Search Results (2,788)

Engineering education is based on experiential learning, but the problem is that in laboratory conditions, it is difficult to give feedback to the students in real time and personalize this feedback. The paper introduces the proposal of an innovative approach to the laboratories, called Adaptive Lab Mentor (ALM), which combines the technologies of Artificial Intelligence (AI), Internet of Things (IoT), and sensor technology to facilitate intelligent and customized laboratory setting. ALM is supported by a new real-time multimodal sensor fusion model in which a sensor-instrumented laboratory is used to record real-time electrical measurements (voltage and current) which are used in parallel with symbolic component measurements (target resistance) with a lightweight, dual-input Convolutional Neural Network (1D-CNN) running on an edge device. In this initial validation, visual context is presented as a symbolic target value, which establishes a pathway for the future integration of full computer vision. The architecture will enable monitoring of the student progress, making error diagnoses within a short time period, and provision of adaptive feedback based on information available in the context. To test this strategy, a high-fidelity model of an Ohm Laboratory was developed. LTspice was used to generate a huge amount of current and voltage time series of various circuit states. The trained model achieved 93.3% test accuracy and demonstrated that the proposed system could be applied. The ALM model, compared to the current Intelligent Tutoring Systems, is based on physical sensing and edge AI inference in real-time, as well as adaptive and safety-sensitive feedback throughout hands-on engineering demonstrations. The ALM framework serves as a blueprint for the new smart laboratory assistant. Full article

In the era of sustainable digital transformation, organizations increasingly rely on artificial intelligence (AI) to enhance efficiency, innovation, and long-term competitiveness. However, employees’ psychological barriers, including technostress and innovation resistance, continue to constrain successful and sustainable AI adoption. Grounded in Social Exchange Theory (SET), Conservation of Resources Theory (COR), Diffusion of Innovation Theory (DOI), and the Technology Acceptance Model (TAM), this study develops an integrated model linking perceived organizational support (POS)—comprising emotional, informational, and instrumental dimensions—to employees’ sustainable AI adoption through the dual mediating roles of technostress and innovation resistance. Based on 426 valid responses collected from multiple industries, a triadic hybrid approach combining Structural Equation Modeling (SEM), Artificial Neural Networks (ANNs), and Necessary Condition Analysis (NCA) was applied to capture both linear and nonlinear mechanisms. The results reveal that Informational Support (IFS) is the most influential factor and constitutes the sole necessary condition for high-level AI adoption, while emotional and instrumental support indirectly promote sustainable adoption by mitigating employees’ stress and resistance. This study contributes to sustainable management and AI adoption research by providing insights into the potential hierarchical and threshold patterns of organizational support systems in digital transformation. It also provides managerial implications for designing transparent, empathetic, and resource-efficient support ecosystems that foster employee-driven intelligent transformation. Full article

The environmental sustainability of cleaning materials used in heritage conservation remains poorly quantified despite growing attention to the replacement of hazardous petroleum-based solvents with bio-based alternatives. This study applies a comprehensive Life Cycle Assessment (LCIA) to compare conventional solvents with innovative bio-based formulations, including Fatty Acid Methyl Esters (FAMEs), Deep Eutectic Solvents (DES), and aqueous or organogel systems used for cleaning painted surfaces. Following ISO 14040/14044 standards and using the Ecoinvent v3.8 database with the EF 3.1 impact method, three functional units were adopted to reflect material and system-level scales. Results demonstrate that water-rich systems, such as agar gels and emulsified organogels, yield significantly lower climate and toxicity impacts (up to 85–90% reduction) compared with petroleum-based benchmarks, while FAME and DES exhibit outcomes highly dependent on allocation rules and baseline datasets. When including application materials, cotton wipes dominate total environmental burdens, emphasizing that system design outweighs solvent substitution in improving sustainability. The study provides reproducible data and methodological insights for integrating LCIA into conservation decision-making, contributing to the transition toward evidence-based and environmentally responsible heritage practices. Full article

This study examines the role of different factors in supporting the sustainable use of Artificial Intelligence (AI) technologies in higher education, particularly in the context of student interactions with intelligent and human-centered learning tools. Using Structural Equation Modeling (SEM) and Artificial Neural Networks (ANN) within the Technology Acceptance Model (TAM), the research provides a detailed look at how trust influences students’ attitudes and behaviors toward AI-based learning platforms. Data were gathered from 200 students at Occidental Mindoro State College to analyze the effects of social influence, self-efficacy, perceived ease of use, perceived risk, attitude toward use, behavioral intention, acceptance, and actual use. Results from SEM indicate that perceived risk and ease of use have a stronger impact on AI adoption than perceived usefulness and trust. The ANN analysis further shows that acceptance is the most important factor influencing actual AI use, reflecting the complex, non-linear relationships between trust, risk, and adoption. These findings highlight the need for AI systems that are adaptive, transparent, and designed with the user experience in mind. By building interfaces that are more intuitive and reliable, educators and designers can strengthen human–AI interaction and promote responsible and lasting integration of AI in education. Full article

Due to its significant environmental impact, the built environment faces growing pressure to transition toward more sustainable practices. Biomimicry, a novel field of practice that entails design and innovation inspired by nature’s time-tested strategies, offers a promising pathway to enhance sustainability in the construction industry. Hence, this study examines the perceived benefits of applying biomimicry principles in the construction sector, aiming to identify the key dimensions that underpin its transformative potential. An exploratory factor analysis (EFA) was conducted using data collected through a structured questionnaire survey, which contained 18 indicators derived from a targeted literature synthesis. The questionnaire was administered to 120 purposively sampled, duly registered, practising construction and biomimicry professionals in South Africa. The instrument captured perceptions of the environmental, economic, and socio-functional benefits of adopting and implementing biomimicry. The EFA revealed four principal factors: socio-economic and health, ecological resilience, performance enhancement and green market efficiency. These four factors cumulatively accounted for approximately 70% of the total variance, indicating a strong internal structure of perceived benefits. The findings demonstrate that stakeholders perceive biomimicry as a tool for reducing environmental footprints and as a catalyst for innovation, circularity, and regenerative design practices in the built environment. This research contributes to the emerging discourse on biomimicry in the built environment by providing empirical evidence on its multifaceted value. It highlights the importance of integrating natural design intelligence into construction to foster more adaptive, efficient, resilient and sustainable systems. The paper recommends policy support, interdisciplinary collaboration, and further research to operationalise biomimicry within mainstream construction processes. Full article

This paper introduces an innovative EEG sensor-based computational framework that establishes a pioneering nexus between personality trait quantification and neural dynamics, leveraging biosignal processing of brainwave activity to elucidate their intrinsic influence on cognitive health and oscillatory brain rhythms. By employing electroencephalography (EEG) recordings from 21 participants undergoing the Trier Social Stress Test (TSST), we propose a machine learning (ML)-driven methodology to decode the Big Five personality traits—Extraversion (Ex), Agreeableness (A), Neuroticism (N), Conscientiousness (C), and Openness (O)—using classification algorithms such as support vector machine (SVM) and multilayer perceptron (MLP) applied to 64-electrode EEG sensor data. A novel multiphase neurocognitive analysis across the TSST stages (baseline, mental arithmetic, job interview, and recovery) systematically evaluates the bidirectional relationship between personality traits and stress-induced neural responses. The proposed framework reveals significant negative correlations between frontal–temporal theta–beta ratio (TBR) and self-reported Extraversion, Conscientiousness, and Openness, indicating faster stress recovery and higher cognitive resilience in individuals with elevated trait scores. The binary classification model achieves high accuracy (88.1% Ex, 94.7% A, 84.2% N, 81.5% C, and 93.4% O), surpassing the current benchmarks in personality neuroscience. These findings empirically validate the close alignment between personality constructs and neural oscillatory patterns, highlighting the potential of EEG-based sensing and machine-learning analytics for personalized mental-health monitoring and human-centric AI systems attuned to individual neurocognitive profiles. Full article

Pesticides remain indispensable for modern agriculture, yet their persistence in soil poses serious ecological and human-health risks through bioaccumulation, groundwater contamination, and impacts on non-target organisms. Although extensive research exists on pesticide degradation, most reviews separate biochemical pathways, environmental controls, and applied bioremediation strategies, limiting the ability to predict real-world field performance. This review integrates mechanistic enzymology, soil ecological responses, quantitative degradation kinetics, and emerging synthetic biology innovations into one unified framework. Soil bacteria including Pseudomonas, Bacillus, Rhodococcus, and Arthrobacter degrade organophosphates, carbamates, triazines, neonicotinoids, pyrethroids, and organochlorines through hydrolysis, oxidation, nitroreduction, and ring-cleavage pathways, often supported by plasmid-encoded genes and horizontal gene transfer. Bioaugmented systems typically achieve 70 to 95 percent removal within 10 to 30 days, with highly efficient cases such as Pseudomonas putida KT2440 removing 96 percent chlorpyrifos in 5 days, Rhodococcus koreensis mineralizing 98 percent endosulfan in 7 days, and Arthrobacter sp. AD26 degrading 95 percent atrazine in 72 h. Field-scale Azotobacter–Pseudomonas consortia have reduced chlorpyrifos from 25 mg kg⁻¹ to less than 1 mg kg⁻¹ within 30 days. Environmental conditions strongly influence degradation efficiency. Acidic soils increase pyrethroid half-lives by two to three times, anaerobic conditions can extend pesticide persistence from months to years, and drought or low organic matter reduces microbial activity by 60 to 80 percent, increasing neonicotinoid DT₅₀ to more than 1000 days. Advances in omics, metagenomics, kinetic assays, and synthetic biology now enable engineered strains and synthetic consortia capable of more than 90 percent mineralization within 7 to 21 days. By linking molecular mechanisms, ecological constraints, quantitative outcomes, and emerging biotechnologies, this review provides a predictive roadmap for climate-resilient, scalable, and sustainable bioremediation strategies. Full article

The beginning of the 21st century is associated with a significant technological leap on a global scale, which has had a substantial impact on production and economic processes at the national and regional levels. This radical technological change in the economy is linked to the emergence and development of artificial intelligence and effective knowledge management, which are the main drivers of economic growth. The use of AI today can be traced in many different areas of applied science—medicine, physics, mathematics, and engineering design, including modeling, planning, and management of territorial systems. The accumulation of large databases and other information necessary for AI to function is directly related to the spatial aspects of economic development, which is also based on local potential (a place-based approach). At the same time, local knowledge resources and innovation potential are not fully utilized in the context of technology diffusion and AI implementation in individual countries and regions. In this regard, this study aims to analyze the role of regional innovation systems, with a focus on AI development, and to track their impact across individual European regions, using NUTS2 spatial-level data to ensure objectivity. The authors consider AI innovation a modern tool for decision-making in the implementation of regional policy, with a specific impact on cohesion between EU regions. The results of the study show a direct link between the localization of regional innovation systems, R & D expenditure, AI implementation, and the economic development of European regions. Important factors influencing this process are the degree of Internet coverage, the capacity to generate innovation, the degree of AI implementation in the individual economic sectors of the countries, the growth of the ICT sector in relation to the overall development of GDP and the economy, and the result of the smart specialisation of regional innovation systems. Full article

The transition to sustainable food systems requires communication strategies that resonate with consumers’ values, not only technological innovation. This study examines how values-centric communication can shape German consumers’ responses to alternative proteins, focusing on insect-based snacks. A desk-based synthesis of recent studies, guided by Schwartz’s value theory, identified Tradition and Security as dominant drivers of food choice and yielded five communication requirements: Cultural familiarity, Emotional safety, Simplicity and clarity, Trust and credibility, and Routine integration. These were operationalised into communication guidelines and short on-pack claims, which were applied to a refined packaging prototype. An exploratory focus group (N = 7) then compared reactions to the original versus the refined packaging, analysed using McGuire’s communication–persuasion stages. Within this small exploratory group, participants reported that familiar formats, a reassuring tone, clear visual hierarchy, and salient trust cues made them more willing to consider trying the product, whereas information overload, claim–image incongruence, value-incongruent brand naming, and delayed recognition of insect content appeared to impede acceptance. The study contributes an integrative analytic lens combining Schwartz’s value theory with McGuire’s model and a set of testable guidelines for value-aligned food communication. Because the empirical evidence is based on a single small student focus group with fixed presentation order, bundled manipulations, and hypothetical intentions, these results are exploratory and self-reported and should be interpreted cautiously; future research should employ counterbalanced factorial designs with behavioural outcomes. Full article

Classical thermodynamics (CT) has become integrated into everyday life, especially through its applications in engineering. In contrast, out-of-equilibrium thermodynamics (OET) is often viewed as a fundamental science that seems distant from daily experiences. While “energy” is a familiar term in households, “entropy,” which refers to degraded energy, remains enigmatic. This gap in understanding has significant implications for developing effective sustainability practices. CT typically emphasizes the efficiency of individual systems that produce work, often overlooking the entropy production that occurs within larger, interconnected systems. This paper aims to establish a philosophical framework that transforms OET into what is referred to as “lived thermodynamics.” This framework is grounded in pragmatism, particularly drawing from the early synthesis of thermodynamics and evolutionary theory proposed by Charles S. Peirce. A central aspect of this approach involves shifting the focus from traditional “systems” to out-of-equilibrium assemblages. In these assemblages, the physical trends of entropy production are often interrupted and redirected by evolutionary innovations and random events. The evolving envelope of open systems within these assemblages manifests an increasing rate of entropy production. This synthesis of thermodynamics and evolutionary theory builds on Lotka’s pioneering contributions and contemporary theories, particularly Vermeij’s work on the evolution of power. The framework introduces a sustainability criterion based on entropy. By applying this criterion, OET can evolve into “lived thermodynamics,” fostering a holistic understanding of energy use in devices and technological systems while considering the broader implications of entropy production in the out-of-equilibrium assemblages in which we live. Full article

This study introduces an innovative Honey Badger Optimization (HBO) designed to address the Optimal Power Flow (OPF) challenge in electrical power systems. HBO is a unique population-based searching method inspired by the resourceful foraging behavior of honey badgers when hunting for food. In this algorithm, the dynamic search process of honey badgers, characterized by digging and honey-seeking tactics, is divided into two distinct stages, exploration and exploitation. The OPF problem is formulated with objectives including fuel cost minimization and voltage deviation reduction, alongside operational constraints such as generator limits, transformer settings, and line power flows. HBO is applied to the IEEE 30-bus test system, outperforming existing methods such as Particle Swarm Optimization (PSO) and Gray Wolf Optimization (GWO) in both fuel cost reduction and voltage profile enhancement. Results indicate significant improvements in system performance, achieving 38.5% and 22.78% better voltage deviations compared to GWO and PSO, respectively. This demonstrates HBO’s efficacy as a robust optimization tool for modern power systems. In addition to the single-objective studies, a multi-objective OPF formulation was investigated to produce the complete Pareto front between fuel cost and voltage deviation objectives. The proposed HBO successfully generated a well-distributed set of trade-off solutions, revealing a clear conflict between economic efficiency and voltage quality. The Pareto analysis demonstrated HBO’s strong capability to balance these competing objectives, identify knee-point operating conditions, and provide flexible decision-making options for system operators. Full article

Low-energy impacts have been demonstrated to cause damage and failure in aircraft structures, thereby affecting the structural load-bearing performance and creating safety hazards. In this study, an innovative damage-monitoring method based on a fiber Bragg grating (FBG) is proposed for honeycomb sandwich composites. The proposed method is applicable to honeycomb sandwich composites and integrates a light gradient boosting machine (LightGBM)-optimized impact localization method with feature-parallel and data-parallel processing in the machine learning architecture. An impact localization algorithm is applied to honeycomb sandwich composites using an array of multiplexed FBG sensors. The proposed algorithm exhibited substantial localization accuracy. The LightGBM method was employed to identify the optimal branching points for impact localization in real time, addressing the low-accuracy challenge in localizing low-energy impacts on the board structure when the fiber grating sensing system operates at a high sampling frequency. Full article

Background: Rheumatoid arthritis (RA) is a systemic chronic inflammatory autoimmune disease causing progressive joint destruction, resulting in significant morbidity and increased mortality. Despite advances in treatment, current pharmacological options, including NSAIDs, DMARDs, and biological agents, have limitations in tissue repair and can lead to severe side effects. Objectives: This study aims to explore drug repurposing as a viable approach to identify novel therapeutic agents for RA by utilizing existing FDA-approved drugs. Methods: We applied an integrated computational strategy that uniquely combines network pharmacology with molecular docking and dynamics simulations. The process began with the construction of a protein–protein interaction (PPI) network from 2723 RA-associated genes, which identified five central targets: TNF-α, IL-6, IL-1β, STAT3, and AKT1. We then built protein–drug interaction (PDI) networks by screening 2637 FDA-approved drugs against these targets. Critically, the top candidates from this network analysis were not just docked but were further validated using 100 ns molecular dynamics simulations to thoroughly evaluate binding affinity, complex stability, and interaction dynamics. Results: This multi-tiered computational workflow identified Rifampicin, Telmisartan, Danazol, and Pimozide as the most promising repurposing candidates. They demonstrated strong binding affinities and, importantly, formed stable complexes with TNF-α, IL-6, IL-1β, and STAT3, respectively, in dynamic simulations. The key innovation of this study is this sequential funnel approach, which integrates large-scale network data with atomic-level simulation to prioritize high-confidence drug candidates for RA. Conclusions: In conclusion, this study highlights the potential of repurposing FDA-approved drugs to target key proteins involved in RA, offering a cost-effective and time-efficient strategy to discover new therapies. Full article

Co-reactants are essential in co-reactant-based electrochemiluminescence (ECL) systems because they generate reactive intermediates that can oxidize or reduce ECL luminophores, thereby driving ECL emission. In the context of ECL, gold nanoclusters (Au NCs) have emerged as innovative luminophores, owing to their tunable electronic structures and excellent biocompatibility. However, their efficiency in ECL applications is often compromised by challenges such as limited excited-state generation and non-radiative losses. To tackle these practical challenges, advanced co-reactant engineering strategies have been developed to improve the performance of Au NCs in ECL systems. This review begins with a brief overview of the mechanisms of ECL. Subsequently, a systematic overview of various co-reactant engineering strategies is presented, including: (1) using innovative co-reactants to replace traditional ones due to their lower toxicity and better biocompatibility; (2) applying co-reaction accelerators to reduce the onset potential and improve the production of reactive intermediates from co-reactants; (3) combining co-reactants with luminophores or creating integrated nanostructure assemblies of co-reactants, co-reaction accelerators, and luminophores to achieve shorter electron transfer paths and reduced energy loss for stable high-intensity ECL emission; (4) utilizing host-guest strategies that encapsulate co-reactants within cavities to stabilize radical intermediates and minimize environmental quenching. This review provides a comprehensive overview of recent developments in co-reactant engineering for Au NCs-based ECL systems, thereby encouraging further exploration and understanding of these systems and expanding their potential applications. Full article

The escalating production and use of lithium-ion batteries (LIBs) have led to a pressing need for efficient and sustainable methods for recycling valuable metals such as cobalt, nickel, manganese, and lithium from spent cathode materials. Traditional hydrometallurgical leaching approaches, based on mineral acids, face significant limitations, including high reagent consumption, secondary pollution, and poor selectivity. In recent years, deep eutectic solvents (DESs) and ionic liquids (ILs) have emerged as innovative, environmentally benign alternatives, offering tunable physicochemical properties, enhanced metal selectivity, and potential for reagent recycling. This review provides a comprehensive analysis of the current state and prospects of leaching LIB cathode materials using DES and ILs. We summarize the structural diversity and composition of common LIB cathodes, highlighting their implications for leaching strategies. The mechanisms, efficiency, and selectivity of metal dissolution in various DES- and IL-based systems are critically discussed, drawing on recent advances in both laboratory and real-sample studies. Special attention is given to the unique extraction mechanisms facilitated by complexation, acid–base, and redox interactions in DES and ILs, as well as to the effects of key operational parameters. A comparative analysis of DES- and IL-based leaching is presented, with discussion of their advantages, challenges, and industrial potential. While DES offers low toxicity, biodegradability, and cost-effectiveness, it may suffer from limited solubility or viscosity issues. Conversely, ILs provide remarkable tunability and metal selectivity but are often hampered by higher costs, viscosity, and environmental concerns. Finally, the review identifies critical bottlenecks in upscaling DES and IL leaching technologies, including long-term solvent stability, metal recovery purity, and economic viability. We also highlight research priorities that emphasize applying circular hydrometallurgy and life-cycle assessment to improve the sustainability of battery recycling. Full article