Search Results (5,829)

As population aging accelerates, the mounting burden on informal family caregivers in areas lacking formal care systems threatens the sustainability of elder care. This study aims at evaluating how the rural living environment and external connectivity jointly alleviate caregiver burden and exploring whether regional accessibility serves as a substitute for local infrastructure deficits. Guided by Ecological Systems Theory, we analyzed a cross-sectional dataset of 327 matched caregiver-recipient dyads from rural China using multivariate regression and mediation models. Results indicate that a favorable local environment reduces burden both directly and indirectly through improved recipient health. Crucially, county-level accessibility moderates this relationship via a substitution effect, where the marginal relief from local environmental improvements is most potent in isolated areas but diminishes where external access is convenient. Dimension-specific analyses show that developmental and physical strains are particularly sensitive to these factors. We conclude that sustaining informal care requires a dual-pathway strategy: prioritizing local “soft” assets like community safety and cultural activities while enhancing regional connectivity to service hubs. Ultimately, this research provides empirical evidence and a theoretical framework for enhancing rural informal care sustainability through environmental optimization, thereby advancing Sustainable Development Goals regarding health, reduced inequalities, and sustainable communities. Full article

Background/Objectives: Detecting copy number variations (CNVs) from next-generation sequencing (NGS) is challenging, particularly in targeted sequencing panels, especially for cell-free DNA (cfDNA), where the signal is weak and noise is high. Methods: We present BayesCNV, a Bayesian hierarchical model for gene-level copy ratio estimation from targeted amplicon read depths compared to a CNV-neutral reference sample. The model provides posterior uncertainty for each gene and supports interpretable calling based on effect size and posterior confidence. The model also provides a principled quality-control strategy based on the marginal log likelihood of each sample, with low values indicating low confidence in the calls. BayesCNV uses thermodynamic integration, a technique to reliably estimate this quantity. We benchmark our method against two publicly available CNV callers using Seracare^® reference samples with known CNVs on the OncoReveal^® Core Lbx panel. Results: Our method achieves a sensitivity of 0.87 and specificity of 0.996, dramatically outperforming two competitor methods, IonCopy and DeviCNV. In a separate FFPE dataset using the OncoReveal^® Essential Lbx panel, we show that the marginal log likelihood cleanly separates, degraded from high-quality samples, even when conventional sequencing QC metrics do not. Conclusions: BayesCNV provides accurate and interpretable gene-level CNV estimates and uncertainty quantification, along with an evidence-based quality control metric that improves robustness in targeted cfDNA workflows. Full article

Security requirements play a critical role in ensuring the trustworthiness and resilience of software systems; however, their automatic classification remains challenging due to limited labeled data, confidentiality constraints, and the heterogeneous nature of requirements across organizations. Existing approaches typically assume centralized access to training data and rely on costly manual annotation, making them unsuitable for distributed industrial settings. To address these challenges, we propose SeqFAL, a communication-efficient and privacy-preserving Federated Active Learning framework for natural language–based security requirements classification. SeqFAL integrates frozen pre-trained sentence embeddings, margin-based active learning, and lightweight federated aggregation of linear classifiers, enabling collaborative model training without sharing raw requirement text. We evaluate SeqFAL on a combined dataset of SeqReq dataset and the PROMISE-NFR dataset under varying federation sizes, query budgets, and communication rounds, and compare it against three baselines: centralized learning, active learning without federated aggregation, and federated learning without active querying. In addition to the proposed margin-based sampling strategy, we investigate alternative query strategies, including least-confidence and random sampling, as well as multiple linear classifiers such as LinearSVC and SGD-based classifiers with logistic and hinge losses. Results show that SeqFAL consistently outperforms FL-only and achieves performance comparable to AL-only centralized baselines, while approaching the optimal upper bound using significantly fewer labeled samples. These findings demonstrate that the joint integration of federated learning and active learning provides an effective and privacy-preserving strategy for security requirements classification in distributed software engineering environments. Full article

Based on panel data from China’s 31 provinces and municipalities covering 2011–2023, this study constructs a multidimensional evaluation system for digital economic development and agricultural industrial chain resilience within the Technology-Organization-Environment (TOE) framework. It systematically examines the impact of the digital economy on agricultural industrial chain resilience and its sub-dimensions, while introducing green finance as a moderating variable. The findings reveal: First, the development of the digital economy significantly enhances the resilience of the agricultural industrial chain. This conclusion withstands multiple robustness tests, and the impact of the digital economy on the three dimensions of agricultural industrial chain resilience (resistance, recovery, and reconstruction) varies, particularly exhibiting a negative effect on reconstruction. Second, the enabling effect of the digital economy on agricultural industrial chain resilience shows a significant spatial gradient. Regionally, resilience is ranked as “Production-Sales Balance Zones > Main Sales Zones > Main Production Zones” within grain functional zones, and “Northeast > West > East > Central” across China’s four major economic regions. Third, green finance development exerts a negative moderating effect on the pathway through which the digital economy enhances agricultural supply chain resilience, higher green finance levels weaken the marginal improvement effect of the digital economy. This study fills research gaps regarding the multidimensional impact of digital economic development on agricultural industrial chain resilience and empirically supplements the lack of evidence on the negative moderating mechanism of green finance and its sub-dimensions, providing policy tools for agricultural modernization and resilience governance. Full article

Road safety and maintenance strategy optimization depend on accurate pavement surface friction prediction. In order to predict the Friction Number for Continuously Reinforced Concrete Pavement (CRCP) sections using data taken from the Long-Term Pavement Performance (LTPP) database, this study presents a hybrid machine learning framework that combines Gradient Boosting Machines (GBMs) with Genetic Algorithm (GA) optimization. Twenty input variables from the structural, climatic, traffic, and performance categories were used in the analysis of 395 data points from 33 CRCP sections. With a mean Root Mean Squared Error (RMSE) of 3.644 and a mean R-squared (R²) value of 0.830, the GA-optimized GBM model outperformed baseline models such as non-optimized GBM, Linear Regression, Random Forest, Support Vector Regression (SVR), and Artificial Neural Networks (ANN). The most significant predictors, according to sensitivity analysis, were AADT, Total Thickness, Freeze Index, and Pavement Age. The marginal effects of these variables on the expected friction levels were illustrated using partial dependence plots (PDPs). The results show that the suggested GA-GBM model offers a strong and comprehensible instrument for forecasting pavement friction, with substantial potential for improving safety evaluations and maintenance scheduling in networks of rigid pavement. Full article

With the widespread adoption of electric vehicles, commercial charging stations (CCS) have grown rapidly as a core component of charging infrastructure. Due to the concentrated and high-power charging load characteristics of CCS, a ‘peak on peak’ phenomenon can occur in the power distribution network. Demand response (DR) serves as an important and flexible regulation tool for power systems, offering a new approach to addressing this issue. However, when CCS participates in DR, it faces a dual dilemma between operational revenue and user satisfaction. To address this, this paper proposes a bi-level, multi-objective framework that co-optimizes station profit and nonlinear user satisfaction. An asymmetric sigmoid mapping is used to capture threshold effects and diminishing marginal utility. Uncertainty in users’ charging behaviors is evaluated using a Monte Carlo scenario simulation together with chance constraints enforced at a 0.95 confidence level. The model is solved using the fast non-dominated sorting genetic algorithm, NSGA-II, and the compromise optimal solution is identified via the entropy-weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Case studies show robust peak shaving with a 6.6 percent reduction in the daily maximum load, high satisfaction with a mean of around 0.96, and higher revenue with an improvement of about 12.4 percent over the baseline. Full article

Dual Carbon Goals are driving transformation in China’s power system, where increased renewable energy penetration is accompanied by heightened fluctuations on the generation and load sides. Energy storage and microgrid coordination have emerged as key solutions. However, existing research faces the challenge of balancing microgrid operations, energy storage services, and the alignment of user demand with stakeholder interests. This paper establishes a tripartite collaborative optimization framework to balance multi-stakeholder interests and enhance system efficiency, assuming fixed energy storage capacity. Centering on a principal-agent game between microgrid operators and consumer aggregators, energy storage service providers are integrated into this dynamic. Microgrid operators set 24-h electricity and heat pricing while adhering to tariff constraints, prompting consumer aggregators to adjust energy consumption and storage strategies accordingly. The KKT conditional method is employed to solve the model, deriving optimal user energy consumption strategies at the lower level while solving marginal pricing equilibrium relationships at the upper level, balancing accuracy with information privacy. The creative contribution of this article lies in the first construction of a tripartite collaborative optimization architecture in which energy storage service providers are embedded in a game of ownership and subordination. It proposes a dynamic coupling mechanism between pricing power, energy consumption decision-making, and energy storage configuration under fixed energy storage capacity constraints, achieving a balance of interests among multiple parties. By building a case study using MATLAB (R2022b), we compare operation costs, benefits, and absorption rates across different scenarios to validate the framework’s effectiveness and provide a reference for engineering applications. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder marked by decreased amyloid-beta (Aβ) clearance, enhanced Aβ aggregation, an increased risk of amyloid-related imaging abnormalities (ARIA), and blood–brain barrier (BBB) dysfunction. The APOE4 allele, being the leading genetic risk factor for AD, contributes strongly to these symptoms. This review covers the relationship between APOE4 status and the efficacy of FDA-approved monoclonal antibody (mAb) therapies, namely aducanumab, lecanemab, and donanemab. Across several clinical trials, APOE4 carriers exhibited higher rates of ARIA-E and ARIA-H compared to non-carriers. While the therapies did often meet biomarker endpoints (i.e., reduced amyloid), benefits were only observed in early and mild AD, and cognitive benefits were often marginal. Going forward, experimental apoE4-targeted immunotherapies may ease the burden of APOE4-related pathology. The field is shifting towards a more integrated approach, focusing on earlier interventions, biomarker-driven precision treatment, and improved drug delivery systems, such as subcutaneous injections, receptor-mediated transport, and antibodies with enhanced BBB penetration. As it stands, high treatment costs, limited accessibility, and strict eligibility criteria all stand as barriers to treatment. By integrating the APOE4 genotype into treatment planning and focusing on disease-stage-specific approaches, a safer and more effective means of treating AD could be achieved. Full article

Despite the growing adoption of hybrid energy systems integrating solar photovoltaic (PV), pumped storage hydropower (PSH), and battery energy storage (BES), comprehensive studies on their dynamic stability and interaction mechanisms remain limited, particularly under weak grid conditions. Due to the high impedance of weak grids, ensuring stability across varied operating scenarios is crucial for advancing grid resilience and energy reliability. This paper addresses these research gaps by examining the interaction dynamics between PV, PSH, and BES on the DC side and the utility grid on the AC side. The study identifies operating-region-dependent instability mechanisms arising from negative incremental resistance behavior and weak grid interactions and proposes a virtual-impedance-based active damping control strategy to suppress poorly damped oscillatory modes. The proposed controller effectively reshapes the converter output impedance, shifts unstable eigenmodes into the left-half plane, and improves phase margins without requiring additional hardware components or introducing steady-state power losses. System stability is analytically assessed using root-locus, Bode, and Nyquist criteria within a developed small-signal state-space model, and further validated through large-signal real-time simulations on an OPAL-RT platform. The main contributions of this study are threefold: (i) a comprehensive stability analysis of a utility-scale grid-connected hybrid PV–PSH–BES system under weak grid conditions, (ii) identification of operating-region-dependent instability mechanisms associated with DC–link interactions, and (iii) development and real-time validation of a practical virtual-impedance-based active damping strategy for enhancing system stability and grid integration reliability. Full article

To address the voltage instability in distribution networks with a high penetration of renewable energy, a multi-objective optimal scheduling method is proposed based on an enhanced static voltage stability margin ratio (SVSMR). The SVSMR_d index suitable for complex distribution networks is constructed by analytical derivation and equivalent impedance correction, and the distributed access characteristics of distributed power generation are considered. Based on the simulation analysis of the PS_CAD simulation platform, the effectiveness and engineering applicability of the SVSMR_d index are compared in the multi-energy station distribution network scenario, and the calculation results of SVSMRF and SDSCR are used to verify it. A multi-objective mixed-integer optimisation model is constructed, with the objective function encompassing electricity purchase cost, network loss cost, and energy storage revenue, and the lowest value of the SVSMR_d index of various new energy nodes is used as the optimisation object to carry out stability targets. Based on the epsilon constraint method, a Pareto frontier solution set is generated through example analysis, which has non-dominant characteristics. The results of the example analysis show that the proposed method can effectively reduce the operating cost, ensure the voltage stability margin of the system, and realise the collaborative optimisation of source–network–load–storage resources. This paper provides a new idea and method for the optimal operation of the distribution network, and optimises the distribution network under a high proportion of new energy access in the distribution network. Full article

To investigate the effects of different Trichosanthes kirilowii Maxim. varieties on leaf nutrients, soil nutrients, and microbial community composition, this study selected Yuelou No. 3 and Huiji No. 2, two major cultivars from the primary production area of Shishou City. The two varieties were cultivated at different locations under standardized agronomic management practices, and a systematic comparative analysis was carried out over a 10-month sampling period from March to December 2024. The analysis encompassed their leaf nutrients (total nitrogen, total phosphorus, total potassium, and relative chlorophyll content), soil nutrients (organic matter, alkali-hydrolyzable nitrogen, available phosphorus, and available potassium), and microbial community characteristics. The results revealed significant varietal differences in leaf nutrient content: the average total phosphorus content of Yuelou No. 3 (0.44%) was higher than that of Huiji No. 2 (0.39%), while Huiji No. 2 exhibited higher total nitrogen (3.73%), total potassium (3.86%), and SPAD (44.72). Leaf nutrient content in both varieties followed a pattern of nitrogen > potassium > phosphorus, with peak phosphorus and potassium demand occurring earlier in Yuelou No. 3. Additionally, Yuelou No. 3 contained higher organic matter (12.73 g/kg) and alkali-hydrolyzable nitrogen (103.02 mg/kg), while Huiji No. 2 showed enhanced soil pH (7.02), available phosphorus (6.96 mg/kg), and available potassium (180.00 mg/kg). Soil available nutrient dynamics displayed a pattern of slow change during the early stage, a rapid increase during the middle stage, and stabilization in the later stage. Microbial analysis revealed no significant differences in alpha diversity between the two varieties, although Yuelou No. 3 showed marginally higher diversity indices during early to mid-growth stages. In contrast, beta diversity showed significant separation in PCoA space. Proteobacteria, Acidobacteria, and Ascomycota were the dominant microbial phyla. Dominant genera included Kaistobacter, Mortierella, and Neocosmospora, among others, with variety-specific relative abundances. Redundancy analysis further supported the variety-specific influence of soil physicochemical properties on microbial community structure, with available phosphorus, available potassium, and alkali-hydrolyzable nitrogen identified as key factors shaping community composition. This study provides a theoretical basis for understanding the impact of different Trichosanthes kirilowii Maxim. varieties on soil–plant–microbe interactions and suggests potential directions for future research on fertilization and management strategies tailored to varietal differences. Full article

This paper examines how logistics performance conditions the relationship between trade openness, economic growth and per capita CO₂ emissions in Mediterranean economies. Using an unbalanced panel of 20 countries over the period 2007–2022, we combine static fixed-effects, dynamic panel generalized method of moments (GMM) estimators and Method-of-Moments Quantile Regression (MM-QR). CO₂ emissions per capita, the World Bank Logistics Performance Index (LPI), trade openness and GDP per capita are drawn from World Bank databases, and interaction terms between LPI and both income and trade openness are constructed to capture conditional effects. The results from fixed-effects and system GMM estimations show that logistics performance exerts a robust and statistically significant negative effect on emissions, whereas GDP per capita is a positive driver and trade openness tends to reduce emissions when logistics capacity is sufficiently strong. Negative and significant interaction terms between LPI and both income and openness indicate that logistics efficiency amplifies the environmental benefits of trade and growth. Quantile regressions reveal that these patterns are most pronounced in high-emission countries, where improvements in logistics performance and its interaction with trade and income generate larger marginal reductions in CO₂ emissions. Overall, the findings highlight the central role of logistics modernization and green trade facilitation in reconciling trade-led growth with decarbonization in the Mediterranean Basin. From a policy perspective, the evidence suggests that prioritizing green logistics and trade facilitation—particularly in high-emission Mediterranean economies—can yield the largest marginal reductions in CO₂ emissions. Full article

Background: Composite restorations are the standard of care for posterior teeth due to their aesthetic properties and conservative nature. However, the choice between direct and semi-direct techniques can influence clinical longevity and performance. Objectives: This study aimed to compare the clinical performance of two restorative approaches: a direct technique and the semi-direct onlay technique in terms of aesthetic quality, surface finish, wear resistance, marginal integrity, and overall clinical efficiency over a two-year period. Methods: A total of 348 composite restorations were placed in 192 patients. Each restoration was evaluated at four timepoints: baseline (T0), 6 months (T1), 1 year (T2), and 2 years (T3). Clinical performance was assessed using standardised 5-point rating scales across the five dimensions. Repeated-measures ANOVA assessed changes over time, while Wilcoxon signed-rank and Mann–Whitney U tests were used for intra- and inter-group comparisons. Results: Significant time effects were observed across all clinical parameters (p < 0.0001). The direct technique exhibited superior initial results in aesthetics and surface finish at T0 and T1 (p < 0.001), but differences diminished by T3. In contrast, the semi-direct technique demonstrated improved performance in wear resistance and marginal integrity at T2 and T3. Both techniques showed progressive deterioration, particularly in marginal adaptation. Conclusions: The direct technique offers enhanced short-term aesthetics and procedural efficiency, while the semi-direct approach provides superior long-term durability and marginal adaptation. Full article

Background: Maximal safe resection remains a central determinant of outcomes in glioma surgery, yet intraoperative discrimination between tumor and normal brain tissue is limited by the speed and subjectivity of frozen-section analysis. Label-free techniques such as Raman spectroscopy, mass spectrometry (MS), and optical coherence tomography (OCT) offer real-time biochemical and structural characterization that may enhance surgical precision. Their comparative diagnostic accuracy across clinically relevant endpoints has not been comprehensively evaluated. Methods: Following PRISMA 2020 guidelines, a systematic review and quantitative meta-analysis were conducted using PubMed, Embase, Scopus, and Web of Science through December 2024. Original human studies evaluating Raman, MS, or OCT for intraoperative glioma margin assessment were included. Pooled sensitivity, specificity, and diagnostic odds ratios (DORs) were calculated using a random-effects model. Subgroup analyses addressed tumor versus normal brain tissue, infiltrated versus non-infiltrated margins, and IDH-mutant versus wild-type gliomas. Results: Twenty-four studies comprising 1768 patients met the inclusion criteria. Across all modalities, pooled sensitivity and specificity were 0.89 (95% CI 0.86–0.92) and 0.88 (95% CI 0.84–0.91), with a pooled DOR of 65.7 (95% CI 42.3–101.8; logDOR 4.18), indicating high overall discriminative performance. Tumor versus normal differentiation achieved DOR 72.4 (logDOR 4.28; I² = 26%), infiltrated margin detection DOR 41.8 (logDOR 3.73; I² = 41%), and IDH classification DOR 52.3 (logDOR 3.96; I² = 29%). No publication bias was observed. Raman and MS outperformed OCT. Conclusions: Raman spectroscopy, mass spectrometry, and OCT demonstrate strong diagnostic accuracy for real-time intraoperative glioma evaluation, enabling reliable tissue differentiation and molecular profiling that may enhance resection extent and support precision, molecularly informed neurosurgery. Full article

Background and Objectives: Cognitive deficits are common in obstructive sleep apnea (OSA), and both intermittent hypoxemia and cardiovascular comorbidity may contribute to poorer outcomes. Arterial hypertension (HTN) has been suggested as a potential modifier of cognitive function in OSA, but findings remain inconsistent. This study examined whether HTN influences baseline cognition or cognitive improvement after continuous positive airway pressure (CPAP) therapy in moderate-to-severe OSA and identified predictors of poorer post-treatment cognitive status. Materials and Methods: This prospective study involved 71 adults with newly diagnosed moderate-to-severe OSA (AHI ≥ 15). Participants underwent baseline polysomnography, Montreal Cognitive Assessment (MoCA) testing, and P300 assessments. Cognitive impairment was defined as MoCA < 26 and HTN by antihypertensive therapy, documented diagnosis, or repeatedly elevated blood pressure. All participants initiated auto-adjusting CPAP and were reassessed after three months for adherence, residual respiratory indices, MoCA, and P300 parameters. Multivariate logistic regression and receiver operating characteristic (ROC) analyses were used to identify independent predictors of poorer cognitive outcomes. Results: CPAP therapy significantly improved apnea severity, daytime sleepiness, global cognition, and P300 latency, while P300 amplitude did not change significantly. After three months, hypertensive and normotensive patients showed similar MoCA scores, respiratory outcomes, and P300 amplitude; P300 latency remained marginally longer in hypertensive individuals. Across multivariate models, lower mean nocturnal oxygen saturation and reduced CPAP adherence independently predicted poorer cognitive outcome at follow-up. CPAP adherence demonstrated greater discriminative ability than mean nocturnal oxygenation. Conclusions: In adequately treated moderate-to-severe OSA, HTN did not significantly affect baseline cognition or short-term cognitive recovery with CPAP. Although P300 latency remained slightly prolonged in hypertensive individuals, this difference was marginal and not accompanied by cognitive deficits. Nocturnal oxygenation and CPAP adherence emerged as the strongest predictors of post-treatment cognitive status, underscoring the importance of sustained and effective therapy. These findings suggest that effective CPAP adherence and improved nocturnal oxygenation are crucial for cognitive recovery in OSA patients, regardless of hypertensive status. Full article