Incremental sheet metal forming (ISMF) is a promising manufacturing technique that has gained significant attention due to its ability to produce complex geometries and high-quality products, particularly for small-scale production and rapid prototyping. The integration of industrial robots into the ISMF process, referred to as roboforming, has enabled advancements in this field. However, the inherent limitations of industrial robots—particularly the reduced rigidity of robotic arms with rotary joints—can lead to dimensional inaccuracies and deviations in the final product. These limitations are primarily due to the lack of precise force control during the forming process. To address these challenges, this study introduces a novel approach to roboforming that incorporates force control alongside the position control of the industrial robot. The contact force between the tool and the workpiece is considered as an additional variable in the control loop, with the objective of improving dimensional accuracy and the overall quality of the formed product. A regression analysis was conducted to determine the mean process force required for conical geometries, with the starting radius, infeed depth, wall angle, and supporting angle serving as input variables. Experimental validation revealed that force-controlled incremental forming with a constant contact force is unfeasible, as the pressure force is highly dependent on the current radius of the workpiece and varies during the forming process. Therefore, a new control strategy is proposed, which involves the dynamic adjustment of the contact force, using the variable pressure force as an input parameter. This approach is expected to significantly enhance the precision and reliability of robot-assisted ISMF, offering a pathway for overcoming current limitations in industrial applications.

Diesel fuel is composed of various molecules of hydrocarbons, so the properties vary from country to country. The sulfur content in Iraqi Diesel ranges from 1% to 2.5%. This abundance of sulfur contributes to a high level of emissions, including sulfur oxides, nitrogen oxides, volatile organic compounds, particulates, and carbon monoxide. To reduce emissions and improve engine performance, a diesel water mixture has been proposed as a fuel for diesel engines. This study examined the performance of a diesel engine under different operating conditions when Diesel was mixed with 10%, 20%, and 30% volume proportions of water, named W10, W20, and W30, respectively. The use of W10 and W20 caused a brake-specific fuel consumption reduction of 2.32% and 4.89%, respectively, compared to conventional Diesel, while using W30 caused an increase in brake-specific fuel consumption of about 5.75%. The brake thermal efficiency improved by 3.6% and 4.63% when using W10 and W20, respectively. While its value decreased when working with W30 by about 2.48% compared to Diesel. Working with W10 and W20 reduced engine emissions of carbon monoxide by an average of 9% and 27%, hydrocarbons by 7.8% and 20%, nitrogen oxides by 8.9% and 20.8%, and particulate matter by 4.92% and 13.1%. Operating with W10 and W20 reduced both particulate matter and nitrogen oxide emissions. The results reveal that water mixing with Iraqi Diesel is an effective means of reducing diesel engine emissions.

The misleading reviews posted on shopping websites and other media platforms sway the opinions and decisions of different customers. On the other hand, dishonest reviewers will make an effort to mimic the writing style of legitimate reviews. There is no guarantee that these text-feature-based approaches will work anytime soon. In addition, the likelihood of an imbalanced category distribution in practice limits detection performance. This paper proposes a fraudulent review detection system that uses ensemble feature selection and multidimensional feature creation to overcome these limitations. Our idea builds three-dimensional characteristics, which include text, reviewer behaviour, and misleading scores. Furthermore, a data resampling approach combines Random Sampling and oversampling techniques to mitigate the effects of an imbalanced distribution of categories. In addition, we combine the outcomes of several feature selection methods that focus on information gain, XGBoost feature importance, and the Chi-square test. On various text datasets, the proposed technique demonstrates exemplary performance in fraudulent review identification according to the experimental findings using feature selection methods, resampling methods, classification, etc. Our technique outperforms existing sophisticated methods when faced with low-quality text or an imbalanced dataset.

Images play a pivotal role in documenting real-life events. With the rapid evolution of digital technology, there has been a significant increase in both the creation and dissemination of photographs. The accessibility of picture editing software has simplified the process of altering images, thereby reducing the time, costs, and expertise needed to create and manage visually manipulated content. Unfortunately, digitally altered photographs have become a primary medium for disseminating misinformation, which affects individuals and society at large. Consequently, the need for effective methods to detect and identify forgeries is more pressing than ever. One prevalent form of picture fraud, image splicing, has been thoroughly examined. In this study, we present a Depth-Wise Convolutional Neural Network (DWCNN) model specifically designed to accurately detect spliced forged images. By converting input RGB images to the HSV color space, known for its ability to withstand color and lighting variations, our model achieves high accuracy in identifying manipulated images. Furthermore, our proposed model is lightweight, based on the MobileNet architecture with seven bottleneck blocks, making it suitable for a wide range of scenarios with constrained resources. To evaluate the model's performance, we tested it on the CASIA v1.0 and CASIA v2.0 datasets. Our model accurately identified forgeries with 99.23% accuracy on the CASIA v1.0 dataset and achieved a remarkable accuracy of 99.37% on the CASIA v2.0 dataset.

Epilepsy seizures are complex neurological phenomena marked by recurrent and unpredictable seizures that can greatly affect an individual’s quality of life. It affects millions of people worldwide. The exact and timely detection of epileptic seizures is crucial in the management and treatment of epilepsy. Many methods have been put forth recently for the diagnosis of epileptic seizures using magnetic resonance imaging (MRI) and electroencephalography (EEG). This work focuses on using deep learning and machine learning techniques, such as Support Vector Machines (SVMs), Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs), to automatically identify epileptic seizures. These techniques have shown promising results in a variety of fields, including time series data processing and medical image analysis. In this work, we present a unique method for detecting epileptic seizures using electroencephalogram (EEG) data by comparing the outcomes of three deep learning architectures: SVM, CNN, and RNN-LSTM (Long-short term memory). The experimental results demonstrate that the SVM, CNN and RNN-LSTM models exhibit promising performance in detecting epileptic seizures from EEG data.

This study aims to advance the detection of production defects at three levels - part, material, and machine - through the application of a few quality tools with a view to proposing solutions. The research focuses on identifying and addressing the most prevalent and critical anomalies observed in the workshop. Despite existing quality control measures, production often falls short of meeting customer specifications due to machine malfunctions, inadequate operator vigilance, and inefficiencies in the maintenance processes. These challenges underscore the need for a systematic approach to visualise and address production system issues. By applying targeted quality tools, this paper seeks to optimise maintenance strategies and improve overall production performance, contributing to enhanced operational efficiency and product quality.

Traffic congestion is a significant challenge in Lagos State, Nigeria. Existing methods rely on limited data sources and simplistic models that fail to capture the complexities of traffic dynamics in a congested urban environment. This study focused on traffic intensity detection in Lagos State using a Bayesian estimation model. Data was obtained from the Kaggle website which involved observing the number of vehicles intersecting junctions at various times of the day for a week. The model captured both spatial and temporal variations, providing real-time estimations of traffic congestion levels across different road segments. Comparative analysis with existing traffic estimation methods showed superior performance in terms of accuracy and reliability. The Gaussian Naive Bayes model achieved a high accuracy of 96% and balanced f1-score of 96%, precision of 0.96, and recall of approximately 0.96. On the other hand, the multinomial Naive Bayes model achieved an accuracy of 69% with a lower f1-score of 69%, precision of 0.67, and recall of 0.69. The model's capacity to provide accurate real-time site traffic facts can significantly contribute to effective traffic control and concrete making plans initiatives.

In order to minimize trial-and-error costs and avoid any faults throughout the production process, sheet metal forming is commonly used in the automotive industry to fabricate body pieces. This study examines how several punch models affect thinning and wrinkling during the deep drawing of aluminum 1050, a 2.50 mm thick material, under 150 KN of pressure. The Forming Limit Diagram (FLD) was used in the simulations to study material deformation and determine safe and essential places on the blank. According to the findings, die pressure-induced material stretching caused the material to significantly rise in major-minor strain, major-minor stress, thinning, and wrinkling by the fifth step. Additionally, for punch 1 material, the safe area grew from 9.20% to 49.36%; punch 2 increased from 9.08% to 46.85%. The non-linear FLD graph analysis verified that both materials stayed in the safe zone the entire time. These results demonstrate how well deep drawing simulations work to improve punch design and aluminum component performance in the automobile industry.

The advancement of technology within the manufacturing sector continually accelerates, frequently resulting in heightened consumer demand driven by enhancements in product quality, including consistency and quality control. This study endeavors to enhance the production process by leveraging reverse engineering alongside Indonesian anthropometry methods. The findings demonstrate that the resultant products can effectively meet customer requirements, exhibiting durability and resilience against wear and tear. By integrating reverse engineering techniques and Indonesian anthropometry methods into the production process, manufacturers can achieve greater precision and tailor products to better suit consumer needs. This approach not only enhances product quality but also contributes to increased customer satisfaction and loyalty. Furthermore, it underscores the importance of incorporating innovative methodologies to keep pace with the evolving demands of the market and to ensure continued success within the manufacturing industry. As technology continues to advance, embracing methodologies like reverse engineering and anthropometry will be crucial for manufacturers seeking to remain competitive and deliver products that surpass consumer expectations in terms of durability, performance, and overall quality.

Sarcasm detection is challenging in sentiment analysis, especially for morphologically complex languages like Telugu. Sarcastic statements often use positive words to convey negative sentiments, complicating automated interpretation. Existing sarcasm detection systems predominantly cater to English, leaving a gap for low-resource languages such as Hindi, Telugu, Tamil, Arabic, and others. This study fills this gap by creating and annotating a Telugu conversational dataset, which includes both standard and sarcastic responses. We employed two deep learning models—Self Attention-based Recurrent Neural Network (SA-RNN) and Gated Recurrent Unit (GRU)—to analyze this dataset. Results showed that the SA-RNN model outperformed the GRU, achieving 96% accuracy compared to 94%. The models utilized GloVe word embeddings and specific linguistic features, such as interjections and punctuation marks, to improve sarcasm detection. This research advances the field of sarcasm detection for low-resource languages, particularly Telugu.

Hand gesture recognition is a technology that enables computers to interpret and understand hand movements and gestures made by users. It has various applications across various domains, including human-computer interaction, gaming, virtual reality, sign language interpretation, and robotics. Hand recognition faces challenges such as lighting conditions, occlusions, and variations in hand shape and size. Creating reliable and precise recognition systems frequently necessitates tackling these issues. Neural Architecture Search (NAS) is a technique employed in deep learning and artificial intelligence to automate the creation and optimization of neural network topologies. The objective of NAS is to identify neural network designs that are optimally aligned with certain objectives, including image classification, natural language processing, or reinforcement learning while reducing the necessity for manual design and adjustment. YOLONAS model's integration of YOLO's speed and efficiency with NAS-driven optimization results in improved accuracy and performance in gesture recognition tasks, making it a compelling choice for real-time applications requiring accurate and efficient gesture analysis. In this research, we implement YOLO with NAS technology and training with the Oxford Hand Dataset. Performance metrics are employed for monitoring and quantifying important data, such as the number of Giga Floating Point Operations Per Second (GFLOPS), the mean average precision (mAP), and the time taken for detection. The results of our study indicate that the utilization of YOLONAS with a training time of 100 epochs produces a more reliable output when compared to other approaches.

This work analyzes and studies the characteristics of three enclosures on a university Campus, which present similar challenges in terms of noise pollution. To carry out an empirical and objective assessment on their acoustic performance, current regulations, and standards, are being used. Theoretical calculations are considered to calculate reverberation time parameters. In order to calculate reverberation time by using the Sabine formula, it is necessary to measure the classrooms, together with the specification of the surface occupied by each of the materials that make up the walls in the rooms under study, resulting in a T60 of between 3.6 s to 6.2 s for classrooms 11 and 12, and between 4.1 s to 7.1 s for classroom 15. To obtain the parameters that define the acoustic capacities of reverberation of the rooms, the guidelines for both measurement and calculation conditions specified in the regulations are followed. Graphical representation and mathematical calculation software are used to achieve the desired results, obtaining a T60 of between 1.8 s to 2.2 s for classroom 11, 2.0 s to 3.0 s for classroom 12, and 1.7 s to 2.7 s for classroom 15. Once the acoustic conditions of the reverberation of the room are defined, it is concluded that none of the rooms has the proper characteristics to carry out the best teaching activities in them, since they exceed the recommended 0.7 s of reverberation time, since they hinder the understanding of speech and the clarity of the word. As a conclusion, the study has served as an analysis of a challenging task in the Miguelete Campus of the Universidad Nacional de San Martin, always based on parameters commonly used in the world of acoustic pollution, both scientifically and legislatively.

The analyzing of friction stir welding is applied to AA6061 with AA5083 using design of experiment (DOE) in Minitab to get optimization of the tensile strength. The analyzing was achieved by varying the main parameters as rotational speed by the values 700, 1050, and 1400 R.P.M, linear velocity of 40, 60, and 80 mm/min and pin depth of 3.5, 3.6, and 3.7 mm less than thickness material weld. A total of 11 runs were included corresponding to the designated experimental design. Analysis of variance (ANOVA) software was used to procedure for full factorial design with 1 replicate and 3 center points analysis. The results clarified that the rotational speed parameter is more significant to be controlled rather than the linear velocity and pin depth of tool. Decreasing rotational speed and increasing linear velocity and pin depth led to higher tensile strength. The profiles welded at 700 RPM, 80 mm/min and 3.7 mm had achieved the optimum case to get the value of maximum tensile strength. It is concluded that the rotational speed was the key parameter that manipulate the tensile strength in friction stir welding applied to AA6061 with AA508.

The chemical response and the collective buoyancy effects of thermal and mass diffusion in magnetohydrodynamic (MHD) convection are analyzed on an infinite vertical surface with porous material that emits gas as it rises. This study investigates how the governing equations perform under a broad range of stringent conditions, incorporating subjective considerations such as determining which factor dominates—suction velocity or Hall current strength. Ancillary currents were also considered to provide a comprehensive analysis. The governing equations for liquid flow were solved using the perturbation technique, yielding results in terms of temperature, concentration, and velocity fields. Dimensionless profiles of temperature, velocity, and concentration are graphically presented for various parameter values. It is observed that an increase in the Dufour number leads to higher primary and secondary velocities, as well as increased temperature. Conversely, primary and secondary velocities decrease with an increase in the chemical reaction number and magnetic field strength.

Heating, Ventilation and Air-Conditioning (HVAC) systems are responsible of 50-60% of energy demand of the building sector. The scientific literature highlights that HVAC units are frequently operated under faulty conditions that can significantly affect their performance. In this paper, the performance of a typical single-duct dual-fan constant air volume Air-Handling Unit (AHU) is investigated through a number of experiments performed during Italian cooling and heating seasons under both fault free and faulty scenarios. The AHU operation is analysed while artificially introducing seven typical faults: return air damper kept always closed; fresh air damper kept always closed; fresh air damper kept always open; exhaust air damper kept always closed; supply air filter clogged at 50%; fresh air filter clogged at 50%; return air filter clogged at 50%. The faulty and fault free tests are compared to assess the environmental and economic performance impacts. The experimental data highlighted that the most adverse fault is that one corresponding to the exhaust air dumper kept always closed; in particular, it increases both the daily global equivalent CO₂ emissions and the daily operating costs up to 110% in comparison with the fault free conditions.

The increasing number of private vehicles has caused massive traffic congestion, and public transport (PT) services are one of the ways to reduce the problem. Even though few types of PT are provided to people, they still prefer private vehicles over the PT. Thus, to encourage them to use PT, we need to understand the factors that trigger people to use PT. This research aims to determine the effects of service quality dimensions on passenger preference for PT by using the structural equation model (SEM) approach. A study was conducted in the main cities of Sarawak state, Malaysia. A total of 199 respondents voluntarily participated in the survey. The result of PLS-SEM showed a significant relationship between customer service (β = 0.443, p < 0.001), safety (β = 0.199, p < 0.001), and accessibility (β = 0.175, p < 0.001) with passenger preferences towards PT services in main cities of Sarawak. The customer service achieved the highest coefficient and showed that customer service is an essential factor that PT providers need to consider in service delivery. Then, safety elements should be emphasized for passenger security, and PT providers should improve their accessibility to passengers’ welfare by increasing the availability of PT when passengers need it.

In big cities, traffic congestion is a prevalent issue. In order to decide how to manipulate traffic in order to alleviate congestion, traffic regulators, who supervise traffic flow, must conduct an analysis of present conditions. Classifying traffic conditions from road information is a critical step that impacts these decisions. Traffic conditions can be categorized using a variety of techniques, each with benefits and drawbacks of its own. Recently, the rapid development of machine learning techniques has accelerated their use in a variety of sectors, including intelligent transportation systems (ITS). In this study, a competitive machine learning system is introduced to support the decision-making process in ITS, specifically in traffic condition classification. The proposed system operates in two stages: first, identifying the best model configuration from various machine learning methods, and second, deciding through a voting system based on the selected models. The proposed system employs six machine learning methods, each with 4-5 variations in model configurations. The methods tested include Neural Networks, k-Nearest Neighbor, Logistic Regression, Bayesian Networks, Decision Trees, and Random Forests, with individual accuracy rates of 66.2%, 70.5%, 44.4%, 46.1%, 72.2%, and 72.6%, respectively. The models that achieved the highest performance for each method proceed to a voting system, both non-weighted and weighted. The experimental results indicate that the non-weighted system achieved an accuracy of 68.6% to 69.3%, while the weighted system reached 71.9% to 72.5%. The findings show that the proposed competitive machine learning system offers a viable solution for classifying traffic conditions with promising results, especially for implementation in Bandung City, Indonesia.

The study presents a comprehensive bibliometric analysis of academic literature focusing on the intersection of transportation and its impact on climate change. Covering the period from 2018 to 2023, the research scrutinized a substantial dataset of 4,373 papers from the Scopus database. Employing VOSviewer for network visualizations and Microsoft 365 for data analysis, the study meticulously mapped publication trends and citation impacts within this period. This systematic approach provided a thorough understanding of the evolution and current state of research in this vital field, highlighting how academic focus on transportation’s role in climate change has intensified over time. Key findings from the study revealed a significant increase in research output, with the number of publications nearly doubling from 483 in 2018 to 932 by 2022, indicating a growing scholarly interest in this area. However, the analysis also uncovered notable variations in citation rates, with a peak citation per publication (CPP) of 24.04 for highly influential papers. This suggests a disparity in the influence of research output, with some studies gaining more recognition than others. Additionally, the study highlighted significant differences in scholarly production and impact across different countries, with the United States leading in publications and citations, followed by China and India. These findings underscore the importance of international collaboration in research, pointing to the need for policies that encourage and support collaborative efforts to tackle the global challenge of climate change through effective transportation strategies.

The sustainability of urban development embodies settlement patterns and transport systems that are affected by residents’ travel preferences and the urban spatial structure. This study aims to analyze the influence of settlement and social and economic systems on movement actors toward the confirmation of cumulative integration in the structure of urban service centers. The research approach was quantitative based on multivariate statistics with the structural equation analysis method (SEM-PLS). Data were collected through observation, documentation, and survey. The results illustrate the distribution and concentration of residents, as well as the socio-economic conditions of the movers that influence the movers. Whereas movement actors have a weak influence on service centers, this is because access and connectivity to service centers can be reached from the periphery to the city center for medium-sized cities. The cumulative integration pattern illustrates that the service center is still dominated by a monocentric spatial structure as evidenced by the distribution of economic and socio-economic activities as well as movement actors who perform mobility to the city service center. This research contributes to urban planning to encourage sustainable urban growth.

A mathematical model has been formulated to optimize the setup of an end-of-production-line (EOL) test bench that is used to evaluate the efficiency of electric motors or axles designed for electric vehicles. The model can forecast the performance of EOL testing benches and electric motors/axles under a variety of conditions, thereby eliminating the need for extensive physical trials and minimizing the associated energy usage. The proposed model can be adjusted to handle different power ratings of electric motors or axles. The model takes the maximum performance that the electric motors or axles need to guarantee according to the vehicle manufacturer’s specifications as inputs. Subsequently, the necessary performance of each primary EOL test bench component is computed, and the corresponding systems available in the market are chosen based on manufacturers’ catalogues. In this research, an EOL test bench for low-power e-axles (approximately 22 kW) has been designed according to the outputs of the proposed model.

In modern surveillance systems intended for surveilled areas, Unmanned Aerial Vehicles (UAVs) equipped with computer vision capabilities fulfill an essential role in tracking objects within dynamic and high-risk monitored regions. This paper presents a novel approach SP-TSA to estimate the areas where objects are likely to be present by analyzing their trajectories, which are estimated through UAV-based computer vision. Each trajectory is represented by a series of points in 3D space, with each point acting as the center of a sphere. The spatial uncertainty of the object’s position is captured by the sphere’s radius, providing a comprehensive probabilistic model of potential object locations. To model the area where an object could be present, the intersections of these spheres are analyzed, and the regions where the spheres overlap are used to form a continuous tubular surface along the trajectory. We introduce a Non-Linear Objective Function to optimize the estimation of these areas and minimize uncertainties in object location. This innovative approach ensures computational efficiency and adaptability to complex trajectories, making it suitable for real-time applications. The method offers a precise and robust way to predict the object’s presence within a given space, providing valuable insights for decision-making in dynamic surveillance environments. Simulation results validate the SP-TSA method, demonstrating its superior accuracy in estimating object presence compared to traditional methods, particularly in scenarios involving non-linear and erratic object trajectories.

A novel approach for road crack detection and segmentation was proposed, incorporating Einstein operators within an Adaptive Neuro-Fuzzy Inference System (ANFIS). This methodology leverages advanced fuzzy aggregation techniques and adaptive mechanisms, combined with dynamic Einstein sum and product operators, to enhance the identification of cracks. The model was designed to effectively manage varying crack intensities, geometries, and noise levels, thereby ensuring high sensitivity and accuracy in real-world road conditions. In the preprocessing stage, robust fuzzification was applied using Gaussian membership functions alongside Einstein operators, which significantly improved feature extraction. The segmentation framework based on ANFIS ensured precise detection and delineation of cracks. The performance of the proposed model was demonstrated through a comparative analysis, showing superior accuracy (95.2%), precision (94.1%), recall (96.4%), and F1-score (95.2%) when compared to state-of-the-art models. Statistical validation was conducted, with p-values < 0.01 for all performance metrics, confirming the reliability and statistical significance of the results. Advanced post-processing techniques, including fuzzy morphological refinement and adjacency matrix-based connectivity analysis, were employed to accurately identify even faint or disconnected cracks. The proposed method exhibits exceptional resilience to environmental variations, offering a reliable and adaptive solution for road maintenance and monitoring. This work highlights the potential of fuzzy logic, statistical validation, and adaptive mechanisms in addressing real-world challenges in road crack detection.

The Kariangau Container Terminal serves as a port facilitating loading and unloading operations in Balikpapan City and the New Capital of the Archipelago. However, its service quality has not yet reached an optimal level for all customers. This is evident from the relatively low user perception ratings across several indicators, including tangibles, reliability, responsiveness, assurance, empathy, and credibility. This study aims to evaluate the quality of container loading and unloading services at Kariangau Container Terminal by examining user perceptions and expectations. The methods employed include Gap Analysis and the Customer Satisfaction Index (CSI). The key assessment indicators are tangibles, reliability, responsiveness, assurance, empathy, and credibility. The findings indicate that all customer satisfaction dimensions have negative gap values, suggesting that the service quality does not fully align with customer expectations. The analysis revealed an overall satisfaction level with an average gap of -0.365, indicating that while the service dimensions meet customer expectations to a certain extent, there is still room for improvement by Kariangau Container Terminal operators.

The shift to electric vehicles in transportation is essential to mitigate pollution and achieve global climate goals. Recent EU regulations and incentives have accelerated the adoption of this strategy, especially in cities, facilitating the development of fleets of electric city buses. This paper explores the integration of biomass gasifiers and battery energy storage systems to develop environmentally sustainable high-power charging stations, focusing on Carpi, Italy, as a case study. By using locally available biomass resources, this approach aims to disconnect power from the electricity grid and reduce emissions. Through the analysis of different configurations, the study demonstrates once again how the economic sustainability of projects based on biomass gasifiers is strongly dependent both on the cost of biomass and the current energy market with which it competes. Only extending the use of the charging station to private vehicles generates a return on investment of around 7 years. However, through gasification is possible to achieve carbon capture and storage that, in the analyzed case study, is almost equivalent to the annual CO_2eq emission of 4 diesel buses.

The modified xTRoad (MxT) model, an innovative route optimization framework, is presented to enhance urban traffic management within disaster-prone regions. This model uniquely integrates static and real-time data derived from the social media platform X (formerly known as Twitter) to improve route mitigation strategies, particularly during emergencies. The methodology employs a refined social media data extraction process using Boolean logic and a score-based evaluation system to identify disruptions, including flooding, obstructive debris, and public demonstrations. To validate the accuracy of the model, ground truth validation techniques were implemented, confirming the system’s efficacy in detecting obstacles and generating alternative routes. Performance testing was conducted on key transport arteries in Jakarta, where the MxT model demonstrated a detection accuracy exceeding 91.6% for traffic disruptions. Furthermore, the model achieved an average reduction in travel time by 15% compared to traditional traffic management systems. The MxT model dynamically adapts to real-time conditions, offering safer and more efficient navigation options in complex urban settings. The results underscore the MxT model’s potential as a scalable, adaptable solution for intelligent transport management during disaster scenarios, thereby contributing to the advancement of resilient urban infrastructure.

In Italy, the transport sector contributes significantly to carbon dioxide (CO₂) emissions, accounting for 30.7% of the total emissions, with road freight transport alone responsible for 25% of this figure. This situation demands urgent emissions reductions to meet the country’s national commitment to achieving net-zero by mid-century. The growing affordability of electric vehicles (EVs) due to improved energy densities and reduced lithium storage system costs is extending to heavy transport, promising emissions reductions. Additionally, short-term alternatives like hydrogen and liquefied natural gas (LNG) are being considered. To evaluate the carbon footprint of emerging transportation technologies, including internal combustion engine vehicles (ICEVs), fuel cell electric vehicles (FCEVs), LNG vehicles, and battery electric vehicles (BEVs), a detailed life cycle analysis (LCA) is essential. This research aims to inform decision-making processes, investment initiatives, and regulatory compliance by assessing emissions per kilometer within future scenarios. The study employs an LCA model integrating global supply chain contributions, offering regional context and scenario analysis. Findings indicate higher GHG emissions per kilometer for FCEVs and diesel vehicles, with BEVs emerging as promising alternatives. Moreover, the study highlights significant Scope 3 emissions associated with FCEV supply chains, emphasizing the broader environmental impacts of different vehicle types.

This paper aimed to adopt the Value Engineering philosophy in enhancing the car seat cushion performance that emphasizes quality and productivity improvements. Value Engineering approaches are used to validate the design of car seat cushion to make the product more cost effective in terms of function and quality. Value Engineering methods are identified in the process of improving the design of car seat component cushion. The model of car seat component is developed using Autodesk® Inventor®. The design of the model is then analyzed using Boothroyd Dewhurst Design for Manufacture and Assembly (DFMA) software. Computer-aided engineering (CAE) ANSYS® software will be used in the analysis of displacement and stress. studies. The result shows that the force applied on the seat frame and seat cushion is set at 1177.2 N (120 kg). Maximum Von Mises Stresses for seat cushion and seat frame are 0.02337 MPa and 13.7 MPa. Maximum displacements for seat cushion and seat frame are 0.4026 mm and 0.006119 mm. FMEA was conducted on the model of car seat components to predict the possible failure and effect on the model. Hence, this paper provides valuable insight on potential car seat cushion improvement through Value Engineering approach.

Modern cars use a hierarchical system of sensors, controlling devices, and controllers, linked via various intra-vehicle systems, to regulate and monitor the vehicle’s status. Researchers have confined numerous academic papers on intrusion detection in the Internet of Things (IoT), employing data mining and machine learning (ML) techniques to secure autonomous vehicles and detect potential attacks. To identify malicious attacks on the Internet of Vehicles (IoV), however, a competent and accurate method is required. This paper presents a model for cyber-attack detection in IoV that employs tree-based ML methods, an Improved Random Forest Classifier (IRFC), and Extra Tree (ET). We build the proposed model using Improved Random Forest (IRF) and ensemble learning techniques. The proposed IRF model employs optimized feature selection and tuning strategies to enhance intrusion sensitivity and decrease false positive rates. We evaluate the proposed model’s performance using the CI-CIDS 2018 dataset. Also, this work focuses mostly on the reduced feature selection and ensemble learning (EL) methods to get a high detection rate while keeping the cost of computing low. The test results show that the proposed method can find DDoS attacks and vehicle intrusions with a 0.99 accuracy rate.