Search Results (22,288)

The integration of generative artificial intelligence, particularly large language models, into education presents opportunities for both personalised learning and pedagogical challenges. This study focuses on electrical engineering laboratory education. We developed a configurable prototype of a generative artificial intelligence powered tutoring tool, implemented it in an undergraduate electrical engineering laboratory course, and analysed 208 student–tutoring tool interactions using a mixed-methods approach that combined research team evaluation with learner feedback. The findings show that student prompts were predominantly procedural or factual, with limited conceptual or metacognitive engagement. Structured prompt styles produced clearer and more coherent responses and were rated the highest by students, while approaches aimed at fostering reasoning and reflection were valued mainly by the research team for their pedagogical depth. This contrast highlights a consistent preference–pedagogy gap, indicating the need to embed stronger instructional guidance into artificial intelligence tutoring. To bridge this gap, a promising direction is the development of pedagogically enriched AI tutors that integrate features such as adaptive prompting, hybrid strategy blending, and retrieval-augmented feedback to balance clarity, engagement, and depth. The results provide practical and conceptual value relevant to educators, developers, and researchers interested in artificial intelligence tutors that are both engaging and pedagogically sound. For educators, the study clarifies how students interact with tutors, helping align artificial intelligence use with instructional goals. For developers, it highlights the importance of designing systems that combine usability with educational value. For researchers, the findings identify directions for further study on how design choices in artificial intelligence tutoring affect learning processes and pedagogical alignment across STEM contexts. On a broader level, the study contributes to a more transparent, equitable, and sustainable integration of generative AI in education. Full article

The ocean, as one of the largest thermal energy storage bodies on earth, has great potential as a thermal-electric energy reserve. Application of the relatively fixed-temperature ocean as the heat sink, and using concentrated solar energy as the heat source, one may construct a mobile power station on the ocean’s surface. However, a traditional solar-based heat source requires a large footprint to concentrate the light beam, resulting in bulky parabolic dishes, which are impractical under ocean engineering scenarios. For buoy-sized applications, the small form factor of the energy collector can only achieve limited temperature differential, and its energy quality is deemed to be unusable by traditional spring-loaded free piston Stirling engines. Facing these challenges, a low-temperature differential free piston Stirling engine is presented. The engine features a large displacer piston (ϕ136, 5 mm thick) made of corrugated board, and an aluminum power piston (ϕ10). Permanent magnets embedded in both pistons couple them through magnetic attraction rather than a mechanical spring. This magnetic “spring” delivers an inverse-exponential force–distance relation: weak attraction at large separations minimizes damping, while strong attraction at small separations efficiently transfers kinetic energy from the displacer to the power piston. Engine dynamics are captured by a lumped-parameter model implemented in Simulink, with key magnetic parameters extracted from finite-element analysis. Initial results have shown that the laboratory prototype can operate continuously across heater-to-cooler temperature differences of 58–84 K, sustaining flywheel speeds of 258–324 RPM. Full article

Although sustainability has become a central concern in project management research, its integration into enterprise risk practices in construction remains limited. This study investigates the complex set of barriers preventing effective implementation of Sustainable Enterprise Risk Management (SERM) within the construction industry of the United Arab Emirates (UAE). SERM focuses on maintaining the system’s long-term effectiveness, adaptability, and resilience. As projects across the region expand in scale and complexity, the need for resilient and sustainability-aligned risk practices has become increasingly urgent. To address this gap, a structured four-stage methodology was adopted. A Systematic Literature Review identified 28 potential barriers, which were refined through a Delphi process to 16 validated barriers. Interpretive Structural Modeling (ISM) and MICMAC analysis were then used to explore their hierarchical relationships and mutual influence. The ISM–MICMAC results showed that weak governance and limited organizational awareness reinforce communication and procedural challenges, while technology-related constraints remain highly dependent within the hierarchy. The sixteen barriers were categorized under four dimensions: leadership, culture, resources, and technology to clarify their structural relationships and dominant influence levels. Among these, the lack of senior management commitment (C01) emerged as the most influential barrier, exerting the strongest driving power (16) and lowest dependence (1), positioning it as the root cause affecting the rest. These findings highlight the need for leadership-driven strategies to embed long-term sustainability within organizational risk governance. The study offers practical direction for policymakers, contractors, and project leaders seeking to strengthen resilience and sustainable risk practices in the UAE construction sector. Full article

Optical fiber technology is becoming essential in modern radiation therapy, enabling precise, real-time, and minimally invasive monitoring. As oncology moves toward patient-specific treatment, there is growing demand for adaptable and biologically compatible sensing tools. Fiber-optic systems meet this need by integrating into clinical workflows with highly localized dosimetric and spectroscopic feedback. Their small size and flexibility allow deployment within catheters, endoscopes, or treatment applicators, making them suitable for both external beam and internal therapies. This paper reviews the fundamental principles and diverse applications of optical fiber sensing technologies in radiation oncology, focusing on dosimetry, spectroscopy, imaging, and adaptive radiotherapy. Implementations such as scintillating and Bragg grating-based dosimeters demonstrate feasibility for in vivo dose monitoring. Spectroscopic techniques, such as Raman and fluorescence spectroscopy, offer real-time insights into tissue biochemistry, aiding in treatment response assessment and tumor characterization. However, despite such advantages of optical fiber sensors, challenges such as signal attenuation, calibration demands, and limited dynamic range remain. This paper further explores clinical application, technical limitations, and future directions, emphasizing multiplexing capabilities, integration and regulatory considerations, and trends in machine learning development. Collectively, these optical fiber sensing technologies show strong potential to improve the safety, accuracy, and adaptability of radiation therapy in personalized cancer care. Full article

The gravity forward modeling algorithm is a compute-intensive method and is widely used in scientific computing, particularly in geophysics, to predict the impact of subsurface structures on surface gravity fields. Traditional implementations rely on CPUs, where performance gains are mainly achieved through algorithmic optimization. With the rise of domain-specific architectures, FPGA offers a promising platform for acceleration, but faces challenges such as limited programmability and the high cost of nonlinear function implementation. This work proposes an FPGA-based co-processor to accelerate gravity forward modeling. A RISC-V core is integrated with a custom instruction set targeting key computation steps. Tasks are dynamically scheduled and executed on eight fully pipeline processing units, achieving high parallelism while retaining programmability. To address nonlinear operations, we introduce a piecewise linear approximation method optimized via stochastic gradient descent (SGD), significantly reducing resource usage and latency. The design is implemented on the AMD UltraScale+ ZCU102 FPGA (Advanced Micro Devices, Inc. (AMD), Santa Clara, CA, United States) and evaluated across several forward modeling scenarios. At 250 MHz, the system achieves up to 179× speedup over an Intel Xeon 5218R CPU (Intel Corporation, Santa Clara, CA, United States) and improves energy efficiency by 2040×. To the best of our knowledge, this is the first FPGA-based gravity forward modeling accelerate design. Full article

U-Space, the European Union’s legal framework for enabling drone traffic in low altitude, has implications extending beyond airspace management, particularly on the sustainable development of urban areas. This article presents a case study involving regional and local level representatives, examining anticipated concerns and strategic interests, as well as managing requirements in urban U-Space planning. Following a three-stage capacity building process conducted in the German federal state of Hamburg, the results specify ambitions for enhancing economic attractiveness coupled with locally embedded visions for improved public service provision. Instruments that have shown apposite in the given setting to address concerns surrounding public order and security, as well as the impairment of area functions, are presented. The challenges of implementing U-Space in alignment with societal expectations are outlined. Based on the discussion of these findings, recommendations for local-level capacity-building policy and the multi-level governance of U-Space are derived. Full article

This study introduces a novel control strategy aimed at achieving projective synchronization in incommensurate fractional-order chaotic systems (IFOCS). The approach integrates the mathematical framework of fractional calculus with the recursive structure of the backstepping control technique. A key feature of the proposed method is the systematic use of the Mittag–Leffler function to verify stability at every step of the control design. By carefully constructing the error dynamics and proving their asymptotic convergence, the method guarantees the overall stability of the coupled system. In particular, stabilization of the error signals around the origin ensures perfect projective synchronization between the master and slave systems, even when these systems exhibit fundamentally different fractional-order chaotic behaviors. To illustrate the applicability of the method, the proposed fractional order backstepping control (FOBC) is implemented for the synchronization of two representative systems: the fractional-order Van der Pol oscillator and the fractional-order Rayleigh oscillator. These examples were deliberately chosen due to their structural differences, highlighting the robustness and versatility of the proposed approach. Extensive simulations are carried out under diverse initial conditions, confirming that the synchronization errors converge rapidly and remain stable in the presence of parameter variations and external disturbances. The results clearly demonstrate that the proposed FOBC strategy not only ensures precise synchronization but also provides resilience against uncertainties that typically challenge nonlinear chaotic systems. Overall, the work validates the effectiveness of FOBC as a powerful tool for managing complex dynamical behaviors in chaotic systems, opening the way for broader applications in engineering and science. Full article

Environmental education (EE) is central to achieving the Sustainable Development Goals (SDGs), particularly where inequalities constrain access to quality learning. Following PRISMA 2020, this review synthesizes 35 peer-reviewed studies and policy documents to examine Morocco’s EE policies and practices against global frameworks and post-pandemic challenges. A systematic search was conducted in Scopus, Web of Science, ERIC, ProQuest/EBSCO, Google Scholar, and national repositories (January 2000–December 2024; executed 15–17 March 2024). Findings show strong discursive alignment with SDG 4.7 and UNESCO’s ESD 2030 Roadmap but persistent implementation gaps: rural and peri-urban schools face resource shortages; teacher preparation for participatory, interdisciplinary approaches remains limited; and environmental clubs often rely on short-term projects without stable institutional support. The COVID-19 period exacerbated these pressures yet opened opportunities to integrate health–environment linkages, digital tools, and adaptive pedagogy. Equity reporting was limited (31% gender; 37% residence; 9% socio-economic status). Arabic-only records were identified (n = 42) and title/abstract-screened (n = 17) but excluded due to translation constraints (language bias). To advance transformative EE, we recommend prioritizing participatory, place-based teacher education; institutionalizing school clubs with light monitoring and baseline grants; targeting support to reduce territorial inequities; and developing an SDG-aligned national dashboard. Expanding longitudinal, quasi-experimental, and participatory designs is critical to strengthen causal claims and inform policy. Full article

Multi-robot ensembles comprising several manipulators are commonly used in industrial settings to process non-deterministic flows of items loaded by an upstream source onto a shared transportation system. After the execution of a given task, the robots regularly deposit the items on a common output flow, which conveys the semi-finished material towards the downstream portion of the plant for further processing. The productivity and reliability of the entire process, which is affected by the plant layout, by the quality of the adopted scheduling and task assignment algorithms, and by the proper balancing of the input and output flows, may be degraded by random disturbances and transient conditions of the input flow. In this paper, a highly accurate event-based simulator of this kind of system is used in conjunction with a rollout algorithm to optimize the performance of the plant in all operating scenarios. The proposed method relies on a simulation of the plant that comprehensively considers the dynamic performance of the manipulators, their actual motion planning algorithms, the adopted scheduling and task assignment methods, and the regulation of the material flows. The simulation environment is built upon computationally efficient maps able to predict the execution time of the tasks assigned to the robots, considering all the determining factors, and on a representation of the manipulators themselves as finite state automata. The proposed formalization of the line balancing problem as a Markov Decision Process and the resulting rollout optimization method are shown to substantially improve the performance of the plant, even in challenging situations, and to be well suited to real-time implementation even on commodity hardware. Full article

Switzerland’s energy transition toward net-zero greenhouse gas emissions by 2050 presents a critical challenge in managing winter electricity demand, particularly due to the widespread electrification of space heating and domestic hot water. In this study, we assess how targeted measures in the building sector can influence heat demand and thereby also the winter electricity gap. To this end, we extended the existing PowerCheck simulation tool by incorporating a detailed bottom-up representation of the Swiss building stock. We model hourly heat and electricity demand across 60 building categories, defined by climate zone, usage type, and insulation standard. Twelve future scenarios are developed based on variations in four key parameters: building renovation rate, hot water heat recovery, heat sources used by heat pumps, and ambient temperature trends. Our results indicate that renovation of old buildings to current insulation standards has by far the greatest effect out of the studied parameters. Increasing the annual thermal renovation rate of building shells from the currently planned 1.1% to 2% can reduce the winter electricity gap from 10.7 TWh to 6.0 TWh, a 44% reduction. Conversely, achieving only a low renovation rate of 0.5% could increase the gap to 13.9 TWh. Additional measures, such as greater use of ground-source instead of air-source heat pumps and implementation of hot water recovery, offer further potential for reduction. These findings underscore the importance of early and sustained investment in thermal renovation of building shells for achieving Switzerland’s 2050 net-zero climate targets. Full article

The increasing emphasis on environmental protection, climate change mitigation, and the transition to a circular economy requires industries, including the wood-processing sector, to integrate sustainability into strategic and operational management. Green growth indicators represent essential tools for evaluating the environmental, economic, and social impacts of business activities, while also contributing to the sustainable economics and responsible management of forest resources and products. This study applies a qualitative research design using structured interviews with 10 executives from medium and large woodworking enterprises in Slovakia. The interviews examined company strategies, practices, and challenges in sustainable development and forest resource utilization. The findings reveal that while many companies actively manage waste, invest in green technologies, and conduct internal audits, the broader implementation of environmental management systems and the uptake of public sustainability funding remain limited. Notably, 90% of respondents emphasized waste volume and recovery rates as critical indicators. Based on the results, a set of green growth indicators was developed and categorized across key thematic areas including waste management, energy efficiency, stakeholder communication, certification, and strategic planning. These indicators not only support the assessment of corporate sustainability but also strengthen efficient forest resource management, responsible use of raw materials, and the long-term economic viability of the sector. The study highlights the importance of systematically designed and practically applicable indicators for guiding companies toward sustainable competitiveness and emphasizes the need for stronger institutional support, improved access to reliable data, and integration of sustainability metrics into core business decision-making. Full article

Background: During the COVID-19 pandemic, transgender and gender-diverse (TGD) people and people with disabilities in India faced disproportionate barriers to accessing vaccination services. Building on previous studies, this study explored the experiences of COVID-19 vaccine access in these two marginalized communities, using the WHO Behavioral and Social Drivers (BeSD) framework. Methods: Keeping community-based participatory methods (CBPR) at heart, we conducted a survey adapted from the BeSD COVID-19 survey tool. The survey was adapted using insights from a prior study, a literature review, stakeholder consultations, and discussions with a community leadership group (CLG) and an advisory board (AdB). Participants were recruited through transgender, gender-diverse, and disability rights networks. Data were analyzed descriptively, using percent analysis, and psychometrically, using exploratory factor analysis on polychoric correlations. Results: The adapted BeSD survey tool showed a high 0.85 (p < 0.05) internal consistency and criterion validity. Moreover, it showed a high willingness to be vaccinated (for ease of access to other services and community responsibility); however, systemic barriers hindered vaccination access. TGD people and people with disabilities faced multiple barriers in being vaccinated. The TGD community reported documentation mismatches and mistrust in health systems. People with disabilities reported mobility challenges, escort dependence, financial challenges, and variable accessibility at vaccination sites. Both groups faced digital exclusion, received inadequate information that did not address their specific needs, and experienced inconsistent implementation of inclusive policies. Community-led facilitation led to more uptake. Conclusions: Vaccine willingness alone is insufficient to ensure that vaccines reach everyone. Addressing trust deficits, infrastructural barriers, and digital exclusions requires diligent attention and commitment from the government to mitigate broader challenges faced by TGD people and people with disabilities. Full article

In recent years, academic performance prediction has evolved as a research field thanks to its development and exploration in the educational context. Early student performance prediction is crucial for enhancing educational outcomes and implementing timely interventions. Conventional approaches frequently struggle on behalf of the complexity of student profiles as a consequence of single activation functions, which prevent them from effectively learning intricate patterns. In addition, these models could experience obstacles such as the vanishing gradient problem and computational complexity. Therefore, this research study designed an Activation Ensemble Deep Neural Network (AcEnDNN) model to gain control of the previously mentioned challenges. The main contribution is the creation of a credible student performance prediction model that comprises extensive data preprocessing, feature extraction, and an Activation Ensemble DNN. By utilizing various methods of activation functions, such as ReLU, tanh, sigmoid, and swish, the ensembled activation functions are able to learn the complex structure of student data, which leads to more accurate performance prediction. The AcEn-DNN model is trained and evaluated based on the publicly available Student-mat.csv dataset, Student-por.csv dataset, and a real-time dataset. The experimental results revealed that the AcEn-DNN model achieved lower error rates, with an MAE of 1.28, MAPE of 2.36, MSE of 4.55, and RMSE of 2.13 based on a training percentage of 90%, confirming its robustness in modeling nonlinear relationships within student data. The proposed model also gained the minimum error values MAE of 1.28, MAPE of 2.97, MSE of 4.77, and RMSE of 2.18, based on a K-fold value of 10, utilizing the Student-mat.csv dataset. These findings highlight the model’s potential in early identification of at-risk students, enabling educators to develop targeted learning strategies. This research contributes to educational data mining by advancing predictive modeling techniques that evaluate student performance. Full article

Antimicrobial resistance (AMR) constitutes a major global health challenge, driven significantly by inappropriate antibiotic use in human medicine. Despite the existence of evidence-based guidelines, variability in antibiotic prescribing persists, influenced by psychosocial factors, diagnostic uncertainty, patient expectations, and local prescribing cultures. Outpatient care, the setting in which most antibiotics are prescribed, is particularly affected by such challenges. Traditional top-down interventions, such as national guidelines, often fail to achieve sustained behavioral change among prescribers. In this comprehensive review, we provide an overview of the psychological and behavioral factors influencing antimicrobial stewardship (AMS) implementation, as well as describe a bottom-up project working to meet these challenges: the “Antibiotic Therapy in Bielefeld” (AnTiB) initiative. AnTiB employs a cross-sectoral strategy aimed at developing rational prescribing culture by means of locally developed consensus guidelines, interdisciplinary collaboration, and regularly held trainings. By addressing both the organizational and psychological aspects of prescribing practices, AnTiB has facilitated a harmonization of antibiotic use across specialties and care interfaces at the local level. The initiative’s success has led to its expansion within Germany, including through the creation of the AMS-Network Westphalia Lippe and the development of AnTiB-based national pediatric recommendations. These projects are all grounded in social structures designed to strengthen the long-term establishment of AMS measures. Our efforts underscore the importance of considering local social norms, professional network, and real-world practice conditions in AMS interventions. Integrating behavioral and social science approaches into outpatient antimicrobial stewardship—exemplified by the practitioner-led AnTiB model—improves acceptability and alignment with stewardship principles; wider adoption will require local adaptation, routine outpatient resistance surveillance, structured evaluation, and sustainable support. Full article

This study proposes a comprehensive and computationally efficient system for the recognition of heart valve diseases (HVDs) in phonocardiogram (PCG) signals, emphasizing an end-to-end workflow suitable for real-world deployment. The core of the system is a lightweight weighted convolutional neural network (WCNN) featuring a key weighting calculation (KWC) layer, which enhances noise robustness by adaptively weighting feature map channels based on global average pooling. The proposed system incorporates optimized feature extraction using Mel-frequency cepstral coefficients (MFCCs) guided by GradCAM, and a band energy ratio (BER) metric to assess signal quality, showing that lower BER values are associated with higher misclassification rates due to noise. Experimental results demonstrated classification accuracies of 99.6% and 90.74% on the GitHub PCG and PhysioNet/CinC Challenge 2016 databases, respectively, where the models were trained and tested independently. The proposed model achieved superior accuracy using significantly fewer parameters (312,357) and lower computational cost (4.5 M FLOPs) compared with previously published research. Compared with the model proposed by Karhade et al., the proposed model use 74.9% fewer parameters and 99.3% fewer FLOPs. Furthermore, the proposed model was implemented on a Raspberry Pi, achieving real-time HVDs detection with a detection time of only 1.87 ms for a 1.4 s signal. Full article