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Purpose The global pandemic and the resulting rapid and large-scale digitization changed the way firms recognized and understood knowledge curation and management. The changing nature of work and work systems necessitated changes in knowledge management (KM), some of which are likely to have a long-term impact. Using the lens of technology in practice, the purpose of this study is to examine the impact of technology agency on KM structures and practices that evolved across five knowledge-intensive global organizations. This study then argues that sustainable knowledge management (SKM) systems evolve in specific contexts. Design/methodology/approach This study adopts a qualitative case study design to examine five multinational knowledge-intensive global organizations’ KM systems and practices across diverse industry sectors. Findings Based on the findings, the authors develop SKM systems and practices model relevant to a post-pandemic organizational context. The authors argue that KM digitization and adoption support socialization in knowledge sharing. Further formalization through organizational enabling systems aids the externalization of knowledge sharing. Deliberate practices promoted with leadership support are likely to sustain in the post-COVID era. Further, organizations that evolved ad-hoc or idiosyncratic approaches to managing hybrid working are more likely to revert to legacy KM systems. The authors eventually theorize about the socialization of human-to-human and technology-mediated human interactions and develop the three emerging SKM structures. Originality/value This study contributed to practitioners and researchers by developing the various tenets of SKM.

In the field of medical science a tremendous amount of data is generated, doctors need to test the patient physically to find out the injuries and disease of the patient. This paper outlines the idea of predicting a particular disease by performing operations on the digital data generated in the medical diagnosis. In this project an efficient genetic algorithm hybrid with the techniques like back propagation and Naive Bayes approach for disease prediction is proposed. Bad clinical decisions would cause death of a patient which cannot be aff orded by any hospital. To achieve a correct and cost eff ective treatment, computer technology Systems can be developed to make good decision. There is a lot of medical information unexplored, which gives rise to an important query of how to make useful information out of the data. The purpose of this project is to construct a basic prototype model which can determine and extract unknown knowledge (patterns, concepts and relations) related with multiple disease from a past database records of specified multiple diseases. It can solve complicated queries for detecting a particular disease and thus assist medical practitioners to make intelligent clinical decisions which traditional decision support systems were not able to. By providing efficient treatments, it can help to reduce costs of treatment. The medical organizations are "rich in data" but their "knowledge utilization is poor ". There is a lack of sufficiency of improved analysis techniques to find relations, concepts and patterns in the medical data. Data mining is science and engineering study of extracting previously undiscovered patterns from a huge set of data. In this paper, data mining methods namely, Decision tree, Naïve Bayes, and Back-Propagation(ANN) algorithms are implemented on medical data sets .The medical datasets will be represented graphically(graphs , charts , shapes)using diff erent visualization techniques. The algorithms are compared and evaluated on basis of their accuracy and time consumption factors. The algorithm which gives out high accuracy and less duration to give the output is analysed and implemented.

Ischemic stroke (IS) causes neurological dysfunction due to a loss of cerebral blood flow with exaggerated cerebral damage in diabetes. Exercise has shown beneficial effects on the vascular system and diabetes by mediating inter-organ communications. However, whether and how exercise contributes preventatively to the brain after IS in type 2 diabetes (T2D) is undefined. Here, we aimed to determine the effects of exercise on the metabolism, cerebral injury, neurological function, and protein expression in the brain after IS. T2D diabetic mice (db/db, 7-8 wks), and age/sex-matched controls (db/c) were subjected to exercise (10 m/min, 5 days/wk for 8-wks) or sedentary. Body weight and blood glucose were recorded once a week. One day after exercise, middle cerebral artery occlusion surgery was performed to induce IS. Sensorimotor deficits were assessed by the adhesive removal and corner tests two days after surgery. Afterward, the brain samples were collected for measuring the infarct size by cresyl violet staining. The proteins from the ipsilateral and contralateral brain were used for Western Blot to measure the levels of endothelial nitric oxide synthase (eNOS), neuronal NOS, NADPH oxidase (Nox2), Nox4, nuclear factor-kappa B (NF-κB), NF-κ light polypeptide gene enhancer in B-cells inhibitor, alpha (IκBα). We found: 1) exercise could stabilize the blood glucose in male db/db mice and have effects on preventing blood glucose increase at the early age of female db/db mice; 2) exercise could improve sensorimotor deficits by reducing tape contact and removal time and balancing the corner turning times (p< 0.05); 3) the infarct size is decreased in exercised group in both db/c (18.5 ± 2.2% and 22.2 ± 2.5%), and db/db mice (25.6 ± 3.1% and 35.6 ± 3.8%, exercise vs. sedentary, p< 0.05); 4) proteins related to oxidative stress and inflammation were downregulated in the brain of exercised diabetic IS mice. In conclusion, exercise could provide protective effects on stabilizing metabolism, improving neurological function, and reducing brain injury by downregulating the protein expression related to inflammation and oxidative stress in the brain.