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This study investigates the effect of employee stock ownership on a company's cost of capital. It takes employees into great consideration and tries to overcome the agency conflict that exists within the company by adopting the employee's stock ownership plan. The panel data regression is run in order to find out the effect of employee stock ownership on a company’s cost of capital. This study hypothesizes that employee stock ownership will tend to reduce the company's cost of capital, due to a decrease in the cost of equity and debt cost of the corporations. From KSE 100 companies were chosen for our research analysis, the study employs a sample size of 209 companies. 11 years of data were collected (2010-2020), a panel data regression was run due to cross sectional and time-series data. The research showed that there is a substantial impact of employees’ stock ownership on the company's cost of capital, equity cost, and company's debt cost, and a negative relationship exists between the independent and dependent variables. Employee stock ownership increases the company's cost of debt, equity cost and capital cost decreases. The research involves employee stock ownership as the variable because there are few research works yet been conducted especially in Pakistan. It also contributes towards the bond found between ESO and agency cost that how it helps to minimize the cost and benefits to the company and shareholders. This study offers a contribution towards the literature concerning ESO and the company’s capital cost. It is useful for the companies, as this shows that adopting the employee's stock ownership plan reduces the company’s capital cost, cost of debt, and equity as a whole, and most importantly the principal-agent conflict is minimized. The financial companies are fully ignored which may represent different results and may vary according to the sectors as well. The given effect can also be checked by taking stock prices as the dependent variable.

For analysis of physical properties of different materials, rectangular porous fins are used to examine the heat transformation through a system. In this paper, a metaheuristic is combined with neural computing modelling to study the effects of temperature changes in a porous fin model. Cuckoo search algorithm is used as an efficient optimization technique to find the best weights to reduce the mean squared error in the required temperature profile. The governing partial differential equation is converted into a non-linear ordinary differential equation subject to certain boundary conditions. Two individual cases, of silicon nitride (Si3N4) and Aluminium (Al), are considered. In the proposed procedure, the Cuckoo Search(CS) algorithm is combined with the artificial neural network (ANN), namely CS-ANN, to solve the differential equations and obtain solutions with better accuracy. Keywords: Heat distribution, Porous fin, Artificial intelligence, Metaheuristics, Cuckoo search algorithm, Differential equations, Optimization problems

Aim/Purpose: The clustering techniques are normally considered to determine the significant and meaningful subclasses purposed in datasets. It is an unsupervised type of Machine Learning (ML) where the objective is to form groups from objects based on their similarity and used to determine the implicit relationships between the different features of the data. Cluster Analysis is considered a significant problem area in data exploration when dealing with arbitrary shape problems in different datasets. Clustering on large data sets has the following challenges: (1) clusters with arbitrary shapes; (2) less knowledge discovery process to decide the possible input features; (3) scalability for large data sizes. Density-based clustering has been known as a dominant method for determining the arbitrary-shape clusters. Background: Existing density-based clustering methods commonly cited in the literature have been examined in terms of their behavior with data sets that contain nested clusters of varying density. The existing methods are not enough or ideal for such data sets, because they typically partition the data into clusters that cannot be nested. Methodology: A density-based approach on traditional center-based clustering is introduced that assigns a weight to each cluster. The weights are then utilized in calculating the distances from data vectors to centroids by multiplying the distance by the centroid weight. Contribution: In this paper, we have examined different density-based clustering methods for data sets with nested clusters of varying density. Two such data sets were used to evaluate some of the commonly cited algorithms found in the literature. Nested clusters were found to be challenging for the existing algorithms. In utmost cases, the targeted algorithms either did not detect the largest clusters or simply divided large clusters into non-overlapping regions. But, it may be possible to detect all clusters by doing multiple runs of the algorithm with different inputs and then combining the results. This work considered three challenges of clustering methods. Findings: As a result, a center with a low weight will attract objects from further away than a centroid with higher weight. This allows dense clusters inside larger clusters to be recognized. The methods are tested experimentally using the K-means, DBSCAN, TURN*, and IDCUP algorithms. The experimental results with different data sets showed that IDCUP is more robust and produces better clusters than DBSCAN, TURN*, and K-means. Finally, we compare K-means, DBSCAN, TURN*, and to deal with arbitrary shapes problems at different datasets. IDCUP shows better scalability compared to TURN*. Future Research: As future recommendations of this research, we are concerned with the exploration of further available challenges of the knowledge discovery process in clustering along with complex data sets with more time. A hybrid approach based on density-based and model-based clustering algorithms needs to compare to achieve maximum performance accuracy and avoid the arbitrary shapes related problems including optimization. It is anticipated that the comparable kind of the future suggested process will attain improved performance with analogous precision in identification of clustering shapes.

In this research, a soft computing approach based on a Nature-inspired technique, the Fractional-Order Darwinian Particle Swarm Optimization (FO-DPSO) algorithm, is hybridized with feed-forward artificial neural network (FF-ANN) to suggest and calculate better solutions for non-linear second-order ordinary differential equation (ODE) representing the corneal shape model (CSM). The unknown weights involved in approximate solutions obtained through ANN are tuned with the help of FO-DPSO. To test the robustness of our approach and conditionality of CSM, we have considered several cases of CSM with different aspects of the problem. Solutions obtained by Adam's method are used as a reference point for the sake of comparison. We establish it that FO-DPSO is a suitable technique for tuning the unknown weights involved in the solution designed with ANNs. Our results suggest that the proposed approach is a suitable candidate for solving real-world problems involving differential equations.

In the last four decades, China has transited from a closed country to major power status by adopting pragmatic policies. This article analyzes the transformation in Chinas foreign policy with its futuristic plan of building a harmonious world through peaceful co-existence and win-win cooperation. By adhering to principles of Panchsheel, foundations for prosperous China were laid. The notion of peaceful coexistence is the hallmark of Chinese foreign policy which has helped in resolving border issues withs neighbors by peaceful means. By remaining neutral and playing the role of a mediator, China has successfully managed regional and global conflicts. President Xi Jinpings vision of the Chinese Dream is, in fact, a holistic concept for taking China forward among leading nations in the world and rejuvenation of the Chinese nation for a prosperous future. Chinese foreign and domestic policies are now delivering and Pakistan being its Iron Brother can gain opportunities from Chinas peaceful rise

This research paper deals with a problem related to the damped materials contained in structural dynamics. The problem dealt with here involves a fractional-order damping coefficient in the form of fractional derivatives that present a better mathematical model of the vibration systems. Fractional derivatives are widely used to characterize the viscoelastic features in structural designs. Unlike the integer order differentiation, fractional-order derivatives consider local as well as the global evolution of the system. Therefore, fractional differential equations can be indicated as a reasonable choice for modeling certain physical phenomena, and to present more accurate mathematical solutions to real-world applications than the ordinary differential equations. We have proposed a novel unsupervised machine learning procedure that first designs general solutions, with the help of Artificial Neural Networks (ANNs), for the fractional-order differential equation containing unknown decision weights. These weights are worked out with the help of Fractional-Order Darwinian Particle Swarm Optimization (FO-DPSO) algorithm by setting a fitness function for each case. Results obtained from our simulations are better in the sense that they are overlapping with the analytical solutions available in the literature.

In this research, we have investigated doubly singular ordinary differential equations and a real application problem of studying the temperature profile in a porous fin model. We have suggested a novel soft computing strategy for the training of unknown weights involved in the feed-forward artificial neural networks (ANNs). Our neuroevolutionary approach is used to suggest approximate solutions to a highly nonlinear doubly singular type of differential equations. We have considered a real application from thermodynamics, which analyses the temperature profile in porous fins. For this purpose, we have used the optimizer, namely, the fractional-order particle swarm optimization technique (FO-DPSO), to minimize errors in solutions through fitness functions. ANNs are used to design the approximate series of solutions to problems considered in this paper. We find the values of unknown weights such that the approximate solutions to these problems have a minimum residual error. For global search in the domain, we have initialized FO-DPSO with random solutions, and it collects best so far solutions in each generation/ iteration. In the second phase, we have fine-tuned our algorithm by initializing FO-DPSO with the collection of best so far solutions. It is graphically illustrated that this strategy is very efficient in terms of convergence and minimum mean squared error in its best solutions. We can use this strategy for the higher-order system of differential equations modeling different important real applications.

BACKGROUND: The WHO in 1977 provided the proper definition of Drug Utilization. It has been defined as the marketing, distribution, prescription and use of drugs in society with special emphasis on the resulting medical and social consequences. Urinary tract infection (UTI) is caused by pathogenic invasion of the urinary tract which leads to an inflammatory response of the urothelium. Urinary tract infections are common burden in patients with diabetes mellitus. Cystitis, ascending infection leading to pyelonephritis, impaired leucocyte function, recurrent vaginitis, emphysematous complications and renal/perinephric abscesses are well recognized in this group of patients if glycemic control is poor. The present study was undertaken to identify the common pathogens and drug sensitivity pattern of the isolate among patients who attended the medicine outpatients department so as to guide empirical treatment. OBJECTIVES: To evaluate prescription pattern of antibiotics in UTI in medicine department in a tertiary care hospital. Study of type of patients along with type of UTI major symptoms, precipitating factors, evaluation of uropathogens. To analyze rationality among the prescriptions. METHODS AND MATERIALS: This prospective, observational and analytical study was done to assess the antibiotics prescribed in UTI in medicine department in a tertiary care hospital. Gender distribution, age wise distribution, type of patients (outpatients, inpatients), type of UTI (complicated, uncomplicated), presenting symptoms, lab investigations related to UTI, microorganisms isolated from urine culture, antibiotics prescribed for UTI, precipitating factors for UTI, route of drug administration, adjuvant drugs along with antibiotics for UTI and outcome of the treatment were evaluated. Patients receiving antibiotic therapy in UTI of either gender of age >18 years, visiting medicine department were included. Approval from the institutional ethics committee was obtained before starting the study. The study was carried out between March, 2014 and July, 2014 (5months). 120 consecutive patients receiving antibiotics for UTI were included in the study. RESULTS AND CONCLUSION: The incidence of urinary tract infections was more in females compared to males (1.18:1). It was more common in the age group between 31-50 years. Majority of the cases were outpatients and majority of them were uncomplicated UTI. The most common presenting symptom was dysuria. The most common organisms isolated from the urine culture and sensitivity test was E. coli. The most commonly prescribed antibiotic was amikacin. Majority of the patients recovered.