The effects of polycarboxylate superplasticizer (PCE) on the rheological properties and workability of cement-based composites were investigated by testing parameters such as static yield stress, dynamic yield stress, plastic viscosity, slump flow, bleeding rate, and penetration depth. The correlation between the dosage of PCE and the rheological parameters of fresh cement-based composites was analyzed. The results indicated that with an increase in the PCE dosage, the static yield stress, dynamic yield stress, and plastic viscosity of fresh cement-based composites decreased, demonstrating that PCE can improve the rheological properties of these composites. As the PCE dosage increased, the slump flow and bleeding rate of fresh cement-based composites also increased, but the rate of change decreased at higher dosages. Additionally, with an increase in PCE dosage, the penetration depth gradually increased, while the penetration depth difference ($\Delta {H}$) decreased. Furthermore, the compressive strength of cement-based composite cubes slightly decreased with an increase in PCE dosage.

The psychometric validity of multiple-choice questions (MCQs) generated by an advanced Artificial Intelligence (AI) language model (ChatGPT) was evaluated in comparison with those developed by experienced human instructors, with a focus on mathematics teacher education. Two parallel 30-item MCQ tests—one human-designed and one AI-generated—were administered to 30 mathematics teacher trainees. A comprehensive psychometric analysis was conducted using six metrics: item difficulty index (P_i), discrimination index (D), point-biserial correlation, item-test correlation (R_it), Cronbach’s alpha (α) for internal consistency, and score variance. The analysis was facilitated by the Analysis of Didactic Items with Excel (AnDIE) tool. Results indicated that the human-authored MCQs exhibited acceptable difficulty (mean P_i = 0.55), moderate discrimination power (mean D = 0.31), and strong internal consistency (Cronbach’s α = 0.752). In contrast, the AI-generated MCQs were found to be substantially more difficult (mean P_i = 0.22), demonstrated weak discrimination (mean D = 0.16), and yielded negative internal consistency reliability (Cronbach’s α = −0.1), raising concerns about their psychometric quality. While AI-generated assessments offer advantages in terms of scalability and speed, the findings underscore the necessity of expert human review to ensure content validity, construct alignment, and pedagogical appropriateness. These results suggest that AI, in its current form, is not yet equipped to autonomously generate assessment instruments of sufficient quality for high-stakes educational settings. A hybrid test design model is therefore advocated, wherein AI is leveraged for initial item drafting, followed by rigorous human refinement. This approach may enhance both efficiency and quality in the development of educational assessments. The implications extend to educators, assessment designers, and developers of educational AI systems, highlighting the need for collaborative human-AI frameworks to achieve reliable, valid, and pedagogically sound testing instruments.

In view of growing global trade complexities and increasing pressure on maritime infrastructure, the strategic implementation of Logistics Information Systems (LIS) has emerged to be a critical enabler of port efficiency and competitiveness. This study aims to evaluate and rank the alternatives to LIS for enhancing port operation in the Black Sea region of Türkiye by employing a hybrid multi-criteria decision-making (MCDM) approach, which integrates fuzzy Full Consistency Method (fuzzy FUCOM) and fuzzy Rough Analytical Weighted Evaluation Criteria (fuzzy RAWEC). Six key evaluation criteria including operational efficiency, cost effectiveness, technological competence, regulatory support, user compatibility, and sustainability impact were determined by expert consultation and literature synthesis. Based on these criteria, assessment were conducted on five LIS alternatives, involving Port Community Systems, Terminal Operating Systems, Blockchain-Based Platforms, IoT-Supported Smart Port Systems, and Cloud-Based Logistics Management Systems. Fuzzy FUCOM method was employed to derive consistent criterion weights under uncertainty, while fuzzy RAWEC facilitated the ranking of alternatives with enhanced sensitivity to expert evaluations. Validation of the results was processed via three methods: sensitivity analysis, benchmarking with five other fuzzy MCDM techniques, and rank reversal test. Terminal Operating Systems was consistently proved to be the most preferred alternative, demonstrating robustness across all validation procedures. The findings highlighted the effectiveness of the proposed hybrid model in handling uncertainty and advocating strategic digital transformation in port management. This research offered both methodological contributions to fuzzy MCDM literature and practical insights, targeting port authorities and policymakers to modernize logistics infrastructure in the Black Sea region.

Congestion in Jakarta is a significant problem that impacts socio-economic aspects. The odd-even policy implemented by the government is considered ineffective because it encourages people to have two vehicles with odd and even license plates so as not to reduce the number of cars on the road significantly. Therefore, the government plans to implement Electronic Road Pricing (ERP), which promotes a more equitable distribution of transportation modes and the reduction of congestion. This research aims to design ERP tariffs that are effective in reducing congestion. This study uses a quantitative approach in data collection, which is then analyzed using a combination of Ability to Pay (ATP), Willingness to Pay (WTP), and Analytical Hierarchy Process (AHP). The results of the study show that the optimal fare is in the range of Rp 18,000 – Rp 22,000 for cars, while for motorcycles, it is in the range of Rp 7,000 – 9,500. The study also showed that the value of ATP > WTP. This imbalance can lead to ineffectiveness, so a pricing strategy must be made so that rates are fairer and more effective for different levels of society. This study highlights the importance of incorporating ATP and WTP analyses in determining fair rate structures. In addition, AHP is important for determining the optimal ERP rate by giving weight to the WTP value factor so that the analysis results are more accurate and objective. With a well-designed tariff, ERP can potentially serve as a viable solution to Jakarta’s congestion problem.

Population growth and technological development are fueling the increasing demand for electricity in Indonesia. By 2023, electricity consumption in Indonesia has reached 1,285 KWH, mostly met by non-renewable energy. This condition raises concerns about the sustainability of energy supply. On the other hand, Indonesia has great potential to utilize ocean wave energy as a source of electricity. The novelty of this research lies in the Generalized Linear Model-based Gamma Regression modelling approach to evaluate the electrical energy potential of ocean wave energy in 175 Indonesian waters. The focus of this research lies on the specific analysis of the impact of wave type on power potential, while wind speed and weather factors have no significant influence. In addition, the selection of the best model was conducted using the Root Mean Square Error (RMSE) approach, which shows that the model predictions are getting closer to the actual values. The results show that low and medium wave types significantly reduce the power potential compared to calm waves, by 0.0000083% and 0.0000113%, respectively. These findings make an important contribution to understanding the potential of ocean wave energy as a renewable energy source in Indonesia.

This study addresses the critical challenge of enhancing thermal efficiency in industrial firetube boilers within the fishing industry, a sector burdened by significant fuel consumption and associated costs amidst rising fuel prices; achieving even marginal improvements in boiler efficiency can result in substantial economic savings and environmental benefits. Utilizing the Peruvian technical standard for efficiency determination, alongside recommendations from boiler manufacturers and operational conditions, this research employs artificial neural networks (ANNs) to model and predict efficiency outcomes based on various operational parameters, including fuel type and combustion conditions specifically, the study explores the impact of excess air and fuel regulation on thermal efficiency and pollutant emissions, employing applied research methods and a comprehensive analysis of boiler operation at 80% and 100% load conditions. Results demonstrate the capability of neural network models to accurately predict thermal efficiency, with optimized configurations achieving significant reductions in CO₂ and CO emissions by 43% and 55%, respectively. The findings underscore the potential for neural network applications in optimizing boiler operations, offering a pathway to economic and environmental improvements in industrial processes. The study concludes with optimal operational parameters that balance efficiency gains with emission reductions, highlighting the practical implications for the fishing industry and beyond.

For sustainability, rail accidents are minimized by continuously upgrading with technology and refining existing methods for wheel life. Indian railways are one of the major transport sectors in the world. Hence, the focus is on defect detection over rail wheels to avoid accidents. An effective mechanism is required to detect wheel issues. The train journey to me made without issues and would experience a smooth journey for the passengers if there were no defects. The proposed system is an automatic visual inspection approach that comprises a set of nondestructive techniques, strain gauge sensors for detecting flat spots, and cracks, infrared cameras used for detecting abnormally hot or abnormally cold areas of the wheel that indicate damage, and the usage of wheelset balancing for achieving the quality of the wheel. Integrating transfer learning with the present working body would significantly make a difference. The combination of required technologies, such as specific non-destructive techniques, ResNet for spotted defects labelling, and transfer learning for comparison of refined and actual objects. Significant metrics such as accuracy and error rate were also analyzed, comparing the existing approaches against the proposed hybrid approach. The known advantages of using a transfer learning approach are faster training, higher accuracy, and better generalization capabilities.

The ability of paratransit to better adapt to demand is not necessarily an argument that justifies the adequacy of its offer in relation to the concerns of users, especially in the African context. Then, the reform of paratransit remains a challenge for stakeholders in the sector. This work focuses on four paratransit networks in Dakar, Bamako and Conakry, namely the “Association de Financement des Professionnels du Transport Urbain” (AFTU), "cars rapides" and "ndiaga ndiaye" in Dakar, "sotramas" in Bamako and "magbanas" in Conakry. The objective of the research is to make a comparison on the level of access to services of these different types of networks. Chi-square and normality tests are used to analyze primary data collected from surveys. Observations, interviews and documentation were also used to complete the information for the analysis. The results show that, even if the AFTU network in Dakar fails to meet the regularity of services as required by the concession contract that binds it to the public authorities, it remains the best network in terms of pricing and its services are closer to the populations.

Accurate, real-time vehicle detection is crucial for autonomous vehicles navigating dynamic traffic environments. This study compares YOLOv11 and the newly released YOLOv12, two state-of-the-art deep learning models for object detection, to assess enhancements in speed, accuracy, and robustness. YOLOv12 has improved upon YOLOv11's architecture with an attention mechanism and Residual Efficient Layer Aggregation Networks (R-ELAN). The improvements for YOLOv12 are designed to obtain better accuracy and improved computational performance as compared to YOLOv11. YOLOv11 and YOLOv12 were trained and tested on a newly developed dataset with 38,500 fully annotated images of seven classes of vehicles taken in different environmental conditions. Results show YOLOv12 achieves higher recall (95.0%), F1-score (96.03%), and mAP@50–95 (88.6%), while both maintain real-time inference speeds. YOLOv12 also demonstrated an improved capacity to detect small or partially occluded objects in challenging scenes. Overall, these findings establish YOLOv12 as a better solution for perceiving real-time data while autonomous driving, with a real prospect for implementation in intelligent transportation systems and edge-computing.

Efficient production management in energy storage systems requires accurate performance modeling of lithium-ion batteries (LIBs), especially under varying load conditions. This study presents a novel simplified lumped parameter approach that predicts battery performance with minimal reliance on internal design specifics. The approach uses a black-box modeling technique to estimate critical parameters—ohmic overpotential, diffusion time constant, and charge exchange current—via a Levenberg–Marquardt optimization algorithm, based on experimental voltage, current, and open circuit voltage data. Results demonstrate high accuracy in predicting cell voltage over dynamic load cycles, achieving standard deviations of 0.015 V and 0.014 V in parameter estimation and load prediction, respectively. These findings have significant implications for advancing energy storage systems by enabling more sustainable production management practices, reducing resource wastage, and improving operational efficiency. By enhancing the adaptability of production processes while maintaining high performance, this model contributes to achieving long-term goals of sustainability and scalability in energy storage applications.

The paper considers the issues of the urgent problem of increasing the efficiency of the organization and planning of the maintenance system of mining dump trucks. The methods of analysis and synthesis of a priori information and statistical data of the enterprise show the need to improve the existing system for ensuring reliable operation of the dump truck fleet. Quantitative indicators are given that characterize the operational reliability of the park as the number of failures of the park as a whole, the main components and assemblies and the coefficient of technical readiness (CTR) of the park, analytical dependencies are obtained that approximate the dynamics of these indicators, depending on the duration of the studied service life. The obtained functional dependencies differ by type, with a significant discrepancy between planned and actual indicators, which in production conditions are eliminated by unscheduled operational measures of technical impact that reduce the overall efficiency of the maintenance and repair system of the fleet. The necessity of increasing the efficiency of the operating system at the stage of planning and organization of maintenance and repair is substantiated.