Proactive coping behavior has been considered an important personal job resource for employees. Organizations have paid considerable attention to the proactive coping behavior of employees to maintain their competitive advantage. The purpose of the current study is to discover the relationship between organizational job resources, work engagement, and proactive coping using structural equation modeling. The participants were 340 licensed Chinese social workers. In the rapidly growing social work sector in China, social work organizations require psychologically connected and dedicated social workers. Findings include the effect of organizational job resources and work engagement on proactive coping. Based on the results, impacts on organizational management are discussed.

In this paper silver nanoparticles (NPs) which are synthesized by a simple plasma arc discharge method, that is a kind of electrochemical methods, are examined. The method is very simple and silver NPs are obtained very fast by means of two polished silver plates and electrochemical cell. The effects of changing some terms of the experiment including using Hydrogen peroxide (H₂O₂), temperature and the medium of experiment on oxygen percent and crystalline structure of silver NPs have been studied by transmission electron microscopy, UV-visible spectrophotometery, and X-ray diffraction. Water medium gets larger nanoparticles with less oxygen content compare to air medium. The size of synthesized nanoparticles become smaller and they also become more spherical by using H₂O₂ in air medium. In water medium, the size and concentration of the silver crystallite increase by temperature growth and adding H₂O₂  respectively. 

The COVID-19 epidemic caused unexpected complications, complexities and challenges in higher educational institutions (HEIs). In order to promote and strengthen the role of women leadership, this study aimed to clarify the unique challenges faced by female leaders at Saudi HEIs during the epidemic, find possible solutions to these challenges, and provide policy as well as management implications. A systematic literature review (SLR) was conducted, examining 27 records (i.e., research papers, articles and conference studies). The data were qualitatively analysed and categorized based on themes like challenges faced, opportunities recognized, and solutions proposed. Findings highlighted women leaders in Saudi HEIs grappled with multiple challenges, including technological barriers, cultural constraints, and increased workloads. Merging challenges with solvable strategies offers a forward-looking perspective, advocating for systemic changes that can shape a resilient and inclusive future for HEIs in Saudi Arabia.

Objective: To evaluate the imaging features of spondyloarthritis on magnetic resonance imaging (MRI) of the sacroiliac (SI) joints in terms of topography (in thirds) and affected margin, since this aspect is rarely addressed in the literature. Methods: Cross-sectional study with MRI (1.5 T) evaluation of the SI in 16 patients with diagnosis of axial spondyloarthritis regarding the presence of acute (subchondral bone edema, enthesitis, synovitis and capsulitis) and chronic changes (erosions, subchondral bone sclerosis, bone bridging and fatty replacement), performed by two radiologists, blinded to clinical data. MRI findings were correlated with clinical data including age, disease duration, medications, HLA-B27, BASDAI, ASDAS-VHS and ASDAS-PCR, BASMI, BASFI, and mSASSS. Results: Bone edema pattern and erosions showed predominance in the upper third of SI (p = 0.050, p = 0.0014, respectively). There was a correlation between the time of disease and structural changes by affected third (p = 0.028-0.037), as well as the presence of bone bridges with BASMI (p = 0.028) and mSASSS (p = 0.014). Patients with osteitis of the lower third had higher ASDAS values (ESRV: p = 0.011 and CRP: p = 0.017). Conclusion: Chronic inflammatory changes and the pattern of bone edema predominated in the upper third of the SI, but there was also concomitant involvement of the middle or lower thirds of the joint. The localization of involvement in the upper third of the SI was insufficient to differentiate between degeneration and inflammation.

How will children now stand in the world in the future, as a preschool teacher, what can we do? The author thinks that only by relying on multiple courses to constantly cultivate, cultivate and forge children, can they become the pillars of the country with high aspirations, equal attention to virtue and ability, common sense and practice, and the courage to explore!

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.