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Many industries use non-Newtonian ternary hybrid nanofluids (THNF) because of how well they control rheological and heat transport. This being the case, this paper aims to numerically study the Casson-Williamson THNF flow over a yawed cylinder, considering the effects of several slips and an inclined magnetic field. The THNF comprises Al₂O₃-TiO₂-SiO₂ nanoparticles because they improve heat transmission due to large thermal conductivity.

Applying suitable nonsimilarity variables transforms the coupled highly dimensional nonlinear partial differential equations (PDEs) into a system of nondimensional PDEs. To accomplish the goal of achieving the solution, an implicit finite difference approach is used in conjunction with Quasilinearization. With the assistance of a script written in MATLAB, the numerical results and the graphical representation of those solutions were ascertained.

As the Casson parameter β increases, there is an improvement in the velocity profiles in both chord and span orientations, while the gradients Re1/2Cf, Re1/2C¯f reduce for the same variations of β. The velocities of Casson THNF are greater than those of Casson-Williamson THNF. Approximately, a 202% and a 32% ascension are remarked in the magnitudes of Re1/2Cf and Re1/2C¯f for Casson-Williamson THNF than the Casson THNF only. When velocity slip attribute S1 jumps to 1 from 0.5, magnitude of both F(ξ,η) and Re1/2Cf fell down and it is reflected to be 396% at ξ=1, Wi=1 and β=1. An augmentation in thermal jump results in advanced fluid temperature and lower Re−1/2Nu. In particular, about 159% of down drift is detected when S2 taking 1.

There is no existing research on the effects of Casson-Williamson THNF flow over a yawed cylinder with multiple slips and an angled magnetic field, according to the literature.

Proper identification, allocation, and pricing of risks are critical to effective procurement and project delivery, particularly when contracts specify the intended performance instead of how the work is to be performed. This paper presents an overview of the sources of project risks when performance specifications are used for highway infrastructure procurement. The findings are based on a comprehensive literature review and interviews with subject-matter experts involved in developing performance specifications for highway infrastructure. The authors conclude that wider use of performance specifications in U.S. highway infrastructure construction requires a fundamental reassessment of risk allocation and pricing. Highway agencies and the contractors need to realign their respective organizational capabilities with the goal of using performance specifications as a facilitator of innovation, a goal that remains elusive after decades of applied research.

Proper identification, allocation, and pricing of risks are critical to effective procurement and project delivery, particularly when contracts specify the intended performance instead of how the work is to be performed. This paper presents an overview of the sources of project risks when performance specifications are used for highway infrastructure procurement. The findings are based on a comprehensive literature review and interviews with subject-matter experts involved in developing performance specifications for highway infrastructure. The authors conclude that wider use of performance specifications in U.S. highway infrastructure construction requires a fundamental reassessment of risk allocation and pricing. Highway agencies and the contractors need to realign their respective organizational capabilities with the goal of using performance specifications as a facilitator of innovation, a goal that remains elusive after decades of applied research.

Electric vehicles are well known for a silent and smooth drive; however, their presence on the road is difficult to identify for road users who may be subjected to certain incidences. Although electric vehicles are free from exhaust emission gases, the wear particles coming out from disc brakes are still unresolved issues. Therefore, the purpose of the present paper is to introduce a smart eco-friendly braking system that uses signal processing and integrated technologies to eventually build a comprehensive driver assistance system.

The parameters obstacle identification, driver drowsiness, driver alcohol situation and heart rate were all taken into account. A contactless brake blending system has been designed while upgrading a rapid response. The implemented state flow rule-based decision strategy validated with the outcomes of a novel experimental setup.

The drowsiness state of drivers was successfully identified for the proposed control map and set up vindicated with the improvement in stopping time, atmospheric environment and increase in vehicle active safety regime.

The present study adopted a unique approach and obtained a brake blending system for improved braking performance as well as overall safety enhancement with rapid control of the vehicle.

This study investigated the potential of partially replacing cement with red mud (RM) in concrete and examined its effects on its mechanical properties and microstructure. This study aims to explore sustainable alternatives to traditional cement and evaluate the performance of concrete mixtures with varying percentages (%) of RM as cement replacement.

This research aims to comprehensively understand the impact of RM on concrete, aiming for both environmental sustainability and improved construction materials. Subsequently, concrete mixtures were prepared with varying RM contents, ranging from 0% to 21% in increments of 3%, replacing cement. The workability of these mixtures was evaluated using the Slump Cone Test, whereas their mechanical properties (compressive strength, flexural strength and split tensile strength) were assessed through standardized tests. The durability was further investigated via water absorption, acid attack, rapid chloride permeability tests, open porosity test and Sorptivity test. To gain deeper insights into the internal structure of concrete, microstructure analysis was conducted using X-ray diffraction and scanning electron microscopy. Finally, the results were analyzed and quantified.

The finding demonstrates that substituting 12% of cement with RM not only boosts the mechanical characteristics of concrete but also mitigates waste disposal. The microstructural analysis identified a denser cement matrix and improved bonding between the cement paste and the aggregates, suggesting potential improvements in strength and durability.

These results suggest that RM can be efficiently used to produce sustainable concrete with potential applications in construction projects with environmental considerations.

The investigation aims to improve Nd: YAG laser technology for precision cutting of carbon fiber reinforcing polymers (CFRPs), specifically those containing newly created resin (NDR) from the polyethylene and polyurea group, is the goal of the study. The focus is on showing how Nd: YAG lasers may be used to precisely cut CFRP with NDR materials, emphasizing how useful they are for creating intricate and long-lasting components.

The study employs a systematic approach that includes complicated factorial designs, Taguchi L27 orthogonal array trials, Gray relational analysis (GRA) and machine learning predictions. The effects of laser cutting factors on CFRP with NDR geometry are investigated experimentally, with the goal of optimizing the cutting process for greater quality and efficiency. The approach employs data-driven decision-making with GRA, which improves cut quality and manufacturing efficiency while producing high-quality CFRP composites. Integration of machine learning models into the optimization process significantly boosts the precision and cost-effectiveness of laser cutting operations for CFRP materials.

The work uses Taguchi L27 orthogonal array trials for systematically explore the effects of specified parameters on CFRP cutting. The cutting process is then optimized using GRA, which identifies influential elements and determines the ideal parameter combination. In this paper, initially machining parameters are established at level L3P3C3A2, and the optimal machining parameters are determined to be at levels L3P2C3A3 and L3P2C1A2, based on predictions and experimental results. Furthermore, the study uses machine learning prediction models to continuously update and optimize kerf parameters, resulting in high-quality cuts at a lower cost. Overall, the study presents a holistic method to optimize CFRP cutting processes employing sophisticated techniques such as GRA and machine learning, resulting in better quality and efficiency in manufacturing operations.

The novel concept is in precisely measuring the kerf width and deviation in CFRP samples of NDR using sophisticated imaging techniques like SEM, which improves analysis and precision. The newly produced resin from the polyethylene and polyurea group with carbon fiber offers a more precise and comprehensive understanding of the material's behavior under different cutting settings, which makes it novel for kerf width and kerf deviation in their studies. To optimize laser cutting settings in real time while considering laser machining conditions, the study incorporates material insights into machine learning models.