Purpose – Purpose of current study is to explore, impact of workplace environment i.e Physical Environmental Factors and Behavioral Environmental Factors on employee productivity (EP) through mediating role of employee health (EH). Research methodology – This study adopted questionnaire survey method and data was collected from 250 employees working in software houses in Pakistan. Data has been analysed using SPSS and AMOS software. Reliability and correlation analysis was performed by using SPSS while; path analysis was performed using AMOS. Findings – Results revealed that one unit variance in PEF incorporates 35% change in EH, 33% change in EH is caused by one unit increase in BEF and one unit increase in EH leads to 80% increase in EP. Physical and Behavioural Environmental Factors are positively affecting EH and EH is positivity affecting EP. Results of the study revealed that: employee health is mediating the relationship between workplace environment factors and employee performance. Research limitations – We used working Environment factors to determine employee health; future studies can consider compensation practices, insurance plans and health benefits by the organisation, a large sample or increased number of mediating variables can be used. The current study has adopted cross-sectional design while future studies can consider longitudinal design. Practical implications – Organisations must maintain a better environment in order to enhance employee productivity as, employee performance and workplace environment have direct and positive relationship, employees productivity and physical as well as behavioural environment are linked through employee health.

Cyberattacks can trigger power outages, military equipment problems, and breaches of confidential information, i.e., medical records could be stolen if they get into the wrong hands. Due to the great monetary worth of the data it holds, the banking industry is particularly at risk. As the number of digital footprints of banks grows, so does the attack surface that hackers can exploit. This paper aims to detect distributed denial-of-service (DDOS) attacks on financial organizations using the Banking Dataset. In this research, we have used multiple classification models for the prediction of DDOS attacks. We have added some complexity to the architecture of generic models to enable them to perform well. We have further applied a support vector machine (SVM), K-Nearest Neighbors (KNN) and random forest algorithms (RF). The SVM shows an accuracy of 99.5%, while KNN and RF scored an accuracy of 97.5% and 98.74%, respectively, for the detection of (DDoS) attacks. Upon comparison, it has been concluded that the SVM is more robust as compared to KNN, RF and existing machine learning (ML) and deep learning (DL) approaches.

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Practitioners and academics have been perplexed over the years by low efficiency and bad performance in construction projects. Several critical factors have been uncovered by previous studies which are governance mechanism, task conflict and opportunism. But an obvious question arises how the mechanism of governance in the presence of conflict can mitigate opportunism. The overarching objective of this study is therefore to create a model to study the effectiveness of these mechanisms of governance in the presence of task conflict. This paper is based on a positivist study philosophy in which a quantitative deductive method was used to collect data from 139 participants. Hypotheses were tested using structural equation modeling (SEM) through SmartPLS3.The research findings show that relational governance affects project efficiency considerably and is helpful in decreasing opportunism and conflict. In addition, there is proof that opportunistic behavior will increase the task conflict among parties but both task conflict and opportunism doesn’t have direct impact on the performance of project

To provide a continuous supply of electricity, renewable energy resources, and smart technological innovation must be exploited, aiming to provide a long-term solution to the existing energy crises faced by today's world. The photovoltaic (PV) panels are deployed to receive solar energy from the sunlight and consequently convert it into electrical energy. The photovoltaic PV panel receives maximum solar radiation only when it is at 90° perpendicular to the sunlight, which is directly proportional efficiency of the system. If proper technique not used to collect the maximum amount of sunlight, solar PV generation can be a more expensive energy source as compared to conventional energy generation. The generated output power of PV panels depends on the quantity of solar energy they collect from sunlight, and this amount can be increased by exploiting tracking systems. The solar tracker tends to move PV panels perpendicular to sunlight radiations to collect the maximum amount of sunlight to increase the system's efficiency.