Journal title SINERGI Initials SINERGI Online ISSN 2460-1217 Print ISSN 1410-2331 Accreditation Status Sinta 1 Accredited Journal, Decree No: 79/E/KPT/2023 Frequency 3 issues per year (February, June, and October) DOI Journal 10.22441/sinergi by Editor-in-chief Prof. Dr. Andi Adriansyah. Publisher Fakultas Teknik, Universitas Mercu Buana Cite Analysis Google Scholar, Sinta Indexing Scopus; Google Scholar; Garuda Ristekbrin, DOAJ, BASE, and EBSCO. SINERGI is a peer-reviewed international journal published three times a year in February, June, and October. The journal is published by the Faculty of Engineering, Universitas Mercu Buana. Each publication contains articles comprising high-quality theoretical and empirical original research papers closely related to the fields of Engineering (Mechanical, Electrical, Industrial, Civil, Chemical, and Architecture). The theme of the paper is focused on new industrial applications and energy development that synergize with global, green and sustainable technologies. The journal is registered in the CrossRef system with a Digital Object Identifier (DOI). The journal has been indexed by Scopus, Google Scholar, DOAJ, BASE, and EBSCO.
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Heat transfer and pressure characteristics of Tri Ethylene Glycol/water and Ethylene Glycol/water mixtures in copper pipe heated flow systems
Enhancing heat transfer efficiency and pressure regulation in copper pipe flow systems is crucial for advancing modern cooling and heating technologies, particularly given the widespread use of copper piping in these applications. This study investigates the thermal and hydraulic performance of ethylene glycol/water (EG/water) and tri ethylene glycol/water (TEG/water) mixtures as working fluids in copper pipe systems. A series of controlled experiments was carried out on a dedicated copper pipe test section to evaluate the effects of varying flow rates on the heat transfer coefficient and pressure drop for each fluid mixture. The results indicate that the TEG/water mixture yielded a ~2.0% increase in heat transfer coefficient and a ~1.0% reduction in pressure drop compared to the EG/water mixture, with a corresponding increase in Reynolds number of approximately 37.0%. The reduction in pressure drop is primarily attributed to the lower viscosity of the TEG/water fluid. These findings provide valuable comparative insights into the thermophysical behaviour of both glycol-based mixtures and offer practical guidance for optimizing the selection of thermal fluids in large-scale cooling and heating systems that utilize copper piping.
Copyright (c) 2025 SINERGI http://creativecommons.org/licenses/by-sa/4.0/
Port perspectives in a changing climate: strategies for enduring impact
More than 50% of the world's trade happens via seaborne line. The sustainability of the ports is crucial as it boosts economic growth. However, climate change and its effects have disturbed the port's activities. This paper highlighted the possibility of climate change effects threatening some ports worldwide. A systematic literature review has been conducted, and 11 resources have been used to summarize their impact on port activities. The climate change effects encountered by the ports and their authorities' adaptation measures are underlined. Ports worldwide are considered. As a result, sea level rise (SLR), storm surges, and flooding are some threats that can affect port activities. Adaptation and mitigation plans can be more successfully implemented with excellent knowledge of the factors leading to increased exposure. From the port expansion to creating a new location of ports, the other mitigation and adaptation plan towards the sustainable ports is by providing an accurate topographic map, a good simulation software, and good resilience infrastructures and adaptation framework. The related ports have suggested and implemented adaptation and mitigation approaches to resolve the problem and sustain their ports and harbour activities. Adaptation and mitigation measures taken will respond to the effects of climate change.
Driver assistance collision warning system using a LIDAR sensor with kinematics and perception algorithms
Road accidents remained a significant global concern, causing loss of life and economic damage. To mitigate this issue, the automotive industry has increasingly invested in Advanced Driver Assistance Systems to enhance vehicle safety. This research presented a Driver Assistance Collision Warning System that incorporated kinematics and perception algorithms to improve collision prevention. The system utilized a LIDAR sensor to capture real-time data regarding the distance to the vehicle in front of it. This data was integrated with an Arduino microcontroller to compute the relative speed and time of collision. Upon detecting a collision risk, the system triggered a warning mechanism, which included an audible alert provided by a buzzer and a visual warning displayed on the head-up display. The system integrated kinematics algorithms, which processed sensor-read values to generate real-time decisions utilizing a specific threshold time to collision, and perception algorithms relied on Fuzzy Logic to handle uncertainty and improve accuracy. Validation was conducted through integration, system, and acceptance testing, demonstrating reliable synchronization of algorithms and accurate operation in real-world environments. The results showed that the system achieved a collision risk detection accuracy of ±5 cm within five different environmental factors. These findings confirmed the system's potential as a reliable solution for real-world collision prevention.
A review of rock slope failures in Malaysia: Exploring types, cases, causes, and consequences
Rock slope failures remain a significant concern in regions of Malaysia with varying geological formations. This review examines the challenges posed by these incidents and addresses key knowledge gaps. By providing a comprehensive, multidisciplinary analysis of case studies, technological advancements, climate influences, disaster management, and socio-economic impacts, it offers valuable insights for researchers, engineers, and policymakers. It examines key failure types, including rock falls, slides, avalanches, and general failures, through notable case studies such as Bukit Lanjan (Selangor), Kinta Valley (Perak), and Mount Kinabalu (Sabah). A comprehensive methodology framework was employed, utilizing manual search techniques (handpicking, snowballing, citation, and reference tracking) alongside advanced keyword-based searches with Boolean operators in Scopus, ScienceDirect, and Google Scholar databases. Findings reveal that Malaysian rock slopes are highly susceptible to collapse due to heavy rainfall, human activities, and natural events such as earthquakes. While these factors can act independently, their combined effects significantly amplify failure risks, particularly during intense rainfall. The consequences extend beyond immediate casualties, injuries, and property damage, leading to infrastructure failures, economic disruptions, and environmental degradation. This review underscores the need for sustainable mitigation strategies to address these risks and highlights the urgency of implementing effective solutions to safeguard lives, infrastructure, and ecosystems.
An intelligent approach for detection and classification of security attacks in a Passive Optical Network using Light Gradient Boosting Machine
Over the past decade, Passive Optical Networks (PONs) have emerged as a leading solution for next-generation broadband access, providing high-speed and cost-effective communication. However, PONs face significant security challenges, including data interception, denial-of-service (DoS) attacks, and resource exhaustion caused by malicious Optical Network Units (ONUs). Machine learning (ML), particularly advanced models like Light Gradient Boosting Machine (LightGBM), has proven to be a promising solution for managing complex security issues in PONs. Leveraging its ability to handle imbalanced, high-dimensional datasets, LightGBM was employed in this study to detect and classify malicious ONUs based on bandwidth usage patterns. The model achieved an impressive accuracy of 95.27%, a Matthews Correlation Coefficient (MCC) of 90%, and a precision rate of 93%. While traditional classifiers, such as Naïve Bayes (NB), achieved an accuracy of 88.53%, LightGBM demonstrated superior robustness in addressing class imbalance and enhancing detection accuracy. This work highlights the potential of LightGBM in enhancing PON security and enabling intelligent, resilient broadband networks.
Experimental investigation of HHO blending in combustion engine performance
The transition to renewable energy sources has become increasingly critical due to the adverse effects of greenhouse gas emissions. One alternative to reducing fossil fuel dependence is hydrogen. Hydrogen technology can be integrated into internal combustion engines without major design modifications. This study investigates the effects of HHO gas blending on engine performance under varying brake load conditions. The carburetor was modified to allow HHO gas from electrolysis to enter the combustion chamber. The results indicate that HHO blending led to a 4.9% increase in brake power, a 1.66% improvement in thermal efficiency, and a 3% reduction in brake-specific energy consumption (BSEC). Additionally, among different potassium hydroxide (KOH) concentrations, the 30% wt solution exhibited the lowest power consumption for electrolysis.
Design and evaluation of a TEG-PV hybrid energy system for sustainable offshore oil and gas operations using PVsyst simulation
The rising demand for sustainable offshore oil and gas operations has accelerated the need for efficient, low-emission energy systems. Conventional fossil fuel-based systems significantly contribute to greenhouse gas emissions, underscoring the need for innovative alternatives. This study proposes and evaluates a hybrid energy system that integrates thermoelectric generators (TEGs) and photovoltaic (PV) panels to enhance energy efficiency, reduce emissions, and improve the sustainability of offshore operations. The system design utilizes both manual calculations and simulations, employing PVsyst software, to evaluate performance metrics. Results show that the TEG + PV hybrid system improves energy efficiency and reduces carbon emissions by approximately 40% compared to traditional fossil fuel systems. TEGs utilize waste heat from natural gas combustion, while PV panels capture solar energy, creating a synergistic effect that significantly reduces environmental impact. This hybrid configuration also aligns with emission reduction regulations in the oil and gas sector. Beyond environmental benefits, the hybrid system offers economic advantages. Fuel consumption and operational costs are notably reduced, with total savings in capital and operational expenditures (CAPEX and OPEX) reaching IDR 2.53 billion. These savings demonstrate the system’s financial viability and support its adoption in real-world offshore applications. In conclusion, the integration of TEG and PV technologies into a hybrid energy system offers a practical and sustainable solution for offshore oil and gas operations. The system achieves emission reduction targets, enhances operational efficiency, and delivers cost savings, thereby supporting the industry's transition toward more environmentally responsible energy practices.
Copyright (c) 2025 SINERGI https://creativecommons.org/licenses/by-sa/4.0
Comprehensive seismic evaluation of existing buildings using ASCE 41-17 standards
This study addresses the lack of comprehensive seismic evaluations for eight-story reinforced concrete buildings in high-seismic zones, such as Jakarta, using the ASCE 41-17 standard. The research evaluates the seismic performance of a 35-year-old office building through a tiered analysis approach, including Tier 1, Tier 2, and Tier 3. The study aims to identify structural deficiencies and propose retrofitting measures to meet modern seismic standards. Defects in soft story behavior and overturning stability were among the five and fourteen items in the Tier 1 assessment that showed noncompliance. In Tier 2, linear analysis revealed critical ductility demands, with Demand-to-Capacity Ratios (DCR) exceeding permissible limits in most structural elements. The nonlinear pushover analysis conducted in Tier 3 revealed an insufficient structural capacity to withstand high seismic loads. Maximum inter-story drifts in the X and Y directions were 2.321% and 2.319%, respectively, surpassing Life Safety standards. The findings indicate that the building's seismic performance falls between the Life Safety and Collapse Prevention levels, emphasizing the urgent need for retrofitting to enhance its resilience. This research presents a comprehensive framework for integrating global standards and local seismic conditions to enhance the safety and performance of existing structures in high-risk areas.
Development of a machine learning model for the classification of healthy and diabetic subjects using electromyography signal
Diabetes can lead to complications like Diabetic Peripheral Neuropathy (DPN), which impacts muscle and nerve function. Electromyography (EMG) is a standard diagnostic tool for detecting DPN, but its complex signals make analysis time-consuming, delaying detection and treatment. This study aims to develop and compare machine learning models for classifying healthy and diabetic individuals using EMG data collected during dorsiflexion movement. The Muscle Sensor V3 recorded EMG signals, which were then transformed into time-domain features—Root Mean Square (RMS), Mean Absolute Value (MAV), Standard Deviation (SD), and Variance (VAR)—for classification purposes. Machine learning models, including K-Nearest Neighbour (KNN), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were optimized using Particle Swarm Optimization (PSO). The analysis revealed that healthy individuals exhibited higher EMG amplitudes than those with diabetes. Among the models, ANN achieved the highest classification accuracy (94.44%) compared to SVM (88.89%) and KNN (77.78%). These results demonstrate the effectiveness of ANN as a reliable classifier for distinguishing between healthy and diabetic individuals, offering a more efficient and accurate approach to EMG data analysis for potential clinical applications.
Improving the implementation of Indonesian halal logistics: a statistical approach
In Indonesia, only a limited number of logistics providers have obtained halal certification, even though halal logistics plays a crucial role in preserving product integrity and preventing cross-contamination. This study analyzes the implementation of halal logistics by identifying key variables that influence the availability of halal products in the Indonesian market. Using a quantitative research approach, data were collected through surveys of 108 companies, including 35 logistics service providers and 73 halal-certified MSMEs, as well as semi-structured interviews with logistics service providers and MSME business actors in the food sector. The data were analyzed using multiple regression analysis to determine the most influential factors affecting halal product availability. The findings indicate that halal product information availability, consistent worker training, and corporate environmental responsibility are the primary variables influencing halal product availability. These insights provide a foundation for policymakers to develop regulations that strengthen halal logistics while enabling logistics providers and MSMEs to allocate resources more effectively to maintain halal product availability in the market. As one of the few studies examining halal logistics in Indonesia’s food sector, this research contributes to the broader discourse on halal supply chain management and policy development. This study emphasizes the need for stronger stakeholder collaboration to enhance halal logistics sustainability in Indonesia.
Experimental investigation on discarded aluminum can waste as an anodic anti-corrosion agent for steel in reinforced concrete under aggressive environments
Corrosion of concrete reinforcement can reduce the service life of buildings, especially in aggressive environments such as coastal areas, where salt content and high humidity accelerate corrosion. Sacrificial Anode Cathodic Protection (SACP) is a commonly utilized method of protecting structures from corrosion by using metals such as zinc (Zn), aluminum (Al), and magnesium (Mg) as sacrificial anodes. However, the high cost of these metals has prompted research into more economical and environmentally friendly alternatives. In this study, recycled aluminum from the beverage can waste was melted at 600˚C and then formed into a circle with a diameter of 11 cm to investigate the utilization and effectiveness of recycled aluminum compared to zinc as a sacrificial anode in the SACP process. The measurement of corrosion potential involved the assessment of current density, on-potential, off-potential, rest-potential, and depolarization. The recycled aluminum anode exhibited a depolarization value of 680 mV, demonstrating that waste cans could serve as an effective sacrificial anode to protect concrete structures.
Performance of Diesel Particulate Filter (DPF) in reducing exhaust gas opacity in diesel engines
This research aims to determine the performance of the Diesel Particulate Filter (DPF) in reducing exhaust gas opacity, smoke temperature and noise levels on the 1997 Isuzu Panther. The DPF used in this research is made of copper and stainless steel with a metallic honeycomb model. This experimental study involved installing a DPF on a standard diesel vehicle exhaust system and measuring exhaust emissions, smoke temperature and noise levels. The measuring instrument used to test the opacity and temperature of the exhaust gas is a smoke opacity meter based on the SAE-J1667 test procedure (snap acceleration test procedure). Meanwhile, to test the noise level, a sound level meter based on ISO/FDIS 5130:2006 (E) standard was used. Tests were carried out under various conditions including standard exhaust testing (Non-DPF), standard exhaust with DPF (DPF), DPF with 50 gram stainless steel material (DPF + SS50), DPF with 100 gram stainless steel material (DPF + SS100), and DPF made of 150 gram stainless steel (DPF + SS150). The test results showed that DPF had a significant effect on reducing exhaust opacity, smoke temperature and noise level of the 1997 Isuzu Panther. The greatest reduction in exhaust opacity, smoke temperature and noise level were produced by DPF + 150 gram stainless steel with exhaust opacity that could be reduced by 87, 1 ± 1.1%, smoke temperature of 43.2 ± 1.7%, and noise level of 15.2 ± 1.3%. These findings open up opportunities for the development of DPF technology that is more efficient in reducing diesel vehicle emissions, contribute to stricter emissions regulations, and support filter design innovation to improve air quality and reduce the negative impact of vehicles on the environment. In addition, the results of this research show that the use of DPF is in accordance with the diesel vehicle emission threshold regulations set in Indonesia.
Positioning of quality systems in lean manufacturing: integrated approach vs independent implementation in the food industry
Despite the widespread adoption of Lean Manufacturing (LM), its effectiveness in the food industry remains underexplored, particularly regarding the integration of the Quality System (QS). The purpose of this research is to compare QS placement and LM implementation strategies in the food industry. This study utilized a comparative approach, analyzing empirical data from four food processing companies in Indonesia over six months, employing qualitative methods (expert interviews, document analysis) and quantitative analysis. Response Surface Methodology (RSM) with the Box-Behnken design was applied for optimization, while Principal Component Analysis (PCA) identified key variables influencing Lean Manufacturing success. Two implementation strategies were compared: phased implementation with a separate Quality System (Companies A and B) and simultaneous implementation with an integrated Quality System (Companies C and D). The findings revealed that Company A achieved the highest performance, with 88% in 5S and 85% in Just-In-Time (JIT), followed by Company B with 80% in JIT and 75% in 5S. In contrast, companies C and D exhibited lower performance. PCA results indicated that PC1 (80.40%) was associated with on-time delivery and sales growth, whereas PC2 (14.47%) was linked to rejection factors. Companies A and B excelled in PC1, while Companies C and D were more dominant in PC2. These findings suggest that phased implementation of LM tools is more effective than simultaneous application. This research not only addresses a critical gap in the literature but also provides practical insights for food industry practitioners seeking to enhance operational efficiency through Lean Manufacturing.
Spatial components in social interaction spaces: a review of Jakarta urban kampongs dwellers
Architecture exists because of the response to the needs of human social relationships. Space is one part of architecture that is very important for use in everyday human life, one of which is social interaction space. Urban kampongs are settlements in Jakarta that contain social interaction space for dwellers. This space is an important space and is often discussed in the scope of architecture, but there has been no special review of the aspects that form the social interaction space used by dwellers of urban kampongs in Jakarta. Therefore, it is important to conduct a special study that discusses the aspects that form the social interaction space in urban kampongs. This study is a literature study that uses a qualitative method with a narrative descriptive analysis approach. The basic literature used is an understanding of spaces from the perspective of sociology, anthropology, and geography. The results of the study show that the social interaction space of dwellers of urban kampongs in Jakarta is greatly influenced by non-physical aspects (socio-cultural) and physical aspects (location). This study is very useful for enriching the theory regarding the social interaction space of urban kampongs in particular and the theory of spatial design in general.
Experimental study and optimisation of flexural properties of 3D-printed polylactic acid for energy-storing-and-returning prosthetic foot
A prosthetic foot with energy-storing-and-returning capabilities requires high strength to prevent damage, high rigidity for stability, and low weight for user comfort. Therefore, efforts are needed to optimise the properties of the 3D-printed prosthetic foot. Based on the literature review, a research gap remains in understanding the complex interactions among 3D printing parameters that improve flexural properties, minimise mass, and reduce printing time. This study investigated how infill density, layer thickness, shell thickness, and their interaction affect the flexural strength-to-mass ratio, flexural modulus of elasticity, strain, and required printing time of the 3D-printed product. The experimental parameter ranges are infill density (40–60%), layer thickness (0.2–0.3 mm), and shell thickness (0.8–1.6 mm). A case study was conducted to optimise these parameters using the Response Surface Methodology with the Box-Behnken Design. The experimental data were fitted to a quadratic model, and Analysis of Variance determined the significance of individual factors. A gradient-based algorithm then identified the optimal parameter combinations. Results indicated that shell thickness was the most influential factor on the flexural strength-to-mass ratio and flexural modulus. Additionally, the interaction between layer height and shell thickness significantly affected strain, while infill density impacted printing time. The optimal values obtained were 32.5722 MPa/gram for the flexural strength-to-mass ratio, 2727.06 MPa for the modulus, 0.0522 for the strain, and 757.7788 seconds for the printing time. The novelty of this research lies in presenting how the interaction between shell thickness, layer thickness, and infill density affects process productivity and material efficiency while preserving product performance.
The influence of Silane Coupling Agent and porogen ratio on 3D scaffold PCL/HA
The design of scaffold based on polycaprolactone (PCL) and hydroxyapatite (HA) has attracted attention as a solution for bone tissue regeneration. However, the main challenge in its development is the difficulty in achieving an optimal balance between porosity and mechanical strength. Suboptimal porosity limits the scaffold ability to support cell proliferation, and weak mechanical properties result in the scaffold being less than optimal as a load-bearing implant material during the bone cell regeneration process. This study aims to address these issues by evaluating the effects of the silane coupling agent 3-aminopropyltriethoxysilane (APTES) and the porogen ratio (NaCl) on the mechanical properties, morphology, and biodegradation of PCL-HA-based 3D scaffold. The scaffold were synthesized using the solvent casting/particulate leaching (SCPL) method with varying APTES concentrations (1%, 3%, and 5%) and porogen ratios. (4:1, 6:1, and 8:1). The results of characterization show that the addition of 1% APTES increases compressive strength by 283% and tensile strength by 138% compared to scaffold without APTES. A higher porogen ratio (8:1) results in the highest porosity of 78.16%, but reduces mechanical strength by 84%. The optimal combination was found in scaffold with 1% APTES and a porogen ratio of 4:1, which have optimal mechanical strength, porosity of 69.65%, and a biodegradation time of up to 380 days. This research offers a comprehensive solution to enhance the properties of PCL-HA based 3D scaffold, making a significant contribution to the development of materials for bone tissue engineering applications.
Optimization of material requirements to support the accuracy of delivery materials based on quantity of cubicost analysis results
Construction projects heavily rely on labor, materials, and equipment to ensure timely completion, with materials being a significant cost component. This study addresses discrepancies between rebar inventory and material demand, which can disrupt production and delay project completion. This study aims to determine the rebar volume using Cubicost TRB and optimize the lowest requirement cost by applying the Silver-Meal and Wagner-Whitin algorithms. Detailed engineering data supports the application, ensuring rebar bends meet technical standards at beam, column, and slab intersections. Material planning incorporates project schedules, inventory records, and rebar volume data from Cubicost. This planning considered the minimization of ordering and storage costs. The results showed that the volume of rebar calculated using the Cubicost application was more accurate than the project's bill of quantity data. The use of the Cubicost application proved to be more advantageous, resulting in a 1% reduction compared to the project data. Then, the Silver Meal algorithm resulted in total costs that were 21% lower than those of the Wagner-Whitin method in minimizing overall inventory costs, particularly for high-demand rebar types such as D13 and D19. These findings highlight the importance of accurate inventory scheduling and selecting appropriate lot-sizing techniques to minimize costs and prevent delays. Future research can compare Cubicost TRB’s accuracy with other software and extend the Silver-Meal and Wagner-Whitin algorithms to other materials.
Car seatbelt monitoring system using real-time object detection algorithm under low-light and bright-light conditions
Seatbelt usage is essential for minimizing injury risk during vehicular accidents. The monitoring seatbelt system in modern vehicles can be easily tricked into not displaying the warning alert. Car seatbelt detection, utilising real-time object detection, is employed to monitor seatbelt usage. However, the accuracy of such systems needs to be further evaluated under low-light and bright-light conditions. This study aims to develop a car seatbelt monitoring system using a real-time object detection algorithm, which will be tested in low-light and bright-light scenarios. The system integrates a trained YOLOv5 model into embedded hardware, which interfaces directly with the vehicle’s ignition system, enabling or disabling engine start based on seatbelt usage. Notifications are also delivered through LEDs, a buzzer, and Telegram messages. This system has an accuracy of 95.75%, precision of 99.1%, recall of 96.2%, and an F1-score of 97.2%. The results show that the system can generate a better confidence score under bright-light conditions than under low-light conditions. This work offers tangible proof of the efficacy of applying intelligent object detection models for real-time driver monitoring, particularly in enhancing compliance through physical intervention and IoT-based alerts.
Optimizing intrusion detection with data balancing and feature selection techniques
The rapid growth of IoT devices has brought significant security challenges, particularly in detecting various types of attacks within heterogeneous network environments. This study explores the effectiveness of data balancing techniques, including Random Undersampling (RUS), Cost-Sensitive Learning (CSL), Synthetic Minority Oversampling Technique (SMOTE), and Randomized Combination Sampling (RCS). Feature selection methods, namely correlation (threshold 0.8) and mutual information (top 15 features), were employed to optimize feature sets. The Decision Tree (DT) and Linear Discriminant Analysis (LDA) classifiers were used to evaluate the performance of balanced datasets. The evaluation metrics included accuracy, precision, recall, F1-score, G-mean, and ROC curves. The results revealed that SMOTE and RCS outperformed other balancing methods, with SMOTE achieving the highest accuracy (98.7%) and RCS demonstrating robust G-mean values across both feature selection techniques. DT consistently showed better performance compared to LDA across all metrics, while feature selection significantly improved the classification results, particularly under mutual information criteria. However, the analysis highlighted limitations of LDA in handling imbalanced datasets and high-dimensional features. This study concludes that a combination of advanced data balancing and effective feature selection significantly enhances the accuracy of intrusion detection in IoT networks. Future work will focus on integrating real-time detection systems and exploring hybrid models to further improve the detection of complex attacks in dynamic IoT environments.
A systematic review of methods for reducing embodied energy in building materials: a quantitative cradle-to-gate analysis
Reducing embodied energy (EE) in building materials is a critical aspect of achieving sustainable building construction. Embodied energy refers to the total energy consumed in the extraction, processing, transportation, and manufacturing of building materials before they reach the construction site. In conventional buildings, total energy consumption is predominantly influenced by operational energy (OE), which includes energy used for heating, cooling, lighting, and ventilation throughout the building’s lifespan. However, in energy-efficient buildings, the proportion of EE to total energy demand becomes more significant, sometimes equaling or surpassing OE. This shift highlights the growing importance of minimizing EE in sustainable building design. This study conducts a systematic review using the PRISMA framework, extracting relevant data from the Scopus database to categorize methods for reducing EE within cradle-to-gate systems. These methods are classified into three phases: the material phase, the construction method phase, and the design phase. The material phase includes three approaches: mixed material intervention, production process intervention, and material substitution. The construction method phase encompasses two approaches: building component substitution and process or method substitution. Finally, the design phase focuses on interventions at the building design level. Despite these classifications, the findings suggest that no single phase or approach demonstrates a significantly greater impact on EE reduction than the others. Each approach contributes comparably to reducing EE, highlighting that while notable progress has been made, the relative effectiveness of individual methods remains consistent across phases. Integrated approaches combining strategies across multiple phases hold promise for substantial reductions in EE, emphasizing the need for future research to refine and innovate these methodologies.
Adaptive high-level control for robot-assisted rehabilitation: A Discrete Event System approach
This research introduces a two-degree-of-freedom rehabilitation robotic platform to enhance Constraint-Induced Movement Therapy (CIMT) for post-stroke upper limb rehabilitation. Unlike conventional CIMT, that depends on therapist intervention, the proposed system integrates a control framework balancing assistance and autonomy to improve patient engagement and recovery efficiency. The main contribution is a hybrid control architecture combining a low-level impedance controller with a high-level discrete event system (DES) controller. This dual-layer control enables real-time adaptation to a patient’s motor impairment stage, offering dynamic and personalized rehabilitation. The high-level controller, structured around the Chedoke-McMaster Assessment (CMA), facilitates intelligent transitions between rehabilitation states, ensuring robotic assistance matches recovery progress. The design emphasizes simplicity, portability, and user-friendliness, employing a lightweight, cable-driven mechanism that produces smooth and natural movements, closely replicating manual therapy. Experiments with healthy subjects simulating impaired conditions demonstrated the system’s ability to adjust assistance levels and movement velocities according to motor function stages. The results confirm the feasibility of an adaptive, patient-centric control framework that enhances motor engagement and supports progressive rehabilitation. Future work will focus on clinical validation with stroke patients, expanding movement directions, and long-term evaluation of therapeutic outcomes in real-world settings. Overall, this study offers a scalable, data-driven approach bridging robotic automation and therapist-guided rehabilitation, opening new possibilities for improving neuroplasticity and motor recovery after stroke.
TE-LSTM: winding temperature prediction for induction motors in the oil and gas industry
Induction motor winding failure repair takes longer compared to other failures, such as bearing failure. This research introduces a hybrid deep learning framework, TE-LSTM, to predict winding temperatures in induction motors used in oil and gas operations, aiming to address the challenges of accurately forecasting potential winding failures and enabling proactive maintenance strategies. The TE-LSTM model combines a transformer encoder-based architecture with long short-term memory to effectively model intricate temporal relationships and sensor dynamics within the dataset. The study utilized data collected from January 2016 to December 2024 at 1-minute intervals from induction motors equipped with stator winding temperature sensors, where the motors were designed with Class F insulation and had stage 1 and stage 2 alarms set at 257°F and 285°F, respectively. The findings highlight the efficiency and performance of the TE-LSTM model in predicting winding temperatures, which can significantly reduce unplanned downtime and associated costs, thereby optimizing maintenance operations and enhancing the reliability of the motor.
An FFT-based vibration characterization on road profile of two-wheeler electric vehicle
Vibration is an inevitable physical phenomenon; excessive and uncontrolled amounts of vibration can result in damage and system failure. In accordance with various automotive product certification standards, vehicle batteries or rechargeable electrical energy stotrage system (REESS) must undergo a vibration test to assess their mechanical integrity. This study aims to broaden the perspective on vibration assessment by examining it during vehicle operation and assessing the protective capabilities of vehicle suspension against vibrations from damaged roads in two-wheeled electric motor vehicles. The proposed method involves installing an accelerometer on the battery pack body placed in the battery compartment. The experimental setup involved conducting tests on a 125-meter track, with the vehicle traversing roads characterized by concrete cracks, uneven surfaces, and potholes. Two distinct speed variations were selected for analysis: 10 and 15 kilometers per hour. The results obtained from the Rion VA 12 portable vibration analyzer are presented as a plot of the fast Fourier transform (FFT) graph. The maximum acceleration recorded was 2.35 and 1.98 G at the same frequency of 7 hertz (Hz). This research method and result aligns with others, including those focused on assessing road damage, passenger comfort, and vehicle component damage, such as shock absorbers. In the future, the development of a vehicle battery support structure is anticipated to further minimize vibration disturbance by reducing the peak acceleration values depicted in the FFT graph. The minimization of incoming vibrations is expected to enhance the safety and durability of the battery pack.
Evaluation of recycled steel properties originating from construction steel waste
This research presents an experiment of constructional recycled steel properties that has been remelted into component-shaped specimens using green sand casting. A series of tensile, compressive and toughness tests were conducted. Then, specimens were observed using Scanning Electron Microscopy (SEM) and Energy-Dispersive X-ray (EDX) for microstructure analysis and chemical composition determination. This experiment was done to determine whether the recycled steel quality met the industrial standards. All tests were conducted at Universiti Teknologi MARA (UiTM), providing data on the stress-strain relationship and toughness of recycled steel. The results indicated that recycled steel exhibited tensile characteristics below the standard strength range required by Eurocode. The changes in Young’s modulus of the steel were attributed to exposure to high temperatures, causing significant vibration within the steel atoms and increasing the distance between atoms, thereby reducing tensile strength. The influence of impurities may also be a factor affecting tensile strength. The tests also concluded that the recycled steel construction waste was a brittle material with an average V-notch toughness of 24J.
Experimental investigation of the Moment Bolted Coupler (MBT) with steel on the bond strength under different monotonic pull-out tests
The bond strength between steel reinforcement bars and mechanical bolted couplers (MBT) is essential for the structural integrity of reinforced concrete structures. However, various pull-out test methods yield inconsistent results when assessing this bond strength. This research examines the impact of reinforcement bar diameter (12mm, 16mm, and 20mm) and three different pull-out test configurations (M1, M2, and M3) on the bond strength of MBT couplers. The M1 method, employing direct tensile loading and a standard clamp zone, consistently produced the highest bond strength values across all bar diameters. Its simplicity, reliability, and adherence to standardized procedures make it the preferred method for determining the maximum bond capacity of MBTs. While the M2 and M3 methods offer insights into coupler behavior under complex loading scenarios, they exhibit lower bond strength values compared to M1. The M1 pull-out test method is recommended as the primary method for evaluating the bond strength of MBT in practical applications, with M2 testing as a potential supplement for a more comprehensive understanding of coupler behavior.