Universitas Ahmad Dahlan: UAD Scientific Journal
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Capitalist exploitation and animal ethics in water for elephants: A Marxist and utilitarian perspective
Full text linkIn early 2025, numerous cases of human rights violations in the circus industry emerged in Indonesia, bringing public attention to the exploitation of both workers and animals. This study explores the portrayal of exploitation in Francis Lawrence’s Water for Elephants through Karl Marx’s theory of labor exploitation combined with the animal ethics perspective of Singer and Regan, presenting an insightful dual perspective in film analysis. This qualitative research employs a literature study method, analyzing scenes, dialogues, and visual elements of the film. The findings reveal that Water for Elephants portrays various indicators of exploitation: hazardous work conditions, low wages, lack of healthcare access, abuse, and poor living conditions. These conditions reflect capitalist-driven oppression for profit maximization in circus environments. The study concludes that the movie serves as a critical commentary on the ethical implications of labor and animal rights violations, reinforcing the need for broader societal awareness and reform in the treatment of both humans and animals in entertainment sectors
Development of an energy management system for palm oil refinery facilities: implementing a systems approach
Full text linkThis study aims to develop a proactive Energy Management System (EnMS) for a palm oil refinery using a comprehensive systems-based approach implemented carefully during the plant design phase. Unlike conventional methods that rely mainly on historical operational data, this research deliberately utilizes engineering design specifications together with simulation modeling to estimate accurate energy consumption baselines and formulate an ISO 50001-compliant EnMS. A regression-based analysis is systematically applied to define reliable Energy Performance Indicators (EnPIs), using production volume and running hours as key variables influencing overall energy utilization. The resulting analytical model estimates a Specific Energy Consumption (SEC) of 2.168 MWh/MT—significantly higher than the 0.45 MWh/MT BAT benchmark—primarily due to assumptions of full-capacity, simultaneous operation under conservative conditions. To support continuous energy performance improvement, the system incorporates PDCA-based review mechanisms and establishes progressive energy-saving targets: an initial 10% reduction, followed by 1–2% annual incremental improvements. Validation through structured feedback sessions from plant management confirmed the system's strong alignment with operational needs, feasibility within industrial contexts, and readiness for phased implementation. Ultimately, this study contributes a novel, simulation-based framework for integrating EnMS during the design stage, offering a scalable and adaptable model for energy-intensive industries that aim to enhance efficiency and achieve long-term sustainability from the outset
A smart city infrastructure implementation framework – insights from smart street lighting implementation optimization
Full text linkIn recent years, the concept of smart cities and infrastructure has gained momentum as a solution to challenges such as population growth, resource management, and environmental sustainability. Rapid urbanization in many developing countries highlights the need for efficient infrastructure planning and management. This framework offers a structured approach for decision-making and resource allocation, enabling prioritization of investments to maximize limited resources while supporting development goals. The framework is tested through an analysis of the Smart Street Lighting Systems (SSLS) in Surabaya, Indonesia, addressing the city's intention to upgrade street lighting to reduce maintenance costs and energy consumption. Currently, the street lighting system faces issues including a high rate of broken or damaged lights and inefficiencies in handling complaints. However, limited funding and varied regional needs constrain any comprehensive upgrade. The proposed framework integrates the Analytical Hierarchy Process (AHP) to prioritize regions as weighting inputs, Mixed Integer Goal Programming (MIGP) to optimize the distribution of SSLS and conventional LED lighting across regions, and Cost-Benefit Analysis (CBA) to evaluate financial feasibility. Results recommend purchasing 11,915 new SSLS units with region-specific distributions, achieving a financially viable Benefit-Cost Ratio (BCR) of 2.059. These findings demonstrate practical implementation of smart city principles, balancing cost-efficiency, service performance, and stakeholder priorities. Policymakers can use this framework to maximize impact within budget constraints. This framework serves as a viable template for other regions and countries embarking on smart city infrastructure implementation
Transformer Models in Deep Learning: Foundations, Advances, Challenges and Future Directions
Full text linkTransformer models have significantly advanced deep learning by introducing parallel processing and enabling the modeling of long-range dependencies. Despite their performance gains, their high computational and memory demands hinder deployment in resource-constrained environments such as edge devices or real-time systems. This review aims to analyze and compare Transformer architectures by categorizing them into encoder-only, decoder-only, and encoder-decoder variants and examining their applications in natural language processing (NLP), computer vision (CV), and multimodal tasks. Representative models BERT, GPT, T5, ViT, and MobileViT are selected based on architectural diversity and relevance across domains. Core components including self-attention mechanisms, positional encoding schemes, and feed-forward networks are dissected using a systematic review methodology, supported by a visual framework to improve clarity and reproducibility. Performance comparisons are discussed using standard evaluation metrics such as accuracy, F1-score, and Intersection over Union (IoU), with particular attention to trade-offs between computational cost and model effectiveness. Lightweight models like DistilBERT and MobileViT are analyzed for their deployment feasibility. Major challenges including quadratic attention complexity, hardware constraints, and limited generalization are explored alongside solutions such as sparse attention mechanisms, model distillation, and hardware accelerators. Additionally, ethical aspects including fairness, interpretability, and sustainability are critically reviewed in relation to Transformer adoption across sensitive domains. This study offers a domain-spanning overview and proposes practical directions for future research aimed at building scalable, efficient, and ethically aligned. Transformer-based systems suited for mobile, embedded, and healthcare applications
Design and Application of a Cyber Physical Based Data Logger System for Charging Stations
Full text linkThe rapid advancement of technology, particularly in transportation, has led to a growing public interest in electric vehicles. Government support, exemplified by Presidential Regulation No. 55 of 2019, further encourages this shift. With more electric vehicles on the road, the need for adequate charging infrastructure is critical. This research aims to design, test, and implement a charging device that records electric vehicle usage, displays data on an LCD, and allows monitoring through a website. Using the research and development (R&D) method, a highly effective design was developed. The data recording system employs the PZEM-004T sensor and ESP32 microcontroller to send data to a database. Validation tests showed high accuracy and precision, with current accuracy at 98.79% and precision at 99.24%, and voltage accuracy at 99.59% and precision at 99.87%. The device was installed in the basement of UPT TIK UNS and tested with three electric vehicles, each with different power requirements. The average power growth every ten minutes was 0.063 kWh for the first vehicle, 0.164 kWh for the second, and 0.139 kWh for the third. These results demonstrate that the device functions well, the design is successful, and it provides consistent, accurate, and precise energy growth measurements
Geographic-Origin Music Classification from Numerical Audio Features: Integrating Unsupervised Clustering with Supervised Models
Full text linkClassifying the geographic origin of music is a relevant task in music information retrieval, yet most studies have focused on genre or style recognition rather than regional origin. This study evaluates Support Vector Machine (SVM) and Convolutional Neural Network (CNN) models on the UCI Geographical Origin of Music dataset (1,059 tracks from 33 non-Western regions) using numerical audio features. To incorporate latent structure, we first applied K-means clustering with the optimal number of clusters (k=2) determined by the Elbow and Silhouette methods. The cluster assignments were used as auxiliary signals for training, while evaluation relied on the true region labels. Classification performance was assessed with Accuracy, Precision, Recall, and F1-score. Results show that SVM achieved 99.53% accuracy (95% CI: 97.38–99.92%), while CNN reached 98.58% accuracy (95% CI: 95.92–99.52%); Precision, Recall, and F1 mirrored these values. The differences confirm SVM’s superior performance on this dataset, though the near-perfect scores also suggest strong separability in the feature space and potential risks of overfitting. Learning-curve analysis indicated stable training, and cluster supervision provided small but consistent benefits. Overall, SVM remains a reliable baseline for tabular music features, while CNNs may require spectro-temporal representations to leverage their full potential. Future work should validate these findings across multiple datasets, apply cross-validation with statistical significance testing, and explore hybrid deep models for broader generalization
A Blockchain-Enabled Internet of Things Framework for Enhancing Trust and Privacy in Social Work Case Management
Full text linkTraditional social work case management systems face critical challenges including data silos, security vulnerabilities, and insufficient inter-agency collaboration, limiting service efficiency and compromising client privacy protection. This study addresses these challenges by developing and evaluating a novel technological framework that integrates blockchain consortium networks with Internet of Things (IoT) devices to establish multi-party trust mechanisms and enhance service delivery. The research contribution is a comprehensive four-layer system architecture featuring 28 smart contracts, decentralized trust mechanisms, and privacy-preserving technologies including homomorphic encryption and differential privacy for social work applications. The methodology employed a mixed-methods approach involving system design and development, followed by a six-month pilot implementation across three social work institutions in Taiwan with 249 participants. Data collection encompassed quantitative performance metrics from system logs and IoT sensors, alongside qualitative feedback through interviews and focus groups. The blockchain network achieved 850 transactions per second with 99.2% system availability, significantly outperforming industry standards. Results demonstrated substantial operational improvements: 37.1% reduction in case processing time, 87.3% increase in service efficiency, and 26-fold increase in inter-agency collaboration frequency. The blockchain-based trust mechanism increased inter-agency data sharing willingness from 61.3% to 84.6%, while maintaining 100% anonymization coverage with 91.3% analytical accuracy. Cost-benefit analysis revealed a 2.8-year payback period with 41.2% return on investment. This research demonstrates the feasibility and effectiveness of blockchain-IoT integration in social work, providing a practical framework for digital transformation while ensuring data security and privacy protection in sensitive social service environments
A Lightweight 1D CNN for Unified Real-Time Communication Signal Classification and Denoising in Low-SNR Edge Environments
No full textThe escalating complexity and pervasive noise in contemporary communication systems have increasingly rendered traditional signal processing methods insufficient for reliable real-time analysis. This research addresses a fundamental void in existing literature by proposing a novel and lightweight deep learning framework, primarily centered on Convolutional Neural Networks (CNNs) for the joint classification and denoising of communication signals. Distinct from prior methodologies that often segregate these crucial tasks our model integrates both objectives within a highly optimized, unified architecture engineered for ultra-low-latency inference, notably achieving a 30–50% reduction in inference time compared to deeper CNN-RNN hybrids or Transformer-based architectures. The framework's effectiveness was comprehensively evaluated using both synthetic and real-world datasets, including RadioML2018.01A which encompasses a diverse range of modulation schemes and signal-to-noise ratio (SNR) levels. Experimental results conclusively demonstrate that the proposed CNN achieved an impressive 96.8% classification accuracy significantly enhanced signal quality to an average of 22.3 dB SNR, and maintained an average processing latency of merely 11.3 ms. These figures consistently demonstrate superior performance compared to traditional baselines including FFT, SVM, and LSTM. Despite these promising results, the current model was primarily trained and evaluated under Additive White Gaussian Noise (AWGN) conditions, and future work will explore its generalization to real-world scenarios involving multipath fading, Doppler shifts, and dynamic channel interference. This study represents a significant leap forward in developing robust, efficient, and intelligent solutions essential for next-generation communication signal processing, particularly for real-time applications in resource-constrained environments
Temperature-Controlled Process for Recycled Waste Tire Polymer-Polymer Composites: An Innovative and Sustainable Solution for Marine Fender Applications
Full text linkMarine fender prototypes play a critical role in protecting the ship and the berthing infrastructure from damage during docking. Recycling waste polymers, such as waste tires, into composite materials for marine fenders, can contribute to environmental sustainability and resource conservation. In marine Fender applications, compression testing often plays a crucial role; we should also test factors such as elasticity, stiffness, and hardness. In this study, pressure and hardness were selected, and Young's modulus was calculated for two types of composite materials: one manufactured from waste tires and high-density polyethylene (HDPE) and the other from waste tires and room-temperature vulcanized (RTV) silicone both in varying proportions. These types of materials were produced using a press machine equipped with a PID controller, which enables the adjustment of the temperature to a desired value, thereby achieving the best results. Prototypes containing 85% waste tire with 15% HDPE and 50% waste tire with 50% RTV silicone showed superior energy absorption and durability for marine fender applications. Despite achieving satisfactory hardness and hardness values, the waste tire and RTV silicone composite did not exceed those of the waste tire and HDPE composites, which had Young's modulus and Shore hardness values of 1.74 MPa and 56.6, respectively. The compression test showed that the waste tire and RTV silicone composites achieved higher values, surpassing 1990 kN. The findings provide a crucial foundation for utilizing waste composite materials in marine fender production
A Hybrid PI–SOSM Control Strategy with Disturbance Observer for Enhanced Dynamic Response of IM Drives
Full text linkThis paper proposes a novel hybrid field-oriented control (FOC) strategy for high-performance induction motor (IM) drives, integrating a conventional Proportional–Integral (PI) controller in the speed loop and a Super-Twisting Second-Order Sliding Mode (SOSM) controller in the current loop. The main novelty lies in combining a sliding mode disturbance observer (OB) with a hybrid PI–SOSM structure, enabling real-time estimation and compensation of unknown load torque. The estimated torque is transformed into an equivalent disturbance current, which is directly added to the torque-producing current reference, thereby achieving feedforward disturbance rejection. The novel hybrid structure achives the improved dynamic response and robustness through self-compensated torque disturbance using OB, reduced chattering in current regulation via SOSM, and maintaining PI simplicity in the outer speed loop. Extensive simulation results by MATLAB/Simulink sotfware demonstrates that the hybrid controller offers superior dynamic performance, enhanced robustness against parameter uncertainties and load disturbances, and significantly reduced chattering effects compared with conventional PI–PI FOC