Cutaneous infections among Human Immunodeficiency Virus (HIV) - infected patients in a single center in Kuala Lumpur, Malaysia

Background: Human immunodeficiency virus (HIV) infection is frequently associated with cutaneous infections. Objective: To determine the spectrum of dermatological infections among HIV positive patients, identify the causative microorganisms and CD4 count. Methods: This is a retrospective study conducted in a tertiary center. HIV-positive patients registered in 2013 to 2018 were identified from casemix database, those with suspected cutaneous infection were selected. Data was obtained from electronic and physical medical records. Results:  A total of 27 patients were enrolled. Mean age was 38.61±11 years, 22(81.5%) were males and 5(18.5%) were females. Most patients 14(52%) developed more than one skin disease, there were a total of 46 diagnoses. The skin infections were fungal 11(24%), viral 4(8.7%) and bacterial 4(8.7%). Disseminated mycoses were mostly due to Penicillium marneffei 4(8.7%). Superficial fungal infections were due to Fusarium sp, Candida sp and Trichosporum sp < /em>. Cutaneus candidiasis constituted 3(6.47%). The average CD4 count in patients with fungal infections was 79 cells/mm3. Other skin diseases documented were adverse cutaneous drug reactions 8(17.4%) and pruritic papular eruption 5(11%). CD4 count was Conclusion: Both disseminated and superficial fungal infections were common in our cohort. Penicillium marneffei caused majority of disseminated infections while superficial infections are due to moulds

Simulation of Assembly Line Balancing in Automotive Component Manufacturing

This study focuses on the simulation of assembly line balancing in an automotive component in a vendor manufacturing company. A mixed-model assembly line of charcoal canister product that is used in an engine system as fuel's vapour filter was observed and found that the current production rate of the line does not achieve customer demand even though the company practices buffer stock for two days in advance. This study was carried out by performing detailed process flow and time studies along the line. To set up a model of the line by simulation, real data was taken from a factory floor and tested for distribution fit. The data gathered was then transformed into a simulation model. After verification of the model by comparing it with the actual system, it was found that the current line efficiency is not at its optimum condition due to blockage and idle time. Various what-if analysis were applied to eliminate the cause. Proposed layout shows that the line is balanced by adding buffer to avoid the blockage. Whereas, manpower is added the stations to reduce process time therefore reducing idling time. The simulation study was carried out using ProModel software

Machine Learning–Based Identity and Access Management for Cloud Security

Cloud computing has the potential to offer an abundance of computing resources on demand due to its high scalability, which eliminates the need for providers to plan far in advance for hardware provisioning. Security remains a significant challenge in promoting cloud computing, but artificial intelligence (AI) can improve cloud services by enhancing security features. The privacy issue arises from the multiple data storage locations and available cloud services. Identity management services are crucial in establishing secure and efficient relationships in the cloud and cross-cloud environments by authenticating users based on their identity properties and past interactions. To incorporate AI, cloud security must be improved to provide an effective solution for data storage. We suggest AI-enabled cloud services security in this proposed architecture to protect cloud service users. Our model uses AI to identify users’ identities and restricts malicious access to cloud services, and it was trained based on CloudSim-generated datasets. Quality of services (QoS) was used to measure the architecture’s efficiency, and the results showed that the proposed architecture was effective and fruitful for users. The results effectively show the improvements in network throughput under congestion control with the proposed approach compared with existing state-of-the-art techniques

Effect of Primary Reference Fuel on Reactivity-Controlled Compression Ignition Engine Emission Produce

Reactivity-Controlled Compression Ignition or also known as RCCI mode engine is a modified of Homogeneous Charge Compression Ignition (HCCI) engine in which have a better control in combustion and wider load range. The strategies of RCCI mode in controlling the combustion is by using dual fuel of different reactivity such as diesel (high reactivity fuel) and gasoline (low reactivity fuel). The objective of this experimental investigation is to evaluate the effect of Primary Reference Fuel towards the emission produce by the RCCI engine mode. Single cylinder CI engine with port injection system is used in this study. Two alkane-based, iso-octane and n-heptane were blend together, PRF80 (80% iso-octane + 20% n-heptane) fuel mixtures were used throughout this study as low reactivity fuel in port injection system and pure diesel as high reactivity fuel in direct injection system. Result found that the performance of RCCI mode engine improve with the use of alkane (iso-octane and n-heptane) in the PRF80 blends especially in comparison to normal mode CI engine (using diesel only). In terms of emission, by using PRF80 in RCCI mode engine, the NOx reduce almost 95% of normal CI engine NOx production. As a result of applying PRF as a low reactivity fuel in an RCCI engine system, knocking resistance may be produced even at high engine compression ratios, resulting in better thermal efficiency and reduced NOx-Soot emissions

Intelligent microservice based on blockchain for healthcare applications

Nowadays, the blockchain, Internet of Things, and artificial intelligence technology revolutionize the traditional way of data mining with the enhanced data preprocessing, and analytics approaches, including improved service platforms. Nevertheless, one of the main challenges is designing a combined approach that provides the analytics functionality for diverse data and sustains IoT applications with robust and modular blockchain-enabled services in a diverse environment. Improved data analytics model not only provides support insights in IoT data but also fosters process productivity. Designing a robust IoT-based secure analytic model is challenging for several purposes, such as data from diverse sources, increasing data size, and monolithic service designing techniques. This article proposed an intelligent blockchain-enabled microservice to support predictive analytics for personalized fitness data in an IoT environment. The designed system support microservice-based analytic functionalities to provide secure and reliable services for IoT. To demonstrate the proposed model effectiveness, we have used the IoT fitness application as a case study. Based on the designed predictive analytic model, a recommendation model is developed to recommend daily and weekly diet and workout plans for improved body fitness. Moreover, the recommendation model objective is to help trainers make future health decisions of trainees in terms of workout and diet plan. Finally, the proposed model is evaluated using Hyperledger Caliper in terms of latency, throughput, and resource utilizationwith varying peers and orderer nodes. The experimental result shows that the proposed model is applicable for diverse resourceconstrained blockchain-enabled IoT applications and extensible for several IoT scenarios. © 2021 Tech Science Press. All rights reserved

Intelligent transmission control for efficient operations in SDN

Although the Software-Defined Network (SDN) is a well-controlled and efficient network but the complexity of open flow switches in SDN causes multiple issues. Many solutions have been proposed so far for the prevention of errors and mistakes in it but yet, there is still no smooth transmission of pockets from source to destination specifically when irregular movements follow the destination host in SDN, the errors include packet loss, data compromise etc. The accuracy of packets received at their desired destination is possible if networks for pockets and hosts are monitored instead of analysis of network snapshot statistically for the state, as these approaches with open flow switches, discover bugs after their occurrence. This article proposes a design to achieve the said objective by defining the Intelligent Transmission Control Layer (ITCL) layer. It monitors all the connections of end hosts at their specific locations and performs necessary settlements when the connection state changes for one or multiple hosts. The layer informs the controller regarding any state change at one period and controller collects information of end nodes reported via ITCL. Then, updates flow tables accordingly to accommodate a location-change scenario with a route-change policy. ICTL is organized on prototype-based implementation using the popular POX platform. In this paper, it has been discovered that ITCL produces efficient performance in the trafficking of packets and controlling different states of SDN for errors and packet loss. © 2022 Tech Science Press. All rights reserved

Multilevel central trust management approach for task scheduling on iot‐based mobile cloud computing

With the increasing number of mobile devices and IoT devices across a wide range of real-life applications, our mobile cloud computing devices will not cope with this growing number of audiences soon, which implies and demands the need to shift to fog computing. Task scheduling is one of the most demanding scopes after the trust computation inside the trustable nodes. The mobile devices and IoT devices transfer the resource‐intensive tasks towards mobile cloud computing. Some tasks are resource‐intensive and not trustable to allocate to the mobile cloud computing resources. This consequently gives rise to trust evaluation and data sync‐up of devices joining and leaving the network. The resources are more intensive for cloud computing and mobile cloud computing. Time, energy, and resources are wasted due to the nontrustable nodes. This research article proposes a multilevel trust enhancement approach for efficient task scheduling in mobile cloud environments. We first calculate the trustable tasks needed to offload towards the mobile cloud computing. Then, an efficient and dynamic scheduler is added to enhance the task scheduling after trust computation using social and environmental trust computation techniques. To improve the time and energy efficiency of IoT and mobile devices using the proposed technique, the energy computation and time request computation are compared with the existing methods from literature, which identified improvements in the results. Our proposed approach is centralized to tackle constant SyncUPs of incoming devices’ trust values with mobile cloud computing. With the benefits of mobile cloud computing, the centralized data distribution method is a positive approach. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Multilevel Central Trust Management Approach for Task Scheduling on IoT-Based Mobile Cloud Computing

With the increasing number of mobile devices and IoT devices across a wide range of real-life applications, our mobile cloud computing devices will not cope with this growing number of audiences soon, which implies and demands the need to shift to fog computing. Task scheduling is one of the most demanding scopes after the trust computation inside the trustable nodes. The mobile devices and IoT devices transfer the resource-intensive tasks towards mobile cloud computing. Some tasks are resource-intensive and not trustable to allocate to the mobile cloud computing resources. This consequently gives rise to trust evaluation and data sync-up of devices joining and leaving the network. The resources are more intensive for cloud computing and mobile cloud computing. Time, energy, and resources are wasted due to the nontrustable nodes. This research article proposes a multilevel trust enhancement approach for efficient task scheduling in mobile cloud environments. We first calculate the trustable tasks needed to offload towards the mobile cloud computing. Then, an efficient and dynamic scheduler is added to enhance the task scheduling after trust computation using social and environmental trust computation techniques. To improve the time and energy efficiency of IoT and mobile devices using the proposed technique, the energy computation and time request computation are compared with the existing methods from literature, which identified improvements in the results. Our proposed approach is centralized to tackle constant SyncUPs of incoming devices’ trust values with mobile cloud computing. With the benefits of mobile cloud computing, the centralized data distribution method is a positive approach

Simulation of the ignition mechanisms of low and high octane number blended fuels in HCCI engine

Homogenous Charge Compression Ignition (HCCI) is an alternative combustion concept for in reciprocating engines which offers significant benefits in terms of its high efficiency and low emissions. HCCI is the most commonly used name for the auto-ignition of various fuels and one of the most promising alternatives to SI and CI combustion. This study focus on the ignition reactions of low and high octane number of fuel blends through comprehensive simulation. This study was carried out by using n-heptane as a base fuel and toluene as a sub fuel use as a fuel mixture in this simulation. Furthermore, for numerical analysis, MATLAB Software has been used to design simplified model of reaction mechanism for n-heptane. The simplified model has been discussed in this study. The highest value of hydroxyl radicals OH was achieved at approximately 0.23 at NTF 10 (Toluene mixture 10%) and the line decreased until 0 This value is gradually decreased when the mixture of toluene (NTF) as sub fuel is elevated until NTF60 Due to the content percentage of toluene added 10% consecutively, HCHO production increased as well. It is because HCHO consumes OH and at the same time affects the amount of OH. By doing this method (mixing n-heptane with toluene), the ignition delay of the fuel becomes longer is described. It is also shows that the simplified model constructed with a consideration of the property of reaction happen in nheptane (base fuel) added with toluene (sub fuel) in which OH reproduction and fuel + OH reaction plays important role. The purpose of this study is to figure out the reaction mechanism of compression ignition at Low Temperature Oxidation (LTO) and design the simplified model of reaction mechanism for n-heptane + toluene (NTF)