Search Results (17,090)

With the increasing penetration of photovoltaic systems in low-voltage distribution networks, new operational challenges arise for distribution system operators. This article focuses on a comprehensive analysis of the impact of single-phase and three-phase photovoltaic systems on voltage magnitude, voltage unbalance, and currents flowing through distribution lines. The steady-state operation was calculated using EA-PSM simulation software, and the assessment of the impact of photovoltaic systems on the network was carried out using the international standard EN 50160. Simulation results show that a high penetration of photovoltaic systems causes significant changes in the network’s voltage profile. The study also includes a proposal of measures aimed at mitigating the adverse effects of decentralized generation in photovoltaic systems on the distribution network. Among the most effective measures is the selection of an appropriate conductor cross-section for distribution lines. The results also indicate that, in terms of negative impact on the network, it is preferable to prioritize three-phase connection over single-phase connection, because for the same impact on the network, three-phase photovoltaic systems can inject several times more power into the network compared to single-phase systems. These and other findings may be beneficial, especially for distribution system operators in planning the operation and development of networks. Full article

Background: Artificial Intelligence (AI) is increasingly applied in endurance sports to optimize performance, enhance recovery, and personalize nutrition and supplementation. This review synthesizes current knowledge on AI applications in endurance sports, emphasizing implications for metabolic health, nutritional strategies, and recovery optimization, while also addressing ethical considerations and future directions. Methods: A narrative review was conducted using targeted searches of PubMed, Scopus, and Web of Science with cross-referencing. Extracted items included sport/context, data sources, AI methods including machine learning (ML), validation type (internal vs. external/field), performance metrics, comparators, and key limitations to support a structured synthesis; no formal risk-of-bias assessment or meta-analysis was undertaken due to heterogeneity. Results: AI systems effectively integrate multimodal physiological, environmental, and behavioral data to enhance metabolic health monitoring, predict recovery states, and personalize nutrition. Continuous glucose monitoring combined with AI algorithms allows precise carbohydrate management during prolonged events, improving performance outcomes. AI-driven supplementation strategies, informed by genetic polymorphisms and individual metabolic responses, have demonstrated enhanced ergogenic effectiveness. However, significant challenges persist, including measurement validity and reliability of sensor-derived signals and overall dataset quality (e.g., noise, missingness, labeling error), model performance and generalizability, algorithmic transparency, and equitable access. Furthermore, limited generalizability due to homogenous training datasets restricts widespread applicability across diverse athletic populations. Conclusions: The integration of AI in endurance sports offers substantial promise for improving performance, recovery, and nutritional strategies through personalized approaches. Realizing this potential requires addressing existing limitations in model performance and generalizability, ethical transparency, and equitable accessibility. Future research should prioritize diverse, representative, multi-site data collection across sex/gender, age, and race/ethnicity. Coverage should include performance level (elite to recreational), sport discipline, environmental conditions (e.g., heat, altitude), and device platforms (multi-vendor/multi-sensor). Equally important are rigorous external and field validation, transparent and explainable deployment with appropriate governance, and equitable access to ensure scientifically robust, ethically sound, and practically relevant AI solutions. Full article

The decarbonization of maritime transport has primarily targeted large vessels, leaving small craft largely dependent on fossil fuel despite their inherent use of wind propulsion. This study addresses that gap by designing and simulating a zero-emission propulsion system for a 12.5 m sailing yacht based on integrated renewable energy. The retrofit replaces the diesel engine with an electric drivetrain supported by static solar panels and wind turbines, as well as dynamic sources, including hydro-generators and a regenerative propeller. In addition to performance under typical weather profiles, we conducted a lifecycle environmental impact estimation and evaluated system resilience under low renewable input. Simulations used real mid-latitude meteorological data to assess operational and environmental sustainability. The results show that during two representative 24 h voyages, propulsion and hotel loads were sustained solely by onboard renewables, with battery state of charge remaining above 28–46%. In an emergency calm scenario, the yacht motored for four hours at 5–6 knots using only stored energy, with solar input extending range. The findings demonstrate that integrated multi-source renewables can provide complete energy autonomy for sailing yachts. The approach illustrates practical feasibility under real conditions, scalability to eco-tour boats and ferries, and alignment with international decarbonization targets. Full article

Artificial intelligence (AI) is profoundly transforming heritage tourism through the incorporation of technological solutions that reconfigure the ways in which cultural heritage is conserved, interpreted, and experienced. This article presents a critical and systematic review of current AI applications in this field, with a special focus on their impact on destination management, the personalization of tourist experiences, universal accessibility, and the preservation of both tangible and intangible assets. Based on an analysis of the scientific literature and international use cases, key technologies such as machine learning, computer vision, generative models, and recommendation systems are identified. These tools enable everything from the virtual reconstruction of historical sites to the development of intelligent cultural assistants and adaptive tours, improving the visitor experience and promoting inclusion. This study also examines the main ethical, technical, and epistemological challenges associated with this transformation, including algorithmic surveillance, data protection, interoperability between platforms, the digital divide, and the reconfiguration of heritage knowledge production processes. In conclusion, this study argues that AI, when implemented in accordance with principles of responsibility, sustainability, and cultural sensitivity, can serve as a strategic instrument for ensuring the accessibility, representativeness, and social relevance of cultural heritage in the digital age. However, its effective integration necessitates the development of sector-specific ethical frameworks, inclusive governance models, and sustainable technological implementation strategies that promote equity, community participation, and long-term viability. Furthermore, this article highlights the need for empirical research to assess the actual impact of these technologies and for the creation of indicators to evaluate their effectiveness, fairness, and contribution to the Sustainable Development Goals. Full article

Greenhouse automation has become increasingly important in facility agriculture, yet multi-span glass greenhouses pose both scientific and practical challenges for autonomous mobile robots. Scientifically, solid-state LiDAR is vulnerable to glass-induced reflections, sparse geometric features, and narrow vertical fields of view, all of which undermine Simultaneous Localization and Mapping (SLAM)-based localization and mapping. Practically, large-scale crop production demands accurate inter-row navigation and efficient rail switching to reduce labor intensity and ensure stable operations. To address these challenges, this study presents an integrated localization-navigation framework for mobile robots in multi-span glass greenhouses. In the intralogistics area, the LiDAR Inertial Odometry-Simultaneous Localization and Mapping (LIO-SAM) pipeline was enhanced with reflection filtering, adaptive feature-extraction thresholds, and improved loop-closure detection, generating high-fidelity three-dimensional maps that were converted into two-dimensional occupancy grids for A-Star global path planning and Dynamic Window Approach (DWA) local control. In the cultivation area, where rails intersect with internal corridors, YOLOv8n-based rail-center detection combined with a pure-pursuit controller established a vision-servo framework for lateral rail switching and inter-row navigation. Field experiments demonstrated that the optimized mapping reduced the mean relative error by 15%. At a navigation speed of 0.2 m/s, the robot achieved a mean lateral deviation of 4.12 cm and a heading offset of 1.79°, while the vision-servo rail-switching system improved efficiency by 25.2%. These findings confirm the proposed framework’s accuracy, robustness, and practical applicability, providing strong support for intelligent facility-agriculture operations. Full article

The expansion of conventional agricultural models in the Colombian Amazon has caused deforestation, biodiversity loss, and socio-environmental degradation. In response, agroecology and bioeconomy are emerging as key strategies to regenerate landscapes and foster sustainable production systems. We evaluated the agroecological performance of 25 farms in the Andean–Amazon transition zone of Colombia using FAO’s Tool for Agroecology Performance Evaluation (TAPE). The analysis included land cover dynamics (2002–2024), characterization of the agroecological transition based on the 10 Elements of Agroecology, and 23 economic, environmental, and social indicators. Four farm typologies were identified; among them, Mixed Family Farms (MFF) achieved the highest transition score (CAET = 60.5%) and excelled in crop diversity (64%), soil health (SHI = 4.24), productive autonomy (VA/GVP = 0.69), and household empowerment (FMEF= 85%). Correlation analyses showed strong links between agroecological practices, economic efficiency, and social cohesion. Land cover dynamics revealed a continuous decline in forest cover (12.9% in 2002 to 7.1% in 2024) and an increase in secondary vegetation, underscoring the urgent need for restorative approaches. Overall, farms further along the agroecological transition were more productive, autonomous, and socially cohesive, strengthening territorial resilience. The application of TAPE proved robust multidimensional evidence to support agroecological monitoring and decision-making, with direct implications for land use planning, rural development strategies, and sustainability policies in the Amazon. At the same time, its sensitivity to high baseline biodiversity and to the complex socio-ecological dynamics of the Colombian Amazon underscores the need to refine the methodology in future applications. By addressing these challenges, the study contributes to the broader international debate on agroecological transitions, offering insights relevant for other tropical frontiers and biodiversity-rich regions facing similar pressures. Full article

This study investigates the environmental dimension of sustainable development across fifteen post-Soviet republics in 2022. While sustainability is generally understood as a triadic construct—economic, social, and environmental—this paper isolates the ecological pillar to highlight cross-country differences shaped by industrial legacies, institutional capacity, and governance models. A composite Environmental Performance Index (EPI) is developed using the Mazziotta–Pareto Index (MPI), which captures both average performance and internal consistency across three SDG-related domains: SDG 6 (Clean Water and Sanitation), SDG 13 (Climate Action), and SDG 15 (Life on Land). The study adds to existing literature as it includes a non-compensatory composite index and cluster analysis, and in policy terms, it provides a benchmarking system for facilitating ecological transition in the post-Soviet context. The results reveal strong divergence across the region: Baltic countries and Moldova achieve higher scores, reflecting policy convergence with the European Union and stronger environmental institutions, while Central Asian republics lag due to resource dependence, water scarcity, and weaker governance. Geographic cluster analysis corroborates these differences, showing clear spatial patterns of environmental convergence and divergence. Correlation analysis further demonstrates that environmental sustainability is positively associated with GDP per capita, HDI, and life expectancy, while negatively linked with inequality and fertility rates. These findings stress the need for context-sensitive and evidence-based policies, intra-regional cooperation, and integrated governance mechanisms to advance ecological transition in line with the 2030 Agenda for Sustainable Development. Full article

Scholars have long shown that post-separation abuse continues through legal channels and that legal institutions often reinforce existing social relations. Nevertheless, little is known about how abused mothers’ legal experiences shape their understanding of legality and how this dynamic may function to perpetuate coercive control. Drawing on in-depth interviews with 32 Israeli mothers co-parenting with abusive ex-partners, this study offers a phenomenological account of how post-separation abused mothers experience family law proceedings, based on a feminist imperative to bring their voices to center stage. The analysis reveals a dialectical legal consciousness comprising three interconnected orientations—characterized by internal contradictions and tensions that paradoxically serve to maintain rather than disrupt existing power relations: Institutional Trust and Disillusionment in the law’s protective promise, Institutional Asymmetry as experienced from the abused mothers’ perspective, and Recognizing Entrapment—the realization that legal processes reproduce the very dynamics they sought to escape. Abused mothers thus describe a paradoxical relationship with the law of both needing and distrusting a system that mandates continued contact with their abusers. Caught in a second-order abusive relationship, they feel compelled to comply with processes they perceive as harmful. We term this Entrapped Legal Consciousness—a form of legal subjectivity shaped by institutional norms that reconfigure resistance and reinscribe coercive control. This study offers empirical and theoretical insight into how legality may become a mechanism for cultivating continued control. Full article

This article examines the global efforts to govern and regulate Artificial Intelligence (AI) in response to its rapid development and growing influence across many parts of society. It explores how governance takes place at multiple levels, including international bodies, national governments, industries, companies, and communities. The study draws on a wide range of official documents, policy reports, and international agreements to build a timeline of key regulatory and standardization milestones. It also analyzes the challenges of coordinating across different legal systems, economic priorities, and cultural views. The findings show that while some progress has been made through soft-law frameworks and regional partnerships, deep divisions remain. These include unclear responsibilities, uneven enforcement, and risks of regulatory gaps. The article argues that effective AI governance requires stronger international cooperation, fair and inclusive participation, and awareness of power imbalances that shape policy decisions. Competing global and commercial interests can create obstacles to building systems that prioritize the public good. The conclusion highlights that future governance models must be flexible enough to adapt to fast-changing technologies, yet consistent enough to protect rights and promote trust. Addressing these tensions is critical for building a more just and accountable future of AI. Full article

Active helidecks systems (AHS) provide an effective solution scheme for the safe landing of helicopters on ships. This article proposes a novel adaptive antidisturbance prescribed performance control law of AHS subject to input saturation, ship motion-induced external disturbances. Specifically, we develop novel saturation-triggered boundaries to guarantee prescribed tracking error constraints under input saturation. This effectively addresses the control singularity issue inherent in traditional prescribed performance control, which occurs when input saturation causes the control error to exceed prescribed constraint boundaries. Subsequently, we design a continuous auxiliary dynamic system to further mitigate the effects of input saturation. Furthermore, leveraging the internal model principle and the periodic nature of ship motion, external disturbances are treated as the outputs of a linear exosystem with known structure but unknown parameters. These unknown parameters are then estimated using adaptive techniques, enabling asymptotic estimation of external disturbances. Building upon these developments and employing the backstepping design tool, we achieve adaptive antidisturbance stabilization of AHS. Both theoretical analysis and comparative simulations validate the proposed control law. Full article

Microgrids with high renewable energy penetration face critical challenges in frequency stability due to reduced system inertia and the presence of parameter uncertainties. This study introduces a novel adaptive virtual inertia control strategy utilizing a combination of the Walrus Optimization Algorithm (WaOA), a recent metaheuristic optimization technique, and Proportional–Integral–Derivative (PID) controllers (WaOA-PID) to improve frequency regulation in islanded microgrids under diverse operating conditions. The proposed method is evaluated across three scenarios: medium inertia, low inertia, and parametric uncertainty. Comparative analyses with conventional, IMC-tuned PID and H∞ Vector Internal Controllers (VIC) reveal that the WaOA-PID controller achieves the lowest overshoot, undershoot, and rate of change of frequency (RoCoF), while maintaining acceptable settling times in all cases. At an estimated load deviation of 0.18, the demand is varied from 200 MW to 250 MW to evaluate the system’s performance. The proposed technique yields an Integral Time Absolute Error (ITAE) of 0.000576, with PID gains of Ki = 0.9994, Kd = 0.185, and Kp = 0.774. Compared to traditional methods, the proposed controller demonstrates high reliability and efficiency in maintaining load frequency control and enhancing power system management, validating its suitability for real-time renewable energy-integrated microgrid applications. Full article

Accurate classification between magnetizing inrush currents and internal faults is essential for reliable transformer protection and stable power system operation. Because their transient waveforms are so similar, conventional differential protection and harmonic restraint techniques often fail under dynamic conditions. This study presents a two-stage classification model that combines a self-organizing map (SOM) and a convolutional neural network (CNN) to enhance robustness and accuracy in distinguishing between inrush currents and internal faults in power transformers. In the first stage, an unsupervised SOM identifies topologically structured event clusters without the need for labeled data or predefined thresholds. Seven features are extracted from differential current signals to form fixed-length input vectors. These vectors are projected onto a two-dimensional SOM grid to capture inrush and fault distributions. In the second stage, the SOM’s activation maps are converted to grayscale images and classified by a CNN, thereby merging the interpretability of clustering with the performance of deep learning. Simulation data from a 154 kV MATLAB/Simulink transformer model includes inrush, internal fault, and overlapping events. Results show that after one cycle following fault inception, the proposed method improves accuracy (AC), precision (PR), recall (RC), and F1-score (F1s) by up to 3% compared with a conventional CNN model, demonstrating its suitability for real-time transformer protection. Full article

Pre-trial detention is intended to be a measure of last resort, yet it is excessively applied across jurisdictions worldwide. This paper examines its use, with particular emphasis on its application to women and its incompatibility with international human rights law, standards, and norms. We demonstrate that the inappropriate and widespread use of custodial remand violates fundamental human rights, while exposing the gendered and intersectional barriers that impede women’s access to bail. We further underscore the far-reaching social, economic, and emotional consequences of women’s incarceration. Drawing on a limited but expanding body of research, we argue that pre-trial detention operates as a form of gendered punishment that reflects and reinforces structural inequalities, producing enduring harms for women, their families, and communities. The paper concludes by calling for investment in gender-sensitive, non-custodial, and community-based alternatives that advance women’s decarceration. These measures must be underpinned by reforms that give practical effect to human rights law, standards, and norms, while also addressing the structural conditions that lead to women’s involvement in the criminal-legal system, and ending the unnecessary imprisonment of those who are legally innocent. Full article

This paper presents a pioneering and novel analysis of the synergistic relationship between China’s leadership in electric vehicle (EV) adoption and the rapid advancement of autonomous driving (AD) technologies within the nation’s mobility ecosystem. Challenging the conventional view of electrification as a parallel trend, this study introduces a new perspective by demonstrating how EV infrastructure serves as a fundamental enabler of autonomy, providing the necessary high-voltage architectures for critical AD functions like real-time sensor fusion and over-the-air updates. In doing so, it addresses the central research question: How does large-scale electrification influence the architecture, deployment, and safety development of autonomous driving vehicles, particularly in the context of China’s intelligent mobility ecosystem? Through technical analysis and industry examples, the paper offers original contributions by illustrating how EV-driven platforms overcome the inherent limitations of internal combustion engine systems, enhancing autonomous execution and system reliability. Furthermore, this research provides novel insights into China’s unique public–private innovation ecosystem, highlighting the role of vertically integrated startups and cross-sector coordination in driving AD development. By analyzing these previously overlooked systemic interactions, the paper posits that China’s EV dominance strategically amplifies its autonomous vehicle ambitions, positioning the nation to lead the next generation of intelligent transportation systems. Full article

Objectives: This study aimed to investigate the diagnostic performance of imaging-based biomarkers from computed tomography (CT) and magnetic resonance imaging (MRI) for prediction of malignant and borderline malignant ovarian tumours. Methods: 195 consecutive patients with suspected primary epithelial ovarian cancer were included from the retrospective “Prognostic and Diagnostic Added Value of Medical Imaging in Staging and Treatment Planning of Gynaecological Cancer” (PRODIGYN) study. The radiological stage, according to the International Federation of Gynaecology and Obstetrics system (rFIGO), magnetic resonance imaging (MRI)-based Ovarian-Adnexal Reporting and Data System (O-RADS-MRI) score, and the mean apparent diffusion coefficient (ADC_mean) were investigated for prediction of ovarian malignancy, with histopathology as reference. The same imaging biomarkers were applied to the borderline tumour cohort (n = 33) to predict malignant/adverse features, such as micro-invasion. Results: The rFIGO stage demonstrated high accuracy for ovarian malignancy, with an area under the curve (AUC) of 0.98 (95% confidence interval (CI) = 0.97–0.99). On lesion level, the sensitivity and specificity of the O-RADS-MRI score to predict ovarian malignancy, after adjusting for correlated data structure, was 1 (CI: 0.96–1) and 0.82 (CI: 0.70–0.90), respectively. The performance of ADC_mean to predict ovarian malignancy on lesion level was moderately high, with AUC = 0.78 (95% CI 0.68, 0.88). Discrimination of adverse features in borderline tumours was not improved. Conclusions: rFIGO and O-RADS-MRI showed excellent performance and outperformed ADC_mean as predictive tools for ovarian malignancy but could not predict adverse features in borderline tumours. Full article