences between experimental results and emulations under various testing conditions. AISI 9310 steel and the Johnson–Cook model are adopted for methodological demonstration. The development of the finite element model, Gaussian process surrogate model, and inverse optimization is detailed, and the results obtained are discussed. This framework can be extended to the parametric identification of other materials and heat treatment conditions using Gleeble testing. [ABSTRACT FROM AUTHOR]

Individual physiotherapy is crucial in treating patients with various pain and health issues, and significantly impacts abdominal surgical outcomes and further medical problems. Recent technological and artificial intelligent advancements have equipped healthcare professionals with innovative tools, such as sensor systems and telemedicine equipment, offering groundbreaking opportunities to monitor and analyze patients' physical activity. This paper investigates the potential applications of mobile accelerometers in evaluating the symmetry of specific rehabilitation exercises using a dataset of 1280 tests on 16 individuals in the age range between 8 and 75 years. A comprehensive computational methodology is introduced, incorporating traditional digital signal processing, feature extraction in both time and transform domains, and advanced classification techniques. The study employs a range of machine learning methods, including support vector machines, Bayesian analysis, and neural networks, to evaluate the balance of various physical activities. The proposed approach achieved a high classification accuracy of 90.6% in distinguishing between left- and right-side motion patterns by employing features from both the time and frequency domains using a two-layer neural network. These findings demonstrate promising applications of precise monitoring of rehabilitation exercises to increase the probability of successful surgical recovery, highlighting the potential to significantly enhance patient care and treatment outcomes. [ABSTRACT FROM AUTHOR]

Opportunistic Network (OppNet) is an enhanced network in the mobile ad hoc network family and has outstanding and updated qualities in the field of Network Technology. The main benefit of OppNet is its ability to store messages that need to be sent to intermediate nodes until the moment of successful communication without imposing a limited time. Developing routes with the best cluster head selection and security are difficult tasks in oppnet. This study uses an improved Secure Fuzzy Trust-Based C-Mean Clustering-based Machine Learning Model (IFTCC) to determine trustworthy nodes. At the same time, an Energy Efficient Harris-Hawks-Remora Routing Protocol (EEHHRR) is proposed to route the cluster head using the secure identify path detection. When selecting the cluster head, the most reliable and energy-efficient node is taken into account. The optimal cluster head among the nodes is chosen using the fuzzy c-means clustering technique. The protection of the intrusive node and the secure transport of data from the source to the destination are the goals of this approach. The best path is found by routing using the EEHHRR. The proposed model finds the safe path using a hybrid optimization technique known as the Harris Hawks and Remora Optimization Algorithm by tracking the node positions and computing the objective function. The proposed model is assessed and contrasted with prevailing methods. The results section shows that PDR, delay, power consumption, message loss, and overhead ratio are 98.8%, 0.02 s, 50.4j, 11,431, 31.15, and 15.14, respectively. [ABSTRACT FROM AUTHOR]