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Centrality analysis is a crucial tool for understanding the role of nodes in a network, but it is unclear how different centrality measures provide much unique information. To improve the identification of influential nodes in a network, we propose a new method called Hybrid-GSM (H-GSM) that combines the K-shell decomposition approach and Degree Centrality. H-GSM characterizes the impact of nodes more precisely than the Global Structure Model (GSM), which cannot distinguish the importance of each node. We evaluate the performance of H-GSM using the SIR model to simulate the propagation process of six real-world networks. Our method outperforms other approaches regarding computational complexity, node discrimination, and accuracy. Our findings demonstrate the proposed H-GSM as an effective method for identifying influential nodes in complex networks.

Wood–plastic composites (WPCs) offer a sustainable alternative to solid wood, yet their heterogeneous structure presents challenges in laser machining due to thermal sensitivity and inconsistent material behaviour. This study investigates the optimization of CO2 laser-cutting parameters for WPCs, focusing on feed rate and assist-gas pressure. Using a 1500 W CO2 laser, a full factorial experimental design was employed to cut 18 mm thick WPC panels at varying feed rates (1000–3000 mm/min) and gas pressures (1–3 bar). Statistical analyses including MANOVA and linear regression were conducted to evaluate their effects on key machining responses: cutting depth, heat-affected zone (HAZ) width, cut-edge quality, and surface finish. Results indicated that feed rate significantly influences both cutting depth and thermal damage, while gas pressure plays a major role in improving surface quality and reducing HAZ. Optimal combinations were identified for various performance goals, and validation trials at the selected parameters confirmed alignment with predicted outcomes. The optimized settings yielded high-quality cuts with reduced HAZ and enhanced surface characteristics. This study demonstrates the effectiveness of a statistical optimization approach in refining CO2 laser-cutting conditions for WPCs, offering insights for improved process control and sustainable manufacturing applications. This study also introduces a multi-objective optimization approach that verifies the interaction effects of feed rate and assist-gas pressure, enabling precise and efficient CO2 laser cutting of 18 mm thick WPCs.

Nanostructuring is known to be an effective method to improve thermoelectric performance but, generally, it requires complex procedures and much labor. In the present study, self-assembled nanometer-sized composite structures of silicon (Si) and chromium disilicide (CrSi2) were easily fabricated by the rapid solidification of a melt with a eutectic composition. Ribbon-like samples were obtained with a dominant nanostructure of fine aligned lamellae with a spacing range of 20–35 nm. The thermoelectric power factor of the ribbon was observed to be 1.2 mW/mK2 at room temperature and reached 3.0 mW/mK2 at 773 K. The thermal conductivity was 65% lower than that of a bulk eutectic sample. The results suggest that this method is promising for fabricating an effective nanostructure for thermoelectric performance.

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.

Dye sensitized solar cell (DSSC) is a well-known photovoltaic device that is used for low power application. One of the main components for DSSC is semiconductor material photoanode which will provide the pathway for electron transportation and thus determine the energy conversion efficiency of the DSSC. The most commonly used material for the semiconductor photoanode is titanium dioxide (TiO2). TiO2 is a semiconductor material with wide bandgap material that is existed in three crystalline phase; rutile, anatase and brookite. This paper emphasizes the best annealing temperature for commercialized TiO2, 98% anatase powder where the temperature varies from 300 oC – 600 oC. Through this research, the best annealing temperature for anatase TiO2 photoanode is at 420 °C (0.094%) with the crystallite size of 18.76 nm and particle size of 19 nm that is favorable for the dye attached and thus enhances the energy conversion efficiency of the DSSC.

Physical interaction in peer learning has been proven to improve students’ learning processes, which is pertinent in facilitating a fulfilling learning experience in learning theory. However, observation and interviews are often used to investigate peer group learning dynamics from a qualitative perspective. Hence, more data-driven analysis needs to be performed to investigate the physical interaction in peer learning. This paper complements existing works by proposing a framework for exploring students’ physical interaction in peer learning based on the graph analytics modeling approach focusing on both centrality and community detection, as well as visualization of the graph model for more than 50 students taking part in group discussions. The experiment was conducted during a mathematics tutorial class. The physical interactions among students were captured through an online Google form and represented in a graph model. Once the model and graph visualization were developed, findings from centrality analysis and community detection were conducted to identify peer leaders who can facilitate and teach their peers. Based on the results, it was found that five groups were formed during the physical interaction throughout the peer learning process, with at least one student showing the potential to become a peer leader in each group. This paper also highlights the potential of the graph analytics approach to explore peer learning group dynamics and interaction patterns among students to maximize their teaching and learning experience.

Dye sensitized solar cell (DSSC) is a promising candidate for a low cost solar harvesting technology as it promised a low manufacturing cost, ease of fabrication and reasonable conversion efficiency. Basic structure of DSSC consists of photoanode, dye, electrolyte and counter electrode. Photoanode plays an important role for a DSSC as it supports the dye molecules and helps in the electron transfer that will determine the energy conversion efficiency. This paper emphasizes the various improvements that had been done on the TiO2 and ZnO photoanode nanostructures synthesized through thermal method. For overall comparisons, ZnO nanoflowers photoanode had achieved the highest energy conversion efficiency of 4.7% due to its ability of internal light scattering that had increased the electron transportation rate. This has made ZnO as a potential candidate to replace TiO2 as a photoanode material in DSSC.

Solar cell is expanding as green renewable alternative to conventional fossil fuel electricity generation, but compared to other land-used electrical generators, it is a comparative beginner. Many applications covered by solar cells starting from low power mobile devices, terrestrial, satellites and many more. To date, the highest efficiency solar cell is given by GaAs based multilayer solar cell. However, this material is very expensive in fabrication and material costs compared to silicon which is cheaper due to the abundance of supply. Thus, this research is devoted to develop multilayer solar cell by combining two different layers of P-I-N structures with silicon carbide and silicon germanium. This research focused on optimising the intrinsic layer thickness, p-doped layer thickness and concentration, n-doped layer thickness and concentration in achieving the highest efficiency. As a result, both single layer a-SiC and a-SiGe showed positive efficiency improvement with the record of 27.19% and 9.07% respectively via parametric optimization. The optimized parameters is then applied on both SiC and SiGe P-I-N layers and resulted the convincing efficiency of 33.80%.

Nearly half a million accidents on Malaysians road occur in 2015. The aim of this research is to detect car speed, capture the photo of the speeding car and then transfer the data like car speed, date and time, location and lane number to an online database. A distance sensor is used to measure the distance range between two points on the road. The ESP8266 NodeMCU will be the control unit to process the data and calculate the speed with the formula of speed equal to distance over time. The ESP8266 NodeMCU is also a Wi-Fi module to help in transferring data via IoT to an online database. The Google spreadsheet acted as an online database and will receive all the data if detected a speeding car. In conclusion, the Smart Rash Driver System is successfully invented and able to detect vehicle speed, capture the photo of over speed vehicle and save it to the SD card and lastly transfer all data via IoT to the Google Spreadsheet. This invention will be able to help to decrease the road accident rate efficiently.