The study aims to explain the relationship between the effectiveness of a business and its management through the analysis of working capital. The findings prove the complementary relationship. The analysis of working capital will always have a significant impact on the effectiveness of business management. The main objective of any corporation is to be effective in business, which can be achieved by analyzing the working capital. The result shows that analysis of working capital based on factors like operational efficiency, the company’s earnings and profitability, cash management, corporate receivable management, and corporate inventory management creates room for improvement and effectiveness in business management. Firms might enhance finances for business expansion by lowering their working capital requirements. It has also been revealed that there is a considerable difference in industries across time. It was observed that there is a high association between working capital efficiency and firm profitability. A highly efficient corporation is less vulnerable to liquidity risk and is also self-sufficient in terms of external finance. Numerous studies have been done to regulate the true rapport between working capital investments and their impact on financial presentation. It demonstrates that effective investment in working capital management may boost profitability and business value. The relationship between accounting and finance was explained by measuring working capital management in demand to illustrate the status of profitability. It was suggested that accountants take a more professional approach to updating their accounting and finance skills in their organization through effective working capital management.

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.

Cartography includes two major tasks: map making and map application, which is inextricably linked to artificial intelligence technology. The cartographic expert system experienced the intelligent expression of symbolism. After the spatial optimization decision of behaviorism intelligent expression, cartography faces the combination of deep learning under connectionism to improve the intelligent level of cartography. This paper discusses three problems about the proposition of “deep learning + cartography”. One is the consistency between the deep learning method and the map space problem solving strategy, based on gradient descent, local correlation, feature reduction and non-linear nature that answer the feasibility of the combination of “deep learning + cartography”; the second is to analyze the challenges faced by the combination of cartography from its unique disciplinary characteristics and technical environment, involving the non-standard organization of map data, professional requirements for sample establishment, the integration of geometric and geographical features, as well as the inherent spatial scale of the map; thirdly, the entry points and specific methods for integrating map making and map application into deep learning are discussed respectively.

Graphene, an innovative nanocarbon, has been discovered as a significant technological material. Increasing utilization of graphene has moved research towards the development of sustainable green techniques to synthesize graphene and related nanomaterials. This review article is basically designed to highlight the significant sustainability aspects of graphene. Consequently, the sustainability vision is presented for graphene and graphene nanocomposites. Environmentally sustainable production of graphene and ensuing nanomaterials has been studied. The formation of graphene, graphene oxide, reduced graphene oxide, and other derivatives has been synthesized using ecological carbon and green sources, green solvents, non-toxic reagents, and green routes. Furthermore, the utilization of graphene for the conversion of industrial polymers to sustainable recycled polymers has been studied. In addition, the recycled polymers have also been used to form graphene as a sustainable method. The implication of graphene in the sustainable energy systems has been investigated. Specifically, high specific capacitance and capacitance retention were observed for graphene-based supercapacitor systems. Subsequently, graphene may act as a multi-functional, high performance, green nanomaterial with low weight, low price, and environmental friendliness for sustainable engineering and green energy storage applications. However, existing challenges regarding advanced material design, processing, recyclability, and commercial scale production need to be overcome to unveil the true sustainability aspects of graphene in the environmental and energy sectors.

Introduction: The selection of genotypes with determinate growth habit in tomato should contemplate adequate selection criteria to increase the efficiency of the breeding program. Objective: The objective of this work was to estimate selection criteria for “chonto” type tomato lines with determined growth habit. Materials and methods: This work was carried out at the Universidad Nacional de Colombia (Palmira Campus), in 2016, with seven lines with determinate growth habit and a control with indeterminate growth. Heritability in a broad sense (h² g), coefficient of environmental variation, coefficient of genetic variation, selection efficiency and genetic gain were determined in parameters of morphological, phonological, fruit quality, fruit shape and production, using the RELM/BLUP procedure of the SELEGEN software. Results: There were three ranges of h² g, the first with values of h² g greater than 0.76, the second between 0.53 and 0.38, and the third with a value less than 0.38. The highest values of h² g were for final plant height with 0.92, plant height at harvest with 0.88, yield per plant with 0.83, days to flowering with 0.83, number of fruits per plant with 0.82, and days to harvest with 0.82. For genetic gain it was found that the control had the highest values for final plant height, plant height at harvest, internode length, days to harvest, harvest duration, soluble solids content, number of fruits per plant, fruit weight and yield per plant; however, in some parameters such as height and phenology for selection by determined growth habit, the lowest values were better. Conclusion: There was evidence of genetic parameters that could be considered as selection criteria for “chonto” type tomato lines with determinate growth habit.

Magnetic graphene oxide nanocomposites (M-GO) were successfully synthesized by partial reduction co-precipitation method and used for removal of Sr(II) and Cs(I) ions from aqueous solutions. The structures and properties of the M-GO was investigated by X-ray diffraction, Fourier transformed infrared spectroscopy, X-ray photoelectron spectroscopy, transmission electron microscopy, scanning electron microscopy, vibrating sample magnetometer (VSM) and N₂-BET measurements. It is found that M-GO has 2.103 mg/g and 142.070 mg/g adsorption capacities for Sr(II) and Cs(I) ions, respectively. The adsorption isotherm matches well with the Freundlich for Sr(II) and Dubinin–Radushkevich model for Cs(I) and kinetic analysis suggests that the adsorption process is pseudo-second-ordered.