Search Results (13,442)

Micro-photosynthetic power cells (μPSCs), also known as biophotovoltaics (BPVs), represent sustainable and self-regenerating solutions for harvesting electricity from photosynthetic microorganisms. However, their practical deployment has been constrained by low voltage, low current output, and scaling inefficiencies. In this work, we address these limitations through a dual-optimization strategy: (i) systematic quantification of how electrode surface area influences key performance metrics, and (ii) based on our previous work we highlighted the novel hybrid modular array architectures that combine series and parallel connections of μPSCs. Three single μPSCs with electrode areas of 4.84, 19.36, and 100 cm² were fabricated and compared, revealing that while open-circuit voltage remains largely area-independent (850–910 mV), both short-circuit current and maximum power scale with electrode size. Building on these insights, two hybrid array configurations fabricated from six 4.84 cm² μPSCs achieved power outputs of 869.2 μW and 926.4 μW, equivalent to ~82–87% of the output of a large 100 cm² device, while requiring only ~29% electrode area and ~70% less reagent volume. Importantly, these arrays delivered voltages up to 2.4 V, significantly higher than a single large device, enabling easier integration with IoT platforms and ultra-low-power electronics. A meta-analysis of over 40 reported BPV/μPSC systems with different electrode surface areas further validated our findings, showing a consistent inverse relationship between electrode area and power density. Collectively, this study introduces a scalable, resource-efficient strategy for enhancing μPSC performance, providing a novel design paradigm that advances the state of the art in sustainable bioenergy and opens pathways for practical deployment in distributed, low-power and IoT applications. Full article

We present the eyeglass-type switch, an eyeglass-mounted eye/blink switch designed for nurse-call operation by people with severe motor impairments, with a particular focus on amyotrophic lateral sclerosis (ALS). The system targets real-world bedside constraints—low illumination at night, supine posture, and network-independent operation—by combining near-infrared (NIR) LED illumination with an NIR eye camera and executing all processing on a small, GPU-free computer. A two-stage convolutional pipeline estimates eight periocular landmarks and the pupil center; eye-closure is detected either by a binary classifier or by an angle criterion derived from landmarks, which also skips pupil estimation during closure. User intent is determined by crossing a caregiver-tunable “off-area” around neutral gaze, implemented as rectangular or sector shapes. Four output modes—single, continuous, long-press, and hold-to-activate—are supported for both oculomotor and eyelid inputs. Safety is addressed via relay-based electrical isolation from the nurse-call circuit and audio feedback for state indication. The prototype runs at 18 fps on commodity hardware. In feature-point evaluation, mean errors were 2.84 pixels for landmarks and 1.33 pixels for the pupil center. In a bedside task with 12 healthy participants, the system achieved $F=0.965$ in single mode and $F=0.983$ in hold-to-activate mode; blink-only input yielded $F=0.993$. Performance was uniformly high for right/left/up and eye-closure cues, with lower recall for downward gaze due to eyelid occlusion, suggesting camera placement or threshold tuning as remedies. The results indicate that the proposed switch provides reliable, low-burden nurse-call control under nighttime conditions and offers a practical input option for emergency alerts and augmentative and alternative communication (AAC) workflows. Full article

Large-scale lithium-ion energy storage systems (ESSs) are indispensable for renewable energy integration and grid support, yet ensuring long-term reliability under field conditions remains challenging. This study investigates degradation trends in a 50 MW-class ESS deployed on Jeju Island, South Korea, focusing on two indicators: direct current internal resistance (DCIR) and state-of-health (SOH). Annual round-trip (capacity) and hybrid pulse power characterization (HPPC) tests conducted from 2023 to 2025 quantified capacity fade and resistance growth. A polynomial-regression-based temperature compensation was applied—compensating DCIR to 23 °C and SOH to 30 °C—which reduced environmental scatter and clarified year-to-year degradation trends. Beyond mean shifts, intra-bank variability increased over time, indicating rising internal imbalance. A focused case study (Bank 03-01) revealed concurrent SOH decline and DCIR escalation localized near specific racks; spatial maps linked this hotspot to heating, ventilation, and air conditioning (HVAC)-driven airflow asymmetry and episodic fan operation. These findings underscore the importance of combining temperature compensation, variability-based diagnostics, and spatial visualization in field ESS monitoring. The proposed methodology provides practical insights for the early detection of abnormal degradation and supports lifecycle management of utility-scale ESSs under real-world conditions. Full article

The accurate classification of plant diseases is vital for global food security, as diseases can cause major yield losses and threaten sustainable and precision agriculture. The classification of plant diseases in low-light noisy environments is crucial because crops can be continuously monitored even at night. Important visual cues of disease symptoms can be lost due to the degraded quality of images captured under low-illumination, resulting in poor performance of conventional plant disease classifiers. However, researchers have proposed various techniques for classifying plant diseases in daylight, and no studies have been conducted for low-light noisy environments. Therefore, we propose a novel model for classifying plant diseases from low-light noisy images called dilated pixel attention network (DPA-Net). DPA-Net uses a pixel aBention mechanism and multi-layer dilated convolution with a high receptive field, which obtains essential features while highlighting the most relevant information under this challenging condition, allowing more accurate classification results. Additionally, we performed fractal dimension estimation on diseased and healthy leaves to analyze the structural irregularities and complexities. For the performance evaluation, experiments were conducted on two public datasets: the PlantVillage and Potato Leaf Disease datasets. In both datasets, the image resolution is 256 × 256 pixels in joint photographic experts group (JPG) format. For the first dataset, DPA-Net achieved an average accuracy of 92.11% and harmonic mean of precision and recall (F1-score) of 89.11%. For the second dataset, it achieved an average accuracy of 88.92% and an F1-score of 88.60%. These results revealed that the proposed method outperforms state-of-the-art methods. On the first dataset, our method achieved an improvement of 2.27% in average accuracy and 2.86% in F1-score compared to the baseline. Similarly, on the second dataset, it aBained an improvement of 6.32% in average accuracy and 6.37% in F1-score over the baseline. In addition, we confirm that our method is effective with the real low-illumination dataset self-constructed by capturing images at 0 lux using a smartphone at night. This approach provides farmers with an affordable practical tool for early disease detection, which can support crop protection worldwide.  Full article

Material handling (MH) plays a critical role in the performance, cost efficiency, and sustainability of high-rise construction projects. Despite its significance, MH practices in such projects remain challenged by complex vertical logistics, space constraints, fragmented supply chains, and increasing pressure to align with decarbonisation goals. This study applies a mixed-methods approach that integrates a systematic literature review, semi-structured expert interviews, and a SWOT–AHP (Strengths, Weaknesses, Opportunities, Threats—Analytic Hierarchy Process) model to identify and prioritise factors influencing MH optimisation in China’s high-rise construction sector. Eighteen factors were evaluated across four SWOT dimensions, and expert pairwise comparisons were aggregated using geometric means. The results revealed that Technological Adoption (S1) and Technological Advancements (O3) are the most critical enablers, while High Implementation Costs (W2) and Resource Scarcity (T3) are the most significant constraints. Interactions among these factors highlight the dual importance of internal digital capabilities and external technological trajectories in shaping MH strategies. Comparative analysis with practices in Europe, the United States, and the Middle East demonstrates that digitalisation, financial mechanisms, and policy incentives are globally consistent drivers of MH innovation. The findings advance theoretical understanding by integrating perspectives from the Resource-Based View, Technology-Organisation-Environment, and Institutional Theory, and they offer practical implications for policymakers and industry stakeholders seeking to align MH optimisation with China’s dual-carbon targets. This study contributes to the development of a comprehensive decision-support framework that enhances the sustainability, resilience, and efficiency of material logistics in high-rise construction projects. Full article

Background/Objectives: As safe, eco-friendly, and legally compliant solutions for the disposal of unwanted medications, drug take-back systems have attracted extensive research attention. However, there is a lack of systematic mapping of global trends, collaborative networks, research themes, and hotspots in this field. This study aimed to conduct a comprehensive bibliometric analysis and review of global academic research on drug take-back programs. Methods: Peer-reviewed research articles on drug take-back programs, published between 2005 and 2025, were retrieved from the Web of Science Core Database. Microsoft Office Excel 2019, VOSviewer (v.1.6.17), and CiteSpace (v.6.1.R3 Advanced) were used to assess publication/citation trends, countries, institutions, authors, journals, disciplines, references, and keywords. Narrative analysis was employed to synthesize data from the included articles and identify core research themes. Results: A total of 149 eligible articles with 4520 citations were included, involving 619 authors, 52 countries/regions, 310 institutions, and 95 journals. Publication/citation counts increased significantly between 2005 and 2025. The United States led in both publication output and collaborative research; Mercer University was the most influential institution, but international and cross-institutional collaboration remained limited. Environmental Sciences ranked first among disciplinary categories in drug take-back research, followed by Pharmacology/Pharmacy. Core research themes underpinning this field included stakeholders’ knowledge–attitude–practice assessment (76 articles), returned medication treatment (37 articles), intervention evaluation (25 articles), policy analysis (7 articles), and the role of drug take-back programs in mitigating environmental and public health hazards caused by medicine wastes (4 articles). Conclusions: Scholarly attention to drug take-back programs has grown steadily. Future research should prioritize cross-sectoral and international cooperation, develop and adopt evidence-based interventions to optimize the safety, sustainability, and accessibility of drug take-back systems on a global scale. Full article

Background: Use of Artificial Intelligence (AI) is proliferating in society and in pharmacy practice. For some, this represents a great advancement that will enhance effectiveness and efficiency of health care. For others, it is an existential risk that will worsen inequalities, lead to deskilling of the workforce, and spiral beyond the comprehension or control of humans. Human-in-the-loop (HiL) vs. human-out-of-the loop (HoL) AI have different potential risks and challenges that raise questions regarding patient safety. Defining principles for responsible adoption of AI in pharmacy practice will be an important safeguard for both patients and the profession. Methods: Semi-structured interviews with 12 pharmacy regulators from across Canada and the United States were undertaken, with informed consent. Constant comparative data analysis using nVivo v15 was used to identify common themes. The COREQ framework was applied to assure quality of research processes used. Results: Pharmacy regulators highlighted the value of a principles-based, rather than rules-based, approach to AI. Core principles related to transparency, redundancy, audit and feedback, quality assurance, privacy/data security, alignment with codes of ethics, and interoperability were identified. There was limited consensus on the role of consent and choice as principles to be considered. Conclusions: The role of regulation in shaping responsible adoption of AI in pharmacy will be significant. This study highlighted a series of agreed-upon principles but also identified lack of consensus with respect to how consent and choice could be operationalized in pharmacy practice. Full article

Thermodynamic fault diagnosis of marine steam turbines remains challenging due to non-stationary multivariate sensor data under stochastic loads and transient conditions. While conventional threshold-based methods lack the sophistication for such dynamics, existing data-driven Transformers struggle with inherent non-stationarity. To address this, we propose a hybrid DLinear–Transformer framework that synergistically integrates localized trend decomposition with global feature extraction. The model employs a dual-branch architecture with adaptive positional encoding and a gated fusion mechanism to enhance robustness. Extensive evaluations demonstrate the framework’s superiority: on public benchmarks (SMD, SWaT), it achieves statistically significant F1-score improvements of 2.7% and 0.3% over the state-of-the-art TranAD model under a controlled, reproducible setup. Most importantly, validation on a real-world marine steam turbine dataset confirms a leading fault detection accuracy of 94.6% under variable conditions. By providing a reliable foundation for identifying precursor anomalies, this work establishes a robust offline benchmark that paves the way for practical predictive maintenance in marine engineering. Full article

This study investigates how urban tourism contributes to sustainable development, with a focus on SDGs 8.9 and 17 in Nanyang City. Drawing on a reflective measurement model and employing Partial Least Squares Structural Equation Modeling (PLS-SEM), this study examines the impact of urban tourism on cultural promotion, employment creation, and multi-stakeholder collaboration. A total of 300 surveys were collected from locals and visitors across Nanyang City to analyze these relationships. The results suggest that urban tourism promotes economic development but is also a means to preserve cultural heritage, and in turn directly supports SDG 8.9 for sustainable tourism, leading to job creation and local culture preservation. The analysis also shows that collaboration among governments, private organizations, and local communities is needed to achieve effective urban tourism governance, as stated in SDG 17. This study contributes a novel theoretical development to the literature, relating SDG-based governance with local tourism dynamics whilst providing an emic perspective of how mid-sized Chinese cities like Nanyang City, through collaborative and inclusive governance of tourism, put SDGs 8.9 and 17 into practice. The results contribute to current tourism–SDG frameworks by showing how the presence of local cultural endowments and decentralized governance structures homogenizes a specific pathway toward sustainable urban tourism. Additionally, the results provided practical guidance for tourism practitioners and policymakers on how to increase urban tourism systems’ diversity, inclusiveness, and resilience. This study’s limitations, being restricted to a single city with a small sample and a lack of longitudinal follow-up, may make findings difficult to generalize. Full article

The transformation of vocational education and training (VET) systems has become a strategic priority for achieving Sustainable Development Goals (SDGs), particularly in the context of accelerating global economic transitions. This article examines how EU member states modify their VET systems to address evolving labor market demands and align with the objectives of SDGs 4, 8, and 10, utilizing system alignment, decentralization, infrastructure development, stakeholder engagement, and investment in green and digital skills. The article analyzed the influence of these five strategies. Using cross-national comparative analysis and multidimensional indicators, the study reveals that strong partnerships with labor market stakeholders and investments in green and digital transitions significantly enhance the responsiveness and sustainability of VET systems. However, assumptions related to the decentralization of governance and infrastructure expansion were not consistently supported, indicating the need for a more nuanced approach to policy reform. The findings offer practical implications for VET policy design, emphasizing flexibility, system coherence, and future-oriented planning. This study contributes to the growing body of research that links education systems to sustainable economic development. The research also concludes that innovative management models—combining flexible governance, labor-market intelligence, and digital innovation—are central to modernizing VET and improving its adaptability to future skill needs. Full article

The enumeration of chemical graphs plays a crucial role in cheminformatics and bioinformatics, especially in the search for novel drug discovery. These graphs are usually tree-like multigraphs, or they consist of tree-like multigraphs attached to a central core. In both configurations, the tree-like components play a key role in determining the properties and activities of chemical compounds. In this work, we propose a dynamic programming approach to precisely count the number of tree-like multigraphs with a given number of n vertices and Δ multiple edges. Our method transforms multigraphs into rooted forms by designating their unicentroid or bicentroid as the root and then defining a canonical representation based on the maximal subgraphs rooted at the root’s children. This canonical form ensures that each multigraph is counted only once. Recursive formulas are then established based on the number of vertices and multiple edges in the largest subgraphs rooted at the root’s children. The resulting algorithm achieves a time complexity of $\mathcal{O}\left(n^2(n+\Delta (n+{\Delta }^2·min\{n,\Delta \}))\right)$ and space complexity of $\mathcal{O}\left(n^2({\Delta }^3+1)\right)$. Extensive experiments demonstrate that the proposed method scales efficiently, being able to count multigraphs with up to 200 vertices (e.g., (200, 26)) and up to 50 multiple edges (e.g., (90, 50)) in under 15 min. In contrast, the available state-of-the-art tool Nauty runs out of memory beyond moderately sized instances, as it relies on explicit generation of all candidate multigraphs. These results highlight the practical advantage and strong potential of the proposed method as a scalable tool for chemical graph enumeration in drug discovery applications. Full article

Distributed temperature sensing (DTS) has long been employed in the oil and gas industry to characterize reservoirs, optimize production, and extend well life. More recently, its application has expanded to geothermal energy development, where DTS provides critical insights into transient wellbore temperature profiles and flow behavior. A comprehensive understanding of such field measurements can be achieved by systematically comparing and interpreting DTS data in conjunction with robust numerical models. However, many existing wellbore models rely on steady-state heat transfer assumptions that fail to capture transient dynamics, while fully coupled wellbore–reservoir simulations are often computationally demanding and mathematically complex. This study aims to address this gap by developing a transient wellbore heat transfer model validated with DTS data. The model was formulated using a thermal-analogy approach based on the theoretical framework of Eickmeier et al. and implemented with a finite-difference scheme. Validation was performed by comparing thermal slug velocities predicted by the model with those extracted from DTS measurements. The results demonstrated strong agreement between modeled and measured slug velocities, confirming the model’s reliability. In addition, the modeled thermal slug velocity was lower than the corresponding fluid velocity, indicating that thermal front propagates more slowly than the fluid front. Consequently, this computationally efficient approach enhances the interpretation of DTS data and offers a practical tool for improved monitoring and management of geothermal operations. Full article

With the rapid development of coastal and nearshore engineering projects in China, geotextile pipe and bag (GPB) structures have been increasingly applied in marine land reclamation and coastal protection works. To better understand the mechanical behavior of GPB structures on soft soil foundations, this study conducts a systematic investigation into the mechanical properties of both soft soils and GPBs using a physical model test system. By integrating numerical simulations, the stress–deformation characteristics of GPB structures on soft soils and the evolution of pore pressure are further analyzed. The results indicate that the compression curve of soft soil exhibits significant nonlinearity, with silt showing higher apparent compressibility than silty clay. Experimental data yielded the compression coefficient λ and rebound coefficient μ for both soil types. As consolidation pressure increases, deviatoric stress in the soft soil rises notably, demonstrating typical strain-hardening behavior. Based on these findings, the critical state effective stress ratio M was determined for both soil types. The study also establishes the development laws of cohesion c and friction angle φ during soil consolidation, as well as the variation of pore water pressure under different confining pressures. Interface tests clarify the relationships between cohesion and friction angle at the interfaces between geotextile pipe bags and sand, and between adjacent pipe bag layers. Numerical simulations reveal that the reclamation construction process significantly influences structural horizontal displacement. Significant stress concentration occurs at the toe of the slope, while the central portion of the pipe-bag structure experiences maximum tensile stress—still within the material’s allowable stress limit. The installation of drainage boards effectively accelerates pore pressure dissipation, achieving nearly complete consolidation within one year after construction. This research provides a scientific foundation and practical engineering guidance for assessing the overall stability and safety of (GPB) structures on soft soil foundations in coastal regions. Full article

In the present work, a new analytical model of polytropic flow in constant-diameter pipelines is developed to accurately describe the flow of compressible gases, including natural gas and hydrogen, explicitly accounting for heat exchange between the fluid and the environment. In contrast to conventional models that assume isothermal or adiabatic conditions, the proposed model simultaneously accounts for variations in pressure, temperature, density, and entropy, i.e., it is based on a realistic polytropic gas flow formulation. A system of differential equations is established, incorporating the momentum, continuity, energy, and state equations of the gas. An implicit closed-form solution for the specific volume along the pipeline axis is then derived. The model is universal and allows the derivation of special cases such as adiabatic, isothermal, and isentropic flows. Numerical simulations demonstrate the influence of heat flow on the variation in specific volume, highlighting the critical role of heat exchange under real conditions for the optimization and design of energy systems. It is shown that achieving isentropic flow would require the continuous removal of frictional heat, which is not practically feasible. The proposed model therefore provides a clear, reproducible, and easily visualized framework for analyzing gas flows in pipelines, offering valuable support for engineering design and education. In addition, a unified sensitivity analysis of the analytical solutions has been developed, enabling systematic evaluation of parameter influence across the subsonic, near-critical, and heated flow regimes. Full article

This review summarizes and analyzes the current status and trends in conservation of agricultural AnGR in Russia. The biodiversity of farm animal species in Russia is significant and is represented by 507 breeds, including 236 aboriginal ones. Based on a summary of global experience in maintaining genetic material of various types, we describe here strategies for preserving native breeds in Russia. Genetic monitoring using modern technologies improves the relevance of biological collections and enables the selection of the most typical and valuable representatives of AnGR for cryopreservation. The National Center for AnGR has developed a key conservation strategy based on the integrated use of genetic and assisted reproductive technologies. This strategy has been successfully implemented in a few cattle and goat breeds. In 2025, genetic monitoring of AnGR reproduced in vivo and preserved in vitro began. These studies and practical measures in Russia play an important role in preserving the genetic diversity of native AnGR in a changing climate to ensure food security for current and future generations. Thus, owing to the joint efforts of research teams and state financial support, a positive trend has emerged in cryopreservation programs and the preservation of living bioresource collections in Russia. Full article