Reasoning by Analogy in a Multi-Level System Architecture for the Design of Mechanisms

Since the first attempts to integrate AI technology and engineering design nearly two decades ago, few expert systems have been shown to demonstrate sufficient reasoning capabilities to solve real-world design problems. The complex nature of design, the lack of understanding of the design process, and the limitations of current expert system technology have all been shown to have adverse effects on the maturity of this research area. Therefore, our direction in this research concentrates on understanding the design process, investigating a novel area of research focusing on creative design, and incorporating the results into a system model feasible for production use. The model presented is based on the concept of reusing past experience and existing cases to solve future design problems in different application domains. The resulting system performs its task by reasoning and learning by ANALOGY while utilizing the Logical-Building Block approach to design. Our method demonstrates the use of a case-based reasoner in conjunction with other existing techniques, such as heuristic reasoning and first principle reasoning, to produce a system with three levels of reasoning strategies. Such a system will exhibit a learning capability by which its performance is enhanced with repeated use. A prototype has been implemented and tested for the synthesis of various mechanisms

Heart Disease Prediction Using Machine Learning Method

The heart disease is also known as coronary artery disease, many hearts affecting symptoms that are very common nowadays and causes death. It is a challenging task to diagnose heart diseases without any intelligent diagnosing system. Many researchers did research on it and developed a diagnostic system to diagnose heart diseases and worked on it. The prediction of cardiovascular disease, required a brief medical history of patients, including genetic information. The world is in acute need of a system for predicting heart disease and it became crucial. Data mining and machine learning are common techniques used in the field of health care to process large and complex data. This research paper presents reasons for heart disease and a model based on Machine learning algorithms for prediction

Pathways to a more peaceful and sustainable world:The transformative power of children in families

This article provides an overview of selected ongoing international efforts that have been inspired by Edward Zigler’s vision to improve programs and policies for young children and families in the United States. The efforts presented are in close alignment with three strategies articulated by Edward Zigler: (a) conduct research that will inform policy advocacy; (b) design, implement, and revise quality early child- hood development (ECD) programs; and (c) invest in building the next generation of scholars and advocates in child development. The intergenerational legacy left by Edward Zigler has had an impact on young children not only in the United States, but also across the globe. More needs to be done. We need to work together with a full commitment to ensure the optimal development of each child

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Unified m-learning model through interactive education satellite: a proposal for an Arab Homeland education satellite

Abstract—in this paper, we propose a unified and interactive mobile learning (M-Learning) model to help with expanding and spreading education in the Arab Homeland countries. The model utilizes a new competitive spot beam satellite communication technology, which enables efficient channel allocation, where communication channels can be allocated to specific and precise areas. The proposed model is referred to as the interactive Arab education satellite (IAESat).The communication satellite can efficiently and effectively cover the entire Arab Homeland and reaches a wide area and mobile users that cannot be reached otherwise. The model implements existing interactivity components to enhance the learning process and meet international standards in education. Index Terms—E-Learning, M-Learning, interactive learning, education satellite, spot beam communication