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The purpose of this study is to investigate the tribological performance of helical gear pairs with consideration of the properties of non-Newtonian lubricant and the real three-dimensional (3D) topography of tooth flanks.

Based on the mixed elastohydrodynamic lubrication (EHL) theory for infinite line contact, this paper proposes a complete model for involute helical gear pairs considering the real 3D topography of tooth flanks and the properties of non-Newtonian lubricant. Film thickness, contact load and contact area ratios at the mid-point of contact line are studied for each angular displacement of pinion. Both the total friction coefficient and surface flash temperature are calculated after obtaining the values of pressure and subsurface stress. Then, the influences of input parameters including rotational speed and power are investigated.

During the meshing process, contact load ratio and area ratio of the two rough surface cases first increase and then decrease; the maximum flash temperature rise (MFTR) on the gear is lower than that on the pinion first, but later the situation converses. For cylindrical gears, on the plane of action, there is a point or a line where the instantaneous friction reduces to a minimum value in a sudden, as the sliding–rolling ratio becomes zero. When rotational speed increases, film thickness becomes larger, and meanwhile, contact load ratio, coefficient of friction and MFTR gradually reduce.

A comprehensive analysis is conducted and a computer program is developed for meshing geometry, kinematics, tooth contact, mixed EHL characteristics, friction, FTR and subsurface stress of involute helical gear pairs. Besides, a numerical simulation model is developed, which can be used to analyze mixed lubrication with 3D machined roughness under a wide range of operating conditions.

This chapter reviews graphical modeling techniques for estimating large covariance matrices and their inverse. The chapter provides a selective survey of different models and estimators proposed by the graphical modeling literature and offers some practical examples where these methods could be applied in the area of health economics.

With burgeoning interest in the low-altitude economy, applications of long-endurance unmanned aerial vehicles (LE-UAVs) have increased in remote logistics distribution. Given LE-UAVs’ advantages of wide coverage, strong versatility and low cost, in addition to logistics distribution, they are widely used in military reconnaissance, communication relay, disaster monitoring and other activities. With limited autonomous intelligence, LE-UAVs require regular periodic and non-periodic control from ground control resources (GCRs) during flights and mission execution. However, the lack of GCRs significantly restricts the applications of LE-UAVs in parallel.

We consider the constraints of GCRs, investigating an integrated optimization problem of multi-LE-UAV mission planning and GCR allocation (Multi-U&G IOP). The problem integrates GCR allocation into traditional multi-UAV cooperative mission planning. The coupling decision of mission planning and GCR allocation enlarges the decision space and adds complexities to the problem’s structure. Through characterizing the problem, this study establishes a mixed integer linear programming (MILP) model for the integrated optimization problem. To solve the problem, we develop a three-stage iterative optimization algorithm combining a hybrid genetic algorithm with local search-variable neighborhood decent, heuristic conflict elimination and post-optimization of GCR allocation.

Numerical experimental results show that our developed algorithm can solve the problem efficiently and exceeds the solution performance of the solver CPLEX. For small-scale instances, our algorithm can obtain optimal solutions in less time than CPLEX. For large-scale instances, our algorithm produces better results in one hour than CPLEX does. Implementing our approach allows efficient coordination of multiple UAVs, enabling faster mission completion with a minimal number of GCRs.

Drawing on the interplay between LE-UAVs and GCRs and considering the practical applications of LE-UAVs, we propose the Multi-U&G IOP problem. We formulate this problem as a MILP model aiming to minimize the maximum task completion time (makespan). Furthermore, we present a relaxation model for this problem. To efficiently address the MILP model, we develop a three-stage iterative optimization algorithm. Subsequently, we verify the efficacy of our algorithm through extensive experimentation across various scenarios.

The purpose of this study is to look at the factors that drive people to participate in the sharing economy (SE). Based on the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), the study proposes a mathematical model. The study’s ultimate objective is to help businesses attract more involved customers and promote collaborative consumption as a sustainable alternative to typical consumption patterns. The study offers a conceptual framework established via a thorough literature review to examine Indian customers’ use behavior toward SE platforms. A one-sample two-tailed t-test is used to assess the framework’s efficacy. The research fills gap in the literature on the SE by investigating the factors that determine subjective norms (SN), attitudes (A), and perceived behavioral control (PBC). A framework is provided that takes behavioral intention (BI) contemplated as a mediating variable. The research improves TAM and TPB by including new factors such as technical characteristics. This research adds to the body of knowledge on the digital SE by underlining the relevance of usage behavior in comprehending Indian customers, where A, SN, and PBC are important aspects. The research presents a paradigm for better understanding customers’ attitudes and behaviors toward various SE platforms, which might help academics, practitioners, and policy makers situate their initiatives within the larger field of sharing. The study’s categorizations of Indian consumers’ A, SN, PBC, and BI toward the SE might potentially advise on future research and government policies.

The chapter explored integrating smart construction techniques in achieving stealth construction objectives, emphasising the development of building cross-sections, visibility management, energy transmission optimisation, and countermeasure implementation. It delved into the multifaceted aspects of smart construction towards achieving stealth construction goals, including environmental protection, enhanced construction safety, accelerated construction duration, cost-effectiveness, and aesthetic considerations. Furthermore, the chapter underscores the importance of leveraging innovative approaches and advanced technologies to meet the evolving demands of stealth construction projects and pave the way for sustainable, safe, and aesthetically pleasing built environments.

The purpose of this paper is to propose a new outer‐rotor permanent‐magnet flux‐switching machine (ORPMFSM) for electric vehicle (EV) in‐wheel propulsion. The paper documents both the design procedure and performance investigation of this novel machine.

The topology and preliminary sizing equations of the ORPMFSM are introduced. The rotor poles are optimized, whilst the machine losses are particularly investigated, using 2‐D finite element analysis (FEA).

An ORPMFSM, with 12 stator poles and 22 rotor poles, is most suitable for the proposed EV application. The analytical sizing equations are quite efficient with a sufficient accuracy for the preliminary design. The optimal rotor pole width from the FEA results is nearly 1.3 times the original value which was proposed in early literatures. The efficiency of the proposed machine under rated load is slightly low, as a result of significant eddy current losses in the permanent magnets. The losses can be effectively suppressed with the technique of magnet segmenting. The predicted outstanding performance implies that by adopting magnet segmentation the proposed machine is a leading contender for EV direct drives.

The outer‐rotor structure of PMFSM was not addressed in early literatures. This paper provides designers with the technical background and an alternative candidate for the EV propulsion.

The paper purposes a novel SFPM machine topology with radial and circumferential permanent magnets (PMs). The paper aims to discuss this issue.

In order to reduce the flux leakage in the stator-outer region and consequently achieve higher magnetic material utilization in switched flux permanent magnet (SFPM) machine, a novel topology with radial and circumferential PMs is proposed. This topology (SFRCPM) has the same structure as conventional SFPM (CSFPM) machine except of the additional set of radially magnetized PMs located around the back iron and surrounded by a laminated ring frame. Using finite element analysis (FEA) the influence of the design parameters on the performance is investigated in order to obtain an effective optimization procedure. Internal and external rotor SFRCPM machines with either NdFeB or ferrite magnets are investigated, optimized and compared with the CSFPM machine having the same size, copper loss and stator/rotor pole combination.

It is concluded that comparing SFRCPM with its CSFPM machine counterpart, internal rotor SFRCPM machine can achieve high PM flux-linkage per magnet volume, however reduced slot area leads to low output torque, whereas external rotor SFRCPM machine can produce higher torque and torque per magnet volume.

This paper proposes a novel SFPM machine topology.

The purpose of this paper is to investigate and compare the influence of end-effect on the torque-speed characteristics of three conventional switched flux permanent magnet (SFPM) machines having different stator/rotor pole combinations, i.e. 12/10, 12/13 and 12/14 as well as three novel topologies with less permanent magnets (PMs), i.e. multi-tooth, E-core and C-core.

SFPM machines combine the advantages of simple and robust rotor and easy management of the temperature due to the location of the PMs and armature windings on the stator. However, due to spoke location of the PMs a large flux leakage in the end region, i.e. end-effect, can be observed which could result in a large reduction in the electromagnetic performance. Therefore, the influence of end-effect on the torque-speed characteristics is investigated. 3D-finite element analyses (FEA) results are compared with their 2D-FEA counterparts in order to account for the end-effect influence.

It has been concluded that due to end flux leakage, lower torque capability in the constant torque region is observed in the six machines. However, improved flux-weakening capability in the conventional machines can be exhibited at high current levels, whereas due to the large inductance lower power capability in the multi-tooth, E-core and C-core machines is obtained.

The influence of temperature rise on the performance is not included.

This paper has analysed the influence of end-effect on the torque-speed characteristics of several SFPM machines.

The purpose of this paper is to study the importance of image data in hotel selection-recommendation using different types of cognitive features and to explore whether there are reinforcing effects among these cognitive features.

This study represents user-generated images “cognitive” in a knowledge graph through multidimensional (shallow, middle and deep) analysis. This approach highlights the clustering of hotel destination imagery.

This study develops a novel hotel selection-recommendation model based on image sentiment and attribute representation within the construction of a knowledge graph. Furthermore, the experimental results show an enhanced effect between different types of cognitive features and hotel selection-recommendation.

This study enhances hotel recommendation accuracy and user satisfaction by incorporating cognitive and emotional image attributes into knowledge graphs using advanced machine learning and computer vision techniques.

This study advances the understanding of user-generated images’ impact on hotel selection, helping users make better decisions and enabling marketers to understand users’ preferences and trends.

This research is one of the first to propose a new method for exploring the cognitive dimensions of hotel image data. Furthermore, multi-dimensional cognitive features can effectively enhance the selection-recommendation process, and the authors have proposed a novel hotel selection-recommendation model.

The purpose of this study is to propose a synthetic post-adoption model based on the expectation-confirmation model (ECM) and flow theory to examine whether the fit factor, network factors and psychological factors as antecedents to end-users’ beliefs can affect their continuance intention of the robo-advisor.

This study used the research model based on ECM and flow theory to examine the effects of the fit factor, network factors and psychological factors on end-users’ beliefs and continuance intention of the robo-advisor. Sample data were collected from end-users at three financial services companies in Taiwan. A total of 450 questionnaires were distributed and 360 (80.0%) usable questionnaires were analyzed using structural equation modeling.

This study proposes a solid research model that based on ECM and flow theory, three types of factors, namely, fit factor, network factors and psychological factors, as antecedents to end-users’ continuance intention of the robo-advisor have been examined and this study’s results strongly support the research model with all hypothesized links being significant.

It is particularly worth mentioning that a synthetic post-adoption model can be proposed in this study by introducing the fit factor extracted from task-technology fit model, network factors originated from the theory of network externalities and psychological factors derived from uses and gratifications theory as antecedents to perceived usefulness, confirmation, satisfaction and continuance intention referred in ECM and flow experience derived from flow theory. Thus, this study’s research model and findings can reveal deep insights into the evaluation of determinants in the field of end-users’ continuance intention of the robo-advisor.