Search Results (19,851)

In the context of a complex and volatile domestic and global environment, Chinese enterprises face frequent risks of supply chain disruption that seriously hinder their operations. The rise of the digital economy offers new opportunities to strengthen supply chain resilience. Building on supply chain collaboration and value co-creation theories, this study conceptualizes supply chain collaboration through three dimensions, namely information collaboration, governance collaboration, and innovation collaboration, and explores their role in enhancing resilience. Using panel data of Chinese A-share listed firms from 2011 to 2023, this study investigates the impact of focal firm digitalization on supply chain resilience and its underlying mechanisms. The results indicate that focal firm digitalization generates significant backward spillover effects, enhancing the resilience of its upstream suppliers. Although its positive influence on supply chain stability (measured by supply chain demand and supply fluctuations) is not statistically significant, it substantially enhances recovery (measured by supply chain efficiency) and adaptability (measured by supplier innovation). Mechanism analysis further reveals that digitalization strengthens supply chain collaboration through information, governance, and innovation channels, thereby reinforcing resilience. Moreover, the positive effects are heterogeneous, varying with industry competition intensity, the closeness of upstream–downstream relationships, and suppliers’ regional resource endowments. These findings highlight the need to design digitalization strategies centered on focal firm leadership and upstream–downstream collaboration, thereby advancing both resilience improvement and collaborative mechanism development through differentiated and targeted approaches. Full article

This research evaluates the performance of the Final Run remote sensing precipitation products from the Integrated Multi-satellite Retrievals for GPM (IMERG-F) in complex terrain river basins (2014–2023). Utilizing decade-long daily precipitation data from 2415 manned national-level ground stations, the evaluation employs eight statistical metrics—probability of detection, false alarm ratio, accuracy, critical success index, Pearson correlation coefficient (PCC), root mean square difference, mean difference, and relative difference—to analyze detection accuracy, correlation, and bias on daily, monthly, and annual scales. The main findings include the following: (1) IMERG-F’s daily precipitation detection capability follows a three-tier spatial pattern (northwest to southeast), aligning with the stepped terrain of China. (2) Stronger correlations (PCC = 0.7–0.9) with gauge data emerge in southeastern regions despite higher biases, while northwestern areas show weaker correlations but fewer deviations. (3) IMERG-F overestimates annual rainy days, but slightly underestimates precipitation intensity compared with ground observations. (4) Annual precipitation estimates exceed gauge measurements, particularly in the Songhua and Liao River Basins (18–20% overestimation). Monthly analysis shows fewer errors during rainy seasons versus winter dry periods, with pronounced seasonal variations in northwestern basins. These findings emphasize the need for terrain-aware calibration to improve satellite precipitation monitoring in hydrologically diverse basins, particularly addressing seasonal and spatial error patterns in water resource management applications in northern China. Full article

Background: Antimicrobial Resistance (AMR) and the emergence of multidrug-resistant organisms (MDROs) represent major public health threats. Although traditionally linked to hospital-acquired infections (HAIs), MDROs are becoming gradually more prevalent in community-onset infections. Objectives: The objective of this study is to identify major risk factors associated with community-onset MDRO infections among patients admitted to the hospital. Methods: This is a retrospective study of patients admitted to the Internal Medicine Departments of the University General Hospital of Ioannina from July 2022 to August 2023 and had a microbiologically confirmed infection. Patients with HAIs were excluded. Data were extracted from both electronic and paper-based medical records and included variables such as demographics, baseline comorbidities, previous antibiotic use, previous hospitalizations, the type of MDRO and infection, and clinical outcomes. Statistical analysis included descriptive statistics, univariate analyses, and subsequently multiple binary regression models. Each regression model was adjusted for age and sex. Results: Our cohort included 125 participants with a mean age of 77.9 years, with the majority (58.4%) being female. The overall prevalence of MDRO infections was 43.2% (54/125). Notably, the presence of a permanent urinary catheter was associated with a nearly fourfold increase in the risk of community-onset MDRO infections (OR = 3.69; 95% CI: 1.35–10.05; p = 0.011), while prior hospitalization (OR = 3.33; 95% CI: 1.48–7.51; p = 0.004), the Charlson index score (OR = 3.08; 95% Cl: 1.1–8.68; p = 0.033) and previous antibiotic use (OR = 2.18; 95% CI: 0.98–4.84; p = 0.057) were also significant potential risk factors. Conclusions: The identification of key risk factors associated with community-onset MDRO infections in patients admitted to the hospital can assist clinicians in early stratification and rational selection of initial empirical antimicrobial treatment, support antimicrobial stewardship programs, promote targeted public health interventions, and encourage more judicious antibiotic use. Full article

Background/Objectives: Artificial intelligence (AI) is gaining importance in clinical pharmacology, supporting therapeutic decisions and the prediction of drug interactions, although its applications have significant limitations. The aim of the study was to evaluate the accuracy of the responses of four large language models (LLMs), namely ChatGPT-4o, ChatGPT-3.5, Gemini Advanced 2.0, and DeepSeek, in the field of clinical pharmacology and drug interactions, as well as to analyze the impact of prompting and questions from the National Specialization Examination for Pharmacists (PESF) on the results. Methods: In the analysis, three datasets were used: 20 case reports of successful pharmacotherapy, 20 reports of drug–drug interactions, and 240 test questions from the PESF (spring 2018 and autumn 2019 sessions). The responses generated by the models were compared with source data and the official examination key and were independently evaluated by clinical-pharmacotherapy experts. Additionally, the impact of prompting techniques was analyzed by expanding the content of the queries with detailed clinical and organizational elements to assess their influence on the accuracy of the obtained recommendations. Results: The analysis revealed differences in the accuracy of responses between the examined AI tools (p < 0.001), with ChatGPT-4o achieving the highest effectiveness and Gemini Advanced 2.0 the lowest. Responses generated by Gemini were more often imprecise and less consistent, which was reflected in their significantly lower level of substantive accuracy (p < 0.001). The analysis of more precisely formulated questions demonstrated a significant main effect of the AI tool (p < 0.001), with Gemini Advanced 2.0 performing significantly worse than all other models (p < 0.001). An additional analysis comparing responses to simple and extended questions, which incorporated additional clinical factors and the mode of source presentation, did not reveal significant differences either between AI tools or within individual models (p = 0.34). In the area of drug interactions, it was also shown that ChatGPT-4o achieved a higher level of response accuracy compared with the other tools (p < 0.001). Regarding the PESF exam questions, all models achieved similar results, ranging between 83 and 86% correct answers, and the differences between them were not statistically significant (p = 0.67). Conclusions: AI models demonstrate potential in the analysis of clinical pharmacology; however, their limitations require further refinement and cautious application in practice. Full article

Measuring the long-term trend of PM2.5 mass-concentration in urban environments is essential as it has a direct impact on human health. PM2.5 levels depend not only on the intensity of local emission sources and on imported pollution, but also on meteorological conditions (e.g., anticyclonic versus windy conditions), which leads to yearly variations in mean PM2.5 values. Two datasets available for Paris, France, are considered: measurements from Airparif air quality agency network and from the Pollutrack network of mobile car-based sensors. Also, meteorological parameters coming from ERA5 analysis (ECMWF) are considered. Annual values are calculated using three different statistical methods, which yield different results. For the 2013–2024 period, a clear relationship between wind speed and PM2.5 mass-concentration levels is established. The results show a linear decrease in both concentration and standard deviation for wind speeds in the 0–6 m.s⁻¹ range, followed by nearly stable values for wind speed above 6 m.s⁻¹. This behavior is explained by the dispersive effect of strong winds on air pollution. Under such conditions, which occur about 10% of the time in Paris, the contribution of persistent background sources can be isolated. Using the 6 m·s⁻¹ threshold, the average annual linear decrease in emissions from local sources is estimated at 4.1 and 4.3% per year for the Airparif and Pollutrack data, respectively. Since 2023, the annual background value attributed to emission has been close to 5 µg.m⁻³, in agreement with WHO recommendations. This approach could be used to monitor the effects of regulations on traffic and heating emissions and could be applied to other cities for estimating background pollution levels. Finally, future studies should therefore prioritize number concentrations and size distributions, rather than mass-concentrations. Full article

This study addresses the limitations of traditional coal calorific value prediction models, which primarily rely on linear regression and single-source proximate analysis data. Based on 465 Chinese coal samples and integrating proximate analysis, ultimate analysis, and constructed derived indicators (combustible content—CC, carbon–hydrogen index—CHI, carbon in combustibles—CIC), a nonlinear modeling method combining mean impact value (MIV) feature selection and support vector regression (SVR) is proposed. The results show that the Pearson correlation coefficients between the derived indicators and net calorific value (NCV) all exceed 0.93, outperforming the original items. Using CC–CHI–CIC–FC_ad as characteristic variables, the established SVR model achieved a mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R²) of 1.838%, 0.544 MJ/kg, and 0.962, respectively, with exceptionally high statistical significance (F = 1485.96, p < 0.001). The predictive accuracy of this model is significantly superior to traditional linear models, while the proposed linear model based on the derived indicators (R² > 0.900) can serve as an alternative for rapid estimation. This method effectively enhances the accuracy and robustness of coal calorific value prediction. Full article

Background: Conflicts are an inevitable part of interpersonal relationships, and personality traits influence how they are resolved. In the nursing work environment, conflicts often arise from poor communication and stress, negatively impacting nurses’ well-being and quality of care. The “Big Five” personality model highlights how traits such as extraversion, agreeableness, and emotional stability shape conflict approaches. Understanding these traits aids in developing effective conflict management strategies. This study investigates intragroup conflicts among nurses by identifying their types and examining how sociodemographic factors and personality traits predict their occurrence. The aim is to provide insights that support targeted interventions and improve team dynamics in nursing practice. Methods: The study was conducted as a cross-sectional analysis within the University Hospital Centre Osijek from March to August 2024, involving nurses and technicians. Data was collected using structured questionnaires with clearly defined inclusion and exclusion criteria. The questionnaire included the Process Conflict Scale, the Big Five Inventory, and a Demographic questionnaire. Appropriate statistical analyses were conducted, including descriptive statistics, normality testing with the Kolmogorov–Smirnov test, non-parametric Spearman and Point-Biserial correlations, and linear regression to examine predictors of intragroup conflicts. All assumptions for regression were met, with significance set at p < 0.05, and analyses were performed using JASP software version 0.17.2.1. Results: The research reveals significant differences among various types of team conflicts, where personality traits such as neuroticism increase, while conscientiousness decreases conflicts. The professional competence of respondents also positively correlates with logistical conflicts, and personality explains the variance in conflicts among nurses. Conclusions: Intragroup conflicts among nurses, particularly task-related, stem from communication issues and high care standards. Neuroticism negatively affects team dynamics, while conscientiousness can reduce conflicts but may also lead to disagreements if expectations are unmet. Education on conflict management and clearly defined roles can improve teamwork and quality of care. Full article

This present investigative work proceeds with the statistical study of the heat transfer coefficient (CTC) in the different flow transitions that are formed in a horizontal pipe with variation in the angles of inclination in a collector/evaporator component of a heat pump of solar assisted direct expansion (DX-SAHP) by using R600a refrigerant as working fluid in Quito - Ecuador. The dimensions of the collector/evaporator are 3.8 and 1000 mm inside diameter and length, respectively. To determine the results obtained, five practical tests are carried out with inclination angles of 10, 20, 30, 40 and 45°, with speeds or mass flows that vary between 203.24 and 222.28 kg·m⁻²·s⁻¹, the heat fluxes reached values between 200.58 and 507.23 W·m⁻². The correlations proposed by Kattan, Kundu, and Mohseni, and the experimental data were considered for the analysis of the effects of heat transfer on flow patterns. The results obtained from the investigation show that the maximum CTC is 6163.83 W·m⁻²·K⁻¹ with an inclination angle of 45°. Statistical analysis was performed considering the direction of Pearson presented results that for the angle of inclination of 10° a greater inverse direction of −0.316 is obtained. Full article

This study aimed to evaluate blood lactate, anion gap, and ionised calcium levels as potential diagnostic biomarkers in calves with atresia coli, and to identify possible predisposing factors such as breed, gender, age, method of conception, number of lactations, and births. The study included twenty-two calves with atresia coli and ten healthy controls, all aged 1–11 days (median, 3 days), brought to Erciyes University Veterinary Faculty from Kayseri and nearby provinces due to non-defecation and abdominal swelling. Prominent clinical findings among the 22 calves with atresia coli included abdominal distension in 90.9%, anorexia in 81.8%, and depressed general posture in 86.4%. Blood gas analysis revealed significantly elevated lactate and anion gap and decreased ionised calcium and pH in atresia coli calves compared to controls (p < 0.05). Anion gap (>14.05 mmol/L) and ionised calcium (<1.205 mmol/L) demonstrated high diagnostic accuracy (AUC: 0.964 and 0.872, respectively), suggesting their potential as supportive biomarkers for early detection of atresia coli. The study data revealed that male gender, artificial insemination, and calves born from the third or subsequent pregnancies are statistically significant risk factors for the development of atresia coli. Atresia coli in calves is characterized by specific clinical signs and significant changes in blood gas parameters, such as elevated lactate and anion gap and reduced ionised calcium and pH. Early detection using these markers can improve diagnosis, and further studies should focus on prevention by addressing these risk factors. Full article

Background: This study aims to evaluate the effect of different types of platelet concentrates (autologous blood biomaterials) on the migration potential of human dental pulp stem (hDPSCs). Materials and Methods: Our team created a model of human dental pulp stem cells (hDPSCs). Various types of AB biomaterials were produced from blood samples from volunteers using the protocols presented: A-PRF+, Gel A-PRF+, and Solid PRF. The scratch wound healing assay was used to examine the closure of the experimental wounds on day 1 and day 14. The wound areas were quantified using Image J software. Statistical analysis was performed with the Kruskal–Wallis and Mann–Whitney U tests, as the data did not follow a normal distribution, which was confirmed by the Shapiro–Wilk test (p < 0.05). Results: The results demonstrate significantly faster closure of the experimental wounds on day 14 of the studied biomaterials AB: A-PRF+, Gel A-PRF+, and Solid PRF compared to the control group of cells. Gel A-PRF+ exhibited the most pronounced stimulatory effect on cell migration (p = 0.0036 vs. control), followed by Solid PRF and A-PRF+. Conclusions: The results indicate that autologous blood platelet concentrates stimulate the migration of hDPSCs in vitro. Gel A-PRF+ demonstrated the strongest effect, underscoring its potential clinical relevance for applications in tissue engineering. Full article

Background: Standard imaging in neurosurgery often fails to visualize infiltrative tumor regions that extend beyond contrast enhancement. Metabolic imaging using hyperpolarized 13C-MRI may offer new intraoperative insights into tumor biology. Objective: To systematically assess the clinical and technical evidence on hyperpolarized MRI for metabolic tumour characterization in patients with malignant brain tumors. Eligibility criteria: We included original human studies reporting on hyperpolarized 13C-MRI for perioperative and diagnostic use in brain tumor patients. Reviews, animal studies, and technical-only reports were excluded. Information sources: Searches were conducted in PubMed, Embase, and Web of Science on 26 December 2024. Risk of bias: Methodological quality was assessed using the QUADAS-2 tool. Synthesis of results: A qualitative synthesis was performed, and where feasible, random-effects meta-analysis was used to calculate standardized mean differences (SMDs) and heterogeneity statistics. Results: Three studies (n = 15 patients) met inclusion criteria. The bicarbonate-to-pyruvate ratio showed a significant difference between tumor and non-tumour brain (SMD = 1.34, p = 0.002), whereas pyruvate-to-lactate ratio (kPL) values showed minimal difference (SMD = 0.06, p = 0.730). Asmall effect was observed for kPL between tumor and normal-appearing white matter (SMD = –0.33). One study provided qualitative data only. Overall heterogeneity was high (I² = 69.4%). Limitations: Limitations include small sample sizes, heterogeneous methodologies, and limited availability of patient-level data. Interpretation: Hyperpolarized 13C-MRI shows metabolic differentiation between tumor and healthy tissue in certain parameters, especially bicarbonate metabolism. While promising, the technology requires further clinical validation before routine intraoperative application. Full article

Objectives: The present study aimed to determine the growth dynamics of the ossification centers of the twelfth thoracic vertebra in the human fetus, focusing on detailed linear, surface, and volumetric parameters of both the vertebral body and neural processes. Methods: The investigation was based on 55 human fetuses (27 males, 28 females) aged 17–30 weeks of gestation. High-resolution low-dose computed tomography, three-dimensional reconstruction, digital image analysis and appropriate statistical modeling were used to obtain detailed morphometric measurements. Results: All measured morphometric parameters of the Th12 vertebral body ossification center—transverse and sagittal diameters, cross-sectional area, and volume—increased linearly with gestational age (R² = 0.94–0.97). A similar linear growth pattern was demonstrated for the length, width, cross-sectional area, and volume of the right and left neural process ossification centers (R² = 0.97–0.98). No statistically significant sex-related or side-related differences were found, allowing the establishment of single normative growth curves for each parameter. Conclusions: This study provides the first comprehensive CT-based normative data for the ossification centers of the fetal Th12 vertebra in the second and early third trimesters. The presented linear growth models and reference values may assist anatomists, radiologists, obstetricians, and pediatric spine surgeons in estimating fetal age, and in the prenatal and postnatal assessment of congenital spinal anomalies, especially at the thoracolumbar junction. Further research on larger and broader gestational cohorts is warranted to validate and extend these findings. Full article

Nurses often work under high stress and heavy workloads, making it critical to understand factors that influence their motivation, psychosocial safety climate (PSC), and work engagement (WE). Objectives: To assess nurses’ levels of motivation, PSC, and WE; examine relationships among these variables; and test whether PSC mediates the association between motivation and WE. Methods: A descriptive correlational study was conducted with 318 nurses from Assiut University Hospital, Egypt, using validated scales for motivation, PSC, and WE. Data were analyzed using descriptive statistics, Pearson’s correlations, multivariate regression, and mediation analysis (bootstrapped, 5000 resamples). Statistical significance was set at p < 0.05. Results: Nurses reported moderate motivation (M = 118.1, SD = 16.4), moderate WE (M = 22.7, SD = 5.8), and low PSC perception (M = 60.5, SD = 16.6). Motivation was positively correlated with PSC (r = 0.48, 95% CI [0.39, 0.56], p < 0.001). Motivation-WE correlation was small and non-significant (r = 0.10, 95% CI [−0.01, 0.21], p = 0.08). Mediation analysis showed PSC partially mediated the motivation-WE link (indirect effect = 0.07, 95% CI [0.02, 0.14]), though the effect size was small. Conclusions: Motivation and PSC reinforce each other, but neither strongly predicts WE in this setting. Targeted strategies to strengthen PSC and intrinsic motivation may indirectly enhance engagement and retention. Full article

Quantifying and removing urbanization-induced biases in existing precipitation datasets is critical for climate change detection, model assessment, and attribution studies in Northwest China (NWC). The precipitation observational stations of NWC were divided into rural (reference) stations and urban stations using the percentage of urban areas calculated from the land use/land cover (LULC) satellite data of the European Space Agency (ESA) Climate Change Initiative (CCI) Land Cover project. The annual extreme precipitation index series for urban stations (all stations) and rural stations from 1961 to 2022 were calculated based on the categorization of meteorological stations, and the urbanization effects and their contributions to precipitation index series were quantitatively evaluated through estimating trends in the difference series between all stations and the rural stations. The results showed that the urbanization effect varies among different regions and indices. The R10mm, R95pTOT, R99pTOT, and PRCPTOT indices in the sampled urban areas of NWC exhibited statistically significant negative urbanization effects, reaching −0.075 days decade⁻¹, −0.038 % decade⁻¹, −0.024 % decade⁻¹, and −0.035 % decade⁻¹, respectively. However, the R95pTOT, SDII, CDD, and CWD indices at the urban station of the largest city, Urumqi, have been significantly positively affected by urbanization, which is inconsistent with the sampled urban areas of NWC, where the urbanization effect reached 0.069 % decade⁻¹, 0.054 mm·d⁻¹ decade⁻¹, 2.319 days decade⁻¹, and 0.112 days decade⁻¹, respectively. Our analysis shows that the previously reported regional increase in total precipitation and extremes has been underestimated due to the negative urbanization effects in the precipitation data series of urban stations. Full article

Background and Objectives: Dynamic nutrient profiling represents a paradigm shift in personalized nutrition, integrating real-time nutritional assessment with individualized dietary recommendations through advanced algorithmic approaches, biomarker integration, and artificial intelligence. This comprehensive systematic review and meta-analysis examines the current state of dynamic nutrient profiling methodologies for personalized diet planning, evaluating their effectiveness, methodological quality, and clinical outcomes. Methods: Following PRISMA 2020 guidelines, we conducted a comprehensive search of electronic databases (PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, and Google Scholar) from inception to December 2024. The protocol was prospectively registered in PROSPERO (Registration: CRD42024512893). Studies were systematically screened using predefined inclusion criteria, quality was assessed using validated tools (RoB 2, ROBINS-I, Newcastle–Ottawa Scale), and data were extracted using standardized forms. Random-effects meta-analyses were performed where appropriate, with heterogeneity assessed using I² statistics. Publication bias was evaluated using funnel plots and Egger’s test. Results: From 2847 initially identified records plus 156 from additional sources, 117 studies met the inclusion criteria after removing 391 duplicates and systematic screening, representing 45,672 participants across 28 countries. Studies employed various methodological approaches: algorithmic-based profiling systems (76 studies), biomarker-integrated approaches (45 studies), and AI-enhanced personalized nutrition platforms (23 studies), with some studies utilizing multiple methodologies. Meta-analysis revealed significant improvements in dietary quality measures (standardized mean difference: 1.24, 95% CI: 0.89–1.59, p < 0.001), dietary adherence (risk ratio: 1.34, 95% CI: 1.18–1.52, p < 0.001), and clinical outcomes including weight reduction (mean difference: −2.8 kg, 95% CI: −4.2 to −1.4, p < 0.001) and improved cardiovascular risk markers. Substantial heterogeneity was observed across studies (I² = 78–92%), attributed to methodological diversity and population characteristics. AI-enhanced systems demonstrated superior effectiveness (SMD = 1.67) compared to traditional algorithmic approaches (SMD = 1.08). However, current evidence is constrained by practical limitations, including the technological accessibility of dynamic profiling systems and equity concerns in vulnerable populations. Additionally, the evidence base shows geographical concentration, with most studies conducted in high-income countries, underscoring the need for research in diverse global settings. These findings have significant implications for shaping public health policies and clinical guidelines aimed at integrating personalized nutrition into healthcare systems and addressing dietary disparities at the population level. Conclusions: Dynamic nutrient profiling demonstrates significant promise for advancing personalized nutrition interventions, with robust evidence supporting improved nutritional and clinical outcomes. However, methodological standardization, long-term validation studies exceeding six months, and comprehensive cost-effectiveness analyses remain critical research priorities. The integration of artificial intelligence and multi-omics data represents the future direction of this rapidly evolving field. Full article