Search Results (6,322)

During late December 2021, an ice accretion disaster occurred in North Xinjiang, especially in the western part. It is found that the meteorological conditions suitable for the occurrence of ice accretion disasters are when the temperature is between −14 °C and −3 °C, the relative humidity is greater than 80%, the wind speed is between 4.5 m s⁻¹ and 7.5 m s⁻¹, and the pressure is between 919 hPa and 928 hPa. The ice accretion disaster is influenced by large-scale circulation, including the two-trough and one-ridge geopotential height structure in the middle troposphere and the spatially moving Ural Mountain blocking high pressure. Furthermore, using the artificial intelligence-based Pangu model and machine learning algorithms within the application of multiple linear regression and the leave-ten-out cross-validation, a skillful forecast correction model for ice accretion thickness in North Xinjiang is constructed. The prediction model has significant prediction skill for ice accretion thickness in North Xinjiang with 24 h, 48 h, and even 72 h in advance. The findings of the study can improve the timeliness of business system in the short-term and immediate forecast of ice accretion thickness, providing more reliable technical support for the ice prevention and disaster reduction of the power grids. Full article

Biochar has emerged as a promising material for carbon storage, exhibiting properties analogous to those of activated carbon. Biochar has a particularly high absorbance due to its high porosity, surface area, and functional groups, although these parameters depend on the feedstock and pyrolysis conditions. The sorbent properties of biochar make it suitable for many applications, including the biological treatment of organic waste. In the context of composting, biochar addition seems to positively impact the process performance and the final compost characteristics. Furthermore, it reduces greenhouse gas and odor emissions, which is a crucial step in preventing the full implementation of composting. The objective of this review is to provide a comprehensive description of the effects of biochar on composting emissions and the reported mechanisms, highlighting the limitations of current research. In summary, the use of biochar in composting is still in its early stages and requires further research and consensus on fundamental issues, such as the optimal biochar dosage and mitigation mechanisms. Moreover, there is a significant lack of full-scale implementation. Accordingly, future work should focus on overcoming these critical challenges to take a step forward towards a consistent and complete picture of the environmental impacts and a rigorous economic analysis of the use of biochar in composting. Full article

Rare genetic diseases (RGDs) affect individuals, families, and healthcare systems worldwide. Population-scale genomic data remain largely restricted to Western cohorts with an estimated 10,000 RGDs. South Asian populations remain underrepresented in molecular, clinical, and genomic databases. This study presents the first preliminary molecular genetic characterization of RGDs in the Punjabi population of Pakistan. Data were collected from the provincial RGD registry at the Punjab Thalassemia and Other Genetic Disorders Prevention and Research Institute (PTGDPRI), Lahore. Families diagnosed using next-generation sequencing (NGS) between 2021 and 2023 were enrolled. Structured questionnaires captured clinical, demographic, and socioeconomic information, and statistical and genetic analyses were performed to assess allele frequencies, and disease distribution. The registry included 167 families with 72 distinct RGDs, with a mean burden of 0.81 ± 0.24 affected children per family. Niemann–Pick disease (NP), progressive familial intrahepatic cholestasis (PFIC), and mucopolysaccharidosis (MPS) were the most common diseases. Consanguinity was observed in 89% of families, 77% of which involved first-cousin marriages, and was significantly associated with RGD incidence. Most families belonged to low-income groups despite high literacy rates, underscoring inequity in healthcare. The primary and secondary variants included 131 variants, including copy number variants (CNVs) and single nucleotide variants (SNVs), annotated as pathogenic, likely pathogenic, or variants of unknown significance (VUS) across 109 genes, including 24 South Asian-enriched variants. This study provides the first genomic and epidemiological overview of RGDs in the Punjabi population. The findings reveal how genetic, socioeconomic, and cultural factors converge to amplify the RGD burden and highlight the need for affordable molecular diagnostics, inclusive genomic databases, and regional genomic surveillance initiatives in South Asia. Full article

Rice is one of the world’s most important staple crops, and outbreaks of insect pests pose a persistent threat to yield stability and food security in major rice-growing regions. Reliable field-scale rice pest detection remains challenging due to limited datasets, heterogeneous imaging conditions, and inconsistent annotations. To address these limitations, we construct RicePest-30, a field-oriented dataset comprising 8848 images and 62,227 annotated instances covering 30 major rice pest species. Images were collected using standardized square-framing protocols to preserve spatial context and visual consistency under diverse illumination and background conditions. Based on RicePest-30, YOLOv11 was adopted as the primary detection framework and optimized through a systematic hyperparameter tuning process. The learning rate was selected via grid search within the range of 0.001–0.01, yielding an optimal value of 0.002. Training was conducted for up to 300 epochs with an early-stopping strategy to prevent overfitting. For fair comparison, YOLOv5s, YOLOv8s, Faster R-CNN, and RetinaNet were trained for the same number of epochs under unified settings, using the Adam optimizer with a learning rate of 0.001. Model performance was evaluated using Precision, Recall, AP@50, mAP@50:95, and counting error metrics. The experimental results indicate that YOLOv11 provides the most balanced performance across precision, localization accuracy, and counting stability. However, all models exhibit degraded performance in small-object scenarios, dense pest distributions, and visually similar categories. Error analyses further reveal that class imbalance and field-scene variability are the primary factors limiting detection robustness. Overall, this study contributes a high-quality, uniformly annotated rice pest dataset and a systematic benchmark of mainstream detection models under realistic field conditions. The findings highlight critical challenges in fine-grained pest recognition and provide a solid foundation for future research on small-object enhancement, adaptive data augmentation, and robust deployment of intelligent pest monitoring systems. Full article

Background: The R21 malaria vaccine is a next-generation, WHO-prequalified vaccine that was introduced to reduce the burden of clinical malaria. In alignment with WHO recommendations, multi-dose vaccine presentations are preferred for large-scale immunization and inclusion in the Expanded Programme on Immunization (EPI). Accurate protein quantification is a critical quality control parameter for lot release, but it remains challenging when the antigen is present at low protein concentrations or formulated with complex matrices, including adjuvants, stabilizers, and preservatives. Methods: In this study, multiple protein estimation methods including Micro-BCA, BCA, and Bradford assays were evaluated to determine their suitability for quantifying the R21 antigen formulated with Matrix-M1 adjuvant and 2-PE preservative. The Bradford assay was selected as the most appropriate method, based on a comparative assessment of precision, accuracy, and linearity. Further optimization was undertaken to identify suitable buffer systems, and the method was validated in accordance with ICH Q2(R2) guidelines. Results: Validation results demonstrated that the assay is specific, accurate, precise, and repeatable, with a limit of quantitation (LOQ) of 2 µg/mL. The method demonstrated comparable performance to ELISA and was found to be sensitive enough to detect changes in antigen concentration resulting from unintended adsorption of R21 to vial surfaces. The assay offers a rapid, high-throughput, and cost-effective solution for protein quantitation in commercial manufacturing, lot release, and stability studies. The protein content of the drug product, quantified using the Bradford method, demonstrated robust in vivo immunogenicity in both release and stability studies. Conclusions: The robustness and reproducibility of the assay establish a new benchmark in quality control for virus-like particle (VLP)-based vaccines with complex formulations, thereby supporting the precision and reliability required for global malaria prevention efforts. Full article

Frailty assessment and monitoring are essential for supporting independent living and preventing adverse outcomes among older adults. This study aimed to develop and evaluate the concurrent validity of a high-accuracy home-monitoring system for assessing and tracking frailty in older adults. The system integrated off-the-shelf, zero-effort technologies, including ultra-wideband (UWB) indoor positioning, a smart scale, a connected hand dynamometer, and a Bluetooth speakerphone, to measure the five components of Fried’s Frailty Phenotype criteria. Twenty-one participants (aged 21–90 years) completed frailty assessments using both traditional clinical measures and the sensor-based system within a simulated home environment within a major rehabilitation hospital. The developed system demonstrated very strong and statistically significant correlations between the sensor-based system and the Fried’s Frailty Phenotype criteria, strong correlations with the Clinical Frailty Scale, and moderate-to-strong correlations with the Edmonton Frailty Scale, confirming the system’s strong concurrent validity. These findings indicate that high-accuracy, home-based monitoring technologies can provide reliable, objective, and non-invasive assessment of frailty in older adults, supporting early detection and continuous monitoring. This approach shows promise for future integration into smart home environments to enhance proactive frailty management and aging-in-place strategies. Full article

Italian inner areas face population decline, limited access to services and fragile infrastructure; however, the micro-mechanisms through which community practices generate tangible improvements often remain unclear. Still, local communitarian initiatives, such as those represented by ecovillages, can be an effective response to the ongoing process of marginalisation, becoming true living labs for place-based transitions. Through the analysis of the Torri Superiore Ecovillage (Imperia, Italy), a recognised and well-known good practice in the national and international ecovillage circuit, we want to find answers to three research questions: (RQ1) To what extent can an ecovillage act as a living lab for social innovation and ecological transition in inner areas? (RQ2) Which demographic and governance conditions enable territorial resilience and which ones block it? (RQ3) Which environmental practices generate locally significant improvements and with what limitations? Based on qualitative and interpretative evidence (2016–2025)—field observations, internal documents and testimonies—and on essential demographic indicators (ISTAT/SNAI), this study examines the Torri Superiore Ecovillage as a small-scale living lab. Torri Superiore and the surrounding municipalities are ageing and have reduced demographic bases; however selective immigration and heterogeneity of skills act as partial buffers. The governance of the Torri Superiore Ecovillage combines clear rules, participatory routines and coordination mechanisms, promoting problem solving while remaining sensitive to leadership burdens. The “bridging” between multiple actors enables terrace maintenance, local water resource management, agroecological practices, renewable energy adoption, waste prevention/composting and light mobility to achieve tangible environmental improvements on a small scale. We frame transferability as analytical (not statistical), specify the enabling conditions (sufficient active participants, stable routines, territorial management) and outline the relevant policy implications for SNAI classes and a lightweight longitudinal observatory. Full article

In order to solve the easily scalable problem of evaporative condensing equipment, re-search was conducted on the development of a new type of self-priming scaling evaporative condenser with a built-in electrochemical scale absorber which changes the traditional water quality treatment cycle method from “preventing scaling” to “self-priming scaling”, integrating treatment devices and an evaporative condenser in order to realize the integration and modularization of the equipment. Experimental results show that the new self-priming scaling evaporative condenser has a condensing temperature range of 36.7~38.9 °C, heat rejection range of 716.69~768.66 kW, and, under different load rates, a condensing temperature range of 22.7~36.7 °C. The new self-priming scaling evaporative condenser system’s energy efficiency ratio COP has a range of 3.26~3.44. The coefficient of performance IPLV is 3.9, which exceeds the design index value. After the unit ECT electrochemistry meter is turned on, it effectively reduces the concentration multiplier of water quality: the total hardness increase per minute is reduced from 0.4 mg/L to 0.23 mg/L, a reduction of 42.5%. Turning on the electrochemistry meter also effectively reduces the increase in the total hardness and reduces the occurrence of scaling in the evaporative condenser unit. Full article

In the face of advancing globalisation processes and intensified contacts between representatives of different cultures and religions, interreligious dialogue is becoming an important component of contemporary social coexistence. This article aims to establish a relationship between interpersonal skills, moral intelligence, and readiness to engage in interreligious dialogue among adult Poles. A total of 519 people aged 18 to 75 (M = 48.44; SD = 15.55) were surveyed. This study used the Readiness to Engage in Interreligious Dialogue Scale (TGDMVE), the Interpersonal Competence Questionnaire (ICQ-R) and the Moral Intelligence Quotient (MIQ). The results of the analyses indicated that, in terms of interpersonal competence, the strongest significant predictor of all five dimensions of readiness to engage in interreligious dialogue was active concern for others. In terms of moral intelligence, it was the ability to recognise spiritual needs. The results may have significant practical implications for the areas of intercultural education, the prevention of xenophobic attitudes, and the building of social capital based on dialogue, trust, and respect. Full article

Background: With ageing populations and increasing labour force participation among women over 50, their mental health and psychological well-being require attention. The multifactorial etiology of depression has been extensively studied at both the individual and societal levels. Longitudinal analyses exploring socioeconomic and political determinants and whether they influence depression severity across European countries are lacking. Objective: The objective of this study was to examine a possible correlation between socioeconomic and political factors with depression severity in women aged 50 and older in Europe and to what extent these possible correlations vary across countries. Methods: This longitudinal observational study was conducted using data from 47,426 women aged 50–89 years across 15 European countries, drawn from seven waves (2004–2015) of the Survey of Health, Ageing and Retirement in Europe (SHARE). Depression symptoms were measured by the validated European Depression Scale (EURO-D). The Andersen Model of Health Service Utilization was applied to contextualize twelve macro-level predictors of depression. These were organized into four overarching domains—health, education/employment/finance, equality, and security. Mean EURO-D scores were calculated with respect to age group and country. Correlations between predictors and depressive symptoms were assessed using Pearson’s and Adjusted Pearson’s correlation coefficients to determine the strength and rank of associations. Results: Significant correlations between predictor variables and depression were identified in nine countries, especially among women aged 80–89 years. Spain and Estonia showed strong predictors across several age groups. Eastern European countries exhibited the broadest range of significant correlations. Italy and France, despite high depression levels, revealed few significant predictors. Sweden, the Netherlands, and Switzerland had lower depression scores and demonstrated clearer correlations. Factors related to LGBTQ+ rights, perceived corruption, and peace indices emerged as influential. Conclusions: Country-specific historical, cultural, and sociopolitical factors appear to shape severity of depression in older women, with the strongest effects in the oldest age groups. Predictors of EURO-D scores varied by country and age group, with differences in explanatory power. The importance of predictors varied across age groups; listing them without context misrepresents the findings. The interplay between objective indicators and public perception, especially concerning minority rights and governance, highlights the need for culturally sensitive interventions. Future prevention efforts should incorporate these determinants to improve mental health across Europe. Full article

To promote the sustainable utilization of desert sand as a regional resource in the infrastructure construction of saline-alkali areas, this paper proposes an accelerated test method based on the coupling of an external electric field (60 V) and a 2% Na₂SO₄ solution for rapid evaluation of its sulfate erosion resistance. The optimal mix proportion (FA 10%, water-to-binder ratio 0.33, cement-to-sand ratio 1:1.5, SF 10%) was determined through orthogonal experiments. By employing multi-scale analytical techniques including electrochemical impedance spectroscopy (EIS), scanning electron microscopy (SEM), X-ray diffraction (XRD) and thermal analysis (TG-DTG), the differentiated deterioration mechanisms driven by the electric field were systematically revealed. The results show that the charge-transfer resistance (Rct) decreased by about 95% within 28 d, demonstrating the characteristic of “micro-scale deterioration preceding macro-scale strength loss.” The anode region was dominated by dissolution of hydration products (porosity 5.1%), while the cathode region, due to enrichment of sulfate ions (S content 3.37 wt.%), generated a large amount of expansive products, leading to more pronounced structural damage (porosity 8.3%) and greater mass loss (cathode 12.56% > anode 9.85%). This study not only elucidates the deterioration mechanisms of desert sand concrete under coupled environmental action, but also provides a mechanism-explicit, rapid and efficient laboratory evaluation method for its sulfate resistance, offering practical guidance for durability design and prevention in engineering structures exposed to saline-alkali conditions. Full article

Aircraft with a twin vertical tail and leading-edge extension configuration may experience vertical tail buffeting during high-angle-of-attack maneuvering flight. This issue can lead to structural fatigue damage in the vertical tail, shortening its service life and increasing maintenance costs, ultimately compromising flight safety. Therefore, accurate prediction of buffeting loads and responses is essential during design. In the preliminary stage, wind tunnel testing is the primary means to obtain dynamic data such as fluctuating pressure and acceleration response, which can be transformed to full-scale conditions through similitude principles. However, the elastic scaling model used in buffeting tests is usually established for a specific flight condition. When the flow velocity or objective flight condition changes, the similitude relationship becomes invalid, limiting the applicability of test results and preventing full-envelope strength verification. To overcome this limitation, this study proposes a multi-objective point response prediction method for vertical tail buffeting. The method enables the prediction of full-scale responses at multiple objective flight conditions using wind tunnel data that do not strictly satisfy similitude criteria. A complete aircraft vertical tail buffet (rigid/elastic) hybrid model was developed for testing, and an Adjusted Model incorporating elastic scaling transformation was established. The proposed method was validated through experiments, demonstrating improved test data utilization and prediction accuracy across multiple-objective flight conditions. Full article

Timely and reliable labor market intelligence is crucial for evidence-based policymaking, workforce planning, and economic forecasting. However, traditional data collection and centralized analytics raise growing concerns about privacy, scalability, and institutional data governance. This paper presents a large language model (LLM)-powered framework for privacy-preserving and scalable labor market analysis, designed to extract, structure, and interpret occupation, skill, and salary information from distributed textual sources. Our framework integrates domain-adapted LLMs with federated learning (FL) and differential privacy (DP) to enable collaborative model training across organizations without exposing sensitive data. The architecture employs secure aggregation and privacy budgets to prevent information leakage during parameter exchange, while maintaining analytical accuracy and interpretability. The system performs multi-task inference—including job classification, skill extraction, and salary estimation—and aligns outputs to standardized taxonomies (e.g., SOC, ISCO, ESCO). Empirical evaluations on both public and semi-private datasets demonstrate that our approach achieves superior performance compared to centralized baselines, while ensuring compliance with privacy and data-sharing regulations. Expert review further confirms that the generated trend analyses are accurate, explainable, and actionable for policy and research. Our results illustrate a practical pathway toward decentralized, privacy-conscious, and large-scale labor market intelligence. Full article

Understanding extreme precipitation (EP) evolution is crucial for global climate adaptation and hazardous disasters prevention. However, spatial non-stationarity of urbanization relationships with EP variations has been rarely discussed in a complex topographic context. Taking the city Liuzhou in China as the example, this study separately quantified the evolution of EP intensity, magnitude, duration, and frequency on different temporal scales with Innovative Trend Analysis (ITA). Based on a finer spatial (5 km grid) scale and multiple temporal (daily, daytime, nighttime, and 14 h) scale analyses, it innovatively identified spatially varying urbanization effects on EP with more details in different elevations. Our results indicate that: (1) from 2009 to 2023, EP events became more intense, persistent, and frequent, particularly for higher-grade EPs and in the steeper north of Liuzhou; (2) despite the globally negative correlations, spatial correlations between comprehensive urbanization (CUB) and each EP index on individual temporal scales were still explicitly categorized into four types using LISA maps—high-high, high-low, low-low, and low-high; (3) Geographically Weighted Regression (GWR) was demonstrated to precisely explain the response of most EP characteristics to multiple manifestation of urbanization with respect to population (POP), economy (GDP), and urban area (URP) expansion (adjusted R²: 0.5–0.8). The predictive accuracy of GWR on urbanization and EPs was spatially non-stationary and variable with temporal scales. The local influential strength and direction varied significantly with elevations. The most significant and positive influences of three urbanization predictors on EPs occurred at different elevation grades, respectively. Compared with POP and GDP, urban area percent (URP) was indicated to positively relate to EP changes in more areas of Liuzhou. The spatial and quantitative relationships between urbanization and EPs can help to guide effective urban planning and location-specific management of flood risks. Full article

Conventional dental materials with organised crystal structures exhibit limitations in corrosion resistance, bioactivity, and drug delivery capability. In contrast, amorphous nanomaterials offer potential advantages in overcoming these limitations due to their unique structural properties. They are characterised by a non-crystalline, disordered atomic structure and are similar to a solidified liquid at the nanoscale. Among the amorphous nanomaterials used in dentistry, there are five major categories: calcium-, silicon-, magnesium-, zirconia-, and polymer-based systems. This study reviewed these amorphous nanomaterials by investigating their synthesis, properties, applications, limitations, and future directions in dentistry. These amorphous nanomaterials are synthesised primarily through low-temperature methods, including sol–gel processes, rapid precipitation, and electrochemical etching, which prevent atomic arrangements into crystalline structures. The resulting disordered atomic configuration confers exceptional properties, including enhanced solubility, superior drug-loading capacity, high surface reactivity, and controlled biodegradability. These characteristics enable diverse dental applications. Calcium-based amorphous nanomaterials, particularly amorphous calcium phosphate, demonstrate the ability to remineralise tooth enamel. Silicon-based amorphous nanomaterials function as carriers that can release antibacterial agents in response to stimuli. Magnesium-based amorphous nanomaterials are antibacterial and support natural bone regeneration. Zirconia-based amorphous nanomaterials strengthen the mechanical properties of restorative materials. Polymer-based amorphous nanomaterials enable controlled release of medications over extended periods. Despite the advances in these amorphous nanomaterials, there are limitations regarding material stability over time, precise control of degradation rates in the oral environment, and the development of reliable large-scale manufacturing processes. Researchers are creating smart materials that respond to specific oral conditions and developing hybrid systems that combine the strengths of different nanomaterials. In summary, amorphous nanomaterials hold great promise for advancing dental treatments through their unique properties and versatile applications. Clinically, these materials could improve the durability, bioactivity, and targeted drug delivery in dental restorations and therapies, leading to better patient outcomes. Full article