The aim of the present research was to study the effect of job satisfaction on the performance of employees working in private sector organizations of Peshawar, Pakistan. For that purpose, one hundred and eighty employees (N = 180) were selected as a sample from private organizations of Peshawar. An equal number of employees, i.e., (n = 60), were selected through random sampling method from three types of organizations, viz., hospitals, banks, and universities. A Minnesota Satisfaction Questionnaire (MSQ-short form) developed by Weiss et al. (1967) and a self-constructed Performance Evaluation Form (PRF) were used as instruments for the study. Initially, the reliability statistics of both the instruments was calculated to know the significance of the scales. According to the findings of the study, the type of occupation has been shown significant correlation with job satisfaction. Similarly, the positive relationship of job satisfaction with performance of employees was also confirmed. Therefore, it is concluded from the study that satisfied employees were better in performance as compared to dissatisfied employees, thus contributing significant role in the uplifting of their organizations. As there are unstable economic and political conditions of Peshawar, it is therefore necessary for every organization to make their employees motivated and satisfied towards high performance by adopting different techniques and methods.

The rise of the Omicron variant has been studied in the state of Karnataka in India for the period March 2021-June 2022. The % contribution of the Omicron rose from 1.5% to 99.2% as time passed. Delta variant which was the major strain before Omicron emerged was completely replaced. The pathogenicity and virulence of all three variants were also compared. The rate of spread of more transmissible variant Omicron has been compared with surge in the daily caseload.

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Federated learning (FL) is a distributed machine learning approach that allows multiple clients to learn a shared model collaboratively without sharing their raw data. State-of-the-art FL systems provide an all-in-one solution; for example, users must install and use whichever data management subsystem that is expected by the FL system. This tightly-coupling paradigm is at best a hindrance to the wide adoption of FL solutions; in some domains, such as scientific applications, it could be a deal-breaker by enforcing a specific type of data solution on special hardware and platforms (e.g., high-performance computing clusters without node-local persistent storage). To this end, one natural solution is to decouple the data management functionalities from the FL system, enabling clients to customize their FL applications with specific data subsystems on which efficient queries can be made. The technical challenges of such decoupling methods include new system architectures, expressive and structured data models for FL parameters, and security guarantees among FL participants. As a starting point for this line of research, this paper conducts a thorough evaluation and comparison of mainstream database solutions as decoupled services in the context of FL systems. To make a fair comparison, we develop a framework called Data-Decoupling Federated Learning (DDFL), which can run FL workloads and is agnostic of the underlying database system by exposing a unified interface. To evaluate the effectiveness and feasibility of our approach, we compare DDFL with state-of-the-art FL systems that tightly couple data management and computational counterparts. We carry out extensive experiments on various datasets and data management subsystems (e.g., PostgreSQL, MongoDB, Cassandra, Neo4j) and show that DDFL achieves comparable or better performance in terms of training time, inference accuracy, and database query time, while providing more options for clients to tune their FL applications regarding data-related metrics, such as performance, resilience, and usability. Additionally, we provide a detailed qualitative analysis of DDFL when being integrated with mainstream database systems.

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