Search results

The aim of this paper is to assess the trend of changes of nutritional status and environmental quality of different decomposing organic wastes in different phases of vermicomposting utilizing an indigenous earthworm's species Perionyx excavatus.

Four different organic wastes were selected as basic ingredients for vermicompost production. Dried waste materials (2 kg) of each were mixed with dried cow dung and placed in experimental plastic trays with 60 individuals of selected earthworm species. Samples were collected from each tray at an interval of 15 days. Six trays were used: three were with both wastes, three trays having only wastes were treated as control.

Regression equation has shown the rate of change of nutrients – both in control and experiment vermicomposting units over time – which revealed significant changes of C, N, P and C/N ratio during different phases of vermicomposting.

Vermitechnology is now being considered as an eco‐friendly, user‐friendly and cost‐effective biotechnological tool for the conversion of decomposable organic wastes into valuable organic manure, ensuring pollution abatement, self employment and sustainability in agriculture.

The paper helps to identify the potential of locally available earthworm species and organic materials for the mass production of vermicompost involving local peoples and locally available bioresources.

The purpose of this paper is to examine the relationship between working capital management (WCM) and firm profitability for a sample of 437 non-financial Indian companies.

The study is based on secondary financial data obtained from Capitaline database, pertaining to a period of ten years. This study employs two-step generalized method of moments (GMM) techniques to arrive at results.

The results of the study confirm the inverted U-shape relationship between WCM and firm profitability. In addition, the authors also found that the firms should complete its CCC on an average by 63 days.

Unlike prior studies that found a linear relationship between WCM and firm profitability. This study provides newer evidence for an inverted U-shaped relation between investment in working capital and firm profitability in India. In addition, this study uses GMM to control the potential problems of endogeneity.

The benefits of global trade are primarily attributed to reducing trade distortions between trading partners. The anticipated promise of a progressive diminution in tariffs throughout the globe was, regrettably, steadily superseded by non-tariff measures (NTMs). However, the impact of these NTMs is only sometimes evident since it occurs in various disguises. NTMs significantly influence trade in the SAARC, mandating prompt attention. The question is how much internal trade will expand if NTMs are repealed. Based on statistics from 2015 to 2020, the study endeavours to quantify the impact of NTMs on Afghanistan's trade volume within the SAARC region, primarily targeting four export destinations (Bangladesh, India, Pakistan and Sri Lanka). Using trade freedom scores as a proxy for trade distortions, it has been determined that Afghanistan's magnitude of export earnings is significantly lower due to NTMs imposed by its importing trading partners. According to the findings, a 1% rise in tariffs and NTMs applied by importing countries diminishes Afghanistan's exports by 1.23%.

In contrast, the impact of tariffs alone lowers Afghanistan's exports by 1.13%. The incidence of NTMs also devoid actual Afghanistan exports by US$ 5.70 million, equal to a 0.029% loss of Afghanistan's GDP. The calculations also reveal that lowering or eliminating non-tariff barriers has diverse trade growth effects in different trade groupings. The study recommends a serious NTM-oriented trade policy dialogue that is liberal and guarantees regional integration, thereby promoting and ensuring the future of Afghanistan's economic laurels and stability.

This study aims to examine factors influencing potential commercial innovators' intention to innovate with open government data (OGD) via a risk perspective.

The authors develop a theoretical model that explains how different forms of uncertainty (i.e. financial, technology, competitive, demand, and data) and their inter-relationships influence potential commercial innovators' intention to innovate with OGD. The model is tested using survey data collected from 144 potential commercial innovators from a developed Asian country.

The results suggest that all other forms of uncertainty, except competitive uncertainty, negatively influence potential commercial innovators' intention to innovate, mediated by their perceived risk of innovating with OGD. The results also show positive relationships between different forms of uncertainty, i.e. competitive and financial, demand and competitive, data and financial uncertainty.

This paper identifies major forms of innovation uncertainty, perceived risk, their inter-relationships, and impacts on the intention to innovate with OGD. It also finds support for a unique form of uncertainty for OGD innovation (i.e. data uncertainty).

The purpose of this paper is to examine the relationship between working capital management and firm performance for a sample of 437 non-financial Indian companies. In addition, this paper examines the impact of financial constraints on working capital management-performance relationship.

This study is based on secondary financial data of 437 non-financial Indian companies obtained from CAPITALINE database, pertaining to a period of ten years. This study employs the two-step generalized method of moments (GMM) technique to arrive at results.

Results of the study confirm the inverted U-shape relationship between working capital management and firm performance. In addition, the authors also found that the firms that are likely to be more financially constrained have lower optimal working levels.

Unlike prior studies, which found a linear relationship between working capital management and firm performance, this study provides newer evidence for an inverted U-shaped relation between investment in working capital and firm performance in India. In addition, this study also tests the impact of financial constraints on this relationship. In contrast to the prior studies, this study uses GMM to control the potential problems of endogeneity.

This study identifies a methodology to explore the issues of enterprise innovation ecosystem health (EIEH).

First, this study constructs the indicator system of EIEH based on the research objective; second, the dynamic vertical projection method (DVPM) and entropy weight method are proposed to analyze the status and influencing factors of EIEH; finally, the future development of EIEH is analyzed using GM (1,1).

In terms of methodology, the DVPM can effectively analyze EIEH, which can not only analyze the development status and potential of EIEH every year but also analyze the comprehensive state of EIEH for many years. In terms of practice, the value and grade of EIEH in China have been gradually increasing from 2016 to 2020, but the overall development is unbalanced, and five key factors affecting EIEH have been identified. The EIEH in China is predicted to steadily grow from 2021 to 2025.

The analytical method employed in this study can effectively analyze EIEH, which provides a new analytical perspective for the evaluation of EIEH and enriches the research content of the enterprise innovation ecosystem (EIE). By analyzing the results, we can gain a comprehensive understanding of the state of different EIEs, enabling each EIE to design tailored remedial measures to enhance EIEH and achieve sustainable development.

The purpose of the paper is to predict mapping of areas vulnerable to flooding in the Ourika watershed in the High Atlas of Morocco with the aim of providing a useful tool capable of helping in the mitigation and management of floods in the associated region, as well as Morocco as a whole.

Four machine learning (ML) algorithms including k-nearest neighbors (KNN), artificial neural network, random forest (RF) and x-gradient boost (XGB) are adopted for modeling. Additionally, 16 predictors divided into categorical and numerical variables are used as inputs for modeling.

The results showed that RF and XGB were the best performing algorithms, with AUC scores of 99.1 and 99.2%, respectively. Conversely, KNN had the lowest predictive power, scoring 94.4%. Overall, the algorithms predicted that over 60% of the watershed was in the very low flood risk class, while the high flood risk class accounted for less than 15% of the area.

There are limited, if not non-existent studies on modeling using AI tools including ML in the region in predictive modeling of flooding, making this study intriguing.

Developing countries like India, accounts for about 40 percent of undernourished children in the World and it is largely due to the result of dietary inadequacy in relation to their needs. The purpose of this paper was to evaluate the changes in the nutritional status of the children, from their preschool days to the present primary school days.

The present investigation was conducted at 20 Integrated Child Development Service (ICDS) centers (Center-A) and 15 primary schools (Center-B) in Bali Gram Panchayat, Arambag, Hooghly District of West Bengal, India, at an interval of three to four years. A total of 1,012 children (boys=498; girls=514) aged two to six years old enrolled in these ICDS centers and a total of 603 children (boys=300, girls=303), aged five to ten years were studied from the 15 primary schools who were the beneficiaries of ICDS centers. Underweight (weight-for-age Z-score (WAZ)) and wasting (weight-for height Z-score (WHZ)) were used to assess the nutritional status.

The nutritional situation (both in case of underweight and wasting) was better in Center B as compared with Center A. In general, the nutritional condition of boys was better than girls. Center had a very significant effect on both WAZ as well as WHZ, irrespective of age and sex. Sex has a significant impact only on WAZ. Interestingly, there was no significant sex-center interaction for both WAZ as well as WHZ. The children of the area were getting Mid Day Meal supplied through the school authorities which was comparatively better than the ICDS centers’ food supplementation. Better monitoring of nutritional supplementation at primary schools may be an important factor.

In ICDS centers, only the Anganwari worker is responsible in running and implementing the programs offered by the Government. However, at primary schools, the active involvement of all the teachers to run the program may have effectively led to have better results. Furthermore, the Government's focus should not be only on the increase the area covered by the ICDS program, but focus should be to increase the quality of food supplied, proper monitoring of the implementation and increase the allocation of funds. Appropriate measures may be taken by the authorities regarding this.

The results of the study will help in policy making in reducing the prevalence of undernutrition.

The purpose of this paper is to formulate the reliability optimization problem in stochastic and interval domain and also to solve the same under different stochastic set up.

Stochastic programming technique has been used to convert the chance constraints into deterministic form and the corresponding problem is transformed to mixed-integer constrained optimization problem with interval objective. Then the reduced problem has been converted to unconstrained optimization problem with interval objective by Big-M penalty technique. The resulting problem has been solved by advanced real coded genetic algorithm with interval fitness, tournament selection, intermediate crossover and one-neighbourhood mutation.

A new optimization technique has been developed in stochastic domain and the concept of interval valued parameters has been integrated with the stochastic setup so as to increase the applicability of the resultant solution to the interval valued nonlinear optimization problems.

The concept of probability distribution with interval valued parameters has been introduced. This concept will motivate the researchers to carry out the research in this new direction.

The application of genetic algorithm is extended to solve the reliability optimization problem in stochastic and interval domain.

The purpose of this paper is to examine the impact of working capital management (WCM) on profitability under different financial conditions (constraint/unconstraint) and WCM policy (aggressive/conservative). Furthermore, the study investigates the existence of optimal working capital levels under different financial conditions and WCM policy.

Two-step system generalized method of moments and fixed effect models are used to analyze the data collected from Prowess database from 2011 to 2020 for a sample of 1,104 Indian manufacturing companies.

The study finds an inverted U-shaped relationship between working capital and profitability in all financial conditions and working capital policy. This finding advocates the existence of an optimal level of working capital that equates the costs and benefits of holding working capital to maximize the companies’ profitability. However, holding working capital beyond the optimal level negatively affects profitability. Companies under financial constraints with aggressive working capital policies have the lowest optimal cash conversion cycle (CCC). Furthermore, the relationship of working capital with profitability and the optimal CCC varies owing to firm age and industry group.

To the best of the authors’ knowledge, this is the first paper that incorporates the impact of working capital on firm’s performance from both financial constraint (unconstraint) and aggressive (conservative) working capital policy perspectives in the Indian context. Furthermore, this study also contributes in terms of reflecting the effect of firm age and industry in determining the optimum CCC of the firms.