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Living in a healthy environment is a need for every human being whether indoor or outdoor. However, pollutions occur everywhere and most people are merely mindful of the importance of having clean outdoor air to breathe and are not concerned about the indoor air quality. Indoor air quality refers to the quality within the building, and relates to the health and comfort of the building occupants. Dangerous particles exist in the outside air, pollute the indoor environment and produce harmful conditions as the polluted air travels into the house or building through windows or doors. Therefore, a wireless Internet of Things-based air quality device is developed to monitor the air quality in the indoor environment. The proposed system integrates a low-cost air quality sensor, temperature and humidity sensors, a single-board computer (Raspberry Pi 2 microprocessor) and cloud storage. The system provides realtime air quality reading, transfers the data through a wireless network to the Internet and displays the data in dedicated webpage. Furthermore, it stores records in cloud storage and sends e-mail notification message to the user when unhealthy condition is met. The study has a significant impact on promoting affordable and portable smart pollution monitoring system as the development of the device utilizing low-cost and off-the-shelf components.

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<p>Insider threat is a significant challenge in cybersecurity. In comparison with outside attackers, inside attackers have more privileges and legitimate access to information and facilities that can cause considerable damage to an organization. Most organizations that implement traditional cybersecurity techniques, such as intrusion detection systems, fail to detect insider threats given the lack of extensive knowledge on insider behavior patterns. However, a sophisticated method is necessary for an in-depth understanding of insider activities that the insider performs in the organization. In this study, we propose a new conceptual method for insider threat detection on the basis of the behaviors of an insider. In addition, gated recurrent unit neural network will be explored further to enhance the insider threat detector. This method will identify the optimal behavioral pattern of insider actions.</p>

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The paper aims to provide an insight into the significance of having a simulation model to forecast the supply of registered nurses for health workforce planning policy using System Dynamics. A model is highly in demand to predict the workforce demand for nurses in the future, which it supports for complete development of a needs-based nurse workforce projection using Malaysia as a case study. The supply model consists of three sub-models to forecast the number of registered nurses for the next 15 years: training model, population model and Full Time Equivalent (FTE) model. In fact, the training model is for predicting the number of newly registered nurses after training is completed. Furthermore, the population model is for indicating the number of registered nurses in the nation and the FTE model is useful for counting the number of registered nurses with direct patient care. Each model is described in detail with the logical connection and mathematical governing equation for accurate forecasting. The supply model is validated using error analysis approach in terms of the root mean square percent error and the Theil inequality statistics, which is mportant for evaluating the simulation results. Moreover, the output of simulation results provides a useful insight for policy makers as a what-if analysis is conducted. Some recommendations are proposed in order to deal with the nursing deficit. It must be noted that the results from the simulation model will be used for the next stage of the Needs-Based Nurse Workforce projection project. The impact of this study is that it provides the ability for greater planning and policy making with better predictions.

Insider threat has become a widely accepted issue and one of the major challenges in cybersecurity. This phenomenon indicates that threats require special detection systems, methods, and tools, which entail the ability to facilitate accurate and fast detection of a malicious insider. Several studies on insider threat detection and related areas in dealing with this issue have been proposed. Various studies aimed to deepen the conceptual understanding of insider threats. However, there are many limitations, such as a lack of real cases, biases in making conclusions, which are a major concern and remain unclear, and the lack of a study that surveys insider threats from many different perspectives and focuses on the theoretical, technical, and statistical aspects of insider threats. The survey aims to present a taxonomy of contemporary insider types, access, level, motivation, insider profiling, effect security property, and methods used by attackers to conduct attacks and a review of notable recent works on insider threat detection, which covers the analyzed behaviors, machine-learning techniques, dataset, detection methodology, and evaluation metrics. Several real cases of insider threats have been analyzed to provide statistical information about insiders. In addition, this survey highlights the challenges faced by other researchers and provides recommendations to minimize obstacles.

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