Search Results (15,152)

This article examines the asymmetric effects of renewable energy on sustainable economic growth across six South Asian countries from 2000 to 2023, employing panel data and threshold regression analysis. The findings indicate that CO₂ emissions must remain below a threshold of 2.38% to support the integration of renewable energy with sustainable growth. Furthermore, access to clean energy and technologies should exceed 3.38%, and urbanization must be managed at a complementary threshold of 3.21%. These results are consistent with various studies investigating the renewable energy transition’s economic impacts globally. It is recommended that South Asia focus on reducing CO₂ emissions below the identified threshold, enhancing clean energy access and innovation above the designated thresholds, and supporting urban growth as part of its policy initiatives. Such actions are essential for fostering economic growth and ensuring the sustainability of the region. The study recommends that the South Asian region take decisive steps to reduce CO₂ emissions and enhance access to clean energy while accommodating urban population growth. It highlights the importance of transitioning to renewable energy to stimulate economic growth and maintain trade and foreign direct investment (FDI) as a viable part of the gross domestic product. The study suggests that investments in Gross Capital Formation (GCF), trade, and FDI will yield long-term benefits, although short-term policy adjustments may disrupt resource allocation and hinder economic and renewable energy development. Future research should explore the complex interactions between CO₂ emissions, clean energy access, FDI, and trade, particularly in light of recent trade policies, including U.S. tariffs. Investigating these relationships through advanced methodologies, such as machine learning, could provide valuable insights into drivers of renewable energy transition and economic outcomes. Full article

Nuclear Magnetic Resonance (NMR)-based metabolomics has emerged as a powerful analytical technique in nutritional science, enabling comprehensive profiling of metabolites in biological samples. This entry explores the integration of NMR metabolomics in nutrition research, highlighting its principles, methodological considerations, and applications in dietary assessment, nutritional interventions, and biomarker discovery. The entry also addresses the advantages and limitations of NMR compared to other metabolomic techniques and discusses its future potential in personalized nutrition and health monitoring. Full article

This paper presents a sensorless control strategy for permanent magnet synchronous motor (PMSM) drives in industrial and automotive air compressor applications. The strategy utilizes an adaptive-gain sliding mode observer integrated with a refined back-EMF model to suppress chattering and improve convergence. The proposed approach achieves precise rotor position and speed estimation across a wide operational range without mechanical sensors. It directly addresses the critical needs of reliability, compactness, and resilience in automotive environments. Unlike conventional observers, its originality lies in the enhanced gain structure, enabling accurate and robust sensorless control validated through both simulation and hardware tests. Comprehensive simulation results demonstrate effective performance from 2000 to 8500 rpm, with steady-state speed tracking errors maintained below 0.4% at 2000 rpm and 0.035% at 8500 rpm under rated load. The control methodology exhibits excellent disturbance rejection capabilities, maintaining speed regulation within ±5 rpm under an 80% load disturbance at 8500 rpm while limiting q-axis current ripple to 2.5% of rated values. Experimental validation on a 2.2 kW PMSM-driven compressor test platform confirms stable operation at 4000 rpm with speed fluctuations constrained to 20 rpm (0.5% error) and precise current regulation, maintaining the d-axis current within ±0.07 A. The system demonstrates rapid dynamic response, achieving acceleration from 1320 rpm to 2365 rpm within one second during testing. The results confirm the method’s practical viability for enhancing reliability and reducing maintenance in industrial and automotive compressors systems. Full article

A global transition in water management is currently underway, marked by the declining reliability of conventional sources and the accelerated adoption of non-conventional alternatives. This shift is driven by escalating pressures from climate change, population growth, and freshwater overexploitation. While the literature on management of water sources (WSs) is extensive, empirical clarity on Hybrid Water Systems Management (HWSM)—the integration of conventional and non-conventional WSs within a single system—remains limited. The present study addresses this gap through a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach, which ensures methodological transparency and applicability. From over 9000+ peer-refereed articles retrieved from three major scientific databases (ScienceDirect, Scopus, and Web of Science Core Collections), published between 1999 and 2024, 44 studies were identified as the most relevant and consequently analyzed. The literature review refines the classification of WSs, distinguishing conventional sources, such as groundwater and surface water, from non-conventional alternatives, such as desalinated water, treated wastewater, gray water, and rainwater harvesting. The analysis also indicates that non-conventional WSs are now more prominent in the literature than conventional ones. Overall, the present study demonstrated that modern water management strategies increasingly emphasize optimization and circular reuse. In contrast, earlier approaches tend to focus more on water conservation and economic efficiency. The literature also indicates a gradual shift from traditional supply-dominant models toward integrated, cost-effective, and sustainability-oriented approaches that combine multiple sources and advanced allocation techniques. Full article

The paper examines the current landscape, as well as the promises and pitfalls, of the digital transition in agricultural production and rural areas in Greece. It questions whether digitalisation is a viable option given the demographic dynamics, gaps in digital infrastructure, and heavy reliance on migrant labour in rural Greece. The methodological approach employs a mixed-methods design, integrating statistical and cartographic analyses of available census data with qualitative methods (semi-structured interviews, ethnographic observations, and a focus group). The main research question is grounded in a brief theoretical framework that addresses critiques of the inevitability of technological innovation and highlights the need to understand the complex dynamics of digital change. The paper analyses the dynamics and challenges of digital change in rural Greece, examining how demographic change and ageing, the structure and size of farms, and dependence on migrant labour relate to gaps and inequalities in digital infrastructure and skills. A critique of the prevailing discourse on digital transformation is supported by a discussion of the recently collected qualitative empirical material. The concluding section highlights the key findings and provides policy recommendations. Full article

Ecological safety has become one of the most critical factors in regional development. This article examines how environmental methodologies can be integrated and effectively applied at the regional level in Russia and Kazakhstan—two countries whose unique natural resource potentials contribute significantly to global sustainable development. The study systematizes and compares data from both countries across national, regional, and local frameworks, identifying key priorities, directions, and specific mechanisms for applying ecological policies in their regions. Additionally, it shows how national priorities align with those identified in regional environmental policies. This article brings together environmental methods for assessing, monitoring, and managing regional ecosystems in Russia and Kazakhstan. The main focus is made on understanding ecosystem connections, predicting changes, and developing strategies for sustainable regional development. By comparing the application of these methods and policy priorities at national and regional levels, the study highlights practical challenges in implementing current frameworks. Key elements include environmental impact assessment, ecological monitoring, and resource management. The proposed approach offers a comprehensive yet flexible way to address regional environmental issues, balancing economic growth with nature conservation. The methodologies and criteria presented can help establish a rigorous framework for systematic assessment and management of environmental impacts, supporting evidence-based decisions that balance environmental concerns with sustainable development. In conclusion, the article summarizes best practices and suggests improvements to ecological policies in Russia and Kazakhstan, pointing out opportunities to enhance ecological resilience amid global environmental change. Full article

The unprecedented increase in the number of objects orbiting the Earth necessitates a comprehensive characterisation of these objects to improve the effectiveness of Space Surveillance and Tracking (SST) operations. In particular, accurate knowledge of the attitude and physical properties of space objects has become critical for space debris mitigation measures, since these parameters directly influence major perturbation forces like atmospheric drag and solar radiation pressure. Characterising a space object beyond its orbital position improves the accuracy of SST activities such as collision risk assessment, atmospheric re-entry prediction, and the design of Active Debris Removal (ADR) and In-Orbit Servicing (IOS) missions. This study presents a novel approach for the simultaneous estimation of the attitude and optical reflective properties of uncontrolled space objects with known shape using light curves. The proposed method also accounts for atmospheric effects, particularly the Aerosol Optical Depth (AOD), a highly variable parameter that is difficult to determine through on-site measurements. The methodology integrates different estimation, optimisation, and data analysis techniques to achieve an accurate, robust, and computationally efficient solution. The performance of the method is demonstrated through the analysis of a simulated scenario representative of realistic operational conditions. Full article

The carbon pool of harvested wood products (HWPs) is an essential part of the national greenhouse gas inventory. Developing a Tier-3 method for the Intergovernmental Panel on Climate Change (IPCC) Production Approach (PA) enhances the accuracy of HWP carbon balance assessments and removal estimates. This is crucial as the PA is a mandatory IPCC approach. A major challenge for developing a Tier-3 PA is the absence of an effective way to allocate HWP production to domestic and overseas end uses, precluding the application of Tier-3 PA in most countries in the world. Here, we integrated the Eora multi-regional input–output (MRIO) model, which effectively allocates HWPs to these end uses into the PA to create a generalizable Tier-3 PA. Using China-produced HWPs from 1990 to 2020 as a case study, we report that these HWPs accumulated a carbon stock of 3376 MtCO₂e by 2020 and provided a carbon sink of 221 MtCO₂e yr⁻¹ from 2016 to 2020. Construction, furniture, other solid HWPs, sanitary and household paper, and other paper products accumulated 1244, 226, 1032, 0, and 189 MtCO₂e, respectively. China’s trade partners consumed 14% of China-produced HWPs and contributed to 13% of the total carbon stock and 15% of the total carbon sink. The generalizable Tier-3 PA is applicable for countries with limited end-use data, and thus enhances their HWP carbon removal estimates. Our first-ever comprehensive PA-based assessment of overseas HWP consumption and carbon removal supports IPCC methodological improvement and future HWP-related international negotiations and mitigation actions. Full article

Spatial Allocation Problems (SAP) in multistory buildings present significant challenges, as they require the simultaneous satisfaction of complex geometric constraints and conflicting functional requirements. To address this problem, this study proposes an integrated pipeline utilizing Generative Design (GD) methodologies within a Building Information Modeling (BIM) environment to automate and optimize a 3.3 m modular multi-story spatial allocation. The core of the proposed methodology lies in the clear distinction and application of design requirements formalized as ‘Hard Constraints’ (mandatory conditions for feasibility) and ’Soft Objectives’ (metrics for performance evaluation). Hard constraints include the implementation of a boundary constraint, ensuring that all spaces remain within defined limits, and a vertical alignment constraint for fixed elements (e.g., cores), thereby ensuring geometric and structural validity. To quantify functional efficiency, three soft objectives were defined: positional preference, circulation efficiency, and functional cohesion. The methodology was validated using a four-story case study. The implemented system successfully generated numerous valid design alternatives that satisfied all hard constraints while simultaneously optimizing the three soft objectives. Aimed at architects, building designers, and computational specialists, this study offers significant practical value by providing a tool that automates the complex initial phases of space planning. This allows designers to rapidly explore and quantitatively evaluate a wide array of optimized and valid layouts. This study contributes to a systematic framework for balancing geometric validity and functional efficiency during the early design stages by presenting a concrete procedure for integrating GD and BIM to solve complex SAPs. Full article

Nondestructive quality detection of characteristic fruits is essential for ensuring nutritional value, economic viability, and consumer safety in global supply chains, yet traditional destructive methods compromise sample integrity and scalability. Visible and near-infrared (Vis/NIR) spectroscopy offers a transformative solution by enabling rapid, non-invasive multi-attribute quantification through molecular overtone vibrations. This review examines recent advancements in Vis/NIR-based fruit quality detection, encompassing fundamental principles, system configurations, and detection strategies calibrated to fruit biophysical properties. Firstly, optical mechanisms and system architectures (portable, online, vehicle-mounted) are compared, emphasizing their compatibility with fruit structural complexity. Then, critical challenges arising from fruit-specific characteristics—such as rind thickness, pit interference, and spatial heterogeneity—are analyzed, highlighting their impact on spectral accuracy. Applications across diverse fruit categories (pitted, thin-rinded, and thick-rinded) are systematically reviewed, with case studies demonstrating the robust prediction of key quality indices. Subsequently, considerations in model development and validation are presented. Finally, persistent limitations in model transferability and environmental adaptability are discussed, proposing future research directions centered on integrating hyperspectral imaging, AI-driven calibration transfer, standardized spectral databases, and miniaturized, field-deployable sensors. Collectively, these methodological breakthroughs will pave the way for autonomous, next-generation quality assessment platforms, revolutionizing postharvest management for characteristic fruits. Full article

Electrocoagulation (EC) employing aluminum–aluminum (Al–Al) electrodes was investigated for hospital wastewater treatment, targeting the removal of turbidity, soluble chemical oxygen demand (sCOD), and total dissolved solids (TDS). A hybrid modeling framework integrating response surface methodology (RSM) and artificial neural networks (ANN) was developed to enhance predictive reliability and identify energy-efficient operating conditions. A Box–Behnken design with 15 experimental runs evaluated the effects of pH, current density, and electrolysis time. Multi-response optimization determined the overall optimal conditions at pH 7.0, current density 20 mA/cm², and electrolysis time 75 min, achieving 94.5% turbidity, 69.8% sCOD, and 19.1% TDS removal with a low energy consumption of 0.34 kWh/m³. The hybrid RSM–ANN model exhibited high predictive accuracy (R² > 97%), outperforming standalone RSM models, with ANN more effectively capturing nonlinear relationships, particularly for TDS. The results confirm that EC with Al–Al electrodes represent a technically promising and energy-efficient approach for decentralized hospital wastewater treatment, and that the hybrid modeling framework provides a reliable optimization and prediction tool to support process scale-up and sustainable water reuse. Full article

This paper presents an innovative approach to efficiently harvesting energy from ocean waves through a buoy-type Wave Energy Converter (WEC). The proposed methodology integrates a buoy, a Mechanical Motion Rectifier (MMR), a Motion Rectifier (MR), an Energy Storage Element (ESE), and an electric generator. A MATLAB-2023 model has been employed to assess the electrical power generated under varying wave heights and frequencies. Experimental data and numerical simulations reveal that the prototype Wave Energy Harvester (WEH) achieved a peak voltage of 6.7 V, peak power of 3.6 W, and an average power output of 8.5 mW, with an overall efficiency of 47.2% for the device’s actual size. Additionally, a theoretical analysis has been conducted to investigate the impact of incorporating additional buoys on the electrical power output. Full article

In response to the limitations of traditional single-signal approaches, which fail to comprehensively reflect fault conditions, and the difficulties of existing feature extraction methods in capturing subtle fault patterns in transformer fault diagnosis, this paper proposes an innovative fault diagnosis methodology. Initially, to address common severe faults in excitation transformers, Principal Component Analysis (PCA) is applied to reduce the dimensionality of multi-source feature data, effectively eliminating redundant information. Subsequently, to mitigate the impact of non-stationary noise interference in voiceprint signals, a Deep Belief Network (DBN) optimized using the Hunter–Prey Optimization (HPO) algorithm is employed to automatically extract deep features highly correlated with faults, thus enabling the detection of complex, subtle fault patterns. For temperature and electrical parameter signals, which contain abundant time-domain information, the Random Forest algorithm is utilized to evaluate and select the most relevant time-domain statistics. Nonlinear dimensionality reduction is then performed using an autoencoder to further reduce redundant features. Finally, a multi-classifier model based on Adaptive Boosting with Support Vector Machine (Adaboost-SVM) is constructed to fuse multi-source heterogeneous information. By incorporating a pseudo-label self-training strategy and integrating a working condition awareness mechanism, the model effectively analyzes feature distribution differences across varying operational conditions, selecting potential unseen condition samples for training. This approach enhances the model’s adaptability and stability, enabling real-time fault diagnosis. Experimental results demonstrate that the proposed method achieves an overall accuracy of 96.89% in excitation transformer fault diagnosis, outperforming traditional models such as SVM, Extreme Gradient Boosting with Support Vector Machine (XGBoost-SVM), and Convolutional Neural Network (CNN). The method proves to be highly practical and generalizable, significantly improving fault diagnosis accuracy. Full article

Background/Objectives: Gamification and game-based learning (GBL) have recently emerged as fresh and appealing ways of health education, and they have been shown to perform better in knowledge acquisition than traditional teaching approaches. Digital serious games are developing as innovative tools for cancer education and behaviour change, yet no review has systematically synthesized their use across key populations. This scoping review aimed to map evidence on serious games for cancer prevention, care, and survivorship among the public, patients, and healthcare professionals, framed through the Capability, Opportunity, Motivation-Behaviour (COM-B) model. Methods: Following Joanna Briggs Institute methodology, we searched Web of Science, MEDLINE, CINAHL, and PsycINFO. Eligible studies evaluated a serious game with a cancer focus and reported outcomes on knowledge, awareness, engagement, education, or behaviour. Data extraction and synthesis followed the PRISMA-ScR checklist. Results: Thirty-five studies met the inclusion criteria, covering diverse cancers, populations, and platforms. Most reported improvements in knowledge, engagement, self-efficacy, and communication. However, heterogeneity in study design and limited assessment of long-term behaviour change constrained comparability. Conclusions: Digital serious games show promise for enhancing cancer literacy and supporting behavioural outcomes across patients, professionals, and the public. By integrating multiple perspectives, this review highlights opportunities for theory-driven design, robust evaluation, and implementation strategies to maximize their impact in cancer education and awareness. Full article

Historic urban alleys encapsulate cultural identity and collective memory but are increasingly threatened by commercialization and context-insensitive redevelopment. Preserving their authenticity while enhancing environmental resilience requires design strategies that integrate both heritage and ecological values. This study explores the potential of generative artificial intelligence (AI) to support biophilic design in historic alleys, focusing on Daegu, South Korea. Four alley typologies—path, stairs, edge, and node—were identified through fieldwork and analyzed across cognitive, emotional, and physical dimensions of place identity. A Flux-based diffusion model was fine-tuned using low-rank adaptation (LoRA) with site-specific images, while a structured biophilic design prompt (BDP) framework was developed to embed ecological attributes into generative simulations. The outputs were evaluated through perceptual and statistical similarity indices and expert reviews (n = 8). Results showed that LoRA training significantly improved alignment with ground-truth images compared to prompt-only generation, capturing both material realism and symbolic cues. Expert evaluations confirmed the contextual authenticity and biophilic effectiveness of AI-generated designs, revealing typology-specific strengths: the path enhanced spatial legibility and continuity; the stairs supported immersive sequential experiences; the edge transformed rigid boundaries into ecological transitions; and the node reinforced communal symbolism. Emotional identity was more difficult to reproduce, highlighting the need for multimodal and interactive approaches. This study demonstrates that generative AI can serve not only as a visualization tool but also as a methodological platform for participatory design and heritage-sensitive urban regeneration. Future research will expand the dataset and adopt multimodal and dynamic simulation approaches to further generalize and validate the framework across diverse urban contexts. Full article