Search Results (13,569)

Climate change represents a serious challenge to agricultural sustainability in arid and semi-arid regions, where farmers increasingly face drought, temperature fluctuations, and resource scarcity. This study aims to assess and compare farmers’ attitudes in Egypt and Iraq toward climate change adaptation mechanisms and to identify the main barriers that limit the effective adoption of adaptive practices. A descriptive–analytical and comparative field approach was applied, and primary data were collected using a structured questionnaire administered to 342 farmers in Egypt and 157 farmers in Iraq. Descriptive statistics and inferential analyses were used to examine attitudes and determine significant differences between the two groups. Farmers’ attitudes toward climate change adaptation mechanisms and practices were measured using a 30-item scale with a three-point Likert response format (1–3), where higher scores indicate more favorable attitudes. The results indicated that farmers in both countries exhibited moderately positive attitudes toward adaptation practices, with mean scores of 2.34 in Egypt and 2.38 in Iraq with no statistically significant difference at the aggregate level, while differences are more clearly expressed at the dimensional and contextual levels rather than in overall attitudes. Major constraints to adaptation included weak institutional support, limited access to financing, absence of early warning systems, and insufficient training opportunities. The study concludes that improving agricultural extension services, expanding credit facilities, and upgrading rural infrastructure are essential to enhance farmers’ adaptive capacity and strengthen the resilience of agricultural systems. Full article

Machine learning (ML) is increasingly influencing the assessment and delivery of movement and exercise, yet its role within adapted physical activity (APA) for individuals with chronic conditions has not been comprehensively synthesized. ML-based approaches have the potential to enhance functional assessment, support individualized exercise prescription, and facilitate scalable monitoring across preventive, community-based, and long-term adapted exercise settings, particularly in populations characterized by functional heterogeneity and variable responses to exercise. The aim of this narrative review is to synthesize and critically discuss current ML applications relevant to the core professional processes of APA practice. A structured narrative review was conducted using searches in PubMed/MEDLINE, Scopus, and Web of Science, complemented by targeted searches in engineering-oriented sources to capture ML methods not consistently indexed in biomedical databases. The search covered the period in which contemporary ML approaches have been increasingly applied to human movement and exercise research and was last updated in January 2026. Evidence was synthesized thematically into application-oriented domains relevant to APA practice. ML applications in APA include markerless motion and gait analysis, wearable-sensor data processing, balance and fall-risk assessment, and functional classification. Predictive and adaptive models support individualized regulation of exercise intensity, progression, and workload, including remote and hybrid delivery models. Applications span oncology, cardiometabolic, respiratory, neuromuscular conditions, and adapted sport contexts. Ethical, legal, and governance issues, such as algorithmic bias, data privacy, and professional accountability, emerge as central considerations for safe and equitable implementation. ML represents a promising decision-support layer for APA, complementing professional expertise through enhanced assessment, personalization, and monitoring. Its effective integration requires robust validation, interpretability, and responsible governance to ensure that ML augments, rather than replaces, professional judgment in APA practice. Full article

Addressing the intertwined challenges of food security and climate vulnerability requires robust and regionally tailored strategies for staple crops such as common beans. Although adjusting sowing dates is a key adaptive practice, spatio-temporal climate variability complicates the identification of optimal planting windows. This study integrates crop modeling with Functional Data Analysis (FDA) to quantify sowing-date-dependent yield losses for rainfed common beans across Central-Southern Brazil. The CSM-CROPGRO-Dry Bean model, driven by long-term climate data (1980–2016), soil properties, and management practices, was used to simulate yields for the BRS Estilo cultivar. FDA was subsequently applied to cluster yield-loss curves across municipalities and growing seasons, generating representative regional risk profiles. The results reveal clear spatial patterns. During the wet season, earlier sowing minimizes losses in Goiás, Minas Gerais, and western Paraná, whereas later sowing is beneficial in São Paulo, Santa Catarina, and eastern Paraná. In the dry season, earlier sowing consistently reduces losses across most regions. These patterns are primarily driven by water deficits and suboptimal temperatures during critical phenological phases. The resulting spatio-temporal sowing calendar provides an evidence-based decision-support tool to help farmers mitigate climatic risks. Moreover, it offers a scientific foundation for policymakers to refine sustainable management practices, improve crop insurance design, and enhance agricultural resilience and productivity under increasing climate uncertainty. Full article

The quality of wind power output data directly impacts the assessment of wind farm operational status and the accuracy of power forecasting models. However, due to factors such as sensor precision, communication interference, and the complex harbor environment, raw data collected from port-area wind turbines often contain noise, outliers, and missing values. Without effective cleaning, the resulting power curves can be distorted, reducing the generalization capability of predictive models. To overcome the limitations of traditional outlier detection methods in terms of adaptability and robustness, this study proposes a two-stage port-area wind power data cleaning approach based on dynamic interquartile range and an improved Sigmoid function fitting. In the first stage, an adaptive binning and density-weighting mechanism dynamically expands the interquartile range to identify and remove local outliers across different wind speed intervals. In the second stage, the cleaned wind speed–power data are subjected to secondary fitting and residual analysis using an improved Sigmoid model to detect hidden anomalies and boundary-type outliers. Using measured data from the #1 WT in the Chuanshan Port area as a case study, the experimental results demonstrate that the proposed method achieves high data retention while outperforming the conventional interquartile range, density-based spatial clustering of applications with noise and isolation forest algorithms in terms of the Pearson correlation coefficient (r = 0.93) and the coefficient of determination (R² = 0.89), with mean squared error and root mean squared error reduced to 446.39 kW and 545.58 kW, respectively. The findings verify the efficiency, stability, and practical feasibility of the method for port-area wind power data cleaning, providing a reliable data foundation for wind power forecasting and operational optimization in port environments. Full article

This study develops a novel AI-based trading framework designed to consistently generate profits across cyclical bullish and bearish futures markets. Unlike conventional strategies that rely on static rules or a single predictive model, the proposed framework introduces a dual-agent deep reinforcement learning (DRL) architecture, where one agent specializes in bullish conditions and the other in bearish conditions, while a trading decision selector dynamically predicts market regimes and allocates execution accordingly. This design enables the system to adapt to regime shifts and mitigate risks arising from market volatility and extreme events. Using Mini Taiwan Stock Exchange Index Futures (MTX) as a case study, a four-year historical backtest is conducted covering multiple disruptive periods, including the tax adjustment and the Russia–Ukraine conflict. The empirical results show that, under a monthly capital reset and loss-compensation rule with a fixed investment of TWD 500,000 per month, the proposed framework achieves an average cumulative return of 2240%, an annualized return of 109%, and a Sharpe ratio of 0.31, with the cumulative ROI exceeding twice the MTX index growth over the same period. Although the Sharpe ratio remains moderate, this outcome reflects the framework’s emphasis on directional trading and absolute return maximization, where profitable trades outweigh intermittent losses despite higher short-term volatility. These findings suggest that adaptive, regime-aware DRL architectures are particularly effective for futures trading in markets characterized by frequent trend reversals, offering both methodological innovation and practical applicability under realistic market conditions, with strong returns achieved at a moderate risk-adjusted level. Full article

The COVID-19 pandemic reshaped the function of the home, shifting it from a private residence to a multifunctional hub for work, education, and daily life. In Saudi Arabia, where homes are traditionally rooted in communal hospitality, this global event prompted an unprecedented inward focus, compelling users to adapt their living spaces. This study investigates how Saudi users perceived and adapted their homes during and after the pandemic, focusing on spatial and functional changes, particularly those that support remote work and multifunctionality. Data was collected from three surveys conducted in those three periods of time and statistically analyzed. These surveys were distributed through the researcher’s social media channels, such as WhatsApp, LinkedIn, and the X platform. It examines changes across the pre-COVID, mid-COVID, and post-COVID periods to determine whether there was a significant shift in the collective spatial priorities of the Saudi domestic landscape across three distinct socio-historical periods, such as remote work and guest spaces, or if they were abandoned as soon as the pandemic and its lockdown were over. Statistical analysis was conducted using JASP to generate chi-squared tests, ANOVA, and descriptive analysis. The outcome aims to better inform future housing design priorities in Saudi Arabia and align them with the housing goals of Vision 2030. The preliminary findings suggest minimal differences in functional space requirements between the pandemic and post-pandemic eras, indicating a lasting shift in users’ spatial needs. The results have practical implications for architects, planners, and policymakers seeking to design adaptable, resilient residential spaces for the post-pandemic era. Full article

Climate change and growing water scarcity necessitate that irrigation districts allocate limited water resources more efficiently, with explicit consideration of multi-source uncertainties. To maximize the effective utilization coefficient of irrigation water, an uncertainty-informed optimization and dynamic regulation framework for agricultural water allocation (UODRA) was developed. The framework quantifies and characterizes uncertainties arising from meteorological forcings, soil heterogeneity, irrigation practices, and water losses during conveyance and field application. The fractional programming model derived therefrom is solved via Dinkelbach’s algorithm, and Monte Carlo simulation is adopted in a reduced scenario space to propagate the dominant uncertainty drivers and assess the distribution characteristics of outcomes and associated risks. A case study was conducted in the Fendong Irrigation District to evaluate three water supply scenarios. The results indicate that with sufficient water supply and diminishing marginal returns, the effective utilization coefficient of irrigation water increases accordingly. Uncertainty mainly exerts an impact on the degree of dispersion and downside risks rather than at the average level. Sensitivity analysis shows that efficiency-related perturbations are the primary drivers of output variability, and their impacts are greater than those of supply-side perturbations and demand-side variation in simulated irrigation demand. Further technical comparison reveals that the adoption of high-efficiency irrigation can significantly improve the performance at the regional level: under drip irrigation conditions, the efficiency reaches 0.614, while that of sprinkler irrigation is 0.499, with a simultaneous improvement in operational stability. Overall, UODRA provides a quantitative decision support method for robust irrigation water resource allocation and adaptive management under uncertain conditions. Full article

The transformation of public spaces to meet the need for shelter in the post-disaster situation is a practice observed in many countries. However, these temporary alterations are meticulously planned and executed within a defined timeframe following the disaster. This approach hinders the effective utilization of available space. The objective of the study is to reach design decisions by determining the adaptive use potential of sports facilities for temporary shelter in the post-disaster process. In addition, the study will reveal which adaptability strategies can be used to adapt spaces with different functions. The design decisions are reached by comparing sports facilities and temporary shelter needs programs based on eleven adaptability strategies (adjustability, versatility, transformability, scalability, portability, flexibility, expandability, dismountability, reuse, modularity, independence). The conversion of sports facilities into temporary shelters was achieved by employing adaptability strategies, thereby demonstrating the potential for a space with 15 different functions to undergo transformation. A transformability strategy has been employed, whereby changing rooms have been converted into laundry rooms, and grandstands into training areas. A scalability strategy has been employed to facilitate the reuse of cafe-restaurant areas as dining halls. The transformation of the playground into sleeping areas is facilitated by strategies of portability and dismountability. Flexibility and expandability strategies are employed in the transition from the first aid room to the infirmary area. A reuse strategy is employed for administrative units, parking areas, restrooms and prayer areas, ensuring that spaces with similar needs are utilized with minimal intervention. By examining a range of adaptability strategies, analogous adaptability applications can be developed for other public spaces. The study contributes a transferable, criteria-driven framework that supports decision-making for the adaptive reuse of public buildings in post-disaster contexts, offering a structured basis for extending similar transformations to other building typologies. Full article

Urbanization and climate change are exerting significant pressure on the living environments of traditional rural settlements. In western Sichuan, the persistently cold and humid winter further intensifies the risks for local residents. Linpan, a distinctive agricultural settlement form that has evolved over centuries, embodies climate-responsive construction wisdom shaped by long-term human–environment interaction. Within Linpan, residential units—composed of outdoor and indoor spaces—serve as the primary activity spaces for inhabitants. Their spatial configuration and construction practices directly regulate the thermal environment and consequently influence daily life. However, whether the winter thermal environment satisfies contemporary thermal comfort requirements, and which landscape and construction determinants can effectively enhance thermal adaptation, remains insufficiently understood. To address this gap, this study integrated meteorological field measurements, thermal comfort questionnaire surveys, and coupled numerical simulations to systematically investigate winter thermal conditions in both outdoor and indoor spaces of Linpan residential units. The optimization performance of key landscape determinants (vegetation configurations and ground materials) and construction determinants (building layouts and envelope materials) was evaluated. The results reveal climate-responsive passive design strategies based on actual inhabitants’ thermal adaptation, establishing a sustainable design framework for improving winter thermal comfort in traditional agricultural settlements. The findings provide scientific support for rural revitalization and contribute theoretical insights into climate-resilient preservation of vernacular dwellings under changing environmental conditions. Full article

Heritage-based enterprises, such as traditional batik businesses in Indonesia, face increasing financial challenges that threaten their long-term sustainability. This study investigates the role of financial risk management in supporting the sustainability of these enterprises, particularly among sole-proprietor batik artisans in Pekalongan, a city designated as a UNESCO Creative City for its cultural heritage. Using the Resource-Based View (RBV) as the theoretical lens, the research explores how internal capabilities like financial literacy and contextual factors such as cultural identity contribute to enterprise resilience. A cross-sectional survey was conducted among 110 artisans, and data were analyzed using structural equation modeling. The findings reveal that financial risk management directly enhances sustainability, while financial literacy serves as a partial mediator, and cultural identity acts as a significant moderator. These interactions suggest that sustainability in heritage sectors is not only a matter of financial competence but also of cultural embeddedness. The study highlights the importance of aligning financial strategies with cultural values to foster adaptive behavior and intergenerational continuity. The results offer practical implications for policymakers and development programs seeking to strengthen traditional enterprises through integrated financial and cultural capacity-building approaches. Full article

To address the challenges of designing geologically compatible, large-scale gas drainage strategies in gassy coal mines, this study introduces an integrated workflow combining detailed gas-geological unit subdivision with the Analytic Hierarchy Process (AHP) for the Baode Coal Mine. This approach aims to transform gas drainage technology selection from empirical judgment to a systematic, quantitative decision-making process, thereby enhancing control precision and mine safety. First, the No. 8 coal seam was refined into ten distinct gas-geological units (II-i to II-x), forming the foundation for a targeted management strategy. For these units, a quantitative evaluation index system was constructed, integrating key factors such as permeability, structural characteristics, and unit area. The AHP was then employed to assess the adaptability of four primary drainage technologies: ULB-uni/bi (underground long borehole unidirectional/bidirectional drainage), UULB (underground ultra-long directional borehole drainage), UDLB-SHF (underground directional long borehole drainage with staged hydraulic fracturing), and FHWS (fractured horizontal wells drilled from the surface). The decision analysis reveals significant regional differentiation in technical suitability. FHWS ranks highest in structurally complex and water-rich zones. UDLB-SHF and UULB serve as viable, cost-effective alternatives to FHWS in various scenarios, with UULB being particularly advantageous for “large-area pre-drainage” in extensive panels with relatively simple geology. ULB-uni/bi is confirmed as the most economical option but is suitable only for minor blocks with simple conditions. Consequently, the study proposes a hierarchical, zone-specific strategy: prioritizing surface-based FHWS for high-risk zones, employing UDLB-SHF for active permeability enhancement in low-permeability resource-rich areas, utilizing UULB for efficient large-area drainage, and restricting ULB-uni/bi to small, geologically normal blocks. Ultimately, this research establishes a robust technical selection system that integrates fine geological subdivision, AHP-based multi-criteria evaluation, and targeted technology matching. It provides a scientific basis for balancing risk control and cost optimization in gas drainage design for the Baode Coal Mine. In summary, the methodological framework proposed in this study provides a systematic approach for coal mine gas control under complex geological conditions. Its core value lies in achieving the unity of scientificity and practicality in gas control technology decisions through standardized analysis logic and differentiated adaptation mechanisms, thereby providing support for the precise and efficient development of coal mine gas control. Full article

Background: Artificial Intelligence as a Medical Device (AIaMD) introduces regulatory, methodological, ethical, and clinical challenges that are not fully addressed by traditional device trial frameworks. Given rapidly evolving and jurisdiction-specific guidance, a consolidated mapping of trial design expectations and regulatory requirements is needed. Objective: To map regulatory requirements and clinical trial design approaches for AIaMD across major jurisdictions and to identify key methodological and implementation gaps relevant to adaptive/continuously learning systems. Methods: A scoping review was conducted in accordance with the PRISMA-ScR reporting guideline. Peer-reviewed literature (2017–2025) was searched in PubMed, Embase, Web of Science, and the Cochrane Library. Gray literature was identified from major regulators and policy bodies (FDA, EMA, MHRA, PMDA, WHO, CDSCO). Eligible records addressed AIaMD clinical evaluation, trial design, regulatory pathways, post-market surveillance, or reporting standards. Data were charted using a predefined extraction framework and synthesized descriptively with thematic analysis across regulatory, methodological, ethical, and clinical implementation domains. Results: Included sources demonstrate substantial heterogeneity in evidence expectations and AI-specific pathways across jurisdictions. Recurrent themes include the need for predefined change management, performance monitoring and drift controls, dataset representativeness and bias evaluation, transparency and versioning, cybersecurity, and real-world evidence integration. Reporting frameworks (SPIRIT-AI, CONSORT-AI, MI-CLAIM) are frequently cited as mechanisms to improve reproducibility and regulatory readiness. Conclusions: Evidence and regulatory expectations for AIaMD remain fragmented. Harmonization of terminology, trial design principles, and post-market governance—supported by standardized reporting—would improve clinical validity, safety assurance, and scalability across regions. This review has several limitations. As a scoping synthesis, it prioritizes breadth of coverage rather than quantitative meta-analysis. Included sources vary in methodological rigor and reporting detail, and evolving regulatory guidance may change rapidly over time. Nevertheless, integrating peer-reviewed and regulatory evidence provides a comprehensive overview of current expectations and emerging gaps. In conclusion, effective evaluation of AIaMD requires a shift from static, one-time validation toward continuous lifecycle oversight that integrates adaptive trial designs, transparent reporting standards, bias surveillance, and structured post-market monitoring. Regulatory heterogeneity currently poses significant barriers to multinational development; however, coordinated adoption of standardized evidence frameworks and collaborative governance mechanisms may reduce duplication while preserving patient safety. By translating methodological principles into operational guidance, this review aims to support regulators, sponsors, and clinical investigators in designing trials that are both scientifically rigorous and practically implementable for continuously learning systems. Full article

Deep learning has achieved expert-level performance in medical imaging analysis. However, models often fail to generalize across patient populations due to cross-population domain shifts, distributional differences arising from demographic variability, variations in imaging protocols, scanner hardware, and differences in disease prevalence. This challenge limits the real-world deployment and can increase health inequities. This review systematically examines the nature, causes, and impact of cross-population domain shift in deep learning-based medical imaging analysis. We analyzed 50 peer-reviewed studies from 2020 to 2025, evaluating the proposed methodologies for handling population shifts, the datasets employed, and the metrics used to assess performance. Our findings demonstrate that performance degradation ranged from 10–25% when models were tested on unseen populations, emphasizing the substantial impact of domain shifts on model generalizability. The literature reveals that mitigation strategies broadly fall into two categories: data-centric approaches, such as augmentation and harmonization, and model-centric approaches, including domain adaptation, transfer learning, adversarial learning, multi-task learning, and continual learning. While domain adaptation and transfer learning are the most widely used, their performance gains across populations remain modest, ranging from 5–15%, and are not supported by external validation. Our synthesis reveals a significant reliance on large, publicly available datasets from limited regions, with an underrepresentation of data from low- and middle-income countries. Evaluation practices are inconsistent, with few studies employing standardized external test sets. This review provides a structured taxonomy of mitigation techniques, a refined analysis of domain shift characteristics, and an in-depth critique of methodological challenges. We highlight the urgent need for more geographically and demographically inclusive datasets, adaptable modeling techniques, and standardized evaluation protocols to enable accurate and equitable AI-driven diagnostics across diverse populations. Finally, we outline future research directions to guide the development of robust, generalizable, and fair models for medical imaging analysis. Full article

Background: CGRP contributes to cerebrovascular regulation, mainly based on experimental and translational data; human evidence remains limited. Gepants, including atogepant, are effective migraine preventives and achieve partial penetration across the blood–brain barrier. However, their neurologic and cerebrovascular safety in heterogeneous patient populations remains incompletely characterized. Objective: To describe acute neurologic events observed during atogepant therapy, provide contextual information regarding their baseline occurrence, and explore potential mechanisms by which CGRP receptor blockade may influence neurovascular resilience. Methods: We report five adults treated with atogepant (30–60 mg/day) who developed acute neurologic symptoms prompting emergency hospital admission. All patients underwent comprehensive diagnostic assessment including neuroimaging, vascular studies, cardiac evaluation, and laboratory testing. To provide context, a retrospective comparison cohort of migraine patients not treated with gepants during a similar period was analyzed. Baseline characteristics were summarized, and event occurrence was compared using Fisher’s exact test. Results: Among 575 individuals treated with atogepant, five experienced acute neurologic events, including one cerebellar infarction and several transient focal syndromes without structural correlates. No cerebrovascular events requiring hospitalization were identified in the non-gepant cohort (n = 610). In an unadjusted analysis, this difference was statistically significant (p = 0.027). The events were clinically heterogeneous, and several lacked radiologic confirmation of ischemia. Conventional vascular risk factors were present in some patients. Conclusions: These findings do not imply causality but raise the possibility that CGRP receptor blockade may reduce cerebrovascular adaptability in susceptible individuals. Clinicians should remain vigilant for ischemia or microvascular dysfunction when patients receiving atogepant present with acute vertigo, diplopia, ptosis, or hemisensory symptoms—even when CT and CTA are normal—and obtain timely MRI and vascular assessment. The absence of comparable events in a retrospective non-gepant cohort provides contextual information but does not permit inference regarding increased risk due to potential confounding and unmeasured factors. The findings are exploratory and hypothesis-generating, underscoring the need for prospective controlled studies to clarify the cerebrovascular safety of CGRP receptor antagonists in routine clinical practice. Full article

The advent of smart farms, enabled by information and communication technologies (ICT) and the Internet of Things (IoT), has improved productivity and sustainable agriculture. However, the large-scale implementation of smart farms is currently hampered by physical constraints. These constraints have led to the concept of open-field smart farming as a viable alternative. In this paradigm, data from unmanned aerial vehicles (UAVs) play a central role in effective and sustainable agricultural management. The quantitative analysis of such data requires highly reliable technological solutions. The objective of this study is to conduct a comparative analysis of image binarization algorithms for UAV-based soybean canopy extraction across growth stages and to contribute to the development of an image labeling methodology. UAVs were used to capture images of soybean fields at different growth stages, and a comparative analysis was performed using binarization image algorithms. The performance of each algorithm was evaluated using Normalized Cross Correlation (NCC) and Mean Absolute Error (MAE). The results indicate that the Excess Green (ExG) and Excess Green minus Excess Red (ExGR) vegetation indices provide accurate and stable soybean canopy extraction across growth stages when combined with Adaptive and Otsu binarization algorithms. These indices are particularly suitable for extracting soybean canopy from UAV-based data, thereby expanding the scope of precision analysis in the agricultural sector and providing data for advancing precision agriculture technology. This study contributes to the standardization and efficient use of UAV-based agricultural data processing. However, since manual weeding was performed prior to image acquisition to ensure that only soybean plants were present, reflecting standard agricultural practices in South Korea, additional validation would be required for application in fields where weeds are naturally present. Full article