Noorul Islam University

The global rise in energy demand, driven by modern lifestyles, necessitates more efficient wind energy harvesting. This research aims to determine the optimal blade angle for enhancing aerodynamic performance in Horizontal Axis Wind Turbines (HAWTs) under specific wind conditions. Computational and experimental analyses were conducted to evaluate lift and drag forces across different blade angles, focusing on maximizing moment and power output. The results identify 82° as the optimal blade angle for peak performance, with maximum moment observed at this angle for wind speeds of 3 m/s, 12.5 m/s, and 25 m/s. CFD simulations using ANSYS Fluent 17.0 with NACA aerofoils were validated through experiments, showing strong agreement between theoretical and experimental results. The study establishes a rated tip speed of 105 m/s for a 5 MW HAWT. By integrating experimental and computational approaches, this research provides valuable insights into the aerodynamic behavior of HAWTs, aiding in the development of efficient airfoils and blades to enhance energy generation. The findings highlight CFD's role as a cost-effective and time-efficient tool for assessing blade and wind characteristics, contributing to the optimization of wind energy systems and the advancement of sustainable energy solutions. • Integration of computational, theoretical, and experimental methods to optimize HAWT performance • Identification of optimal blade angles to enhance efficiency at low wind speeds • Analysis of wind turbine performance using ANSYS Fluent and wind tunnel experiments • Insights into the impact of wind speed variations on electricity production • Comprehensive evaluation of aerodynamic forces influencing HAWT performance

Electric motors represent critical assets in industrial systems, where reliability and longevity directly influence operational continuity. Although the nominal service life of induction motors is typically 20–25 years, many units remain in operation beyond this threshold under effective maintenance. This study evaluates the continued performance and insulation health of a 40-year-old, 150 kW low-tension induction motor deployed in a water transfer pump system. A comprehensive diagnostic protocol was applied, including insulation resistance, polarization index, dielectric absorption ratio, leakage current, and DC winding resistance measurements. Results indicated insulation resistance values between 2.39 GΩ and 10.3 GΩ, an R-phase polarization index of 1.87, and marginal performance in Y and B phases. Infrared thermography identified localized temperature gradients associated with incipient faults. AI-assisted analytics using a Random Forest classifier achieved an overall accuracy of 86.7% and ROC-AUC of 0.81, demonstrating moderate predictive capability. The framework illustrates the potential for integrating conventional diagnostics with data-driven decision support in condition-based maintenance applications. The motor maintained an availability of 99.94%, confirming its extended viability under structured monitoring. The combined framework merging conventional electrical diagnostics, thermal imaging, and machine-learning inference provides a scalable approach for condition-based maintenance and life-extension assessment of aged assets.

Hepatitis is a serious liver disease which must be diagnosed early and precisely to avoid serious complications. The application of ultrasound imaging is quite popular as it is non-invasive and inexpensive, but there are issues like the presence of speckle noise, low contrast, and intricate patterns of tissues that restrict the accuracy of the obtained diagnosis. This paper presents a lean hybrid system of automated hepatitis prediction with the use of ultrasound images. The effective pre-processing is combined with the model of Adaptive Pearson Residual Normalization of Ultrasound (APRN-U) and Contrast Limited Adaptive Histogram Equalization (CLAHE). It utilizes a dual-branch approach to feature extraction, based on deep spatial features of a lightweight Convolutional Neural Network (CNN) and Granular Texture Encoding (GTE) which incorporates GLCM, LBP, wavelet energy features, and fractal features. The features extracted are combined with an attention-based mechanism and trimmed down with the help of Principal Component Analysis (PCA). Final classification is performed using a hypertuned Bayesian Neural Network (BNN) which in turn allows uncertainty-aware prediction. As experimental data show, the proposed model has a 92% accuracy with an AUC of 0.92 and is better than current approaches. The contribution of each component is further authenticated in the ablation study. The suggested framework provides a computationally effective and valid solution to detect hepatitis in clinical cases.

ABSTRACT Data mining is a technique that involves evaluating big data in order to reveal patterns or connections that can aid in solving business problems through the analysis of data. Exceptional model mining (EMM) is a system for finding local patterns in a dataset by identifying subgroups whose behavior deviates substantially from that which is anticipated. Incremental learning allows the model to continuously learn from new examples while retaining prior knowledge. In the instance of EMM, incremental learning increases the process by adding new training samples, which helps to enhance the model's performance over time. This research presents an incremental learning approach in EMMiner (InEMMiner) using a deep learning model. A novel hybrid optimizing algorithm, double exponential‐running city game optimizer (DE‐RCGO), is proposed by putting together double exponential smoothing (DES) and running city game optimizer (RCGO) for the purpose of finding outstanding subgroups in within‐individual clusters. To assess quality of these subgroups, four different metrics are suggested: (1) a combination of weighted Kraskov entropy with a support vector machine (SVM), (2) a combination of weighted Fuzzy entropy with an Autoencoder, (3) a combination of weighted Boltzmann entropy with a gated recurrent unit (GRU), and (4) a metric that combines weighted Gibbs entropy with isolation forest. Such measures are incorporated in the EMMiner system in order to effectively filter and prioritize most appropriate exceptional subgroups with an improvement on overall EMM task performance. InEMMiner incorporates an incremental learning approach using a deep learning model, TabNet, which is further optimized by the proposed DE‐RCGO algorithm. Experimental results validate the efficiency of the model, with quality metric achieving a value of 98.9%, memory usage of 985.65 MB, and a computational time of 81.52 s.

Abstract This workshop aims to improve the competence of elementary school teachers using digital learning media, in particular, Wordwall, Pickerwhell and Ahaslides. This activity is a Community Service Program of the Elementary School Teacher Education Lecturer Team of Universitas Pelita Bangsa which was carried out online involving 2 Principals, 15 Elementary School Teachers, and 79 Students spread across regions in Indonesia, especially Bekasi, West Java. The workshop was carried out through three stages, namely preparation, material delivery, and evaluation. In the preparation stage, the service team prepared an online workshop by inviting the cooperation of several schools and involving students. In the implementation stage, workshop participants participated in the presentation of theory and practice in the use of digital platforms through Zoom Meetings and then the evaluation was carried out through the help of Google Form after the implementation. The results of the evaluation showed that 82% of participants gave a very useful, solution, and innovative assessment of the implementation of the workshop, while 56.1% of participants rated YouTube as the easiest platform to use, followed by Wordwall at 34.1%. Overall, this workshop received an average satisfaction of 4.88 out of 5. Thus, this digital learning workshop is effective in improving teachers' competence in smartphone-based digital learning. It is hoped that there will be no more limitations for teachers to implement digital learning by utilizing gadgets owned by all groups, namely smartphones. Keywords: Workshop; Digital Learning; Elementary School Teachers; Smartphone Abstrak Workshop ini bertujuan meningkatkan kompetensi Guru SD memanfaatkan media pembelajaran digital, khususnya, Wordwall, Pickerwhell dan Ahaslides. Kegiatan ini merupakan Program Pengabdian Masyarakat Tim Dosen Pendidikan Guru Sekolah Dasar Universitas Pelita Bangsa yang dilaksanakan secara daring dengan melibatkan 2 Kepala Sekolah, 15 Guru SD, dan 79 Mahasiswa yang tersebar di wilayah di Indonesia khususnya Bekasi, Jawa Barat. Workshop dilaksanakan melalui tiga tahapan, yaitu persiapan, penyampaian materi, dan evaluasi. Pada tahap persiapan tim pengabdian mempersiapkan workshop secara daring dengan mengajak kerjasama beberapa sekolah dan melibatkan mahasiswa. Tahap implementasi, peserta workshop mengikuti pemaparan teori dan praktik dalam penggunaan platform digital melalui Zoom Meeting kemudian pada evaluasi dilaksanakan melalui bantuan Google Form usai pelaksanaan. Hasil evaluasi menunjukkan 82% peserta memberikan penilaian sangat Bermanfaat, Solutif, dan Inovatif terhadap pelaksanan workshop, sementara 56,1% peserta menilai YouTube sebagai platform yang paling mudah digunakan dan disusul Wordwall 34,1%. Keseluruhan, workshop ini mendapatkan rata-rata kepuasan 4,88 dari 5. Dengan demikian workshop pembelajaran digital ini efektif meningkatkan kompetensi guru dalam pembelajaran digital berbasis smartphone. Diharapkan tidak ada lagi keterbatasan guru mengimplementasikan pembelajaran digital dengan memanfaatkan gawai yang dimiliki semua kalangan yaitu Smartphone. Kata kunci: Workshop; Pembelajaran Digital;Guru SD; Smartphone

This preprint has been withdrawn at the authors’ request due to serious scientific errors that cannot be corrected by updating the paper.

This study examines the ideas of Ziauddin Sardar and Azyumardi Azra in three major areas: Islamic education, digital religious authority, and religious moderation. Using a library research approach, this article analyzes how Sardar’s Postnormal Times (PNT) framework explains global complexity, chaos, and contradictions that shape the future of Islamic thought and education. Meanwhile, Azra’s concept of Islam Nusantara and wasathiyah provides a historical and cultural foundation for constructing moderate Islamic identity in Indonesia. Findings indicate that Sardar emphasizes adaptive education oriented toward future literacy, while Azra highlights the integration of tradition, modernity, and local culture. In the context of digital authority, Sardar views the transformation as a structural effect of postnormal conditions driven by algorithmic systems, while Azra stresses the need to strengthen scholarly legitimacy based on sanad, institutions, and ethical guidance. Both perspectives converge on the importance of moderation. Sardar presents moderation as a strategy to manage global complexity, whereas Azra positions wasathiyah as the inherent identity of Islam in the archipelago. This study concludes that synthesizing both frameworks can strengthen Islamic education, stabilize digital religious authority, and reinforce Indonesia’s moderate Islamic identity in responding to contemporary challenges.

Purpose The primary purpose of this study is to develop an eco-sustainable and high-performance three-dimensional (3D)-printed polymer composite by reinforcing polyethylene terephthalate glycol (PETG) with short bamboo fibre (SBF) and nano-scale coconut shell ash (CSA-n). The study aims to enhance the mechanical, structural and interfacial characteristics of PETG through a hybrid reinforcement strategy that uses renewable natural fibres and waste-derived nano-fillers. By integrating SBF for strength and nano-scale CSA for improved interfacial bonding and thermal stability, the work seeks to address the limitations of conventional PETG composites, such as poor stiffness and limited fibre–matrix adhesion. The overall objective is to contribute towards the advancement of sustainable materials in additive manufacturing, enabling environmentally friendly and structurally efficient bio-composites suitable for engineering and structural applications. Design/methodology/approach The hybrid PETG composites were fabricated using the fused filament fabrication process. The methodology began with the preparation of SBF through cleaning, alkali treatment, and silane coupling to improve surface roughness and adhesion with the polymer matrix. CSA was converted into nano-scale particles via controlled carbonization, calcination, high-energy ball milling and ultrasonic dispersion to achieve fine particle size and uniform morphology. Composite formulations were prepared at a fixed 70:30 volume ratio of PETG to reinforcement (SBF + CSA-n), and filaments were produced through twin-screw extrusion. Standardized specimens were 3D printed using optimized process parameters and tested for tensile, flexural, impact and hardness properties according to ASTM standards. Scanning electron microscopy was used to analyse fibre–matrix interaction, filler dispersion and fracture characteristics, supported by quantitative image analysis to assess void content and alignment. Findings The experimental results demonstrated that the incorporation of nano-scale CSA substantially enhanced the interfacial bonding, mechanical strength and filler dispersion of PETG-based composites. Among all the developed specimens, the hybrid composite containing SBFs and nano-CSA (Specimen D) exhibited superior performance, with tensile strength of 54.1 MPa, flexural strength of 74.8 MPa, impact toughness of 5.6 kJ/m², and hardness of 101 HRM. SEM analysis revealed dense fibre distribution, minimal void formation and strong interfacial adhesion in the nano-CSA-reinforced composites, confirming efficient stress transfer and structural integrity. In contrast, fibre-only composites showed poor adhesion and higher porosity, while micro-CSA-filled composites presented moderate improvements but exhibited uneven filler dispersion. Overall, the findings confirm that the synergistic interaction of SBFs and nano-scale CSA leads to significant gains in stiffness, strength and toughness without compromising printability or weight efficiency. Originality/value To the best of the authors’ knowledge, this study is the first to report the development of a 3D-printed hybrid PETG composite reinforced simultaneously with SBF and nano-scale CSA . The novelty lies in demonstrating the synergistic effect of natural fibre and nano-filler reinforcement in enhancing interfacial bonding, load transfer and overall composite performance. Unlike previous single-reinforcement PETG composites, this hybrid system introduces a dual mechanism that significantly improves both mechanical and thermal stability, contributing to the design of next-generation bio-based engineering materials. The research holds substantial value for advancing sustainable additive manufacturing practices by using renewable bamboo fibres and waste-derived nano-fillers, thereby supporting circular economy initiatives and offering a scalable pathway towards environmentally responsible, high-performance composite materials.