Search Results (1,733)

China boasts abundant cultivated resources of pitahaya, with Guizhou Province being one of its core producing areas. Quality differences in red-fleshed pitahaya among local producing areas have not been fully clarified, and a standardized quantitative evaluation system for these differences remains lacking. This study seeks to identify the key factors influencing regional variations in quality and establish a comprehensive evaluation standard. In this study, 15 samples of red-fleshed pitahaya were collected from four major producing areas in Guizhou and used as research materials. Based on 15 quality characteristic indicators of the fruits, an analysis of quality differences and establishment of an evaluation system were carried out using multivariate statistical analysis. The results showed that 14 of the 15 quality indicators exhibited significant differences among pitahaya samples from different producing areas (p < 0.05), with the a* value being the sole exception. Cluster analysis classified the 15 samples into four groups. Principal component analysis (PCA) extracted four principal components, with a cumulative variance contribution rate of 81.07%, which clearly identified betacyanin, betaxanthin, 1,1-diphenyl-2-picrylhydrazyl (DPPH) free-radical scavenging rate, vitamin C, fruit shape index, and transverse diameter as the core evaluation indicators. This study systematically clarifies the differences in quality characteristics and the internal correlations among quality indicators of red-fleshed pitahaya from different major producing areas in Guizhou. It further provides an important scientific basis for pitahaya variety breeding, cultivation regulation, and market positioning in this region and fills the research gap existing in the field of comprehensive quality evaluation of pitahaya. This is of significant practical importance for promoting the standardized upgrading of local specialty fruit industries, enhancing the market competitiveness of products, and facilitating the high-quality development of the agricultural economy. Full article

This study evaluated the fruit quality of 12 apple cultivars grafted onto the cold-resistant dwarfing interstock SH40 in the arid region of Lingwu, Ningxia, to identify well-adapted varieties for local production. A total of 21 indicators were measured, encompassing three major aspects: external quality (e.g., fruit size, shape index, peel color), internal flavor (e.g., soluble solids, soluble sugars, titratable acids, vitamin C content), and textural attributes (e.g., hardness, crispness, chewiness), and data were analyzed using principal component analysis and membership function methodology. The cultivars exhibited distinct quality profiles under identical management: ‘Red General’ performed well in fruit size, weight, and sugar–acid balance; ‘Yanfu 6’ showed the highest firmness and crispness; ‘Shengli’ had the greatest soluble solids content; and ‘Granny Smith’ was richest in vitamin C. Four principal components were extracted, explaining 80.06% of the total variance and simplifying the quality evaluation system. Based on the comprehensive membership function scores, ‘Red General’, ‘White Winter Pearmain’, and ‘Huashuo’ ranked highest in overall fruit quality. In conclusion, these three cultivars perform excellently on SH40 and are recommended for promotion, whereas ‘Red Delicious’ is not recommended due to poor performance. These findings offer a practical reference for selecting apple cultivars paired with SH40 in similar arid regions. Full article

Geothermal energy is recognized as one of the most reliable and environmentally sustainable energy sources. This study presents a comprehensive energy, exergy, economic, and exergoenvironmental assessment of the Mis I binary geothermal power plant (GPP) operating with a low-temperature geothermal resource. This study fills a critical gap in the literature by providing a four-dimensional (4-E) assessment—energy, exergy, economic, and exergoenvironmental—of the Mis I binary geothermal power plant (GPP). Unlike conventional studies that focus on theoretical models, this research utilizes real-time operational data to identify potential improvements at the component level by evaluating exergy-based environmental sustainability and economic performance. The energy efficiency of the n-pentane Rankine cycle was calculated as 39.76%, indicating that a substantial portion of the geothermal heat is rejected as waste. The exergy input to the plant was determined to be 18,580.29 kW, while the net electrical power output was 8990 kW, resulting in an overall exergy efficiency of 48.38%. These results highlight the clear disparity between energy and exergy efficiencies and underline the importance of exergy-based performance evaluation for low-temperature geothermal power systems. Component-level exergy balance analyses were conducted using real operating data, revealing that the cooling towers are the dominant sources of exergy destruction, whereas the turbine units exhibit comparatively high thermodynamic effectiveness. Improvement potential analysis identified cooling towers I–II, evaporator II, and preheater I as key components requiring further optimization. Economic evaluation showed that approximately 64% of the total investment cost is associated with turbine units, with a total plant cost of about USD 6.7 million. The levelized cost of electricity was calculated as 0.0136 USD/kWh, and the payback period was approximately 1.5 years. Exergoenvironmental results indicate that the Mis I GPP achieves the highest sustainability index (1.94) among comparable plants, confirming its superior thermodynamic, economic, and environmental performance. Full article

Background/Objectives: Childhood obesity remains a major public health issue, particularly in Mediterranean countries such as Greece. Although parental influences on children’s weight have been extensively studied, fewer studies have jointly examined parental mental health, feeding practices, sociodemographic factors, and biological stress markers. This study aimed to investigate associations between psychological status, educational level, feeding behaviors, and children’s Body Mass Index (BMI) in a Greek sample. A pilot assessment of salivary cortisol was included in evaluating its feasibility as an objective biomarker of parental stress. Subjects and Methods: A total of 103 parent–child dyads participated in this cross-sectional study. Children’s BMI was classified using World Health Organization (WHO) growth standards. Parental stress, anxiety, and depressive symptoms were assessed using the Perceived Stress Scale-14 (PSS-14) and the Depression Anxiety Stress Scale-21 (DASS-21) questionnaires. Feeding practices were evaluated with the Comprehensive Feeding Practices Questionnaire (CFPQ). Statistical analyses included Pearson correlations, independent samples t-tests, one-way ANOVA, Mann–Whitney U, and Kruskal–Wallis tests. A subsample provided saliva samples for cortisol analysis to assess feasibility and explore the potential associations with parental stress indicators. Results: Parental BMI showed a strong positive association with child BMI (p = 0.002). Higher parental anxiety (p = 0.002) and depression (p = 0.009) were also associated with increased child BMI. Restrictive (p < 0.001) and emotion-driven (p < 0.001) feeding practices were associated with higher child BMI, whereas monitoring (p = 0.013) and health-promoting feeding practices (p = 0.001) appeared protective. Lower parental education was related to a higher BMI in both parents (p = 0.001) and children (p = 0.002) and to more frequent use of restrictive feeding strategies (p = 0.001). WHO charts identified a greater proportion of children as overweight or obese compared with the Centers for Disease Control and Prevention (CDC) criteria. The analysis showed statistically significant differences between the two classification systems (χ² (4) = 159.704, p < 0.001), indicating that BMI categorization varies considerably depending on the reference system used. No significant associations were observed with residential environment or salivary cortisol, likely due to the limited size of the pilot biomarker subsample. Conclusions: The findings highlight the combined effect of parental mental health status, educational level, and feeding practices on child BMI within the Greek context. The preliminary inclusion of a biological stress marker provides added value to the existing research in this area. These results underscore the importance of prevention strategies that promote parental psychological wellbeing and responsive feeding practices while addressing socioeconomic disparities to reduce the childhood obesity risk. Full article

In the context of a low-carbon economy, automotive parts supply chains face multifaceted risks, making an effective supply chain risk assessment model a crucial means of ensuring supply chain stability. Traditional evaluation methods struggle to comprehensively and accurately identify all influencing factors and their interrelationships in automotive parts supply chains. This article constructs an evaluation model based on the principle of symmetry. The “structural symmetry” is determined by the ratio of the completeness of risk dimension coverage in the indicator system to the precision of indicators, while “fusion symmetry” refers to the degree of equilibrium in information contribution during the fusion of subjective and objective weights. First, Fault Tree Analysis (FTA) and the Delphi method are adopted to establish a risk evaluation index system, whereby structural symmetry is ensured by the equilibrium between the completeness of risk dimension coverage and the accuracy of indicators in the index system. Second, drawing on the symmetric fusion principle, this study proposes a hybrid evaluation approach integrating hesitant fuzzy DEMATEL with entropy weight-coefficient of variation (HDEC), and the fusion symmetry is guaranteed by the balanced integration of subjective and objective weight information. Finally, a case study of an automotive parts supply chain enterprise quantitatively assesses and ranks risk factors, with corresponding countermeasures proposed. The symmetry-guided HDEC method achieves high accuracy, identifying indicator–causal relationships. Compared with the traditional entropy-weighted AHP algorithm, the Pearson correlation coefficient is 0.8566, and Spearman’s rank correlation coefficient is 0.88, indicating strong weight correlation and robust stability. The integration of mathematical symmetry enhances the model’s theoretical rigor, which aligns with symmetry-oriented optimization research. Full article

Historic waterfront streets are not only an important component of urban public spaces but also highlight the distinctive features and historical contexts of the city. High-quality streetscape visual perception plays a crucial role in advancing the cultural, social, environmental, and economic sustainability of the urban street space. This study was initiated to construct a multi-dimension and multi-scale comprehensive evaluation framework to assess the visual quality of waterfront streets, taking “Water City” Liaocheng as a typical case. Technical methods of semantic segmentation, sDNA (Spatial Design Network Analysis), GIS (Geographic Information System), and statistical analysis were utilized. Following the extraction and classification of street space elements, a multi-dimensional evaluation index system of natural coordination, artificial comfort, and historical culture for the visual assessment was established. Space syntax was performed on waterfront streets by sDNA to quantify macro-level scale spatial structure and meso-level scale pedestrian accessibility. The results of micro-scale visual perception, meso-scale behavioral walkability, and macro-scale spatial structure, were integrated to construct a multi-scale diagnostic framework for eight classifications. This framework provides a scientific basis to put forwards the refined and sustainable optimization strategies for historic waterfront streets. Full article

With the deepening implementation of agricultural full-cost insurance and crop income insurance, agricultural insurance has gradually become a significant force in promoting agricultural and rural modernization and achieving the strategic goal of building a strong agricultural nation. Based on data from 30 provinces in China from 2011 to 2023, a comprehensive evaluation index system for agricultural economic resilience was constructed, and the impact of agricultural insurance on agricultural economic resilience, along with its underlying mechanisms, was systematically analyzed. The findings reveal that: (1) There exists a nonlinear “U-shaped” relationship between agricultural insurance and agricultural economic resilience, a conclusion that remains robust after a series of tests; (2) Agricultural insurance can positively influence agricultural economic resilience by promoting agricultural technological progress; (3) When the level of industrial structure exceeds 7.108, agricultural insurance has a significant effect on agricultural economic resilience, and as the industrial structure level improves, the promoting effect of agricultural insurance becomes more pronounced; (4) The “U-shaped” impact of agricultural insurance on agricultural economic resilience is more prominent in the eastern, central, and northeastern regions, while it is not significant in the western region. Full article

With the rapid growth of vessel traffic and the widespread adoption of the Automatic Identification System (AIS) in recent years, analyzing maritime traffic flow characteristics has become an essential component of modern maritime supervision. Clustering analysis is one of the primary data-mining approaches used to extract traffic patterns from AIS data. Addressing the challenge of assigning appropriate weights to the multidimensional features in AIS trajectories, namely latitude and longitude, speed over ground (SOG), and course over ground (COG). This study introduces an adaptive parameter optimization mechanism based on evolutionary algorithms. Specifically, Particle Swarm Optimization (PSO), a representative swarm intelligence algorithm, is employed to automatically search for the optimal feature-distance weights and the core parameters of Density-Based Spatial Clustering of Applications with Noise (DBSCAN), enabling dynamic adjustment of clustering thresholds and global optimization of model performance. By designing a comprehensive clustering evaluation index as the objective function, the proposed method achieves optimal parameter allocation in a multidimensional similarity space, thereby uncovering maritime traffic clusters that may be overlooked when relying on single-dimensional features. The method is validated using AIS trajectory data from the Xiamen Port area, where 15 traffic clusters were successfully identified. Comparative experiments with two other clustering algorithms demonstrate the superior performance of the proposed approach in trajectory pattern analysis, providing valuable reference for maritime regulatory and traffic management applications. Full article

The study addresses the assessment of environmental sustainability in agriculture, noting that the existing scientific literature has predominantly focused on negative environmental impacts, particularly greenhouse gas emissions from the livestock sector. It argues that a comprehensive evaluation of farming systems should go beyond impact-based metrics and instead compare the demand and supply of natural capital, using appropriate methodologies such as the ecological footprint (EF). Accordingly, the objective of the study is to analyze the environmental sustainability of fattening pig farming systems in Italy by applying the EF to compare a virtuous case-study farm (located in Umbria, 72.4 ha of utilized agricultural area, and 1960 pigs per year) with a representative sample of ninety-four specialized pig-fattening farms drawn from the Italian FADN 2023 database. The results show the following marked differences between the two systems: the case study exhibits a positive ecological balance (EB = +50.1 gha; IEP = +0.69 gha/ha), while the FADN sample displays, on average, a negative ecological balance (EB = −167.6 gha) and a strongly negative sustainability index (IEP = −3.84 gha/ha). These findings indicate that, in a sector characterized by generalized environmental unsustainability, the preservation of natural capital can be achieved not only through low-impact technical solutions, but also by addressing structural factors (e.g., livestock density per unit area and the presence of non-productive land uses). Overall, the study demonstrates that sustainability assessment requires explicitly comparing natural capital demand and supply, rather than merely quantifying emissions. Full article

Against the backdrop of global climate change and China’s “dual-carbon” goals, carbon emissions from the construction phase of transportation infrastructure, particularly the rapidly expanding railway network, have garnered significant attention. However, systematic research and general evaluation models targeting the factors influencing carbon emissions during the railway bridge construction phase remain insufficient. To address this gap, this study presents a novel low-carbon evaluation model that integrates the analytic network process (ANP) and the fuzzy comprehensive evaluation (FCE) method. First, a carbon accounting model covering four stages—material production, transportation, construction, and maintenance—is established based on life cycle assessment (LCA) theory, providing a data foundation. Second, an innovative low-carbon evaluation index system for railway bridges, comprising 5 criterion layers and 23 indicator layers, is constructed. The ANP method is employed to calculate weights, effectively capturing the interdependencies among indicators, while the FCE method handles assessment ambiguities, forming a comprehensive evaluation framework. A case study of the bridge demonstrates the model’s effectiveness, yielding an evaluation score of 82.38 (“excellent” grade), which is consistent with expert judgement. The ranking of indicator weights from the model is highly consistent with the actual carbon emission inventory ranking (Spearman coefficient of 0.714). Key indicators—C21 (use of high-performance materials), C22 (concrete consumption), and C25 (transportation energy consumption)—collectively account for approximately 60% of the total impact, accurately identifying the major emission sources. This research not only verifies the model’s efficacy in pinpointing critical carbon sources but also provides a scientific theoretical basis and practical tool for low-carbon decision-making and optimization in the planning and design stages of railway bridge projects. Full article

Based on the interaction mechanism between higher education and industrial structure, this paper constructs an evaluation index system for the higher education development (HED) and the industrial upgrading (IU) by integrating their core characteristics. Using the entropy weight method, TOPSIS method, and coupling coordination model, as well as Kernel Density Estimation (KDE), it measures the comprehensive development levels and synergistic level of HED and IU in Chinese provinces (cities) from 2009 to 2020 and explores their spatiotemporal evolution characteristics. The findings reveal the following: (1) The overall level of China’s HED and IU shows an upward trend, but the absolute scores remain low, with significant regional disparities, and are divided into Balanced Development, Structural Imbalance, Industry-Supported, and Education-Supported. (2) The interaction between HED and IU is progressing toward a higher level, characterized by a reduction in low-value regions and a narrowing of regional disparities. However, the overall coordination remains in a “running-in stage”. (3) The eastern region has formed a virtuous cycle of interaction. The central region has achieved rapid improvement, benefiting from policy support, while the western region, constrained by resource limitations and policy lag, experiences slower progress in coordination. The northeastern region, lacking coupling momentum, exhibits long-term stagnation at a low level. Full article

Soil salinity is a major constraint to wheat production worldwide. Efficient screening of salt-tolerant cultivars is essential for breeding programs, yet a rapid and reliable evaluation system based on full-life-cycle salt stress treatment is lacking. To address this, we conducted a hydroponic experiment encompassing the entire growth cycle of 37 wheat cultivars under control and salt stress (85.5 mM NaCl). Using principal component and stepwise regression analyses on 15 agronomic and yield-related traits, we identified five key indicators—total dry weight, root dry weight, plant height, thousand-grain weight, and number of grains per spike—that effectively represent overall salt tolerance. Based on a comprehensive evaluation value (D-value), the cultivars were classified into five distinct categories: highly salt-tolerant, salt-tolerant, moderately salt-tolerant, weakly salt-tolerant, and salt-sensitive. Notably, the highly salt-tolerant cultivar ‘Yangfumai 8′ and the salt-sensitive cultivar ‘Yangmai 22’ were selected as representative extremes. A subsequent pot experiment confirmed significant physiological differences between them in antioxidant enzyme activities (SOD, POD, CAT) and proline accumulation under salt stress. This study establishes a practical and efficient screening framework, providing breeders with a simplified index set for high-throughput evaluation and offering ideal contrasting materials for in-depth physiological research on salt tolerance mechanisms in wheat. Full article

To improve the oral bioavailability of oleanolic acid (OA), this study developed a menthol–fatty acid-based hydrophobic deep eutectic solvent (HDES) system. Through a comprehensive evaluation using in vitro simulated digestion and Caco-2 cell transport models, the short-chain HDES was found to increase the apparent in vitro bioavailability index of OA by 9.3-fold compared to conventional ethanol systems, with efficacy showing clear fatty acid chain-length dependence. The mechanism was systematically investigated through spectral characterization and cellular studies, revealing a two-stage enhancement process: during the digestion phase, HDES significantly improved OA bioaccessibility to 14.30% compared to 4.90% with ethanol; during the absorption phase, it markedly increased cellular uptake to 25.79% versus 4.71% with ethanol. Molecular analysis indicated that the optimal hydrophobicity and diffusion properties of HDES contributed to this enhancement. This study reveals a fatty acid chain-length-dependent mechanism in HDES-facilitated OA delivery, providing a tunable strategy for enhancing the absorption of hydrophobic bioactive compounds. Full article

Progressive rock slope destabilization poses significant geohazard risks, necessitating advanced monitoring frameworks to detect precursory failure signals. This study presents a comprehensive time-dependent evaluation of the displacement probability (CTEDP) model, which integrates GNSS-derived spatiotemporal data with multi-parameter reliability indices to enhance landslide risk assessment. Five monitoring points on a destabilizing rock slope were analyzed from mid-November 2024 to early January 2025 using kinematic metrics (velocity, acceleration, and jerk), statistical measures (e.g., moving averages), and reliability indices (RI₀, RI₁, RI₂, and RI_combined). Point 1 exhibited the most critical behavior, with a cumulative displacement of ~60 mm, peak velocities of 34.5 mm/day, and accelerations up to 1.15 mm/day². The CTEDP for active points converged to 0.56–0.61, indicating sustained high risk. The 90th percentile displacement threshold was 58.48 mm for Point 1. Sensitivity analysis demonstrated that the GNSS-derived reliability indices dominated the RI_combined variance (r = 0.999, explaining 99.8% of variance). The first- and second-order reliability indices (RI₁, RI₂) at Point 1 exceeded the 60-index threshold, indicating a transition to Class B (“Low Risk—Trend Surveillance Required”) status, while other points showed coherent deformation of 37–45 mm. Results underscore the framework’s ability to integrate spatiotemporal displacement, kinematic precursors, and statistical variability for early-warning systems. This approach bridges gaps in landslide prediction by accounting for spatial heterogeneity and nonlinear geomechanical responses. Full article

Background: The aim of this study was to evaluate the diagnostic and prognostic performance of albumin-based inflammatory–nutritional indices in hyperemesis gravidarum (HG) and to determine their associations with disease severity and risk of re-hospitalization. Methods: This retrospective case–control study included 246 women with HG and 246 gestational-age-matched healthy pregnant controls at 6–16 weeks of gestation. Disease severity was classified as mild, moderate, or severe using the Pregnancy-Unique Quantification of Emesis (24 h scale) (PUQE-24) score. A comprehensive panel of albumin-based inflammatory indices—including C-reactive protein-to-albumin ratio (CAR), fibrinogen-to-albumin ratio (FAR), neutrophil-to-albumin ratio (NAR), leukocyte-to-albumin ratio (LAR), neutrophil percentage-to-albumin ratio (NPAR), monocyte-to-albumin ratio (MAR), hemoglobin–albumin–lymphocyte–platelet (HALP) score, modified HALP (m-HALP) score, prognostic nutritional index (PNI) score, systemic immune-inflammation index-to-albumin (SII/Alb), and systemic inflammatory response index-to-albumin (SIRI/Alb)—was calculated from routine complete blood count and serum biochemistry results obtained at diagnosis. Receiver operating characteristic analysis, along with univariate and multivariate logistic regression models, was performed to evaluate diagnostic performance and identify predictors of severe HG and re-hospitalization. Results: Albumin-based indices exhibited severity-associated alterations, with an overall trend toward worsening immuno-nutritional status across increasing HG severity. Among these, m-HALP score demonstrated the strongest inverse correlations with PUQE-24 score, ketonuria grade, length of hospital stay, and re-hospitalization risk (r = −0.74 to −0.52; all p < 0.001) and achieved the highest discriminative accuracy for both severe HG (AUC 0.864, 95% CI 0.836–0.892, p < 0.001) and re-hospitalization (AUC 0.722, 95% CI 0.675–0.766, p < 0.001). In multivariable analysis, higher HALP, m-HALP, and PNI were independently associated with a lower likelihood of severe HG. For re-hospitalization, higher m-HALP and HALP were independently associated with a lower risk, whereas higher NPAR, higher ketonuria grade, and higher PUQE-24 score were independently associated with an increased risk of re-hospitalization. Conclusions: Albumin-based indices, particularly m-HALP, demonstrated robust diagnostic and prognostic performance in HG compared with conventional biomarkers. These readily available, cost-neutral composite biomarkers enable objective severity stratification and accurate identification of patients at elevated risk of recurrent hospitalization, offering immediate potential to guide personalized, evidence-based clinical management. Full article