As a country with a high percentage of motorbikes, the appearance of potholes has a serious impact on people. Today, detecting and repairing potholes takes a long time. Therefore, this research paper aims to provide solutions for inspectors to save time in detecting potholes. In this study, the authors used drones to col-lect data combined with the common computer vision model YOLOv5 to pro-duce pothole detection models for images taken from drones. From the identifi-cation results, we calculate the size and location of the potholes so that the inspectors can identify the danger range and come up with a reasonable repair plan. The current model reached the precision, recall, and F1 score at 0.946, 0.961, and 0.95, respectively.

Micro-perforated panels (MPPs) with natural fiber backing hold great potential for the development of acoustic panels due to their various advantages over conventional porous materials. Some aesthetic restrictions and the bulky nature posed by conventional porous materials have made natural fiber-reinforced MPP a more adaptable and compelling solution for acoustic efficacy. Therefore, in this paper, MPPs backed by coconut coir, kenaf fiber, and palm leaf fiber are the focused for assessing the sound transmission loss (STL) performance. The selection of these natural fibers is largely due to their favorable inherent natural characteristics and acoustic properties. To assess STL, the current study employed the impedance tube approach to analyze the performance of materials in attenuating sound pressure across varying frequencies. Results reveal that the MPP reinforced with coconut coir achieved the maximum STL at 42 dB in the mid-frequency range of 190 to 230 Hz, demonstrating that it may be a highly suitable material for applications that require soundproofing. Undoubtedly, natural fiber-backed panels offer a viable alternative and an effective solutions to mitigate different forms of sound transmission, including airborne and structure-borne noise, thereby enhancing overall acoustic insulation.

The aim of this work is to experiment and compare the operation of two solar cookers, namely a parabolic solar cooker and a box solar cooker in order to encourage the population to use solar thermal equipment. For this purpose a parabolic cooker fitted with a mirror as a reflector and a box cooker were created. These two sensors are tested under the same conditions following the international standard procedure for testing solar cookers and reporting performance. Under a solar flux varying between 250 W/m2 and 1009.78 W/m2, the maximum water temperature is 108.87°C and 98.02°C at the parabolic cooker and the box cooker, respectively. The parabolic cooker has a standardized cooking power than the box cooker. The specific boiling time is 0.41 h.m2.Kg-1 and 0.54 h.m2.Kg-1 for the parabolic cooker and the box cooker respectively. The output of the parabolic cooker is higher than the canned one in the morning. The two efficiencies decrease as a function of time and in the evening the efficiency of the parabolic collector becomes lower compared to that of the box cooker. In view of these results, solar cookers can be used in our climate zone to meet certain cooking energy needs.

As smart vehicles are becoming more common, protecting them from cyberattacks has become very important. To address this issue, this paper introduces an automatic intrusion response system (IRS) designed especially for intelligent vehicles. The system is able to quickly analyze the effect of a cyberattack and choose the best response method in real time, making vehicle operations more secure. The proposed IRS offers several key features. First, it provides a clear analysis of different response methods that can be used during cyber intrusions. Second, it introduces a framework that evaluates both the cost and the impact of each response. Third, it applies decision-making tools such as Simple Additive Weighting (SAW), Linear Programming (LP), game theory, and artificial intelligence–based approaches to select the most effective response strategy. Extensive testing shows that the system performs strongly in terms of response quality, time efficiency, and resource usage. This study proposes a hybrid SAW-LP method that combines rapid multi-criteria ranking with constraint-based optimization. Experimental evaluation shows that the hybrid approach reduced response selection time by 32% and improved decision accuracy by 12% compared to standalone algorithms.

This study investigates the basement excavation of the Opal Skyview building in Thu Duc, Ho Chi Minh City, using 3D Plaxis numerical simulations for reverse analysis. The research aims to evaluate excavation behavior, particularly diaphragm wall displacement, by comparing numerical predictions with field monitoring data. The Hardening Soil (HS) and Mohr-Coulomb (MC) models were employed to simulate wall deflections at six monitoring locations (SID1–SID6) across various excavation stages. The results indicate that numerical simulations overestimated displacement at specific points, such as SID2 and SID6, by 1.3 and 1.1 times, respectively. The HS model demonstrated superior accuracy, closely aligning with observed data, whereas the MC model exhibited greater deviations. The findings highlight the critical role of accurate geotechnical investigations, appropriate design methodologies, and real-time monitoring in ensuring excavation safety and structural stability. Furthermore, the study underscores the necessity of integrating numerical simulations with field observations to enhance excavation efficiency and mitigate construction risks. By providing insights into excavation-induced deformations, this research contributes to more reliable and cost-effective deep excavation practices. The results serve as a valuable reference for engineers working on deep excavations in urban environments with complex geotechnical conditions.

Traffic congestion has become a critical issue in urban areas worldwide, leading to increased accidents and delays for emergency vehicles. This paper proposes a solution utilizing IoT-enabled technology to alleviate these challenges by implementing "Green Corridors" for emergency vehicles. By leveraging Intelligent Traffic Management Systems (ITMS), which integrate sensors, cameras, and data analysis, this research aims to evaluate the effectiveness of such systems in reducing congestion and improving road safety. A comprehensive evaluation framework will be employed to analyze the impact of ITMS implementation on traffic flow patterns and safety outcomes. The system utilizes UWB technology to identify emergency vehicles, triggering traffic lights to switch to green and notifying nearby vehicles to clear lanes, facilitating unimpeded passage for emergency services. This integrated approach addresses the dual challenges of traffic congestion and emergency response, offering valuable insights for policymakers and urban planners seeking effective solutions for smart city transportation management.

As a tropical country, the government of Indonesia issued energy conservation standards for air conditioning systems in buildings through the Indonesian National Standard (SNI) 6390:2020. However, this SNI has not provided specific calculations to identify the comfort level of parameters in a building. This study aims to provide clear limits by providing conditions for fulfilling comfort conditions for indoor temperature parameters and buildings using a new proposed equation, namely the Room Comfort Equation (RCE) and Building Comfort Equation (BCE). These methods are based on the average ratio of room data parameters to SNI limited by a widening factor of 1% to 5% as comfort temperature tolerance threshold values. RCE is an Indoor temperature measurement compared to the SNI, resulting in a temperature parameter ratio . At the same time, BCE is the sum of the RCE across the building. The room temperature was measured for 4 weeks manually using a digital Environmental meter in four rooms , , and at the Integrated Laboratory and Technology Innovation Center (LTSIT) Building, University of Lampung. The calculation results obtained a temperature parameter ratio (RCE) of 0.958, 1.012, 1.002, and 0.990, so a BCE of 3.96 was obtained. This result shows that the building temperature is ideal by a 5% widening factor, but there are two rooms ( and ) has a passing tolerance limit of 1% widening factor. This finding successfully identified that the cause was the behavioural factor of busy users during practical hours. These results also show that room temperature comfort is more straightforward to measure and report. Therefore, additional efforts are needed to obtain the ideal/optimal temperature, such as upgrading HVAC systems with distributed adaptive control, which can be an effective temperature control solution while compensating for disturbances due to occupant behaviour and the influence of differing ambient weather conditions.

Photocatalytic is an advanced technology for water remediation which potentially to be applied for river pollution treatment. The key to the highest photocatalytic efficiency is the type of photocatalyst used and the process variables including light sources, light intensity, and time operations. However, the study on the interaction of process variables on photocatalytic efficiency for polluted river water treatment has not been well studied. Thus, the present study focused on the performance of pilot-scale photocatalytic reactor incorporated with ZnO-Kaolin under various light intensities such as 100, 125, and 225 Watts with varied time operation at 20, 40, 60 and 80 minutes for reduction of ammoniacal nitrogen (AN) and chemical oxygen demand (COD). Followed by the elucidation of kinetic study through pseudo-first and pseudo-second-order models. It was found that 225 Watts of UV-light contributed to the highest reduction of AN (59.34%) and COD (90.91%) for 60 minutes of treatment. Furthermore, the kinetic study revealed that the photocatalytic process follows pseudo-first-order model with a rate constant of 0.0184 min-1. Hence, the highest photocatalytic efficiency of ZnO-Kaolin is mainly attributed to the intensive light intensity, leading to the higher generation of reactive species for rapid photocatalytic degradation, consequently improving the performance of pilot-scale photocatalytic reactor.

Step-by-step guidance for new practitioners is essential for understanding the general engineering principles adopted for slope stability analysis within the Malaysian context. In view of this, this paper presents the basic geotechnical assessment conducted for a new residential development on hilly terrain in Kulim. Three objectives were outlined, including desk study based on the secondary data of related maps and reports, designing soil parameters, and assessing the slope stability analysis through numerical modelling for ensuring compliance with the long-term slope stability safety guidelines provided by the Malaysian Public Works Department (JKR). Practical sustainable stabilisation methods were identified, and a feasible foundation design for the development was proposed for the development, considering both cost-effectiveness and minimal environmental impact.

Changes in the behavior of small fish have recently been commonly used in assessing the impact of water pollutants, especially those of the group of endocrine disruptors. Behavioral studies mostly use visual observations, which can introduce bias and inconsistency in observational results. Recent studies have developed computer vision tools for tracking fish movements that allow automatic detection of small fish movements with high accuracy and consistency. In addition, computer vision combined with machine learning can help analyze, identify, and predict changes in fish behavior, easily integrated into environmental and ecological monitoring systems. This study uses YOLO (You Only Look One) algorithm models to detect fish in video data. Comparing the effectiveness of YOLO versions with the training data set shows that the YOLOv8s model has the highest efficiency and is selected for detecting and analyzing fish behavior in the environmental impact assessment model. The amount of image data for training the YOLOv8s model is also determined to be approximately 800 images. The training results show that YOLOv8s has high detection efficiency with a high frequency of detecting 11 fish in video frames. Results from detecting and analyzing fish positions in video data using the trained YOLOv8s model showed that the males of both mosquitofish (Gambusia affinis) and medaka (Oryzias latipes) species were affected following a two-day acute exposure to the estrogenic stressor, 17a-ethinylestradiol, in the aquatic environment at a concentration of 5 ng/L. While male mosquitofish when not exposed to estrogen tended to pay more attention towards the tank compartment containing female medaka, when exposed to estrogen, they increased their tendency towards the compartment containing male medaka. Additional research is needed to increase the accuracy and effectiveness of the YOLO algorithms in fish detection for behavior evaluation in an environmental impact assessment model.