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In a boundary layer ingestion (BLI) propulsion system, the fan operates continuously under distorted inflow conditions, leading to an increment of aerodynamic loss and in turn impacting the potential fuel burn reduction of the aircraft. Usually, in the preliminary design stage of a BLI propulsion system, it is essential to assess the impact of fuselage boundary layer fluids on fan aerodynamic performances under various flight conditions. However, the hub region flow loss is one of the major loss sources in a fan and would greatly influence the fan performances. Moreover, the inflow distortion also results in a complex and highly nonlinear mapping relation between loss and local physical parameters. It will diminish the prediction accuracy of the commonly used low-fidelity computational approaches which often incorporate traditional physics-based loss models, reducing the reliability of these approaches in evaluating fan performances. Meanwhile, the high-fidelity full-annulus unsteady Reynolds-averaged Navier–Stokes (URANS) approach, even though it can give rather accurate loss predictions, is extremely time-consuming. This study aims to develop a fast and accurate hub loss prediction method for a BLI fan under distorted inflow conditions.

This paper develops a data-driven hub loss prediction method for a BLI fan under distorted inflows. To improve the prediction accuracy and applicability, physical understandings of hub flow features are integrated into the modeling process. Then, the key physical parameters related to flow loss are screened by conducting a sensitivity analysis of influencing parameters. Next, a quasi-steady assumption of flow is made to generate a training sample database, reducing the computational time by acquiring one single sample from the highly time-consuming full-annulus URANS approach to a cost-efficient single-blade-passage approach. Finally, a radial basis function neural network is used to establish a surrogate model that correlates the input parameters and the output loss.

The data-driven hub loss model shows higher prediction accuracy than the traditional physics-based loss models. It can accurately capture the circumferentially and radially nonuniform variation trends of the losses and the associated absolute magnitudes in a BLI fan under different blade load, inlet distortion intensity and rotating speed conditions. Compared with the high-fidelity full-annulus URANS results, the averaged relative prediction errors of the data-driven hub loss model are kept less than 10%.

The originality of this paper lies in developing a new method for predicting flow loss in a BLI fan rotor blade hub region. This method offers higher prediction accuracy than the traditional loss models and lower computational time cost than the full-annulus URANS approach, which could realize fast evaluations of fan aerodynamic performances and provide technical support for designing high-performance BLI fans.

Half of adulthood mental health challenges begin by the age of 14-years-old, making the need for early-intervention clear. This study aims to evaluate a new service model that promotes early-intervention through a community based low-intensity Hub.

Clinical data from 2,384 young people were analysed through within-group, pre- and post-comparisons and qualitative survey, and interview data was analysed through content analysis.

Overall, participants reported that they were highly satisfied with the Hub and the low-intensity brief interventions met their needs. Participants reported that learning new skills, having a place to talk and positive therapeutic relationships were beneficial. The Hub appeared to be less successful for young people with complex mental health difficulties. As a service, the adoption of the Hub model reduced waiting list times by more than half.

The quantitative data demonstrated that engaging with the Hub reduced symptoms of psychological distress. Qualitative analyses suggest that access to local, community, welcoming and “less clinical” support was beneficial, and the type of brief interventions offered was less important than therapeutic relationships.

This is the first study of a novel “Hub” model for low-intensity brief interventions in a socio-economically deprived area of England. Local knowledge, community integrated support, therapeutic relationships and a welcoming environment were viewed as more beneficial than the type of brief interventions offered. Consequently, community spaces can be created to be therapeutic and beneficial for mental health outside of a traditional conceptualisation of clinical support.

Pin-loaded hubs with fitted bush are used in industrial connector-type elements. They are subjected to varying radial forces leading to variable stress distribution. The literature provides various pressure distribution expressions adapted essentially for symmetric geometries and fixed load condition (circular hubs, half-infinite geometries, axial load, tangential load, etc.). This study aims to take into account the geometrical conditions of industrial connector-type elements and presents a model for pressure distribution based only on geometric parameters, maximal pressure and contact angle value for the case of fit pin-loaded hub.

The finite element computation for the contact problem shows that the pressure distribution of the pin-loaded hub under various inclined forces (from 0° to 180°) is a parabolic distribution. This distribution can be defined by three parameters which are θ_A, θ_B and P_max. The study assumes that the distribution is symmetric and that P_max can be modeled using force F, hub radius R, hub thickness b and the half contact angle are θ_A.

The new proposal pressure distribution parameters are easy to identify. Even for the non-symmetric pressure distribution, the study denotes that the errors on evaluating θ_A and θ_B keep the analytical model still in good agreement with finite element computations.

Only the neat fit case was studied.

Pin-loaded joints are connector-type elements used in mechanical assemblies to connect any structural components and linkage mechanisms such as connecting rod ends of automotive or shear joints for aircraft structure.

The good correlation between finite element computations and model results shows the validity of the assumptions adopted here. Analytical fatigue models, based on this stress distribution, could be derived in view of a fatigue lifetime calculation on connecting hub. Friction, pin deformation and local plastic effects under pin-loading are the main phenomena to take into account to further enrich this model.

The paper aims to study the challenges and solutions of city logistics in the new retail era. The new retail, which is characterized by omni-channel, fragmented orders and decentralized 2C distribution, is becoming the mainstream of the retail industry worldwide. In order to achieve a comprehensive breakthrough in new retail, the change of order fulfillment mode is the most noteworthy issue. The aim of this paper is to design a city logistics operation model for new retail and verify its feasibility and efficiency.

A physical internet (PI) enabled two-tier city logistics solution is proposed by redefining the key facilities in city logistics with the PI concept. A “Container-as-a-Warehouse” operation mode is designed to provide a more flexible store and transfer solution. A mathematical model of the proposed solution is established. An adaptive large neighborhood search (ALNS) is designed based upon an iterative procedure, which ensures consistent and optimal results.

To quantitatively assess the feasibility of the proposed solution, a computational experiment is designed to compare the performance of the proposed model against the conventional two-tier city logistics operation. The effects of geographical location pattern, utilization of PI-hub as well as the fluctuation of customer orders are analyzed. The results show that the PI-enabled city logistics is more advantageous than the conventional solution.

This study does not consider the impact of new technologies in city logistics; for example, the replenishment problem of unmanned vending machines and the charging problem of electric vehicles.

The proposed PI-enabled solution and analysis results in this paper have positive guiding significance for future practical application.

Based on the concept of PI, this paper proposes an innovative and practical operation model to solve the city logistics challenges.

The mischievous nodes that defy the standard corrupt the exhibition of good nodes considerably. Therefore, an intrusion discovery mechanism should be included to the mobile ad-hoc network (MANET). In this paper, worm-hole and other destructive malignant attacks are propelled in MANET.

A wireless ad-hoc network also called as mobile ad-hoc network (MANET) is a gathering of hubs that utilizes a wireless channel to exchange information and coordinate together to establish information exchange among any pair of hubs, without any centralized structure. The security issue is a major difficulty while employing MANETs.

Consequently, the attacks due to the malicious node activity are detected using Hybrid Reactive Search and Bat (HRSB) mechanism to prevent the mischievous nodes from entering the network beneath the untruthful information. Moreover, the attack detection rate and node energy are predicted for determining the lifetime of the node.

The simulation outcomes of the proposed HRSB technique are evaluated with the prevailing methods. The comparison studies have proven the efficacy of the current research model by attaining high attack detection rate and achieving more network lifetime.

In this paper, a first‐order approximation coupling (FOAC) model is investigated to analyze the dynamics of the hub‐beam system, which is based on the Hamilton theory and the finite element discretization method. The FOAC model for the hub‐beam system considers the second‐order coupling quantity of the axial displacement caused by the transverse displacement of the beam. The dynamic characteristics of the system are studied through numerical simulations under twos cases: the rotary inertia of the hub is much larger than, and is close to, that of the flexible beam. Simulation and comparison studies using both the traditional zeroth‐order approximation coupling (ZOAC) model and the FOAC model shows that when large motion of the system is unknown, possible failure exists by using the ZOAC model, whereas the FOAC model is valid. When the rotary inertia of the hub is much larger than that of the beam, the result using the ZOAC model is similar to that using the FOAC model. But when the rotary inertia of the hub is close to that of the beam, the ZOAC model may lead to a large error, while the FOAC model can still accurately describe the dynamic hub‐beam system.

This paper aims to investigate the location of regional and international hub ports in liner shipping by proposing a hierarchical hub location problem.

This paper develops a mixed-integer linear programming model for the authors’ proposed problem. Numerical experiments based on a realistic Asia-Europe-Oceania liner shipping network are carried out to account for the effectiveness of this model.

The results show that one international hub port (i.e. Rotterdam) and one regional hub port (i.e. Zeebrugge) are opened in Europe. Two international hub ports (i.e. Sokhna and Salalah) are located in Western Asia, where no regional hub port is established. One international hub port (i.e. Colombo) and one regional hub port (i.e. Cochin) are opened in Southern Asia. One international hub port (i.e. Singapore) and one regional hub port (i.e. Jakarta) are opened in Southeastern Asia and Australia. Three international hub ports (i.e. Hong Kong, Shanghai and Yokohama) and two regional hub ports (i.e. Qingdao and Kwangyang) are opened in Eastern Asia.

This paper proposes a hierarchical hub location problem, in which the authors distinguish between regional and international hub ports in liner shipping. Moreover, scale economies in ship size are considered. Furthermore, the proposed problem introduces the main ports.

The purpose of this paper is to explore how hubs’ social influence on social network decisions can cause the behavior of information cascades in a market.

The authors establish understanding of the fundamental mechanism of information cascades through a computational simulation approach.

Eigenvector centrality, betweenness centrality, and PageRank are statistically correlated with the occurrence of information cascades among agents; the hubs’ incorrect decisions in the early diffusion stage can significantly cause misled shift cascades; and the bridge role of hubs is more influential than their pivotal position role in the process of misled shift cascades.

This implication can be extendable in the field of marketing, sequential voting, and technology, or innovation adoption.

Lessons are drawn from the experience of the logistics hub at Amsterdam's Schiphol Airport for Korea s Inc he on International Airport. After providing an overview of the air cargo market the key factors underpinning the success of air cargo hubs are outlined. Examples are provided of Schiphol's multi-level strategy that aims at being simultaneously an air transport hub, a multimodal hub, and a logistics hub as a part of the airport city strategy. Consideration needs to be given to how the successful development of an air transport hub depends on the capacity problems at other competing air traffic nodes in the region. With this background, interest is centered upon the prospects of the Incheon International Airport becoming a cargo hub for Japan.

This paper aims to study the hub transportation system in supply chain networks which would contribute to reducing costs and environmental pollution, as well as to economic development and social responsibility. As not all customers tend to buy green products, several customer groups should be considered in terms of need type.

In this paper, a multi-objective hub location problem is developed for designing a sustainable supply chain network based on customer segmentation. It deals with the aspects of economic (cost reduction), environment (minimizing greenhouse gas emissions by the transport sector) and social responsibility (creating employment and community development). The epsilon-constraint method and augmented epsilon-constraint (AEC) method are used to solve the small-sized instances of this multi-objective problem. Due to the non-deterministic polynomial-time hardness of this problem, two non-dominated sorting genetic algorithm-II (NSGA-II) and multi-objective grey wolf optimizer (MOGWO) metaheuristic algorithms are also applied to tackle the large-sized instances of this problem.

As expected, the AEC method is able to provide better Pareto solutions according to the goals of the decision-makers. The Taguchi method was used for setting the parameters of the two metaheuristic algorithms. Considering the meaningful difference, the MOGWO algorithm outperforms the NSGA-II algorithm according to the rate of achievement to two objectives simultaneously and the spread of non-dominance solutions indexes. Regarding the other indexes, there was no meaningful difference between the performance of the two algorithms.

The model of this research provides a comprehensive solution for supply chain companies that want to achieve a rational balance between the three aspects of sustainability.

The importance of considering customer diversity on the one hand and saving on hub transportation costs, on the other hand, triggered us to propose a hub location model for designing a sustainable supply chain network based on customer segmentation.