International Conference on Statistical Methods for Analyzing Engineering Data

  Semiconductor market size worldwide from 1987 to 2023   In 2022, semiconductor sales reached 580.13 billion U.S. dollars worldwide. Semiconductors are crucial components of electronics devices and the industry is highly competitive. The year-on-year growth rate in 2022 reached 4.4 percent. Semiconductor market A semiconductor is a substance that conducts electricity under some but not all circumstances. Manufacturers are able to customize the conductivity of a semiconductor, such as introducing a sensitivity to heat or light, or altering conductivity based on the direction of the current. Semiconductors are an important component of many commonly used electronic devices including smartphones, tablets, and PCs. Notable semiconductor chip makers include Intel and Samsung Electronics , with Intel generating 58.4 billion U.S. dollars and Samsung generating 65.6 billion U.S. dollars in semiconductor revenue in 2022, placing them among the largest companies in terms of semiconduct

  Number of fintechstartups worldwide from 2018 to 2023, by region   As of May 2023, there were 11,651 fintech (financial technology) startups in the Americas, making it the region with the most fintech startups globally. In comparison, there were 9,681 fintech startups in the EMEA region (Europe, the Middle East, and Africa) and 5,061 in the Asia Pacific region. In 2023, the United States ranked first in terms of the number of fintech unicorns globally , having roughly five times more of these companies than the United Kingdom, that ranked second. Fintech investment landscape Investment into the fintech sector grew sharply during the last decade, with global investment value reaching an all-time high in 2021. In 2022, however, investment activity slowed down considerably, with the Americas seeing a particularly large drop in investment value. The decline in the value of investments was most likely caused by the economic contraction . Leading fintech companies Services provided

                                                                           M oduli spaces In engineering statistics, the concept of moduli spaces is not as commonly used as in some branches of mathematics or theoretical physics. However, moduli spaces and related mathematical concepts can still find applications in certain areas of engineering statistics. Here are a few examples: 1. Parameter Estimation : In engineering, there are often situations where we need to estimate unknown parameters of a system or model based on observed data. Moduli spaces can be used to define the space of possible parameter values and explore the relationships between different parameter settings. This can aid in finding the best estimates or assessing the uncertainty associated with the estimates. 2. Design of Experiments : In engineering, designing experiments to gather data for analysis is a crucial task. Moduli spaces can be used to define the space of experimental design choices, such as th

                Statistical process control Statistical process control (SPC) is defined as  the use of statistical techniques to control a process or production method . SPC tools and procedures can help you monitor process behavior, discover issues in internal systems, and find solutions for production issues.   Why use Statistical Process Control Today companies face increasing competition and operational costs, including raw materials increasing. So, it is beneficial for organizations to have control over their operation. Organizations must try to continuously improve quality, efficiency, and cost reduction. Many organizations still follow inspection after production for quality-related issues. SPC helps companies to move towards prevention-based quality control instead of detection-based quality controls. By monitoring SPC graphs, organizations can easily predict the behavior of the process. Statistical Process Control Benefits Reduce scrap and rework Increas

The Mathieu equation is a second-order linear ordinary differential equation that arises in various areas of physics and engineering. It is named after Emile Léonard Mathieu, a French mathematician who studied the equation in the late 19th century. The general form of the Mathieu equation is: d²y/dθ² + (a - 2qcos(2θ))y = 0 where y is a periodic function of the angle θ, and a and q are parameters that determine the behavior of the equation. The Mathieu equation is most commonly encountered in the field of vibration theory and the study of periodic motions. Applications of the Mathieu equation can be found in a wide range of scientific disciplines, including: Mechanics and Vibrations: The Mathieu equation describes the motion of a vibrating mechanical system subjected to periodic forces or oscillations. It is used to analyze stability and resonance phenomena in various systems such as rotating machinery, pendulums, and gyroscopes. Quantum Mechanics: The Mathieu equation has applicat

The Impact of Different Teaching Methods on Student Achievement: A Comparative Analysis using ANOVA Abstract:  This study aims to investigate the effects of various teaching methods, including lecture-based instruction, group work, and hands-on activities, on students' academic performance. The research will employ an Analysis of Variance (ANOVA) to analyze the differences in achievement scores among students exposed to different teaching methods. By comparing the outcomes, the study seeks to provide valuable insights into the effectiveness of these instructional approaches and their potential implications for enhancing student learning outcomes. Introduction:  The selection of appropriate teaching methods plays a vital role in promoting effective learning and academic achievement. However, educators face challenges in determining which teaching methods are most effective for facilitating student success. This study aims to address this issue by examining the impact of three commo

  The Role of Statistics in Engineering Statistics have long been of great importance to engineers, providing a powerful tool for understanding the data collected from experiments and other activities. Engineering relies heavily upon statistics in many ways, from using statistical models for problem-solving to helping make decisions based on probability. To fully comprehend how invaluable statistics is within engineering, it's important to look at some of its practical applications and understand its significance when developing inventions or creating new products. Here is the role of statistics in engineering. Design of Experiments Design of experiments (DOE) is a statistical method to optimize designs, processes, and products. The process involves using various experiments to test different parameters and measure the results. Statistical models are used to analyze the data collected during these experiments, providing insights into which combinations of factor levels or var

  Asian Development Bank Signs $44.2 Million Blue Loan With ALBA to Reduce Ocean Plastic Waste in Indonesia The Asian Development Bank (ADB) signed a $44.2 million blue loan with PT ALBA Tridi Plastics Recycling Indonesia, an ALBA Group Asia company, to establish a polyethylene terephthalate (PET) recycling facility in Central Java . ADB and the Leading Asia’s Private Infrastructure Fund (LEAP) will each provide $22.1 million in funding for the project. Blue loans are financing instruments that aim to safeguard access to clean water, protect underwater environments, and invest in a sustainable water economy. “Plastic pollution causes billions of dollars in irreversible harm to our marine ecosystem, and also has severe impacts on economies and public health,” said ADB Vice-President for Private Sector Operations and Public–Private Partnerships Ashok Lavasa. “This project showcases the potential for PET recycling in Indonesia, while the certified blue loan aims to attract more investors

Research team creates statistical model to predict COVID-19 resistance   Researchers have created and preliminarily tested what they believe may be one of the first models for predicting who has the highest probability of being resistant to COVID-19 in spite of exposure to SARS-CoV-2, the virus that causes it. Researchers from Johns Hopkins Medicine and The Johns Hopkins University have created and preliminarily tested what they believe may be one of the first models for predicting who has the highest probability of being resistant to COVID-19 in spite of exposure to SARS-CoV-2, the virus that causes it. The study is reported online today in the journal PLOS ONE. "If we can identify which people are naturally able to avoid infection by SARS-CoV-2, we may be able to learn -- in addition to societal and behavioral factors -- which genetic and environmental differences influence their defense against the virus," says lead study author Karen (Kai-Wen) Yang, a biomedical engineeri