Search Results (4,834)

While existing curricula often focus on theoretical aspects of sustainability, they frequently fail to equip students with practical design skills required by the green industry. To address this disconnect, this study seeks to answer: How can a structured pedagogical framework effectively enhance students’ ability to translate abstract sustainability principles into concrete technical solutions? This study introduces a comprehensive CDIO-based framework reform for Embedded Intelligent Systems education, weaving sustainability throughout every phase. We put forward a “Sustainable CDIO Capability Model” that charts a progressive pathway—starting from basic resource awareness and advancing through to sophisticated sustainable system innovation. Our four-dimensional teaching strategy brings this model to life: first, project-based learning driven by real sustainability challenges; second, a hybrid ecosystem blending online resources, hands-on practice, and immersion in green industry contexts; third, hierarchical team-based pedagogy backed by personalized support mechanisms; and fourth, a multi-dimensional assessment system that weights energy efficiency, resource stewardship, and social value creation alongside conventional metrics. We implemented this approach with Intelligent Science and Technology majors at Wuhan Institute of Technology. The results show the model effectively bridges the persistent gap between dry technical content and the practical demands of green industry. Students made substantial gains not merely in core engineering capabilities—system architecture, hardware-software co-development—but crucially in sustainable design awareness and their capacity to untangle complex sustainability challenges. This work offers a readily transferable framework for embedding Education for Sustainable Development (ESD) into engineering curricula worldwide. It provides practitioners with a concrete, tested model for cultivating the next generation of engineers who naturally think and act with sustainability in mind. Full article

Food irradiation is a well-established technology endorsed by the World Health Organization (WHO) to enhance food safety. However, consumer awareness of irradiated foods remains remarkably low across diverse cultural contexts. This study aimed to cross-culturally adapt and validate the Turkish form of the Awareness Scale for Consumption of Irradiated Foods (ASCIF). The ASCIF, originally developed and validated in Brazil, encompasses 31 items distributed across four factors: safety of irradiated foods (S), concepts (C), labeling (L), and awareness (A). The cross-cultural adaptation process adhered to the International Test Commission (ITC) guidelines, involving forward–backward translation and expert panel review. A total of 346 university-affiliated individuals (82.9% female; mean age 21.3 ± 4.8 years) from Gazi University, Ankara, completed an online survey with the adapted Turkish version of the ASCIF. Although the internal consistency results indicated a high level of reliability (α = 0.963), other indicators, such as ESEM analysis (RMSEA (90% CI) = 0.143 (0.139–0.148), CFI = 0.880, and TLI = 0.870), suggest that the cross-cultural adaptation of the ASCIF scale into Turkish encountered significant challenges, particularly regarding its psychometric validation. These complications indicate that the original construct may not have translated seamlessly across the cultural and linguistic nuances of the Turkish context in this study. Full article

The rapid expansion of online real estate (RE) platforms has intensified information overload, making property search and decision-making increasingly complex. Real estate recommendation systems (RERSs) have emerged as essential decision-support tools; however, their development has not kept pace with advances in explainable artificial intelligence (XAI), transfer learning (TL), and fairness-aware machine learning. This PRISMA-compliant systematic review synthesizes 59 peer-reviewed studies published between 2005 and 2025 to critically examine algorithmic approaches, data modalities, evaluation practices, and ethical considerations in RERS research. Our analysis reveals a substantial lag in the adoption of state-of-the-art AI techniques: While deep learning is employed in 15% of studies, no reviewed work implements state-of-the-art post hoc XAI or TL frameworks, despite their relevance for addressing interpretability and data scarcity challenges. Furthermore, we identify systemic research biases, including reliance on proprietary datasets (80%), geographic concentration in Asia (56%), the dominance of residential property studies (91%), and limited fairness auditing despite documented discrimination risks in housing markets. To address these gaps, we propose a trust-based evaluation (T-EVAL) framework that integrates predictive accuracy, user trust, fairness, and market efficiency, and introduces a comprehensive nine-layer conceptual architecture for transparent, ethical, and data-efficient next-generation RERS. This review establishes an empirical benchmark for technology adoption gaps and outlines a research agenda for advancing responsible AI in RE decision-support systems. Full article

To boost the local microalgae sector, Switzerland needs to better understand the current state of the industry, which is not fully represented in the existing literature. Only by identifying the strengths and weaknesses of the Swiss microalgae industry, will the country be able to develop strategies toward a strong and sustainable sector in the future. This work provides the first structured assessment of Switzerland’s fragmented and poorly documented microalgae sector through desktop research and an online survey of the country’s microalgae stakeholders. First, research articles with Swiss authors and patents with Swiss applicants were mapped. Then, a survey consisting of 8 questions was designed to gather information about the location, purpose, employees, production capacity, activities, and installations of 42 organizations with a research and/or commercial focus. The growing number of organizations working with microalgae in Switzerland is dominated by small companies (<50 employees) that provide services rather than biomass or bioproducts. Microalgae biomass production is about 2 tons DW per year and is also dominated by small-scale producers (<100 kg DW per year). One third of Swiss companies that sell microalgae-based products produce their own biomass abroad or purchase from abroad. Our findings highlight the growth potential of the Swiss microalgae sector. This systematic summary of research interests, technological innovations, and current market parameters is the first step toward future improvements in the sector. Full article

As transportation-related environmental pressures intensify, understanding the psychological mechanisms underlying battery electric vehicle (BEV) adoption has become increasingly important. Drawing on the Value–Attitude–Behavior (VAB) framework, this study investigates how perceived green value, hedonic value, and utilitarian value shape Chinese consumers’ attitudes and purchase intentions toward BEVs, while examining the moderating roles of technophilia, range anxiety, and battery cost. A cross-sectional online survey was conducted in China, yielding 596 valid responses. Partial Least Squares Structural Equation Modeling (PLS-SEM) and Necessary Condition Analysis (NCA) were employed for data analysis. The results show that perceived hedonic value exerts the strongest positive effect on Attitude Toward Using BEVs (β = 0.591, p < 0.001), followed by perceived utilitarian value (β = 0.135, p < 0.001) and perceived green value (β = 0.074, p = 0.026). Attitude Toward Using significantly predicts BEV purchase intention (β = 0.151, p = 0.002). Technophilia significantly moderates the relationship between attitude and purchase intention (β = −0.096, p = 0.002), whereas the moderating effects of range anxiety and battery cost are not significant. The structural model explains 40.9% of the variance in attitude and 24.2% of the variance in purchase intention. NCA results further reveal that hedonic value constitutes the most critical necessary condition for forming favorable attitudes toward BEVs (d = 0.079, p < 0.001). This study contributes to the sustainable mobility literature by extending the VAB framework through the integration of multidimensional perceived value and necessary condition logic within the Chinese BEV context. The findings highlight that experiential and technological enjoyment, rather than environmental concern alone, has become a central driver of BEV adoption in emerging electric mobility markets. Full article

Background: The management of diabetic foot disease and knee osteoarthritis (OA) with smart orthotics holds significant importance during the early stages of these conditions, given their potential consequences, including functional impairment, chronic pain, and economic burden. Real-time monitoring of plantar foot pressure enables early detection of abnormal force distribution and gait biomechanics, allowing for the redirection of forces away from affected ulcers or arthritic joints. This is the first systematic review to synthesise clinical evidence for smart orthotics technology with real-time plantar pressure sensor biofeedback across both diabetic foot ulcer prevention and knee osteoarthritis management simultaneously. A search of the PROSPERO register confirmed no existing registration covers this specific combination. Objectives: To examine the clinical evidence for the use of standard and smart orthotics in the prevention and management of diabetic foot ulcers (DFUs) and knee OA, and to evaluate their impact on plantar pressure redistribution, ulcer recurrence, pain, biomechanics, and economic burden. Eligibility criteria: Studies published in English involving human adult participants (≥18 years) with a clinical diagnosis of diabetes mellitus (at risk of DFU or with peripheral neuropathy) or knee OA, where the intervention involved any orthotic device or smart/intelligent insole with clinical outcomes reported, were included. Studies on healthy individuals only, those not reporting participant age, and non-weight-bearing protocols not differentiated from weight-bearing were excluded. Information sources: Five databases were searched: CINAHL (EBSCO Information Services, Ipswich, MA, USA), PubMed Advanced (National Library of Medicine, Bethesda, MD, USA), Wiley Online Library (John Wiley & Sons, Hoboken, NJ, USA), Cochrane Library (Cochrane Collaboration, London, UK), and Google Scholar (Google LLC, Mountain View, CA, USA). Searches were completed in May 2026. Methods: We conducted a comprehensive literature review. This review was structured and reported with reference to the PRISMA 2020 statement (Preferred Reporting Items for Systematic Reviews and Meta-Analysis; University of Ottawa, Ottawa, ON, Canada) to guide transparency of reporting. It does not constitute a full Cochrane-style systematic review; risk of bias assessment was applied to key included studies and GRADE (Grading of Recommendations Assessment, Development and Evaluation; McMaster University, Hamilton, ON, Canada) certainty ratings were applied informally and narratively rather than as formal per-outcome evidence profiles. Five databases were searched yielding 92,637 records. After removal of 398 duplicates by Rayyan, 92,239 records remained. A subsequent automated keyword-based relevance filter applied within Rayyan (Rayyan AI, Doha, Qatar), prior to human screening, excluded 84,572 records that did not contain any terms related to orthotics, diabetic foot, or knee osteoarthritis, yielding 7667 records for human title/abstract screening. A narrative synthesis approach was adopted owing to the heterogeneity of study designs and outcome measures across included studies, which precluded meta-analysis. This review was not prospectively registered. A complete list of all 78 included studies, including those not individually discussed in the results and discussion. Results: The available clinical studies report promising findings for orthotics and smart orthotics in pain reduction, ulcer prevention, and potential reduction in economic burden, though conclusions are limited by small sample sizes, heterogeneity, and predominantly open-label designs. Recent research found that orthotics can be used to alter the gait pattern that influences knee OA by reducing excessive force on the affected joint. A randomised controlled trial demonstrated an 80% relative risk reduction in DFU recurrence (RR = 0.20; 95% CI: 0.06–0.79; p = 0.022), with absolute event rates of 6.3% in the intervention group versus 30.8% in controls (ARR = 24.5%); a second trial reported a 71% reduction in ulcer incidence over 18 months; and a third randomised controlled trial demonstrated statistically significant plantar pressure reduction (p < 0.01) in patients with diabetic neuropathy. Conclusions: The available evidence suggests that orthotics may be associated with improved pressure redistribution, reduced ulcer incidence, and benefit in the management of knee OA. Although the number of studies directly comparing smart orthotics with standard orthotics remains limited, the limited comparative studies suggested that smart orthotics showed promising results in reducing ulcer incidence, providing the patient with real-time feedback to offload via their electronic devices. These findings, while preliminary, highlight the potential of smart orthotic technology as an adjunct to standard orthotic care in reducing the overall burden of diabetic foot disease and knee osteoarthritis. Limitations: The primary methodological limitation of this review is the open-label design of all included smart orthotic trials, which precludes participant blinding and introduces performance bias. However, this limitation is structural and inherent to the wearable technology field—analogous to surgical trials—and is substantially mitigated by the use of objective primary outcome measures (plantar pressure and ulcer recurrence) across the three included RCTs, the consistency of effect direction across independent RCTs conducted in different countries, and a narrative sensitivity analysis confirming robustness of findings (Risk of Bias Across Studies Section). Formal per-outcome GRADE evidence profiles were not produced; overall certainty of evidence was assessed narratively with reference to GRADE domains and is judged to be low to moderate for smart orthotics in DFU prevention and low for knee OA management, consistent with the Level 2–3 evidence base and open-label study designs. Future adequately powered, multi-site RCTs with standardised outcome reporting, minimum 24-month follow-up, and integrated health economic modelling are the highest priority to extend these preliminary findings. Registration: This review was not prospectively registered. Full article

Non-contact magnetic sensing technology is widely adopted in transmission line online monitoring scenarios including current measurement and fault location for its non-contact measurement capability, strong environmental robustness and low deployment cost. However, existing magnetic-sensing-based galloping monitoring methods suffer from two critical limitations: no theoretical guidance is provided for sensor placement, and a high false detection rate is observed under current fluctuation conditions. To address these issues, a novel transmission line galloping monitoring method based on spatial magnetic field distribution features is proposed in this paper. A conductor galloping-power frequency magnetic field coupling model is first established to derive the optimal magnetic sensor array arrangement strategy. Subsequently, a galloping detection algorithm fusing multi-node frequency-domain features and phase difference information is proposed to eliminate current fluctuation induced false detection. Simulations conducted based on actual 500 kV transmission line parameters and verification tests carried out on a scaled-down laboratory platform confirm that reliable galloping detection can be realized by the proposed method under both current low-frequency oscillation and random fluctuation scenarios. With advantages of non-contact deployment, high anti-interference performance and detection accuracy, the proposed method has promising application potential in engineering-oriented high-voltage transmission line monitoring. Full article

The integration of online Digital Twin (DT) technologies with industrial control systems represents an important step toward real-time monitoring and synchronization of manufacturing processes within Industry 4.0 environments. However, reproducible approaches for connecting simulation environments with real industrial control hardware using standardized communication protocols remain insufficiently described in the existing literature. This study presents the development of an online Digital Twin for real-time monitoring of manufacturing processes using OPC UA communication and programmable logic controller (PLC) data exchange. The proposed approach combines discrete-event simulation with real-time industrial data acquisition to enable synchronization between a physical manufacturing system and its virtual representation. The implementation was experimentally validated in a laboratory-scale cyber–physical production system using Tecnomatix Plant Simulation, Siemens S7-1200 PLC, and KEPServerEX middleware. The developed architecture enables real-time process state monitoring, event-driven synchronization, and verification of selected control and safety functions within the simulation environment. The results demonstrate stable synchronization between the physical and digital systems with response times ranging from 50 to 200 ms, confirming the feasibility of near-real-time integration. The implemented light barrier scenario further demonstrated the capability of the online DT to reflect safety-related events occurring in the physical system. The main contribution of this study lies in the implementation and experimental verification of an OPC UA-based online Digital Twin architecture for manufacturing process monitoring in a laboratory environment. The presented approach provides a foundation for future extensions toward predictive analytics, scenario-based simulation, and advanced manufacturing optimization applications. Full article

Coal mining has historically been a central economic, cultural, and social cornerstone of Appalachian communities. The decline of the coal industry, driven by technological changes, competition from natural gas and renewable energy, environmental regulations, and evolving energy markets, has created major economic and environmental challenges for coal-dependent regions. This study examines Kentucky residents’ perceptions of coal decline and how socio-demographic factors shape environmental concern. Data was collected from 685 Kentucky residents through a statewide online survey conducted in December 2023. Ordered logistic regression was used to examine the influence of gender, age, rural residence, and political affiliation on concerns regarding climate change, environmental degradation, extinction of endangered species, air pollution, and water pollution. Respondents identified health and safety concerns, cleaner energy alternatives, government incentives, and technological changes as major contributors to coal decline, while climate change was viewed as less significant. The findings also reveal support for workforce retention and training in sectors such as construction, transportation, utility work, and renewable energy. Female respondents expressed high levels of environmental concern, while rural residents and Republicans reported lower concern regarding climate change and environmental degradation. Full article

With the advancement in electronic attack technologies, intelligent jamming poses a significant challenge to the reliable transmission of wireless communications. Traditional anti-jamming methods often fail to adapt to dynamic nonlinear jamming environments. This paper addresses nonlinear swept-frequency jamming by modeling anti-jamming communication as a sequential decision-making problem and proposes an intelligent anti-jamming method based on proximal policy optimization (PPO) to optimize dynamic channel selection. Firstly, the channel selection problem is formalized as a Markov decision process (MDP), where a state space integrating jamming patterns and communication status is designed, the channel set is defined as the action space, and a multi-objective reward function trades off jamming avoidance against switching overhead. A dual-network architecture comprising a policy network and a value network is constructed, and the PPO algorithm is employed for policy updates, where a clipping mechanism is used to enhance training stability. The system optimizes the anti-jamming strategy online through a closed-loop process of “sensing–decision–learning–communication”. Simulation results demonstrate that compared to conventional methods, the proposed method significantly improves key performance indicators such as packet success rate and throughput. It can rapidly track changes in jamming, exhibiting excellent real-time performance and environmental robustness, and thus provides an effective solution for reliable communication in dynamic jamming environments. Full article

Educational information systems have evolved into highly interconnected digital landscapes that support learning management platforms, student information systems, institutional repositories, and online assessment environments. As these systems increasingly operate across cloud infrastructures and mobile devices, ensuring the confidentiality, integrity, and availability (CIA Triad) of educational data is critical for safeguarding institutional operations and maintaining trust in digital education services. This paper investigates how next-generation security protocols, such as adaptive multi-factor authentication and advanced access control and data protection mechanisms, can reinforce ISO/IEC 27001:2022 requirements within contemporary educational information systems. The analysis maps emerging protocol capabilities to relevant new ISO/IEC 27001:2022 control domains, illustrating how they mitigate threats associated with unauthorized access, data manipulation, and service disruption. The proposed framework is further supported by an implementation-oriented mapping and an illustrative operational architecture that demonstrates the feasibility of translating prioritized security determinants into practical mechanisms. The FAHP analysis identifies access control mechanisms, backup and recovery, and data validation as the three highest-weighted determinants, with aggregate weights of 0.061, 0.059, and 0.057, respectively. These determinants are translated into a determinant-driven Security Operationalization Matrix that connects ISO/IEC 27001:2022 control domains, CIA dimensions, and technology recommendations, and is complemented by implementation feasibility considerations tailored to the budgetary, infrastructural, and resource constraints characteristic of educational institutions. Based on the prioritization results and conceptual operationalization, the proposed integrative approach provides a structured and progressively adoptable foundation for CIA-oriented security governance in digital educational environments. Full article

Smart Mobility plays a key role in Smart Cities, given its ability to support the rollout of intelligent transport systems, allowing for more sustainable urban transportation and greater interoperability across diverse mobility modes. Furthermore, Smart Mobility is essential to maximize the quality of life for the community while advancing principles of sustainability, economic development, technological innovation, and collaborative governance. Real-Time Traffic Management (RTTM) emerges as a vital technology for optimizing traffic management in Smart Mobility. Using the PRISMA framework, the proposed systematic literature review examines 165 peer-reviewed publications related to RTTM research work published between 2019 and 2025. This review identified eleven application domains, with Urban Traffic Management Systems (36.97%) and Artificial Intelligence (AI) and Predictive Analytics (12.73%) representing the most prominent areas. A retrospective analysis of the literature on control architecture used in closed-loop feedback systems indicates that most studies (89%) have adopted a more dynamic control model, while 7.8% adopted a Digital Twin (DT)-based approach. However, several implementation barriers persist, including limited integration of online optimization and learning loops into RTTM systems, gaps in performance comparisons between simulation and reality, scalability issues due to heterogeneous environments, inconsistent data quality caused by various sensor types, and difficulties integrating sensors into a control system. In addition, this paper proposes a taxonomy of RTTM applications and control architectures, while outlining key practical barriers to implementation and charting future research directions for advancing Smart Mobility through robust RTTM. Full article

Robotic belt grinding is an effective and widely adopted finishing method for superalloys, offering notable advantages such as high material removal capability, low heat input, and reduced workpiece damage. In addition, robots can readily integrate multiple sensors—such as infrared radiation cameras, force sensors, and high-speed cameras—which facilitate real-time monitoring of the grinding process and thereby enhance grinding quality control. With the establishment and continuous advancement of large-scale artificial intelligence (AI) data models, new breakthroughs have emerged in the optimization of robotic grinding processes. Owing to its dexterous workspace and advantages in high flexibility and cost-effectiveness, robotic belt grinding has become a critical process for the precision forming of complex curved components such as aero-engine blades and blisks. However, factors such as the limited absolute accuracy of industrial robots, time-varying grinding contact states, and significant transient boundary effects make it difficult for the current constant-parameter open-loop machining mode to simultaneously meet the demands for high material removal efficiency and high surface integrity on complex profiles. This paper systematically reviews the technologies for precision control and process optimization of robotic belt grinding aimed at pointwise precise material removal. First, the structural composition of the robotic belt grinding system and the material removal mechanism are analyzed. Then, centered on the compensation concept, a hierarchical progressive technical framework is outlined, covering geometric calibration compensation, force/position hybrid online compensation, transient entry boundary compensation, and system-level comprehensive compensation of multi-source errors, with a comparison of the applicable scenarios and the effects on shape and property control at each level. Furthermore, under the support of effective compensation, the collaborative optimization methods of material removal modeling, multi-objective optimization of process parameters, force-constrained trajectory planning, and intelligent adaptive processes are elaborated. Finally, current technical bottlenecks are summarized, and future trends in next-generation adaptive grinding technology driven by digital twins and embodied intelligence are envisioned. This review aims to provide a systematic theoretical reference for the high-precision and intelligent upgrading of robotic precision grinding systems. Full article

Vegetable mechanized transplanting is a key link bridging industrial seedling raising and field cultivation, whose technical level directly determines operating efficiency and planting standardization. Despite its importance, current transplanting systems still struggle with instability and limited coordination between modules. This review adopts a systematic literature analysis methodology, covering core databases including Web of Science, Scopus, CNKI, and CAB Abstracts. In response to prominent issues in current transplanting equipment, such as continuous seedling supply, low-damage seedling picking, synchronization of conveying and planting actions, and adaptability to high-speed operation, this paper systematically reviews and evaluates the latest research progress in related key technologies worldwide. From the perspective of kinematic chain coupling, the transplanting process is deconstructed into four core stages: “seedling supply—seedling picking—seedling delivery—seedling planting,” with a focus on analyzing the temporal coordination, spatial constraints, state transitions, and their dynamic coupling relationships within the machine-seedling-soil system. Research indicates that vegetable transplanting technology is evolving from localized mechanism optimization toward whole-process collaborative design and system stability control, with typical high-speed operation efficiency reaching 60–140 plants per minute per row. However, significant challenges remain in low-damage seedling picking and planting at high speeds, adaptability to diverse varieties and seedling states, online perception and real-time error correction, as well as engineering reliability. The seedling damage rate under high-speed operation exceeds 8% in most existing equipment, and the planting upright rate drops by more than 5% when the operating speed increases from 60 plants/min to 120 plants/min. Future research should prioritize multi-stage collaborative optimization design, in-depth investigation of machine-seedling-soil interaction mechanisms, innovation in intelligent perception and precise control strategies, and the development of modular, low-cost, and high-performance transplanting equipment. These efforts will drive vegetable mechanized transplanting technology toward greater intelligence, efficiency, and versatility. Full article

Natural gas metering is shifting from volume-based measurement to energy-based assessment as gas sources diversify, pipeline networks become more interconnected, and gas quality varies more strongly across time and space. This review examines the key technologies required for natural gas energy metering and evaluates how they support full-chain traceability from production to end use. The reviewed topics include flow measurement, gas composition analysis, calorific value determination, temperature-pressure compensation, state correction, uncertainty evaluation, intelligent data acquisition, and metrological traceability. The literature shows that individual technologies have advanced substantially. Ultrasonic flowmeters, rapid gas-quality sensing methods, dynamic calorific value allocation models, high-accuracy equations of state, and digital metering platforms have improved the technical basis of energy metering. However, these advances remain more mature at the level of individual links than at the level of the complete metering chain. Under multi-source supply, gas-quality fluctuation, hydrogen blending, and digitalized operation, the main challenge is to maintain consistency, uncertainty control, online verification, data credibility, and auditability across different metering stages. Future development should therefore focus on dynamic calorific value allocation, robust state correction under variable gas quality, full-chain uncertainty propagation, online verification, and secure data management for traceable natural gas energy metering. Full article