Search Results (8)

Wood is a naturally occurring renewable resource widely used in various industries, including in construction, packaging, furniture, and paneling. In Malaysia, 80% of furniture products are made from wood, making it a crucial material in this sector. Laser cutting is an advanced machining technique that enhances precision and minimizes material waste, yet its thermal effects, particularly the heat-affected zone (HAZ), remain a challenge. This study investigates how laser cutting parameters—including the laser power, traverse speed, and focus position—affect HAZ formation in two fast-growing Malaysian wood species, Acacia mangium and Azadirachta excelsa. This research seeks to determine the optimal laser settings that minimize HAZ dimensions while maintaining cutting precision. A diode laser cutting system was used to analyze the effects of three laser power levels (800, 1500, and 2400 mW), three traverse speeds (2, 5, and 10 mm/s), and three focus positions (on-focus, +0.2 mm, and −0.2 mm). We employed statistical analysis, including a two-way ANOVA, to assess the significance of these parameters and their interactions (p < 0.001). The results indicate that a higher laser power and slower speeds significantly increase the HAZ’s width and depth, with Azadirachta excelsa exhibiting a greater HAZ width but shallower penetration compared to Acacia mangium. A slight above-focus position (+0.2 mm) reduces the HAZ’s width, whereas a below-focus position (−0.2 mm) increases the HAZ’s depth. The optimal parameters for minimizing HAZ dimensions while ensuring efficient cutting were identified as a 1500 mW laser power, a 10 mm/s traverse speed, and an on-focus position (0 mm). This study provides practical insights into laser parameter optimization for tropical wood species, contributing to improved precision in laser machining and sustainable wood processing practices. These findings support industries in adopting advanced, high-quality laser cutting techniques tailored to fast-growing wood resources. Full article

The COVID-19 pandemic has been an extraordinary event, the type of event that rarely occurs but that has major impacts on the stock market. The pandemic has created high volatility and caused extreme fluctuations in the stock market. The stock market can be characterized as either linear or nonlinear. One method that can detect extreme fluctuations is extreme value theory (EVT). This study employed a semisystematic literature review on the use of the EVT method to estimate investment risk in the stock market. The literature used was selected by applying the preferred reporting items for systematic review and meta-analyses (PRISMA) guidelines, sourced from the ScienceDirect.com, ProQuest, and Scopus databases. A bibliometric analysis was conducted to determine the study characteristics and identify any research gaps. The results of the analysis show that studies on this topic are rarely carried out. Research in this field is generally performed only in univariate cases and is very complicated in multivariate cases. Given these limitations, further research could focus on developing a conceptual model that is dynamic and sensitive to extreme fluctuations, with multivariable inputs, in order to predict investment risk. The model developed here considered the variables that affect stock price fluctuations as the input data. The combination of VaR–EVT and machine-learning methods is effective in increasing model accuracy because it combines linear and nonlinear models. Full article

This research examines the utilization of Web-Based Library and Information Services (WBLIS) in academic libraries. Digital technology promotes the use of WBLIS, including during the COVID-19 pandemic. Few Malaysian studies have investigated utilization and its factors. Three factors of WBLIS utilization were identified: library technology, support, and the environment. WBLIS’s output and outcome were emphasized. A conceptual model was developed and tested using non-probability sampling. A 38-item, five-point Likert Scale online survey was distributed to postgraduates from 20 public universities. Raosoft sampled 383 research, comprehensive, and focused universities using stratified sampling. SMARTPLS version 3 was used to test hypotheses on 527 respondents. Harmon’s Single Factor test eliminated single-source bias. All measurement and model criteria were met. All hypotheses on the relationships between library technology, support, and environment on WBLIS utilization were supported. The findings will contribute to academic librarianship and related fields. Malaysian universities and the Ministry of Higher Education will benefit from improving academic libraries’ impact on learning, research, and universities’ institutional value. Future research may include private university, polytechnic, and community college students and academicians. Comparative studies and qualitative research can be conducted. Full article

The depletion of petroleum-based resources and the adverse environmental problems, such as pollution, have stimulated considerable interest in the development of environmentally sustainable materials, which are composed of natural fiber–reinforced polymer composites. These materials could be tailored for a broad range of sustainable industrial applications with new surface functionalities. However, there are several challenges and drawbacks, such as composites processing production and fiber/matrix adhesion, that need to be addressed and overcome. This review could provide an overview of the technological challenges, processing techniques, characterization, properties, and potential applications of NFRPC for sustainable industrial applications. Interestingly, a roadmap for NFRPC to move into Industry 4.0 was highlighted in this review. Full article

Particleboard is not entirely a wood replacement but a particular material with its properties, making it more effective at different times than heavy or solid wood. The world’s biggest concern is environmental problems with formaldehyde as a particulate board binder that can lead to human carcinogenic agents. A cradle-to-gate life cycle assessment (LCA) of particleboard production was performed using openLCA software. The impact assessment was carried out according to the software’s features. This preliminary investigation aims to analyze the chemical composition of particleboard and identify its environmental impact. The Fourier-transform infrared spectroscopy (FTIR) system was used to track the functional group of aliphatic hydrocarbons, inorganic phosphates, and main aliphatic alcohols found in particleboards made in Malaysia. Based on the FTIR results, aliphatic groups were found in numerous aggravates that the spectroscopic infrared was likely to experience. The most important vibrational modes were C–H, at approximately 3000 cm⁻¹, and –CH deformations around 1460 cm⁻¹ and 1380 cm⁻¹. Eight effect groups demonstrated that 100% of the input and all analyses produced the same relative outcome. The life cycle of a product is determined by pollution of the air, water, and soil. Thus, particleboard has a minimal impact on the environment, except for global warming. Full article

The rapid growth in biomedical datasets has generated high dimensionality features that negatively impact machine learning classifiers. In machine learning, feature selection (FS) is an essential process for selecting the most significant features and reducing redundant and irrelevant features. In this study, an equilibrium optimization algorithm (EOA) is used to minimize the selected features from high-dimensional medical datasets. EOA is a novel metaheuristic physics-based algorithm and newly proposed to deal with unimodal, multi-modal, and engineering problems. EOA is considered as one of the most powerful, fast, and best performing population-based optimization algorithms. However, EOA suffers from local optima and population diversity when dealing with high dimensionality features, such as in biomedical datasets. In order to overcome these limitations and adapt EOA to solve feature selection problems, a novel metaheuristic optimizer, the so-called improved equilibrium optimization algorithm (IEOA), is proposed. Two main improvements are included in the IEOA: The first improvement is applying elite opposite-based learning (EOBL) to improve population diversity. The second improvement is integrating three novel local search strategies to prevent it from becoming stuck in local optima. The local search strategies applied to enhance local search capabilities depend on three approaches: mutation search, mutation–neighborhood search, and a backup strategy. The IEOA has enhanced the population diversity, classification accuracy, and selected features, and increased the convergence speed rate. To evaluate the performance of IEOA, we conducted experiments on 21 biomedical benchmark datasets gathered from the UCI repository. Four standard metrics were used to test and evaluate IEOA’s performance: the number of selected features, classification accuracy, fitness value, and p-value statistical test. Moreover, the proposed IEOA was compared with the original EOA and other well-known optimization algorithms. Based on the experimental results, IEOA confirmed its better performance in comparison to the original EOA and the other optimization algorithms, for the majority of the used datasets. Full article

Carbon fibre-reinforced polymer (CFRP) composite materials play an increasingly important role in modern manufacturing, and they are among the more prominent materials used in aircraft manufacturing today. However, CFRP is highly prone to delamination and other damage when drilled due to it being extremely strong with a good strength-to-weight ratio and high thermal conductivity. Because of this problem and CFRP’s growing importance in aircraft manufacture, research has focused on the entry and exit holes as indicators of damage occurrence during drilling of screws, rivets, and other types of holes. The inside of the hole was neglected in past research and a proper way to quantify the internal side of a hole by combining the entry and exit hole should be included. To fill this gap and improve the use of CFRP, this paper reports a novel technique to measure the holes by using the extension of the adjusted delamination factor (S_FDSR) for drilling thick CFRP composites in order to establish the influence of machining input variables on key output measures, i.e., delamination and other damages. The experimental results showed a significant difference in interpretation of the damage during the analysis. Improvement was made by providing better perspectives of identifying hole defects. Full article

Grey model GM(1,1) has attained excellent prediction accuracy with restricted data and has been broadly utilized in a range of areas. However, the GM(1,1) forecasting model sometimes yields large forecasting errors which directlyaffect the simulation and prediction precision directly. Therefore, the improvement of the GM(1,1) model is an essential issue, and the current study aims to enhance the prediction precision of the GM(1,1) model. Specifically, in order to improve the prediction precision of GM(1,1) model, it is necessary to consider improving the initial condition in the response function of the model. Consequently, the purpose of this paper is to put forward a new method to enhance the performance of the GM(1,1) model by optimizing its initial condition. The minimum sum of squared error was used to optimize the new initial condition of the model. The numerical outcomes show that the improved GM(1,1) model provides considerably better performance than traditional grey model GM(1,1) . The result demonstrates that the improved grey model GM(1,1) achieves the objective of minimizing the forecast errors. Full article