Traffic management is a significantly difficult and demanding task. It is necessary to know the main parameters of road networks in order to adequately meet traffic management requirements. Through this paper, an integrated fuzzy model for ranking road sections based on four inputs and four outputs was developed. The goal was to determine the safety degree of the observed road sections by the methodology developed. The greatest contribution of the paper is reflected in the development of the improved fuzzy step-wise weight assessment ratio analysis (IMF SWARA) method and integration with the fuzzy measurement alternatives and ranking according to the compromise solution (fuzzy MARCOS) method. First, the data envelopment analysis (DEA) model was applied, showing that three road sections have a high traffic risk. After that, IMF SWARA was applied to determine the values of the weight coefficients of the criteria, and the fuzzy MARCOS method was used for the final ranking of the sections. The obtained results were verified through a three-phase sensitivity analysis with an emphasis on forming 40 new scenarios in which input values were simulated. The stability of the model was proven in all phases of sensitivity analysis.

One of the most common tools for achieving optimization and adequate production process management is linear programming (LP) in various forms. However, there are specific cases of the application of linear programming when production optimization implies several potential solutions instead of one. Exactly such a problem is solved in this paper, which integrates linear programming and a Multi-Criteria Decision-Making (MCDM) model. First, linear programming was applied to optimize production and several potential solutions lying on the line segment AB were obtained. A list of criteria was created and evaluated using the Improved Fuzzy Stepwise Weight Assessment Ratio Analysis (IMF SWARA). To obtain the final solution, a novel Rough compromise ranking of alternatives from distance to ideal solution (R-CRADIS) method was developed and verified through comparative analysis. The results show that the integration of linear programming and a Fuzzy-Rough MCDM model can be an exceptional solution for solving specific optimization problems.

This paper presents the concept and design of a system that embeds piezoelectric sensors to measure the voltage of a mechanical load applied to it. COMSOL Multiphysics, a finite element simulation tool, was used to design the system and analyze the data to find a possible fingerprint of voltage changes. The sensors’ voltage readings were affected by the load applied to the surface of the structure with different magnitudes and speeds. The analyzed data show the effect of position and mass on the voltage readings and indicates the possibility of speed prediction. The obtained dataset results validated the concept of the proposed system, where the collected data can serve as a digital data pipeline model for future research on different artificial intelligence (AI) or machine learning (ML) modeling applications. From the obtained data, a reasonable view shows that voltage reading matrices can be utilized for the detection of vehicle speed, location, and mass if used as training data for machine learning modeling, which can benefit the Internet of Things (IoT) technology.

Computational thinking has been identified as an important approach for enabling students' better comprehension of science concepts as well as scientific procedures. Computation is useful in physics because it permits physics theories to be applied to issues that are complicated to fix analytically. Visual Python provides a 3D environment where learners may design 3D objects, apply a physical framework, and study the effects of altering parameters. The implementation and outcomes of a 6-week teacher-led computational thinking intervention with groups of 12th graders (n=60) are described in this study. Two research questions are being addressed using quantitative analysis and a quasi-experimental approach involving a pre- and post-test. The participants who received the six-week implementation on the experimental group performed significantly better on the post-test than the control group, which received only standard teaching lectures. The results indicated a statistically significant difference in mean scores between the experimental group (M = 24.03, SD = 4.68) and the control group (M = 20.3, SD = 5.38). The findings indicate that implementing computational thinking activities not only improves students' knowledge of physics concepts, but it also improves visual thinking, allowing students to better cognitively comprehend the problem.

This paper introduces a novel and cost-effective method for detecting adulterated diesel, specifically targeting contamination with kerosene, by leveraging machine learning and the refractive index values of mixed diesel samples. It proposes a laser-based sensor, employing COMSOL simulations for synthetic data generation to facilitate machine learning training. This innovative approach not only streamlines the detection process by eliminating the need for expensive equipment and specialized personnel but also enables on-site testing without extensive sample preparation. The sensor’s design, utilizing light refraction and reflection principles, allows for the accurate measurement of diesel adulteration levels. Validation results showcase the machine learning models’ high precision in predicting adulteration percentages, as evidenced by an R-squared value of 0.999 and a mean absolute error of 0.074. This research signifies a leap in sensor technology, offering a practical solution for rapid diesel adulteration detection, especially in developing countries, by minimizing reliance on advanced laboratory analyses. The sensor’s design aligns with the requirements for low-cost IoT technology, presenting a versatile tool for various applications.

This research paper introduces a sensor that utilizes a machine-learning model to predict water salinity. The sensor’s concept and design are established through a simulation software which enables accurate modeling and analysis. Operating on the principle of light transmission physics, the sensor employs data collected from the simulation software as input parameters to predict the salinity parameter, serving as the output. The results of the prediction model exhibit excellent performance, showcasing high accuracy with a coefficient of determination value of 0.999 and a mean absolute error of 0.074. These outcomes demonstrate the model’s ability, particularly the multi-layer perceptron model, to effectively predict salinity values for previously unseen input data. This performance underscores the model’s accuracy and its proficiency in handling unfamiliar input data, emphasizing its significance in practical applications.