Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features

An Empirical Investigation of the Performance of Japanese Mutual Funds: Skill or Luck?

This paper assesses the performance of 355 actively managed Japanese Equity Mutual Funds between April 2011 and April 2016. The equal weight portfolio and Jensen’s alpha measures of active management provide strong evidence that Japanese Mutual Funds fail to outperform the benchmark four-factor capital asset pricing model. When it comes to market timing, the Treynor and Mazuy measure shows that 33 funds have significant positive market timing ability which is largely offset by 31 funds with significant negative timing ability. To ensure the statistical inference is robust to the non-normality found in 33 funds we employ Fama and French’s cross-sectional bootstrap. The results show that a large proportion of funds fail to outperform a hypothetical world with no skill. On the persistence of skill we find that there is stronger persistence for poor performing funds than for strong performing funds

The contribution of data mining to information science

The information explosion is a serious challenge for current information institutions. On the other hand, data mining, which is the search for valuable information in large volumes of data, is one of the solutions to face this challenge. In the past several years, data mining has made a significant contribution to the field of information science. This paper examines the impact of data mining by reviewing existing applications, including personalized environments, electronic commerce, and search engines. For these three types of application, how data mining can enhance their functions is discussed. The reader of this paper is expected to get an overview of the state of the art research associated with these applications. Furthermore, we identify the limitations of current work and raise several directions for future research

A Project Based Approach to Statistics and Data Science

In an increasingly data-driven world, facility with statistics is more important than ever for our students. At institutions without a statistician, it often falls to the mathematics faculty to teach statistics courses. This paper presents a model that a mathematician asked to teach statistics can follow. This model entails connecting with faculty from numerous departments on campus to develop a list of topics, building a repository of real-world datasets from these faculty, and creating projects where students interface with these datasets to write lab reports aimed at consumers of statistics in other disciplines. The end result is students who are well prepared for interdisciplinary research, who are accustomed to coping with the idiosyncrasies of real data, and who have sharpened their technical writing and speaking skills

The global classroom for supply chain management, any time, anywhere!

Academia is facing increasing demands in the design and delivery of their degree programmes due to resource constraints and the demands to embrace. The purpose of this article is to examine the requirement for quality education in the field of supply chain management. The approach adopted here is a reflective one, looking at recent trends in postgraduate Supply Chain Management (SCM) education and focusing in particular on a new mode of delivery, that of e-learning. The paper considers the development of SCM education and presents the range of supply chain management programmes and modules being offered across a selection of UK universities. The article also highlights the dynamic character of SCM education and considers whether the e-learning format is capable of responding to the requirements for quality in this field. Through a focus on one particular programme, the wholly online postgraduate programme in Operations and Supply Chain Management at the University of Liverpool. The conclusions are that new forms of teaching and learning are opening up to Higher Education Institutions (HEIs). The aim of the research was to discover the real time dynamic of SCM practice and theory, objective and subjective perspectives