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Breast cancer has become the largest cancer in the world today. Health problems for women with breast cancer need to be addressed urgently. This study aims to select the best method for preparing temperature-sensitive sports underwear, and to verify the feasibility of using K-type thermocouple threads in underwear fabrics.

In the experiments, two samples were designed for temperature-sensitive performance tests and the effects produced by different outer layer structures were investigated. In the second step, K-type thermocouple wires were integrated into sports underwear. The comfort and feasibility of the temperature-sensitive underwear were investigated.

It was finally verified to obtain the best comfort and temperature-sensing performance of K-type thermocouple filaments integrated into sports underwear with plain stitching.

The underwear has a certain prospect for the application of smart apparel based on breast cancer health monitoring, which is of some significance for monitoring smart apparel.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

The purpose of this paper is to develop a prototype of a wearable medical device in the form of a bandage with a real-time data monitoring platform, which can be used domestically for diabetic patients to identify the possibility of foot ulceration at the early stage.

The prototype can measure blood volumetric change and temperature variation in the forefoot area simultaneously. The waveform extracted using a pulsatile-blood-flow signal was used to assess blood perfusion-related information, and hence, predict ischemic ulcers. The temperature difference between ulcerated and the reference was used to predict neuropathic ulcers. The medical device can be used as a bandage during the application wherein the sensory module is placed inside the hollow pocket of the bandage. A platform was developed through a mobile application where doctors can extract real-time information, and hence, determine the possibility of ulceration.

The height of the peaks in the pulsatile-blood-flow signal measured from the subject with foot ischemic ulcers is significantly less than that of the subject without ischemic ulcers. In the presence of ischemic ulcers, the captured waveform flattens. Therefore, the blood perfusion from arteries to the tissue of the forefoot is considerably low for the subject with ischemic ulcers. According to the temperature difference data measured over 25 consecutive days, the temperature difference of the subject with neuropathic ulcers occasionally exceeded the 4 °F range but mostly had higher values closer to the 4 °F range. However, the temperature difference of the subject who had no complications of neuropathic ulcers did not exceed the 4 °F range, and the majority of the measurements occupy a narrow range from −2°F to 2 °F.

The proposed prototype of wearable medical apparatus can monitor both temperature variation and pulsatile-blood-flow signal on the forefoot simultaneously and thereby predict both ischemic and neuropathic diabetes using a single device. Most importantly, the wearable medical device can be used domestically without clinical assistance with a real-time data monitoring platform to predict the possibility of ulceration and the course of action thereof.

This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.

This study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.

A comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.

Based on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.

The aim of this paper is to investigate the potential impact of Industry 4.0 (I4.0) digital technologies on promoting sustainability in small and medium-sized enterprises (SMEs) within developing economies such as Brazil. Additionally, we present a comprehensive framework that consolidates this correlation.

Qualitative research was conducted through semi-structured interviews with leaders of SMEs to identify the specific challenges in achieving sustainability. Additionally, interviews were conducted with technology provider firms to evaluate the existing solutions available to SMEs. The interview results were analyzed, and technological solutions were proposed through a focus group session involving four experts in I4.0. These proposed solutions were then compared with the offerings provided by the technology providers. Based on this, a second round of meetings was conducted to gather feedback from the SMEs.

The findings of this study confirm the feasibility of implementing I4.0 and sustainable practices in SMEs. However, it is crucial to tailor the technologies to the specific circumstances of SMEs. The study presents propositions on how specific applications of technology can address the economic, environmental and social demands of SMEs. Furthermore, a framework is proposed, emphasizing the integration of smart technologies as essential components across sustainability dimensions.

This study makes a significant contribution to the current body of literature as it pioneers the examination of the relationship between I4.0 technologies and sustainability, focusing specifically on SMEs in a developing country context.

El objetivo de este estudio es investigar el potential impacto de las tecnologías digitales de la Industria 4.0 en la promoción de la sostenibilidad en las pequeñas y medianas empresas (PYMES) en economías en desarrollo, como Brasil.

Realizamos una investigación cualitativa mediante entrevistas semiestructuradas a líderes de PYMES para identificar los desafíos que enfrentan en la búsqueda de la sostenibilidad. También llevamos a cabo entrevistas con empresas proveedoras de tecnología para evaluar las soluciones existentes. Los resultados de las entrevistas se analizaron y se propusieron soluciones tecnológicas a través de una sesión de grupo focal con cuatro expertos en la Industria 4.0. Estas soluciones se compararon con las ofertas proporcionadas por los proveedores de tecnología. Posteriormente, se realizaron una segunda reunión para recopilar comentarios de las PYMES.

Los hallazgos de este estudio confirman la viabilidad de implementar la Industria 4.0 y prácticas sostenibles en las PYMES. Sin embargo, es crucial adaptar las tecnologías a las circunstancias de las PYMES. Presentamos propuestas sobre cómo las aplicaciones de la tecnología pueden abordar las demandas económicas, ambientales y sociales de las PYMES. Además, proponemos un marco que destaca la integración de tecnologías como componentes esenciales de la sostenibilidad.

Este estudio es pionero en examinar la relación entre las tecnologías de la Industria 4.0 y la sostenibilidad, centrándose específicamente en las PYMES en un contexto de país en desarrollo.

Living walls (LWs), vegetated walls with an integrated growth layer behind, are being increasingly incorporated in buildings. Examining plant characteristics’ comparative impacts on LWs’ energy efficiency-related thermal behavior was aimed, considering that studies on their relative effects are limited. LWs of varying leaf albedo, leaf transmittance and leaf area index (LAI) were studied for Antalya, Turkey for typical days of four seasons.

Dynamic simulations run by Envi-met were used to assess the plant characteristics’ influence on seasonal and orientation-based heat fluxes. After model calibration, a sensitivity analysis was conducted through 112 simulations. The minimum, mean and maximum values were investigated for each plant characteristic. Energy need (regardless of orientation), temperature and heat flux results were compared among different scenarios, including a building without LW, to evaluate energy efficiency and variables’ impacts.

LWs reduced annual energy consumption in Antalya, despite increasing energy needs in winter. South and west facades were particularly advantageous for energy efficiency. The impacts of leaf albedo and transmittance were more significant (44–46%) than LAI (10%) in determining LWs’ effectiveness. The changes in plant characteristics changed the energy needs up to ca 1%.

This study can potentially contribute to generating guiding principles for architects considering LW use in their designs in hot-humid climates.

The plant characteristics’ relative impacts on energy efficiency, which cannot be easily determined by experimental studies, were examined using parametric simulation results regarding three plant characteristics.

Complex cost structures and multiple conflicting objectives make selecting an appropriate cloud service difficult. The purpose of this study is to propose a novel group consensus decision making method for cloud services selection with knowledge deficit by trust functions.

This article proposes a knowledge deficit-based multi-criteria group decision-making (MCGDM) method for cloud-service selection based on trust functions. Firstly, the concept of trust functions and a ranking method is developed to express the decision-making opinions. Secondly, a novel 3D normalized trust degree (NTD) is defined to measure the consensus levels. Thirdly, a knowledge deficit-based interactive consensus model is proposed for the inconsistent experts to modify their decision opinions. Finally, a real case study has been carried out to illustrate the framework and compare it with other methods.

The proposed method is practical and effective which is verified by the real case study. Knowledge deficit is an important concept in cloud service selection which is verified by the comparison of the proposed recommended mechanism based on KDD with the conventional recommended mechanism based on average value. A 3D NTD which considers three values (trust, not trust and knowledge deficit) is defined to measure the consensus levels. A knowledge deficit-based interactive consensus model is proposed to help decision-makers reach group consensus. The proposed group consensus model enables the inconsistent decision-makers to accept the revised opinions of those with less knowledge deficit, rather than accepting the recommended opinions averagely.

The proposed a knowledge deficit-based MCGDM cloud service selection method considers group consensus in cloud service selection. The concept of knowledge deficit is considered in modeling the group consensus measuring and reaching method.

The purpose of this study is to design a wearable medical device as a human care platform and to introduce the design details, key technologies and practical implementation methods of the system.

A multi-channel data acquisition scheme based on PCI-E (rapid interconnection of peripheral components) was proposed. The flexible biosensor is integrated with the flexible data acquisition card with monitoring capability, and the embedded (device that can operate independently) chip STM32F103VET6 is used to realize the simultaneous processing of multi-channel human health parameters. The human health parameters were transferred to the upper computer LabVIEW by intelligent clothing through USB or wireless Bluetooth to complete the transmission and processing of clinical data, which facilitates the analysis of medical data.

The smart clothing provides a mobile medical cloud platform for wearable medical through cloud computing, which can continuously monitor the body's wrist movement, body temperature and perspiration for 24 h. The result shows that each channel is completely accurate to the top computer display, which can meet the expected requirements, and the wearable instant care system can be applied to healthcare.

The smart clothing in this study is based on the monitoring and diagnosis of textiles, and the electronic communication devices can cooperate and interact to form a wearable textile system that provides medical monitoring and prevention services to individuals in the fastest and most accurate way. Each channel of the system is precisely matched to the display screen of the host computer and meets the expected requirements. As a real-time human health protection platform technology, continuous monitoring of human vital signs can complete the application of human motion detection, medical health monitoring and human–computer interaction. Ultimately, such an intelligent garment will become an integral part of our everyday clothing.

This study aims to improve the availability of regenerative braking for urban metro vehicles by introducing a sensorless operational temperature estimation method for the braking resistor (BR) onboard the vehicle, which overcomes the vulnerability of having conventional temperature sensor.

In this study, the energy model based sensorless estimation method is developed. By analyzing the structure and the convection dissipation process of the BR onboard the vehicle, the energy-based operational temperature model of the BR and its cooling domain is established. By adopting Newton's law of cooling and the law of conservation of energy, the energy and temperature dynamic of the BR can be stated. To minimize the use of all kinds of sensors (including both thermal and electrical), a novel regenerative braking power calculation method is proposed, which involves only the voltage of DC traction network and the duty cycle of the chopping circuit; both of them are available for the traction control unit (TCU) of the vehicle. By utilizing a real-time iterative calculation and updating the parameter of the energy model, the operational temperature of the BR can be obtained and monitored in a sensorless manner.

In this study, a sensorless estimation/monitoring method of the operational temperature of BR is proposed. The results show that it is possible to utilize the existing electrical sensors that is mandatory for the traction unit’s operation to estimate the operational temperature of BR, instead of adding dedicated thermal sensors. The results also validate the effectiveness of the proposal is acceptable for the engineering practical.

The proposal of this study provides novel concepts for the sensorless operational temperature monitoring of BR onboard rolling stocks. The proposed method only involves quasi-global electrical variable and the internal control signal within the TCU.

This paper aims to explore factors impacting wearable fitness tracking (WFT) device continued usage intention from perspectives of technology attributes (autonomy benefits), health attributes (self-health management benefits, diet-control benefits and health self-efficacy), and consumer attributes (age, gender, technological innovativeness, symbolic benefits, social benefits and hedonic benefits).

The study integrates constructs from the technology acceptance theories and the health promotion model to develop the research model and hypothesis. The empirical analysis was conducted using data from 217 respondents from India. Logistic regression was used to identify factors that discriminate between groups with low and high continued usage intentions.

Results indicate higher continued usage intention for WFT devices is driven by perceived benefits-health, autonomy, social and hedonic, and individual characteristics-technological innovativeness and perceived health self-efficacy. Further, perceived symbolic benefits, diet control benefits, age, and gender does not discriminate between the groups with low and high continued usage intentions.

The results may be limited to the context of the sample and the factors considered. The study suggests future research areas.

The paper offers insights for marketers, governments, insurance firms, and related healthcare services on promoting higher usage of WFT devices to yield dual benefits of preventive healthcare and higher profitability.

The study extends existing research by examining factors across consumer, health, and technological domains in a single framework and adds to the limited research in the context of usage of WFT devices in developing countries.