Journal of Novel Engineering Science and Technology
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Assessing Site Management Practices in the Planning of a Market Building: A Case Study Based on Indonesian Green Building Regulations
Get PDFGovernment infrastructure development plays a vital role in improving quality of life and supporting national development goals. In recent years, sustainable development principles, particularly those related to green building practices, have become increasingly relevant. However, these principles remain underutilized in certain public facilities, such as traditional markets. One critical yet often overlooked component is site management, which significantly affects environmental performance from the early planning stage. This study aims to develop a green building assessment framework specifically focused on site management in market building planning. Employing a qualitative descriptive research method with an applied approach, the study uses a case study of a market building in Indonesia, evaluated using site management criteria derived from Indonesian Green Building regulations. To ensure credibility, the framework and assessment are validated through expert judgment from professionals in green building and sustainable site planning. The results reveal that parking lot provision, site processing and accessibility, and private green open space planning are the most influential sub-parameters, contributing to the highest scores in the site management category. The case study building achieved a score of 23 out of 38, indicating a moderate level of compliance. However, deficiencies were identified in sub-parameters such as green open spaces and the lack of Electric Vehicle Charging Station (EVCS) facilities. This study contributes to the field by proposing a targeted, site-specific assessment framework for market buildings, a building typology that is rarely examined in green building literature. Integrating site management early in the design process can improve sustainability outcomes by enhancing public space quality and environmental awareness in urban communities
Bridging Internal Constraints and Performance: A SEM-PLS Analysis of Public Infrastructure Projects in the Buton Islands
Get PDFPublic infrastructure projects in developing regions often face performance issues due to internal capacity gaps and weak management systems. This study emphasizes both the direct and mediating effects of internal factors on project outcomes, with stronger justification for using SEM-PLS and additional comparative methods. Regression-based robustness checks and PLSpredict cross-validation are incorporated to validate the results. The research investigates the mediating role of construction management in linking internal organizational factors to project performance in the Buton Islands, Southeast Sulawesi. Key elements examined include planning quality, supervisory practices, communication effectiveness, and equipment readiness, measured against cost, time, and quality outcomes. A quantitative design using Structural Equation Modeling with Partial Least Squares (SEM-PLS) was applied to data from 54 construction professionals in government and private sectors. To address statistical concerns with the small sample size and many indicators, redundant items were eliminated (loading < 0.70), and discriminant validity was tested using HTMT ratios. Findings indicate that internal organizational conditions have a moderate but significant direct effect on project performance, while this influence is substantially strengthened when mediated by effective construction management. Novelty is underscored by situating the analysis in the Buton Islands, a geographically isolated and resource-constrained region rarely studied in international literature, extending systems theory and the Iron Triangle to new environments. The strongest predictive paths involve budgeting systems, scheduling mechanisms, and quality assurance frameworks. Beyond technical dimensions, managerial practices also foster institutional trust and resilience, offering practical guidance for policymakers and practitioners
Multimodal Gait Analysis Using IMU and EMG Sensors with HMM Classification to Differentiate Obese and Normal Body Types
Get PDFGait analysis is essential for diagnosing movement disorders and monitoring rehabilitation progress; conventional methods are often costly and complex. This study aims to differentiate gait characteristics between individuals with obesity and those with normal body composition using a multimodal approach that integrates Inertial Measurement Unit (IMU) and electromyography (EMG) sensors. Data were collected from ten male participants (five classified as obese and five with normal body composition). IMU sensors were used to measure acceleration, angular velocity, and step count, while EMG sensors recorded muscle activity from the tibialis anterior and gastrocnemius muscles. We developed a real-time acquisition using ESP32 microcontrollers and Bluetooth Low Energy (BLE), and gait phase classification was performed using the Hidden Markov Model (HMM). Using heel-mounted sensors, the average step detection error ranged from 2.5% to 3.6%. IMU signals from obese participants indicated a shift in dominant gait phase from Initial Contact during slow walking to Loading Response during fast walking, with relative errors up to 27%. In contrast, participants with normal body composition exhibited more diverse and accurate phase distributions. EMG-based analysis provided more precise segmentation (with error rates as low as 0.47%). It revealed distinct muscle activation patterns: gastrocnemius activity was dominant during the Midswing or Midstance phases, while tibialis anterior activity peaked during Initial Contact, Initial Swing, or Loading Response. These findings suggest body composition significantly affects gait stability, phase transitions, and muscle activation patterns. Future work should explore advanced machine learning algorithms such as Long Short-Term Memory (LSTM) or Convolutional Neural Networks (CNN), integrate pressure sensors, and validate the system in real-world environments to enhance accuracy and reliability
Channel Coding Analysis for High-Speed Telecommunication System
Get PDFThe paper mainly focuses on the channel coding design for high-speed telecommunication systems. The challenging issues in this study are based on (1) the growing demand for high data speed and an increase in subscribers, and (2) high-speed telecommunication networks allow users to avoid them due to better speed and more bandwidth. The objectives of this study are (1) to obtain a higher data rate, higher spectral efficiency, higher throughput, higher bandwidth, and higher energy efficiency at lower latency and (2) to detect/correct errors caused when information is transmitted through noisy channels. Therefore, high-speed telecommunication channel coding techniques will play a major role in achieving fast communication with minimum errors. The linear block and turbo codes are fundamental to analyzing the channel coding scheme for specific purposes. Theoretical concepts with numerical simulation are used to conduct the analyses. The simulation results on BER analyses confirm that the performance criteria could be met with real-world applications
Optimizing Brain Tumor Classification with Freeze-5 VGG16 and Dataset Fusion
Get PDFMagnetic resonance imaging (MRI)-based brain tumor classification is pivotal for early diagnosis and treatment planning. This study enhances the VGG16 pretrained model through freeze-5 fine-tuning (i.e., freezing the first five convolutional layers) and dataset fusion of two public repositories, yielding 5,023 training and 1,311 testing images. Preprocessing includes normalization and grayscale-to-RGB conversion, followed by moderate augmentation (rotation ≤ 15°, shift ≤ 0.1, zoom ≤ 0.1, brightness [0.9–1.1]). The base VGG16 (without top layers) is extended with GlobalAveragePooling2D, Dense (1024, ReLU), Dropout (0.5), and Dense (4, softmax) layers. The model is compiled with the Adam optimizer (lr=1e-4), EarlyStopping, and ReduceLROnPlateau callbacks. On the test set, the proposed configuration achieves peak accuracy of 99.16 % and macro-F1 of 0.99, outperforming prior hybrid approaches. An ablation study confirms that the freeze-5 strategy combined with data augmentation significantly boosts generalization without overfitting. These results underscore the critical role of optimal layer-freezing and dataset fusion in brain tumor classification. Future work will explore ensemble architecture and real-time clinical deployment
Analysis on Wideband Channel Model for High Speed Wireless Communication Systems
Get PDFThe paper emphasizes on the analysis on wideband channel model for high speed wireless communication systems. The research problem in this study are based on the following concepts such as (i) Firstly, it is necessary to change to the appropriate physical devices that can support 5G system, (ii) It is required to design a channel that will adapt to the medium that will be convenient for the changed physical devices, and (iii) Mobile terminals that currently use 4G cannot be used in 5G system. The objectives in this study are - to analyze the existing channel model for mobile communication, to analyze the mathematical and dynamical model for wireless propagation channel, to implement the wireless propagation channel with specific purposes, to implement the optimized channel model performance, and to evaluate the performance of the developed channel design. The numerical analyses in this study are conducted by using MATLAB language. The research direction in this study are based on the channel system functions, and tapped delay-line models. The simulation results are confirmed that the 12 taps in this study for the high speed wireless communication system design
Analysis of High-Performance Step-Down DC to DC Converter Design Based on Zero Voltage Switching with Pulse Width Modulation Technique for Electric Vehicles
Get PDFThe paper mainly focuses on analyzing high-performance step-down DC to DC converter based on zero voltage switching with pulse width modulation technique for electric vehicles. In this paper, PI, fuzzy PI, and adaptive network-based fuzzy inference system (ANFIS)control methods are applied to the phase shift full-bridge (PSFB) zero voltage switching (ZVS) converter for auxiliary components in electric vehicles. The robust analysis of three control methods is compared by using the AC small-signal mathematical model. Traditional PI control uses specific mathematical equations with errors and derivatives. Fuzzy PI control utilizes fuzzy logic rules with linguistic variables such as high, medium, and low. ANFIS combines fuzzy logic and neural networks to capture both benefits. The three control designs' switching losses and load changes are analyzed and implemented with the MATLAB/SIMULINK Software platform. From the simulation results, traditional PI control works with 92% efficiency. Fuzzy PI control and ANFIS work with 93% efficiency at full load capacity
Hybrid Ensemble Retrieval-Augmented Generation for Indonesian Legal Consultation with Keyword Boosting
Get PDFThis study presents the design and evaluation of a fully local, hybrid ensemble Retrieval-Augmented Generation (RAG) system tailored for Indonesian legal consultation. By integrating sparse (BM25), dense (FAISS), and keyword-aware retrieval mechanisms, the system balances lexical, semantic, and domain-specific relevance to retrieve high-quality legal context. A curated dataset of 8,450 legal consultation articles was scraped from a trusted legal platform, cleaned through multi-stage pre-processing, and indexed for efficient retrieval. Retrieved documents are formatted into structured prompts and fed into locally hosted large language models (LLMs) using Ollama, allowing for complete offline operation. Experiments comparing five retrieval configurations TF-IDF, BM25, FAISS, ensemble BM25+FAISS, and ensemble with keyword boosting demonstrate that the hybrid ensemble with keyword boosting yields the most relevant and grounded answers. Both quantitative (retrieval score analysis) and qualitative (manual relevance rating) evaluations were performed, confirming the effectiveness of the ensemble strategy in improving answer quality. Additionally, the system achieves practical response times (12–20 seconds) on consumer-grade hardware without reliance on cloud services. This work makes a novel contribution by demonstrating that a hybrid ensemble retrieval framework, specifically tuned to the linguistic characteristics and retrieval challenges of Indonesian legal texts, can significantly enhance the performance of local RAG-based legal QA systems. Future directions include real-time indexing, fine-tuning of legal-domain LLMs, and extending the system to support other legal domains such as statutory law, regulations, and court rulings
The Evaluation of Seismic Fragility Curves for High-rise RC Frames
Get PDFIn Yangon City's downtown area, Latha Township is a congested area with a high population density. Most of the buildings in the downtown area are RC residential buildings. Yangon is regarded as a medium seismicity region and is about 40 km west of the Sagaing Fault, which is a major active fault in Myanmar. For disaster seismic risk reduction on human lives and infrastructure, this study evaluates the seismic performance of high-rise reinforced concrete buildings under Service Level Earthquake (SLE), Design Basis Earthquake (DBE), and Maximum Considered Earthquake (MCE) hazard levels. The first objective is to evaluate the seismic performance of such buildings with varying length-to-width ratios under possible future earthquakes by using the mean values of maximum %ISDR, following FEMA 356 criteria. The second objective is to develop fragility curves based on results obtained from Incremental Dynamic Analysis (IDA) using SAP 2000. Hypothetical building models represent structures in Latha Township with material properties aligned with current construction practices, as informed by the Yangon City Development Committee (YCDC). Nonlinear Time History Analysis (NTHA) evaluates seismic performance, correlating inter-story drift ratios (%ISDR) with ground motion intensities, specifically peak ground acceleration (PGA). Nonlinear properties are modeled in compliance with ASCE 41-13 guidelines. This analysis aims to provide a risk transfer measure for those vulnerable high-rise structures in order to protect the properties of residents under possible future earthquakes
Comparison of Multi-Face Detection Performance on Images Using Haarcascade, Dlib, and RetinaFace
Get PDFMulti-face detection presents a significant challenge in computer vision, especially in environments with limited hardware resources. This study compares the performance of three multi-face detection methods—Haarcascade, Dlib (HOG and CNN), and RetinaFace—using a subset of the WIDER FACE dataset in a CPU-only environment without GPU acceleration. The experiment was conducted in two stages using a total of 300 images from the WIDER FACE dataset, which reflect real-world variations such as pose, scale, illumination, expression, and occlusion. Performance evaluation was carried out using precision, recall, F1-score, accuracy, and processing time as metrics. The results show that RetinaFace consistently outperforms the other methods, achieving superior metrics in Recall (0.92), F1-score (0.93), and Accuracy (0.88) on Subset A, and leading across all metrics on Subset B. While Dlib-CNN demonstrates high detection performance, it suffers from very slow processing time. In contrast, Haarcascade delivers the fastest processing speed but performs poorly in terms of evaluation metrics. The experiments also reveal that RetinaFace is the most consistent and reliable method based on standard deviation values of precision (0.01), recall (0.11), F1-score (0.07), and accuracy (0.11). Overall, this study contributes valuable insights for selecting efficient face detection methods under constrained resource conditions