Analysis of Factors Influencing the Benefits of Microcredit in Farm Production: A Welfare Economic Perspective from Punjab Province of Pakistan

Microcredit seems to be the dire need of small farmers and believed to be an appropriate tool for facilitating small enterprises. This study examined the effects of farmers’ certain internal and external factors on microcredit major benefits i.e. farm production and income. The study was confined to the four districts of Punjab Province of Pakistan. Data was randomly collected from 118 small farmers who had borrowed microcredit from different microfinance institutions (MFIs). Data analysis was performed in such a way that influence of specific variables under six categories of internal and external factors was estimated by employing logit model.  Most influencing variable observed from each logit model was selected for overall multiple regression analysis.  Findings from data analysis revealed that farmers’ education and their saving habit were positively influencing farm production and income. Number of livestock animals and more off-farm income sources reduced the changes of credit money to be used on non-income generation activities. These results were significant at 1% significance level. Inter-cropping had positive relationship for crop productivity. Suitable weather conditions was taken as environmental factor and its influence was positive for microcredit benefits. Farmers’ long distance from major agricultural market and strict repayment plan of MFIs were negative influencing microcredit benefits and were significant at 1% significance level. Multiple regression model overall 54.81% explained the impacts of six important variables on microcredit benefits. Econometric models also showed that many of the variables were inter-related and inter-dependent to each other and were affecting farm production and income on varying degree. Keywords: microcredit benefit, farm production, impact assessment, inter-dependence, Punjab

Bio-nanotechnology application in wastewater treatment

The nanoparticles have received high interest in the field of medicine and water purification, however, the nanomaterials produced by chemical and physical methods are considered hazardous, expensive, and leave behind harmful substances to the environment. This chapter aimed to focus on green-synthesized nanoparticles and their medical applications. Moreover, the chapter highlighted the applicability of the metallic nanoparticles (MNPs) in the inactivation of microbial cells due to their high surface and small particle size. Modifying nanomaterials produced by green-methods is safe, inexpensive, and easy. Therefore, the control and modification of nanoparticles and their properties were also discussed

Advancement in Mixing Hydrodynamics using Motionless Mixer

A large number of scientists have been conducting research to improve the hydrodynamic characteristics of mixing of fluids. Out of these techniques, static mixing is adopted in this study to improve the mixing of fluids, which has a lead of negligible energy consumption in comparison with dynamic mixers. Air Water system have been cast-off for mixing in which reduction in pressure, energy consumed, bubble diameter and mass transfer rate was mainly taken into account to design the static mixer element. Five different types of elements (Baffle, Plate, Blade, Needle and Wheel) were tested to observe and compare above mentioned hydrodynamic properties. Two point source characteristics i.e. reduction in pressure and bubble size, were carried out using Hg manometer and still photography respectively. Other nonpoint source characteristics (Energy depletion, rate of mixing) were found to be directly influenced by these point source characteristics. From the experimentations baffle element catches more importance, in terms of less energy depletion, more mixing rate, when compared with the other elements tested. This element becomes also comparable with other elements renowned in literature

Artificial Intelligence in Corporate Business and Financial Management: A Performance Analysis from Pakistan

This paper attempts to explore many signs of progress enabled by Artificial Intelligence (AI) in financial and corporate business management. It also amid to identify the benefits and cons of AI applications in social life. A systematic content analysis approach has been used to demonstrate the developmental phases of AI. Four distinct organizational maturity clusters i.e. Pioneers, Investigators, Experimenters, and Passives have been developed on basis of dataset. Data collections was carried through emails, customizable chatbots, live chat softwares and automated helpers of top ten online companies and various banking and financial institutions located in Lahore and Karachi cities for making behavioral analysis. The data results revealed that all aspects of financial managements and corporate business activities have been highly influenced by the application of AI. The study demonstrated that 80% senior business executives were of view that AI boost productivity and creates new business avenues. The results also demonstrated that 88% Pioneer organizations have understand and adopted AI techniques according to organization requirements, 82% Investigator organizations are not using it beyond the pilot stage whereas 24% Experimental organizations were adopting AI without understanding it. These results seem to reflect that AI has profound effects on financial industry to streamline its credit decisions from quantitative trading to financial risk management and fraud detection. This study also discovered that the widespread use of AI have raised a number of ethical, moral and legal challenges that are yet to be addressed. Although AI is gaining popularity day by day and it is believed that AI will improve work performance beyond human standards but it could not replace human resources fully

Evaluation of Systematic Monetary Influences in Pakistan’s Perspective

Traditional macroeconomic theories establish relationship among certain macroeconomic variables based on assumptions of perfect competition and resulting flexible prices. Theories based on these assumptions might not hold for developing economies due to imperfect market structure and fragile financial institutions. This study attempts to analyze the quantity theory of money (QTM) and Phillips curve (PC) relationship from long-run perspective for economy of Pakistan. QTM relates complete absorption of money growth effect into inflation, and PC establishes negative relationship between inflation and unemployment. In the long-run, money is assumed to have only inflationary or nominal effect. Therefore, presence of any long-run tradeoff between inflation and unemployment, once inflations is a pure monetary phenomenon in the long-run, cast serious doubts regarding long-run neutrality of money. Autoregressive distributed lag (ARDL) modelling approach is opted to analyze long-run impact of money growth on inflation, and long-run effect of inflation on unemployment. The long-run relationship between inflation and unemployment is statistically insignificant for economy of Pakistan. Furthermore, results of this study show that inflation, even in the long-run, does not adjust as theorized in QTM

Knowledge sharing and innovation capabilities: The moderating role of organizational learning

The main purpose of this research is to analyze those factors that affect the knowledge sharing and can lead the conventional banking sector towards enhancing the innovation capabilities by creating the culture of organizational learning. The model of this research paper is tested using a sample of 300 employees occupying the position of officer grade I, II and III in conventional banking sector of Bahawalpur. Researchers have used the simple random sampling technique for the collection of data with the help of questionnaire. SPSS version 21 is used to analyze data collected for this research paper. The result shows that out of nine factors, seven factors namely individual personality, individual attitude, reward and recognition, competence based trust, benevolence based trust, ICT infrastructure and availability and ICT know how are all significantly and positively related with the innovation capabilities as well as knowledge sharing and thus knowledge sharing also mediates between them. But the two factors centralization and formalization have an insignificant relationship with the innovation capabilities and knowledge sharing. Hence, no mediation takes place between them. Moderator; organizational learning also plays a significant role between knowledge sharing and innovation capabilities. This research paper has a significant managerial implication that it helps the managing bodies of conventional banks to pay an attention on these factors like individual personality, individual attitude, reward and recognition, competence based trust, benevolence based trust, ICT infrastructure and availability and ICT know how in order to enhance the innovation capabilities. The incorporation of individual personality, individual attitude, reward and recognition, competence based trust, benevolence based trust, ICT infrastructure and availability and ICT know how in order to enhance the innovation capabilities. Model offers a new theoretical lens and an alternative explanation for the determinants influencing the knowledge sharing that leads to innovation capabilities

Economic Interactions among Stock Market Performance and Macroeconomic Variables with Mediating Role of Gold Prices Volatilities: An Evidence from Pakistan

In all emerging economies, one of the most challenging issues for investors is the multifaceted inter-relationship between volatility of gold prices and stock market index. During the COVID-19 sub-periods, gold has shown a strong hedging behavior against stock market performance. The main objective of this study was to quantify the long-run relationship among multiple independent macroeconomic variables (predictors) on stock market index (response variable) using the volatilities of gold prices as a mediator factor. This study applied the descriptive statistics, correlation, t-test and OLS multiple regression Model. The specific data comprised of period 2011-2020 regarding the fluctuations in gold prices, exchange rate, interest rate, inflation rate and performance of stock market index has been utilized. The statistical outputs of models showed that exchange rate (Dollar to PKR) was positively affecting the performance of Karachi Stock Exchange (KSE)-100 Index, whereas inflation rate and interest rate were negatively affecting the overall performance of KSE100 index. The findings of this study suggested that to achieve better performance of stock market, relatively low interest rate and inflation rate contribute a significant role. However, to increase the generalization capabilities of this study the impact of mentioned macroeconomic variables in other sectors like industrial production, oil & gas and energy sectors with wider time span can be more helpful

Enhancing software defect prediction: a framework with improved feature selection and ensemble machine learning

Effective software defect prediction is a crucial aspect of software quality assurance, enabling the identification of defective modules before the testing phase. This study aims to propose a comprehensive five-stage framework for software defect prediction, addressing the current challenges in the field. The first stage involves selecting a cleaned version of NASA’s defect datasets, including CM1, JM1, MC2, MW1, PC1, PC3, and PC4, ensuring the data’s integrity. In the second stage, a feature selection technique based on the genetic algorithm is applied to identify the optimal subset of features. In the third stage, three heterogeneous binary classifiers, namely random forest, support vector machine, and naïve Bayes, are implemented as base classifiers. Through iterative tuning, the classifiers are optimized to achieve the highest level of accuracy individually. In the fourth stage, an ensemble machine-learning technique known as voting is applied as a master classifier, leveraging the collective decision-making power of the base classifiers. The final stage evaluates the performance of the proposed framework using five widely recognized performance evaluation measures: precision, recall, accuracy, F-measure, and area under the curve. Experimental results demonstrate that the proposed framework outperforms state-of-the-art ensemble and base classifiers employed in software defect prediction and achieves a maximum accuracy of 95.1%, showing its effectiveness in accurately identifying software defects. The framework also evaluates its efficiency by calculating execution times. Notably, it exhibits enhanced efficiency, significantly reducing the execution times during the training and testing phases by an average of 51.52% and 52.31%, respectively. This reduction contributes to a more computationally economical solution for accurate software defect prediction

Forecasting the strength of graphene nanoparticles-reinforced cementitious composites using ensemble learning algorithms

Integrating nanomaterials into concrete is a promising solution to improve concrete strength and durability. However, the intricacies of such nanoscale cementitious composites are highly complex. Traditional regression models encounter limitations in capturing these intricate compositions to provide accurate and reliable estimations. This study focuses on developing robust prediction models for the compressive strength (CS) of graphene nanoparticle-reinforced cementitious composites (GrNCC) through machine learning (ML) algorithms. Three ML models, bagging regressor (BR), decision tree (DT), and AdaBoost regressor (AR), were employed to predict CS based on a comprehensive dataset of 172 experimental values. Seven input parameters, including graphite nanoparticle (GrN) diameter, water-to-cement ratio (wc), GrN content (GC), ultrasonication (US), sand content (SC), curing age (CA), and GrN thickness (GT), were considered. The models were trained with 70 % of the data, and the remaining 30 % of the data was used for testing the models. Statistical metrics such as mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (R) were employed to assess the predictive accuracy of the models. The DT and AR models demonstrated exceptional accuracy, yielding high correlation coefficients of 0.983 and 0.979 for training, and 0.873 and 0.822 for testing, respectively. Shapley Additive exPlanation (SHAP) analysis highlighted the influential role of curing age and GrN thickness (GT), positively impacting CS, while an increased water-to-cement ratio (w/c) negatively affected CS. This study showcases the efficacy of ML techniques in accurately predicting CS of graphene nanoparticle-modified concrete, offering a swift and cost-effective approach for assessing nanomaterial impact on concrete strength and reducing reliance on time-consuming and expensive experiments.Validerad;2024;Nivå 1;2024-04-08 (marisr);Full text license: CC BY</p

Optimizing durability assessment: Machine learning models for depth of wear of environmentally-friendly concrete

The use of fly ash in cementitious composites has gained popularity. However, assessing the depth of wear (DW) of concrete requires expensive and destructive laboratory tests utilizing specialized equipment like the rotating-cutter method. Therefore, there is a need for alternative methods to predict the depth of wear of such composites more efficiently and cost-effectively. Accordingly, the objective of this research is to utilize machine learning (ML) approaches, including one individual algorithm (Decision Tree) and two ensemble algorithms (AdaBoost Regressor and Bagging Regressor) to estimate the depth of wear of fly-ash-based concrete. A collection of 216 experimental records was obtained from the existing literature. The efficiency of the models was examined with multiple statistical indexes. The bagging regressor (BR) model provided superior estimation performance with a correlation coefficient (R) of 0.999 compared to AdaBoost regressor (R = 0.965) and decision tree (R = 0.962). The ensemble models, notably BR, provided more accurate predictions with an 87.8 % lower mean absolute error (MAE) and an 85 % lower root mean square error (RMSE) compared to the decision tree model. In addition, the BR model exhibited the lowest performance index (ρ) values of 0.016 for training and 0.012 for validation. The SHapley Additive exPlanation (SHAP) revealed that the time of testing and age are the most dominant controlling features that significantly contribute to the estimation of the depth of wear. In conclusion, utilizing ML techniques and SHAP interpretation to estimate the DW of fly ash concrete significantly reduces reliance on expensive lab tests, making durability assessment more practical and cost-effective