Search Results (26,658)

Intelligent connected vehicles (ICVs) are increasingly emerging as a pivotal driving force for promoting sustainable development in global transportation due to their outstanding performance in mitigating carbon emissions, alleviating traffic congestion, and enhancing travel efficiency. Enhancing the digital innovation capability of ICV enterprises is an imperative path to explore high-quality industry development and effectively respond to the latest technological revolution and industrial transformation. Based on the panel data from 20 listed companies in the ICV industry in China from 2019 to 2022, this study utilizes a fixed-base index and fuzzy set qualitative comparative analysis (fsQCA) method within the framework of technology–organization–environment (TOE) theory to investigate the influencing factors, driving paths, and temporal trends of digital innovation in ICV enterprises in China. The results show the following: (1) No single factor can constitute a sufficient condition for achieving a high level of digital innovation. (2) The level of digital innovation of ICV enterprises in China showed a fluctuating growth trend during 2019–2022. Digital infrastructure and market competition are the two critical factors for digital innovation, while technology and organizational conditions have become bottlenecks. (3) There are five driving paths of digital innovation in ICV enterprises in China, including the Entrepreneurial Navigation + Technology Accumulation Driving Path, R&D Advantage + Environment Driving Path, Application Oriented + Environment Driving Path, Knowledge Absorption Driving Path, and Total Factor Driving Path. (4) From 2019 to 2022, the digital innovation of ICV enterprises in China underwent two stages of evolution, namely “Knowledge Absorption + Government Promotion” to “Technology Embedded + Market Driven”. (5) A substitutive relationship exists between digital resource acquisition capabilities and digital infrastructure, which promotes digital innovation through different paths leading to the same goal. The findings of this study have important theoretical and practical implications for ICV enterprises in making scientific and reasonable digital innovation decisions, thereby fostering the sustainable development of the automotive industry. Full article

Precision agriculture techniques contribute to optimizing the use of agricultural inputs, as they consider the spatial and temporal variability in the production factors. Prescription maps of limestone and fertilizers at variable rates (VRA) can be generated using various soil sampling techniques, such as point grid sampling, cell sampling, and management zone sampling. However, low-density grid sampling often fails to capture the spatial variability in soil properties, leading to inaccurate fertilizer recommendations. Sampling techniques by cells or management zones can generate maps of better quality and at lower costs than the sampling system by degree of points with low sampling density. Thus, this study aimed to compare the accuracy of different sampling techniques for mapping soil attributes in precision agriculture. For this purpose, the following sampling techniques were used: high-density point grid sampling method, low-density point grid sampling method, cell sampling method, management zone sampling method, and conventional method (considering the mean). Six areas located in the Brazilian states of Bahia, Minas Gerais, Mato Grosso, Goias, Mato Grosso do Sul, and Sao Paulo were used. The Root-Mean-Square-Error (RMSE) method was determined for each method using cross-validation. It was concluded that the cell method generated the lowest error, followed by the high-density point grid sampling method. Management zone sampling showed a lower error compared to the low-density point grid sampling method. By comparing different sampling techniques, we demonstrate that management zone and cell grid sampling can reduce soil sampling while maintaining comparable or superior accuracy in soil attribute mapping. Full article

Rice is an important agricultural product in Vietnam; however, the rice industry faces several challenges, particularly weak linkages between farmers and enterprises. The Vietnamese government has introduced the Large Field Model (LFM) program to improve cooperation between farmers and agribusiness companies. Despite these efforts, its implementation remains limited, and contract violations are common. This study investigated the factors affecting relationship quality (RQ) and loyalty (LO) between farmers and agricultural companies in the LFM, focusing on comparing cooperative and non-cooperative participating farmers. Using the Partial Least Squares–Multi-Group Analysis (PLS-MGA) method, this study assessed the different effects of factors such as price satisfaction (PS), payment terms (PTs), and support policies (SPs) on RQ and LO among two groups. The findings indicated that RQ has a stronger influence on LO in farmers with cooperative participation than those who exhibit non-cooperative participation. PS was the most significant factor influencing RQ in both groups, whereas PT was influential only for cooperative participating farmers. SP did not significantly affect RQ in either group. Finally, agricultural companies and policymakers should strengthen partnership management in agricultural supply chains by addressing gaps in support policies, developing appropriate pricing strategies, being flexible in negotiating payment terms, and improving the legal framework related to contract enforcement. Full article

The Northeastern Black Soil Region in China is recognized as one of the three major black soil regions globally and is often regarded as a cornerstone of national food security. However, prolonged agricultural practices have led to increasingly severe soil degradation, and the mechanisms and driving factors behind the degradation of soil quality remain unclear. Therefore, this study examines the historical and current characteristics of soil quality, focusing on major influencing factors, such as the 70-year history of reclamation and climate change. By accessing different databases, reviewing the relevant literature, and performing Pearson correlation and redundancy analyses (RDA), this study investigated the variation patterns of significant soil quality indicators and their driving factors in the 0–20 cm soil layer along the latitudinal direction (Nenjiang, Beian, Hailun, and Harbin) in the typical black soil region of Northeast China. The main conclusions are as follows: the soil organic matter (SOM) content experienced a rapid decline in the 30 years preceding cultivation (1950~1980), with the greatest decline rate in the Beian area (about 1.10 g/kg per year). The SOM in the Beian, Hailun, and Harbin areas decreased from north to south, changing at rates of 9.40–21.67 g/kg/degree and 0.15–0.34 g/kg/m with latitude and elevation, respectively. Elevation impacts the annual rate of change in soil quality indicators through its influence on the annual mean maximum temperature (AMXT) and annual atmospheric pressure (AP). AMXT and AP exhibit a linear relationship with elevation, based on which regression models were established. The key factors influencing soil quality indicators in the black soil region include cultivation years (Y), annual mean maximum and minimum temperatures (AMXT and AMNT), annual relative humidity (ARH), and AP. An increase in chemical fertilizer application is among the critical factors affecting soil pH. Additionally, the extensive use of agricultural machinery can reduce soil porosity and cause water and salt accumulation, ultimately leading to a decline in soil pH. This study offers theoretical support for mitigating soil degradation in Northeast China’s black soil region, thereby contributing to national food security and promoting sustainable development. Full article

Meteorological factors are the key drivers of air pollution. Stable weather conditions, the boundary layer height, and temperature inversion significantly influence the dispersion of particulate matter, which is also associated with the aerodynamic properties of particles. However, limited studies have been conducted on this topic in northeast China. This study investigates the influence of meteorological factors on PM_2.5 pollution under cold weather conditions, employing both qualitative and quantitative methods. The key meteorological factors considered include temperature difference, relative humidity, wind speed and direction, the boundary layer height (BLH), and temperature inversion. The stable weather index (SWI) is introduced as a quantitative measure of the stability of weather based on data from the last five winters in a typical megacity of northeast China. The monthly PM_2.5 concentrations recorded during the last five Februarys ranged from 59.79 μg/m³ to 85.68 μg/m³, with the highest daily concentration reaching 417 μg/m³. A new parameter, ‘temperature difference (ΔT)’, is defined in this study as the difference in temperature between two consecutive days, calculated by subtracting the previous day’s temperature from the current day’s. The temperature differences were found to have a significantly positive correlation with the differences in PM_2.5 concentrations (p < 0.01). The results showed that PM_2.5 pollution was associated with increased temperature, higher relative humidity, and lower wind speed, or any combination of these factors. The SWI explained 65% and 64% of the variances in air quality index (AQI) and PM_2.5 pollution, respectively. When the predicted SWI exceeds 10, the likelihood of particle pollution increases. A lower BLH, in conjunction with a thicker inversion layer, contributes to the formation of severe particle pollution. In the early stages of a winter pollution episode in Harbin, the temperature inversion layer thickened and intensified, with the inversion top height reaching approximately 200 m. The boundary layer remained below 200 m, resulting in a poor vertical dispersion condition. PM_2.5 pollution, therefore, is influenced by the combined effects of multiple meteorological factors. Our study quantitatively analyzed the characteristics of weather conditions and their impacts on air quality, which could provide scientific evidence for air pollution prediction and assist in making specific policy interventions, particularly for the upcoming ninth Asian Winter Games in Harbin in February 2025. Full article

Long COVID, or post-acute sequelae of SARS-CoV-2 infection, includes a variety of enduring symptoms that endure beyond the acute phase of the illness, impacting multiple facets of patients’ psychological and physical health. The persistent symptoms encompass fatigue, breathing difficulties, musculoskeletal pain, and cognitive impairments, which can significantly affect daily functioning and overall quality of life. The objective of this study was to create and validate the accuracy of the Post-COVID Cognitive Impairment Scale, which is used to evaluate cognitive impairments resulting from a COVID-19 infection. This study was conducted in Iran between January and September 2023. It consisted of three phases: developing the scale, evaluating its content validity with experts, and validating its structure with 454 participants using exploratory and confirmatory factor analysis. The exploratory factor analysis revealed two variables, namely memory and attention, which accounted for 40.38% of the variation. Confirmatory factor analysis verified the model’s fit, with indices indicating satisfactory alignment: CMIN/DF = 2.80, RMSEA = 0.06, SRMR = 0.05, CFI = 0.93, and TLI = 0.92. The factor loadings were statistically significant (p < 0.001), and Cronbach’s Alpha values indicated strong internal consistency (working memory = 0.81, attention = 0.80). These results affirm the Post-COVID Cognitive Impairment Scale is a valid and reliable instrument for evaluating cognitive deficiencies in individuals with long COVID. Its application in clinical and research environments aids in the prompt detection and tracking of the treatment of such impairments. Full article

Over the past few years, the incorporation of generative Artificial Intelligence (AI) techniques, particularly the Retrieval-Augmented Generator (RAG) framework, has opened up revolutionary opportunities for improving personalized travel recommendation systems. The RAG framework seamlessly combines the capabilities of large-scale language models with retriever models, facilitating the generation of diverse and contextually relevant recommendations tailored to individual preferences and interests, all of which are based on natural language queries. These systems iteratively learn and adapt to user feedback, thereby continuously refining and improving recommendation quality over time. This dynamic learning process enables the system to dynamically adjust to changes in user preferences, emerging travel trends, and contextual factors, ensuring that the recommendations remain pertinent and personalized. Furthermore, we explore the incorporation of personality models like the Myers–Briggs Type Indicator (MBTI) and the Big Five (BF) personality traits into personalized travel recommendation systems. By incorporating these personality models, our research aims to enrich the understanding of user preferences and behavior, allowing for even more precise and tailored recommendations. We explore the potential synergies between personality psychology and advanced AI techniques, specifically the RAG framework with a personality model, in revolutionizing personalized travel recommendations. Additionally, we conduct an in-depth examination of the underlying principles, methodologies, and technical intricacies of these advanced AI techniques, emphasizing their ability to understand natural language queries, retrieve relevant information from vast knowledge bases, and generate contextually rich recommendations tailored to individual personalities. In our personalized travel recommendation system model, results are achieved such as user satisfaction (78%), system accuracy (82%), and the performance rate based on user personality traits (85% for extraversion and 75% for introversion). Full article

Cavitation is a critical phenomenon for improving melt quality in casting processes by reducing hydrogen porosity, and it can be classified into two major types based on bubble dynamics, stable and transient cavitation. In this study, the relationship between stable and transient cavitation and the degassing efficiency of A356 alloy was evaluated. Cavitation intensity was quantified based on the Karman vortices method, and the measured cavitation intensities were processed through FFT transformation to analyze the acoustic spectra. The line spectrum and continuous spectrum were characterized separately to quantify stable and transient cavitation in distilled water. Negligible change in stable cavitation was observed, while transient cavitation increased with amplitude. On the other hand, both stable and transient cavitation increased proportionally with frequency. By employing the characterized cavitation indices, the effects of stable and transient cavitation on ultrasonic degassing of A356 were assessed. It was confirmed that transient cavitation was the dominant factor in the degassing before the degassing efficiency reached a steady state. This study clearly demonstrates that optimizing frequency to enhance transient cavitation is a more effective approach for increasing intensity and, consequently, improving degassing efficiency. Full article

Background and Objectives: Type 2 diabetes (T2DM) affects millions across the globe, generating a veritable public health issue through quality-of-life-reducing chronic complications, among which urinary tract infections are the most common. A shift in the disease managing paradigm from a glucose-centered view to a concept of cardio-reno-metabolic health has uniquely placed SGLT2 inhibitors as viable medication for the complex management of T2DM and its comorbidities. Some concerns have been raised over the increased likelihood of urinary tract infections (UTIs) associated with SGLT2 inhibitor use. The current study aims to evaluate the risk of developing urinary tract infections if patients with type 2 diabetes take SGLT2 inhibitors and determine those factors which make these patients more prone to develop this undesired complication. Materials and Methods: A cross-sectional, noninterventional evaluation of 328 patients with type 2 diabetes consecutively admitted to the Diabetes Clinic of “Pius Brinzeu” County Emergency Hospital in Timisoara, between January and February of 2024, was performed by examining medical charts and running statistical analyses using MedCalc version 22.26.0.0. Results: There was no statistical difference between patients taking SGLT2 inhibitors and those taking other glucose lowering medications when examining the presence of UTIs. Those patients with a higher HbA1c or BMI showed an increased predisposition to contracting UTI. The female gender was also associated with an increased likelihood of UTI. A further evaluation of the sublot of patients taking SGLT2 inhibitors revealed that not only higher BMI or HbA1c could be a predictor for the likelihood of developing UTI, but also a longer duration of T2DM was a predisposing factor. Conclusions: The use of SGLT2 inhibitors did not increase the likelihood of developing a urinary tract infection in this patient population. Full article

Introduction: The concept of clinical readiness for practice among nursing students is yet to be analyzed, and there is a lack of empirical evidence on its usage among academics and clinicians. Methods: This concept analysis is anchored on a systematic literature review that adhered to the PRISMA guidelines and incorporated the eight iterative steps of Walker and Avant’s concept analysis method. This concept analysis method involved: (1) choosing a concept; (2) determining the objectives of the analysis; (3) identifying usages of the concept; (4) determining the defining attributes; (5) identifying a model case; (6) identifying other cases, including borderline, contrary, and related cases; (7) identifying antecedents and consequences; and (8) defining empirical references. The integrative thematic data synthesis method was adopted. Results: The concept of nursing students’ clinical readiness for practice is said to have four interrelated attributes. These attributes included (1) professional skills, (2) communication skills, (3) self-management skills, and (4) self-confidence. The two antecedents for nursing students’ clinical readiness to practice are (1) personal factors, including demographic characteristics, prior healthcare experience, income, and emotional intelligence; and (2) educational factors, including the clinical learning environment, clinical internship program, learning resource, and learning strategy. The consequence of clinical readiness for the practice of nursing students includes obtaining practice skills that can lead to more personal and job-related satisfactory outcomes. Conclusions: clinical readiness for practice in nursing encompasses the acquisition and integration of professional knowledge, skills, effective communication abilities, and self-management capabilities and the application of these competencies with confidence toward the provision of high-quality care to patients. Clinical Relevance: Understanding the components of clinical readiness is crucial for nursing educators, preceptors, and healthcare institutions to ensure that nursing students are adequately prepared for the challenges they will face in clinical practice. By recognizing the importance of professional knowledge, skills, communication, and self-management in clinical readiness, educators and training institutions can tailor their curricula, programs, and support systems to better prepare nursing students for the demands of real-world healthcare settings. This focus on clinical readiness ultimately delivers safe, effective, and compassionate patient care. Full article

Atmospheric particulate matter (PM) samples were collected in Riyadh, Saudi Arabia, to assess air quality, quantify, heavy metal concentrations, and evaluate related ecological and health risks. This study’s uniqueness stems from its focused and detailed analysis of PM pollution in Riyadh, including an extensive assessment of heavy metal concentrations across different PM sizes by applying diverse pollution and health indices. This brings to light critical health and ecological issues and provides foundation for targeted pollution control efforts in the region. The study focused on two PM size fractions, PM_2.5 and PM₁₀ and analyzed the presence of heavy metals, including iron (Fe), nickel (Ni), chromium (Cr), zinc (Zn), cobalt (Co), copper (Cu), silver (Ag), arsenic (As), cadmium (Cd), and lead (Pb), using inductively coupled plasma emission spectrometry. Results showed significantly higher levels of PM₁₀ (223.12 ± 66.12 µg/m³) compared to PM_2.5 (35.49 ± 9.63 µg/m³), suggesting that local dust is likely a primary source. Air quality varied from moderate to unhealthy, with PM₁₀ posing substantial risks. Heavy metal concentrations in PM_2.5 followed the order Fe (13.14 ± 11.66 ng/m³) > As (2.87 ± 2.08 ng/m³) > Cu (0.71 ± 0.51 ng/m³) > Zn (0.66 ± 0.46 ng/m³) > Cr 0.50 ± 0.23 ng/m³) > Pb (0.14 ± 0.10 ng/m³) > Ni (0.03 ± 0.04 ng/m³) > Cd (0.004 ± 0.002 ng/m³) > Ag (0.003 ± 0.003 ng/m³) > Co (0.002 ± 0.004 ng/m³). In PM10, they followed the order Fe (743.18 ± 593.91 ng/m³) > As (20.12 ± 13.03 ng/m³) > Cu (10.97 ± 4.66 ng/m³) > Zn (9.06 ± 5.50 ng/m³) > Cr (37.5 ± 2.70 ng/m³) > Ni (1.72 ± 01.54 ng/m³) > Pb (1.11 ± 0.64 ng/m³) > Co (0.25 ± 0.28 ng/m³) > Ag (0.10 ± 0.26 ng/m³) > Cd (0.04 ± 0.02 ng/m³). Enrichment factor analysis revealed elevated levels for the metals Cu, Zn, As, Ag, Cd, and Pb. Pollution indices indicated various contamination levels, with Ag and As showing particularly high contamination and ecological risks. The study highlighted significant health concerns, especially from As, which poses a substantial long-term carcinogenic threat. The findings emphasize the urgent need to reduce hazardous metal levels in Riyadh’s air, especially with high child exposure. Full article

Anxiety-induced sleep disturbance is a common phenomenon that affects the overall well-being and quality of life of school-going adolescents. Nonetheless, evidence on anxiety-induced sleep disturbance among adolescents in Benin is scarce. This study examined the gender differences in the prevalence and correlates of anxiety-induced sleep disturbance among in-school adolescents in the Republic of Benin. We conducted a secondary analysis of Benin’s 2016 Global School-Based Health Survey dataset involving 2496 in-school adolescents aged 13 years and above. The proportion of male and female students who reported anxiety-induced sleep disturbance was computed. The multivariable binary logistic regression analysis was conducted to determine the factors associated with anxiety-induced sleep disturbance. Adjusted odds ratios (aOR) were estimated with corresponding confidence intervals at a 0.05 significance level. The overall prevalence of anxiety-induced sleep disturbance among adolescents was 21.3%. The prevalence of anxiety-induced sleep disturbance was 22.4% and 19.9% among the male and female participants, respectively. Among male in-school adolescents, hunger (aOR = 0.51, 95% CI = 0.37–0.71), loneliness (aOR = 0.29, 95% CI = 0.20–0.41), suicide ideation (aOR = 0.37, 95% CI = 0.22–0.61), suicide attempt (aOR = 1.79, 95% CI = 1.12–2.87), ever having sex (aOR = 0.66, 95% CI = 0.48–0.91), and having understanding parents (aOR = 0.64, 95% CI = 0.47–0.87) were significantly associated with anxiety-induced sleep disturbance. On the other hand, hunger (aOR = 0.63, 95% CI = 0.42–0.94), loneliness (aOR = 0.42, 95% CI = 0.28–0.61), and suicide ideation (aOR = 0.52, 95% CI = 0.33–0.83) were significantly associated with anxiety-induced sleep disturbance among the female in-school adolescents. The prevalence of anxiety-induced sleep disturbance among in-school adolescents in Benin poses a great concern for their mental health and well-being. Collaborative efforts between schools, parents, and healthcare professionals should focus on culturally appropriate, gender-specific factors that may contribute to a reduction in anxiety-induced sleep disturbance among in-school adolescents in Benin. Full article

This study aims to reveal the extent of the impact of mining activities on groundwater in a specific mining area in Hunan Province, assess the current quality of groundwater in the study area, investigate the correlations among heavy metal elements, and understand the migration and enrichment processes of different elements in groundwater. The findings will provide scientific data support for the establishment of groundwater monitoring systems by local governments and environmental protection departments. This paper presents the monitoring work conducted on the current status of heavy metal ions in groundwater within a mining area. Sampling and analysis were performed to measure pH, total dissolved solids (TDS), manganese (Mn), arsenic (As), cobalt (Co), molybdenum (Mo), barium (Ba), antimony (Sb), and selenium (Se) in both groundwater and surface water surrounding the study area. An improved hierarchical ladder evaluation method was used to comprehensively assess the degree of heavy metal pollution in the groundwater of the mining area. Additionally, statistical analysis was conducted to examine the correlations between heavy metal evaluation factors and TDS. The results show that the TDS values at various water quality monitoring points range from 187.34 to 1359.67, with a median of 400 and an average of 460.83. Additionally, the analysis of heavy metal pollution characteristics indicates that the water bodies in the mining area are severely impacted by mining activities, falling into the category of severe pollution. Among the heavy metals, As and Sb are the most serious pollutants, while the other heavy metal evaluation factors are less affected by mining activities. Correlation analysis further reveals that the correlation coefficients between Mo and Se and between Se and TDS are 0.772 and 0.778, respectively, indicating strong correlations. The correlation coefficients between As and Sb, Ba and Sb, and Mo and TDS are 0.455, 0.55, and 0.499, respectively, indicating moderate correlations. Other factor pairs exhibit weak correlations or even negative correlations. Full article

Access to drinking water in Cuenca Alto Atoyac (CAA), serving over 3.5 million people, is challenged by contamination and inadequate disinfection processes. This study investigates the use of oxidation–reduction potential (ORP) as a tool to optimize disinfection in CAA’s water systems. The research was conducted in three stages: an initial pilot phase with 10 sampled sources, expanding to 14 supply sources in the experimental phase, followed by analyses defining optimal chlorine dosing and contact times across two distinct hydrogeological zones. Principal component analysis (PCA) identified key factors, explaining 53.52% of water quality variability, while ORP measurements revealed significant differences in water quality among sources, with “Ameca” and “Pozo #2 Santa María Atlihuetzían” representing the extremes. Adjusted chlorine doses and contact times were tailored to local conditions, achieving target ranges and ensuring improved microbial control. In the final stage, significant correlations were established between ORP levels, chlorine doses, and total coliform concentration, supporting the effectiveness of ORP in guiding disinfection processes. Overall, chlorine doses of 0.3–1.5 mg/L with contact times around 15 min yielded ORP values (594–687 mV) optimal for CAA water safety. Full article

Host genetics and environmental factors have been associated with effects on the mouse fecal microbiome; however, the commercial source of mice remains the dominant factor. Increasing evidence indicates that supplier-specific microbiomes confer differences in disease susceptibility in models of inflammatory conditions, as well as baseline behavior and body morphology. However, current knowledge regarding the compositional differences between suppliers is based on targeted-amplicon sequencing data, and functional differences between these communities remain poorly defined. We applied a multi-omic (metagenomic and metatranscriptomic) approach to biomolecules extracted from murine feces representative of two U.S. suppliers of research mice, which differ in composition, and influence baseline physiology and behavior as well as disease severity in models of intestinal disease. We reconstructed high-quality metagenome-assembled genomes, frequently containing genomic content unique to each supplier. Transcriptional activity and pathway analyses revealed key functional differences between the metagenomes associated with each supplier including carbohydrate, fatty acid, and sulfite metabolism. These data provide a detailed characterization of the baseline differences in the fecal metagenome of mice from two U.S. commercial suppliers, suggesting that these functional differences are influenced by differences in the initial inoculum of colony founders, as well as additional taxa gained during growth of the production colony. Full article