Gravitational Water Vortex Power Plant (GWVPP) is an appropriate means to convert kinetic energy of water to rotational mechanical energy at the very low head site. This study aims to establish a basic reference for the design of the runner for the Gravitational Water Vortex Turbine (GWVT) with a conical basin. Seven different geometrical parameters have been identified for runner design, and the effect of these parameters on the system efficiency has been studied numerically and experimentally. The effect of these parameters has been studied over the range of speed with torque. The results from performance tests of these runners suggest that runner height is the most significant parameter to be considered in the design of a turbine runner for GWVPP with a conical basin. The results show that the efficiency of GWVT has improved up to 47.85% as obtained from experiments.
The application of Artificial Intelligence (AI) and Machine Learning (ML) to cybersecurity challenges has gained traction in industry and academia, partially as a result of widespread malware attacks on critical systems such as cloud infrastructures and government institutions. Intrusion Detection Systems (IDS), using some forms of AI, have received widespread adoption due to their ability to handle vast amounts of data with a high prediction accuracy. These systems are hosted in the organizational Cyber Security Operation Center (CSoC) as a defense tool to monitor and detect malicious network flow that would otherwise impact the Confidentiality, Integrity, and Availability (CIA). CSoC analysts rely on these systems to make decisions about the detected threats. However, IDSs designed using Deep Learning (DL) techniques are often treated as black box models and do not provide a justification for their predictions. This creates a barrier for CSoC analysts, as they are unable to improve their decisions based on the model's predictions. One solution to this problem is to design explainable IDS (X-IDS). This survey reviews the state-of-the-art in explainable AI (XAI) for IDS, its current challenges, and discusses how these challenges span to the design of an X-IDS. In particular, we discuss black box and white box approaches comprehensively. We also present the tradeoff between these approaches in terms of their performance and ability to produce explanations. Furthermore, we propose a generic architecture that considers human-in-the-loop which can be used as a guideline when designing an X-IDS. Research recommendations are given from three critical viewpoints: the need to define explainability for IDS, the need to create explanations tailored to various stakeholders, and the need to design metrics to evaluate explanations.INDEX TERMS Explainable intrusion detection systems, explainable artificial intelligence, machine learning, deep learning, white box, black box, explainability, cybersecurity.
This study attempts to explore the involvement of fathers of children under two years of age in Maternal and Child health care in the Dhading district of Nepal. Four focus groups discussions with 38 fathers were conducted. Six major themes emerged from the analysis as follows: Access to health facility; knowledge on ANC and PNC visits; helping the pregnant and lactating mother; family decision-making; male parent's preference of health facility and the male's suggestions on how to improve the health care system for MCH care. The results revealed that priority was given to faith healers for health services; male parents were less aware of the importance of ANC and PNC visits and that social stigma negatively impacts the help given to the pregnant and lactating mother. Most of the participants were helpful and supportive of their wives during pregnancy and lactating. The mistrust created by the unavailability of health workers in the health facility, long distances to the health facility with roads inaccessible to ambulances and a lack of human resources in the village were all reasons for home delivery. Involvement of the male in health care activities and providing them with health education opens a window of opportunity to help achieve Maternal and Child health-related goals.
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