Ali Khan

This chapter seeks to trace the changing relations between India and Pakistan, paying particular attention to how the changing political influences in both countries have affected the cricket teams and the cricketing relations between them. More specifically, the chapter examines the recent decline in relations which is acutely influenced by Pakistan’s worsening security situation as a result of increased terrorist activity in the country following the war on terror. This was most dramatically demonstrated in the attack on the Sri Lankan cricket team which subsequently led to Pakistan losing the right to hold cricket matches at home. It also manifested itself in the Mumbai attacks that led to the severance of cricket relations between India and Pakistan in 2008. Relations have also been affected by the dramatic rise of extremist Hindu ideology in India and more specifically under Narendra Modi’s Bharatiya Janata Party government since 2014. An analysis of these political changes is key to understanding the plummeting cricketing relations between India and Pakistan. Where in the past support from the Asian bloc had guaranteed Pakistan would not be isolated, today the rupture in political relations with India has meant Pakistan has been increasingly sequestered. There have also been significant economic changes that have seen the meteoric rise of India as the financial powerhouse of the global cricketing world. This has meant that India is able to use the withdrawal of cricketing relations as a means of further punishing and isolating Pakistan.

Introduction The integration of artificial intelligence (AI) into healthcare introduces innovative possibilities but raises ethical, legal and professional concerns. Assessing the performance of AI in core components of the United States Medical Licensing Examination (USMLE), such as communication skills, ethics, empathy and professionalism, is crucial. This study evaluates how well ChatGPT versions 3.5 and 4.0 handle complex medical scenarios using USMLE-Rx, AMBOSS and UWorld question banks, aiming to understand its ability to navigate patient interactions according to medical ethics and standards. Methods We compiled 273 questions from AMBOSS, USMLE-Rx and UWorld, focusing on communication, social sciences, healthcare policy and ethics. GPT-3.5 and GPT-4 were tasked with answering and justifying their choices in new chat sessions to minimise model interference. Responses were compared against question bank rationales and average student performance to evaluate AI effectiveness in medical ethical decision-making. Results GPT-3.5 answered 38.9% correctly in AMBOSS, 54.1% in USMLE-Rx and 57.4% in UWorld, with rationale accuracy rates of 83.3%, 90.0% and 87.0%, respectively. GPT-4 answered 75.9% correctly in AMBOSS, 64.9% in USMLE-Rx and 79.6% in UWorld, with rationale accuracy rates of 85.4%, 88.9%, and 98.8%, respectively. Both versions generally scored below average student performance, except GPT-4 in UWorld. Conclusion ChatGPT, particularly version 4.0, shows potential in navigating ethical and interpersonal medical scenarios. However, human reasoning currently surpasses AI in average performance. Continued development and training of AI systems can enhance proficiency in these critical healthcare aspects.

Plastic consumption is an important aspect of contemporary living, and studies that systematically examine consumers’ plastic waste recycling intentions from an ethical perspective are scarce. Considering the severity of plastic waste recycling problems globally based on the stimulus-organism-response paradigm, this study analyses how the information publicity influences consumers’ plastic waste recycling intentions from an ethical perspective in an emerging market economy. We investigate this link by focusing on the indirect effect of perceived social pressure and the moderating role of media disposition (i.e., media richness and content trustworthiness). Data from a three-wave field survey of 469 household consumers from China showed that social pressure mediated the positive relationship between information publicity and ethical recycling intentions. Additionally, media richness and content trustworthiness moderate the link between information publicity and consumers’ perceptions of social pressure. These findings provide encouraging evidence for applying the SOR paradigm to explain the ethical recycling intention of consumers. The findings may assist policymakers in motivating consumers to think ethically about plastic waste recycling.

Cardiac disease is the leading cause of death worldwide. Cardiovascular diseases can be prevented if an effective diagnostic is made at the initial stages. The ECG test is referred to as the diagnostic assistant tool for screening of cardiac disorder. The research purposes of a cardiac disorder detection system from 12-lead-based ECG Images. The healthcare institutes used various ECG equipment that present results in nonuniform formats of ECG images. The research study proposes a generalized methodology to process all formats of ECG. Single Shoot Detection MobileNet v2-based Deep Neural Network architecture was used to detect cardiovascular disease detection. The study focused on detecting the four major cardiac abnormalities with 98% accuracy results were calculated. The work is relatively rare based on their dataset; a collection of 11,148 standard 12-lead-based ECG images used in this study were manually collected from health care institutes and annotated by the domain experts. The study achieved high accuracy results to differentiate and detect four major cardiac abnormalities. Several cardiologists manually verified the proposed system’s accuracy result and recommended that the proposed system can be used to screen for a cardiac disorder.

Half a million species of plants could be existing in the world. Classification of plants based on leaf features is a critical job as feature extraction from binary images of leaves may result in duplicate identification. However, leaves are an effective means of differentiating plant species because of their unique characteristics like area, diameter, perimeter, circularity, aspect ratio, solidity, eccentricity, and narrow factor. This paper presents the extraction of plant leaf gas alongside other features from the camera images or a dataset of images by applying a convolutional neural network. The extraction of leaf gas enables identification of the actual level of chlorophyll and nitrogen which may help to interpret future predictions. Our contribution includes the study of texture and geometric features, analyzing ratio of Ch and N in both healthy and dead leaves, and the study of color-based methods via CNN. Several steps are included to obtain the results: image preprocessing, testing, training, enhancement, segmentation, feature extraction, and aggregation of results. A vital contrast of the results can be seen by considering the kind of image, whether a healthy or dead leaf.