Urban trees are one of the valuable storage in metropolitan areas. Nowadays, a particular attention is paid to the trees and spends million dollars per year to their maintenance. Trees are often subjected to abiotic factors, such as fungi, bacteria, and insects, which lead to decline mechanical strength and wood properties. The objective of this study was to determine the potential degradation of Elm tree wood by Phellinus pomaceus fungi, and Biscogniauxia mediteranae endophyte. Biological decay tests were done according to EN 113 standard and impact bending test in accordance with ASTM-D256-04 standard. The results indicated that with longer incubation time, weight loss increased for both sapwood and heartwood. Fungal deterioration leads to changes in the impact bending. In order to manage street trees, knowing tree characteristics is very important and should be regularly monitored and evaluated in order to identify defects in the trees.

In recent years, the construction of Jiafeng (家风)has become an important research topic in the field of street-level governance. A systematic literature review method is used to review 504 journal articles sourced from China National Knowledge Infrastructure (CNKI). The research overview is presented from the perspectives of overall research characteristics, highly cited literature, theoretical foundations, and research methods. The research systematically elaborates on the results of literature analysis from the perspective of the connotation and extension of Jiafeng, the practical mechanisms and related suggestions for Jiafeng construction. The research has found that the practical mechanisms of Jiafeng construction includes institutional support mechanism, theoretical consolidation mechanism, collaborative mechanism, social education mechanism, application innovation mechanism, and efficiency evaluation mechanism. On the basis of constructing a framework for the study of Jiafeng, this article provides prospects for future research: consolidating the theoretical foundation of Jiafeng construction, defining the connotation and extension of Jiafeng, refining the practical mechanism of Jiafeng construction, enriching the research methods of Jiafeng and measuring tools for governance effectiveness.

Homelessness is a global social issue that has affected various nations around the world, including South Africa. The instances of homelessness began during the apartheid era in South Africa and have since risen to alarming levels in provinces such as Gauteng, Western Cape, and KwaZulu-Natal, as reported in the 2022 census. Despite the lack of comprehensive research on homelessness in South Africa, this study conducted a scoping review to evaluate research completed on homelessness from independence to 2020 in the country. The scoping review followed the Preferred Reporting Item for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and involved a systematic search of the Development Southern Africa and Urban Forum databases. A total of 72 research articles were identified, with 10 meeting the inclusion and exclusion criteria for the review, which were then analyzed using thematic analysis. The study identified several key themes, including homelessness as a reflection of patriarchal systems, gender-based conflicts leading to homelessness, proactive and reactive interventions by non-state actors for homeless individuals, and the quantitative focus of research on homelessness in South Africa from independence to the present day. The study presents the applicability of these findings to tackle homelessness in Papua New Guinea and recommends the use of mixed methods approaches to research homelessness in South Africa to gain a more comprehensive understanding of the various dimensions of homelessness in the country.

This study thoroughly examined the use of different machine learning models to predict financial distress in Indonesian companies by utilizing the Financial Ratio dataset collected from the Indonesia Stock Exchange (IDX), which includes financial indicators from various companies across multiple industries spanning a decade. By partitioning the data into training and test sets and utilizing SMOTE and RUS approaches, the issue of class imbalances was effectively managed, guaranteeing the dependability and impartiality of the model’s training and assessment. Creating first models was crucial in establishing a benchmark for performance measurements. Various models, including Decision Trees, XGBoost, Random Forest, LSTM, and Support Vector Machine (SVM) were assessed. The ensemble models, including XGBoost and Random Forest, showed better performance when combined with SMOTE. The findings of this research validate the efficacy of ensemble methods in forecasting financial distress. Specifically, the XGBClassifier and Random Forest Classifier demonstrate dependable and resilient performance. The feature importance analysis revealed the significance of financial indicators. Interest_coverage and operating_margin, for instance, were crucial for the predictive capabilities of the models. Both companies and regulators can utilize the findings of this investigation. To forecast financial distress, the XGB classifier and the Random Forest classifier could be employed. In addition, it is important for them to take into account the interest coverage ratio and operating margin ratio, as these finansial ratios play a critical role in assessing their performance. The findings of this research confirm the effectiveness of ensemble methods in financial distress prediction. The XGBClassifier and RandomForestClassifier demonstrate reliable and robust performance. Feature importance analysis highlights the significance of financial indicators, such as interest coverage ratio and operating margin ratio, which are crucial to the predictive ability of the models. These findings can be utilized by companies and regulators to predict financial distress.