Internet of things–based vital sign monitoring system

Wireless network technology-based internet of things (IoT) has increased significantly and exciting to study, especially vital sign monitoring (body temperature, heart rate, and blood pressure). Vital sign monitoring is crucial to carry out to strengthen medical diagnoses and the continuity of patient health. Vital sign monitoring conducted by medical personnel to diagnose the patient's health condition is still manual. Medical staff must visit patients in each room, and the equipment used is still cable-based. Vital sign examination like this is certainly not practical because it requires a long time in the process of diagnosis. The proposed vital sign monitoring system design aims to assist medical personnel in diagnosing the patient's illness. Vital sign monitoring system uses HRM-2511E sensor for heart detection, DS18b20 sensor for body temperature detection, and MPX5050DP sensor for blood pressure detection. Vital sign data processing uses a raspberry pi as a data delivery media-based internet of things (IoT). Based on the results of the vital sign data retrieval shows that the tool designed functioning correctly. The accuracy of the proposed device for body temperature is 99.51%, heart rate is 97.90%, and blood pressure is 97.69%

途上国のための統合的電気電子機器廃棄物管理システムのライフサイクル評価 : ヨルダンでの評価

学位の種別: 課程博士審査委員会委員 : （主査）東京大学客員教授 田崎 智宏, 東京大学教授 多部田 茂, 東京大学准教授 小貫 元治, 東京大学特任准教授 松田 浩敬, 国立環境研究所主任研究員 小口 正弘University of Tokyo(東京大学

Comparing neural network with linear regression for stock market prediction

There are both gains and losses possible in stock market investing. Brokerage firms' stock investments carry a higher risk of loss since their stock prices are not being tracked or analyzed, which might be problematic for businesses seeking investors or individuals. Thanks to progress in information and communication technologies, investors may now easily collect and analyze stock market data to determine whether to buy or sell. Implementing machine learning algorithms in data mining to obtain information close to the truth from the desired objective will make it easier for an individual or group of investors to make stock trades. In this study, we test hypotheses on the performance of a financial services firm's stock using various machine learning and regression techniques. The relative error for the neural network method is only 0.72 percentage points, while it is 0.78 percentage points for the Linear Regression. More training cycles must be applied to the Algortima neural network to achieve more accurate results

Aligning South Africa's National Development Plan with the 2030 Agenda's Sustainable Development Goals: Guidelines from the Policy Coherence for Development movement

Sustainable development depends on coherence between the development policies of recipients and the providers of development assistance. Yet achieving coherence is difficult. This paper examines the extent to which the Policy Coherence for Development movement offers guidelines for aligning national development priorities with global development priorities. This qualitative paper focuses on alignment between South Africa's National Development and its Medium‐Term Strategic Frameworks and the 2030 Agenda and its Sustainable Development Goals. Based on an analysis of policy documents and peer‐reviewed research on the Policy Coherence for Development movement, it is argued that five guidelines might be of relevance for South Africa, namely (i) prioritizing political buy‐in, (ii) safeguarding country ownership of development priorities, (iii) using and improving existing institutional structures and processes, (iv) stimulating cooperation across government departments by using an issue‐based approach and (v) including a long‐term and transnational perspective when considering policy impacts.https://wileyonlinelibrary.com/journal/sd2020-11-01hj2018Business Managemen

An overview of methods used for estimating e-waste amount

Electrical and electronics products become obsolete with technological innovations or changes in customer preferences. Thus, each new product or a new version of an existing product pushes the old one into the E-waste stream. E-waste is one of the fastest growing waste types and it seems that this rapid increase will continue due to shortening lifecycles of technological products. Therefore, the estimation of E-waste amount has a crucial role in designing efficient E-waste management and treatment systems. There are various estimation methods in the literature, some of which are drawn from general estimation and forecasting models and others that are specially designed depending on the E-waste constraints and boundaries. This chapter offers a detailed and comparative presentation of E-waste estimation methods. An example for each estimation method is provided to further explain the method. © 2019 Elsevier Inc. All rights reserved