Search Results (58,951)

The process of pulping and papermaking is a complicated, resource-demanding operation that requires energy, water, and chemicals. When not managed properly, the process can also contribute significantly to pollution. The washing process is one critical operation that impacts the process’s economics and environmental footprint. Most mills utilize rotary vacuum washers to separate black liquor from pulp, ensuring clean pulp for further processing downstream. Numerous factors influence the efficiency of a brown stock washer, and the washing operation itself is intricate. This study employs the system dynamics modeling approach to examine the critical role of brown stock washing in the pulp and paper industry, emphasizing optimizing process parameters for improved efficiency and sustainability. In the first part of the paper, a single stage of the washer system is modeled by establishing mass balance equations for key streams, including pulp, liquor, and dissolved solids. Within the system dynamics environment, separate models are developed for each stream, allowing for a detailed analysis of their behavior. To enhance modeling efficiency, the brown stock washing process is divided into four distinct operations: dilution, pulp formation, washing, and filtration. Breaking down the process into these operations makes it possible to focus on optimizing each step for improved overall performance. Furthermore, a control strategy is implemented to ensure stability in critical areas such as dilution vat level, discharged pulp consistency, and filtration tank level. In the final phase of the research, a multistage countercurrent brown stock washing line comprising three washers is modeled. Researchers can gain insights into how different components interact and influence overall performance by evaluating various parameters and analyzing the line’s efficiency. This comprehensive analysis enables them to identify potential improvements and optimize the washing process for enhanced productivity and quality output. The conclusions drawn from this work offer valuable guidance for optimizing water management practices in the pulp and paper sector, contributing to the industry’s sustainability goals and regulatory compliance. Full article

Trachelospermum jasminoides (Lindl.) Lem. is a well-known herb with important medicinal and economic values. It is widely used in the treatment of inflammations in China. As global climate change intensifies, the ecological niche of plants has correspondingly shifted. Therefore, understanding the distribution of suitable habitats for T. jasminoides under different climate conditions is of great significance for its cultivation, introduction, and conservation. This research utilizes the MaxEnt model in combination with the Geographic Information System (ArcGIS) to analyze the present and future potential habitat distributions of T. jasminoides. Based on 227 documented occurrence points and 15 ecological variables, the results emphasize that the key environmental limitations influencing the optimal habitats of T. jasminoides are the precipitation during the coldest quarter, the mean temperature of the driest quarter, precipitation in the warmest quarter, temperature seasonality (standard deviation × 100), and the human impact index. At present, the combined area of suitable and highly suitable habitats for T. jasminoides amounts to 15.76 × 104 km², with the highly suitable habitats predominantly situated in East and Central China. Based on climate scenario forecasts, within the SSP1-2.6 climate scenario, the total suitable habitat area for T. jasminoides is projected to increase relative to the current situation. Nevertheless, in the SSP2-4.5 and SSP5-8.5 climate scenarios, the suitable habitat area is anticipated to initially rise and then decline. The distribution center is mainly concentrated in the provinces of Hunan and Jiangxi, with the centroid shifting southeastward compared to the current situation. The findings of this research offer valuable insights for the effective cultivation, preservation, and sustainable use of T. jasminoides resources. Full article

This study aims to examine context-specific post-migration stress factors and their differential impacts on the mental health of Syrian refugee women resettled in Houston, Texas, and urban communities in Jordan. A cross-sectional survey investigated sociodemographic and health-related conditions, psychological distress and coping (Perceived Stress Scale [PSS]), mental health-related symptomatology (Self-Report Questionnaire [SRQ]), conflict-related psychological distress (Afghan Symptom Checklist [ASC]), and post-migration stress (Refugee Post-Migration Stress Scale [RPMS]). Linear regression models examined factors associated with post-migration stress and mental health outcomes. A total of 127 Syrian refugee women participated in the study. Participants were in their mid-30s (mean age = 34.79 ± 11.2 years), married (66.9%), and reported low levels of education (44.8% below high school), low employment (27.2%), and elevated financial strain (91% below the poverty line). Jordan-based refugees exhibited higher scores on mental distress measures compared to their Houston-based counterparts; specifically more elevated psychological distress (p < 0.001), symptomatology (p < 0.001), and conflict-related distress (p < 0.001). Syrian refugee women in Houston reported higher social strain, while those in Jordan experienced greater financial hardship and barriers to accessing healthcare services. Mental distress among Syrian refugee women is influenced by specific post-migration stressors that vary by resettlement location. Targeted interventions are necessary to improve mental health outcomes in this population. Full article

Background/Objectives: Respiratory syncytial virus (RSV) represents a significant cause of acute respiratory infections (ARIs) in adults aged 60 years and older, often leading to severe clinical outcomes and high healthcare costs. This study aimed to evaluate the clinical and economic burden of RSV compared to other ARIs, focusing on specific age groups, comorbidities, and demographic factors. Methods: A retrospective observational study was conducted using the electronic medical records of adults aged ≥60 years hospitalized for ARIs, including RSV, in Spain. Direct costs related to hospitalizations, intensive care unit (ICU) admissions, and treatments were analyzed. The study also assessed demographic, clinical, and comorbidity-related factors influencing the economic burden. Results: RSV infections resulted in significantly higher direct costs compared to other ARIs, particularly in patients aged 70–80 years. Comorbidities such as asthma and smoking history were associated with increased costs in RSV cases. Although ICU costs were comparable between groups, hospitalizations for RSV required longer stays and more intensive treatments, amplifying the overall economic burden. Differences in costs by age and sex highlighted the need for tailored clinical management strategies. Conclusions: RSV poses a substantial economic and clinical burden on adults aged 60 years and older, particularly in those with comorbidities. Preventive measures, such as vaccination, could reduce healthcare costs and improve outcomes in this vulnerable population. These findings support the inclusion of RSV vaccines in immunization programs, especially in aging populations like Spain, to alleviate healthcare pressures during peak respiratory disease seasons. Full article

Elucidating the effects of nitrogen and water addition on N₂O dynamics is critical, as N₂O is a key driver of climate change (including nitrogen deposition and shifting precipitation patterns) and stratospheric ozone depletion. The temperate steppe is a notable natural source of this potent greenhouse gas. This study uses field observations and soil sampling to investigate the seasonal pattern of N₂O emissions in the temperate steppe of Inner Mongolia and the mechanism by which nitrogen and water additions, as two different types of factors, alter this seasonal pattern. It explores the regulatory roles of environmental factors, soil physicochemical properties, microbial community structure, and abundance of functional genes in influencing N₂O emissions. These results indicate that the effects of nitrogen and water addition on N₂O emission mechanisms vary throughout the growing season. Nitrogen application consistently increase N₂O emissions. In contrast, water addition suppresses N₂O emissions during the early growing season but promotes emissions during the peak and late growing seasons. In the early growing season, nitrogen addition primarily increased the dissolved organic nitrogen (DON) levels, which provided a matrix for nitrification and promoted N₂O emissions. Meanwhile, water addition increased soil moisture, enhancing the abundance of the nosZ (nitrous oxide reductase) gene while reducing nitrate nitrogen (${\mathrm{N}\mathrm{O}}_3^{-}$-N) levels, as well as AOA (ammonia-oxidizing archaea) amoA and AOB (ammonia-oxidizing bacteria) amoA gene expression, thereby lowering N₂O emissions. During the peak growing season, nitrogen’s role in adjusting pH and ammonium nitrogen (${\mathrm{N}\mathrm{H}}_4^{+}$-N), along with amplifying AOB amoA, spiked N₂O emissions. Water addition affects the balance between nitrification and denitrification by altering aerobic and anaerobic soil conditions, ultimately increasing N₂O emissions by inhibiting nosZ. As the growing season waned and precipitation decreased, temperature also became a driver of N₂O emissions. Structural equation modeling reveals that the impacts of nitrogen and water on N₂O flux variations through nitrification and denitrification are more significant during the peak growing season. This research uncovers innovative insights into how nitrogen and water additions differently impact N₂O dynamics across various stages of the growing season in the temperate steppe, providing a scientific basis for predicting and managing N₂O emissions within these ecosystems. Full article

Eating behavior encompasses the psychological, physiological, and environmental factors influencing food intake. Dysregulation in eating behavior, such as emotional eating, binge eating, or loss of satiety signals, contributes to excessive caloric intake and weight gain. These behaviors are often linked to hormonal imbalances, stress, or genetic predisposition. Obesity is a chronic, multifactorial disease characterized by excessive body fat accumulation, with a body mass index (BMI) ≥ 30 kg/m² often used for diagnosis. It is associated with significant morbidity, including type 2 diabetes, cardiovascular disease, and obstructive sleep apnea. Pathophysiological mechanisms underlying obesity include insulin resistance, leptin dysregulation, and altered gut microbiota, which perpetuate metabolic derangements. Lifestyle interventions remain first-line treatment, but sustained weight loss is challenging for many patients. Bariatric surgery is a therapeutic option for individuals with severe obesity (BMI ≥ 40 kg/m² or ≥35 kg/m² with comorbidities) who have failed conservative management. Procedures such as Roux-en-Y gastric bypass and sleeve gastrectomy alter gastrointestinal anatomy, promoting weight loss through restriction, malabsorption, and hormonal modulation (e.g., increased GLP-1 secretion). Bariatric surgery improves obesity-related comorbidities and enhances quality of life. However, it requires lifelong medical follow-up to address potential nutritional deficiencies and ensure sustainable outcomes. Full article

The interaction between supply and demand of civil aviation passenger transport serves as an important reference for airport planning, transportation structure optimization and dynamic matching of supply and demand. Based on the panel data of 31 province-level administrative divisions (excluding Hong Kong, Macao, and Taiwan) in China spanning from 2004 to 2019, this study employs the entropy-weighted TOPSIS method to evaluate the supply level and demand level of civil aviation passenger transport. On this foundation, this study uses the modified coupling coordination degree model to measure the coupling coordination degree of civil aviation passenger transport supply and demand, further analyzes the spatio-temporal evolution characteristics of coupling coordination degree and employs the double fixed-effect model to investigate the influencing factors. The results indicate that China’s civil aviation passenger supply, demand and supply and demand coupling coordination degree are increasing year by year. The level of provincial supply and demand coupling coordination has risen to the upper-middle level, and has shown significant spatial differences. Economically developed regions demonstrate a higher level of coordination. Economic development, urbanization rate and the degree of opening to the outside world have a positive impact on the coupling and coordinated development. Full article

This study investigates the distribution and mobility of metals and metalloids (M&Ms) in soils, rocks, and groundwater within the geologically complex southwestern region of Sicily. The study aims to highlight how natural sources, like rocks and soils, can release elements potentially harmful to human health. It underlines their dual role as both natural reservoirs and active sources of M&M release, driven by leaching processes influenced by physicochemical factors such as pH and redox potential (Eh). Lithological characteristics significantly influence the retention and release of elements, with clay-rich formations exhibiting higher immobilization capacity. However, environmental parameter variations can enhance element mobilization, increasing bioavailability and the risk of groundwater contamination. Water quality analyses reveal regulatory exceedances for As, B, Ni, and Be, underscoring potential health and ecological risks. Concurrently, microbiological investigations identify diverse microbial communities capable of altering the oxidative states of specific elements through oxidation and reduction processes, further influencing their mobility. This study underscores the importance of understanding natural sources of M&Ms and their interactions with geochemical and microbiological processes for effective environmental risk assessment. The findings provide a foundation for developing integrated and sustainable water resource management strategies to mitigate contamination risks and safeguard ecosystems and public health. Full article

This study investigates the factors influencing users’ intention to use generative AI by employing a Bayesian network-based probabilistic structural equation model approach. Recognizing the limitations of traditional models like the technology acceptance model and the unified theory of acceptance and use of technology, this research incorporates novel constructs such as perceived anthropomorphism and animacy to capture the unique human-like qualities of generative AI. Data were collected from 803 participants with prior experience of using generative AI applications. The analysis reveals that social influence (standardized total effect = 0.550) is the most significant predictor of use intention, followed by effort expectancy (0.480) and perceived usefulness (0.454). Perceived anthropomorphism (0.149) and animacy (0.145) also influence use intention, but with a lower relative impact. By utilizing a probabilistic structural equation model, this study overcomes the linear limitations of traditional acceptance models, allowing for the exploration of nonlinear relationships and conditional dependencies. These findings provide actionable insights for improving generative AI design, user engagement, and adoption strategies. Full article

(1) Background: Global warming has intensified dry heatwaves, threatening urban tree health and ecosystem services. Crown damage in trees is a key indicator of heat stress, linked to physiological changes and urban habitat characteristics, but the specific mechanisms remain to be explored. (2) Methods: This study investigated the heatwave-induced crown damage of Wuhan’s urban tree species, focusing on the influence of physiological responses and urban habitats. Crown damage was visually scored, and physiological responses were measured via stomatal conductance (Gs) and transpiration rate (Tr). (3) Results: Significant interspecific differences in crown damage were identified, with Prunus × yedoensis showing the highest degree of crown damage, while Pittosporum tobira displayed the lowest. A strong correlation was observed between crown damage and Gs and Tr, albeit with species-specific variations. The Degree of Building Enclosure (DegBE) emerged as the most prominent habitat factor, with a mitigating effect on crown damage, followed by the Percentage of Canopy Coverage (PerCC), in contrast with the Percentage of Impermeable Surface (PerIS) that showed a significant positive correlation. (4) Conclusions: The above findings suggest that species traits and habitat configurations interact in complex ways to shape tree resilience under heatwave stress, informing strategies for urban vegetation protection against heat stress in Central Chinese cities. Full article

Background/Objectives: Short tandem repeat expansions are the most common cause of inherited neurological diseases. These disorders are clinically and genetically heterogeneous, such as in myotonic dystrophy and spinocerebellar ataxia, and they are caused by different repeat motifs in different genomic locations. Major advances in bioinformatic tools used to detect repeat expansions from short read sequencing data in the last few years have led to the implementation of these workflows into next generation sequencing pipelines in healthcare. Here, we aimed to evaluate the clinical utility of analysing repeat expansions through exome sequencing in a large cohort of genetically undiagnosed patients with neurological disorders. Methods: We here analyse 27 disease-causing DNA repeats found in the coding, intronic and untranslated regions in 12,496 exomes in patients with a range of neurogenetic conditions. Results: We identified—and validated by polymerase chain reaction—29 repeat expansions across a range of loci, 48% (n = 14) of which were diagnostic. We then analysed the genotyping performance across all repeat loci and found that, despite high coverage in most repeats in coding regions, some loci had low genotyping rates, such as those that cause spinocerebellar ataxia 2 (ATXN2, 0.1–8.4%) and Huntington disease (HTT, 0.2–58.2%), depending on the capture kit. Conversely, while most intronic repeats were not genotyped, we found a high genotyping rate in the intronic locus that causes spinocerebellar ataxia 36 (NOP56, 30.1–98.3%) and in the one that causes myotonic dystrophy type 1 (DMPK, myotonic dystrophy type 1). Conclusions: We show that the key factors that influence the genotyping rate of repeat expansion loci analysis are the sequencing read length and exome capture kit. These results provide important information about the performance of exome sequencing as a genetic test for repeat expansion disorders. Full article

Tobacco smoking remains a major global health concern, causing preventable deaths and economic strain. Although new tobacco products such as heat-not-burn (HnB) are safer alternatives to traditional cigarettes, research on their associated risks remains limited. This study aimed to investigate the effects of HnB smoke exposure on the lungs compared to those of traditional cigarettes and the combined use of HnB and cigarettes using experiments with a mouse model. We quantitatively analyzed changes in the levels of 92 blood plasma proteins using the proximity extension assay method and observed significant changes in their levels in mice exposed to different smoke conditions; specifically, the levels of certain proteins, including Ccl20, Cxcl1, and Pdgfb, increased in the HnB smoke-exposed group, suggesting activation of nicotine pathways. Comparative analysis with traditional cigarette smoke-exposed mice further highlighted similarities and differences in their protein expression profiles. This study contributes to an improved understanding of the biological mechanisms underlying the harmful effects of alternative nicotine delivery systems and identifies potential biomarkers associated with the harmful effects of HnB smoke exposure. However, the precise impact of nicotine on the immune system may be influenced by various factors, necessitating further research. Full article

Predicting seafood consumption behavior is essential for fishing companies to adjust their production plans and marketing strategies. To achieve accurate predictions, this paper introduces a model for forecasting seafood consumption behavior based on an interpretable machine learning algorithm. Additionally, the Shapley Additive exPlanation (SHAP) model and the Accumulated Local Effects (ALE) plot were integrated to provide a detailed analysis of the factors influencing Shanghai residents’ intentions to purchase salmon. In this study, we constructed nine regression prediction models, including ANN, Decision Tree, GBDT, Random Forest, AdaBoost, XGBoost, LightGBM, CatBoost, and NGBoost, to predict the consumers’ intentions to purchase salmon and to compare their predictive performance. In addition, Bayesian optimization algorithm is used to optimize the hyperparameters of the optimal regression prediction model to improve the model prediction accuracy. Finally, the SHAP model was used to analyze the key factors and interactions affecting the consumers’ willingness to purchase salmon, and the Accumulated Local Effects plot was used to show the specific prediction patterns of different influences on salmon consumption. The results of the study show that salmon farming safety and ease of cooking have significant nonlinear effects on salmon consumption; the BO-CatBoost nonlinear regression prediction model demonstrates superior performance compared to the benchmark model, with the test set exhibiting RMSE, MSE, MAE, R² and TIC values of 0.155, 0.024, 0.097, 0.902, and 0.313, respectively. This study can provide technical support for suppliers in the salmon value chain and help their decision-making to adjust their corporate production plan and marketing activities Full article

This study explores how the environmental conditions of sustainable classrooms influence the concentration and academic performance of students in university environments, integrating the use of sustainable design strategies. Within the framework of education for sustainability, the importance of sustainable urban spaces, buildings, interiors, and green infrastructure products in communicating and promoting scientific and environmental knowledge is recognized. Using a systematic mapping methodology, the research examines how real-time monitoring of environmental variables such as air quality, humidity, temperature, CO₂, particulate matter, and lighting, through the Internet of Things (IoT), can enhance learning. The study focuses on examining the impact of environmental factors on students’ academic performance, as well as exploring how sustainable educational spaces can promote greater awareness and favorable attitudes towards the environment. Based on an analysis of 454 articles and success stories on green educational infrastructure projects, the results reveal a significant correlation between optimal environmental conditions such as good ventilation and temperature control and increased student concentration and performance. This study also highlights the role of educational interventions, both formal and informal, that integrate sustainably built environments to reinforce occupants’ environmental engagement. The conclusion is clear: improving classroom environmental conditions, especially in terms of ventilation and temperature control, not only optimizes learning, but also acts as a powerful environmental education tool, fostering education for sustainable development and strengthening ecological attitudes among students. Full article

Accurately identifying the impact of different factors on water conservation is influenced by the spatial grid scale. However, existing studies on water conservation often overlook the Modifiable Areal Unit Problem (MAUP). MAUP is one of the key factors contributing to the uncertainty in spatial analysis results. The Qinling Mountains are a critical water conservation area, with the Yiluo River Basin (YLRB) as a key sub-basin. This study uses the Optimized Parameter GeoDetector (OPGD) model to analyze water conservation changes and influencing factors in the YLRB from 1990 to 2020. By optimizing spatial scale (2 km grid) and driving factor discretization, the OPGD model addresses spatial heterogeneity and the MAUP, enhancing analysis accuracy. Results show a fluctuating upward trend in water conservation depth, averaging 0.94 mm yearly, with a spatial decline from southwest to northeast. High–high and low–low clusters dominate the region, with some areas consistently showing high or low values. Key conservation zones expanded by 2748 km², reflecting significant enhancement. Natural factors, particularly precipitation, predominantly influence water conservation, outweighing human activities. The interaction between precipitation and temperature notably affects dynamic changes, while human impacts, such as land use, play a secondary role. The findings suggest water management should prioritize climatic factors and integrate land-use policies to enhance conservation. The OPGD model’s application improves factor identification and supports targeted ecological and water management strategies. Full article