Search Results (17,757)

Aim: There is an urgent need for systematic and well-designed studies to clarify the role of systemic inflammatory parameters, especially the neutrophil–lymphocyte-ratio (NLR), in the pathophysiology and clinical management of unregulated substance addiction. This review aims to synthesize current evidence on the relationship between unregulated substance addiction and systemic inflammatory parameters, focusing specifically on the NLR as a potential biomarker. Methods: To ensure a transparent approach in the collection of evidence, this review was carried out following the recommendations of the PRISMA 2020 guidelines and registered in PROSPERO (CRD420251151136). We searched the PubMed and Scopus databases in July2025 using combinations of MeSH terms and keywords related to unregulated substance use and inflammatory biomarkers. The strategy included terms such as “cocaine,” “cannabis,” “opioids,” “heroin,” “fentanyl,” “methadone,” “buprenorphine” “nitazene”, “MDMA”, and “methamphetamine,” combined with “neutrophil-to-lymphocyte ratio.” Filters were applied to limit results to human studies published between 2015 and 2025 in English. The methodological quality of the studies included was assessed using the STROBE 22-item checklist. Results: Fifteen studies were included in this review. Methamphetamine and opioid users showed higher NLR and MLR values. For cocaine abuse, although the evidence is limited to a single population-based study, a significant increase in NLR was reported. Controversial results were observed for cannabis use. Conclusions: Systemic inflammation markers are related to unregulated substance abuse disorders; however, the sparse available evidence encourages the need for well-designed large, prospective clinical trials. Full article

Forecasting the spatio-temporal evolutions of cellular traffic is crucial for urban management. However, achieving accurate forecasting is challenging due to “complex correlation modeling” and “model-blindness” issues. Specifically, cellular traffic is generated within complex urban systems characterized by an intricate structure and human mobility. Existing approaches, often based on proximity or attributes, struggle to learn the latent correlation matrix governing traffic evolution, which limits forecasting accuracy. Furthermore, while substantial knowledge about urban systems can supplement the modeling of correlations, existing methods for integrating this knowledge—typically via loss functions or embeddings—overlook the synergistic collaboration between data and knowledge, resulting in weak model robustness. To address these challenges, we develop a data–knowledge collaborative learning framework termed the knowledge-empowered spatio-temporal neural network (KESTNN). This framework first extracts knowledge triplets representing urban structures to construct a knowledge graph. Representation learning is then conducted to learn the correlation matrix. Throughout this process, data and knowledge are integrated collaboratively via backpropagation, contrasting with the forward feature injection methods typical of existing approaches. This mechanism ensures that data and knowledge directly guide the dynamic updating of model parameters through backpropagation, rather than merely serving as a static feature prompt, thereby fundamentally alleviating the “model-blindness” issue. Finally, the optimized matrix is embedded into a forecasting module. Experiments on the Milan dataset demonstrate that the KESTNN exhibits excellent forecast performance, reducing RMSE by up to 23.91%, 16.73%, and 10.40% for 3-, 6-, and 9-step forecasts, respectively, compared to the best baseline. Full article

Lignans are naturally occurring compounds found in a wide variety of plant species, including flaxseed, soybean, pumpkin seed, broccoli, sesame seed, and some berries. Lignans have been used for centuries in both food and traditional herbal medicine. Recently, numerous new lignans and lignan derivatives with diverse biological properties have been identified. Lignans are considered promising for human health due to their hydrogen-donating antioxidant activity together with their ability to complex divalent transition metal cations. They have demonstrated beneficial effects for cardiovascular disease, as well as in maintaining blood glucose levels, supporting cardiac health, promoting anti-obesity effects, decreasing the risk of renal diseases, enhancing brain function, improving skin and gut health, among others. This review explores the biosynthesis and biological effects of lignans, with a particular focus on their antihypertensive and anti-obesity properties, as well as the molecular mechanisms involved. It also highlights recent advances in sustainable lignan extraction techniques that are suitable for human use. The mechanisms underlying these bioactivities are thought to involve hormonal metabolism and availability, antioxidant action, modulation of angiogenesis, and more. However, further research is needed to fully elucidate the molecular pathways through which lignans exert their therapeutic effects. Overall, lignans from various plant sources hold significant potential for application in functional foods, dietary supplements, and pharmaceutical products aimed at preventing and managing a range of health conditions, including hypertension and obesity. Full article

Cataracts remain the leading cause of blindness worldwide, and surgery is currently the only effective clinical treatment, as no pharmacological therapy has yet been validated. Here, we explore Fullerenol, a hydroxylated fullerene derivative formulated as eye drops, as a potential nanomedicine for delaying cataract onset and progression. In UVB-induced mouse cataract models, topical Fullerenol preserved the lens transparency and histological structure. In human lens epithelial cells, Fullerenol reduced the oxidative stress, restored the mitochondrial function, alleviated the DNA damage, and suppressed the cellular senescence. RNA sequencing and pathway enrichment analyses further indicated that Fullerenol modulated the oxidative stress- and senescence-associated signaling pathways, including MAPK and TGF-β cascades, while downregulating the p53–CDKN1A (p21) axis. These findings provide new evidence that Fullerenol can mitigate photo-oxidative damage and age-related cellular dysfunction, highlighting its promise as a non-invasive and clinically translatable nanomedicine strategy for cataract management. Full article

In corporate management practices, human resources are among the most active and critical elements, and frequent employee turnover can impose substantial losses on firms. Accurately predicting employee turnover dynamics and identifying turnover propensity in advance is therefore of significant importance for organizational development. To improve turnover prediction performance, this study proposes an employee turnover prediction model that integrates an improved ADASYN data rebalancing algorithm with a grid-search-optimized CatBoost classifier. In practice, turnover instances typically constitute a minority class; severe class imbalance may lead to overfitting or underfitting and thus degrade predictive performance. To mitigate imbalance, we employ ADASYN oversampling to reduce skewness in the dataset. However, because ADASYN is primarily designed for continuous features, it may generate invalid or meaningless values when discrete variables are present. Accordingly, we improve ADASYN by introducing a new distance metric and an enhanced sample generation strategy, making it applicable to turnover data with mixed (continuous and discrete) features. Given CatBoost’s strong predictive capability in high-dimensional settings, we adopt CatBoost as the base learner. Nonetheless, CatBoost performance is highly sensitive to hyperparameter choices, and different parameter combinations can yield markedly different results. Therefore, we apply grid search (GS) to efficiently optimize CatBoost hyperparameters and obtain the best-performing configuration. Experimental results on three datasets demonstrate that the proposed improved-ADASYN GS-CatBoost model effectively enhances turnover prediction performance, exhibiting strong robustness and adaptability. Compared with existing models, our approach improves predictive accuracy by approximately 4.6112%. Full article

Coal mine ventilation systems face coupled and systemic risks characterized by structural interconnection and disaster chain propagation. In order to accurately quantify and evaluate this overall system risk, this study presents a new method of risk assessment of the coal mine ventilation system based on fuzzy polymorphic Bayesian networks. This method effectively addresses the shortcomings of traditional assessment approaches in the probabilistic quantification of risk. A Bayesian network with 44 nodes was established from five dimensions: ventilation power, ventilation network, ventilation facilities, human and management factors, and work environment. The risk states were divided into multiple states based on the As Low As Reasonably Practicable (ALARP) metric. The probabilities of evaluation-type root nodes were calculated using fuzzy evaluation, and the subjective bias was corrected by introducing a reliability coefficient. The concept of distance compensation is proposed to flexibly calculate the probabilities of quantitative-type root nodes. Through the verification of the ventilation system of H Coal Mine in Shanxi, China, it is concluded that the high risk of the ventilation system is 18%, and the high-risk probability of the ventilation system caused by the external air leakage of the mine is the largest. The evaluation results are consistent with real-world conditions. The results can provide a reference for improving the safety of the ventilation systems. Full article

The abrupt COVID-19 lockdown in early 2020 offered a unique natural experiment to examine vegetation productivity responses to sudden declines in human activity. Although vegetation often responds to environmental changes with time lags, how such lags operate under short-term, intensive disturbances remains unclear. This study combined multi-source environmental data with an interpretable machine learning framework (XGBoost-SHAP) to analyze spatiotemporal variations in net primary productivity (NPP) across the Beijing-Tianjin-Hebei region during the strict lockdown (March–May) and recovery (June–August) periods, using 2017–2019 as a baseline. Results indicate that: (1) NPP showed a significant increase during lockdown, with 88.4% of pixels showing positive changes, especially in central urban areas. During recovery, vegetation responses weakened (65.31% positive) and became more spatially heterogeneous. (2) Integrating lagged environmental variables improved model performance (R² increased by an average of 0.071). SHAP analysis identified climatic factors (temperature, precipitation, radiation) as dominant drivers of NPP, while aerosol optical depth (AOD) and nighttime light (NTL) had minimal influence and weak lagged effects. Importantly, under lockdown, vegetation exhibited stronger immediate responses to concurrent temperature, precipitation, and radiation (SHAP contribution increased by approximately 7.05% compared to the baseline), whereas lagged effects seen in baseline conditions were substantially reduced. Compared to the lockdown period, anthropogenic disturbances during the recovery phase showed a direct weakening of their impact (decreasing by 6.01%). However, the air quality improvements resulting from the spring lockdown exhibited a significant cross-seasonal lag effect. (3) Spatially, NPP response times showed an “urban-immediate, mountainous-delayed” pattern, reflecting both the ecological memory of mountain systems and the rapid adjustment capacity of urban vegetation. These findings demonstrate that short-term removal of anthropogenic disturbances shifted vegetation responses toward greater immediacy and sensitivity to environmental conditions. This offers new insights into a “green window period” for ecological management and supports evidence-based, adaptive regional climate and ecosystem policies. Full article

Despite existing studies on creativity, examining human resource management practices alongside knowledge management models for constructing creativity remains lacking. This study investigates employee creativity in small and medium enterprises (SMEs) in Indonesia, using data from 508 respondents within a 254-sample frame and employing partial least squares structural equation modeling (PLS-SEM). The results indicate that human resource management practices and technological innovation significantly influence knowledge management and cultivate competitive work environments that foster creativity. The PLS-SEM model confirmed that human resource management practices and technological innovation have a significant direct effect on employee creativity, as well as indirect effects through knowledge management and competitive work environments. Knowledge management and competitive work environment served as double mediators in the mediation mechanism tested in this model. The findings provide practical insights for managers seeking to optimize human resources and technological innovation to enhance knowledge management and create competitive work environments that boost creativity. Full article

The continuous degradation of mangrove ecosystems, considered among the most vulnerable worldwide, reveals multiple threats driven by human activities and climate change. In the Peruvian context, particularly in the Tumbes Mangrove ecosystem, these pressures are intensified by the absence of integrated spatial and educational infrastructures capable of supporting conservation efforts while engaging local communities. In response, this research proposes a Sustainable Interpretation Center for Conservation and Environmental Education in Ecologically Sensitive Areas of the Tumbes Mangrove, Peru. The methodology includes climate data analysis, identification of local flora and fauna, and site topography characterization, supported by digital tools such as Google Earth, AutoCAD 2025, Revit 2025, and 3D Sun Path. The results are reflected in an architectural proposal that incorporates sustainable materials compatible with sensitive ecosystems, including eco-friendly structural solutions based on algarrobo timber, together with resilient strategies addressing climatic variability, such as lightweight structures, elevated platforms, and passive environmental solutions that minimize impact on the mangrove. Furthermore, the proposal integrates a photovoltaic energy system consisting of 12 solar panels with a unit capacity of 450 W, providing a total installed capacity of 5.4 kWp, complemented by a 48 V LiFePO₄ battery storage system designed to ensure energy autonomy during periods of low solar availability. In conclusion, the proposal adheres to principles of sustainability and energy efficiency and aligns with the Sustainable Development Goals (SDGs) 7, 8, 12, 14, and 15, reinforcing the use of clean energy, responsible tourism, sustainable resource management, and the conservation of marine and terrestrial ecosystems. Full article

This study aimed to develop a credit risk index to classify coffee producers according to socioeconomic, agronomic, and financial performance variables, with the purpose of strengthening financial inclusion. We combined qualitative and quantitative methods to understand credit risk factors among smallholder coffee producers. The study followed a descriptive-analytical approach structured in consecutive methodological phases. The systematic review, conducted following the Kitchenham protocol, identified theoretical factors associated with credit risk, while fieldwork with 300 producers provided the socioeconomic and productive contexts of coffee-growing households. Producer income, cost of living, and farm management expenses were modeled using regression, statistical, and machine learning methods. Subsequently, these variables were integrated to construct a financial risk index, which was normalized using expert scoring. The index was validated using data from 100 additional producers, for whom annual repayment capacity and maximum loan amounts were estimated according to their risk level. The results indicated that incorporating municipal-level economic variables, such as estimated average prices, income, and expenses, enhanced predictive accuracy and improved the rational allocation of loan amounts. The study concludes that credit risk analysis based on variables related to human, productive, and economic capital constitutes an effective strategy for improving access to finance in rural areas. Full article

Background: Postnatal growth failure in very preterm infants remains a major concern in neonatal care and clinical management is complicated by the lack of a standardized definition. This study aims to identify risk factors for growth faltering (GF) and undernutrition (UN) at hospital discharge, defined according to the latest consensus definitions established by the European Society for Paediatric Gastroenterology, Hepatology and Nutrition (ESPGHAN). Methods: We conducted a retrospective observational study of 416 preterm infants (gestational age < 32 weeks and/or birth weight < 1500 g). Growth was monitored using the Intergrowth 21st standards. In line with ESPGHAN criteria, GF was defined longitudinally as a weight for age (WFA) z-score decline ≥ 1 SD from birth, while UN was defined cross-sectionally as a WFA or length for age z-score < −2 SD at discharge. Logistic regression models were used to determine independent predictors for both growth phenotypes. Results: At discharge, the prevalence of GF and UN was 45.3% and 33.1%, respectively. In infants born without growth restriction (GR), UN was almost entirely driven by GF (89.7%). In contrast, 85.5% of infants born with GR remained undernourished at discharge. Multivariate analysis identified bronchopulmonary dysplasia and higher maximal postnatal weight loss as major independent risk factors for GF, while female sex and human milk feeding at discharge were associated with a lower risk of GF. For infants born with adequate weight, maternal hypertension, extremely low birth weight, and the co-occurrence of GF were the strongest predictors of UN. Conclusions: Nearly half of very preterm infants experience significant growth impairment before discharge. By assessing the dynamic process of GF and the static endpoint of UN, we identified distinct clinical trajectories. Standardized ESPGHAN criteria allow for the identification of high-risk “phenotypes”—particularly those with GR at birth or severe neonatal morbidity—enabling more targeted and intensive nutritional management during the critical developmental window. Full article

Background/Objectives: Greater than 50% of surviving very preterm infants are affected by postnatal growth failure and are at high risk of associated development of bronchopulmonary dysplasia (BPD). Given the influence of enteral feeding on growth failure, we aimed to determine the impact of type, volume, and time of introduction of enteral feeds on mitigating the risk of postnatal growth failure and BPD risk. Methods: This was a retrospective chart review of mothers’ own milk (MOM), pooled pasteurized donor human milk (PDHM) feeding, postnatal growth, and BPD severity in preterm infants <33 weeks of gestation admitted to the Children’s Hospital of Richmond at VCU neonatal intensive care unit between 2021 and 2024. Statistical analysis included linear regression with moderation analysis using the Hayes Process model, chi-square tests, linear and multinomial logistic regression, with p-value < 0.05 considered significant. Results: After controlling for the percentage of MOM received at 34 weeks corrected gestational age (cGA), greater severity of BPD was associated with lower infant weight and growth failure, p < 0.001. Early introduction of MOM (3 days of life) and greater volume of MOM showed better linear growth and decreased risk of severe BPD, respectively (p < 0.001). Conclusions: Provision of MOM to preterm infants within 3 days of life was associated with a moderation of the relationship between gestational age and growth velocity, with improved growth velocity trajectory. Preterm infants who received a greater volume of MOM through 34 weeks cGA experienced less severe BPD compared to those fed higher volumes of PDHM. As the incidence of growth failure paralleled the incidence of BPD severity, identification of key MOM components becomes important to address and augment the value of PDHM in the management of preterm infants. Full article

Human Capital Agility is increasingly recognized as a critical capability for achieving sustainable project success in the highly dynamic construction sector, yet an original and empirically testable Human Capital Agility model rooted in Human Capital theory is still lacking. This study aims to develop and validate a Human Capital Agility framework that integrates Leadership Agility and Knowledge Management and to construct a hierarchical roadmap for the gradual development of Human Capital Agility. Using a multi-method design, survey data from 141 construction professionals were analyzed with Partial Least Squares Structural Equation Modeling to test the structural relationships among Knowledge Management, Leadership Agility, Human Capital Agility, Sustainable Project Success, and the moderating role of Firm Size, while expert judgments from nine practitioners were modeled using Modified Total Interpretive Structural Modeling to derive the internal hierarchy of Human Capital Agility components. The results show that Leadership Agility is a dominant driver of Human Capital Agility and that Human Capital Agility significantly enhances Sustainable Project Success, whereas the direct effect of tacit knowledge on Leadership Agility is not supported. The hierarchical model maps nine key components of Human Capital Agility into six levels, separating foundational drivers such as attitudes and predisposition from higher-level outcome capabilities such as generative behavior, responsiveness, adaptability, and resilience. These findings provide an integrated and empirically grounded Human Capital Agility model that offers both a causal explanation and a practical roadmap for strengthening human capital capabilities in construction projects. Full article

Climate change and land-use intensification are speeding up the loss of ecosystem services that support human health, food security, and environmental stability. Vegetative interventions—such as urban forests in cities and cover crops in farming systems—are increasingly seen as nature-based solutions for climate adaptation. However, their benefits are often viewed separately. This review combines 20 years of research to explore how these strategies, together, improve provisioning, regulating, supporting, and cultural ecosystem services across various landscapes. Urban forests help reduce urban heat islands, improve air quality, manage stormwater, and offer cultural and health benefits. Cover crops increase soil fertility, regulate water, support nutrient cycling, and enhance crop yields, with potential for carbon sequestration and biofuel production. We identify opportunities and challenges, highlight barriers to adopting these strategies, and suggest integrated frameworks—including spatial decision-support tools, incentive programs, and education—to encourage broader use. By connecting urban and rural systems, this review underscores vegetation as a versatile tool for resilience, essential for reaching global sustainability goals. Full article

The COVID-19 pandemic led to an unprecedented slowdown in global economic and transportation activities, offering a unique opportunity to assess the relationship between human activity and atmospheric pollution. This study analyzes global variations in major air pollutants and meteorological conditions during the pandemic period using multi-satellite and reanalysis datasets. Nitrogen dioxide (NO₂) data were obtained from the OMI sensor aboard NASA’s Aura satellite, while carbon monoxide (CO) observations were taken from the MOPITT instrument on Terra. Reanalysis products from MERRA-2 were used to assess CO, sulfur dioxide (SO₂), black carbon (BC), organic carbon (OC), and key meteorological variables, including temperature, precipitation, evaporation, wind speed, and direction. Average concentrations of pollutants for April, May, and June 2020, representing the lockdown phase, were compared with the average values of the same months during 2017–2019, representing pre-pandemic conditions. The difference between these multi-year means was used to quantify spatial changes in pollutant levels. Results reveal widespread reductions in NO₂, CO, SO₂, and BC concentrations across major industrial and urban regions worldwide, consistent with decreased anthropogenic activity during lockdowns. Meteorological analysis indicates that the observed reductions were not primarily driven by short-term weather variability, confirming that the declines are largely attributable to reduced emissions. Unlike most previous studies, which examined local or regional air-quality changes, this work provides a consistent global-scale assessment using harmonized multi-sensor datasets and uniform temporal baselines. These findings highlight the strong influence of human activities on atmospheric composition and demonstrate how large-scale behavioral and economic shifts can rapidly alter air quality on a global scale. The results also provide valuable baseline information for understanding emission–climate interactions and for guiding post-pandemic strategies aimed at sustainable air-quality management. Full article