Ergonomics Concept in Inclusive Public Playground Targeting on Children with Disabilities

Nowadays, playgrounds are underused to improve the physical and social development of young children with special needs. Therefore, this study aims to identify the key criteria of ergonomic inclusive public children playground aim for children with disabilities (CWDs). The study explored the process of universal design and the ergonomics function of play equipment that focuses on CWDs through physical site observation and interview protocol done among caregivers, parents of children with disabilities, and all stakeholders involved in the development of the public playground in Malaysia. The result presents three major themes as a guideline to create an ergonomic inclusive playground. Keywords: Ergonomics; Universal Design; Public Playground; Children with disabilities. eISSN: 2398-4287© 2020. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open-access article under the CC BY NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: 10.21834/ebpj.v5i15.249

Deterministic Static Sensor Node Placement in Wireless Sensor Network based on Territorial Predator Scent Marking Behaviour

An optimum sensor node placement mechanism for Wireless Sensor Network (WSN) is desirable in ensuring the location of sensor nodes offers maximum coverage and connectivity with minimum energy consumption. This paper proposes a sensor node placement algorithm that utilizes a new biologically inspired optimization algorithm that imitates the behaviour of a territorial predator in marking their territories with their odours known as Territorial Predator Scent Marking Algorithm (TPSMA). The main objectives considered in this paper are to achieve maximum coverage and minimum energy consumption with guaranteed connectivity. A simulation study has been carried out to compare the performance of the proposed algorithm implemented in two different single objective approaches with an Integer Linear Programming based algorithm. The proposed single objective approaches of TPSMA studied in this paper are TPSMA with minimum energy and TPSMA with maximum coverage. Simulation results show that the WSN deployed using the proposed TPSMA sensor node placement algorithm is able to arrange the sensor nodes according to the objective required; TPSMA with maximum coverage offers the highest coverage ratio with fewer sensor nodes up to 100% coverage while TPSMA with minimum energy consumption utilized the lowest energy as low as around 4.85 Joules. Full connectivity is provisioned for all TPSMA approaches since the constraint of the optimization problem is to ensure the connectivity from all sensor nodes to the sink node

Protein produced by Bacillus subtilis ATCC21332 in the presence of Cymbopogon flexuosus essential oil

Proteins levels produced by bacteria may be increased in stressful surroundings, such as in the presence of antibiotics. It appears that many antimicrobial agents or antibiotics, when used at low concentrations, have in common the ability to activate or repress gene transcription, which is distinct from their inhibitory effect. There have been comparatively few studies on the potential of antibiotics or natural compounds in nature as a specific chemical signal that can trigger a variety of biological functions. Therefore, this study was focusing on the effect of essential oil from Cymbopogon flexuosus in regulating proteins production by Bacillus subtilis ATCC21332. The Minimum Inhibition Concentration (MIC) of the C. flexuosus essential oil on B. subtilis was determined by using microdilution assay, resulting 1.76mg/ml. The bacteria cells were further exposed to the C. flexuosus essential oil at concentration of 0.01 MIC for 72 h. The proteins were then isolated and analyzed by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). Proteins profile showed that a band with approximate size of 30 kDa was appeared for the treated bacteria with C. flexuosus essential oil. Thus, B. subtilis ATCC21332 in stressful condition with the presence of C. flexuosus essential oils at low concentration could induce the protein production. The isolated protein also showed antimicrobial activity against selected Gram-positive and Gram-negative bacteria

5G Technology: ML Hyperparameter Tuning Analysis for Subcarrier Spacing Prediction Model

Resource optimisation is critical because 5G is intended to be a major enabler and a leading infrastructure provider in the information and communication technology sector by supporting a wide range of upcoming services with varying requirements. Therefore, system improvisation techniques, such as machine learning (ML) and deep learning, must be applied to make the model customisable. Moreover, improvisation allows the prediction system to generate the most accurate outcomes and valuable insights from data whilst enabling effective decisions. In this study, we first provide a literature study on the applications of ML and a summary of the hyperparameters influencing the prediction capabilities of the ML models for the communication system. We demonstrate the behaviour of four ML models: k nearest neighbour, classification and regression trees, random forest and support vector machine. Then, we observe and elaborate on the suitable hyperparameter values for each model based on the accuracy in prediction performance. Based on our observation, the optimal hyperparameter setting for ML models is essential because it directly impacts the model’s performance. Therefore, understanding how the ML models are expected to respond to the system utilised is critical

Criteria Selection Using Machine Learning (ML) for Communication Technology Solution of Electrical Distribution Substations

In the future, as populations grow and more end-user applications become available, the current traditional electrical distribution substation will not be able to fully accommodate new applications that may arise. Consequently, there will be numerous difficulties, including network congestion, latency, jitter, and, in the worst-case scenario, network failure, among other things. Thus, the purpose of this study is to assist decision makers in selecting the most appropriate communication technologies for an electrical distribution substation through an examination of the criteria’s in-fluence on the selection process. In this study, nine technical criteria were selected and processed using machine learning (ML) software, RapidMiner, to find the most optimal technical criteria. Several ML techniques were studied, and Naïve Bayes was chosen, as it showed the highest performance among the rest. From this study, the criteria were ranked in order of importance from most important to least important based on the average value obtained from the output. Seven technical criteria were identified as being important and should be evaluated in order to determine the most appropriate communication technology solution for electrical distribution substation as a result of this study

A New Machine Learning-based Hybrid Intrusion Detection System and Intelligent Routing Algorithm for MPLS Network

Machine Learning (ML) is seen as a promising application that offers autonomous learning and provides optimized solutions to complex problems. The current Multiprotocol Label Switching (MPLS)-based communication system is packed with exponentially increasing applications and different Quality-of-Services (QoS) requirements. As the network is getting complex and congested, it will become challenging to satisfy the QoS requirements in the MPLS network. This study proposes a hybrid ML-based intrusion detection system (ML-IDS) and ML-based intelligent routing algorithm (ML-RA) for MPLS network. The research is divided into three parts, which are (1) dataset development, (2) algorithm development, and (3) algorithm performance evaluation. The dataset development for both algorithms is carried out via simulations in Graphical Network Simulator 3 (GNS3). The datasets are then fed into MATLAB to train ML classifiers and regression models to classify the incoming traffic as normal or attack and predict traffic delays for all available routes, respectively. Only the normal traffic predicted by the ML-IDS algorithm will be allowed to enter the network domain, and the route with the fastest delay predicted by the ML-RA is assigned for routing. The ML-based routing algorithm is compared to the conventional routing algorithm, Routing Information Protocol version 2 (RIPv2). From the performance evaluations, the ML-RA shows 100 percent accuracy in predicting the fastest route in the network. During network congestion, the proposed ML outperforms the RIPv2 in terms of delay and throughput on average by 57.61 percent and 46.57 percent, respectively

Antimicrobial protein produced by Bacillus subtilis ATCC 21332 in the presence of Allium sativum

Introduction: Proteins levels produced by bacteria may be increased in stressful surroundings, such as in the presence of antibiotics. It appears that many antimicrobial agents or antibiotics, when used at low concentrations, have in common the ability to activate or repress gene transcription, which is distinct from their inhibitory effect. There have been comparatively few studies on the potential of antibiotics or natural compounds in nature as a specific chemical signal that can trigger a variety of biological functions. Objective: To study the effect of Allium sativum in regulating proteins production by Bacillus subtilis ATCC 21332. Methods: The bacteria cells were exposed to the A. sativum at concentration of 0.025 MIC for 24 h. The extracellular proteins were then isolated and screening for antimicrobial activity before being further analyzed by using Sodium Dodecyl Sulfate Polyacrylamide Gel Electrophoresis (SDS-PAGE) and Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS). Results & Discussion: The isolated extracellular proteins showed antimicrobial activity against selected Gram-positive and Gram-negative bacteria. Proteins profile showed that two new bands with approximate sizes of 51.36 kDA and 9.74 kDA were appeared for the treated bacteria with A. sativum. LC-MS/MS analysis revealed that four and two possible proteins were identified for each of isolated proteins with approximate sizes of 51.36 kDA and 9.74 kDA. Conclusion: B. subtilis ATCC 21332 in stressful condition with the presence of A. sativum at low concentration (0.025 MIC) could induce the production of bioactive protein with antimicrobial activity

Active Electric Distribution Network: Applications, Challenges, and Opportunities

Traditional electrical power grids are transitioning from centralised operation with unidirectional energy and information flows (from the generation domain to customers) to smart grids with decentralised mode of operation and bidirectional flows. This reversal of traditional power flow direction is due to the connections of active loads such as distributed energy resources (DERs) and renewable energy sources close to the distribution network. Through advanced and sophisticated information and communication technologies (ICTs), efficient DER management and various applications for reliable and secure power delivery are enabled. However, before the adoption of any ICT solution in the grid, several challenges remain, which include interoperability, security and privacy concerns, and the ever-increasing demands to support various services and applications. Although the information within the grid is becoming more visible because of bidirectional communication flow, this only applies to transmission networks and not active distribution networks, which house numerous smart grid applications. There is also little research that supports the automatic operation of active distribution networks. Hence, this article explores and reviews active distribution network communication technologies, as well as the applications and communication standards. This review paper also highlights issues and challenges with active distribution networks and opportunities and research trends in the distribution domain from an ICT perspective

The Implementation of a Machine Learning-based Routing Algorithm in a Lab-Scale Testbed

High quality of service (QoS) requires monitoring and controlling parameters such as delay and throughput. Due to network complexity, conventional QoS-improving routing algorithms (RAs) may be impractical. Thus, researchers are developing intelligent RAs, including machine learning (ML)-based algorithms to meet traffic Q oS r equirements. However, most current studies evaluate performance using simulations. Validation requires real-world environment studies, but lab-scale testbed studies are limited. Therefore, we proposed an ML-based RA (ML-RA-t) to improve delay and throughput, evaluated using simulation and a lab-scale testbed. The results show that ML-RA-t predicted the fastest route as compared to RIPv2 routing protocol in simulation and testbed