Journal of Advanced Research in Applied Sciences and Engineering Technology

Analysing the Effect of using Waste Ceramic Aggregates as Coarse Aggregate and Concrete Dust as Filler in Pavement to the Permeability

M Kalpana, G. Venkatesan , S. Padma — Mon, 07 Oct 2024 00:00:00 +0700

The recent degradation of the common environment is a result of the swift advancement of human demands on the climate. Around the world, there have been a lot of construction, demolition, and repair projects that have increased the amount of waste. Waste items contribute to the landfill and also cause unfavourable environmental and public contamination. On the other side, paver materials from common resources are used for construction. In the past thirty years, there has been an unquestionable increase in asset use, which has led to asset double dealing. Therefore, we can see a solution by using waste material in place of developing asphalts. Porous asphalts are used in pedestrian and vehicular routes to facilitate surface spill penetration. Porous asphalts can capture suspended materials, thereby filtering contaminants from storm water, in addition to reducing surface overflow. The purpose of the test investigation is to determine the likelihood of using ceramic waste as a replacement to coarse aggregate with concrete dust as filler material when developing porous asphalt. Ceramic waste with a 12.5mm diameter that is used appropriately can be used to develop penetrable asphalt. The experimental trial with 30% replacement of coarse aggregate can be used as a substitute for conventional mix because the experimental results are almost identical to those of the conventional sample. The trials evaluated using 40% ceramic waste in place of coarse aggregate can be used to build parking lots and a pathway because it is lower in weight and has more voids. The trial mix of 50/50 failed to bear the load used during the experimental trials, hence it cannot be used as a replacement.

Multi-Criteria Decision-Making in SMEs Venture Capital Allocation: Analytic Hierarchy Process and Double Auction Approach

Mon, 07 Oct 2024 00:00:00 +0700

Small and medium-sized enterprises (SMEs) make a significant contribution to the economy and can reduce the unemployment rate. To improve their success, SMEs often need outside support, such as venture capital or other sources of financing. For governments, private investors, and banks to provide financial support, it is important to identify relevant criteria to evaluate the future success of SMEs. To evaluate SMEs and allocate venture capital effectively, this study proposes a multi-criteria decision-making technique that combines the Analytic Hierarchy Process (AHP) and the double auction method. The double auction approach is used to match SMEs with investors based on their AHP scores and funding needs. The AHP is used to assess the importance of various factors that influence SME success. This study aims to support an effective and informed decision-making process for the distribution of venture capital to SMEs by bringing these two methods together to promote their growth and success.

Optimisation and Cross-Validation of Hybrid Pricing Algorithm using SL’s CART Model for E-Hailing

Mon, 07 Oct 2024 00:00:00 +0700

Machine learning (ML) transforms the world and creates new technological avenues. The ML models such as supervised, unsupervised, and reinforcement learning (RL) offer various simple, medium, and complex solutions to the real world. Banking, transportation, and e-commerce rely on ML models. The e-hailing falls under the transport industry which relies heavily on ML’s RL model to build its dynamic pricing (DP) strategy. However, associated pricing issues due to limitations of the RL model could potentially jeopardize e-hailing grandeur and impact the revolutionary sector. The DP assists e-hailing providers with surging prices during peak hours or high demand, which many consumers believe is discriminatory pricing for riders and drivers. This paper aims to explain and highlight how to address underlying issues caused by the RL model with supervised learning’s classification and regression tree (CART) model by constructing a hybrid pricing model. The prototype will demonstrate the potential to enhance the pricing model and downgrade pricing issues. The e-hailing is an essential sector of Malaysia that will remain relevant. If issues arising from RL’s dynamic pricing are unaddressed, it can create a more significant impact. Hence, the researcher initiated a study to prove that the proposed CART model has an edge over the existing RL model of the transportation industry. The test result shows improved pricing offered by the hybrid pricing model. Further, the solution can act as good research for further related to dynamic pricing and a similar solution can be extended to tourism, airlines, or other industries that utilize the RL model to offer dynamic pricing.

Improving Lung Region Segmentation Based on Lazy Snapping and Clustering for Aiding COVID-19 Diagnosis

Mon, 07 Oct 2024 00:00:00 +0700

The COVID-19 global pandemic, brought on by the rapid spread of the new coronavirus (SARS-CoV-2), has developed into one of the healthcare industry's most significant challenges in recent memory. Early detection of positive patients is essential to prevent the further spread of the COVID-19 virus. Chest x-ray (CXR) images of patients reporting shortness of breath initially led clinicians to suspect the presence of this novel virus. On CXR images, among the alterations detected in the lungs are indications of cloud region, also known as Ground-Glass Opacity. Consequently, the primary objective of this study is to develop a robust segmentation and to acquire an accurate segmented lung region in a CXR image, as this is a necessary step for accurate diagnosis using computer-aided diagnostic systems (CADS). The proposed methodology employs a multi-level segmentation strategy to improve the performance of lung region segmentation, where Lazy Snapping is utilized as pre-segmentation step to automatically remove the bone of the chest area, followed by clustering to achieve the complete segmentation. Furthermore, the advantage of fast k-means (FKM) clustering has also been utilized to obtain the desired lung region. The proposed strategy using Lazy Snapping and FKM was experimented on 150 CXR images and has achieved an average accuracy, sensitivity of and specificity of 92.38%, 85.23% and 96.27%, respectively. Based on the results obtained, this approach demonstrated efficacy in lung segmentation in chest x-ray images and has a significant potential for clinical use.

A SWMR Algorithm Approach for the Vendor Managed Inventory Problem when Static Demand Rates

Mon, 07 Oct 2024 00:00:00 +0700

In a two-phase supply chain with Vendor Managed Inventory (VMI) policies, the supplier takes on the duty of keeping its retailers' inventory up to date and making sure they never run out of stock. This is in addition to his inbound inventory. This study aims to combine the retailers in various set partitions or to maximize their distribution and inventory holding costs within the same set partition. This paper focuses on a two-stage supply chain made of a single warehouse and multiple retailers (SWMR) facing a static demand and implementing VMI. We proposed a model for coordinating the system with a two-phase solution method. The first phase is to minimize the overall inventory costs and, in the second phase, a constructive vehicle routing problem (VRP) heuristic is used to optimize the distribution costs by satisfying some additional restrictions. Results of this implementation of VMI in an illustrative supply chain case are shown and discussed in detail.

Tidal Level Short-Term Prediction using Back-Propagating Artificial Neural Network (BP-ANN)

Imi Raihanasha Zaki, Ahmad Zaki Annuar — Mon, 07 Oct 2024 00:00:00 +0700

This research paper presents a back-propagating artificial neural network (BPANN) model for short-term prediction of tidal levels in Penang, Malaysia. The model is trained using historical tidal data and meteorological parameters such as tides height within 3 hours, tides coefficient, and tides cycles for four months data were taken from website in May, June, July and August. The study aims to develop an accurate and efficient prediction model to aid in tidal energy management and forecasting for simulating tidal level value that closely matches the actual tide from the input-output relationships in the short-term tidal records through the unknown parameters determine by ANN. The prediction determination used modelling with artificial neural network (ANN) with the back-propagation method. The optimal predictions using ANN were obtained by conducting five input layers, five hidden layers, and one output layer (5-5-1). The results end with mean absolute percentages error (MAPE) for May, June, July and August was 1.76, 0.39, 0.26 and 0.07 respectively. ANN proved very effective in predictions tidal level.

A Study of the High-Performance Computing Parallelism in Solving Complexity of Meteorology Data and Calculations

Masnida Hussin, Mohd Ridhuan Noor Affendi , Dana Hasan — Mon, 07 Oct 2024 00:00:00 +0700

This paper investigates the high-performance computing (HPC) implementation in the field of meteorology, the challenges, and the future benefits. It is associated with utilizing HPC parallelism to simultaneously execute multiple tasks or operations for meteorological research. Merely a few people are aware of HPC's role in generating weather forecasting, climate modeling, and data assimilation. Our investigation elaborates on, identifies, and analyzes the features and characteristics of parallel computing that are utilized in it. The paper also focuses on examining parallelization modeling, the algorithms involved, and optimization strategies employed in HPC-enabled meteorological simulations. By addressing significant aspects of HPC in meteorological research, it helps the scientific community identify emerging trends and future directions for leveraging HPC in meteorology. Further issues can be studied for integrating big data analytics and machine learning into HPC computing architectures.

Innovative Mobile Solutions for Elevating Restroom Maintenance in Malaysian Shopping Malls

Bui Lin Wee Wee, Hui Hui Wang, Poh Yin Gan, Soo See Chai — Mon, 07 Oct 2024 00:00:00 +0700

Public toilets, crucial for our basic physiological needs, are often plagued by inadequate maintenance and poor hygiene, further exacerbated by their communal nature, which increases the risk of pathogen contamination, including SARS-CoV-2. Restroom hygiene significantly influences consumer choices and satisfaction when selecting shopping malls, impacting in-store spending. Furthermore, facility managers consistently identify restrooms as challenging to maintain. In Malaysia, public toilet cleanliness has long been a concern, with 20% receiving low ratings due to inadequate maintenance. Unfortunately, limited attention and resources are allocated to address these ongoing issues, as many mall restrooms still rely on inflexible paper-based management methods. Despite some technological solutions being introduced for public toilet management, they remain relatively novel and expensive in Malaysia, posing budgetary constraints for mall operators. Therefore, cost-effective solutions take precedence when implementing a new toilet management system. This research introduces an innovative, budget-friendly mobile-based system featuring an interactive panel with Raspberry Pi technology to revolutionize shopping mall toilet management. This mobile application allows supervisors to efficiently schedule cleanings, monitor progress, and assess cleanliness based on user feedback. Cleaning staff can promptly respond to user requests via the app, and users can request services and rate conditions. Usability tests reveal that 81% of users are satisfied with the system, meeting their expectations and enhancing operational efficiency. This comprehensive system facilitates effective communication among cleaning supervisors, cleaners, and users, ensuring consistently satisfactory restroom conditions. It promotes hygiene standards in shopping malls, which positively impacts both consumer perceptions and business outcomes for the malls.

Evaluation of SongketChain: A Framework to Protect Unique Cultural Product using Blockchain Technology

Mon, 07 Oct 2024 00:00:00 +0700

The traditional art of Songket weaving in Sarawak, Malaysia, embodies a rich cultural heritage that has faced threats from counterfeit practices, undermining the authenticity and economic value of this artisan craft. In response, this research introduces SongketChain, a blockchain-based framework aimed at preserving the originality and enhancing the traceability and transparency of Songket production. By leveraging both public and private blockchain technologies, SongketChain offers a novel approach to securing pattern rights and supply chain logistics, ensuring that each Songket product's journey from weaver to consumer is documented and verifiable. This study evaluates the effectiveness of SongketChain through a comparative analysis of blockchain platforms and consensus algorithms, highlighting the framework's potential to mitigate counterfeit issues and support the sustainable development of the Songket market. The findings demonstrate that blockchain technology can play a crucial role in protecting cultural heritage, offering a replicable model for other traditional crafts facing similar challenges. This research contributes to the discourse on the application of blockchain in cultural preservation and supply chain management, laying the groundwork for future explorations into its broader implications for the artisan sector. Quantitatively, the study showcases a 100% improvement in transparency and traceability across the Songket supply chain, effectively integrating over 12 unique data points per product to authenticate and secure the Songket production process from inception to customer delivery.

Fuzzy Logic Controller- Proportional-Integral for Motor Velocity Control of Electric Rail Train Using DC-DC Converter

Mon, 07 Oct 2024 00:00:00 +0700

DC motor utilization is obtained in various of industrial activities and non- industrial activities. The utilization of DC motors in various purposes, it is necessary to control a velocity of the DC motor in accordance with a required velocity and this control can be conducted through a power converter. In this study, the utilization of DC chopper for velocity control of DC motor through Fuzzy logic controller proportional integral (FLC-PI) method for electric rail train is developed. The FLC-PI supports to achieve an adaptive controller. It has been adapted to achieve a good performance for auto tuning PI controller. The development process is conducted through simulation work with Matlab software. This study compares the performance between the system with PI control and FLC-PI control. The results of this study obtained the comparison between the PI and FLC-PI performances for the system without load are 1.06% and 0% overshoots respectively. In other to the comparison between the PI and FLC-PI performances for the system with load of 100 Nm are 0.5% and 0% overshoots respectively. And when the comparison between the PI and FLC-PI performances for the system with load of 200 Nm are 0.27% and 0% overshoots respectively. Thus, FLC-PI has shown more better performance than PI control.