This study investigates the relationship between corporate social responsibility (CSR), capital structure, and financial distress in Jordan’s financial services sector. It tests the mediating effect of capital structure on the CSR-distress linkage. Utilizing a panel data regression approach, the analysis examines a sample of 35 Jordanian banks and insurance firms from 2015–2020. CSR is evaluated through content analysis of sustainability disclosures. Financial distress is measured using Altman’s Z-score model. The findings reveal an insignificant association between aggregated CSR engagement and bankruptcy risk. However, capital structure significantly mediates the impact of CSR on financial distress. Specifically, enhanced CSR enables higher leverage capacity, subsequently escalating distress risk. The results advance academic literature on the nuanced pathways linking CSR to financial vulnerability. For practitioners, optimally balancing CSR and financial sustainability is recommended to strengthen resilience. This study provides novel empirical evidence on the contingent nature of CSR financial impacts within Jordan’s understudied financial services sector. The conclusions offer timely insights to inform policies aimed at achieving sustainable and stable financial sector development.

Inflammation of the lungs, called pneumonia, is a disease characterized by inflammation of the air sacs that interfere with the exchange of oxygen and carbon dioxide. It is caused by a variety of infectious organisms, including viruses, bacteria, fungus, and parasites. Pneumonia is more common in people who have pre-existing lung diseases or compromised immune systems, and it primarily affects small children and the elderly. Diagnosis of pneumonia can be difficult, especially when relying on medical imaging, because symptoms may not be immediately apparent. Convolutional neural networks (CNNs) have recently shown potential in medical imaging applications. A CNN-based deep learning model is being built as part of ongoing research to aid in the detection of pneumonia using chest X-ray images. The dataset used for training and evaluation includes images of people with normal lung conditions as well as photos of people with pneumonia. Various preprocessing procedures, such as data augmentation, normalization, and scaling, were used to improve the accuracy of pneumonia diagnosis and extract significant features. In this study, a framework for deep learning with four pre-trained CNN models—InceptionNet, ResNet, VGG16, and DenseNet—was used. To take use of its key advantages, transfer learning utilizing DenseNet was used. During training, the loss function was minimized using the Adam optimizer. The suggested approach seeks to improve early diagnosis and enable fast intervention for pneumonia cases by leveraging the advantages of several CNN models. The outcomes show that CNN-based deep learning models may successfully diagnose pneumonia in chest X-ray pictures.

Benzoxazine resin, a new type of phenolic resin, has many advantages, such as a strong molecular design, no small molecular release in the curing process, excellent thermal stability and mechanical properties, and a high residual carbon ratio. Thus, it is important for electronic communication industry matrix material. To meet the needs of high-frequency and high-speed communication technology for low-dielectric polymer resin, the low-dielectric modification of benzoxazine resin is of great significance to the high frequency and high-speed propagation of the signal, which attracts a wide range of materials researchers’ attention. In this paper, we review a series of studies on the low dielectric modification of benzoxazine resin in recent years, including the synthesis of new monomers, inorganic - organic hybridization, copolymerization with other resins, and low molecular weight benzoxazine resin research trends.

In China, ideological and political education is currently the hot direction of teaching reform in various colleges and universities, yet the development of appropriate teaching evaluation methods needs to catch up. This study addresses the pressing need for a preliminary investigation into the complex relationships among ideological and political education, the students’ learning satisfaction and teaching quality. This research examines the influence of teaching and ideological and political education quality on students’ satisfactions by designing a set of scales, collecting about 3800 questionnaires. Utilizing Structural Equation Modeling (SEM) and qualitative interviews, this study reveals that the teaching quality directly affects students’ learning satisfaction and ideological and political education. Notably, ideological and political education can also affect students’ learning satisfaction. The findings underscore the importance of including ideological and political education assessments in evaluating courses. This research contributes to the ongoing dialogue on effective teaching evaluation methods in the context of evolving educational practices.

This study empirically examines the complex relationship between materialism and economic motivation, proposing an inverted U-shaped relationship. The research analyzes three dimensions of materialism: happiness pursuit, social recognition, and uniqueness, and their impact on economic motivation. The findings suggest that materialism, when balanced, positively influences economic motivation without causing adverse effects. This relationship remains consistent across demographic characteristics and life satisfaction levels, challenging the traditional negative view of materialism. The implications of these findings extend to marketing strategies, policy design, and infrastructure development, offering actionable insights for real-world contexts. This research underscores the importance of balancing materialistic values to foster sustainable economic growth and well-being.

Urban planning is critical to managing rapid urban growth, particularly in African regions experiencing high urbanization rates. This study focuses on Bol, Lake Chad Province, a city facing significant challenges due to inadequate planning frameworks compounded by recurrent humanitarian and climate crises. It fills an empirical gap by analyzing how local planning mechanisms respond to these socio-environmental complexities, with a focus on the interplay between institutional structures, legislative frameworks, and resource allocation. The study assesses urban planning practices in Bol to identify challenges and opportunities, with the aim of improving institutional effectiveness, aligning policies with realities, and integrating climate resilience strategies. Using a qualitative methodology, it combines field surveys, stakeholder interviews, and document analysis, using SWOT (Strengths, Weaknesses, Opportunities, Threats) and PESTEL (Political, Economic, Sociocultural, Technological, Environmental, Legal) frameworks for data analysis. The findings reveal that ineffective institutions, poor inter-sectoral coordination, outdated legislative frameworks and resource constraints hamper sustainable urban development in Bol. To address these issues, the study proposes to strengthen local institutional capacities, foster stakeholder collaboration, and modernize urban planning policies through participatory approaches. The study highlights the need to integrate resilience strategies into urban settings to mitigate climate change impacts and improve governance. These measures not only address immediate challenges, but also advance urban planning theory and provide a basis for future research on adaptation strategies in crisis-prone regions. This study offers practical insights for policy makers and contributes to developing more sustainable and resilient urban planning systems in similar contexts.