Search Results (13,717)

Solar greenhouses are an important component of modern facility agriculture, and the dynamic changes in their internal environment directly affect crop growth and yield. Among these factors, crop transpiration releases water vapor through transpiration, directly altering the indoor humidity balance and forming a dynamic coupling with factors such as temperature and light. The environment of solar greenhouses exhibits highly nonlinear and multivariate coupling characteristics, leading to insufficient prediction accuracy in existing models. However, accurate predictions are crucial for regulating crop growth and yield. However, current mainstream greenhouse environmental prediction models still have obvious limitations when dealing with such complexity: traditional machine learning models and single-variable-driven models have issues such as insufficient accuracy (average MAE is 15–20% higher than in this study) and weak adaptability to nonlinear environmental changes in multi-environmental factor coupling predictions, making it difficult to meet the needs of precision farming. A review of relevant research over the past five years shows that while LSTM-based models perform well in time series prediction, they ignore the spatial correlations between environmental factors. Models incorporating attention mechanisms can capture key variables but suffer from high computational costs. To address these issues, this study proposes a prediction model based on multi-strategy optimization and gradient-boosting (GBDT) algorithms. By introducing a multi-scale feature fusion module, it addresses the accuracy issues in multi-factor coupling prediction. Additionally, it employs a lightweight network design to balance prediction performance and computational efficiency, filling the gap in existing research applications under complex greenhouse environments. The model optimizes data preprocessing and model parameters through Sobol sequence initialization, adaptive t-distribution perturbation strategies, and Gaussian–Cauchy mixture mutation strategies and combines CatBoost for modeling to enhance prediction accuracy. Experimental results show that the MSCSO–CatBoost model performs excellently in temperature prediction, with the mean absolute error (MAE) and root mean square error (RMSE) reduced by 22.5% (2.34 °C) and 24.4% (3.12 °C), respectively, and the coefficient of determination (R²) improved to 0.91, significantly outperforming traditional regression methods and combinations of other optimization algorithms. Additionally, the model demonstrates good generalization capability in predicting multiple environmental variables such as temperature, humidity, and light intensity, adapting to environmental fluctuations under different climatic conditions. This study confirms that combining multi-strategy optimization with gradient-boosting algorithms can significantly improve the prediction accuracy of solar greenhouse environments, providing reliable support for precision agricultural management. Future research could further explore the model’s adaptive optimization in complex climatic regions. Full article

Comprehensive healthcare seeks to uphold the right to health by providing patient-centred care in both personal and work environments. However, the unequal distribution of healthcare services significantly restricts access in remote or underserved areas—a challenge that is particularly critical in mental health care within low-income countries. On average, there is only one psychiatrist for every 200,000 people, which severely limits early diagnosis and continuous monitoring in patients’ daily environments. In response to these challenges, this research explores the feasibility of implementing an information system that integrates cloud computing with an intelligent Facial Expression Recognition (FER) module to enable psychologists to remotely and periodically monitor patients’ emotional states. This approach enhances comprehensive clinical assessments, supporting early detection, ongoing management, and personalised treatment in mental health care. This applied research follows a descriptive and developmental approach, aiming to design, implement, and evaluate an intelligent cloud-based solution that enables remote monitoring of patients’ emotional states through Facial Expression Recognition (FER). The methodology integrates principles of user-centred design, software engineering best practices, and machine learning model development, ensuring a robust and scalable solution aligned with clinical and technological requirements. The development process followed the Software Development Life Cycle (SDLC) and included functional, performance, and integration testing. To assess overall system quality, we defined an evaluation framework based on ISO/IEC 25010 quality characteristics: functional suitability, performance efficiency, usability, and security. The intelligent FER model achieved strong validation results, with a loss of 0.1378 and an accuracy of 96%, as confirmed by the confusion matrix and associated performance metrics. Full article

South African cattle comprise diverse breeds with distinct evolutionary histories, potentially reflecting differences in recombination landscapes. This study assessed genome-wide recombination rates and hotspots in Bonsmara (n = 190) and Nguni (n = 119) cattle using three-generation half-sib pedigrees genotyped with the Illumina Bovine SNP50 BeadChip. Phasing across 29 autosomes was conducted using SHAPEIT v2, and crossover events were inferred using the DuoHMM algorithm. The total number of crossover events detected was higher in Nguni (n = 8982) than in Bonsmara (n = 7462); however, the average recombination rate per 1 Mb window was significantly higher in Bonsmara (0.31) compared to Nguni (0.18) (p < 0.01). This apparent discrepancy reflects differences in genomic distribution and crossover clustering across breeds, rather than overall recombination frequency. A critical limitation of the study is the reliance on half-sib families with small family sizes, which may underestimate recombination rates due to limited meiotic sampling and increased variance in crossover detection. We identified 407 recombination hotspots in Bonsmara and 179 in Nguni, defined as intervals exceeding 2.5 standard deviations above the mean recombination rate. Genes such as PDE1B and FP which are associated with productions traits were located within hotspot-enriched regions. However, functional causality between these genes and local recombination activity remains unverified. Our results provide statistically supported evidence for breed-specific recombination patterns and hotspot distributions, underscoring the importance of incorporating recombination architecture into genetic improvement strategies for South African cattle. Full article

Background: Dental professionals are particularly susceptible to occupational stress and burnout, which are amplified during armed conflicts. Civilian dentists continuing to provide care under wartime conditions face unique psychological challenges. This study aimed to evaluate their psychological wellbeing and professional quality of life during military conflict. Methods: A cross-sectional study was conducted using an anonymous online questionnaire distributed through the national dental association. The survey included the Professional Quality of Life Scale (ProQOL, version 5) to assess compassion satisfaction, burnout, and secondary traumatic stress; and the Generalized Anxiety Disorder 7-item scale (GAD-7) to measure anxiety severity. Additional items captured demographic information, professional experience, pre-conflict workload, current work status, family circumstances, and subjective financial impact. The final sample included 239 civilian dentists. Statistical analysis included descriptive statistics, Pearson correlations, chi-square tests for categorical variables, Mann-Whitney U and Kruskal-Wallis tests for between-group comparisons, and multiple regression to identify predictors of psychological outcomes. Results: High compassion satisfaction was reported by 38.9% of respondents, while 70.3% exhibited average burnout levels; only 0.4% had high burnout. Secondary traumatic stress was low in 85.4% of participants. Minimal anxiety was found in 54% of respondents. Significant correlations were found between professional satisfaction and lower anxiety (p < 0.001), lower burnout (p < 0.001), and higher compassion satisfaction (p < 0.001). Dentists with more years of experience and older age reported lower anxiety and burnout levels. Higher pre-conflict workloads were associated with increased anxiety during the conflict (p < 0.001). Dentists working in Health Maintenance Organizations (HMOs) reported significantly higher anxiety levels compared to their non-HMO counterparts (p = 0.022), although reported income loss was similar between groups. Conclusions: Civilian dentists demonstrated resilience and overall positive professional functioning during prolonged conflict. However, public sector dentists, especially those in HMOs, showed greater vulnerability to anxiety. These findings underscore the need for systemic strategies to support dental professionals’ mental health during national crises, with emphasis on those in the public health system. Full article

The present study focused on 10 wt.% TiB₂@Ti/AlCoCrFeNi_2.1 eutectic high-entropy alloy matrix composites (EHEAMCs), which were treated with furnace cooling (FC), air cooling (AC), and water cooling (WC) after being held at 1000 °C for 12 h, aiming to investigate the effect of cooling methods on their microstructure and mechanical properties. The results showed that the composites in all states consisted of FCC phase, BCC phase, TiB₂ phase, and Ti phase. The cooling methods did not change the phase types but affected the diffraction peak characteristics. With the increase in cooling rate, the diffraction peaks of FCC and BCC phases gradually separated from overlapping, and the diffraction peak of the FCC (111) crystal plane shifted to a lower angle (due to the increase in lattice constant caused by Ti element diffusion), while the diffraction peak intensity showed a downward trend. In terms of microstructure, all composites under the three cooling conditions were composed of eutectic matrix, solid solution zone, and grain boundary zone. The cooling rate had little effect on the morphology but significantly affected the element distribution. During slow cooling (FC, AC), Ti and B diffused sufficiently from the grain boundary to the matrix, resulting in higher concentrations of Ti and B in the matrix (Ti in FCC phase: 7.4 at.%, B in BCC phase: 8.1 at.% in FC state). During rapid cooling (WC), diffusion was inhibited, leading to lower concentrations in the matrix (Ti in FCC phase: 4.6 at.%, B in BCC phase: 4.3 at.%), but the element distribution was more uniform. Mechanical properties decreased with the increase in cooling rate: the FC state showed the optimal average hardness (627.0 ± 26.1 HV), yield strength (1574 MPa), fracture strength (2824 MPa), and fracture strain (24.2%); the WC state had the lowest performance (hardness: 543.2 ± 35.4 HV and yield strength: 1401 MPa) but was still better than the as-sintered state. Solid solution strengthening was the main mechanism, and slow cooling promoted element diffusion to enhance lattice distortion, achieving the synergistic improvement of strength and plasticity. Full article

Surface ozone pollution poses a significant threat to human health and ecosystems. However, its highly variable spatiotemporal distribution, especially at hourly scales across China, complicates effective risk management. This variability presents substantial challenges for accurate estimation and forecasting, underscoring the importance of evaluating current hourly surface ozone estimation methods. Therefore, this study collaboratively evaluated the performance of chemical transport model simulations and satellite-based estimates of hourly surface ozone concentrations over mainland China in 2019. Using data from 3185 ground monitoring stations operated by the Ministry of Ecology and Environment, as well as six independent observation sites in Hong Kong, Xianghe, Nam Co, Akedala, Longfengshan, and Waliguan, this study found that both datasets exhibited systematic biases and lacked spatiotemporal consistency. The Community Atmosphere Model with Chemistry simulation results exhibited an average relative bias of 23.17%, generally overestimated ozone concentrations in high-altitude regions, but outperformed the satellite-based estimates at the independent sites, while consistently underestimating ozone concentrations in densely populated urban areas. In contrast, the satellite-based estimates performed better in regions with dense monitoring sites, with mean biases typically within 10% of observations, but their accuracy was limited in remote areas due to sparse ground-based calibration. It is particularly noteworthy that both datasets showed deficiencies in capturing extremely high-value events, nighttime ozone variations, and dynamic transport processes, underscoring challenges in the representation of photochemical processes in the model and in the design of satellite estimation algorithms. The results highlight the importance of optimizing model parameterization schemes, improving satellite estimation algorithms, and integrating multi-source data to enhance the accuracy and stability of hourly ozone estimates. This study provides multi-scale quantitative insights into the relative strengths and limitations of different ozone estimation methods, laying a solid scientific foundation for future data integration, regional air quality management, and policy development. Full article

The extraordinary mechanical flexibility, high electrical conductivity, and nanoscale instability of freestanding graphene make it an excellent candidate for vibration energy harvesting. When freestanding graphene is stretched taut and subject to external forces, it will vibrate like a drum head. Its vibrations occur at a fundamental frequency along with higher-order harmonics. Alternatively, when freestanding graphene is compressed, it will arch slightly out of the plane or buckle under the load. Remaining flat under compression would be energetically too costly compared to simple bond rotations. Buckling up or down, also known as ripple formation, naturally creates a bistable situation. When the compressed system vibrates between its two low-energy states, it must pass through the high-energy middle. The greater the compression, the higher the energy barrier. The system can still oscillate but the frequency will drop far below the fundamental drum-head frequency. The low frequencies combined with the large-scale movement and the large number of atoms coherently moving are key factors addressed in this study. Ten ripples with increasing compressive strain were built, and each was studied at five different temperatures. Increasing the temperature has a similar effect as increasing the compressive strain. Analysis of the average time between curvature inversion events allowed us to quantify the energy barrier height. When the low-frequency bistable data were time-averaged, the authors found that the velocity distribution shifts from the expected Gaussian to a heavy-tailed Cauchy (Lorentzian) distribution, which is important for energy harvesting applications. Full article

Citation networks are fundamental for analyzing the mechanisms and patterns of knowledge creation and dissemination. While most studies focus on pairwise attachment between papers, they often overlook compound relational structures, such as co-citation. Combining two key empirical features, superlinear node inflow and the temporal decay of node influence, we propose the Triangular Evolutionary Model of Superlinear Growth and Aging (TEM-SGA). The fitting results demonstrate that the TEM-SGA reproduces key structural properties of real citation networks, including degree distributions, generalized degree distributions, and average clustering coefficients. Further structural analyses reveal that the impact of aging varies with structural scale and depends on the interplay between aging and growth, one manifestation of which is that, as growth accelerates, it increasingly offsets aging-related disruptions. This motivates a degenerate model, the Triangular Evolutionary Model of Superlinear Growth (TEM-SG), which excludes aging. A theoretical analysis shows that its degree and generalized degree distributions follow a power law. By modeling interactions among triadic closure, dynamic expansion, and aging, this study offers insights into citation network evolution and strengthens its theoretical foundation. Full article

This paper presents a domestic hot water (DHW) consumption model for multi-family residential buildings that captures demand variability across monthly, daily, and hourly timescales. The model enables both the disaggregation of annual consumption for dynamic simulations and the generation of synthetic yet realistic DHW load profiles when detailed measurements are unavailable. It is developed from a dataset of 42 buildings containing 1376 apartments. The analysis identifies seasonal, weekly, and hourly usage patterns, reflecting the influence of apartment layout, building size, and user behavior under Polish climatic and cultural conditions. The proposed model reproduces seasonal deviations of up to 23%, with average daily demand falling to 77% of the annual mean in August and rising above the yearly average during winter months. It also captures weekly variability, with weekend consumption exceeding weekday levels by more than 16%. On working days, the hourly profile exhibits a clear dual-peak structure, with approximately 18% of daily demand occurring in the morning and up to 45% in the evening, whereas weekends show a flatter distribution without pronounced peaks. These results provide a robust basis for more accurate demand representation in energy modeling, system design, and optimization under local conditions. Full article

In this study, physical factors influencing the efficiency of adhesive bonding in External Thermal Insulation Composite Systems (ETICS) using the ribbon-and-dab bonding method were analyzed. Tests were carried out to show the distribution of pressure transmitted through thermal insulation to adhesive mortar and substrate during bonding, and to demonstrate the relationship between pressure, adhesive layer thickness, and bond strength of mortar to concrete substrate. The analysis was also based on in situ observations, laboratory experiments, and numerical modeling, with particular attention paid to contact pressure and adhesive strength depending on cement-based mortar layer thickness. Example pull-off tests (CAST, DAST) performed on dabs showed that increasing thickness from 10 mm to 20 mm caused a decrease in bond strength in the central area by about 86% for tested adhesive mortars and substrate—values dropped from 1.8 MPa to below 0.25 MPa, while edge zones often showed no adhesion. Pressure-mapping tests (PMAST) revealed distinct pressure zones within dabs and perimeter ribbons. The analysis showed that average normalized pressures in adhesive dabs reached about 52% of the maximum value, while the [0.9; 1.0] pressure interval covered about 12% of the contact area. Based on empirical data, a decay function was developed to build a model of radial pressure attenuation. Monte Carlo simulations defined ranges of random model parameters and variability of average pressures in a 10 mm adhesive dab. The model allowed inclusion of a peripheral zero-pressure ring and enabled simulation for a 20 mm layer, confirming that increased thickness led to reduced contact pressure and explained the decrease in bonding performance. Full article

This research has been performed to optimize the management of cultivated land fragmentation in the Henan Section of the Yellow River Basin, (“the research area”), coordinate the contradiction between increasing food demand and environmental constraints, maintain regional food security, and promote agricultural and rural modernization. The spatial and temporal evolution characteristics have been summarized by calculating the fragmentation index of the cultivated land landscape, and the driving factors explored with geographical detectors. Results show the following: (1) between 2000 and 2023, the landscape fragmentation index of cultivated land in the research region exhibited a pattern of initial decline followed by a subsequent rise. It decreased by 69.33% from 2000 to 2015 and increased by 138.42% from 2015 to 2023. Over the period from 2000 to 2023, the cultivated land landscape fragmentation index in the study area saw an overall reduction of 26.87%. (2) ”The reduction in cultivated land area and the decrease in landscape fragmentation” index accounted for 82.46% in the county unit. (3) The kernel density curve of the cultivated land landscape fragmentation index showed a unimodal distribution, but the shape was flat. The regions with a fragmentation index mainly range from 4 to 6. The regional cultivated land fragmentation distribution was more dispersed. (4) The average altitude, the distance from the Yellow River, the proportion of the construction land area and population density are the main driving factors. The combined impact of the proportion of the construction land area and population density contributes more than 46% to the cultivated land landscape fragmentation index. The interaction among various factors exerts a more pronounced effect than any individual factor alone. The intensity of the main interaction factors reaches above 0.67. The findings of this study can serve as a theoretical foundation for the sustainable utilization and development of cultivated land resources, as well as for ecological protection and construction in the Henan segment of the Yellow River Basin. Full article

Habitat and climate changes driven by human activities are altering the distribution of organisms globally. In South Korea, recent temperature increases have exceeded twice the global average, and habitats have markedly changed and shrunk due to urban development driven by population growth and economic expansion. Despite its high biodiversity and over 500 years of preservation, Gwangneung Forest in South Korea has experienced habitat alterations due to the urbanization of surrounding rural areas since the 1990s. In this study, we aimed to evaluate how butterfly communities respond to urbanization and climate change using long-term monitoring data (1998–2015) from the conserved Gwangneung Forest. We considered the thermal adaptation types (cold-, warm-, and moderately adapted species), habitat types (forest edge, forest inside, and grassland), diet breadth (monophagous, oligophagous, and polyphagous), and wingspan of butterflies. Linear regression analysis of the abundance trends for each species revealed that cold-adapted species experienced population declines, while warm-adapted species showed increases. Changes in butterfly abundance were associated with both thermal adaptation type and wingspan, with larger, more mobile species showing greater resistance to habitat loss in surrounding areas. To preserve butterfly diversity in Gwangneung Forest and across South Korea, it is crucial to conserve open green habitats—such as gardens, small arable lands, and grasslands—within urban areas, especially considering the impacts of climate change and habitat loss, which disproportionately affect smaller species with limited mobility. Full article

The mutual twisting of steel wires is widely used in construction, engineering, and everyday applications, as it is relatively easy to perform and imparts new and useful properties to the wires. Since the process involves large deformations and high stress levels, understanding the mechanical behavior of twisted pairs is essential for both their manufacturing and in-service performance. This study provides a detailed analysis of the stresses and deformations that arise during the twisting of two galvanized steel wires with a diameter of 4 mm. Comprehensive information is presented on the development and validation of a suitable finite element model, with emphasis on geometry definition, the selection of appropriate initial and boundary conditions, and the meshing strategy. Special attention is devoted to the material properties, which are obtained and processed based on original tensile and torsion tests. Both the maximum and residual stresses are investigated. It is found that, for small twist pitches, the equivalent stresses during twisting can exceed the material’s yield strength by a factor of two or more, posing a risk of failure. The residual equivalent stresses show complex spatial distributions that vary with pitch, yet their average magnitudes remain within a narrow range, indicating a consistent residual stress level across different twisting configurations. Full article

Purpose: The synthesis of nanoscale particles with antibacterial properties has garnered significant attention in pharmaceutical research, driven by the escalating threat of antibiotic-resistant bacteria. This study investigates the antibacterial efficacy of Zn–Co ferrite nanoparticles against virulent, antibiotic-resistant, and biofilm-forming strains of Escherichia coli. Methods: Three nanoparticle variants—S1 (Zn_0.7Co_0.3Fe₂O₄), S2 (Zn_0.5Co_0.5Fe₂O₄), and S3 (Zn_0.3Co_0.7Fe₂O₄)—were synthesized using the solution combustion method by systematically varying the Zn:Co molar ratio. The Scanning Electron Micrograph, X-ray diffraction analysis, Complementary Fourier-transform infrared, Minimum Inhibitory Concentration, and Minimum Bactericidal Concentration were performed. Results: The SEM spectroscopy study revealed distinct morphological differences as a function of the cobalt substitution level within the spinel ferrite matrix. At the highest level of cobalt substitution (Zn_0.3Co_0.7Fe₂O₄), the microstructure displayed significant irregularities, with enhanced agglomeration and a notably broader particle size distribution. X-ray diffraction analysis confirmed the formation of crystalline structures, with an average crystallite size of 12.65 nm. Complementary Fourier-transform infrared spectroscopy revealed characteristic absorption bands in the 400–600 cm⁻¹ range, indicative of the cubic spinel structure of the ferrite nanoparticles. The higher-frequency band was associated with metal–oxide stretching in the tetrahedral sites, while the lower-frequency band corresponded to stretching in the octahedral sites. The Minimum Inhibitory Concentration and Minimum Bactericidal Concentration assays revealed that Zn–Co ferrite nanoparticles possess potent antibacterial activity against virulent, antibiotic-resistant, and biofilm-forming strains of E. coli. Conclusion: Increasing the molar ratio of Zn to Co enhances the antibacterial activity of the nanoparticles. These findings suggest that Zn–Co ferrite nanoparticles could serve as a promising alternative to conventional antibacterial agents for combating multidrug-resistant pathogenic bacteria in the future. Full article

This study presents a comprehensive analysis of climate evolution in the Porto region (Northern Portugal) using 148 years (1863–2010) of continuous meteorological data from the Serra do Pilar weather station (WMO station 08546). The research employs both traditional linear statistical methods and advanced non-linear analysis techniques, including polynomial trend fitting and multidecadal oscillation analysis, to accurately characterize long-term climate patterns. Results reveal that linear trend analysis is misleading for this dataset, as both temperature and precipitation follow parabolic (U-shaped) distributions with minima around 1910–1970. The early period (1863–1900) exhibited higher values than the recent period, contradicting linear trend interpretations. Advanced analysis shows that the mean temperature follows a parabolic pattern (R² = 0.353) with the minimum around 1935, while precipitation exhibits similar behavior (R² = 0.053) with the minimum around 1936. Multidecadal oscillations are detected with dominant periods of 46.7, 15.6, and 10.0 years for temperature, and 35.0, 17.5, and 4.5 years for precipitation. Maximum temperatures show complex oscillatory behavior with a severe drop around 1890. Seasonal analysis reveals distinct patterns across all seasons: winter (+0.065 °C/decade) and autumn (+0.059 °C/decade) show warming trends in maximum temperatures, while spring (−0.080 °C/decade) and summer (−0.079 °C/decade) demonstrate cooling trends in minimum temperatures, with no significant trends in spring (+0.012 °C/decade) and summer (+0.003 °C/decade) maximum temperatures or winter (−0.021 °C/decade) and autumn (−0.035 °C/decade) minimum temperatures. The study identifies a significant change point in mean temperature around 1980, which occurs approximately one decade earlier than the global warming acceleration typically observed in the 1990s, suggesting regional Atlantic influences may precede global patterns. Extreme event analysis indicates stable frequencies of hot days (averaging 3.6 days/year above 25.0 °C) and heavy precipitation events (averaging 1.2 days/year above 234.6 mm) throughout the study period. These findings demonstrate that the Porto region’s climate is characterized by natural multidecadal variability rather than monotonic trends, with the climate system showing oscillatory behavior typical of Atlantic-influenced coastal regions. The results contribute to understanding regional climate variability and provide essential baseline data for climate change adaptation strategies in Northern Portugal. The results align with broader patterns of natural climate variability in the Iberian Peninsula while highlighting the importance of non-linear analysis for comprehensive climate assessment. Full article