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The spatial–temporal conflicts in the construction process of concrete arch dams are related to the construction quality and duration, especially for pouring blocks with a continuous high-strength and high-density construction process. Furthermore, the complicated construction technology and limited space resources aggravate the spatial–temporal conflicts in the process of space resource allocation and utilization, directly affecting the pouring quality and progress of concrete. To promote the high-strength, quality-preserving and rapid construction of dams and to clarify the explosion moment and influence degree of the spatial–temporal conflicts of construction machinery during the pouring process, a quantification method and algorithm for a “Conflict Bubble” (CB) between construction machines is proposed based on the “Time–Space Microelement” (TSM).

First, the concept of a CB is proposed, which is defined as the spatial overlap of different entities in the movement process. The subsidiary space of the entity is divided into three layered spaces: the physical space, safe space and efficiency space from the inside to the outside. Second, the processes of “creation,” “transition” and “disappearance” of the CB at different levels with the movement of the entity are defined as the evolution of the spatial–temporal state of the entity. The mapping relationship between the spatial variation and the running time of the layered space during the movement process is defined as “Time–Space” (TS), which is intended to be processed by a microelement.

The quantification method and algorithm of the CB between construction machinery are proposed based on the TSM, which realizes the quantification of the physical collision accident rate, security risk rate and efficiency loss rate of the construction machinery at any time point or time period. The risk rate of spatial–temporal conflicts in the construction process was calculated, and the outbreak condition of spatial–temporal conflict in the pouring process was simulated and rehearsed. The quantitative calculation results show that the physical collision accident rate, security risk rate and efficiency loss rate of construction machinery at any time point or time period can be quantified.

This study provides theoretical support for the quantitative evaluation and analysis of the spatial–temporal conflict risk in the pouring construction process. It also serves as a reference for the rational organization and scientific decision-making for pouring blocks and provides new ideas and methods for the safe and efficient construction and the scientific and refined management of dams.

The present numerical investigation examines the magnetohydrodynamic (MHD) double diffusion natural convection of power-law non-Newtonian nano-encapsulated phase change materials (NEPCMs) in a trapezoidal cavity.

The governing Navier-Stokes, energy and concentration equations based on the Cartesian curvilinear coordinates are solved using the collocated grid arrangement’s finite volume method. The in-house FORTRAN code is validated with the different benchmark problems. The NEPCM nanoparticles consist of a core-shell structure with Phase Change Material (PCM) at the core. The enclosure, shaped as a trapezoidal hollow, features a warmed (T_h) left wall and a cold (T_c) right wall. Various parameters are considered, including the power law index (0.6 ≤ n ≤ 1.4), Hartmann number (0 ≤ Ha ≤ 30), Rayleigh number (10⁴ ≤ Ra ≤ 10⁵) and fixed variables such as buoyancy ratio (Br = 0.8), Prandtl number (Pr = 6.2), Lewis number (Le = 5), fusion temperature (Θ_f = 0.5) and volume fraction (ϕ = 0.04).

The findings indicate a decrease in local Nusselt (Nu) and Sherwood (Sh) numbers with increasing Hartmann numbers (Ha). Additionally, for a shear-thinning fluid (n = 0.6) results in the maximum local Nu and Sh values. As the Rayleigh number (Ra) increases from 10⁴ to 10⁵, the structured vortex in the streamline pattern is disturbed. Furthermore, for different Ra values, an increase in n from 0.6 to 1.4 leads to a 67.43% to 76.88% decrease in average Nu and a 70% to 77% decrease in average Sh.

This research is for two-dimensioal laminar flow only.

PCMs represent a class of practical substances that behave as a function of temperature and have the innate ability to absorb, release and store heated energy in the form of hidden fusion enthalpy, or heat. They are valuable in these systems as they can store significant energy at a relatively constant temperature through their latent heat phase change.

As per the literature review and the authors’ understanding, an examination has never been conducted on MHD double diffusion natural convection of power-law non-Newtonian NEPCMs within a trapezoidal enclosure. The current work is innovative since it combines NEPCMs with the effect of magnetic field Double diffusion Natural Convection of power-law non-Newtonian NEPCMs in a Trapezoidal enclosure. This outcome can be used to improve thermal management in energy storage systems, increasing safety and effectiveness.

Our result of this paper aims to indicate that the beta pricing formula could be applied in a long-term model setting as well.

In this paper, we show that the capital asset pricing model can be derived from a three-period general equilibrium model.

We show that our extended model yields a Pareto efficient outcome.

The capital asset pricing model (CAPM) model can be used for pricing long-lived assets.

Long-term modelling and sustainability can be modelled in our setting.

Our results were only known for two periods. The extension to 3 periods opens up a large scope of applicational possibilities in asset pricing, behavioural analysis and long-term efficiency.

The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs include data on vehicles with and without intended driving behavior changes, they do not explicitly demonstrate a type of data on vehicles that intend to change their driving behavior but do not execute the behaviors because of safety, efficiency, or other factors. This missing data is essential for autonomous driving decisions. This study aims to extract the driving data with implicit intentions to support the development of decision-making models.

According to Bayesian inference, drivers who have the same intended changes likely share similar influencing factors and states. Building on this principle, this study proposes an approach to extract data on vehicles that intended to execute specific behaviors but failed to do so. This is achieved by computing driving similarities between the candidate vehicles and benchmark vehicles with incorporation of the standard similarity metrics, which takes into account information on the surrounding vehicles' location topology and individual vehicle motion states. By doing so, the method enables a more comprehensive analysis of driving behavior and intention.

The proposed method is verified on the Next Generation SIMulation dataset (NGSim), which confirms its ability to reveal similarities between vehicles executing similar behaviors during the decision-making process in nature. The approach is also validated using simulated data, achieving an accuracy of 96.3 per cent in recognizing vehicles with specific driving behavior intentions that are not executed.

This study provides an innovative approach to extract driving data with implicit intentions and offers strong support to develop data-driven decision-making models for autonomous driving. With the support of this approach, the development of autonomous vehicles can capture more real driving experience from human drivers moving towards a safer and more efficient future.

Competitiveness is an important personality trait that has been studied in various disciplines and has been shown to predict critical work outcomes at the individual level. Despite this, the role of competitiveness in groups and teams has received scant attention amongst organizational researchers. Aiming to promote future research on the role of competitiveness as both an adaptive and maladaptive trait – particularly in the context of work – the authors review competitiveness and its effects on individual and team stress and Well-Being, giving special attention to the processes of cohesion and conflict and situational moderators. The authors illustrate a dynamic multilevel model of individual and team difference factors, competitive processes, and individual and team outcomes to highlight competitiveness as a consequential occupational stressor. Furthermore, the authors discuss the feedback loops that inform the different factors, highlight important avenues for future research, and offer practical solutions for managers to reduce unhealthy competition.

This study examines the importance of exchange rate and credit growth fluctuations when designing monetary policy in Thailand. To this end, the authors construct a small open economy New Keynesian dynamic stochastic general equilibrium (DSGE) model. The model encompasses several essential characteristics, including incomplete financial markets, incomplete exchange rate pass-through, deviations from the law of one price and a banking sector. The authors consider generalized Taylor rules, in which policymakers adjust policy rates in response to output, inflation, credit growth and exchange rate fluctuations. The marginal likelihoods are then employed to investigate whether the central bank responds to fluctuations in the exchange rate and credit growth.

This study constructs a small open economy DSGE model and then estimates the model using Bayesian methods.

The authors demonstrate that the monetary authority does target exchange rates, whereas there is no evidence in favor of incorporating credit growth into the policy rules. These findings survive various robustness checks. Furthermore, the authors demonstrate that domestic shocks contribute significantly to domestic business cycles. Although the terms of trade shock plays a minor role in business cycles, it explains the most significant proportion of exchange rate fluctuations, followed by the country risk premium shock.

This study is the first attempt at exploring the relevance of exchange rate and credit growth fluctuations when designing monetary policy in Thailand.

The current study aims to develop a 3D-printed continuous metal fiber-reinforced recycled thermoplastic composite using an in-nozzle impregnation technique.

Recycled acrylonitrile butadiene styrene (RABS) plastic was blended with virgin ABS (VABS) plastic in a ratio of 60:40 weight proportion to develop a 3D printing filament that was used as a matrix material, while post-used continuous brass wire (CBW) was used as a reinforcement. 3D printing was done by using a self-customized print head to fabricate the flexural, compression and interlaminar shear stress (ILSS) test samples to evaluate the bending, compressive and ILSS properties of the build samples and compared with VABS and RABS-B samples. Moreover, the physical properties of the samples were also analyzed.

Upon three-point bend, compression and ILSS testing, it was found that RABS-B/CBW composite 3D printed with 0.7 mm layer width exhibited a notable improvement in maximum flexural load (L_max), flexural stress at maximum load (sf_max), flex modulus (E_f) and work of fracture (WOF), compression modulus (E_c) and ILSS properties by 30.5%, 49.6%, 88.4% 13.8, 21.6% and 30.3% respectively.

Limited research has been conducted on the in-nozzle impregnation technique for 3D printing metal fiber-reinforced recycled thermoplastic composites. Adopting this method holds the potential to create durable and high-strength sustainable composites suitable for engineering applications, thereby diminishing dependence on virgin materials.

This study aims to develop a measurement model of the halal supply chain resilience (HSCRES) index, which represents the capability of the supply chain (SC) to handle disruption caused by halal risks. A case study is conducted to apply the HSCRES index in the halal chicken SC in Yogyakarta, Indonesia, to test the proposed methodology.

A literature synthesis was conducted to establish the main capability and vulnerability factors and their relevant indicators. The indicators were validated using the confirmatory factor analysis approach. Then, applying an analytical hierarchy process involving ten experts – practitioners and academicians – the weight of each indicator was obtained. A survey of 20 employees of slaughterhouses, 35 sellers and 100 consumers was conducted to obtain the value of each indicator. Finally, the HSCRES index was calculated by comparing the total weighted capability value to vulnerability.

The results revealed that the resilience of halal chicken SC in Yogyakarta is at a good level, with an index of 3.459, and “halal team” is the most significant indicator. The findings also revealed several capabilities that need improvement, including dedicated halal facilities, employees’ halal competence and halal regulation. However, the lack of a halal certification board, lack of management commitment and packaging contamination were found as vulnerability indicators that need to be reduced.

The case of this study is limited to the halal chicken SC in Yogyakarta, Indonesia. As a consequence, the obtained results are limited to a specific context. The application of this method to different areas and objects enables the establishment of different capability and vulnerability indicators.

The halal resilience measurement model offers a comprehensive understanding of the strengths and weaknesses of the HSC. The findings can help stakeholders improve preparedness for halal risks, deal with halal risks better and recover more quickly. Measuring the HSCRES index can be particularly useful for policymakers in developing evidence-based strategies to increase HSCRES.

The current study is the first to define and classify the contributing halal resilience attributes and also to calculate the halal resilience index.

The purpose of this study is to formulate a new class of vehicle routing problem with an objective to minimise the total cost of raw material collection and derive a new approach to solve optimization problems. This study can help to select the optimum number of suppliers based on cost.

To model the raw material vehicle routing problem, a mixed integer linear programming (MILP) problem is formulated. An interesting phenomenon added to the proposed problem is that there is no compulsion to visit all suppliers. To guarantee the demand of semiconductor industry, all visited suppliers should reach a given raw material capacity requirement. To solve the proposed model, the authors developed a novel hybrid approach that is a combination of block and edge recombination approaches. To avoid bias, the authors compare the results of the proposed methodology with other known approaches, such as genetic algorithms (GAs) and ant colony optimisation (ACO).

The findings indicate that the proposed model can be useful in industries, where multiple suppliers are used. The proposed hybrid approach provides a better sequence of suppliers compared to other heuristic techniques.

The data used in the proposed model is generated based on previous literature. The problem derives from the assumption that semiconductor industries use a variety of raw materials.

This study provides a new model and approach that can help practitioners and policymakers select suppliers based on their logistics costs.

This study provides two important contributions in the context of the supply chain. First, it provides a new variant of the vehicle routing problem in consideration of raw material collection; and second, it provides a new approach to solving optimisation problems.