A medium-sized combine harvester has been recently deployed to harvest newly introduced high-value glutinous rice in Malaysia. Thus, efficient utilisation of combine harvesters during harvest is essential to minimise operating costs and grain loss. This study evaluated a medium-sized combine harvester’s performance and energy consumption for harvesting glutinous rice. The experiment was carried out on a one-hectare paddy field with three sub-plots using a central composite design (CCD). A time-motion study was conducted during the harvesting operation to determine the combine harvester’s performance parameters, which included field operating speed (FS), field efficiency (FE), theoretical field capacity (TFC), effective field capacity (EFC), grain throughput capacity (GTC), fuel consumption (FC) and field machine index (MI). The energy expended during the operation, which included machinery energy (ME), fuel energy (FCE), human energy (HE), and total energy (TE) input, were also computed. The average FS, FE, TFC, EFC, FC, and MI values were 2.42 km/h, 59.78%, 0.56 ha/h, 0.33 ha/h, 14.89 l/ha, and 0.30, respectively. The mean values of ME, FCE, HE, and TE were 305.35, 711.69, 3.62, and 1020.66 MJ/ha, respectively. The combine harvester achieved an average grain throughput capacity (GTC) of 1796.91 kg/h, demonstrating its effectiveness in handling glutinous rice harvesting. The average time distribution for the harvesting operation, such as effective harvesting time, turning/reversing time, and unloading time, was 1.85 h/ha, 0.38 h/ha, and 1.05 h/ha, respectively. Based on the results, it is concluded that the medium-sized combine harvester is technically and economically suitable for harvesting glutinous rice.

Over the past 20 years, there has been a significant increase in publication in complex network analysis research, especially in community detection. Many methods were proposed to identify community structure. Each community identification algorithm has strengths and weaknesses due to the complexity of information. Among them, the optimisation methods are widely focused on. This paper focuses on an empirical study of two community detection algorithms based on agglomerative techniques using modularity metric: Louvain and Leiden. In this regard, the Louvain algorithm has been shown to produce a bad connection in the community and disconnected when executed iteratively. Therefore, the Leiden algorithm is designed to successively resolve the weaknesses. Performance comparisons between the two and their concept were summarised in detail, as well as the step-by-step learning process of the state-of-the-art algorithms. This study is important and beneficial to the future study of interdisciplinary data sciences of network analysis. First, it demonstrates that the Leiden method outperformed the Louvain algorithm in terms of modularity metric and running time. Second, the paper displays the use of these two algorithms on synthetic and real networks. The experiment was successful as it identified better performance, and future work is required to confirm and validate these findings.

Cocoa is one of the main plantation crops in Malaysia. Significant efforts have been made to modernise the industry using mechanisation and automation technologies. Thus, determination of the physical properties of cocoa pods at different maturity stages is paramount for an appropriate design and development of a post-harvest machinery system for pod breaking, sorting and grading. This study investigated the effect of five different maturity stages (unripe, under-ripe, ripe, very-ripe and over-ripe) on the physical properties of cocoa pods. A total of 80 cocoa pods (Clone PBC140) were used as samples, where 16 pods represented each maturity stage. For the unripe stage, the largest mean dimensional values were 172.45, 89.3 and 111.18 mm for the pods’ length, diameter and geometric mean diameter, respectively. The highest mean values of 0.66 and 0.54 for the over-ripe stage were recorded for sphericity and aspect ratio, respectively. The geometric mean diameter, weight and firmness were found to be inversely proportional to the maturity stages. The advanced maturity stages experience greater colour intensity, as reflected in the chroma (C*) and higher L*, a* and b* values. This study has found that the colour and texture of the cocoa pods change throughout their maturity. In conclusion, the results reported in this study can be used as a reference for designing and developing a machinery system for post-harvest fruit processing.

This paper presents a new hybrid energy harvesting on electromagnetic solar for wireless energy harvesting of ambient from sensors of low-power devices. The axial ratio (AR) requirements produce Left-Hand Circular Polarization (LHCP) and Right-Hand Circular Polarization (RHCP) and simultaneously produce a 90-degree phase difference during energy harvesting, adopting a new design in designing a dual-feed broadband circular polarized (CP) antenna. To get the frequency band 2.3–2.4 GHz, we propose a C-Slot antenna with a circular patch dual feed. To estimate the diversity of the phase and magnitude output of the feed configuration under AR value, we used a 50 Ohm feed network output of the characteristic analysis for a dual feed CP antenna. An Axial ratio frequency range of less than 3 dB is achieved using polarization analysis with different branch channel couplers. To produce a DC output voltage, a high-frequency rectifier circuit embedded with a thin-film solar cell on the antenna is then connected to two T-junction power divider rectifiers, resulting in a high-efficiency design. A complete system-level analysis will include multiple signal classification methods of powered ambient RF energy using a wireless energy harvesting array that proposes a compact structure and demonstrates optimal configuration. Reliable operation in typical indoor environments indicates a self-contained sensor Node. Therefore, it has significant implications for powering small electronics and wireless sensor applications independently of the IoT Network or real implementation telecommunications industry.

This study presents a model-driven approach to enhance the efficiency of sago drying utilizing a two-dimensional fluidized bed dryer (FBD). ANSYS® DesignModelerTM 2020 R2 software was employed to simulate the drying profile, considering variations in sago bagasse particle diameter (ranging from 500 to 2000 µm), hot air temperature (ranging from 50 to 90 °C), and inlet air velocity (ranging from 1.5 to 2.1 m/s). The simulation results provided valuable insights into the interplay between these critical drying parameters. The model enabled the prediction of moisture content profiles during the sago drying process under different conditions, thereby facilitating comprehension of the system’s behavior. Using Design Expert® 7.00 (DX7), considering energy efficiency and product quality, an optimal set of conditions for sago drying was determined at 2000 µm, 90 °C and 2.1 m/s. This approach not only streamlined the drying process but also significantly reduced energy consumption while ensuring consistent and high-quality sago. The findings of this research offer a practical and sustainable solution for sago producers, which, when applied, can contribute to improved product quality, reduced production costs, and enhanced food security in the region. Furthermore, the model-driven approach and the integration of specialized software tools demonstrate the potential for broader applications in optimizing various drying processes in the food industry.

Lightning strikes can cause equipment damage and power outages, so the distribution system's reliability in withstanding lightning strikes is crucial. This research paper presents a model that aims to optimise the configuration of a lightning protection system (LPS) in the power distribution system and minimise the System Average Interruption Frequency Index (SAIFI), a measure of reliability, and the associated cost investment. The proposed lightning electromagnetic transient model considers LPS factors such as feeder shielding, grounding design, and soil types, which affect critical current, flashover rates, SAIFI, and cost. A metaheuristic algorithm, PSOGSA, is used to obtain the optimal solution. The paper's main contribution is exploring grounding schemes and soil resistivity's impact on SAIFI. Using 4 grounding rods arranged in a straight line under the soil with 10 Ωm resistivity reduces grounding resistance and decreases SAIFI from 3.783 int./yr (no LPS) to 0.146 int./yr. Unshielded LPS has no significant effect on critical current for soil resistivity. Four test cases with different cost investments are considered, and numerical simulations are conducted. Shielded LPSs are more sensitive to grounding topologies and soil resistivities, wherein higher investment, with 10 Ωm soil resistivity, SAIFI decreases the most by 73.34%. In contrast, SAIFIs for 1 kΩm and 10 kΩm soil resistivities show minor decreases compared to SAIFIs with no LPS. The study emphasises the importance of considering soil resistivity and investment cost when selecting the optimal LPS configuration for distribution systems, as well as the significance of LPS selection in reducing interruptions to customers.

Diabetes is recognized as one of the most detrimental diseases worldwide, characterized by elevated levels of blood glucose stemming from either insulin deficiency or decreased insulin efficacy. Early diagnosis of diabetes enables patients to initiate treatment promptly, thereby minimizing or eliminating the risk of severe complications. Although years of research in computational diagnosis have demonstrated that machine learning offers a robust methodology for predicting diabetes, existing models leave considerable room for improvement in terms of accuracy. This paper proposes an improved ensemble machine learning approach using multiple classifiers for diabetes diagnosis based on the Pima Indians Diabetes Dataset (PIDD). The proposed ensemble voting classifier amalgamates five machine learning algorithms: Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), Random Forests (RF), and XGBoost. We obtained the individual model accuracies and used the ensemble method to improve accuracy. The proposed approach uses a pre-processing stage of standardization and imputation and applies the Local Outlier Factor (LOF) to remove data anomalies. The model was evaluated using sensitivity, specificity, and accuracy criteria. With a reported accuracy of 81%, the proposed approach shows promise compared to prior classification techniques.

Controlling weed infestation is pivotal to achieving the maximum yield in paddy fields. At a time of exponential human population growth and depleting arable land mass, finding the solution to this problem is crucial. For a long time, herbicides have been the most favoured approach for weed control due to their efficacy and ease of application. However, adverse effects on the environment due to the excessive use of herbicides have prompted more cautious and effective herbicide usage. Many weed species tend to dominate the field, and the weed thrived in patches, rendering conventional broad herbicide spraying futile. Site-specific weed management (SSWM) consists of two strategies: weed mapping and selective herbicide application. Since its introduction into the agriculture sector, unmanned aerial vehicles (UAV) have become the platform of choice for carrying both the remote sensing system for weed mapping and the selective application of herbicide. Red-Green-Blue (RGB), multispectral and hyperspectral sensors on UAVs enable highly accurate weed mapping. In Malaysia, adopting this technology is highly possible, given the nature of government-administrated rice cultivation. This review provides insight into the weed management practice using remote sensing techniques on UAV platforms with potential applications in Malaysia's paddy field. It also discusses the recent works on weed mapping with imaging remote sensing on a UAV platform.

Sedge is one type of weed that can infest the rice field, as well as broadleaf and grasses. If sedges are not appropriately controlled, severe yield loss will occur due to increased competition with cultivated rice for light, space, nutrients, and water. Both sedges and grasses are monocots and have similar narrowed leaf characteristics, but most sedge stems have triangular prismatic shapes in cross sections, which differ them from grasses. Event sedges and grasses differ in morphology, but differentiating them in rice fields is challenging due to the large rice field area and high green color similarity. In addition, climate change makes it more challenging as the distribution of sedge weed infestation is influenced by surrounding abiotic factors, which lead to changes in weed control management. With advanced drone technology, agriculture officers or scientists can save time and labor in distributing weed surveys in rice fields. Using hyperspectral sensors on drones can increase classification accuracy and differentiation between weed species. The spectral signature of sedge weed species captured by the hyperspectral drone can generate weed maps in rice fields to give the sedge percentage distribution and location of sedge patch growth. Researchers can propose proper countermeasures to control the sedge weed problem with this information. This review summarizes the advances in our understanding of the hyperspectral reflectance of weedy sedges in rice fields. It also discusses how they interact with climate change and phenological stages to predict sedge invasions.

Most people now prefer to live in cities, and the urban population has grown significantly. The decrement of urban green corridors causes an essential need for livability in highly populated areas. The needs of population health and sustainable city development are put under prolonged stress by the shortage of publicly accessible green infrastructure and its placement in inappropriate areas. The inventory of green corridor areas and the distribution of significant infrastructure are not fully understood, thus encouraging conflicts between residents and wildlife and increasing flood risk in their vicinity. Optical satellite images were required to (1) develop a green corridor (GC) map, (2) investigate the green infrastructure (GI) demand with the existing land use, and (3) propose new green trails (GT) in the Kuantan district using a geospatial approach. NDVI and site suitability analysis were carried out on Landsat OLI and Sentinel-2 MSI images, which were integrated with GIS tools to achieve all the objectives. A GC map has been developed, and five new proposed areas for GI development have been identified. Based on the results, 5 proposed green trails (3 long GTs, 2 short GTs) with a total length of 79.3 km are scattered in the Kuantan district at the most suitable site, identified using a geospatial approach. This study supports SDG 15: Life on Land, where green corridors reinforce biological connectivity, provide conducive space for high-populated areas, and minimize the negative impact of urbanization on the designed areas of GC, GI and GT in the developing city of Kuantan.