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.

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.

The structure and diversity of tree species in a temperate forest in northwestern Mexico was characterized. Nine sampling sites of 50 × 50 m (2,500 m²) were established, and a census of all tree species was carried out. Each individual was measured for total height and diameter at breast height. The importance value index (IVI) was obtained, calculated from the variable abundance, dominance and frequency. The diversity and richness indices were also calculated. A total of 12 species, four genera and four families were recorded. The forest has a density of 575.11 individuals and a basal area of 23.54/m². The species of Pinus cooperi had the highest IVI (79.05%), and the Shannon index of 1.74.

For five different regions in Kırklareli province, heavy metals; such as Pb, Ni, Cu, Mn, Cd, Cr, Co, Zn, Mo, and Fe in the mixture of leaves and flowers from linden trees (Tilia tomentosa L.) were analyzed by using flame atomic absorption spectroscopy after the samples were dissolved with microwave method. Also, organochloride pesticides; such as ∑BHC: [α-BHC, β-BHC, γ-BHC, and δ-BHC], ∑DDT: [4,4’-DDD, 4,4’-DDE, and 4,4’-DDT], α-Endosulfan, β-Endosulfan, Endosulfan sulfate, Heptachlor, Heptachlor-endo-epoxide, Aldrin, Dieldrin, Endrin aldehyde, Endrin ketone, Endrin and Methoxychlor in these samples were determined by utilizing gas chromatography mass spectroscopy after the samples were prepared for analyses by using QuEChERS method. The metal concentrations in the samples were in the range of 45.3 to 268 mg/kg for Mn, 0.25 to 18.8 mg/kg for Cu, 11.5 to 46.1 mg/kg for Zn, 128 to 1310 mg/kg for Fe, 10.4 to 38.6 mg/kg for Mo, 0.82 to 1.34 mg/kg for Cd, 0 to 6.45 mg/kg for Ni, 0 to 19.2 mg/kg for Pb, and 0 to 8.25 mg/kg for Cr. Moreover, the concentrations of organochloride pesticides in samples were usually determined to be lower than their maximum residue level values given the pesticide residue limit regulation of Turkish Food Codex.