Targeted Poverty Alleviation refers to the targeted funding work completed in the process of higher education development. However, at present, in the process of implementing the requirements of Targeted Poverty Alleviation in China's universities, some students' families are difficult to complete identification, and there are also some problems in the information management of the funders, which has seriously affected the funding for students with financial difficulties in their families during the period of higher education in China. With the rapid development and progress of Big data technology, through the establishment of a sound information technology system, we must help students actively change the funding model in the future and greatly improve the funding, which is of great significance to the development of university funding supervision and management.

To evaluate the efficiency of decision-making units, researchers continually develop models simulating the production process of organizations. This study formulates a network model integrating undesirable outputs to measure the efficiency of Vietnam’s banking industry. Employing methodologies from the data envelopment analysis (DEA) approach, the efficiency scores for these banks are subsequently computed and comparatively analyzed. The empirical results indicate that the incorporation of undesirable output variables in the efficiency evaluation model leads to significantly lower efficiency scores compared to the conventional DEA model. In practical terms, the study unveils a deterioration in the efficiency of banking operations in Vietnam during the post-Covid era, primarily attributed to deficiencies in credit risk management. These findings contribute to heightening awareness among bank managers regarding the pivotal importance of credit management activities.

The Organic Rankine Cycle (ORC) is an electricity generation system that uses organic fluid instead of water in the low temperature range. The Organic Rankine cycle using zeotropic working fluids has wide application potential. In this study, data mining (DM) model is used for performance analysis of organic Rankine cycle (ORC) using zeotropik working fluids R417A and R422D. Various DM models, including Linear Regression (LR), Multi-Layer Perceptron (MLP), M5 Rules, M5 Model Tree, Random Committee (RC), and Decision Tree (DT) models are used. The MLP model emerged as the most effective approach for predicting the thermal efficiency of both R417A and R422D. The MLP’s predicted results closely matched the actual results obtained from the thermodynamic model using Genetron software. The Root Mean Square Error (RMSE) for the thermal efficiency was exceptionally low, at 0.0002 for R417A and 0.0003 for R422D. Additionally, the R-squared (R²) values for thermal efficiency were very high, reaching 0.9999 for R417A and R422D. The findings demonstrate the effectiveness of the DM model for complex tasks like estimating ORC thermal efficiency. This approach empowers engineers with the ability to predict thermal efficiency in organic Rankine systems with high accuracy, speed, and ease.

Although dykes are a predominant and widely distributed phenomenon in S-Algeria, N-Mali and N-Niger, a systematic, standardized inventory of dykes covering these areas has not been published so far. Remote sensing and geo information system (GIS) tools offer an opportunity for such an inventory. This inventory is not only of interest for the mining industry as many dykes are related to mineral occurrence of economic value, but also for hydrogeologic investigations (dykes can form barriers for groundwater flow). Surface-near dykes, major fault zones, volcanic and structural features were digitized based on Landsat 8 and 9, Sentinel 2, Sentinel 1 and ALOS PALSAR data. High resolution images of World Imagery files/ESRI and Bing Maps Aerial/Microsoft were included into the evaluations. More than 14,000 dykes were digitized and analyzed. The evaluations of satellite images allow a geomorphologic differentiation of types of dykes and the description of their characteristics such as dyke swarms or ring dykes. Dykes are tracing zones of weakness like faults and zones with higher geomechanically strain. Dyke density calculations were carried out in ArcGIS to support the detection of dyke concentrations as stress indicator. Thus, when occurring concentrated, they might indicate stressed areas where further magmatic and earthquake activity might potentially happen in future.

The provision of clean drinking water is an important public service as more than 700 million people do not have access to this basic need. When it comes to delivering public services in developing countries, government capacity is a crucial element. This study investigates whether state capacity is a significant determinant in the provision of safe drinking water using panel data from 88 developing countries from 1990 to 2017. The paper applies ordinary least squares and fixed effects regression approaches and uses the Bureaucratic Quality Index and the Tax/GDP ratio as metrics of state capacity. The findings indicate that in developing nations, the availability of clean drinking water is positively correlated with state capacity.

Objective: to achieve accurately and rapidly the mapping of agricultural land use and crop distribution at the township scale. Methods: this study, based on specific methods, such as, time-series remote sensing index threshold classification and maximum likelihood, classifies each land use type and extracts crop spatial information, under the guidance of Sentinel-2A remote sensing images, to carry out agricultural land use mapping at township scale. And the mapping concerned will be verified by comparing with an agricultural spatial information map of a 0.5 m resolution, which is based on WorldVieW-2 fused images. Results: (1) the area accuracy of grain and oil crop land, vegetable land, agricultural facilities land and garden land is fairly good, with 92.93%, 98.98%, 95.71% and 95.14% respectively, and within 8% variation from actual area; (2) the spatial information of plot boundary, farmland road network, and canal network produced by OSM road data and historical high-resolution images was overlayed with the classification results of Sentinel-2A multi-spectral image for mapping, which can improve the accuracy of plot boundary information of classification results for the image with 10 m resolution. Conclusions: the use of multi-source information fusion method, agricultural land use and crop distribution space big data produced by Sentinel-2A optical image, can effectively improve the accuracy and timeliness of land use mapping at the township scale, to provide technical reference for the application of remote sensing big data to carry out agricultural landscape analysis at the township scale, optimization and adjustment of agricultural structure, etc.