Characterization of Bottom Ash from the Combustion of Palm Oil Empty Fruit Bunches (EFB)

The palm oil industry generates large volumes of biomass waste, particularly Empty Fruit Bunches (EFB), which pose environmental disposal challenges but offer potential as a renewable energy source. This study focuses on the characterization and combustion analysis of bottom ash derived from pelletized EFB, with special attention to how combustion temperature affects ash quality at a fixed time duration. Pelletized EFB offers improved energy density and uniform combustion behavior compared to loose EFB. Combustion was conducted for 30 minutes at three different temperatures: 400°C, 600°C, and 800°C. The results showed that combustion temperature significantly influences ash yield and composition. Lower temperatures produced darker ash with higher residual carbon, while higher temperatures generated lighter ash with more fused and mineral-rich phases containing silicon, potassium, and calcium. XRD analysis confirmed a transition from simple crystalline phases at low temperatures to more complex silicate and glassy phases at higher temperatures, suggesting potential for construction-related applications. TGA revealed that major mass loss occurred between 200-375 °C due to decomposition of hemicellulose and cellulose, leaving about 33% inorganic residue forming the ash. Overall, this study highlights the critical role of combustion temperature in determining the physicochemical and mineralogical characteristics of EFB bottom ash and supports its potential for sustainable utilization within the palm oil industry.  

Effect of Calcination on The Bioactivity of Hydroxyapatite (HAp) from Black Tilapia Fish Scale

Hydroxyapatite, HAp is extensively used for orthopaedic and dental reconstruction as implant material due to their chemical and biological similarity to human hard tissue. Recently, vigorous research efforts made to obtain HAp from an animal bone in providing alternative feedstock materials for biomedical applications. Therefore, the extraction of natural HAp from the Black Tilapia (Oreochromis Niloticus) fish scales was produced via a conventional heat treatment (calcination) at 1000 °C. To produce HAp fine powder, the natural HAp from the tilapia fish scale went through a grinding process before characterization and testing. The sample was characterized using powder X- rays Diffraction (XRD), Field Emission Scanning Electron Microscopy (FESEM), and Energy Dispersive X-ray spectroscopy (EDX). The bioactivity of the samples was characterized using a Simulated Body Fluid (SBF) Test, Anti-Microbial and MTT-assay using a Human Fetal Osteoblast (hFOB) 1.19 cell line. XRD result shows the crystallinity of extracted HAp is similar to the standard HAp. The FESEM image shows the particles have different morphologies. The EDX analysis shows that the Ca/P ratio is 1.69 that slightly different from the standard HAp (1.67). The SBF result shows apatite deposition on top of the pellet sample surface after immersion for 7 days. Anti-Microbial shows that there are no anti-microbial properties on the extracted HAp and the MTT-assay analysis shows that the samples were not toxic to the cell. This work shows that studies on the extraction of fish scale into high value-added product are the promising alternative to produce natural HAp that is beneficial to medical applications. The bioactivities show that the natural HAp produced is bioactive and not toxic

Burst-Aware Weighted Fair Queueing for Serverless Inference: Mitigating Noisy Neighbor Effects in Multi-Tenant Systems

Multi-tenant serverless inference often devolves into noisy-neighbor scenarios where a single tenant’s bursty LLM batch floods the fleet, pushing interactive calls beyond their latency budgets. We are proposing a Burst-Aware Weighted Fair Queueing (BWFQ) - a scheduler that requires only two counters per tenant (tokens earned, tokens spent) and a constant-time heap pop to pick the next invocation. In BWFQ, we use the classic token-bucket shaper where tokens accumulate at a tenant-specific base rate and are reduced on each dispatch. When a tenant exhausts all its tokens, its requests are queued, giving chances to other quieter tenant s to run. Techniques described in other papers like Dominant-Resource Fairness, BWFQ requires neither per-invocation resource profiling nor multi-dimensional share accounting, making it easy to integrate onto existing Lambda-style dispatchers. To evaluate our algorithm, we built a prototype using AWS Lambda and observed that BWFQ reduces the P99 latency gap between interactive and batch tenants from 8.5s to 2.1s; a 4.0X improvement, while preserving 94% of the throughput achieved by First-Come-First-Serve. The algorithm adds only 35 µs of scheduling overhead per decision and fits in approximately in 150 lines of Go code. These results demonstrate that a simple token-bucket fair queueing is a practical, immediately usable step towards building fairness in production serverless inference

Performance Analysis of YOLOv8, YOLOv9, and YOLOv11 for Corn Leaf Disease Detection

Corn is a crucial crop for agricultural yield and food security, yet it faces significant threats from various foliar diseases that can diminish both growth and quality. Conventional visual assessment techniques often require substantial labor and are susceptible to errors in diagnosis. This study introduces a Corn Leaf Disease Detection System that employs Convolutional Neural Networks (CNNs) and evaluates the performance of three models: YOLOv8, YOLOv9, and YOLOv11. The methodology involves capturing high-resolution images of corn leaves afflicted by diseases such as Northern Leaf Blight and Common Rust. These images undergo a pre-processing phase to enhance clarity and are standardized for input into the detection framework. CNN is employed for intricate classification tasks, while YOLOv11 is utilised for real-time disease detection. The dataset comprises 3,000 images, which are augmented to expand the training set to 6,000 samples. Among the evaluated models, YOLOv11 demonstrated superior performance, achieving an F1-score of 0.93, with precision at 0.94 and recall at 0.92 by epoch 100. These findings highlight the system\u27s operational efficiency and robustness in effectively detecting corn leaf diseases

Assessment of a Safe Work-Impulse for Farmers: A Case Study of Ilara-Mokin, Ondo State, Nigeria

The study measured the heart rates of farmers in Ilara-mokin while they carried out a few regular farm tasks with a view to create a safe work-impulse chart for farmers in that locality. This is expected to provide the information required for the adequate use of muscular force and the prevention of fatigue or physical exertion, during farming. Farmers who were available and were willing to participate in the study were divided into five age groups, such as below 25, 25-34, 35-44, 45-54, and above 54 years, and their heart rates were monitored and recorded under three different categories of farm activities or load (light load, medium load, and heavy load) using a premium pressure monitor with comfit cuff. The time and the heart rate at which the farmers feel a sense of discomfort or tiredness were used to evaluate the safe work-impulse for each category of activities. The result shows that farmers below 45 years of age record a work-impulse of about 9.0M kNs and 7.2H kNs for medium and heavy loads respectively while older farmers manage to sustain medium and heavy loads at a work-impulse of 7.2M kNs, 5.4M kNs and 9.0M kNs, 5.4M kNs respectively. In conclusion, the study revealed that while farmers of any age can handle a light activity for a longer period of time than 18 minutes, they may not be able to sustain heavier loads for a longer period

Simulation of Phase Change Material Solidification for High-Performance Aero-Thermal Energy Storage

Phase Change Materials (PCM) can be used in aerospace thermal management systems due to their ability to absorb and release large amounts of latent heat during phase transitions, making them effective for controlling temperature fluctuations in high-performance aero components. The research paper examines the solidification of paraffin wax PCM in a rectangular enclosure through the computational fluid approach provided by ANSYS Fluent. These three geometrical regions comprised Geometry 1, whose size was 150 mm x 100 mm; Geometry 2, of 200 mm x 150 mm; and Geometry 3, of 250 mm x 200 mm, and were examined under three sets of thermal boundaries, which were 363 K-294 K, 380 K-300 K, and 400 K-310 K. The transient simulation with the enthalpy-porosity approach was run to assess the temperature distribution, the pressure profiles, and the phase change advancement. The findings indicate that increased geometry sizes and greater thermal differences favor a stronger natural convection and a rapid solidification. The numerical model demonstrates the correlation between design parameters and PCM behavior, allowing for the optimization of TES systems\u27 thermal performance

Heritage Building Maintenance Practices: The Case of Muzium Diraja Kedah (MDK)

Maintaining heritage buildings requires a well-structured maintenance management system to ensure their continued usability and cultural significance. The Muzium Diraja (MDK) in Alor Setar, Kedah, an important historical landmark, faces several maintenance-related challenges, including financial constraints, limited access to original building materials, and a constrained maintenance budget, which affect the effectiveness of ongoing maintenance activities. This study investigates the current maintenance practices adopted at the museum using a qualitative case study methodology. Information was obtained through in-depth interviews with seven key personnel selected through purposive sampling, representing conservation, development, monument-related agencies, and the Public Works Department (JKR). The analysis identifies critical weaknesses in several key aspects of maintenance management, including organizational structure, asset management, maintenance strategies, workforce capabilities, financial provision, and documentation practices. Based on these findings, the study proposes targeted improvement measures, including the adoption of a structured maintenance management system, the implementation of proactive and planned maintenance strategies, the enhancement of technical skills among maintenance personnel, sufficient financial allocation, and systematic record-keeping. These measures are intended to support the sustainable preservation of the Muzium Diraja Kedah and safeguard its heritage value for future generations

Environmental Risk Assessment of Sarimukti Landfill Postfire in Indonesia

Indonesia aims to ensure that 100% of urban waste is properly managed focusing 80% on waste collection and 20% on reduction, while transitioning toward a processing-based waste management system. Despite various solutions, achieving substantial progress remains difficult. Waste pollution, including unsightly waste, foul odors, and hazardous leachate, negatively impacts the environment. The fire at Sarimukti landfill was caused by careless disposal of cigarette butts during the dry season. Exacerbated the situation and affected more than 15 hectares. Therefore, an environmental quality evaluation using an Integrated Risk-Based Approach (IRBA) is required. According to Ministry of Public Works Regulation Number 03 of 2013, this evaluation is crucial before deciding whether to rehabilitate or close the landfill. This study characterizes waste during a fire disaster, assesses leachate quality in Sarimukti landfill treatment facility, and conducts a rapid environmental assessment using the IRBA method to determine landfill feasibility. The burned waste had an average moisture content of 10.41%, volatile matter of 49.04%, ash content of 50.95%, fixed carbon of 31.05%, and a calorific value of 3,391.19 cal/g. The leachate quality exceeded standards for BOD, COD, and total nitrogen, while pH, TSS, mercury, and cadmium remained within acceptable limits. The final Environmental Risk Index assessment yielded a very high hazard evaluation of 622.24, indicating that the landfill should be closed due to its significant environmental and social impacts

A Lightweight CNN Model Using Depthwise Separable Convolutions for Brain Tumour Classification

Every year, the number of patients with brain cancers (BCs) or brain tumours (BTs) increases. This trend emphasises the necessity of a computerised system for rapid and accurate detection during the diagnosis of BTs. This paper presents a lightweight deep learning (DL) model based on a convolutional neural network (CNN) for a fast and accurate BC detector. The core component of the BC detector is a depthwise separable convolution (DSConv) on top of the 24-layer CNN architectures. The usage of DSConv with Adam’s optimiser achieves comparable effectiveness to conventional convolutional layers, although using fewer parameters. Additionally, L2 regularisation, dropout, and data augmentation were implemented to mitigate the issues of overfitting. The proposed model was trained and tested using the publicly available dataset consisting of MRI images collected from 233 patients in Nanfang Hospital and General Hospital, with 3063 images in total. In summary, the DSConv-based CNN model demonstrates an average accuracy of 97.50% and has an average inference time of 2.1 milliseconds per classification. It consistently surpasses 96.50% accuracy in the classification of the three types of BTs. These findings indicate that the model is well-suited for accurate BTs classification, particularly for glioma, meningioma, and pituitary tumours from MRI images

Implementation of APPGM-SDG Solutions Initiatives and Impact Evaluations in Sarawak\u27s South Region

As public awareness of the Sustainable Development Goals 2030  has grown, the Malaysian Parliament formed the All-Party Parliamentary Group Malaysia on the Sustainable Development Goals (APPGM-SDG). As part of a bipartisan effort to enhance the implementation of SDG targets in Malaysian parliamentary seats, one of the goals of this research is to investigate the SDG implications of beneficiary solution initiatives. A team is tasked with assembling a report that combines the impact evaluation of SDG initiatives in the Sarawak South Region. Through document analysis, focus groups, and site visits, the regional research team investigated, from December 2023 to February 2024, how the knowledge, skills, networks, and systems of these projects as well as psychology affect the beneficiaries of the solution projects. The paper also addresses the efficiency with which solution providers manage and execute projects, the impact on the beneficiaries, the alignment and mapping of the solution providers\u27 and impact evaluation with the SDG goals and APPGM-SDG modules, and the identification of obstacles and recommended solutions. The study also includes the qualitative and quantitative analysis that the assessors conducted using standardised questionnaires to measure the following six (6) crucial factors: Deep, Clear, Wide, High, SDG and Gender. The primary conclusion is that the goal of all solution initiatives is to eradicate poverty. It is intended that this paper\u27s discussion of the difficulties would better assist project implementers’ and policymakers in making decisions, designing programmes, and writing policy papers, especially when it comes to the economic, social, and environmental domains with specific SDG deliverables and target audiences in mind.  With the aim to improve communication between stakeholders and provide more substantial prospects for society after project execution, the research suggests more visitations approaches