Search Results (1,407)

Ensuring affordable and reliable electricity access to areas with low population density is challenging, as network sparsity and lower connectivity rates can make it nearly impossible for electric utilities to cover the cost of interconnection without raising electricity tariffs. Utility providers that consider extending their networks to remote households must balance multiple and often conflicting objectives, including investment cost, grid resilience, geographical coverage, and environmental impacts. In this paper, a multi-objective decision-making framework is proposed for the electrification of rural households, considering traditional distribution network extension as well as microgrid deployment. In order to condense a wide range of spatial inputs into a tractable problem, a multi-criteria decision-making approach is adopted to identify and rank candidate sites for microgrid deployment that offer superior performance over a variety of technical, environmental, and economic criteria. A novel optimization model is then proposed using multi-objective Chebyshev goal programming, in which project costs, environmental impacts, and energy justice criteria are jointly optimized. The applicability of this framework is demonstrated through a case study of the Shiprock region within the Navajo Nation. The results indicate that the proposed methodology provides a balanced trade-off among conflicting objectives and identifies a priority order of loads to energize first under marginally increasing budgets. Full article

Against the backdrop of intensifying global population aging, lower-limb exoskeleton robots serve as core devices for rehabilitation and power assistance. Their control accuracy and motion smoothness rely on precise dynamic models. However, parameter uncertainties caused by variations in human lower limbs, assembly errors, and wear pose a critical bottleneck for accurate modeling. Aiming to achieve high-precision dynamic modeling for a two-degree-of-freedom lower-limb exoskeleton, this paper proposes a parameter identification method named Tent-GA-GWO. A dynamic model incorporating joint friction and link inertia was constructed and linearized. An excitation trajectory based on Fourier series, conforming to human physiological constraints, was designed. To enhance algorithm performance, Tent chaotic mapping was employed to optimize population initialization, a nonlinear control parameter was used to balance search behavior, and genetic algorithm operators were integrated to increase population diversity. Simulation results show that, compared to the traditional GWO algorithm, Tent-GA-GWO improved convergence efficiency by 32.1% and reduced the fitness value by 0.26%, demonstrating superior identification accuracy over algorithms such as GA and LIL-GWO. Validation on a physical prototype indicated a close agreement between the computed torque based on the identified parameters and the actual output torque, confirming the method’s effectiveness and engineering feasibility. This work provides support for precise control of exoskeletons. Full article

Background and Objectives: Accurate breast tumor segmentation in dynamic contrast-enhanced MRI (DCE-MRI) is crucial for treatment planning, therapy monitoring, and quantitative studies of breast cancer response. However, deep learning models often have worse performance when applied to new hospitals because scanner hardware, acquisition protocols, and patient populations differ from those in the training data. This study investigates how such center-related domain shift affects automated breast DCE-MRI tumor segmentation on the multi-center MAMA-MIA dataset. Methods: We trained a standard 3D U-Net for primary tumor segmentation under two evaluation settings. First, we constructed a random patient-wise split that mixes cases from the three main MAMA-MIA center groups (ISPY2, DUKE, NACT) and used this as an in-distribution reference. Second, we designed a balanced leave-one-center-out cross-validation (LoCoCV) protocol in which each center is held out in turn, while training, validation, and test sets are matched in size across folds. Performance was assessed using the Dice similarity coefficient, 95th percentile Hausdorff distance (HD95), sensitivity, specificity, and related overlap measures. Results: On the mixed-center random split, the best three-channel model achieved a mean Dice of about 0.68 and a mean HD95 of about 19.7 mm on the held-out test set, indicating good volumetric overlap and boundary accuracy when training and test distributions match. Under balanced LoCoCV, the one-channel model reached a mean Dice of about 0.45 and a mean HD95 of about 41 mm on unseen centers, with similar averages for the three-channel variant. Compared with the random split baseline, Dice and sensitivity decreased, while HD95 nearly doubled, showing that boundary errors become larger and segmentations less reliable when the model is applied to new centers. Conclusions: A model that performs well on mixed-center random splits can still suffer a substantial loss of accuracy on completely unseen institutions. The balanced LoCoCV design makes this out-of-distribution penalty visible by separating center-related effects from sample size effects. These findings highlight the need for robust multi-center training strategies and explicit cross-center validation before deploying breast DCE-MRI segmentation models in clinical practice. Full article

Efficient operation of electric vehicle (EV) charging stations is vital in the development of green transportation infrastructure. To address the challenge of balancing profitability, resource utilization, user behavior, and grid stability, this paper proposes a multi-objective dynamic pricing optimization framework based on a chaotic genetic algorithm (CGA). The model jointly maximizes operator profit and charging pile utilization while incorporating price-responsive user demand and grid load constraints. By integrating chaotic mapping into population initialization, the algorithm enhances diversity and global search capability, effectively avoiding premature convergence. Empirical results show that the proposed strategy significantly outperforms conventional methods: profits are 41% higher than with fixed pricing and 40% higher than with traditional time-of-use optimization, while charging pile utilization is 32.27% higher. These results demonstrate that the proposed CGA-based framework can efficiently balance multiple objectives, improve operational profitability, and enhance grid stability, offering a practical solution for next-generation charging station management. Full article

Background/Objectives: Artificial intelligence (AI) has gained substantial prominence in pediatric dentistry, offering new opportunities to enhance diagnostic precision and clinical decision-making. AI-based systems are increasingly applied in caries detection, early childhood caries (ECC) risk prediction, tooth development assessment, mesiodens identification, and other key diagnostic tasks. This systematic review and meta-analysis aimed to synthesize evidence on the diagnostic performance of AI models developed specifically for pediatric dental applications. Methods: A systematic search was conducted in PubMed, Scopus, Web of Science, and Embase following PRISMA-DTA guidelines. Studies evaluating AI-based diagnostic or predictive models in pediatric populations (≤18 years) were included. Reference screening, data extraction, and quality assessment were performed independently by two reviewers. Pooled sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated using random-effects models. Sources of heterogeneity related to imaging modality, annotation strategy, and dataset characteristics were examined. Results: Thirty-two studies met the inclusion criteria for qualitative synthesis, and fifteen were eligible for quantitative analysis. For radiographic caries detection, pooled sensitivity, specificity, and AUC were 0.91, 0.97, and 0.98, respectively. Prediction models demonstrated good diagnostic performance, with pooled sensitivity of 0.86, specificity of 0.82, and AUC of 0.89. Deep learning architectures, particularly convolutional neural networks, consistently outperformed traditional machine learning approaches. Considerable heterogeneity was identified across studies, primarily driven by differences in imaging protocols, dataset balance, and annotation procedures. Beyond quantitative accuracy estimates, this review critically evaluates whether current evidence supports meaningful clinical translation and identifies pediatric domains that remain underrepresented in AI-driven diagnostic innovation. Conclusions: AI technologies exhibit strong potential to improve diagnostic accuracy in pediatric dentistry. However, limited external validation, methodological variability, and the scarcity of prospective real-world studies restrict immediate clinical implementation. Future research should prioritize the development of multicenter pediatric datasets, harmonized annotation workflows, and transparent, explainable AI (XAI) models to support safe and effective clinical translation. Full article

Understanding regional disparities in Chinese modernization is essential for achieving coordinated and sustainable development. This study develops a multi-dimensional evaluation framework, integrating grey relational analysis, entropy weighting, and TOPSIS to assess provincial modernization across China from 2018 to 2023. The framework operationalizes Chinese-style modernization through five dimensions: population quality, economic strength, social development, ecological sustainability, innovation and governance, capturing both material and institutional aspects of development. Using K-Means clustering, kernel density estimation, and convergence analysis, the study examines spatial and temporal patterns of modernization. Results reveal pronounced regional heterogeneity: eastern provinces lead in overall modernization but display internal volatility, central provinces exhibit gradual convergence, and western provinces face widening disparities. Intra-regional analysis highlights uneven development even within geographic clusters, reflecting differential access to resources, governance capacity, and innovation infrastructure. These findings are interpreted through modernization theory, linking observed patterns to governance models, regional development trajectories, and policy coordination. The proposed framework offers a rigorous, data-driven tool for monitoring modernization progress, diagnosing regional bottlenecks, and informing targeted policy interventions. This study demonstrates the methodological value of integrating grey system theory with multi-criteria decision-making and clustering analysis, providing both theoretical insights and practical guidance for advancing balanced and sustainable Chinese-style modernization. Full article

The rapid expansion of wind energy in Southeast Europe has raised concerns about its long-term impacts on bird populations, particularly through collisions with wind turbines. Here, we analyze systematic collision monitoring data collected between 2010 and 2024 within the Integrated System for Protection of Birds in the Kaliakra Protected Area (northeast Bulgaria). Monitoring covered 52 wind turbines until 2017 and 114 turbines from 2018 onwards, using daily carcass searches within standardized 200 × 200 m plots around each turbine. Collision rate was analyzed using effort-normalized statistical models and spatial (GIS-based) analyses to assess temporal trends and habitat context derived from land-cover data. Effort-normalized analyses indicate that collision rate per turbine varied over time and exhibited a pronounced long-term decline, together with clear spatial heterogeneity. Turbines located in open steppe landscapes were associated with consistently higher collision rates compared to turbines situated in other habitat types. These results provide long-term empirical evidence from an operational wind farm area, contributing robust baseline information for cumulative impact assessment and spatial planning. From a sustainability perspective, long-term, effort-standardized collision monitoring represents a critical tool for balancing renewable energy expansion with biodiversity conservation. By providing empirical evidence on how collision occurrence evolves under sustained operational conditions, this study supports adaptive mitigation, cumulative impact assessment, and spatial planning frameworks essential for the sustainable development of wind energy in ecologically sensitive regions. Full article

Background/Objectives: Specific pharmacological osteoporosis therapy (SPOT) is regarded as a key intervention to reduce fracture risk and improve musculoskeletal function. Real-life data, particularly regarding functional muscular outcomes and pain trajectories, remain limited. This study aimed to longitudinally analyze bone mineral density, laboratory parameters, handgrip strength, functional performance, and pain symptoms under guideline-based SPOT. Methods: In this monocentric prospective real-life observational study, 178 patients (80.9% women; median age 82 years) with confirmed osteoporosis were followed for a median of four years. All patients received guideline-recommended antiresorptive or osteoanabolic therapy. Analyses included T-scores, 25(OH)D, calcium, handgrip strength, Chair Rise Test (CRT), tandem stance (TS), pain parameters, alkaline phosphatase (AP), HbA1c, fractures, comorbidities, and body mass index (BMI). Time-dependent changes were evaluated using linear mixed-effects models. Results: Bone mineral density improved highly significantly (ΔT-score ≈ +0.45 SD; p < 0.001), with no differences between therapy groups (antiresorptive vs. osteoanabolic) or BMI categories. Serum 25(OH)D levels increased markedly (Δ ≈ +20 nmol/L; p < 0.001), while calcium levels showed a small but highly significant decrease (Δ ≈ −0.047 mmol/L; p < 0.001), particularly under antiresorptive treatment. Dominant (Δ ≈ −1.95 kg; p < 0.001) and non-dominant handgrip strength (Δ ≈ −0.83 kg; p = 0.046) decreased significantly. In contrast, functional performance improved significantly: CRT time decreased by ~1 s (p = 0.004), and TS time increased by ~1 s (p = 0.007). Back pain decreased highly significantly (Δ ≈ −1.5 NRS; p < 0.001), while pain-free walking time (Δ ≈ +38 min; p = 0.031) and pain-free standing time (Δ ≈ +31 min; p = 0.038) both increased significantly. AP levels decreased significantly (p = 0.003), particularly among normal-weight patients. HbA1c changes were not significant. Overall, 73% of patients had at least one major osteoporotic fracture. Conclusions: In this real-life cohort, guideline-based specific pharmacological osteoporosis therapy was associated with significant improvements in bone mineral density, vitamin D status, functional performance, and pain-related outcomes. Despite a moderate decline in handgrip strength, balance- and mobility-related functional parameters improved, suggesting preserved or even enhanced functional capacity in daily life. These findings provide real-world evidence on the associations between SPOT, laboratory parameters, functional performance, and pain outcomes in a very elderly and multimorbid population. Full article

Background/Objectives: Falls are a leading cause of morbidity and mortality in older adults, even among those who are physically active. This study examined the associations between skeletal muscle mass, muscle strength, and muscle power and fall risk in physically active, community-dwelling older adults. Methods: A cross-sectional analysis was conducted with 280 participants (71.9 ± 5.3 years; 75% women) enrolled in the Stay Up–Falls Prevention Project. Assessments included skeletal muscle mass (anthropometric prediction equation), handgrip strength, lower limb strength and power (Five Times Sit-to-Stand test, 5×STS), and fall history over the past 12 months. Muscle power was calculated from 5×STS performance using the equation proposed by Alcazar and colleagues. Logistic regression models and receiver operating characteristic (ROC) curve analyses were performed. Results: Overall, 26.4% of participants reported at least one fall in the previous year, with a higher prevalence among women (28.9%) than men (18.8%). Fallers showed significantly lower handgrip strength (23.1 vs. 25.4 kg, p = 0.022) and poorer lower limb strength (9.2 vs. 8.7 s, p = 0.007) compared with non-fallers. However, no significant differences were found for skeletal muscle mass or sit-to-stand–derived power. In multivariable models adjusted for age, sex, body mass index, comorbidities, and medications, lower limb strength remained the only independent variable associated with fall status (OR = 1.78, 95% CI: 1.11–2.85, p = 0.016). ROC analysis confirmed fair discriminative capacity for 5×STS performance (AUC = 0.616, p = 0.003), with an optimal cut-off of 8.62 s (sensitivity = 78.4%, specificity = 33.0%). Handgrip strength, muscle mass, and power did not show independent associations with fall status. Conclusions: These findings indicate that the 5×STS test provides a simple, cost-effective, and functional indicator for fall-risk stratification in physically active older adults. Clinicians should consider the 5×STS as a sensitive functional indicator that contributes to fall risk stratification, ideally combined with complementary assessments (e.g., balance, gait, cognition) to improve risk stratification and guide preventive interventions in ageing populations. Full article

To achieve accurate voltage sag prediction and early warning, thereby improving power quality, a hybrid voltage sag prediction framework is proposed by integrating Kernel Entropy Component Analysis (KECA) with an improved Northern Goshawk Optimization (NGO) algorithm for hyperparameter tuning of a CNN-LSTM model. First, to address the limitations of the original NGO, such as proneness to falling into local optima and high randomness of the initial population distribution, a refraction-opposition-based learning mechanism is introduced to enhance population diversity and expand the search space. Furthermore, a sine–cosine strategy (SCA) with nonlinear weight coefficients is integrated into the exploration phase to dynamically adjust the search step size, optimizing the balance between global exploration and local exploitation, thereby boosting convergence speed and accuracy. The improved algorithm (SCNGO) is then utilized to optimize the hyperparameters of the CNN-LSTM model. Second, KECA is applied to voltage-sag-related data to extract key features and eliminate redundant information, and the resulting dimensionally reduced data are fed as input to the SCNGO-CNN-LSTM model to further improve prediction performance. Experimental results demonstrate that the SCNGO-CNN-LSTM model outperforms other comparative models significantly across multiple evaluation metrics. Compared with NGO-CNN-LSTM, GWO-CNN-LSTM, and the original CNN-LSTM, the proposed method achieves a mean squared error (MSE) reduction of 53.45%, 44.68%, and 66.76%, respectively. The corresponding root mean squared error (RMSE) is decreased by 25.33%, 18.61%, and 36.92%, while the mean absolute error (MAE) is reduced by 81.23%, 77.04%, and 86.06%, respectively. These results confirm that the proposed framework exhibits superior feature representation capability and significantly improves voltage sag prediction accuracy. Full article

The global rise in nursing home (NH) populations presents substantial challenges, as residents frequently experience physical and cognitive decline, low physical activity, and high fall risk. This study evaluates the effectiveness of the BeSt Age App, a tablet-based, staff-supported mHealth intervention designed to promote physical activity and prevent falls among NH residents. Primary outcomes were fall incidence and fall risk (assessed using Berg Balance Scale [BBS] and Timed Up and Go [TUG]); fear of falling was a secondary outcome. In a cluster-randomized controlled trial across 19 German NHs, 229 residents (mean age = 85.4 ± 7.4 years; 74.7% female) were assigned to an intervention group (IG) or control group (CG). The 12-week intervention comprised twice-weekly, tablet-guided exercise sessions implemented by NH staff. Mixed models and generalized estimating equations were used under an intention-to-treat framework. The IG showed significantly greater improvement in BBS scores than the CG (group × time: F(1, 190.81) = 8.25, p = 0.005, d = 0.22), while group × time changes in TUG performance, fear of falling, and fall incidence were nonsignificant. These findings demonstrate the feasibility of a staff-mediated mHealth approach to fall prevention in NH residents, showing significant improvements in BBS scores as one functional indicator of fall risk, while TUG, fall incidence and fear of falling showed no change. Full article

Endometrial lesions are common in gynecology, exhibiting considerable clinical heterogeneity across different subtypes. Although ultrasound imaging is the preferred diagnostic modality due to its noninvasive, accessible, and cost-effective nature, its diagnostic performance remains highly operator-dependent, leading to subjectivity and inconsistent results. To address these limitations, this study proposes a hierarchical feature selection and optimization framework for endometrial lesions, aiming to enhance the objectivity and robustness of ultrasound-based diagnosis. Firstly, Kernel Principal Component Analysis (KPCA) is employed for nonlinear dimensionality reduction, retaining the top 1000 principal components. Secondly, an ensemble of three filter-based methods—information gain, chi-square test, and symmetrical uncertainty—is integrated to rank and fuse features, followed by thresholding with Maximum Scatter Difference Linear Discriminant Analysis (MSDLDA) for preliminary feature selection. Finally, the Whale Migration Algorithm (WMA) is applied to population-based feature optimization and classifier training under the constraints of a Support Vector Machine (SVM) and a macro-averaged F1 score. Experimental results demonstrate that the proposed closed-loop pipeline of “kernel reduction—filter fusion—threshold pruning—intelligent optimization—robust classification” effectively balances nonlinear structure preservation, feature redundancy control, and model generalization, providing an interpretable, reproducible, and efficient solution for intelligent diagnosis in small- to medium-scale medical imaging datasets. Full article

As global population aging accelerates, the growing burden of age-related diseases is driving a shift in medical research from single-disease treatment to interventions targeting the aging process itself. Organ-specific interventions have emerged as a promising strategy to modulate systemic aging. Among organs, the brain, muscle, and gut have attracted particular attention due to their central roles in neural regulation, metabolic homeostasis, and immune balance. In this review, we focus on these three key organs, systematically summarizing their roles and regulatory mechanisms in organismal aging and discussing how exercise influences the aging process by affecting these organs. Crucially, we propose a novel “local-to-global” regulatory model, positing that preserving homeostasis in these specific tissues is sufficient to orchestrate systemic anti-aging effects. This work represents a conceptual advance by providing the theoretical rationale to move beyond non-specific systemic treatments toward precise, organ-targeted interventions. Full article

Urban sustainability depends on balancing transportation accessibility, housing affordability, and social equity. Displacement—defined in this study as the population-level loss of low-income households from a census block over time—poses a growing challenge to inclusive urban development. This study examines statistical relationships and spatial patterns linking transit accessibility, housing affordability, and low-income household displacement across the four largest counties in Tennessee. Negative binomial regression models are used to quantify relationships between transit accessibility, housing affordability, and displacement, revealing that housing affordability is consistently linked to displacement, while the effects of transit accessibility vary substantially across counties. Bivariate Local Indicators of Spatial Association (LISA) identify localized clusters where displacement coincides with transit or housing constraints, and Multivariate Cluster Typology Analysis classifies census blocks into distinct typologies, highlighting region-specific trade-offs between accessibility and affordability. Together, the results demonstrate that displacement dynamics are highly context dependent, underscoring the need for place-based and sustainability-oriented policy responses. The findings provide an empirical basis for integrating transportation and housing strategies to reduce displacement risks and support equitable and sustainable urban development in diverse metropolitan contexts. Full article

As a key strategic sector underpinning China’s future development, the artificial intelligence (AI) industry is essential to enhancing national competitiveness and advancing sustainable economic and social development. Based on Chinese provincial panel data from 2012 to 2022, we explore how industrial structure upgrading (ISU) affects the resilience of China’s AI industry chain (RAIIC) and empirically test the underlying transmission mechanism using a mediation effect model. The results indicate that (1) ISU significantly enhances the RAIIC, thereby providing a solid structural foundation for its long-term stability and sustainable evolution; (2) the impact of ISU on the RAIIC can be realized by enhancing regional financial agglomeration and human capital levels; (3) the positive impact of ISU on the RAIIC is significantly stronger in regions with larger population sizes, higher levels of economic development, higher technological sophistication, and more advanced digital inclusive finance. These findings imply that policy design should emphasize regional coordination and dynamic adaptability so as to support the balanced and sustainable nationwide development of the AI industry. According to these findings, we propose corresponding policy recommendations aimed at providing theoretical support and practical guidance for the sustainable and high-quality development of China’s AI industry. Full article