Search Results (3,922)

The use of solar photovoltaic technology is among the most promising approaches to achieving SDG7—Affordable and Clean Energy—which seeks to provide modern, reliable, sustainable, and efficient energy for everyone globally, especially in developing areas with high irradiation, where both energy access and decarbonization are major challenges. South Khorasan Province, Iran, is one of the most highly irradiated regions in the world. However, despite the abundance of solar resources, most previous research in Iran on solar potential has focused on technical potential, with little emphasis on actual energy consumption patterns and economic viability. To the best of our knowledge, this is the first demand-driven assessment at the county level and the first national-scale implementation of the MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution) method for selecting solar energy sites in Iran. A spatially explicit integrated framework based on GIS-MARCOS was established for each of the eleven counties of South Khorasan Province, and five benefits were used as criteria (solar irradiance, population, per capita electrical consumption in residential, industrial, and agricultural sectors). Objective weights were calculated using Shannon’s Entropy. The analysis indicates that residential electricity demand emerges as the most influential factor in the prioritization process. Therefore, the counties of Birjand, Qaenat, and Tabas were identified as top priority counties, while counties with high irradiation levels but low demand (for example, Boshruyeh) received the least priority. These results clearly indicate the need to transition from irradiation-based to demand-based planning to minimize transmission losses and maximize the ability to integrate solar-generated electricity into the electric power grid. This proposed methodology provides a transferable decision-support tool for other high-irradiation, demand-heterogeneous regions around the globe. Full article

This study presents a unified mechanics-based framework for evaluating bonded composite patch repairs. Discrete fracture, fatigue, and adhesive responses are transformed into continuous master equations over the design space. Low-order polynomial surfaces model stress intensity and concentration responses, enabling continuous prediction of repair performance without repeated finite-element analyses. A fracture-based repair efficiency index is derived from the analytical master surface. This index quantifies the average reduction in crack-driving force across the domain. Combined with adhesive stiffness and strength, it defines an adhesive-based repair efficiency index (A-REI), providing a direct link between structural response and material properties. The results show that repair effectiveness is strongly influenced by both geometric severity and adhesive properties. Fatigue performance decreases significantly with increasing notch ratio in single-sided repairs. Double-sided configurations maintain consistently higher efficiency. Symmetric reinforcement more effectively reduces stress concentration, with improvements exceeding 40% at intermediate notch ratios. Adhesive selection is governed by stiffness and strength. Structural adhesives achieve significantly higher A-REI values, whereas compliant adhesives contribute negligibly. Overall, repair symmetry controls the magnitude of improvement, while adhesive properties determine performance ranking. This framework provides a clear, practical basis for design and material selection. Full article

Background: Serum uric acid (SUA) may predict incident type 2 diabetes mellitus (T2DM), but longitudinal evidence from Middle Eastern populations remains limited. Methods: We conducted a retrospective cohort study using electronic health records from Qatar’s Primary Health Care Corporation over a six-year period (2018–2023). Adults aged ≥18 years with at least one valid serum uric acid (SUA) measurement and no prior diabetes at baseline were eligible. All eligible participants were retained; no propensity score matching was performed. Baseline SUA was defined at the first valid measurement, and repeated-measure exposures included current SUA, cumulative-average SUA, and landmark time-weighted average (TWA) SUA. Sex-specific SUA categories were low <208, normal 208–428, and high >428 µmol/L in males and low <149, normal 149–357, and high >357 µmol/L in females. Sex-stratified Cox models, restricted cubic spline analyses, prespecified sensitivity analyses, and complementary explainable boosting machine (EBM) models were used to evaluate associations with incident type 2 diabetes mellitus (T2DM). Results: The cohort included 169,876 adults (85,361 males and 84,515 females) and 18,714 incident T2DM events. In fully adjusted baseline Cox models, high baseline SUA was associated with higher T2DM hazard in females (hazard ratio [HR]: 1.44; 95% CI: 1.36–1.53), whereas low baseline SUA was associated with higher hazard in males (HR: 1.60; 95% CI: 1.44–1.78), and high SUA was not. In women, positive SUA–T2DM associations persisted in time-varying and landmark analyses, including current high- versus- normal SUA (HR: 1.50; 95% CI: 1.41–1.58) and 2-measurement landmark TWA SUA per 1 mg/dL (HR: 1.17; 95% CI: 1.13–1.20). In men, unlagged whole-cohort analyses showed inverse continuous associations, but lagged and repeated-measure analyses shifted toward positive associations, including 365-day lagged high- versus- normal baseline SUA (HR: 1.19; 95% CI: 1.11–1.28) and 2-measurement landmark TWA SUA per 1 mg/dL (HR: 1.06; 95% CI: 1.03–1.09). Restricted cubic splines showed a steadily rising risk gradient in females above approximately 262 µmol/L and a J-shaped pattern in males, with the lowest risk near 374 µmol/L. In EBM models, TWA SUA ranked third in women and fifth in men in the 2-measurement landmark cohorts. Conclusions: In this large Qatar cohort, longitudinal SUA was associated with incident T2DM in a sex-specific manner, with consistent positive associations in females and exposure-definition-dependent patterns in males. Repeated SUA measurements may improve diabetes risk stratification, but causal and therapeutic implications require further study. Full article

This study presents a city-scale physical coastal vulnerability assessment of the 21 km Monrovia coastline (Liberia) using a multi-parameter coastal vulnerability index (CVI). Nine physical parameters—geology/geomorphology, shoreline change rate, elevation, slope, bathymetry, wave height, tidal range, relative sea level rise, and coastal landform characteristics—were integrated within an equal-weight ranking framework. The results identify spatially concentrated high vulnerability segments associated with low elevation, sandy geomorphology, and persistent shoreline retreat. The CVI represents a relative exposure screening rather than a predictive risk model. Limitations related to parameter weighting, classification dependency, and temporal heterogeneity are acknowledged. The findings support preliminary spatial prioritization for coastal adaptation planning Full article

With the increasing penetration of converter-based devices, harmonic distortion has become a major challenge for power quality monitoring in large-scale power systems. This study presents a systematic review of methods for modeling harmonic sources and their applicability to real-time monitoring of power distribution systems. The review was conducted following PRISMA guidelines, considering literature published between 2000 and 2026. Searches were performed across Scopus, IEEE Xplore, Web of Science, ScienceDirect, and MDPI using predefined keywords. A total of 128 peer-reviewed journal articles were included. Potential sources of bias were qualitatively assessed, including selection, retrieval, and classification bias; however, residual bias may still arise from database selection, keyword design, and study classification. A structured comparative framework is introduced, based on a six-dimension coverage scoring scheme and maturity analysis, enabling consistent evaluation across both methodological and deployment aspects. The robustness of this framework was evaluated using leave-one-out and perturbation analyses, indicating low variability in coverage scores and stable rankings across both corpora. A taxonomy of harmonic source modeling approaches is proposed. Comparative synthesis indicates that measurement-based approaches, particularly those leveraging distribution-level PMUs, show strong potential for real-time monitoring. Key challenges include D-PMU placement, data integration, and computational scalability. Future work should focus on physics-informed AI and digital twin-based monitoring. Full article

Within a waste biorefinery framework, integrating agro-industrial by-products into the circular economy requires a detailed understanding of the thermochemical conversion behaviour of low-grade carbonaceous materials. This study evaluates the co-pyrolysis characteristics of Soma lignite (SL) and pectin-rich sugar beet pulp (SBP) as a sustainable route for upgrading these resources into clean energy carriers. Interactions between the two feedstocks were analysed by thermogravimetric measurements, triple-region kinetic modelling, and quantitative synergy indices at six mixing ratios, including the pure samples (100:0, 80:20, 60:40, 40:60, 20:80, and 0:100 wt% SL:SBP). The Reactivity Index ($R_{\mathrm{m}}$) increased from 0.97 × 10⁻⁴ s⁻¹K⁻¹ for pure SL to 8.65 × 10⁻⁴ s⁻¹K⁻¹ for the 20:80 blend, showing that SBP acts as a highly reactive biomass component that accelerates devolatilisation in the main pyrolysis region. Synergy analysis indicated a shift from inhibitory behaviour in coal-rich blends to slightly positive synergy in SBP-rich mixtures, with the onset of positive $\Delta TC$ around 60 wt% SBP under the present single-heating-rate, non-replicated TGA conditions. This tentative threshold-like behaviour suggests that a critical level of literature-supported, hypothesised hydrogen-donating biomass radicals may be required to overcome the structural resistance of the coal matrix. Within these experimental limitations, the apparent macro-kinetic deviations and first-order Arrhenius parameters suggest that SL/SBP co-pyrolysis follows a complex, non-additive pathway that should be further validated by multi-heating-rate and product characterisation studies in future work. The primary contribution of this work lies in proposing this distinct threshold-like biomass fraction at the macro-kinetic level that governs the transition from heat-transfer-limited antagonism to radical-influenced synergy in low-rank coal and pectin-rich biomass blends. Overall, the combined $\Delta TC$, $\Delta E$ and $R_{\mathrm{m}}$ descriptors provide useful macro-kinetic benchmarks for guiding the optimisation of thermochemical processes for low-grade carbonaceous resources. Full article

Exploring public awareness, participation, and emotional inclination toward intangible cultural heritage (ICH) clarifies public attitudes and demands toward traditional culture, providing a crucial basis for targeted ICH protection and inheritance. Based on ICH text big data collected from China’s mainstream social media platform Weibo, this study improves the TF-IDF algorithm, integrates LDA topic analysis for semantic feature mining, and trains a new sentiment analysis model to explore public emotional attitudes and their formation mechanisms. The study is geographically limited to China and covers the entire year of 2023. The results show that: (1) Public ICH perception is multi-dimensional, with close attention to crafts like paper-cutting and traditional Chinese medicine; action-oriented terms reflect dynamic inheritance demands. Public discussions focus on three dimensions: ICH inheritance and development (39%), introduction and promotion (45%), and public experience and participation (16%), with the latter accounting for a low proportion. (2) Public sentiment toward ICH is predominantly positive, with all regions scoring above 0.730 (full score = 1), and Zhejiang (0.751) and Jiangsu (0.750) ranking significantly higher. (3) Spatial econometric analysis reveals marked regional differences in ICH sentiment distribution, mainly affected by three key factors—the number of ICH projects, the number of inheritors, and regional GDP—with regression coefficients of 0.699, 0.632, and 0.458 (p < 0.01). This finding provides a basis for formulating targeted ICH protection strategies. Full article

Adverse drug reaction (ADR) detection is essential for ensuring drug safety and effective pharmacovigilance. The rapid growth of users’ medication reviews posted on social media has introduced a valuable new data source for ADR detection. However, the large scale and high noise inherent in social media text pose substantial challenges to existing detection methods. Although large language models (LLMs) exhibit strong robustness to noisy and interfering information, they are often limited by issues such as stochastic outputs and hallucinations. To address these challenges, this paper proposes two generative detection frameworks based on Chain of Thought (CoT), namely LLaMA-DetectionADR for Supervised Fine-Tuning (SFT) and DetectionADRGPT for low-resource in-context learning. LLaMA-DetectionADR automatically generates CoT reasoning sequences to construct an instruction tuning dataset, which is then used to fine-tune the LLaMA3-8B model via Quantized Low-Rank Adaptation (QLoRA). In contrast, DetectionADRGPT leverages clustering algorithms to select representative unlabeled samples and enhances in-context learning by incorporating CoT reasoning paths together with their corresponding labels. Experimental results on the Twitter and CADEC social media datasets show that LLaMA-DetectionADR achieves excellent performance, with F1 scores of 92.67% and 86.13%, respectively. Meanwhile, DetectionADRGPT obtains competitive F1 scores of 87.29% and 82.80% with only a few labeled examples, approaching the performance of fully supervised advanced models. The overall results demonstrate the effectiveness and practical value of the proposed CoT-based generative frameworks for ADR detection from social media. Full article

The rise in aggregation platforms has reshaped the competitive ride-hailing market. Display slots (i.e., platform-determined ranking priority) have become a key tool for influencing order allocation. Their interaction with drivers’ multi-homing behavior poses new challenges for platform sustainability. This paper constructs a two-stage Stackelberg game model with one aggregator and two underlying ride-hailing platforms. Display slots enhance supply-side lock-in, while a waiting time function links passenger utility to demand allocation. Building on theoretical analysis of two-sided market competition and multi-homing effects, we propose two hypotheses: (H1) under specific conditions, competition for display slots may lead to a Prisoner’s Dilemma equilibrium, and (H2) the proportion of multi-homing drivers positively moderates this dilemma, thereby expanding its occurrence range. Numerical simulation results under baseline parameter settings reveal that display slots generate a supply-side amplification effect by locking in multi-homing drivers. In symmetric markets, a prisoner’s dilemma range exists where mutual purchase erodes collective profits; this range expands with the share of multi-homing drivers. Higher driver profit sensitivity raises the threshold required for display slots to be profitable. In asymmetric markets, dominant platforms (strong brands, low costs) gain more from display slots, potentially leading to unilateral purchasing. Social welfare effects of display slot competition depend on a critical threshold of waiting-time sensitivity: social welfare improves above the threshold and declines below it. This study clarifies the boundaries of display slots as supply-side non-price competitive tools, offering quantitative insights for aggregator platform design and regulatory policy. The findings carry managerial implications for platform strategy and policy aimed at sustainable development. Full article

Deep learning has emerged as an effective tool for automatic waste classification, supporting cleaner cities and more sustainable recycling systems. Because environmental protection is central to the United Nations Sustainable Development Goals (SDGs), improving the sorting and processing of everyday waste is a practical step toward this broader objective. In many real-world settings, however, waste is still sorted manually, which is slow, labor-intensive, and prone to human error. Although convolutional neural networks (CNNs) can automate this task with high accuracy, many state-of-the-art models remain too large and computationally demanding for low-cost edge devices intended for deployment in homes, schools, and small recycling facilities. In this work, we investigate lightweight waste-classification models suitable for TinyML deployment while preserving competitive accuracy. We first benchmark multiple CNN architectures to establish a strong baseline, then apply complementary compression strategies including quantization, pruning, singular value decomposition (SVD) low-rank approximation, and knowledge distillation. In addition, we evaluate an RL-guided multi-teacher selection benchmark that adaptively chooses one teacher per minibatch during distillation to improve student training stability, achieving up to 85% accuracy with only 0.496 M parameters (FP32 ≈ 1.89 MB; INT8 ≈ 0.47 MB). Across all experiments, the best accuracy–size trade-off is obtained by combining knowledge distillation with post-training quantization, reducing the model footprint from approximately 16 MB to 281 KB while maintaining 82% accuracy. The resulting model is feasible for deployment on mobile applications and resource-constrained embedded devices based on model size and TensorFlow Lite Micro compatibility. Full article

Metaheuristic optimization algorithms have attracted considerable research interest for solving complex optimization problems, yet many existing algorithms suffer from premature convergence and an inadequate balance between exploration and exploitation. The Coati Optimization Algorithm (COA) is a recently proposed nature-inspired metaheuristic that models the hunting and escape behaviors of coatis; however, it exhibits limited search diversity and tends to stagnate in local optima on high-dimensional, multimodal landscapes. This paper proposes an Improved Coati Optimization Algorithm (ICOA) that integrates four complementary enhancement strategies: (1) a Dynamic Adaptive Step-Size strategy that combines Lévy flights with Student’s t-distribution perturbations for heavy-tailed exploration; (2) a Population-Adaptive Dynamic Perturbation strategy that incorporates differential evolution operators with fitness-proportional scaling; (3) an Iterative-Cyclic Differential Perturbation strategy that employs sinusoidal scheduling and population-differential guidance; and (4) a Cosine-Adaptive Gaussian Perturbation strategy for refined exploitation with time-decaying intensity. ICOA is evaluated on 29 CEC2017, 10 CEC2020, and 12 CEC2022 benchmark functions across dimensions ranging from 10 to 100, compared against seven state-of-the-art algorithms in each benchmark suite. A statistical analysis using the Friedman test and the Wilcoxon rank-sum test confirms that ICOA achieves overall rank 1 on all three benchmark suites, with Friedman mean ranks of 1.207 (CEC2017, $D=100$), 1.000 (CEC2020, $D=10$), and 2.208 (CEC2022, $D=10$); the CEC2020 result should be interpreted in the context of its low dimensionality. A scalability analysis across four dimensionalities (10D, 30D, 50D, 100D) demonstrates consistent first-place rankings with mean ranks between 1.000 and 1.207. An ablation study and a sensitivity analysis of the strategy activation probability validate the contribution of each individual strategy and the optimality of the 50% activation setting. Furthermore, ICOA achieves the best results on all six constrained engineering design problems tested, with all improvements confirmed as statistically significant ($p<0.05$). Full article

Background: Chronic obstructive pulmonary disease (COPD) is associated with systemic inflammation and frequent exacerbations, leading to disease progression and increased morbidity. Vitamin D deficiency has been suggested to contribute to COPD inflammation and exacerbations. Aim: This study investigated the association between serum 25-hydroxyvitamin D (25(OH)D) levels, systemic inflammation, and exacerbation frequency among patients with COPD GOLD group E. Methods: A cross-sectional study was conducted on 111 patients with stable COPD. Patients were divided into two groups based on their serum 25(OH)D levels (<50 nmol/L vs. ≥50 nmol/L). Data on exacerbation frequency for the past year, inflammatory markers, spirometric lung function parameters, and symptom burden were collected. Results: Patients with low serum 25(OH)D (<50 nmol/L) had a significantly higher CAT score and level of serum high-sensitivity (hs)-CRP and exhibited significantly more exacerbations compared to those with higher 25(OH)D levels (p < 0.001, p < 0.001, and p < 0.0001, respectively). Furthermore, lower vitamin D levels were associated with higher CAT scores (Pearson’s correlation coefficient, r = −0.30, p < 0.01) and higher serum hs-CRP levels (Spearman’s rank correlation coefficient, r = −0.25, p < 0.01), as well as a higher number of exacerbations (Pearson’s correlation coefficient, r = −0.74, p < 0.0001). Conclusions: Low vitamin D levels are significantly associated with greater symptom burden, elevated hs-CRP, and increased exacerbation frequency, indicating a strong relationship between vitamin D deficiency, systemic inflammation, and disease burden in patients with COPD belonging to GOLD group E. However, due to the cross-sectional design, no causal relationship can be inferred and prospective interventional studies are required to determine whether treating vitamin D deficiency improves clinical outcomes. Full article

Low-rank recovery has emerged as a powerful methodology for the restoration of degraded images. Conventional low-rank recovery techniques, however, predominantly rely on nuclear norm or weighted nuclear norm minimization to separate sparse noise. A significant limitation of this approach is its dependence on full singular value decomposition, which imposes a substantial computational burden, thereby hindering its practical applicability. This paper presents a novel image denoising model integrating the weighted nuclear norm and deep image prior. The weighted nuclear norm is introduced to accurately characterize the global structural properties of images, ensuring the consistency of the overall image structure after denoising. Meanwhile, the deep image prior is employed to effectively capture local details, which helps avoid the blurring of textures and edges often caused by excessive noise removal. The complementary advantages of the two components enable the proposed model to achieve superior performance compared with existing denoising methods. To efficiently compute the proposed model, we design the bilinear factorization method and the alternating direction method of multipliers. Experiments show that the proposed method outperforms mainstream approaches in both restoration accuracy and computational efficiency, exhibiting rapid convergence and robust algorithm stability, thereby demonstrating excellent comprehensive performance. Full article

Sorghum, an essential crop in Niger, ranks second to pearl millet in importance for food, feed, and commerce. However, its yields are hindered by various factors, including diseases such as leaf blight caused by Exserohilum turcicum. In this study, field phenotypes were analyzed on 102 accessions (including checks SC748-5 and BTx623) grown and evaluated at two locations in Niger for leaf blight incidence and severity. The panel included accessions originally collected from Niger and Senegal. Genotypes were generated for 120 accessions, and GWAS/ML analyses were performed on 102 accessions due to missing phenotypic data. Among the accessions, S39, N23, and N38 exhibited mean leaf blight incidence below 50%, while S3, S43, N23, and N38 displayed the lowest severity levels, with a mean severity in Niger of 24.5 ± 0.64. Accession N23 showed relatively low incidence and severity levels across the Niger field evaluations. Using genome-wide association studies and machine learning, candidate SNPs associated with leaf blight phenotypes were identified. Genes near these SNPs were associated with functions related to plant defense mechanisms and stress responses, providing preliminary targets for future validation in sorghum leaf blight studies. Full article

Background/Objectives: We aim to investigate whether tracking pan-immune–inflammation value (PIV) dynamics during radiotherapy (RT) can inform real-time prognosis in patients with head and neck cancer (HNC). Methods: We retrospectively reviewed the medical records of patients with HNC who received RT at our institution between 2005 and 2013. Temporal changes in the PIV throughout the RT were evaluated using the Friedman test and Wilcoxon signed-rank test. The PIV dynamics were quantified using PIV ratios, defined as the PIV at three distinct time points (PIV-2, PIV-4, and PIV-6) during treatment divided by the pretreatment PIV (PIV-0). Overall survival (OS) and progression-free survival (PFS) served as the primary and secondary endpoints analyzed. Results: A total of 676 patients with HNC were enrolled, with a median follow-up of 8.1 years. The PIV demonstrated a continuously ascending trend over time, with the most dramatic increase occurring six weeks after the start of RT. Compared with patients with a low PIV ratio at six weeks (PIV-6/PIV-0), those with a high PIV ratio showed more favorable survival outcomes (five-year OS: 58.9% versus 70.8%, p = 0.002; five-year PFS: 62.0% versus 71.1%, p = 0.013). The subgroup analyses yielded consistent results. Notably, the real-time risks of death and recurrence changed in parallel with the PIV dynamics. Multivariate analysis confirmed PIV-6/PIV-0 as an independent prognostic factor for both OS and PFS. Conclusions: Monitoring longitudinal PIV dynamics may assist in forecasting the OS and PFS in patients with HNC being treated with RT, thus enabling individualized, risk-adapted treatment management. Full article