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Objective: To assess the combined impact of interval training programme on haemodynamic variables, specifically blood pressure and heart rate, in patients having undergone coronary artery bypass graft surgery. Methods: The meta-analysis was conducted from October to December 2022, and comprised search on PubMed, MEDLINE, Web of Science, PeDro, EMBASE, Science Direct, Cochrane Central Register of Controlled Clinical Trials, Google Scholar and Scopus electronic databases by two researchers independently for literature published between 2009 and 2020 related to the effects of interval training on coronary artery bypass graft patients. Meta-analysis was then performed to analyse the effects of an interval training on systolic blood pressure, diastolic blood pressure, and heart rate. The meta-analysis was performed in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Results: Of the 122 studies initially located, 7 were related to the effects of different forms of interval trainings and their impact on systolic and diastolic blood pressure of the patients, and 5 studies analysing the effects of interval trainings on heart rate. Interval training showed mild improvement in decreasing SBP compared to control groups. Standardized mean difference suggested an effect size of -0.226 in random effect model (I2=52.01; p=0.064). Similarly, a small effect size of 0.136 (I2=39.19%, p=0.14) was observed for diastolic blood pressure. Also, there was a small effect size of 0.155 (I2= 28.08, p=0.23) observed for heart rate. ---Continue

CSR appears to affect employee attitudes and behaviors, according to previous studies. However, actual evidence is scarce, especially in developing economies like Pakistan. As a result, the purpose of this research is to enrich the present literature on corporate social responsibility by analyzing the influence of CSR on employee performance by employing social identity theory. In addition, the role of Organization Commitment (OC) has been investigated in the current study. To conduct the study, the data were collected from employees of the six Multinationals Companies (MNCs) of Pakistan which are: “Jazz, Careem, Attock Refinery Limited, Berger Paints Pakistan, and McDonald's, Pakistan”. The findings of the study show that corporate social responsibility (CSR) has a major effect on employee performance and commitment. The results also show that OC mediates the link between CSR and employee performance to some degree. In addition, it helps in the administration of organizations by promoting employee commitment through corporate social responsibility activities. Additionally, the organization's reputation can be enhanced as a result of improved employee performance and increased corporate social responsibility. Keywords: Organizational Commitment (OC), Employee Performance, Multinationals Companies (MNCs), Pakistan, and The Social Identity Theory

Leukemia is a kind of blood cancer that influences people of all ages and is one of the leading causes of death worldwide. Acute lymphoblastic leukemia (ALL) is the most widely recognized type of leukemia found in the bone marrow of the human body. Traditional disease diagnostic techniques like blood and bone marrow examinations are slow and painful, resulting in the demand for non-invasive and fast methods. This work presents a non-invasive, convolutional neural network (CNN) based approach that utilizes medical images to perform the diagnosis task. The proposed solution consisting of a CNN-based model uses an attention module called Efficient Channel Attention (ECA) with the visual geometry group from oxford (VGG16) to extract better quality deep features from the image dataset, leading to better feature representation and better classification results. The proposed method shows that the ECA module helps to overcome morphological similarities between ALL cancer and healthy cell images. Various augmentation techniques are also employed to increase the quality and quantity of training data. We used the classification of normal vs. malignant cells (C-NMC) dataset and divided it into seven folds based on subject-level variability, which is usually ignored in previous methods. Experimental results show that our proposed CNN model can successfully extract deep features and achieved an accuracy of 91.1%. The obtained findings show that the proposed method may be utilized to diagnose ALL and would help pathologists.

In engineering, most structural components suffer from local damage in service or fabrication. Under different loading conditions, such local damage will further expand to large cracks and structural collapse. For ensuring longevity and reliability, the study of brittle crack growth is important. In structural damage prediction, various methodologies are commonly used and have demonstrated success in the past. Most traditional simulation methods, however, have the problems of large calculation quantities and long calculation times. At the same time, the mutual influence of crack opening stress and crack image on crack widening is rarely considered in existing works based on the deep learning framework. Herein, a deep learning model is developed to predict the spread of fracture of brittle materials under stress to improve the aforementioned problems. By developing a nonlinear relationship between crack stress and damage effects, this approach predicts crack growth, thus avoiding the loss of required information on crack growth behavior. The model emphasizes repeatability and extensibility, which is verified using extended finite element method (XFEM) Abaqus software through multiple sets of material crack data generated. This model's potential applications are material design and evaluation of residual life. [ABSTRACT FROM AUTHOR]