Journal of Advanced Research Design

Challenges in the Enforcement of Syariah Criminal Law on Social Media in Malaysia: A Review from the Perspective of Syariah Evidence Law

Suhaizad Saifuddin — 2025-10-07

The widespread use of social media has transformed the global communication landscape, making the sharing of information more accessible. However, it has also introduced new challenges in the enforcement of laws. The enforcement of Syariah criminal law serves as a key mechanism for safeguarding the welfare of Muslims in Malaysia. Nonetheless, its implementation faces significant obstacles in this era of borderless technology, particularly with the pervasive use of social media. Syariah criminal offences committed via social media encompass a variety of issues, including offences against the faith such as the dissemination of deviant teachings, religious defamation and the spread of doctrines contrary to Islamic teachings. While these offences contravene Syariah principles, enforcement is often hindered by various challenges, particularly those related to evidentiary law. These challenges render the enforcement of Syariah criminal law misaligned with contemporary technological advancements. This study aims to analyse the enforcement of Syariah criminal offences committed on social media and to identify the challenges encountered, particularly from the perspective of evidentiary law. Furthermore, it seeks to propose recommendations to address the issues identified. A qualitative, doctrinal approach was adopted for this research. Data were collected through library-based research, drawing from sources such as books, journal articles, statutory provisions and both reported and unreported cases. The data gathered was analysed using content and descriptive analysis methods. The findings reveal that gaps in the Syariah Evidence Law and outdated legal provisions constitute major obstacles to the effective enforcement of Syariah criminal law on social media platforms. Consequently, this study recommends that the legislative body take immediate steps to amend the existing evidentiary laws. Such amendments are essential to ensure that the enforcement of Syariah criminal law on social media is aligned with technological developments and becomes more effective in addressing contemporary challenges.

Machinability Assessment of Hole-Making in Rubberwood–Plastic Composites Using Abrasive Waterjet Cutting

Kunlapat Thongkaew — 2025-10-08

The application of wood-plastic composites (WPCs) in industrial and structural applications has increased; however, the secondary machining process of hole-making for assembly remains challenging due to tool wear, dimensional inaccuracy, and surface defects in conventional drilling.

Therefore, this study examines the machinability of abrasive waterjet (AWJ) cutting as an alternative approach for creating holes in single-layer and three-layer WPCs. Experiments were conducted using different cutting parameters, including water pressure, traverse speed, abrasive mass flow rate, and hole diameter. The effect on hole characteristics, roundness, and surface quality was analyzed. The results indicated that AWJ achieved through-cuts in both single-layer and three-layer WPCs, providing greater hole roundness and surface polish, while WJ produced complete cuts mainly at 350 MPa. The recommended AWJ parameters are abrasive flow rate of 6.67 g/s, water pressure of 350 MPa, and traverse speed of 30 mm/s, results in a roundness of 0.351 mm. A regression model was created to predict roundness, attaining an acceptability of 84.8%. A comparison with drilling bit suggested that AWJ cutting enabled shorter processing times, more flexibility, and without tool changes, although yielding lower roundness values. The results indicate that AWJ is a feasible and efficient alternative for hole fabrication in WPCs for manufacturing purposes.

Performance of Hyperthermia Applicator under ISM Frequency for Breast Cancer Treatment

Bibi Sarpinah Sheikh Naimullah — 2025-10-07

Hyperthermia is a treatment procedure that applies heat on the target tissue with a temperature range of 40º to 45º C for a specific duration without affecting the healthy surrounding tissue. The efficacy of hyperthermia treatment relies on the suitable frequency within the Industrial, Scientific, and Medical (ISM) band. The frequency influences both penetration depth and Specific Absorption Rate. This study aims to investigate and compare the performance of the applicator with operating frequencies 434MHz, 915MHz, and 2450MHz with a tumor situated in the middle of the breast phantom. The hyperthermia model is simulated with SEMCAD X. Tissue properties include relative permittivity and conductivity represented with the Generic Dispersive Model. The penetration SAR distribution varies with different frequencies applied to the applicator. The applicator with 434MHz has greater penetration than 915MHz and 2450MHz. The SAR distribution on 2450MHz is more focused on the areola of the breast phantom, while on 915MHz, heat focuses on the middle of the breast phantom. For applicator 434MHz, the heat covers the middle part of the breast phantom to the chest wall. The appropriate applicator is recommended for effective hyperthermia treatment.

Experimental Investigation of Flame Stability and Temperature Profiles in an LPG-Fuelled Combustion System for Industrial Hot Air Generation

Tiew Por Xiong — 2025-10-07

A combustion system is used to produce hot air for a variety of industrial and commercial purposes. This system works by burning a fuel source like natural gas, propane, or diesel in a controlled mixture of fuel and air inside a sealed chamber. However, achieving steady combustion has proven to be difficult since specific air pressure and flow rates are critical. The goal of this experiment was to investigate the combustor's combustion characteristics, particularly flame stability and temperature profile. Low air flow rates required 3 bar of air pressure to ensure flame stability, whereas greater flow rates were able to do so at 2 bar. Air flow rates of 30 l/min and above demonstrated stable flames at a 3 bar pressure. It was found that higher air flow rates were important for flame stability, as a fully opened valve sustained the flame for up to 15 minutes compared to only 4 minutes with a half-opened valve. The temperature profile showed decreased stability at lower air flow rates. Additionally, the maximum temperature of the combustor reached 235 ^oC, below the auto-ignition range of LPG between 410 ^oC to 580 ^oC. Therefore, this study highlights the critical role of air flow rate and pressure in maintaining flame stability and thermal consistency in combustion systems. These findings support efforts to optimize operating parameters of this combustor for improved efficiency, safety, and reliability in industrial applications.

The Symbiotic Evolution of AI and Media: A Technological Leap in Knowledge Sharing and Decision-Making

Faerozh Madli — 2025-10-07

Artificial Intelligence (AI) is more than just an innovation, it is a force reshaping how information flows through society. But how does this impact on media communication? This study uncovers the answer. AI is no longer a new or unfamiliar concept in today's world. Its widespread use across almost every industry highlights its growing influence. AI adoption has largely had a positive impact, driven by various factors. One of the most significant is its role in information dissemination. However, the dynamics of this relationship remain unclear. This study explores the symbiotic relationship between AI and media communication, examining how AI shapes and enhances the flow of information. Additionally, this symbiotic relationship is examined in the context of a crucial global concern which is public health. The findings reveal that AI and media technology work together harmoniously to provide individuals with essential information, empowering them to make well-informed decisions more quickly. In this essence, this research highlights how AI, when integrated with media technology, serves as a powerful tool for knowledge sharing within society. Additionally, the insights from this study offer valuable implications for various stakeholders, particularly those navigating the rapid evolution of AI-driven technologies. Understanding these dynamics can help industries and policymakers maximize AI’s potential for relevance benefits.

Advancing Solar Power Predictions: Exploring Neural Network Architecture through Transfer Function and Hidden Layer Analysis

Siti Nur Afifah Mohd Suhaimi — 2025-10-07

Artificial Neural Network (ANN) models have demonstrated robustness in capturing variations in the input-output relationship between weather parameters and photovoltaic (PV) output power. However, despite their success in solar power forecasting, the challenge of determining the optimal architecture. This study aims to enhance the precision of photovoltaic (PV) output prediction by focusing on four inputs where irradiance, ambient temperature, PV module temperature, and humidity. The primary objective is to discern the most effective neural network architecture, specifically identifying the optimal number of neurons and hidden layers. The study employs the Multilayer Perceptron (MLP) technique, a type of ANN, to develop the model. Training shows that varying the number of neurons in the hidden layer, explicitly using 18 neurons, leads to optimal performance. Furthermore, it compares the best activation function between different activation functions, including linear, Hyperbolic Tangent Sigmoid (tan-sig), and Logistic Sigmoid (log-sig). The analysis concludes that the best activation function is achieved when the MLP model is designed using log-sig- linear- log-sig in the hidden layer with a structure of (4-18-18-18-4) for 560 data entries, using the MATLAB Deep Learning Toolbox. The results obtained from the neural network are thoroughly analyzed, indicating that using 18 neurons across the 3 hidden layers proves to be the most effective configuration for training, testing, and validation. This configuration yields minimal Root Mean Square Error (RMSE), emphasizing its strong performance in this study. The obtained result from this study, highlights the significance of fine-tuning neural network architecture for accurate and reliable solar power forecasting, contributing to advancements in renewable energy applications.