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Human skin temperature data can provide reference advice for diagnosing many diseases, but current wearable skin temperature monitoring systems have few points and may miss temperature information in critical areas. This paper aims to develop a wearable system that can be used for local temperature monitoring and restore better the actual state of local human temperature by exploring ways to extend more temperature measurement points. This will provide better assistance in developing medical targeting and intelligent clothing.

The temperature measurement system contains modules for temperature monitoring, digital-to-analog conversion and regulated power supply, enabling fast reading of 12 channels of monitoring data. The microprocessor unit is the STM32F407. The instructions and control modes are written in C. The waist area was chosen as the monitoring area because it is susceptible to temperature, and many diseases are associated with skin temperature. Twelve points, including temperature-related acupuncture points and heat-sensitive points, were selected for testing and the data results agreed well with the infrared imaging results.

The waist is selected as the monitoring object, and an easy-to-wear waist temperature monitoring belt is designed to verify the application value of the system. The development of the system provides reference suggestions for the exploration of multi-point temperature monitoring systems and the integration capabilities of temperature measurement modules in wearable multifunctional systems.

In addition to waist temperature monitoring, the wearable temperature measurement system developed in this study can also be applied to other body parts. In addition, the system can be efficiently and effectively combined with various garments, making it a useful tool for researching human skin temperature.

The wearable temperature monitoring system designed in this paper extends the number of temperature test points to 12. The number of test points covers as many localized body areas as possible to indeed reproduce the temperature distribution of the human body. In addition, the selection of test points combines medically relevant body points with physiologically relevant heat-sensitive areas, which makes the temperature measurement data more valuable.

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.

The purpose of this study is to clarify the mechanism of ambient and transient temperature effects on piezoelectric pressure sensors, and to propose corresponding compensation measures. The temperature of the explosion field has a significant influence on the piezoelectric sensor used to measure the shock wave pressure. For accurate shock wave pressure measurement, based on the actual piezoelectric pressure sensors used in the explosion field, the effects of ambient and transient temperatures on the sensor should be studied.

The compensation method of the ambient temperature is discussed according to the sensor size and material. The theoretical analysis method of the transient temperature is proposed. For the transient temperature conduction problem of the sensor, the finite element simulation method of structure-temperature coupling is used to solve the temperature distribution of the sensor and the change in the contact force on the quartz crystal surface under the step and triangle temperatures. The simulation results are highly consistent with the theory.

Based on the analysis results, a transient temperature control method is proposed, in which 0.5 mm thick lubricating silicone grease is applied to the sensor diaphragm, and 0.2 mm thick fiberglass cloth is wrapped around the sensor side. Simulation experiments are carried out to verify the feasibility of the control method, and the results show that the control method effectively suppresses the output of the thermal parasitic.

The above thermal protection methods can effectively improve the measurement accuracy of shock wave pressure and provide technical support for the evaluation of the power of explosion damage.

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.

This paper aims to detect the printing failures (such as warpage and collapse) in material extrusion (MEX) process effectively and timely to reduce the waste of printing time, energy and material.

The approach is designed based on the frequently observed fact that printing failures are accompanied by abnormal material phenomena occurring close to the nozzle. To effectively and timely capture the phenomena near the nozzle, a camera is delicately installed on a typical MEX printer. Then, aided by the captured phenomena (images), a smart printing failure predictor is built based on the artificial neural network (ANN). Finally, based on the predictor, the printing failures, as well as their types, can be effectively detected from the images captured by the camera in real-time.

Experiments show that printing failures can be detected timely with an accuracy of more than 98% on average. Comparisons in methodology demonstrate that this approach has advantages in real-time printing failure detection in MEX.

A novel real-time approach for failure detection is proposed based on ANN. The following characteristics make the approach have a great potential to be implemented easily and widely: (1) the scheme designed to capture the phenomena near the nozzle is simple, low-cost, and effective; and (2) the predictor can be conveniently extended to detect more types of failures by using more abnormal material phenomena that are occurring close to the nozzle.

The purpose of this paper is to explore the distribution of local thermal sensitivity of human body heating and the local preferred heating temperature, and the influence of this sensitive division on thermal response when heating human body in cold environment.

Eight subjects were invited to use carbon fiber heating patches in an environment of 5 and RH 50%, and eight body parts were selected to explore the heating sensitivity. By measuring the skin temperature and evaluating the subjective thermal sensation and thermal comfort, the thermal sensitivity of local body segments and the influence of single-zone and double-zone heating on human thermal response were explored.

The sensitivity of local heating on overall thermal sensation (OTS) was foot > back > chest > abdomen > waist > elbow > hand > knee. Both single-zone and double-zone heating can improve the OTS, but double-zone heating can reach thermal neutrality and thermal comfort. In order to prevent the high temperature of heating patches from damaging human body, the local skin temperature should be monitored in the design of local heating clothing, and 39.6 should be taken as the upper limit of local skin temperature.

The results provide a theoretical basis for the selection of heating position in local electric heating clothing (EHC) and the design of intelligent temperature adjustment heating clothing, improve the performance of local EHC and reduce energy consumption.