Mohamad Asrul Mustapha

This bibliometric study examined 179 Scopus-indexed publications on the physical and mechanical properties of medium-density fiberboard (MDF) published between 2016 and 2025. BiblioMagika^® was used for performance analysis, and Biblioshiny was used for keyword co-occurrence, thematic mapping, and thematic evolution. The papers identified as the cohort for analysis had received 2830 citations in total, with an average of 15.81 citations per paper, and an average h-index of 30. The European Journal of Wood and Wood Products and BioResources were the most productive sources. Three distinct categories were identified through keyword mapping among the studies reviewed: (1) advanced composites and reinforcement, (2) adhesive and emission-related studies, and (3) circular-material strategies. Thematic evolution showed a trend away from traditional resin-performance topics toward broader sustainability-related themes, particularly bio-based adhesives and recycling-related topics. Overall, this review provides a quantitative overview of publication patterns, influential sources, and thematic development in MDF research. It also provides direction for future MDF research, focusing on durability, large-scale feasibility, life-cycle assessments, and practical implementation.

Acoustic energy harvester (AEH) studies are commonly applied in the transportation sector, often overlooking industrial areas despite their substantial contribution to noise pollution in surrounding neighbourhoods. Furthermore, AEH approaches are primarily experimental and less common in real-world applications due to the significant knowledge gap in applying AEH systems for industrial noise. Therefore, this paper provides a comprehensive review of the latest advancements in AEH system, development aiming to convert industrial noise into useful electrical energy. Subsequently, this paper presents a case study demonstrating the feasibility of converting industrial noise from district cooling plant into useful electrical energy for powering small electronic devices. The case study result shows that noise mapping of the district cooling plant indicates that the chiller generates a continuous sound level approximately 91 to 96 dB, making it suitable for powering electronic devices within the chiller plants. Additionally, the findings reveal a notable voltage output of 0.0258 V from the AEH system, achieved using the LDT1-028 K PT piezo film.

Volume of the forecasting data and good data analysis are the key factors that influence the accuracy of forecasting algorithm because it depends on data identification and model parameters. This paper focuses on data selection approach for short-term load forecasting. It involves formulating data selection algorithm to identify factors (variables) that influence energy demand at utility level. Correlation Analysis (CA) and Hypothesis Test (HT) are used in the selection, where Wavelet Transform (WT) is applied to bridge the gap between the forecasting variables. This results to three groups of data; data without CA, HT and WT, data with CA, HT but without WT and data with CA, HT and WT. An optimized adaptive neuro-fuzzy inference system (ANFIS) using Cuckoo Search Algorithm (CS) is used to conduct the forecasting. The essence is to reduce the computational difficulty associated with the gradient descent (GD) algorithm in traditional ANFIS. With the three data groups, it is observed that CHW data can give satisfactory results more than the NCNHNW and NCNHW data. Also the numerical results shows that CHW data selection approach can give a MAPE of 0.63 against the bench-mark approach with MAPE of 3.55. This indicates that it is good practice to select the actual data and process it before the forecasting.

Non-Intrusive Load Monitoring (NILM) is a process of identifying the connected loads in a premises from the measurements obtained at the service entry. That is, through NILM one can tell the operating conditions (ON or OFF) of the house appliances from the aggregated measurements taken at the service drop. The method is more advanced than the traditional method which require measuring sensors for every load of interest. In an effort to explore the applicability of NILM in home appliances’ recognition, this paper presents the development of a home NILM using arduino microcontroller. Aggregated real power (P), rms current (Irms) and power factor (pf) of the connected appliances are used as an offline data to train a feed forward ANN whose output is the pattern of the connected loads. Four different home appliances are experimented in generating the training data and the ANN model is implemented in the arduino program to identify the loads. Experimental analysis on the monitoring system shows that it can accurately recognize the load patterns when the supply voltage is within the range of 240 V and the pattern may not be recognized when the input voltage deviated from 240 V. The developed system can be applied into home appliances management and control for efficient energy utilization, the operation of which can be assessed without entering into the consumer privacy.