Software engineering is a discipline of Computer Science in which the new sub-areas are constantly added, especially in the area of quality, data management, and architectural design. Nowadays software development languages and processes are rapidly changing to deliver high-quality software products, i.e., usable systems, hybrid, and fulfill users' needs. This paper aims to identify and classify different process models proposed by the researchers based on characteristics of software quality, data management, and software integration and redesign. From a study of several models through a systematic mapping study, we identify different parameters and presented them in a traceability matrix. The parameters are classified into six areas. This paper provides an in-depth theoretical insight into the models and characteristics. A systematic mapping study was conducted through a literature review. The methodology used in this paper is both qualitative and quantitative. Initially, through a systematic mapping study, we study different models working on different parameters. And then proposed a model that can cover all the aspects of software implementation and management. We select ERP systems for it. Later we perform the GAP analysis and statistical evaluation of the model. It has been observed that all of the models are area specific either focused on quality parameters or management issues or architectural-based. The proposed model covers all aspects. The primary research shows that industrialists also need a better model for quality implementation. Our statistical analysis can serve as a decision-making tool for them to add to their decision-making processes. The other could use it to further enhance the framework for quality management. This model will enhance further in the future for better implementation.

Big-Data is one of the most useful technologies available nowadays to understand behaviorsand patterns. However, in addition to its societal benefits technology might also be used bypractitioners in industrial settings. The Retail industry is also treated as the one which might receive major benefits from the use of Big-Data and therefore this study is purposively associated with implications of Big-Data for the retail sector. The Study uses store layout as the dependent variable as it has the most influence on purchase as the real purpose of Big-Data is to analyze behavior and patterns, therefore, the selection of variable is legitimate. However, the technology is not well-known in emerging markets like Pakistan therefore study is linked with quota sampling and uses SMART-PLS to analyze results. Results indicated that Big-Data was perceived as the potent tool for operations of the organized retail sector of Karachi.

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Big-Data is the recent trend in data sciences prevailing all over the globe. The tool aids significantly in optimization of knowledge and has predominant use in optimization of knowledge and productivity. However, there is lack of understanding of concept and its application in Pakistan as indicated by Gallup Pakistan (2018) and stream of data is going to be doubled in two years’ time Tankard (2012). Therefore, there is a definite need of research which optimizes understanding associated with technology and its application from the context of Pakistan. Hence considering the application of big-data in retail sector this study aims to explore the impact of sentiment analysis through relating impact of big-data with effective assortment s of online stores. Although data has been collected from IT experts associated with online retail sector via quota sampling and SMART-PLS has been incorporated for the purpose of analysis. Results of the study highlights that big-data is perceived as the major tool for the betterment of assortment in online retail stores although data scientist and their applicability might diminish the impact of the use of big-data.

Objective: To determine the correlation of 18F-FDG SUV value with bone scintigraphy findings, i.e. uptake of Tc-99m in diagnosed cases of malignancy with sclerotic bone metastasis. Study Design: Prospective longitudinal study. Place and Duration of Study: Armed Forces Institute of Radiology and Imaging, Rawalpindi Pakistan, from Sep 2020 to Mar 2021. Methodology: This study included 30 patients of age 18 to 80 years who had sclerotic bone metastasis as confirmed in histopathology. All patients underwent whole-body PET/CT scanning to evaluate sclerotic bone metastasis and determined 18-FDG SUV after 5MBq/kg body weight of 18-FDG was injected. After two weeks of PET/CT scan, Tc-99m bone scintigraphy was carried out, and SUV of Tc-99m was determined after 20-25mCi technetium-99 methylene disphosphonate was injected, and the correlation was assessed between SUV of 18-FDG and Tc-99m. Results: The mean 18-FDG SUVmax and mean Tc-99m SUVmax, were, 21.31±8.77g/ml and 15.29±6.49g/ml respectively.21(70%) lesions on 18-FDG PET/CT and 8(26.7%) on Tc-99m bone scintigraphy were metastatic in bones. 18-FDG SUV on PET/CET and Tc-99m SUV on bone scintigraphy correlated positively with each other, and this correlation was found to be statistically significant (r=0.491, p=0.006). Conclusion: 18-FDG SUV PET/CT significantly correlated with Tc-99m SUV on bone scintigraphy and helped detect metastatic lesions earlier and modulate treatment response.Keywords: Bone metastasis, Bone scintigraphy, 18-FDG PET/CT, Tc-99m SUV.