Increasing the environmental friendliness of production systems is largely dependent on the effective organization of waste logistics within a single enterprise or a system of interconnected market participants. The purpose of this article is to develop and test a methodology for evaluating a data-based waste logistics model, followed by solutions to reduce the level of waste in production. The methodology is based on the principle of balance between the generation and beneficial use of waste. The information base is data from mandatory state reporting, which determines the applicability of the methodology at the level of enterprises and management departments. The methodology is presented step by step, indicating data processing algorithms, their convolution into waste turnover efficiency coefficients, classification of coefficient values and subsequent interpretation, typology of waste logistics models with access to targeted solutions to improve the environmental sustainability of production. The practical implementation results of the proposed approach are presented using the production example of chemical products. Plastics production in primary forms has been determined, characterized by the interorganizational use of waste and the return of waste to the production cycle. Production of finished plastic products, characterized by a priority for the sale of waste to other enterprises. The proposed methodology can be used by enterprises to diagnose existing models for organizing waste circulation and design their own economically feasible model of waste processing and disposal.

Surveys are one of the most important tasks to be executed to get valued information. One of the main problems is how the data about many different persons can be processed to give good information about their environment. Modelling environments through Artificial Neural Networks (ANNs) is highly common because ANN’s are excellent to model predictable environments using a set of data. ANN’s are good in dealing with sets of data with some noise, but they are fundamentally surjective mathematical functions, and they aren’t able to give different results for the same input. So, if an ANN is trained using data where samples with the same input configuration has different outputs, which can be the case of survey data, it can be a major problem for the success of modelling the environment. The environment used to demonstrate the study is a strategic environment that is used to predict the impact of the applied strategies to an organization financial result, but the conclusions are not limited to this type of environment. Therefore, is necessary to adjust, eliminate invalid and inconsistent data. This permits one to maximize the probability of success and precision in modeling the desired environment. This study demonstrates, describes and evaluates each step of a process to prepare data for use, to improve the performance and precision of the ANNs used to obtain the model. This is, to improve the model quality. As a result of the studied process, it is possible to see a significant improvement both in the possibility of building a model as in its accuracy.

Land use as for human-circumstance interaction is as we all know changed the global land surface sharply and continuously. Farmland abandonment is the phenomenon of going extreme of marginal of land use, which exert positive and negative impacts on our living circumstances. In order to map the extent of farmland abandonment of Zhejiang Province, we try to use the geo-big data analysis platform to perform the massive data preprocessing and map the extent of farmland abandonment of the study area based on multi-source land use and land cover data. Then we execute landscape pattern analysis using landscape pattern analysis software and spatial auto-correlation (Moran's I) analysis based on ArcGIS and Fragstats software. We found that the area of farmland is about 16.32% on account of all land use types, which is 1.89104 km2. While the whole area of FA is 1.72 × 108 m2, and the farmland abandonment ratio is 1.65%. AF's area is about 1.95 × 109 m2, and the continuous cultivation ratio is 18.69%. The landscape fragmentation, landscape aggregation and landscape diversity of FA, AF and FL are different. At the same time, the spatial auto-correlation of FA and AF are dominant high congregation and low discrete. At last, we compared our calculated results with the existed research results which demonstrate our research does scientific convincible. We also make futural prospects prediction and show the research deficiency as well as bring out some policy implications based on our research, which means build proper land use management regulation and decrease the farmland abandonment on account of the premise of suitable land use policies.

This study deals with the impact of Vietnam bank size, loans, credit risk, and liquidity on Vietnam banks’ net interest margin, which are crucial for economic development. High profit margins result in a lower bad debt ratio due to timely loan collection and good liquidity. This study applies a panel data model to evaluate the relationship among bank size, loans, credit risk, liquidity, and marginal profitability, which are increasingly important in commercial bank growth. Data were collected from 2010 to 2022, and test methods were applied to select a good-fit model. Realizing that the factors that have a close correlation and affect the profit margin are 33.6% and 16.07%, 75.2%, 37.51%, 64.30%, and 41.11%, and R2 is 59.04%, respectively, this suggests that financial managers need to develop appropriate strategies and policies to adjust the factors that adversely affect commercial bank profitability.

This study aimed to measure the impact of implementing mechanisms of accounting data governance, represented by International Accounting Standards, internal auditing, external auditing, audit committees, disclosure and transparency, and performance evaluation, on the quality of financial reporting data for the commercial banks listed on the Amman Stock Exchange, totaling (15) banks. To achieve the objectives of this study, a descriptive-analytical approach was adopted by developing a questionnaire to collect the primary data measuring the study variables. The questionnaire was distributed to employees in the financial and control departments of these banks, with a total of (375) respondents from the total study population of (733) individuals. Appropriate statistical methods were used to analyze the data, test hypotheses, and the results of this study revealed a strong positive impact of five variables of accounting data governance mechanisms on achieving the quality of financial reporting data. These variables are ranked from highest to lowest in terms of the strength of impact and correlation with the quality of financial reports: disclosure and transparency, external auditing, International Accounting Standards, internal auditing, and audit committees. However, there was no impact of the performance evaluation governance variable on achieving the quality of financial reporting data. These results call on the management of commercial banks in the study to commit to the objective implementation of the requirements of accounting data governance mechanisms as stipulated by international professional assemblies.