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This paper investigates the temporal inflow profile that minimises the total cost of travel for a single route. The problem is formulated to consider the case in which the total demand to be serviced is fixed. The approach used here is a direct calculation of the first order variation of total system cost with respect to variations in the inflow profile. Two traffic models are considered; the bottleneck with deterministic queue and the kinematic wave model. For the bottleneck model a known solution is recovered. The wave model proves more difficult and after eliminating the possibility of a smooth inflow profile the restricted case of constant inflow is solved. As the space of possible profiles is finite dimensional in this case, the standard techniques of calculus apply. We establish a pair of equations that are satisfied simultaneously by the optimal inflow and time of first departure.

Unlike many existing methods that are primarily focused on two-dimensional localization, this research paper extended the scope to three-dimensional localization. This enhancement is particularly significant for unmanned aerial vehicle (UAV) applications that demand precise altitude information, such as infrastructure inspection and aerial surveillance, thereby broadening the applicability of UAV-assisted wireless networks.

The paper introduced a novel method that employs recurrent neural networks (RNNs) for node localization in three-dimensional space within UAV-assisted wireless networks. It presented an optimization perspective to the node localization problem, aiming to balance localization accuracy with computational efficiency. By formulating the localization task as an optimization challenge, the study proposed strategies to minimize errors while ensuring manageable computational overhead, which are crucial for real-time deployment in dynamic UAV environments.

Simulation results demonstrated significant improvements, including a channel capacity of 99.95%, energy savings of 89.42%, reduced latency by 99.88% and notable data rates for UAV-based communication with an average localization error of 0.8462. Hence, the proposed model can be used to enhance the capacity of UAVs to work effectively in diverse environmental conditions, offering a reliable solution for maintaining connectivity during critical scenarios such as terrestrial environmental crises when traditional infrastructure is unavailable.

Conventional localization methods in wireless sensor networks (WSNs), such as received signal strength (RSS), often entail manual configuration and are beset by limitations in terms of capacity, scalability and efficiency. It is not considered for 3-D localization. In this paper, machine learning such as multi-layer perceptrons (MLP) and RNN are employed to facilitate the capture of intricate spatial relationships and patterns (3-D), resulting in enhanced localization precision and also improved in channel capacity, energy savings and reduced latency of UAVs for wireless communication.

This paper aims to investigate the impact of quality loss in transit on e-commerce supply chain pricing, production and financing decisions.

The authors consider a Stackelberg game model with a supplier, logistics firm and e-commerce platform. The logistics firm is capital-constrained and obtains funding from the e-commerce platform by debt financing or equity financing. Through backward induction, this paper first solves the equilibrium results under the two financing schemes and then reveals the financing preferences of all parties.

The results demonstrate that equity financing reduces financing costs and promotes production significantly. However, it may also lead to overproduction, particularly in markets with poor profitability and high cost factors. When the percentage of product quality loss is large, equity financing is preferable. With the increasing of transportation level, the benefits of debt finance are steadily growing. In addition, equity financing is the Pareto dominant scheme for all firms under certain circumstances. The extensions consider hybrid financing and another quality loss type.

The paper derives the equilibrium solutions and financing preferences, then specifies the threshold for applying financing schemes. Provide guidance for logistics firms’ finance model innovation and core enterprise involvement in the logistics industry.

The paper investigates how logistics firms’ financing strategies are impacted by product quality loss.

The paper aims to study the constraint solutions of the periodic coupled operator matrix equations by the biconjugate residual algorithm. The new algorithm can solve a lot of constraint solutions including Hamiltonian solutions and symmetric solutions, as special cases. At the end of this paper, the new algorithm is applied to the pole assignment problem.

When the studied periodic coupled operator matrix equations are consistent, it is proved that constraint solutions can converge to exact solutions. It is demonstrated that the solutions of the equations can be obtained by the new algorithm with any arbitrary initial matrices without rounding error in a finite number of iterative steps. In addition, the least norm-constrained solutions can also be calculated by selecting any initial matrices when the equations of the periodic coupled operator matrix are inconsistent.

Numerical examples show that compared with some existing algorithms, the proposed method has higher convergence efficiency because less data are used in each iteration and the data is sufficient to complete an update. It not only has the best convergence accuracy but also requires the least running time for iteration, which greatly saves memory space.

Compared with previous algorithms, the main feature of this algorithm is that it can synthesize these equations together to get a coupled operator matrix equation. Although the equation of this paper contains multiple submatrix equations, the algorithm in this paper only needs to use the information of one submatrix equation in the equation of this paper in each iteration so that different constraint solutions of different (coupled) matrix equations can be studied for this class of equations. However, previous articles need to iterate on a specific constraint solution of a matrix equation separately.

This paper aims to explore blockchain integration strategies within a three-level livestock meat supply chain in which consumers have a preference for quality trust in livestock meat products. The paper investigates three questions: First, how does consumers’ preference for quality trust affect blockchain integration and transaction decisions among supply chain participants? Second, under what circumstances will retailers choose to participate in the blockchain? Finally, how can other factors such as blockchain costs and supplier–retailer partnership value affect integration decisions?

This paper formulates a supply chain network equilibrium model and employs the logarithmic-quadratic proximal prediction-correction method to obtain equilibrium decisions. Extensive numerical studies are conducted using a pork supply chain network to analyze the implications of blockchain integration for different supply chain participants.

The results reveal several key insights: First, suppliers’ increased blockchain integration, driven by higher quality trust preference, can negatively affect their profits, particularly, with excessive trust preferences and high blockchain costs. Second, an increase in consumers’ preference for quality trust expands the range of unit operating costs for retailers engaging in blockchain. Finally, the supplier–retailer partnership drives retailer blockchain participation, facilitating enhanced information sharing to benefit the entire supply chain.

This study provides original insights into blockchain integration strategies in an agricultural supply chain through the application of the supply chain network equilibrium model. The investigation of several key factors on equilibrium decisions provides important managerial implications for different supply chain participants to address consumers’ preference for quality trust and enhance overall supply chain performance.

The purpose of the study is to analytically as well as numerically investigate the weight of throughflow on the onset of Casson nanofluid layer in a permeable matrix. This study examines both the marginal and over stable kind of convective movement in the system.

A double-phase model is used for Casson nanofluid, which integrates the impacts of thermophoresis and Brownian wave, whereas for flow in the porous matrix the altered Darcy model is occupied under the statement that nanoparticle flux is disappear on the boundaries. The resultant eigenvalue problem is resolved analytically as well as numerically with the help of Galerkin process with the Casson nanofluid Rayleigh–Darcy number as the eigenvalue.

The findings revealed that the throughflow factor postpones the arrival of convective flow and reduces the extent of convective cells, whereas the Casson factor, the Casson nanoparticle Rayleigh–Darcy number and the reformed diffusivity ratio promote convective motion and also decrease the extent of convective cells.

Controlling the convective movement in heat transfer systems that generate high heat flux is a real mechanical challenge. The proposed framework proved that the use of throughflow is one of the most important ways to control the convective movement in Casson nanofluid. To the best of the authors’ knowledge, no inspection has been established in the literature that studies the outcome of throughflow on the Casson nanofluid convective flow in a porous medium layer. However, the convective flow of Casson nanofluid finds many applications in improving heat transmission and energy efficiency in a range of thermal systems, such as the cooling of heat-generating elements in electronic devices, heat exchangers, pharmaceutical practices and hybrid-powered engines, where throughflow can play a significant role in controlling the convective motion.

Transient response of continuous composite material (CCM) fin made of high thermally conductive composite material is presented. The continuously varying effective properties of composite material such as thermal conductivity, heat capacity and density have been modelled using the Mori-Tanaka homogenization theory and rule of mixture. Additionally, temperature dependency of thermal conductivity, heat generation (composite materials) and convection coefficient (fluid properties) have also been incorporated. Different base boundary conditions are addressed such as oscillating heat flow, oscillating temperature, step-changing heat flow and step-changing temperature. At the other boundary, the fin is assumed to have a convective tip.

Lattice Boltzmann method is implemented using an in-house source code for obtaining the numerical solution of typical non-linear heat balance equation of the aforementioned problem under various transient base boundary conditions.

The effects of various thermal parameters such as material diffusivity ratio and conductivity ratio, area ratio and Biot number on transient response of fin and temperature distribution of fins are studied and interpreted. The heat transfer rate and time for attainment of steady state temperature of metal matrix composite (MMC) fin are found to be proportionally dependent on their diffusivity ratio. Additionally for higher values of area ratio and biot number, MMC fins are reported to dissipate the heat more efficiently in comparision to homogeneous fins in terms of time required to attain the steady state and surface temperature.

Response of transient fin associated with advanced class of material can facilitates the practicing engineers for designing high-performance and/or miniaturized thermal management devices as used in electronic packaging industries.

Studies of composite fin consisting of laminating second layer of material over the first layer have been reported previously, however transient response of CCM fin fabricated by continuously varying the volume fraction of two materials along the fin length has not been reported till date. Such material finds its application in thermal management and electronic packaging industries. Results are plotted in form of a graph for different application-wise material combinations that have not been reported earlier, and it can be treated as design data.

The influence of the temperature discrepancy parameter and higher order of the time-derivative is discussed. Classical coupled and generalized hygrothermoelasticity models are recovered by considering the various special cases and illustrated graphically.

The theory of integral transformations has been used to study a new hygrothermal model that includes higher-order time derivatives with three-phase-lags and memory-dependent derivatives (MDD). This model considers the microscopic structure’s influence on a non-simple hygrothermoelastic infinitely long cylinder. The generalized Fourier and Fick’s law was adopted to derive the linearly coupled partial differential equations with higher-order time-differential with the two-phase lag model, including memory-dependent derivatives for the hygrothermal field. The investigation of microstructural interactions and the subsequent hygrothermal change has been undertaken as a result of the delay time and relaxation time translations.

These two-phase-lag models are also practically applicable in modeling nanoscale heat and moisture transport problems applied to almost all important devices. This work will enable future investigators to gain insight into non-simple hygrothermoelasticity with different phase delays of higher order in detail.

To the best of my knowledge, and after completing an intensive search of the relevant literature, the author could not learn any published research that presents a general solution for a higher-order time-fractional three-phase-lag hygrothermoelastic infinite circular cylinder with memory memory-dependent derivative.

The null-space projection method is commonly adopted for controlling redundant robots, which undoubtedly requires the robot Jacobian matrix inverse. This paper aims to provide a novel control scheme, which enables null-space control of redundant robots without conflict with the main task space.

In this paper, an impedance-based null-space control approach for redundant robots is proposed. The null-space degrees of freedom are separated from the primary task space by using the eigenvalue decomposition. Then, a joint impedance controller spans the null space and is reflected into the joint space to manage the redundancy. Finally, several experiments have been conducted to evaluate and validate the performance of the proposed approach in comparison with the null-space projection method under various situations.

Experiment results show that no significant differences were observed between the different filling eigenvalues in the proposed approach under different null-space dimensions and motion velocity. Besides, comparative experiment results demonstrate that the proposed method can achieve comparable results to the null-space projection method. Nevertheless, the suggested approach has benefits regarding the quantity of control parameters in addition to not requiring a Jacobian inverse. Notably, the performance of the proposed method will improve as the null-space dimension increases.

This study presents a new control method for redundant robots, which has advantages for dealing with the problems of controlling redundant robots compared to the existing methods.

Supply chain management has become critical in today’s globalized environment, with growingly intense competition on the international level. The particular characteristics of modern trade have led companies to globalize and devise increasingly sophisticated supply chains to meet customer demand worldwide. Motivated by the need to address these challenges, we have developed a new model for a global supply chain that incorporates uncertainties in exchange rates, demand fluctuations, and the quantity of produce.

The objective of the proposed model is to maximize supply chain profitability. Our model optimizes several critical decisions in the proposed global supply chain, including the location of domestic and foreign distribution centers, allocating the centers to customers, transportation mode selection, storage temperature, optimal farm purchase quantities, product flows across the network, and the shelf-life of products. Scenario-based stochastic programming approach is employed to account for the inherent uncertainties within the model. A pistachio supply chain is examined as a case study in this article, and the efficiency of the proposed model is demonstrated through computational results.

The model was solved using the CPLEX solver in GAMS and the results, the Sirjan DDC and Turkey FDC have been selected. In general, 40% of demand for customers from FDC (turkey) and 60% of demand from DDC (sirjan) is provided. Changes in the demand of foreign customers make the net profit more effective than changes in the demand for domestic customers. The decrease in exchange rate decreases the network profit with a higher slope and the increase in exchange rate will increase network profit with a relatively stable slope.

While research on GSCs for perishable products has been ongoing for several years, the importance of the subject necessitates continued investigation in this area. This paper aimed to address this gap by presenting an optimization model for designing GSCs for perishable products under uncertainty and with various transportation modes. The proposed model was designed with the aim of improving supply chain performance and real-world applicability.