Adaptive Control of IoT/M2M Devices in Smart Buildings using Heterogeneous Wireless Networks

With the rapid development of wireless communication technology, the Internet of Things (IoT) and Machine-to-Machine (M2M) are becoming essential for many applications. One of the most emblematic IoT/M2M applications is smart buildings. The current Building Automation Systems (BAS) are limited by many factors, including the lack of integration of IoT and M2M technologies, unfriendly user interfacing, and the lack of a convergent solution. Therefore, this paper proposes a better approach of using heterogeneous wireless networks consisting of Wireless Sensor Networks (WSNs) and Mobile Cellular Networks (MCNs) for IoT/M2M smart building systems. One of the most significant outcomes of this research is to provide accurate readings to the server, and very low latency, through which users can easily control and monitor remotely the proposed system that consists of several innovative services, namely smart parking, garden irrigation automation, intrusion alarm, smart door, fire and gas detection, smart lighting, smart medication reminder, and indoor air quality monitoring. All these services are designed and implemented to control and monitor from afar the building via our free mobile application named Raniso which is a local server that allows remote control of the building. This IoT/M2M smart building system is customizable to meet the needs of users, improving safety and quality of life while reducing energy consumption. Additionally, it helps prevent the loss of resources and human lives by detecting and managing risks.Comment: Accepted in IEEE Sensors Journa

Deep reinforcement learning based driving strategy for avoidance of chain collisions and its safety efficiency analysis in autonomous vehicles

Vehicle control in autonomous traffic flow is often handled using the best decision-making reinforcement learning methods. However, unexpected critical situations make the collisions more severe and, consequently, the chain collisions. In this work, we first review the leading causes of chain collisions and their subsequent chain events, which might provide an indication of how to prevent and mitigate the crash severity of chain collisions. Then, we consider the problem of chain collision avoidance as a Markov Decision Process problem in order to propose a reinforcement learning-based decision-making strategy and analyse the safety efficiency of existing methods in driving security. To address this, A reward function is being developed to deal with the challenge of multiple vehicle collision avoidance. A perception network structure based on formation and on actor-critic methodologies is employed to enhance the decision-making process. Finally, in the safety efficiency analysis phase, we investigated the safety efficiency performance of the agent vehicle in both single-agent and multi-agent autonomous driving environments. Three state-of-the-art contemporary actor-critic algorithms are used to create an extensive simulation in Unity3D. Moreover, to demonstrate the accuracy of the safety efficiency analysis, multiple training runs of the neural networks in respect of training performance, speed of training, success rate, and stability of rewards with a trade-off between exploitation and exploration during training are presented. Two aspects (single-agent and multi-agent) have assessed the efficiency of algorithms. Every aspect has been analyzed regarding the traffic flows: (1) the controlling efficiency of unexpected traffic situations by the sudden slowdown, (2) abrupt lane change, and (3) smoothly reaching the destination. All the findings of the analysis are intended to shed insight on the benefits of a greater, more reliable autonomous traffic set-up for academics and policymakers, and also to pave the way for the actual carry-out of a driver-less traffic world

Predicting primary sequence-based protein-protein interactions using a Mercer series representation of nonlinear support vector machine

© 2022 The Authors. Published by IEEE. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://ieeexplore.ieee.org/document/9956991The prediction of protein-protein interactions (PPIs) is essential to understand the cellular processes from a medical perspective. Among the various machine learning techniques, kernel-based Support Vector Machine (SVM) has been commonly employed to discriminate between interacting and non-interacting protein pairs. The main drawback of employing the kernel-based SVM to datasets with many features, such as the primary sequence-based protein-protein dataset, is the significant increase in computational time of training stage. This increase in computational time is mainly due to the presence of the kernel in solving the quadratic optimisation problem (QOP) involved in nonlinear SVM. In order to fix this issue, we propose a novel and efficient computational algorithm by approximating the kernel-based SVM using a low-rank truncated Mercer series as well as desired. As a result, the QOP for the approximated kernel-based SVM will be very tractable in the sense that there is a significant reduction in computational time of training and validating stages. We illustrate the novelty of the proposed method by predicting the PPIs of “S. Cerevisiae” where the protein features extracted using the multiscale local descriptor (MLD), and then we compare the predictive performance of the proposed low-rank approximation with the existing methods. Finally, the new method results in significant reduction in computational time for predicting PPIs with almost as accuracy as kernel-based SVM.The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number IF-2020-NBU-412.Published versio

Multiple vehicle cooperation and collision avoidance in automated vehicles : Survey and an AI‑enabled conceptual framework

Prospective customers are becoming more concerned about safety and comfort as the automobile industry swings toward automated vehicles (AVs). A comprehensive evaluation of recent AVs collision data indicates that modern automated driving systems are prone to rear-end collisions, usually leading to multiple-vehicle collisions. Moreover, most investigations into severe traffic conditions are confined to single-vehicle collisions. This work reviewed diverse techniques of existing literature to provide planning procedures for multiple vehicle cooperation and collision avoidance (MVCCA) strategies in AVs while also considering their performance and social impact viewpoints. Firstly, we investigate and tabulate the existing MVCCA techniques associated with single-vehicle collision avoidance perspectives. Then, current achievements are extensively evaluated, challenges and flows are identified, and remedies are intelligently formed to exploit a taxonomy. This paper also aims to give readers an AI-enabled conceptual framework and a decision-making model with a concrete structure of the training network settings to bridge the gaps between current investigations. These findings are intended to shed insight into the benefits of the greater efficiency of AVs set-up for academics and policymakers. Lastly, the open research issues discussed in this survey will pave the way for the actual implementation of driverless automated traffic systems

Adsorption of fluoride on a green adsorbent derived from wastepaper: Kinetic, isotherm and characterisation study

The excessive concentration of fluoride (F−) in water represents a grave problem for several countries, especially those that depend on groundwater as a main source of drinking water. Therefore, many treatment methods, such as chemical precipitation and membrane, were practised to remove F− from water. However, the traditional methods suffer from many limitations, such as the high cost and the slowness. Hence, many studies have been directed towards developing novel and effective water defluoridation methods. In this context, the current study investigates the development of an eco-friendly adsorbent by extracting Ca, Al, and Fe from industrial by-products, precipitating them on sand particles, and using this new adsorbent to remove F− from water. The removal experiments were commenced under different pH levels (3-10), contact times (0–240 minutes) and concentrations of F− (7.5–37.5 mg/L). X-ray fluorescence (XRF), X-ray diffraction Investigator (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDX) methods were used to characterise the green adsorbent. Adsorption isotherm and kinetic studies were also conducted to define the adsorption type. The results confirmed that the new adsorbent could remove as high as 86% of F− at pH, contact time, agitation speed and adsorbent dose of 10, 180 minutes, 200 rpm and 15 mg/L, respectively. The characterisation studies prove the occurrence of the sorption process and the suitability of the morphology of the adsorbent for F− removal. Adsorption kinetics follow better with a pseudo-first-order model that indicates the predominance of physisorption, which agrees with the FTIR results. The isotherm study indicated that Langmuir isotherm is more suitable for representing data with an R2 value of 0.992, which means the adsorption of F− occurs as monolayer adsorption on homogeneous sites on the surface of the new adsorbent. In summary, it can be concluded that the developed adsorbent in this study could be a promising alternative to the traditional F− removal methods

Antitrypanosomal and antileishmanial activity of chalcones and flavanones from Polygonum salicifolium

Trypanosomiasis and leishmaniasis are a group of neglected parasitic diseases caused by several species of parasites belonging to the family Trypansomatida. The present study investigated the antitrypanosomal and antileishmanial activity of chalcones and flavanones from Polygonum salicifolium, which grows in the wetlands of Iraq. The phytochemical evaluation of the plant yielded two chalcones, 2′,4′-dimethoxy-6′-hydroxychalcone and 2′,5′-dimethoxy-4′,6′-dihydroxychalcone, and two flavanones, 5,7-dimethoxyflavanone and 5,8-dimethoxy-7-hydroxyflavanone. The chalcones showed a good antitrypanosomal and antileishmanial activity while the flavanones were inactive. The EC50 values for 2′,4′-dimethoxy-6′-hydroxychalcone against Trypanosoma brucei brucei (0.5 μg/mL), T. congolense (2.5 μg/mL), and Leishmania mexicana (5.2 μg/mL) indicated it was the most active of the compounds. None of the compounds displayed any toxicity against a human cell line, even at 100 µg/mL, or cross-resistance with first line clinical trypanocides, such as diamidines and melaminophenyl arsenicals. Taken together, our study provides significant data in relation to the activity of chalcones and flavanones from P. salicifolium against both parasites in vitro. Further future research is suggested in order to investigate the mode of action of the extracted chalcones against the parasites

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

Deep reinforcement learning-based driving strategy for avoidance of chain collisions and its safety efficiency analysis in autonomous vehicles

© 2022 The Authors. Published by IEEE. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://ieeexplore.ieee.org/document/9758806Vehicle control in autonomous traffic flow is often handled using the best decision-making reinforcement learning methods. However, unexpected critical situations make the collisions more severe and, consequently, the chain collisions. In this work, we first review the leading causes of chain collisions and their subsequent chain events, which might provide an indication of how to prevent and mitigate the crash severity of chain collisions. Then, we consider the problem of chain collision avoidance as a Markov Decision Process problem in order to propose a reinforcement learning-based decision-making strategy and analyse the safety efficiency of existing methods in driving security. To address this, A reward function is being developed to deal with the challenge of multiple vehicle collision avoidance. A perception network structure based on formation and on actor-critic methodologies is employed to enhance the decision-making process. Finally, in the safety efficiency analysis phase, we investigated the safety efficiency performance of the agent vehicle in both single-agent and multi-agent autonomous driving environments. Three state-of-the-art contemporary actor-critic algorithms are used to create an extensive simulation in Unity3D. Moreover, to demonstrate the accuracy of the safety efficiency analysis, multiple training runs of the neural networks in respect of training performance, speed of training, success rate, and stability of rewards with a trade-off between exploitation and exploration during training are presented. Two aspects (single-agent and multi-agent) have assessed the efficiency of algorithms. Every aspect has been analyzed regarding the traffic flows: (1) the controlling efficiency of unexpected traffic situations by the sudden slowdown, (2) abrupt lane change, and (3) smoothly reaching the destination. All the findings of the analysis are intended to shed insight on the benefits of a greater, more reliable autonomous traffic set-up for academics and policymakers, and also to pave the way for the actual carry-out of a driver-less traffic world.This work was supported in part by the Ministry of Higher Education of Malaysia through the Fundamental Research Grant Scheme under Grant FRGS/1/2018/TK08/UMP/02/2; and in part by the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia under Project IF-2020-NBU-418.Published versio

Mobile-IRS Assisted Next Generation UAV Communication Networks

Prior research on intelligent reflection surface (IRS)-assisted unmanned aerial vehicle (UAV) communications has focused on a fixed location for the IRS or mounted on a UAV. The assumption that the IRS is located at a fixed position will prohibit mobile users from maximizing many wireless network benefits, such as data rate and coverage. Furthermore, assuming that the IRS is placed on a UAV is impractical for various reasons, including the IRS's weight and size and the speed of wind in severe weather. Unlike previous studies, this study assumes a single UAV and an IRS mounted on a mobile ground vehicle (M-IRS) to be deployed in an Internet-of-Things (IoT) 6G wireless network to maximize the average data rate. Such a methodology for providing wireless coverage using an M-IRS assisted UAV system is expected in smart cities. In this paper, we formulate an optimization problem to find an efficient trajectory for the UAV, an efficient path for the M-IRS, and users' power allocation coefficients that maximize the average data rate for mobile ground users. Due to its intractability, we propose efficient techniques that can help in finding the solution to the optimization problem. First, we show that our dynamic power allocation technique outperforms the fixed power allocation technique in terms of network average sum rate. Then we employ the individual movement model (Random Waypoint Model) in order to represent the users' movements inside the coverage area. Finally, we propose an efficient approach using a Genetic Algorithm (GA) for finding an efficient trajectory for the UAV, and an efficient path for the M-IRS to provide wireless connectivity for mobile users during their movement. We demonstrate through simulations that our methodology can enhance the average data rate by 15\% on average compared with the static IRS and by 25\% on average compared without the IRS system.Comment: 11 pages, 8 figure

Development of Nursing Research in Saudi Arabia: Implications for Policies and Practice

Background: Nursing research in Saudi Arabia can be evaluated based on productivity as well as the quality of publications. The scope of scientific inquiry in nursing research expands to include clinical, health system, and outcome-based research, education, and administration. Aim: The purpose of this article is to track the development of nursing research in the Kingdom of Saudi Arabia. Design: Systematic review. Methods: This study used keywords, databases including MEDLINE, CINAHL, and PubMed to search for published articles on nursing in Saudi Arabia. The search resulted in the identification of 681 publications, from which 360 articles met the inclusion criteria and were included in the review. Results: The highest percentage of studies (56.7% of articles) focused on nursing clinical practice, and 76.0% of the studies were conducted in a hospital setting, followed by an educational setting. Most of the studies were quantitative and non-funded. More than 50.0% of the studies were first authored by Saudi scholars. Conclusions: This study concluded that nursing research in Saudi Arabia is still in its infancy, with notable improvements in the last 5 years. This correlated with an increasing number of nurses holding postgraduate degrees. With the Saudi government’s strong support, the number of scientific research papers published on Saudi nursing has steadily increased over the last year