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Science mapping is an essential application of visualization technology widely used in safety, construction management and environmental science. The purpose of this study was to explore thermal comfort in residential buildings (TCinRB) research in India, identify research trends using a science mapping approach and provide a perspective for recommending future research in TCinRB.

This study used the VOSviewer tool to conduct a systematic analysis of the development trend in TCinRB studies in India based on Scopus Index articles published between 2001 and 2020. The annual numbers of articles, geographical locations of studies, major research organizations and authors, and the sources of journals on TCinRB were presented based on the analysis. Then, using co-authorship analysis, the collaborations among the major research groups were reported. Furthermore, research trends on TCinRB studies were visually explored using keyword co-occurrence analysis. The emerging research topics in the TCinRB research community were discovered by analyzing the authors’ keywords.

The findings revealed that studies had been discovered to pay more attention to north-east India, vernacular architecture, Hyderabad apartments and temperature performance in the past two decades. Thermal adaptation, composite climate, evaporative cooling and clothing insulation are emerging research areas in the TCinRB domain. The findings summarized mainstream research areas based on Indian climatic zones, addressed current TCinRB research gaps and suggested future research directions.

This review is particularly significant because it could help researchers understand the body of knowledge in TCinRB and opens the way for future research to fill an important research gap.

Occupant behavior can lead to considerable uncertainties in thermal comfort and air quality within buildings. To tackle this challenge, the use of probabilistic controls to simulate occupant behavior has emerged as a potential solution. This study seeks to analyze the performance of free-running households by examining adaptive thermal comfort and CO2 concentration, both crucial variables in indoor air quality. The investigation of indoor environment dynamics caused by the occupants' behavior, especially after the COVID-19 pandemic, became increasingly important. Specifically, it investigates 13 distinct window and shading control strategies in courtyard houses to identify the factors that prompt occupants to interact with shading and windows and determine which control approach effectively minimizes the performance gap.

This paper compares commonly used deterministic and probabilistic control functions and their effects on occupant comfort and indoor air quality in four zones surrounding a courtyard. The zones are differentiated by windows facing the courtyard. The study utilizes the energy management system (EMS) functionality of EnergyPlus within an algorithmic interface called Ladybug Tools. By modifying geometrical dimensions, orientation, window-to-wall ratio (WWR) and window operable fraction, a total of 465 cases are analyzed to identify effective control scenarios. According to the literature, these factors were selected because of their potential significant impact on occupants’ thermal comfort and indoor air quality, in addition to the natural ventilation flow rate. Additionally, the Random Forest algorithm is employed to estimate the individual impact of each control scenario on indoor thermal comfort and air quality metrics, including operative temperature and CO2 concentration.

The findings of the study confirmed that both deterministic and probabilistic window control algorithms were effective in reducing thermal discomfort hours, with reductions of 56.7 and 41.1%, respectively. Deterministic shading controls resulted in a reduction of 18.5%. Implementing the window control strategies led to a significant decrease of 87.8% in indoor CO2 concentration. The sensitivity analysis revealed that outdoor temperature exhibited the strongest positive correlation with indoor operative temperature while showing a negative correlation with indoor CO2 concentration. Furthermore, zone orientation and length were identified as the most influential design variables in achieving the desired performance outcomes.

It’s important to acknowledge the limitations of this study. Firstly, the potential impact of air circulation through the central zone was not considered. Secondly, the investigated control scenarios may have different impacts on air-conditioned buildings, especially when considering energy consumption. Thirdly, the study heavily relied on simulation tools and algorithms, which may limit its real-world applicability. The accuracy of the simulations depends on the quality of the input data and the assumptions made in the models. Fourthly, the case study is hypothetical in nature to be able to compare different control scenarios and their implications. Lastly, the comparative analysis was limited to a specific climate, which may restrict the generalizability of the findings in different climates.

Occupant behavior represents a significant source of uncertainty, particularly during the early stages of design. This study aims to offer a comparative analysis of various deterministic and probabilistic control scenarios that are based on occupant behavior. The study evaluates the effectiveness and validity of these proposed control scenarios, providing valuable insights for design decision-making.

The purpose of the study is to examine the experiences of emerging adults transitioning from college to career and the implications of this transition on clothing choice and identity formation.

This study utilized a phenomenological approach to address how appearances are used by emerging adults during the transition from college to the workplace and how those appearances help form identity.

The study found that participants have a desire for high-status consumption, primarily fueled by social comparison and the desire to keep up with colleagues, a desire to express identity through clothing, even if they are working from home, and the tendency to convey maturity during this transitory time by dressing the part.

The main limitation of this study is the homogenous nature of participants. Most are white females in their 20s who work in the fashion industry. It would be fruitful to consider a more representative population of emerging adults to examine the role of clothing choice on identity formation during this critical time.

This study highlights the need for change in the retail sector, regarding which garments create a professional wardrobe. Since the pandemic, many companies have shifted to a casual dress code, thus rendering the historically professional wardrobe of business attire obsolete.

Examining what it means to be an emerging adult joining the workforce in today's post-pandemic world is a complex and ongoing process. This study provides insight into how this experience is navigated via clothing and how identities are shaped during this transition in a person's life.

The textile industry holds immense significance in the economy of any nation, particularly in the production of synthetic yarn and fabrics. Consequently, the pursuit of acquiring high-quality products at a reduced cost has become a significant concern for countries. The primary objective of this research is to leverage data mining and data intelligence techniques to enhance and refine the production process of texturized yarn by developing an intelligent operating guide that enables the adjustment of production process parameters in the texturized yarn manufacturing process, based on the specifications of raw materials.

This research undertook a systematic literature review to explore the various factors that influence yarn quality. Data mining techniques, including deep learning, K-nearest neighbor (KNN), decision tree, Naïve Bayes, support vector machine and VOTE, were employed to identify the most crucial factors. Subsequently, an executive and dynamic guide was developed utilizing data intelligence tools such as Power BI (Business Intelligence). The proposed model was then applied to the production process of a textile company in Iran 2020 to 2021.

The results of this research highlight that the production process parameters exert a more significant influence on texturized yarn quality than the characteristics of raw materials. The executive production guide was designed by selecting the optimal combination of production process parameters, namely draw ratio, D/Y and primary temperature, with the incorporation of limiting indexes derived from the raw material characteristics to predict tenacity and elongation.

This paper contributes by introducing a novel method for creating a dynamic guide. An intelligent and dynamic guide for tenacity and elongation in texturized yarn production was proposed, boasting an approximate accuracy rate of 80%. This developed guide is dynamic and seamlessly integrated with the production database. It undergoes regular updates every three months, incorporating the selected features of the process and raw materials, their respective thresholds, and the predicted levels of elongation and tenacity.

The aim of this review is to present together the studies on textile-based moisture sensors developed using innovative technologies in recent years.

The integration levels of the sensors studied with the textile materials are changing. Some research teams have used a combination of printing and textile technologies to produce sensors, while a group of researchers have used traditional technologies such as weaving and embroidery. Others have taken advantage of new technologies such as electro-spinning, polymerization and other techniques. In this way, they tried to combine the good working efficiency of the sensors and the flexibility of the textile. All these approaches are presented in this article.

The presentation of the latest technologies used to develop textile sensors together will give researchers an idea about new studies that can be done on highly sensitive and efficient textile-based moisture sensor systems.

In this paper humidity sensors have been explained in terms of measuring principle as capacitive and resistive. Then, studies conducted in the last 20 years on the textile-based humidity sensors have been presented in detail. This is a comprehensive review study that presents the latest developments together in this area for researchers.

Today's marketing is dominated by decision-making based on artificial intelligence and machine learning. This study focuses on one semi- and unsupervised machine learning technique, generative adversarial networks (GANs). GANs are a type of deep learning architecture capable of generating new data similar to the training data that were used to train it, and thus, it is designed to learn a generative model that can produce new samples. GANs have been used in multiple marketing areas, especially in creating images and video and providing customized consumer contents. Through providing a holistic picture of GANs, including its advantage, disadvantage, ethical considerations, and its current application, the study attempts to provide business some strategical orientations, including formulating strong marketing positioning, creating consumer lifetime values, and delivering desired marketing tactics in product, promotion, pricing, and distribution channel. Through using GANs, marketers will create unique experiences for consumers, build strategic focus, and gain competitive advantages. This study is an original endeavor in discussing GANs in marketing, offering fresh insights in this research topic.

The purpose of this study is to examine the factors that affect managers’ ethical decision-making in export-oriented readymade garments in Bangladesh.

This is an empirical study based on the quantitative approach undertaking a cross-sectional survey method where a convenience sampling technique was applied. The analysis was done using partial least square structural equation model applying Smart-PLS version 3.0.

This study confirmed that all the components of cognitive appraisal processes, including perceived severity, perceived vulnerability, response efficacy and self-efficacy, have a significant influence on attitude. Attitude, in turn, mediates the relationship between these variables and the behavioural intention of ethical practice, except for perceived vulnerability. Besides, moral obligation is found to mediate the relationship between attitude, self-efficacy and the behavioural intention of ethical decision-making. The study also found that ethical climate and subjective norms have a direct influence on behavioural intention. Furthermore, behavioural intention, ethical climate and self-efficacy are positively related to actual decision-making behaviour. However, this study did not find any direct effect of subjective norms on moral obligation.

The organization should include an emphasis on building ethical culture and setting an ethical code of conduct within the organization to sustain ethical practice within employees. However, the practitioner should work on enhancing self-efficacy to curb unethical practices by individuals.

This research contributes to the management of garments manufacturers by a practical and theoretical understanding of what influences the ethical behavioural decision-making process. Valuable guidelines are provided on the ethical decision-making process in the garments manufacturing companies for future researchers.

While consumer preferences for sporting goods have been widely researched within sport management, literature is lacking on aspects of social and environmental sustainability. Accordingly, this study aims to investigate the role of social and environmental sustainability for purchase decisions of sportswear and compares them to the role of price and functionality.

Based on a conjoint analysis among 1,012 Europeans, the authors conducted a two-step cluster analysis. First, the authors investigated the number of segments via Ward’s method. Second, the authors ran a k-means analysis based on part-worth utilities from the conjoint analysis.

The authors identified four segments which differ in terms of preferred product attributes, willingness to pay, and sociodemographic, behavioral, and psychographic characteristics: undecided, sustainable, price-focused and function-oriented consumers. Based on this segmentation, the authors found that the importance of social and environmental sustainability is growing, but not among all consumers.

The generalizability of the study is limited since it is not built on a sample representative for the included European countries, it focuses on a single product, and participants are potentially subject to a social desirability bias.

The consumer analysis comprises the uptake of attributes related to social and environmental sustainability. The authors thereby address a literature gap as previous research (thematizing sporting goods) in the sport management field has often neglected sustainability elements despite their rapidly growing importance within the sport sector.

For privacy protection, federated learning based on data separation allows machine learning models to be trained on remote devices or in isolated data devices. However, due to the limited resources such as bandwidth and power of local devices, communication in federated learning can be much slower than in local computing. This study aims to improve communication efficiency by reducing the number of communication rounds and the size of information transmitted in each round.

This paper allows each user node to perform multiple local trainings, then upload the local model parameters to a central server. The central server updates the global model parameters by weighted averaging the parameter information. Based on this aggregation, user nodes first cluster the parameter information to be uploaded and then replace each value with the mean value of its cluster. Considering the asymmetry of the federated learning framework, adaptively select the optimal number of clusters required to compress the model information.

While maintaining the loss convergence rate similar to that of federated averaging, the test accuracy did not decrease significantly.

By compressing uplink traffic, the work can improve communication efficiency on dynamic networks with limited resources.

This study aims to investigate if engineered compression variations using moisture-responsive knitted fabric design can improve breast support in seamless knitted sports bras.

An experimental approach was used to integrate a novel moisture-responsive fabric panel into a seamless knitted bra, and the resulting compression variability in dry versus wet conditions were compared with those of a control bra. Air permeability and elongation testing of between breasts fabric panels was conducted in dry and wet conditions, followed by three-dimensional body scanning of eight human participants wearing the two bras in similar conditions. Questionnaires were used to evaluate perceived comfort and breast support of both bras in both conditions.

Air permeability test results showed that the novel panel had the highest variance between dry and wet conditions, confirming its moisture-responsive design, and increased its elongation coefficient in both wale and course directions in wet condition. There were significant main effects of bra type and body location on breast compression measurements. Breast circumferences in the novel bra were significantly larger than in the control bra condition. The significant two-way interaction between bra type and moisture condition showed that the control bra lost compressive power in wet condition, whereas the novel bra became more compressive when wet. Changes in compression were confirmed by participants’ perception of tighter straps and drier breast comfort.

These findings add to the limited scientific knowledge of moisture adaptive bra design using engineered knitted fabrics via advanced manufacturing technologies, with possible applications beyond sports bras, such as bras for breast surgery recovering patients.