AI/ML Algorithms and Applications in VLSI Design and Technology

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations

A human factors methodology for real-time support applications

A general approach to the human factors (HF) analysis of new or existing projects at NASA/Goddard is delineated. Because the methodology evolved from HF evaluations of the Mission Planning Terminal (MPT) and the Earth Radiation Budget Satellite Mission Operations Room (ERBS MOR), it is directed specifically to the HF analysis of real-time support applications. Major topics included for discussion are the process of establishing a working relationship between the Human Factors Group (HFG) and the project, orientation of HF analysts to the project, human factors analysis and review, and coordination with major cycles of system development. Sub-topics include specific areas for analysis and appropriate HF tools. Management support functions are outlined. References provide a guide to sources of further information

AI Knowledge Transfer from the University to Society

AI Knowledge Transfer from the University to Society: Applications in High-Impact Sectors brings together examples from the "Innovative Ecosystem with Artificial Intelligence for Andalusia 2025" project at the University of Seville, a series of sub-projects composed of research groups and different institutions or companies that explore the use of Artificial Intelligence in a variety of high-impact sectors to lead innovation and assist in decision-making. Key Features Includes chapters on health and social welfare, transportation, digital economy, energy efficiency and sustainability, agro-industry, and tourism Great diversity of authors, expert in varied sectors, belonging to powerful research groups from the University of Seville with proven experience in the transfer of knowledge to the productive sector and agents attached to the Andalucía TECH Campu

Social science perspectives on managing agricultural technology

TechnologyAgricultural researchResource managementFarmer participationEvaluation

Social science perspectives on managing agricultural technology

Experiences of 15 social science research fellows who recount their roles in particular research projects at the International Agricultural Research Centers they were appointed. In addition to highlighting the contributions social scientists can make in the field of agricultural research, their papers offer a candid look at the kinds of work in which the Centers currently are engaged.Technology, Agricultural research, Resource management, Farmer participation, Evaluation, Farm Management, Research Methods/ Statistical Methods,

Principles and practical implementation of farming systems research and farmer participatory research. Including Vulindlela District and Sobantu Village case studies

This publication integrates theory and practical work arising from courses in Farming Systems and Farmer Participatory Research held at the Institute of Natural Resources and associated institutions in KwaZulu-Natal during 1996 and 1997. The courses were conducted as part of a project supported by the UK. Government's Department for International Development and managed by the UK Natural Resources Institute (NRI). Objectives of this publication are 1) to provide reference material in Farming Systems and Farmer Participatory Research for interested audiences in KwaZulu-Natal and elsewhere; 2) by integrating theory and practice, to demonstrate how the principles, approaches and methods of FSRJFPR can be applied to real situations; 3) to record the situation, suggestions and priorities of rural and peri-urban families in Vulindlela District, as recorded by course participants; 4) to provide a springboard of information for further development initiatives in Vulindlela and elsewhere in KwaZulu-Natal; 5) to present the Urban Agriculture workshop held in Sobantu Village as a case study of participatory workshop methods

A methodology for the assessment of experiential learning lean: The lean experience factory case study

Purpose \u2013 The purpose of this paper is to present a methodology to assess the experiential learning processes of learning lean in an innovative learning environment: the lean model factories. Design/methodology/approach \u2013 A literature review on learning and lean management literatures was carried out to design the methodology. Then, a case study methodology was used to test the framework. Findings \u2013 The methodology permitted to asses learning processes and course contents of educational dynamics carried out in model factories and to theoretically ground such learning processes. The test showed that learning lean management is supported through a complete coverage of the eight phases of the learning path. Research limitations/implications \u2013 The methodology contributes to the literatures of lean management and experiential learning, proposing a methodology of assessment. Part of the framework could also be applied to other disciplines. Practical implications \u2013 The methodology could be used for two purposes: to design training courses or to assess existing experiential learning courses. Originality/value \u2013 Due to its intrinsic complexity, learning literature presents few practical framework or tools. Among them, none have provided practical and theoretical-based advice on how to use experiential learning precepts to teach lean managemen