Species distribution modeling has become a very popular tool for anticipation and decision-making in biological resource conservation. This study aims to assess changes in the future distribution of Brachytrupes membranaceus habitats in Kinshasa between 2055 and 2100.\nThree variables contributed most to the model: rainfall in the driest month (38.4%), soil (28.9%) and rainfall in the coldest quarter (13.9%). Currently, 89.3% of Kinshasa\'s surface area is highly favorable to the development and conservation of B. membranaceus, compared with 69.5% and 47.5% in 2055 (optimistic and pessimistic scenarios respectively) and 61.5% and 39.2% in 2100 (optimistic and pessimistic scenarios respectively), mainly in the urban zone. From the current to the future climate, the areas potentially favorable to the development and conservation of B. membranaceus shift from the periphery to the urban center of Kinshasa (for all scenarios for the years 2055 and 2100).\nThis shows that threats to the development and conservation of B. membranaceus are mainly due to anthropogenic activities (anarchic construction and slash-and-burn agriculture) and less to climatic/environmental factors (rainfall and soil). These results contribute to Sustainable Development Goals 11 and 15 by 2030.

This study originates from the notion that trade openness promotes inclusive growth as it promotes economic growth. Hence, this study investigates the effects of trade openness on inclusive growth focusing on the selected SADC countries, aiming to inveterate the flow of inclusive growth. The study findings reveal that selected SADC countries require utilitarian policy to be formed in a country context to return responsive inclusive growth standards. The panel autoregressive distributed lag approach was employed from 2000 to 2020. This study has uncovered a shortage of inclusive principles within SADC, to promote inclusive growth. This study confirms that SADC-inclusive channels should be mended. This study contributes by narrowing the inadequacy gap set between SADC trade openness and inclusive growth. Besides its contribution to the field, the study is envisaged to offer insights into selected SADC member states policymakers to reconsider and prioritize benefits that are tied to inclusive growth through trade openness. The findings of this study provide an important scientific basis to the understanding of the relationship between trade openness and inclusive growth in the SADC region. The study plays an important supporting role by sensitizing policymakers and researchers interested in promoting equitable economic development through inclusive growth.

The study \"Bankruptcy Forecast Analysis in the Manufacturing Subsector of Norte de Santander, Colombia (2015 – 2022)\" offers an empirical evaluation of financial stability in this region, using the capital structure theory of Modigliani and Miller and the Altman Z-Score model for bankruptcy risks as a theoretical foundation. Through a quantitative approach that includes time series analysis, the internal and external factors that affect the financial competitiveness of the subsector are analyzed. The research highlights the contributions and limitations of capital structure theory and the Z-Score model in real practices, and discusses the integration of advanced technologies such as artificial intelligence to fine-tune financial risk predictions. Using purposeful sampling and documentary analysis, patterns are identified and business solvency is assessed, providing a detailed view of the sector\'s resilience in the face of economic challenges. This study provides valuable insights for those interested in the financial sustainability of the manufacturing sector, highlighting the importance of adapting risk management strategies to regional particularities. Its contribution to the specialized literature lies in offering an updated perspective on bankruptcy forecasting and financial management adapted to the specific context of the Colombian emerging economy.

Background The hilly region of the central Sichuan basin is one of the most severely affected areas in China in terms of soil and water loss. For this reason, a large area of mixed alder and cypress forest has been established for protection. However, the uneven distribution of rainfall significantly impacts the stability of water usage in the plantations. Therefore, accurately understanding the response of trees to real-time rainfall is essential for effective forests management that optimizes hydrology and water resources. Aims To examine how the sap flux of the cypress forest responds to environmental factors and develop response models for two different conditions.

The purpose of this research is to compare the content of phenolic compounds (catechin, esculetin, polydatin, rutin, resveratrol, quercetin) and anthraquinones (aloe-emodin, emodin, chrysophanol, physcion) in Polygonum cognatum Meissn. and Rumex crispus L. plant taxa from the flora of Turkey. Extraction parameters such as solvent, solvent ratio, and time for soxhlet method were optimized, and the most effective extraction was carried out for eight hours with methanol-water (3:1 v/v). The development and validation of an HPLC-DAD method allowed for the simultaneous quantification of phenolic compounds and anthraquinones in the plant extracts. The analytical method conditions were defined through an investigation of various parameters, including mobile phase composition, flow rate, and column temperature. The method was validated with good linearity, precision, sensitivity, and recovery. The LOD and LOQ values of the compounds were in the range of 0.032–0.457 µg/mL and 0.106–1.523 µg/mL, respectively. The findings of the study showed that this analytical method was successfully applied to identify and measure the phenolic and anthraquinone compounds in Polygonum cognatum Meissn. and Rumex crispus L. plants.

Background: The Coronavirus Disease (COVID-19) pandemic indeed had a far-reaching and significant impact on the quality of life (QoL) worldwide. Its implications affected various facets of people\'s lives, including health, social interactions, economy, and mental well-being. Aim of the Study: This study aimed to elucidate the influence of the COVID-19 pandemic on QoL of adults in Riyadh, Saudi Arabia. Methods: A quantitative, non-experimental, descriptive, and correlational cross-sectional design was utilized, 440 adult residents of Riyadh participated in a Microsoft e-survey between October and December 2021 were recruited. The WHOQOL-BREF, a self-administered questionnaire, was employed for data collection; t-tests, one-way ANOVA, and the Kolmogorov–Smirnov test were applied. Results: Results showed the majority of participants were Saudi females, married, aged between 30 and 48, possessing a bachelor\'s degree in education. On average, participants rated their QoL at 4.12 ± 0.86. The highest-rated domain was physical health (67.30 ± 16.89), followed by social relationships (66.55 ± 22.34), psychological well-being (65.62 ± 17.17), and environment (62.86 ± 17.04). Correlation analysis revealed a positive relationship between participants\' perception of QoL and all variables, particularly with their general physical health. Only age emerged as a statistically significant predictor of participants’ perception of QoL. Conclusion: The findings indicate that adults in Riyadh demonstrated a positive perception of their QoL and general health post-COVID-19 pandemic, reflecting a substantial level of satisfaction.

Background: Thanatophobia refers to the anxiety related to fear of death. Nurses who care for critically ill children and their families may encounter death and dying recurrently, which can be emotionally challenging and distressing. Thanatophobia reported to significantly influence the emotional well-being and quality of life of healthcare professionals, especially those working in PICUs. Methods: A comparative study aimed to investigate and compare the attitudes toward death and dying patients among 176 participants included pediatric intensive care units (PICUs) nurses, nurse interns, and student nurses and to explore the relationship between various factors such as work experience, palliative care training, and personal experience with dying patients utilizing a quantitative descriptive correlation cross sectional research design and convenience sampling technique. Data was collected using a two part online self-reported questionnaire survey with the first section included sociodemographic data and second section was the thanatophobia scale by Merrill, Lorimor, Thornby and Woods (1998). Data was analyzed using the Statistical Software (JMP).

Pereskia bleo, belongs to the Cactaceae family has been traditionally used for treating various diseases. This study aimed to determine the phytochemical analysis, antibacterial activity, antioxidant activity, total phenolic content, and total flavonoid content of Pereskia bleo flowers. Phytochemical screening revealed the presence of glycoside, alkaloid, flavonoid, saponin, tannin, steroid, and terpenoid. The antioxidant activity test indicated that the crude extract of Pereskia bleo flowers had an average IC50 value of 6 ± 0.4359, confirming positive results in the phenolic content test. The total phenolic content of the Pereskia bleo flower crude extract was calculated as 75.295 mg/g at a concentration of 15.059 μg/ml and absorbance of 0.165. In contrast, the calculated total flavonoid content was 7.385 mg/g at a concentration of 9.477 μg/ml, suggesting the present of flavonoid compounds in the flowers of Pereskia bleo. The antibacterial activity of this sample was also tested using the disc-diffusion method against Gram-negative (Salmonella sp., Pseudomonas sp., E. coli) and Gram-positive (Staphylococcus aureus, Bacillus sp., Staphylococcus epidermidis) bacterial strains. The collected data showed that both 100 μg/ml and 500 μg/ml sample concentrations exhibited potent activity against Salmonella sp. and Pseudomonas sp.

The aim of this study was to explore the lived experiences of family caregivers of persons with serious mental illnesses at home. A hermeneutic phenomenology study was utilized. Data were collected via purposive and snowball sampling. In-depth phone calls and semi-structured interviews of 15 family caregivers using open-ended questions were used. All interviews were audio-recorded and transcribed verbatim. Data was analyzed using Diekelmann’s process and guided by Ray\'s Transcultural Caring Dynamics Model. Findings identified an overall theme of “the dynamic of an endless cycle of care” and three relational themes: (a) development of caring experiences, (b) living culture values and beliefs, and (c) meaning of care. The findings informed the development of culturally appropriate systems through an understanding of families’ experiences from a cultural perspective to value patient and family-centered care, social support, and enhanced community mental health care.

During the manufacturing process of electronic products, one of the most critical stages is the soldering of components onto the printed circuit board (PCB). Even a minor defect in the PCB can lead to significant issues in the final product. Therefore, a rigorous\ndefect inspection process is employed during manufacturing, which can be categorized into manual inspection and automated optical inspection (AOI). Manual inspection suffers from drawbacks such as slow speed and the expenditure of manpower and costs. Hence, most manufacturers opt for automated optical inspection to expedite production. However, most current automated optical inspection systems rely on traditional optical inspection algorithms. These computational methods are susceptible to variations in lighting conditions caused by slight differences in the placement of PCBs or the amount of solder. Consequently, these variations often result in misjudgments, where qualified PCBs are mistakenly categorized as defective, leading to high false positive rates in AOI systems.\nThis paper presents a deep learning based method for PCB defect detection. The proposed approach involves the creation of two separate models for classifying defective components individually. Once both models demonstrate a basic recognition capability, they are combined into a master model using the method proposed in this study to enhance overall recognition accuracy. The model has\nbeen trained on datasets for capacitors and resistors, and the experimental results indicate an accuracy of over 99% for both component types.