IMPACT OF RESEARCH & DEVELOPMENT ON THE PERFORMANCE OF PHARMACEUTICAL FIRMS: EVEDENCE FROM PAKISTAN

The goal of this study is to investigate the financial performance of listed Pharmaceuticalcompanies in Pakistan impacted by different board characteristics. These board characteristics are discussedthrough two theories: agency theory and resource dependency. The understudy characteristics include research& development, independent board directors, leverage, CEO/Chair duality, board size and audit committee. Thepaper used panel regression analysis on 11 firms from period of 2010 to 2019. It was found that investment inresearch & development and audit committee have significant and positive impact on the performance of firmsas per agency theory. Whereas the characteristics like Independent directors, CEO duality, leverage and boardsize had negative impact on the performance of the firms. The study helps to clarify the Board's performancerelationship and offers academic proof of existing and future governance changes for policy makers in Pakistan.The conclusions add to the literature by presenting fresh and original perspectives into how the existingknowledge of corporate governance and financial performance is applied within a developing context ofPakistan

Digital-Twins-Based Internet of Robotic Things for Remote Health Monitoring of COVID-19 Patients

The deadly coronavirus disease (COVID-19) has highlighted the importance of remote health monitoring (RHM). The digital-twins (DTs) paradigm enables RHM by creating a virtual replica that receives data from the physical asset, representing its real-world behavior. However, DTs use passive Internet of Things (IoT) sensors, which limit their potential to a specific location or entity. This problem can be addressed by using the Internet of Robotic Things (IoRT), which combines robotics and IoT, allowing the robotic things (RTs) to navigate in a particular environment and connect to IoT devices in the vicinity. Implementing DTs in IoRT, creates a virtual replica [virtual twin (VT)] that receives real-time data from the physical RT [physical twin (PT)] to mirror its status. However, DTs require a user interface for real-time interaction and visualization. Virtual reality (VR) can be used as an interface due to its natural ability to visualize and interact with DTs. This research proposes a real-time system for RHM of COVID-19 patients using the DTs-based IoRT and VR-based user interface. It also presents and evaluates robot navigation performance, which is vital for remote monitoring. The VT operates the PT in the real environment (RE), which collects data from the patient-mounted sensors and transmits it to the control service to visualize in VR for medical examination. The system prevents direct interaction of medical staff with contaminated patients, protecting them from infection and stress. The experimental results verify the monitoring data quality (accuracy, completeness, and timeliness) and high accuracy of PT's navigation.- Qatar National Library - Qatar University Internal Gran

Contributing Factors of Capital Structure: A Case of Non-Financial Companies Listed at KSE 100

The current study has taken the firms listed on KSE (Karachi Stock Exchange) now called Pakistan stock exchange. The data for the said purpose is collected for five years of time period from 2005 to 2010. The results obtained demonstrate that all the selected variables under study shows a highly significant impact on the determinants of capital structure except the tangibility of the asset. The insignificant relationship of tangibility with the capital structure supports the financing hierarchy theory. While the Growth, Size and profitability shows a significant and negative relationship with leverage. The negative relationship of growth shows that higher the growth of the firms lower will be the leverage maintained by the firm. Similarly, firms with smaller size show that such firms prefer high leverage as compared to firms of larger size. The results reveal that higher the profitability of the firm lower will be the leverage ratio. While the positive relationship of the volatility of the earnings states that firms with higher risks has high leverage ratio. Overall a detailed description and impact of the different variables on leverage is provided in the current study

Perception layer security in internet of things

Internet of Things (IoT) is one of the rising innovations of the current era that has largely attracted both the industry and the academia. Life without the IoT is entirely indispensable. To dispel the doubts, if any, about the widespread adoption, the IoT certainly necessitates both technically and logically correct solutions to ensure the underlying security and privacy. This paper explicitly investigates the security issues in the perception layer of IoT, the countermeasures and the research challenges faced for large scale deployment of IoT. Perception layer being one of the important layers in IoT is responsible for data collection from things and its successful transmission for further processing. The contribution of this paper is twofold. Firstly, we describe the crucial components of the IoT (i.e., architectures, standards, and protocols) in the context of security at perception layer followed by IoT security requirements. Secondly, after describing the generic IoT-layered security, we focus on two key enabling technologies (i.e., RFID and sensor network) at the perception layer. We categorize and classify various attacks at different layers of both of these technologies through taxonomic classification and discuss possible solutions. Finally, open research issues and challenges relevant to the perception layer are identified and analyzed. © 2019 Elsevier B.V

ARPN Journal of Agricultural and Biological Science ANTIBACTERIAL ACTIVITY OF FRUITS AGAINST Escherichia coli

ABSTRACT Numerous fruits are unquestionably utilized to prevent food borne illness diseases. Fruits were analyzed for their antibacterial activity. The antibacterial activity was determined by disc diffusion method. Nearly eight fruits with their various concentrations (25%, 50%, 75%, 100%,) were prepared in order to check their antibacterial activity opposing E. coli. Mango (Mangifera indica L), Apricot (Prunus armeniaca), Grapes (Vitisvinifera), Apple (Malus domestica), Peach (Prunus Persica), Lemon (Citrus limonum), Melon (Cucumismelo) and watermelon (Cirtrullus lanatus) were the selected fruits. The highest inhibition zone was observed in the juice extract of Apricot with concentration of 100%. The mean value of inhibition zone was (8.2± 1.1121). The minimum inhibition was surely noticed in the juice extract of mango with also concentration of 100%. The mean value of inhibition zone was (5± 0.9574). Other fruits showed different inhibition zones along with different concentrations and observed that the effect of fruits against E. coli was concentration dependent. Response against in increase and decrease in concentration was varied among all the fruits

Predicting the Ultimate Axial Capacity of Uniaxially Loaded CFST Columns Using Multiphysics Artificial Intelligence

The object of this research is concrete-filled steel tubes (CFST). The article aimed to develop a prediction Multiphysics model for the circular CFST column by using the Artificial Neural Network (ANN), the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Gene Expression Program (GEP). The database for this study contains 1667 datapoints in which 702 are short CFST columns and 965 are long CFST columns. The input parameters are the geometric dimensions of the structural elements of the column and the mechanical properties of materials. The target parameters are the bearing capacity of columns, which determines their life cycle. A Multiphysics model was developed, and various statistical checks were applied using the three artificial intelligence techniques mentioned above. Parametric and sensitivity analyses were also performed on both short and long GEP models. The overall performance of the GEP model was better than the ANN and ANFIS models, and the prediction values of the GEP model were near actual values. The PI of the predicted Nst by GEP, ANN and ANFIS for training are 0.0416, 0.1423, and 0.1016, respectively, and for Nlg these values are 0.1169, 0.2990 and 0.1542, respectively. Corresponding OF values are 0.2300, 0.1200, and 0.090 for Nst, and 0.1000, 0.2700, and 0.1500 for Nlg. The superiority of the GEP method to the other techniques can be seen from the fact that the GEP technique provides suitable connections based on practical experimental work and does not rely on prior solutions. It is concluded that the GEP model can be used to predict the bearing capacity of circular CFST columns to avoid any laborious and time-consuming experimental work. It is also recommended that further research should be performed on the data to develop a prediction equation using other techniques such as Random Forest Regression and Multi Expression Program

Fingertip Gestures Recognition Using Leap Motion and Camera for Interaction with Virtual Environment

The emergence in computing and the latest hardware technologies realized the use of natural interaction with computers. Gesture-based interaction is one of the prominent fields of natural interactions. The recognition and application of hand gestures in virtual environments (VEs) need extensive calculations due to the complexities involved, which directly affect the performance and realism of interaction. In this paper, we propose a new interaction technique that uses single fingertip-based gestures for interaction with VEs. The objective of the study is to minimize the computational cost, increase performance, and improve usability. The interaction involves navigation, selection, translation, and release of objects. For this purpose, we propose a low-cost camera-based system that uses a colored fingertip for the fastest and accurate recognition of gestures. We also implemented the proposed interaction technique using the Leap Motion controller. We present a comparative analysis of the proposed system with the Leap Motion controller for gesture recognition and operation. A VE was developed for experimental purposes. Moreover, we conducted a comprehensive analysis of two different recognition setups including video camera and the Leap Motion sensor. The key parameters for analysis were task accuracy, interaction volume, update rate, and spatial distortion of accuracy. We used the Standard Usability Scale (SUS) for system usability analysis. The experiments revealed that camera implementation was found with good performance, less spatial distortion of accuracy, and large interaction volume as compared to the Leap Motion sensor. We also found the proposed interaction technique highly usable in terms of user satisfaction, user-friendliness, learning, and consistency

Mechanical behaviour of E-waste aggregate concrete using a novel machine learning algorithm: Multi expression programming (MEP)

Technological advancement encourages the usage of electronic appliances in daily life and makes it possible for users to switch to more advanced devices very easily and at a reasonable cost. As new devices are produced and manufactured at an alarming rate around the world, outdated old devices become e-waste. This research work aims at using a popular machine learning (ML) method known as Multi-expression programming (MEP) to examine the compressive strength (CS) and tensile strength (TS) of E-waste aggregate-based concrete (EWAC). 279 and 105 scientific entries for CS and TS, respectively, were culled from reputable literature. The ten convincing input parameters selected based on multicollinearity analysis (correlation matrix and variance inflation factor) were E-waste coarse aggregate (ECA%), E-waste fine aggregate (EFA%), water-cement ratio (w/c), age of concrete (A in days), fine aggregate water-absorption (WAF%), coarse aggregate water-absorption (WAC%), E-waste aggregate water-absorption (WAE%), E-waste aggregate specific-gravity (SGE), coarse aggregate specific-gravity (SGC), and fine aggregate specific-gravity (SGF). To estimate the functioning of the projected models, root-squared-error (RSE), mean-absolute error (MAE), mean-absolute-percent error (MAPE), Nash-Sutcliffe-efficiency (NSE), root-mean-squared error (RMSE), objective-function (OF), coefficient-of-correlation (R), root-mean-squared-logarithmic error (RMSLE), and performance-index (PI) were used. The R-value for both MEP models exceeds 0.9, showing “excellent” with MAPE values in the testing stage equals to 6.68% and 6.78% for the CS-MEP and TS-MEP models, respectively. While for non-linear regression (NLR) models, the MAPE exceeds 20% and 30%, respectively, making them unsuitable for future prediction. Moreover, the sensitivity analysis carried out to evaluate the MEP equations' consistency with the observed physical phenomena, indicates that for both CS and TS, the w/c, ECA%, and EFA% remain the most sensitive parameters with a sensitivity index greater than 0.60. Due to the accuracy and viability of developed models, they can be used to reduce the time needed for laborious laboratory tests