Search Results (101)

Design and use of wearable technology have grown exponentially, particularly in consumer products and service sectors, e.g., healthcare. However, there is a lack of a comprehensive understanding of wearable technology in consumer acceptance. This systematic review utilized a PRISMA on peer-reviewed articles published between 2014 and 2024 and collected on WoS, Scopus, and ScienceDirect. A total of 38 full-text articles were systematically reviewed and analyzed using bibliometric, thematic, and descriptive analysis to understand the technical functions of digital wearable products (DWPs) in consumer acceptance. The findings revealed four key functions: (i) wearable technology, (ii) appearance and design, (iii) biomimetic innovation, and (iv) security and privacy, found in eight types of DWPs, among them smartwatches, medical robotics, fitness devices, and wearable fashions, significantly predicted the customers’ acceptance moderated by the behavioral factors. The review also identified five key outcomes: health and fitness, enjoyment, social value, biomimicry, and market growth. The review proposed a comprehensive acceptance model that combines biomimetic principles and AI-driven features into the technical functions of the technical function model (TAM) while addressing security and privacy concerns. This approach contributes to the extended definition of TAM in wearable technology, offering new pathways for biomimetic research in smart devices and robotics. Full article

Elevators are a vital part of urban infrastructure, playing a key role in smart cities where increasing population density has driven the rise in taller buildings. As an essential means of vertical transportation, elevators have become an integral part of daily life, making their design, construction, and maintenance crucial to ensuring safety and compliance with evolving industry standards. The safety of elevator systems depends on the continuous monitoring and fault-free operation of Permanent Magnet Synchronous Motor (PMSM) drives, which are critical to their performance. Furthermore, the fault-free operation of PMSM drives reduces operating costs, increases service life, and improves reliability. The PMSM drive components may be susceptible to electrical, mechanical, and thermal faults that, if undetected, can lead to operational disruptions or safety risks. The integration of artificial intelligence and Internet of Things (IoT) technologies can enhance fault prediction, reducing downtime and improving efficiency. Ongoing challenges such as managing machine thermal load and developing more durable materials for PMSMs require the development of suitable models that are adapted to existing drive systems. The proposed framework for fault prediction is validated on a real residential elevator equipped with a PMSM drive. Multimodal signal data is processed through a Generative Adversarial Network (GAN)-enhanced Positive Unlabeled (PU) classifier and a Reinforcement Learning (RL)-based adaptive decision engine, enabling robust and scalable fault prediction in a non-intrusive fashion. Full article

The fashion industry is facing increasing pressure to adopt sustainable practices given its significant impact on the environment. This research aims to analyze the implications of marketing strategies in the fashion industry’s transition towards a sustainable and responsible business model. The study starts from the premise that marketing can influence consumer behavior and turn sustainability into a competitive advantage. To investigate this aspect, SmartPLS software was used and hypotheses were tested on the relationship between marketing strategies to educate and sensitize consumers on sustainability issues in the fashion industry and the creation of a sustainably engaged community. Moreover, it becomes essential to collaborate with non-governmental organizations and other brands that share their sustainability values. The research was based on a sample of 227 respondents, and the data were analyzed using structural equation modeling. The results indicate that marketing strategies that promote transparency in supply chain and production processes, enhance brand reputation and credibility and, promote innovation in sustainable materials and production processes through marketing strategies contributing to creating an engaged community, as well as through brand commitment to sustainability through concrete actions and access to new markets and growth opportunities. Marketing strategies to educate and sensitize consumers on sustainability issues in the fashion industry contribute to increasing consumer interest in sustainable products. The implications of the study highlight the need for coherent marketing approaches to support the sustainable transformation of the fashion industry. Full article

The fashion industry is undergoing significant transformation through blockchain technology, which enhances product traceability, authenticity, and transactional transparency. This study explores blockchain’s potential to revolutionize the fashion supply chain by enabling detailed monitoring from design and manufacturing to certification, quality control, storage, transportation, and delivery. To assess customers’ readiness to adopt these authenticated products, an innovative model for fashion product traceability and authenticity based on blockchain was proposed. Since the adoption of blockchain models relies on widespread user involvement, it is crucial to examine the factors that motivate individuals to take part. To this end, an acceptance study was conducted using the modified UTAUT2 (Unified Theory of Acceptance and Use of Technology) framework, with data analyzed using SMART PLS software. The results indicate that the proposed blockchain model can improve transparency, authenticity, and customer trust in fashion products. Furthermore, the findings identify expected effort, perceived efficiency, and social influence as key factors influencing blockchain adoption in the fashion industry. These insights show the importance of targeted education and customer engagement strategies for successful implementations of blockchain technology in the fashion industry. Full article

Exploiting novel crosslinking chemistry, this study pioneers the use of waterborne polyurethane (WPU) to chemically crosslink porcine-derived gelatin, producing enhanced gelatin hydrogel films through a solvent-casting method. Our innovative approach harnesses the reactive isocyanate groups of WPU, coupling them effectively with gelatin’s hydroxyl and primary amino groups to form robust urea and urethane linkages within the hydrogel matrix. This method not only preserves the intrinsic elasticity of polyurethane but also significantly augments the films’ tensile strength and strain. Comprehensive characterizations of these hydrogel films and pre-formed hydrogel reaction mixtures were conducted using viscosity measurements, Fourier Transform Infrared Spectroscopy (FTIR), Thermogravimetric Analysis (TGA), and the universal testing machine (UTM) for tensile-recovery assessments, alongside evaluations of their biocompatibility. The results demonstrated a reduction in pore size with an increase in WPU concentration from 2 to 6% in the developed hydrogels with a decrease in the equilibrium swelling ratio from 15% to 9%, respectively. Further, hydrogels with 6% WPU exhibited the highest tensile stress in both a dry and wet state. The gelatin hydrogel formed with 6% WPU blend also demonstrated the growth and proliferation of CCD-986K (fibroblast) and CCD-1102 (keratinocyte) cells for up to 5 days of co-culturing. The results indicate a notable enhancement in the mechanical properties and biocompatibility of gelatin hydrogels upon the introduction of WPU, positioning these films as superior candidates for biomedical applications such as tissue engineering and wound dressing. Full article

Power system flexibility is becoming increasingly critical in modern power systems due to the quick switch from fossil fuel-based power generation to renewables, old-fashioned infrastructures, and a sharp rise in demand. If a power system complies with financial restrictions and responds quickly to unforeseen shifts in supply and demand, it can be considered flexible. It can ramp up production during periods of high demand or increase it during unanticipated or scheduled events. The broad use of renewable energy in the power grid can provide environmental and economic benefits; nevertheless, renewables are highly stochastic in nature, with variability and uncertainty. New management with adequate planning and operation in the power system is necessary to address the challenges incorporated with the penetration of renewable energy. The primary aim of this review is to provide a comprehensive overview of power system flexibility, including appropriate definitions, parameters, requirements, resources, and future planning, in a compact way. Moreover, this paper potentially addresses the effects of various renewable penetrations on power system flexibility and how to overcome them. It also presents an emerging assessment and planning of influential flexibility solutions in modern power systems. This review’s scientific and engineering insights provide a clear vision of a smart, flexible power system with promised research direction and advancement. Full article

This study addresses the impact of urban parks on the self-rated health of office workers under 40, a demographic experiencing significant increases in depressive symptoms during the pandemic. This study in Baise City, China, aims to fill this gap by exploring the relationships between landscape quality, leisure time spent in parks, place attachment, and self-rated health among 411 office workers aged 18 to 40. Structural equation modeling was used to assess these relationships, and multigroup analysis (MGA) in SmartPLS evaluated differences between subgroups. The findings reveal a strong link between urban park landscape quality and leisure time spent in parks, place attachment, and self-rated health. Although the old-fashioned park showed lower overall performance in the study variables compared to the modern park, it had a stronger relationship between landscape quality and place attachment. Leisure time spent in parks did not directly impact self-rated health but was mediated by place attachment. MGA results indicated that while leisure time in parks positively affected self-rated health for single participants, it had a negative effect for married participants. These results underscore the importance of tailoring urban park design and management to accommodate the varying needs of different demographics. This research provides new insights into enhancing office workers’ self-rated health through environmental design and supports the objectives of the Healthy China strategy and Sustainable Development Goal 11. Full article

This study explores the impact of social media platforms on enhancing social responsibility, employing a rigorous research framework based on the Uses and Gratifications Theory. We developed and tested a model to investigate how motivations for using social media influence social responsibility. A quantitative methodology was utilized, analyzing data from a sample of 520 participants using SmartPLS 4. The findings reveal various social media motivations—specifically information seeking, information sharing, self-status, social interaction, entertainment, being fashionable, and relaxation—significantly and positively impact social responsibility. The results underscore the constructive role of social media motivations in fostering social responsibility. They also suggest that further investigations into additional dimensions could provide deeper insights into how digital media might be leveraged to benefit society more broadly and enhance the concept of social responsibility. This study contributes to the expanding discourse on digital media’s potential to effect positive societal change. Full article

With the improvement of the energy density and sensing accuracy of wearable devices, there is increasing interest in applying wearable electronics in daily life. However, traditional rigid plate-structured wearable devices cannot meet the human body’s wearing habits and make users may feel uncomfortable after wearing them for a long time. Fabric-type wearable electronics can be conformably coated on human skin without discomfort from mismatches in mechanical properties between the human body and electronics. Although state-of-the-art textile-based wearable devices have shown unique advantages in the field of e-textiles, real-world scenarios often involve stretching, bending, and wetting. Further efforts should be made to achieve “comfortable wearing” due to the great challenge of achieving both promising electrical properties and comfort in a single device. This review presents a comprehensive overview of the advances in smart fabric-based wearable electronics toward comfortable wearing, emphasizing their stretchability, hydrophobicity, air permeability, stability, and color-change abilities. Through addressing the challenges that persist in fabric-type wearable electronics, we are optimistic that these will be soon ubiquitous in our daily lives, offering exceptionally comfortable wearing experiences for health monitoring, sports performance tracking, and even fashion, paving the way for a more comfortable and technologically advanced future. Full article

This study demonstrates a novel methodology for developing a textile-based adsorption sensor via mixed solvent dyeing with aggregation-induced emission (AIE) dyes on recycled fabrics. AIE dyes were incorporated into the fabrics using a mixed solvent dyeing method with a co-solvent mixture of H₂O and organic solvents. This method imparted unique fluorescence properties to fabrics, altering fluorescence intensity or wavelength based on whether the AIE dye molecules were in an isolated or aggregated state on the fabrics. The precise control of the H₂O fraction to organic solvent during dyeing was crucial for influencing fluorescence intensity and sensing characteristics. These dyed fabrics exhibited reactive thermochromic and vaporchromic properties, with changes in fluorescence intensity corresponding to variations in temperature and exposure to volatile organic solvents (VOCs). Their superior characteristics, including a repetitive fluorescence switching property and resistance to photo-bleaching, enhance their practicality across various applications. Consequently, the smart fabrics dyed with AIE dye not only find applications in clothing and fashion design but demonstrate versatility in various fields, extending to sensing temperature, humidity, and hazardous chemicals. Full article

The application of augmented reality (AR) in the beauty and hairdressing industry provides customers with a rich experience, especially in terms of virtual hair styling. Through virtual hair color tests, customers can quickly decide on the most suitable hair dye for them. Teenagers use multimedia communication channels to distinctively express their enjoyment of and aesthetic experiences with the interactive, emotional, and spatial aspects of AR. They can also preview diverse hair dyes and hairstyles in a virtual environment and, ultimately, select the option that suits them the most. This study applied the Stimulus–Organism–Response (S–O–R) theoretical framework and collected 337 valid samples through a SmartPLS-4-supported questionnaire survey for analysis. The results revealed that spatiality significantly influenced behavioral intention in the context of aesthetic experiences, whereas interactivity significantly influenced behavioral intention in the context of entertainment experiences. Overall, the hair dye consumption behavior of Taiwanese teenagers was positively influenced by spatiality and interactivity, and the overall model was well structured. The findings of this study can serve as a reference for businesses to develop innovative technology products that enhance consumer marketing experiences and can contribute to the future development of the hair dye segment of the fashion industry. Full article

This study adopts a data-driven methodology to address the challenge of garment fitting for individuals with diverse body shapes. Focusing on young Chinese women aged 18–25 from Central China, we utilized the German VITUS SMART LC3 3D body scanning technology to measure 62 body parts pertinent to fashion design on a sample of 220 individuals. We then employed a hybrid approach, integrating the circumference difference classification method with the characteristic value classification method, and applied the K-means clustering algorithm to categorize these individuals into four distinct body shape groups based on cluster center analysis. Building upon these findings, we formulated specific linear regression models for key body parts associated with each body shape category. This led to the development of an intelligent software capable of automatically calculating the dimensions of 28 body parts and accurately determining the body shape type for young Central Chinese women. Our research underscores the significant role of intelligent predictive systems in the realm of fashion design, particularly within a data-driven framework. The system we have developed offers precise body measurements and classification outcomes, empowering businesses to create garments that more accurately conform to the wearer’s body, thus enhancing both the fit and aesthetic value of the clothing. Full article

This paper investigates the utilisation of smart interactive products by millennial consumers in the fashion industry and how their perceptions and experiences influence the adoption of such products. To achieve this, it employs a generational perspective. It utilises Midgley and Dowling’s theory of predisposition to innovate as its theoretical framework, providing a comprehensive exploration of consumers’ experiences with these products. To bridge the gap in understanding consumers’ limited adoption of smart textile (ST) products, this research employs Interpretive Phenomenological Analysis (IPA). This methodological choice is driven by uncovering how real-life experiences impact consumer behaviour in this context. Expanding on previous work, the research comprised two separate qualitative studies utilising Interpretive Phenomenological Analysis (IPA). Participants interact with specific interactive smart textiles, namely, the Levi’s Jacket by Google. Participant recruitment utilised the snowballing method, which was adapted due to the constraints imposed by the COVID-19 pandemic. Full article

This study introduces ETLBOCBL-CNN, an automated approach for optimizing convolutional neural network (CNN) architectures to address classification tasks of varying complexities. ETLBOCBL-CNN employs an effective encoding scheme to optimize network and learning hyperparameters, enabling the discovery of innovative CNN structures. To enhance the search process, it incorporates a competency-based learning concept inspired by mixed-ability classrooms during the teacher phase. This categorizes learners into competency-based groups, guiding each learner’s search process by utilizing the knowledge of the predominant peers, the teacher solution, and the population mean. This approach fosters diversity within the population and promotes the discovery of innovative network architectures. During the learner phase, ETLBOCBL-CNN integrates a stochastic peer interaction scheme that encourages collaborative learning among learners, enhancing the optimization of CNN architectures. To preserve valuable network information and promote long-term population quality improvement, ETLBOCBL-CNN introduces a tri-criterion selection scheme that considers fitness, diversity, and learners’ improvement rates. The performance of ETLBOCBL-CNN is evaluated on nine different image datasets and compared to state-of-the-art methods. Notably, ELTLBOCBL-CNN achieves outstanding accuracies on various datasets, including MNIST (99.72%), MNIST-RD (96.67%), MNIST-RB (98.28%), MNIST-BI (97.22%), MNST-RD + BI (83.45%), Rectangles (99.99%), Rectangles-I (97.41%), Convex (98.35%), and MNIST-Fashion (93.70%). These results highlight the remarkable classification accuracy of ETLBOCBL-CNN, underscoring its potential for advancing smart device infrastructure development. Full article

Nowadays, the integration of home automation systems with smart thermostats is a common trend, designed to enhance resident comfort and conserve energy. The introduction of smart thermostats that can run machine learning algorithms has opened the door for on-device training, enabling customized thermal experiences in homes. However, leveraging the flexibility offered by on-device learning has been hindered by the absence of a tailored learning scheme that allows for accurate on-device training of thermal models. Traditional centralized learning (CL) and federated learning (FL) schemes rely on a central server that controls the learning experience, compromising the home’s privacy and requiring significant energy to operate. To address these challenges, we propose PePTM, a personalized peer-to-peer thermal modeling algorithm that generates tailored thermal models for each home, offering a controlled learning experience with a minimal training energy footprint while preserving the home’s privacy, an aspect difficult to achieve in both CL and FL. PePTM consists of local and collaborative learning phases that enable each home to train its thermal model and collaboratively improve it with a set of similar homes in a peer-to-peer fashion. To showcase the effectiveness of PePTM, we use a year’s worth of data from US homes to train thermal models using the RNN time-series model and compare the data across three learning schemes: CL, FL, and PePTM, in terms of model performance and the training energy footprint. Our experimental results show that PePTM is significantly energy-efficient, requiring 695 and 40 times less training energy than CL and FL, respectively, while maintaining comparable performance. We believe that PePTM sets the stage for new avenues for on-device thermal model training, providing a personalized thermal experience with reduced energy consumption and enhanced privacy. Full article