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The e-freight program launched by the International Air Transport Association (IATA) has gradually become a standard specification for international air freight operations. This study examined critical factors affecting air freight forwarders’ decision to adopt the IATA e-freight using a technology-organization-environment model with air freight forwarders in Taiwan as the base. Our findings show that ‘information technology (IT) competence’, ‘trading partner pressure’, ‘government policy’ and ‘competitive pressure’ all have significant positive effects on air freight forwarders’ decision to adopt the e-freight and the top three factors among these are ‘government funding’, ‘government’s active promotion’ and ‘government’s requirement of electronic air waybill (e-AWB)’. Finally, this study proposes strategies that can encourage air freight forwarders to decide on e-freight adoption for the information of relevant oK regyawniozradtison International Air Transport Association (IATA); IATA e-freight; Technology organization environment model; Air freight forwarder

Despite the growing research interest in gender diversity, the presence of female executives and organizational outcomes, the relationship between female executives and employment relations outcomes remains under-researched. This study aims to examine the potential relationship between female executives and employment relations outcomes, with the gender gap as a focus.

A cross-sectional survey was used to collect data from 2,682 workers from 119 manufacturing firms in Guangdong Province, southern China.

Results show that firms with female executives are more likely to comply with labor laws and promote staff development. The association between female executives and promotion opportunities is stronger for female employees than for male employees. However, there is no significant association between female executives and employee salaries.

This research contributes to employment relations literature and extends the application of social role theory to studies of employment relations in particular societal contexts. This study also provides possible boundary conditions for the existence of queen bee behavior by using data from Chinese factories.

Nowadays, artificial intelligence (AI) technology has demonstrated extensive applications in the field of art design. Attribute editing is an important means to realize clothing style and color design via computer language, which aims to edit and control the garment image based on the specified target attributes while preserving other details from the original image. The current image attribute editing model often generates images containing missing or redundant attributes. To address the problem, this paper aims for a novel design method utilizing the Fashion-attribute generative adversarial network (AttGAN) model was proposed for image attribute editing specifically tailored to women’s blouses.

The proposed design method primarily focuses on optimizing the feature extraction network and loss function. To enhance the feature extraction capability of the model, an increase in the number of layers in the feature extraction network was implemented, and the structure similarity index measure (SSIM) loss function was employed to ensure the independent attributes of the original image were consistent. The characteristic-preserving virtual try-on network (CP_VTON) dataset was used for train-ing to enable the editing of sleeve length and color specifically for women’s blouse.

The experimental results demonstrate that the optimization model’s generated outputs have significantly reduced problems related to missing attributes or visual redundancy. Through a comparative analysis of the numerical changes in the SSIM and peak signal-to-noise ratio (PSNR) before and after the model refinement, it was observed that the improved SSIM increased substantially by 27.4%, and the PSNR increased by 2.8%, serving as empirical evidence of the effectiveness of incorporating the SSIM loss function.

The proposed algorithm provides a promising tool for precise image editing of women’s blouses based on the GAN. This introduces a new approach to eliminate semantic expression errors in image editing, thereby contributing to the development of AI in clothing design.

The purpose of this paper is to bridge the gap between human emotions and wearable technologies for interactive fashion innovation. To consider the reasons why smart clothing should satisfy the internet of things (IoT) technical functions and human emotional expression simultaneously, to investigate the manner in which artistic design perspectives and engineering methods combined effectively, to explore the R&D elements of future smart clothing based on the IoT technology.

This study combines artistic design perspectives with information-sensing engineering methods as well as kansei evaluation method. Micro-sensors and light-emitting diodes (LEDs) embedded in couples clothing prototype. The first experiment step in the design and production of prototype clothing, and do the initial emotional evaluation. The second experiment is the comparative evaluation of the prototype and other typical smart clothing.

The interactive clothing prototype was proven to correlate well with human emotional expressive patterns. The evaluation I indicated the prototype can stimulate the emotional response of the participants to achieve a higher score in the activate sensor state. Evaluation II revealed that in the process of interactive clothing design, the technical functionality should synchronize with the requirements of human emotional expression.

This study builds the research and development theoretical model of interactive clothing that can be integrated into daily smart clothing life design, and analyze the methods and means of blending IoT smart information-sensing technology with emotional design. By means of this experimental demonstration of human-centered interactive clothing design, the authors provide smart clothing 3.0 evolutionary roadmap and propose a new concept of internet of clothes (IoC) for further research reference.

Precast construction has become increasingly popular in the construction industry. Nonetheless, the logistics of construction materials has been a neglected topic, and this neglect has resulted in delays and cost overruns. Careful planning that considers all of the factors affecting construction logistics can ensure project success. The purpose of this paper is to examine the potential for using genetic algorithms (GAs) to derive logistics plans for materials production, supply and consumption.

The proposed GA model is based on the logistics of precast components from the supplier’s production yard, to the intermediate warehouse and then to the construction site. Using an activity-based costing (ABC) approach, the model not only considers the project schedule, but also takes into account the production and delivery schedule and storage of materials.

The results show that GAs are suitable for solving time-cost trade-off problems. The optimization process helps to identify the activity start time during construction and the delivery frequency that will result in the minimal cost. What-if scenarios can be introduced to examine the effects of changes in construction logistics on project outcomes.

This paper presents a method for using GAs and an ABC approach to support construction logistics planning decisions. It will help construction planners and materials suppliers to establish material consumption and delivery schedules, rather than relying on subjective judgment.

Recently, deep learning (DL) has been widely applied in various aspects of human endeavors. However, studies have shown that DL models may also be a primary cause of data leakage, which raises new data privacy concerns. Membership inference attacks (MIAs) are prominent threats to user privacy from DL model training data, as attackers investigate whether specific data samples exist in the training data of a target model. Therefore, the aim of this study is to develop a method for defending against MIAs and protecting data privacy.

One possible solution is to propose an MIA defense method that involves adjusting the model’s output by mapping the output to a distribution with equal probability density. This approach effectively preserves the accuracy of classification predictions while simultaneously preventing attackers from identifying the training data.

Experiments demonstrate that the proposed defense method is effective in reducing the classification accuracy of MIAs to below 50%. Because MIAs are viewed as a binary classification model, the proposed method effectively prevents privacy leakage and improves data privacy protection.

The method is only designed to defend against MIA in black-box classification models.

The proposed MIA defense method is effective and has a low cost. Therefore, the method enables us to protect data privacy without incurring significant additional expenses.

In the absence of previous work, this study investigates how the psychological contract (PC) influences the safety performance of construction workers in China.

The literature is first consulted to obtain a set of PC and safety performance measures that fits the specific situation of construction workers, which is then moderated by five construction experts. A questionnaire survey of 206 workers from 4 different construction sites is followed by a descriptive statistical analysis of the nature of the PC and level of the safety performance of the respondents. Finally, a regression analysis is used to ascertain the level of influence of the PS, and an analysis is made of the influence of PC on safety performance.

A set of PC and safety performance measures is identified that fits in the construction workers' specific situation. The PC of the respondents is found to be intact and well-performed, and their safety performance is maintained at a high level. Safety performance is highly influenced by the state of the PC, with the three dimensions of safety performance (safety result, safety compliance and safety participation) positively correlated with the three dimensions of the PC (normative, interpersonal and developmental).

Suggestions are made to improve safety production management and safety performance by providing adequate material and economic conditions, helping the workers establish good interpersonal relationships and realize their personal values.

With the increasing awareness of global warming and the important role of last mile distribution in logistics activities, the purpose of this paper is to build an environmental and effective last mile distribution model considering fuel consumption and greenhouse gas emission, vehicle capacity and two practical delivery service options: home delivery (HD) and pickup site service (PS). This paper calls the problem as the capacitated pollution-routing problem with pickup and delivery (CPRPPD). The goal is to find an optimal route to minimize operational and environmental costs, as well as a set of optimal speeds over each arc, while respecting capacity constraints of vehicles and pickup sites.

To solve this problem, this research proposes a two-phase heuristic algorithm by combining a hybrid ant colony optimization (HACO) in the first stage and a multiple population genetic algorithm in the second stage. First, the HACO is presented to find the minimal route solution and reduce distribution cost based on optimizing the speed over each arc.

To verify the proposed CPRPPD model and algorithm, a real-world instance is conducted. Comparing with the scenario including HD service only, the scenario including both HD and PS option is more economical, which indicates that the CPRPPD model is more efficient. Besides, the results of speed optimization are significantly better than before.

The developed CPRPPD model not only minimizes delivery time and reduces the total emission cost, but also helps logistics enterprises to establish a more complete distribution system and increases customer satisfaction. The model and algorithm of this paper provide optimal support for the actual distribution activities of logistics enterprises in low-carbon environment, and also provide reference for the government to formulate energy-saving and emission reduction policies.

This paper provides a great space for the improvement of carbon emissions in the last mile distribution. The results show that the distribution arrangement including HD and PS services in the last mile adopting speed optimization can significantly reduce the carbon emission. Additionally, an integrated real-world instance is applied in this paper to illustrate the validity of the model and the effectiveness of this method.

Drawing on the extended self-theory, this study explores the dynamic process through which reactive helping could influence proactive helping through self-investment and investigate the moderating role of task difficulty in affecting this process.

This study, with a sample of 582 diary surveys from 66 employees, used experience sampling techniques to analyze the proposed hypotheses.

The results revealed that self-investment could mediate the positive relationship between reactive helping and proactive helping. Additionally, task difficulty acts as an essential role in facilitating the process raised by reactive helping. Further examination revealed that the moderated mediation effect in this model was also significant.

Managers should encourage help-seeking and positive responses to requests, especially in groups with difficult tasks, which could build helpers’ extended self at work and increase their proactive helping behaviors at the following episode.

As verifying the dynamic trajectory of reactive helping, this study enriches our understanding of whether and how helping behaviors are likely to grow over time. Besides, it complements current pieces of literature by exploring the potential positive implication of reactive helping with a helper-centric perspective.

The purpose of this study was expected to simultaneously monitor the surface roughness of each solidified layer, the surface roughness of the metal powder, the outline of the solidified layer, and the height difference between the solidified layer and the metal powder.

In the proposed approach, color images with red, green and blue fringes are used to measure the shape of the built object using a three-step phase-shift algorithm and phase-unwrapping method. In addition, the surface roughness is extracted from the speckle information in the captured image using a predetermined autocorrelation function.

The feasibility and accuracy of the proposed system were validated by comparing it with a commercial system for an identical set of samples fabricated by a selective laser melting process. The maximum and minimum errors between the two systems are approximately 24% and 0.8%, respectively.

In the additive manufacturing field, the authors are the first to use fringe detection technology to simultaneously measure the profile of the printed layer and its surface roughness.