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While today's customer steadily adapts to various modes of shopping, their beliefs around fluency through each shopping channel, and personal factors such as income level, can impact their intention to patronage or purchase from omnichannel department stores. Hence, this study analysed the customers of omnichannel fashion department stores, using perceived fluency and income as indirect factors that help understand customers' patronage intention and purchase intention.

The overarching framework for this research is the theory of reasoned action, in which patronage and purchase intentions represent the specific likelihood-of-performance behaviours. A Seemingly Unrelated Regression model was empirically used to analyse the relationships between generational cohorts, income, and perceived channel fluency and the behaviours that lead to patronage intention and purchase intention. Researchers conducted a survey among 552 omnichannel fashion department store consumers to examine today's retail environment.

The results of this study suggest that (1) consumers between the ages of 50 and 69 years, including older Generation X and younger Baby Boomers, who earn between $60,000 and $79,999 in annual salary show a significantly positive relationship with both patronage and purchase intentions through perceived fluency and (2) consumers between the ages of 38 and 49 years, including older Millennials and younger Generation X, who earn between $80,000 and $99,999 in annual salary show a significantly positive relationship with purchase intention through perceived fluency

This study analyses correlations between a generational cohort, perceived fluency as moderated by income and the relationship between these variables and customers' patronage and purchase intentions, which has not been studied before.

Influenced by the constantly changing manufacturing environment, no single dispatching rule (SDR) can consistently obtain better scheduling results than other rules for the dynamic job-shop scheduling problem (DJSP). Although the dynamic SDR selection classifier (DSSC) mined by traditional data-mining-based scheduling method has shown some improvement in comparison to an SDR, the enhancement is not significant since the rule selected by DSSC is still an SDR.

This paper presents a novel data-mining-based scheduling method for the DJSP with machine failure aiming at minimizing the makespan. Firstly, a scheduling priority relation model (SPRM) is constructed to determine the appropriate priority relation between two operations based on the production system state and the difference between their priority values calculated using multiple SDRs. Subsequently, a training sample acquisition mechanism based on the optimal scheduling schemes is proposed to acquire training samples for the SPRM. Furthermore, feature selection and machine learning are conducted using the genetic algorithm and extreme learning machine to mine the SPRM.

Results from numerical experiments demonstrate that the SPRM, mined by the proposed method, not only achieves better scheduling results in most manufacturing environments but also maintains a higher level of stability in diverse manufacturing environments than an SDR and the DSSC.

This paper constructs a SPRM and mines it based on data mining technologies to obtain better results than an SDR and the DSSC in various manufacturing environments.

In this paper, improvements in reducing transmitted accelerations in a full vehicle are obtained by optimizing the gain parameters of an active control in a roughness road profile.

For a classically designed linear quadratic regulator (LQR) control, the vibration attenuation performance will depend on weighting matrices Q and R. A methodology is proposed in this work to determine the optimal elements of these matrices by using a genetic algorithm method to get enhanced controller performance. The active control is implemented in an eight degrees of freedom (8-DOF) vehicle suspension model, subjected to a standard ISO road profile. The control performance is compared against a controlled system with few Q and R parameters, an active system without optimized gain matrices, and an optimized passive system.

The control with 12 optimized parameters for Q and R provided the best vibration attenuation, reducing significantly the Root Mean Square (RMS) accelerations at the driver’s seat and car body.

The research has positive implications in a wide class of active control systems, especially those based on a LQR, which was verified by the multibody dynamic systems tested in the paper.

Better active control gains can be devised to improve performance in vibration attenuation.

The main contribution proposed in this work is the improvement of the Q and R parameters simultaneously, in a full 8-DOF vehicle model, which minimizes the driver’s seat acceleration and, at the same time, guarantees vehicle safety.

To improve the accuracy of stock price trend prediction in the field of quantitative financial trading, this paper takes the prediction accuracy as the goal and avoid the enormous number of network structures and hyperparameter adjustments of long-short-term memory (LSTM).

In this paper, an adaptive genetic algorithm based on individual ordering is used to optimize the network structure and hyperparameters of the LSTM neural network automatically.

The simulation results show that the accuracy of the rise and fall of the stock outperform than the model with LSTM only as well as other machine learning models. Furthermore, the efficiency of parameter adjustment is greatly higher than other hyperparameter optimization methods.

(1) The AGA-LSTM algorithm is used to input various hyperparameter combinations into genetic algorithm to find the best hyperparameter combination. Compared with other models, it has higher accuracy in predicting the up and down trend of stock prices in the next day. (2) Adopting real coding, elitist preservation and self-adaptive adjustment of crossover and mutation probability based on individual ordering in the part of genetic algorithm, the algorithm is computationally efficient and the results are more likely to converge to the global optimum.

This paper aims to focus on the spatial docking task of unmanned vehicles under ground conditions. The docking task of military unmanned vehicle application scenarios has strict requirements. Therefore, how to design a docking robot mechanism to achieve accurate docking between vehicles has become a challenge.

In this paper, first, the docking mechanism system is described, and the inverse kinematics model of the docking robot based on Stewart is established. Second, the genetic algorithm-based optimization method for multiobjective parameters of parallel mechanisms including workspace volume and mechanism flexibility is proposed to solve the problem of multiparameter optimization of parallel mechanism and realize the docking of unmanned vehicle space flexibility. The optimization results verify that the structural parameters meet the design requirements. Besides, the static and dynamic finite element analysis are carried out to verify the structural strength and dynamic performance of the docking robot according to the stiffness, strength, dead load and dynamic performance of the docking robot. Finally, taking the docking robot as the experimental platform, experiments are carried out under different working conditions, and the experimental results verify that the docking robot can achieve accurate docking tasks.

Experiments on the docking robot that the proposed design and optimization method has a good effect on structural strength and control accuracy. The experimental results verify that the docking robot mechanism can achieve accurate docking tasks, which is expected to provide technical guidance and reference for unmanned vehicles docking technology.

This research can provide technical guidance and reference for spatial docking task of unmanned vehicles under the ground conditions. It can also provide ideas for space docking missions, such as space simulator docking.

The objective of the present work is to present the design optimization of composite cylindrical shell subjected to an axial compressive load and lateral pressure.

A novel optimization method is developed to predict the optimal fiber orientation in composite cylindrical shell. The optimization is carried out by coupling analytical and finite element (FE) results with a genetic algorithm (GA)-based optimization scheme developed in MATLAB. Linear eigenvalue were performed to evaluate the buckling behaviour of composite cylinders. In analytical part, besides the buckling analysis, Tsai-Wu failure criteria are employed to analyse the failure of the composite structure.

The optimal result obtained through this study is compared with traditionally used laminates with 0, 90, ±45 orientation. The results suggest that the application of this novel optimization algorithm leads to an increase of 94% in buckling strength.

The proposed optimal fiber orientation can provide a practical and efficient way for the designers to evaluate the buckling pressure of the composite shells in the design stage.

Since the 1970s and 1980s, subsequent waves of so-called ‘new immigration’ have arrived in the United States and Europe. In the United States, this immigration started with the arrival of immigrants and asylum-seekers from Mexico, Central America and Asia. In Europe, the trend began with the influx of Turkish and Moroccan immigrants and continues today with the ongoing refugee crisis. Anti-immigrant politicians on both sides of the Atlantic have adopted exclusionary and often xenophobic rhetoric to further their policies, arguing that these new immigrants and their children cannot assimilate into Western society. A literature review reveals why the classical linear theory of second-generation assimilation is no longer relevant and proposes the contemporary segmented assimilation and comparative integration context theories developed by US and European researchers. A presentation of the findings of two state-of-the-art studies – the CILS project for the United States context and the TIES project for the European context – provides empirical evidence that, despite undeniable obstacles, the new second generation can assimilate into Western education systems and labour markets. Nonetheless, gaps in the existing literature also suggest the need for further research to create a more generalisable theory of second-generation assimilation before appropriate policy measures can be implemented.

This study aims to investigate the influence of wall curvature in a semicircular thermal annular system on magneto-nanofluidic flow, heat transfer and entropy generation. The analysis is conducted under constant cooling surface and fluid volume constraints.

The mathematical equations describing the thermo-fluid flow in the semicircular system are solved using the finite element technique. Four different heating wall configurations are considered, varying the undulation numbers of the heated wall. Parametric variations of bottom wall undulation (f), buoyancy force characterized by the Rayleigh number (Ra), magnetic field strength represented by the Hartmann number (Ha) and inclination of the magnetic field (γ) on the overall thermal performance are studied extensively.

This study reveals that the fluid circulation strength is maximum in the case of a flat bottom wall. The analysis shows that the bottom wall contour and other control parameters significantly influence fluid flow, entropy production and heat transfer. The modified heated wall with a single undulation exhibits the highest entropy production and thermal convection, leading to a heat transfer enhancement of up to 21.85% compared to a flat bottom. The magnetic field intensity and orientation have a significant effect on heat transfer and irreversibility production.

Further research can explore a wider range of parameter values, alternative heating wall profiles and boundary conditions to expand the understanding of magneto-nanofluidic flow in semicircular thermal systems.

This study introduces a constraint-based analysis of magneto-nanofluidic thermal behavior in a complex semicircular thermal system, providing insights into the impact of wall curvature on heat transfer performance. The findings contribute to the design and optimization of thermal systems in various applications.

The purpose of this study is to gain more understanding of how competence might matter from the perspective of well-being at work. The authors explore how perceived competence is connected to perceived work-related well-being among Baby Boomer, Generation X and Generation Y employees.

The authors explore how perceived competence is connected to perceived work-related well-being among Baby Boomer, Generation X and Generation Y employees. The frames of reference of the study are based on literature on employee well-being (measured with work engagement and overcommitment) and competence, as well as on generational discussions. The quantitative, questionnaire-based study was conducted in 88 companies in Finland, with the total number of respondents being 4,418.

The main finding was that perceived competence related to current duties is statistically significantly connected to employee well-being. The results indicate that high competence results in high employee well-being in all generational groups. Further, Generation Y estimated their work well-being, both in terms of work engagement and overcommitment, lower than Baby Boomers or Generation X. The results suggest that developing competence of employees in organizations seems to be an important means to also support work well-being. It is especially important to pay attention to that among Generation Y, who take their first steps in working life. Competence is a meaningful factor for coping in working life in continually changing work environments. Incompetence is not just a factor for poor performance but also a potential threat to employee well-being.

Most of the competence/workplace learning results research concentrates on cognitive competence and skills, often from the employers’ benefit viewpoint (useful skills, productivity increase). This study starts from the finding that new generations of workers rather look for a meaningful work life, and thus, a feeling of having the necessary competences directly improves their well-being and, thus, life quality. Furthermore, the study is based on an original questionnaire-based study conducted in 88 companies in Finland, with the total number of respondents being 4,418.

The Covid-19 pandemic has brought unexpected implications for the world of work, accelerating the use of digital technologies and hybrid workspaces, and posing new questions on how to manage working relationships. This chapter explores whether employee empowerment experiences can ensure better work-life connections. Empowerment involves a permanent transfer of power from the line manager to the employee. Although not all line managers are willing to use it as a development tool for fear of seeing their role downsized, research has been conducted to better understand the empowerment experiences of Generation Z employees, identifying positive and negative aspects of the relationship with their line managers. Generation Z employees have certain expectations when it comes to their job that are not always met, and understanding and managing these expectations through empowerment has great value to shape the future of organizations and create a better work-life connection for upcoming generations of workers. The chapter adopts a new conceptual framework for understanding employees’ empowerment experiences, proposes specific structural actions that line managers can take and reflects on the implications of employee empowerment for the HR function in terms of work-life interface.