Search results
1 – 10 of 983Although the fitness switching costs scale (FSCS) was shown to have sound psychometric properties, the length of the 54-item may impose burdens on survey participants and present…
Abstract
Purpose
Although the fitness switching costs scale (FSCS) was shown to have sound psychometric properties, the length of the 54-item may impose burdens on survey participants and present methodological and analytic challenges for researchers and practitioners. Therefore, the present study shortened and validated two versions of the FSCS, namely the 33-item FSCS (FSCS-33) and the 11-item FSCS (FSCS-11).
Design/methodology/approach
In Study 1 (n = 411), the most useful items from the FSCS for the FSCS-33 and FSCS-11 were identified using item response theory (IRT). Study 2 (n = 391) and Study 3 (n = 400) assessed the psychometric properties of the FSCS-33 and FSCS-11, respectively, using partial least squares structural equation modeling.
Findings
The FSCS-33 and FSCS-11 demonstrated strong reliability and validity in assessing switching costs in fitness centers.
Originality/value
The psychometrically sound short-form scales provide researchers and practitioners with convenient and accurate means of measuring switching costs in fitness centers.
Details
Keywords
The purpose of this paper is to explore teacher candidates’ response to young adult literature (prose and comics) featuring fat identified protagonists. The paper considers the…
Abstract
Purpose
The purpose of this paper is to explore teacher candidates’ response to young adult literature (prose and comics) featuring fat identified protagonists. The paper considers the textual and embodied resources readers use and reject when imagining and interpreting a character’s body. This paper explores how readers’ meaning making was influenced when reading prose versus comics. This paper adds to a corpus of scholarship about the relationships between young adult literature, comics, bodies and reader response theory.
Design/methodology/approach
At the time of the study, participants were enrolled in a teacher education program at a Midwestern University, meeting monthly for a voluntary book club dedicated to reading and discussing young adult literature. To examine readers’ responses to comics and prose featuring fat-identified protagonists, the author used descriptive qualitative methodologies to conduct a thematic analysis of meeting transcripts, written participant reflections and researcher memos. Analysis was grounded in theories of reader response, critical fat studies and multimodality.
Findings
Analyses indicated many readers reject textual clues indicating a character’s body size and weight were different from their own. Readers read their bodies into the stories, regarding them as self-help narratives instead of radical counternarratives. Some readers were not able to read against their assumptions of thinness (and whiteness) until prompted by the researcher and other participants.
Originality/value
Although many reader response scholars have demonstrated readers’ tendencies toward personal identification in the face of racial and class differences, there is less research regarding classroom practices around the entanglement of physical bodies, body image and texts. Analyzing reader’s responses to the constructions of fat bodies in prose versus comics may help English Language Arts (ELA) educators and students identify and deconstruct ideologies of thin-thinking and fatphobia. This study, which demonstrates thin readers’ tendencies to overidentify with protagonists, suggests ELA classrooms might encourage readers to engage in critical literacies that support them in reading both with and against their identities.
Details
Keywords
Liyi Zhang, Mingyue Fu, Teng Fei, Ming K. Lim and Ming-Lang Tseng
This study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem.
Abstract
Purpose
This study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem.
Design/methodology/approach
This study involves cooling, commodity damage and carbon emissions and establishes the site selection model of low-carbon cold chain logistics distribution center aiming at minimizing total cost, and grey wolf optimization algorithm is used to improve the artificial fish swarm algorithm to solve a cold chain logistics distribution center problem.
Findings
The optimization results and stability of the improved algorithm are significantly improved and compared with other intelligent algorithms. The result is confirmed to use the Beijing-Tianjin-Hebei region site selection. This study reduces composite cost of cold chain logistics and reduces damage to environment to provide a new idea for developing cold chain logistics.
Originality/value
This study contributes to propose an optimization model of low-carbon cold chain logistics site by considering various factors affecting cold chain products and converting carbon emissions into costs. Prior studies are lacking to take carbon emissions into account in the logistics process. The main trend of current economic development is low-carbon and the logistics distribution is an energy consumption and high carbon emissions.
Details
Keywords
Wei-Zhen Wang, Hong-Mei Xiao and Yuan Fang
Nowadays, artificial intelligence (AI) technology has demonstrated extensive applications in the field of art design. Attribute editing is an important means to realize clothing…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
Details
Keywords
Angi Martin and Julie Cox
The education of deaf and hard of hearing (d/DHH) students is largely dependent on the preferred mode of communication. Historically, the mode of communication for d/DHH students…
Abstract
The education of deaf and hard of hearing (d/DHH) students is largely dependent on the preferred mode of communication. Historically, the mode of communication for d/DHH students was determined by society rather than by students and families. This resulted in divisiveness between the Deaf culture and proponents of oral communication. The adoption of IDEA allowed family participation in the decision-making process. Advances in technology increased student access to sound, resulting in more educational placement options. Despite the positive changes, the complex nature of hearing loss and the wide variety in cultural considerations have made it difficult to determine the best approach to deaf education. Thus, educators and providers are left in a conundrum of which version of “traditional” deaf education is best for students.
Details
Keywords
Erol Can and Ugur Kilic
Static inverters are very important for the emergency energy distribution system of aircraft and similar machines. At the same time, the electrical energy produced at high…
Abstract
Purpose
Static inverters are very important for the emergency energy distribution system of aircraft and similar machines. At the same time, the electrical energy produced at high frequency for electrical devices is used to reduce the weight of the cables in the aircraft and spacecraft because of the skin effect. In the high-frequency system, a thinner cable cross-section is used, and a great weight reduction occurs in the aircraft. So, fuel economy, less and late wear of the materials (landing gear, etc.) can be obtained with decreasing weight. This paper aims to present the development of a functional multilevel inverter (FMLI) with fractional sinus pulse width modulation (FSPWM) and a reduced number of switches to provide high-frequency and quality electrical energy conversion.
Design/methodology/approach
After the production of FSPWM for FMLI with a reduced component, which, to the best of the authors’ knowledge, is presented for the first time in this study, is explained step by step, and eight operating states are given according to different FSPWMs operating the circuit. The designed inverter and modulation technique are compared by testing the conventional modular multilevel inverter on different loads.
Findings
According to application results, it is seen that there is a 50% reduction in cross-section from 100 Hz to 400 Hz with the skin effect. At 1000 Hz, there is a 90% cross-section reduction. The decrease can be in cable weights that may occur in aircraft from 10 kg to 100 kg according to different frequencies. It causes less harmonic distortion than conventional converters. This supports the safer operation of the system. Compared to the traditional system, the proposed system provides more amplitude in converting the source to alternating voltage and increases the efficiency.
Practical implications
FSPWM is developed for multilevel inverters with reduced components at the high frequency and cascaded switching studies in the power electronics of aircraft.
Social implications
Although the proposed system has less current and power loss as mentioned in the previous sections, it contains fewer power elements than conventional inverters that are equivalent for different hardware levels. This not only reduces the cost of the system but also provides ease of maintenance. To reduce the cable load in aircraft and create more efficient working conditions, 400 Hz alternative voltage is used. The proposed system causes less losses and lower harmonic distortions than traditional systems. This will reduce possible malfunctions and contribute to aircraft reliability for passengers and cargo. As technology develops, it is revealed that the proposed inverter system will be more efficient than traditional inverters when devices operating at frequencies higher than 400 Hz are used. With the proposed inverter, safer operation will be ensured, while there will be less energy loss, less fuel consumption and less carbon emissions to the environment.
Originality/value
The proposed inverter structure shows that it can provide energy transmission for electrical devices in space and aircraft by using the skin effect. It also contains less power elements than the traditional inverters, which are equivalent for different levels of hardware.
Details
Keywords
Ziming Zhou, Fengnian Zhao and David Hung
Higher energy conversion efficiency of internal combustion engine can be achieved with optimal control of unsteady in-cylinder flow fields inside a direct-injection (DI) engine…
Abstract
Purpose
Higher energy conversion efficiency of internal combustion engine can be achieved with optimal control of unsteady in-cylinder flow fields inside a direct-injection (DI) engine. However, it remains a daunting task to predict the nonlinear and transient in-cylinder flow motion because they are highly complex which change both in space and time. Recently, machine learning methods have demonstrated great promises to infer relatively simple temporal flow field development. This paper aims to feature a physics-guided machine learning approach to realize high accuracy and generalization prediction for complex swirl-induced flow field motions.
Design/methodology/approach
To achieve high-fidelity time-series prediction of unsteady engine flow fields, this work features an automated machine learning framework with the following objectives: (1) The spatiotemporal physical constraint of the flow field structure is transferred to machine learning structure. (2) The ML inputs and targets are efficiently designed that ensure high model convergence with limited sets of experiments. (3) The prediction results are optimized by ensemble learning mechanism within the automated machine learning framework.
Findings
The proposed data-driven framework is proven effective in different time periods and different extent of unsteadiness of the flow dynamics, and the predicted flow fields are highly similar to the target field under various complex flow patterns. Among the described framework designs, the utilization of spatial flow field structure is the featured improvement to the time-series flow field prediction process.
Originality/value
The proposed flow field prediction framework could be generalized to different crank angle periods, cycles and swirl ratio conditions, which could greatly promote real-time flow control and reduce experiments on in-cylinder flow field measurement and diagnostics.
Details
Keywords
Pingyang Zheng, Shaohua Han, Dingqi Xue, Ling Fu and Bifeng Jiang
Because of the advantages of high deposition efficiency and low manufacturing cost compared with other additive technologies, robotic wire arc additive manufacturing (WAAM…
Abstract
Purpose
Because of the advantages of high deposition efficiency and low manufacturing cost compared with other additive technologies, robotic wire arc additive manufacturing (WAAM) technology has been widely applied for fabricating medium- to large-scale metallic components. The additive manufacturing (AM) method is a relatively complex process, which involves the workpiece modeling, conversion of the model file, slicing, path planning and so on. Then the structure is formed by the accumulated weld bead. However, the poor forming accuracy of WAAM usually leads to severe dimensional deviation between the as-built and the predesigned structures. This paper aims to propose a visual sensing technology and deep learning–assisted WAAM method for fabricating metallic structure, to simplify the complex WAAM process and improve the forming accuracy.
Design/methodology/approach
Instead of slicing of the workpiece modeling and generating all the welding torch paths in advance of the fabricating process, this method is carried out by adding the feature point regression branch into the Yolov5 algorithm, to detect the feature point from the images of the as-built structure. The coordinates of the feature points of each deposition layer can be calculated automatically. Then the welding torch trajectory for the next deposition layer is generated based on the position of feature point.
Findings
The mean average precision score of modified YOLOv5 detector is 99.5%. Two types of overhanging structures have been fabricated by the proposed method. The center contour error between the actual and theoretical is 0.56 and 0.27 mm in width direction, and 0.43 and 0.23 mm in height direction, respectively.
Originality/value
The fabrication of circular overhanging structures without using the complicate slicing strategy, turning table or other extra support verified the possibility of the robotic WAAM system with deep learning technology.
Details
Keywords
Hossein Shakibaei, Mohammad Reza Farhadi-Ramin, Mohammad Alipour-Vaezi, Amir Aghsami and Masoud Rabbani
Every day, small and big incidents happen all over the world, and given the human, financial and spiritual damage they cause, proper planning should be sought to deal with them so…
Abstract
Purpose
Every day, small and big incidents happen all over the world, and given the human, financial and spiritual damage they cause, proper planning should be sought to deal with them so they can be appropriately managed in times of crisis. This study aims to examine humanitarian supply chain models.
Design/methodology/approach
A new model is developed to pursue the necessary relations in an optimal way that will minimize human, financial and moral losses. In this developed model, in order to optimize the problem and minimize the amount of human and financial losses, the following subjects have been applied: magnitude of the areas in which an accident may occur as obtained by multiple attribute decision-making methods, the distances between relief centers, the number of available rescuers, the number of rescuers required and the risk level of each patient which is determined using previous data and machine learning (ML) algorithms.
Findings
For this purpose, a case study in the east of Tehran has been conducted. According to the results obtained from the algorithms, problem modeling and case study, the accuracy of the proposed model is evaluated very well.
Originality/value
Obtaining each injured person's priority using ML techniques and each area's importance or risk level, besides developing a bi-objective mathematical model and using multiple attribute decision-making methods, make this study unique among very few studies that concern ML in the humanitarian supply chain. Moreover, the findings validate the results and the model's functionality very well.
Details
Keywords
Huaxiang Song, Chai Wei and Zhou Yong
The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of…
Abstract
Purpose
The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.
Design/methodology/approach
This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.
Findings
This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.
Originality/value
This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.
Details