Real-time Temperature Research Articles

A 4H-SiC JFET with a monolithically integrated temperature sensor

In this paper, we present a monolithically integrated temperature sensor for a 4H-SiC JFET. This uses a lateral resistor formed by the P+ gate implant. The sensor resistance is dependent on the number of ionized dopants, which increases with temperature. Drift-diffusion simulations considering device self-heating have been carried out, which show the JFET exhibits a breakdown voltage of 1480 V and a nominal threshold voltage (Vth) of −4.3 V. We show that the sensor has a high degree of linearity (R2) of 0.996 between 25–150∘C. Breakdown voltage of the JFET with the integrated sensor is found to reduce as the spacing (S) between the gate junction, the adjacent floating guard ring (FGR) and the sensor junction increases. However, it is found that a large variation in sensor current (ΔIsens) is experienced in the off-state if S is small. An optimum value of S = 0.95 µm was found to maintain 90% of the JFET breakdown voltage, whilst only exhibiting ΔIsens= 3.4%. The impact of contact resistance to the total sensor resistance is ≤1%. In addition, the sensor robustness during switch-off is good, due to the high doping in the P+ layer. The proposed sensor can be integrated into 4H-SiC JFET without any modification of existing process flows, or additional mask layers to provide real-time temperature monitoring with high accuracy.

Flight Testing Of A Single Point Frequency Sweeping Filtered Rayleigh Scattering Probe

"We present a successful in-flight application of filtered Rayleigh scattering (FRS) for temperature measurement in close proximity to the air plane using a newly developed single point, frequency sweeping probe. The temperature measurement is of relevance in the quantification of the density required for the evaluation of velocity from Pitot-probe measurements to obtain the true air speed (TAS). Laser optical air data sensors have the potential for contactless measurements outside the aerodynamic boundary layer. They are capable of self-diagnosis of fault conditions (e.g. signal loss, increased measurement uncertainty or spectral distortion for FRS) thereby increasing the safety of the aircraft. Compared to classical probes, they are less susceptible potential icing and blockage issues. The FRS-results presented here, are the outcome of two separate flight campaigns, carried out in the Alpine region as well as in the North Sea area. Measurement data provided information on the functionality of the FRS-system under various flight and weather conditions. Furthermore, the implementation of the FRS measurement system in the aircraft is presented; application aspects and improvements are discussed. In particular, the measurement accuracy at the actually achievable measurement rate was analyzed. For straight ahead flights at constant height, temperature mean values were in good agreement with the flight data system of the DLR research aircraft Dassault Falcon 20 (D-CMET). Aiming at in-flight air data measurements, the optical FRS measurement system requires a real-time temperature sample rate of a few Hz. We demonstrate that 2 FRS spectra per second can be acquired by a newly developed method using frequency sweeps for temperature determination. The low signal intensity of cw-laser based FRS is countered using a combined transmitter/receiver optic that focusses the entire cw-laser power into one measuring point. The light backscattered from the probe volume is collected by the same optics, filtered and imaged in a confocal arrangement onto a photomultiplier. An improved and miniaturized version of the presented FRS system could be a part of a future optical air data system (OADS). However, the strength of FRS signal relative to bright ambient conditions (e.g. sunlight, cloud backscatter) must be further improved in order to be able to measure well above cloud cover. The potential for further development is foreseen even if the accuracy required for flight data systems (~0.5 K) has not yet been achieved at a temperature measurement rate of 2 Hz. "

Smart Solutions for Sustainable Agriculture: Design and Development of IoT-Powered Soil Health Identification Systems

This research examines smart solutions for agricultural sustainability using IoT-powered soil health detection systems. Innovative solutions are needed to manage resources and protect the environment as the world population grows and agricultural resources are under strain. IoT-enabled soil health monitoring has great potential. The suggested system uses IoT sensors to measure soil moisture, pH, nutrient content, and temperature in real time. This data is analyzed using powerful data analytics and machine learning algorithms to provide farmers soil health information. Monitoring soil quality allows farmers to make educated irrigation, fertilization, and crop selection choices, optimizing resource use and yields while minimizing environmental effect. The study also examines sensor selection, data transmission methods, and data security for IoT-powered soil health detection systems. Widespread use of these technologies might boost agricultural output, lower input costs, and enhance food security. Finally, IoT-powered soil health detection systems are a major step toward agricultural sustainability. These devices enable precision farming that is ecologically friendly and profitable by giving real-time soil data. However, effective implementation would need agricultural value chain stakeholders to collaborate and continued research to address growing issues and possibilities.

Experimental study on mechanical damage and creep characteristics of Gonghe granite under real-time high temperature

It is significant to study the mechanical properties of hot dry rock (HDR) for the development of deep geothermal energy. At present, the creep behavior of granite under real-time high temperature is not fully understood. The creep behavior of granite at 25 ∼ 800°C was investigated by real-time high-temperature uniaxial compression and graded load creep tests, and the thermal damage mechanism of granite was studied by scanning electron microscopy (SEM) experiments. The paper systematically analyzes the evolution of mechanical indexes such as uniaxial compressive strength (UCS), elastic modulus, creep deformation, steady creep rate and long-term strength of granite under thermal-force coupling. The results show that the UCS and elastic modulus of granite increase with increasing temperature in the range of 25 ∼ 200 °C, and decrease with increasing temperature in the range of 200 ∼ 800 °C. The damage speed of granite is the fastest in the temperature range of 400 ∼ 600 °C. The steady creep rate of granite increases with the increase of temperature and stress level. The ratio of long-term strength to UCS decreases with increasing temperature, from 93.6% at 25 °C to 73.2% at 800 °C. The research results provide relevant thermal damage mechanical parameters and theoretical basis for the development of HDR.

Dynamic Spectral Metafabric with Unidirectional Moisture Transport Property for Personal Thermal Management.

Personal thermal management technology, which adjusts the heat exchange between the human body and the environment, can passively heat or cool the body to maintain a comfortable core temperature, thereby enhancing comfort and reducing energy consumption. However, most existing personal thermal management materials have static properties, such as fixed solar reflectance and infrared emissivity, which do not support real-time dynamic temperature regulation. Moreover, sweat accumulation on the skin surface, while contributing to temperature regulation, can significantly reduce comfort. This study constructs a unidirectional moisture-permeable intelligent thermal management fabric system to achieve superior thermal and moisture comfort in complex environments. The fabric incorporates thermochromic microcapsules into PAN nanofibers by using electrospinning technology for intelligent thermal management. Subsequent hydrophobic treatment of the fiber film surface imparts the fabric with unidirectional wetting properties. The nanofibrous structure provides intrinsic elasticity and breathability. In heating mode, the fabric's average sunlight reflectance is 42.1%, which increases to 82.2% in cooling mode, resulting in a reflectance difference of approximately 40%. The hydrophobic treatment endows the fabric with excellent moisture absorption and perspiration properties, demonstrated by a unidirectional moisture transport index of 696.63 and a perspiration evaporation rate of 5.88 mg/min. When the fabric temperature matches the ambient temperature, the photothermal conversion power difference of the Janus metafabric in two modes reaches 248.37 W m-2. Additionally, Janus metafabrics show the potential for temperature-responsive design and repeated writing applications. The outstanding wearability and dynamic spectral properties of these metafabrics open new pathways for sustainable energy, smart textiles, and thermal-moisture comfort applications.

City-Scale Air Quality Network of Low-Cost Sensors

In this paper, we present a city-scale (possibly global) air pollution network made of low-cost sensors for particulate matter concentration in the air. The components of the presented system are based on our research and experience from previous studies focused on air quality instruments and sensors for addressing urban air pollution. Sensor nodes are produced locally and distributed over Sarajevo, Bosnia and Herzegovina. The system collects, calibrates, and displays particulate matter concentrations in real-time. In comparison to the available measurements from governmental institutions, our system demonstrated good agreement of measured parameters, with several advantages including, higher resolution in space and time, lower costs, both horizontal and vertical measurements. One of the capabilities of the system is the real-time air pollution city map with animations. By installing multiple sensor nodes over a slope, we receive vertical measurements of temperature, humidity, and particulate matter concentration in real time, which gives a valuable insight into the dynamics of temperature inversion episodes and air pollution below the inversion layer.

Investigation of Chemiresisitive Electronic Nose for the Analysis of Multiple Analytes Using Pattern Recognition Algorithm

The multiple analytes produced during the operation of nuclear facilities are required to monitor the smooth operation of the plant in the environment of high temperature and radioactivity in real time. A chemiresisitive electronic nose was investigated and developed to analyze the multiple analytes generated in the nuclear reactor/allied facilities. An electronic nose consists of chemiresisitive sensor, array, housing, hardware, software, and pattern recognition algorithm. The sensor and array of different semiconductor metal oxides were prepared, processed, and developed to sense the multiple analytes. The hardware and data acquisition software (DAS) was designed and developed to acquire the dynamic responses from the array of four sensors. The hardware provides a low excitation voltage for measurement of the dynamic response of four sensors towards the improvement of the life of the sensor. The various experiments were conducted with multiple analytes at different temperatures to study the analysis of analytes. The performance of the hardware and DAS were tested and evaluated with the sensor array responses towards three analytes, viz., hydrogen (H2), formaldehyde (HCHO), and hydrazine (NH2NH2). Different features evaluated from the response traces were processed to teach the instrument using pattern recognition algorithms. The training and real-time testing of the sensor array realized the qualitative discrimination and quantitative estimation of the analytes.

Converter Capacitor Temperature Estimation Based on Continued Training LSTM under Variable Load Conditions.

Capacitors are crucial components in power electronic converters, responsible for harmonic elimination, energy buffering, and voltage stabilization. However, they are also the most susceptible to damage due to their operational environment. Accurate temperature estimation of capacitors is essential for monitoring their condition and ensuring the reliability of the converter system. This paper presents a novel method for estimating the core temperature of capacitors using a long short-term memory (LSTM) algorithm. The approach incorporates a continued training mechanism to adapt to variable load conditions in converters. Experimental results demonstrate the proposed method's high accuracy and robustness, making it suitable for real-time capacitor temperature monitoring in practical applications.

Real-time temperature control in rubber extrusion lines: a neural network approach

In rubber extrusion, precise temperature control is critical due to the process’s sensitivity to fluctuating parameters like compound behavior and batch-specific material variations. Rapid adjustments to temperature deviations are essential to ensure stable throughput and extrudate surface integrity. Based on our previous research, which initiated the development of a feedforward neural network (FNN) without real-world empirical application, we now present a real-time control system using artificial neural networks (ANNs) for dynamic temperature regulation. The underlying FNN was trained on a dataset comprising different ethylene propylene diene monomer (EPDM) rubber compounds, totaling 14,923 measurement points for each temperature value. After training, the FNN achieves remarkable precision, evidenced by a mean absolute percentage error (MAPE) of 0.68% and a mean squared error (MSE) of 0.63°C2 in predicting temperature variations. Its integration into the control system enables real-time responsiveness, allowing for adjustments to temperature deviations within an average timeframe of 68 ms. A key advantage over proportional-integral-derivative (PID) controllers is the ability to continuously learn and adjust to complex, non-linear, and batch-specific process dynamics. This adaptability results in enhanced process stability, as evidenced by inline manufacturing validation. Our paper presents the first ANN-based rubber extrusion control, demonstrating how machine learning techniques can be effectively leveraged for real-time, adaptive temperature control. Beyond rubber extrusion, this strategy has potential applications in various polymer processing and other industries requiring precise temperature control. Future trends may involve the integration of online learning techniques and the expansion of interconnected manufacturing processes.

Synchronously in vivo real-time monitoring bacterial load and temperature with evaluating immune response to decipher bacterial infection.

Determining the precise course of bacterial infection requires abundant in vivo real-time data. Synchronous monitoring of the bacterial load, temperature, and immune response can satisfy the shortage of real-time in vivo data. Here, we conducted a study in the joint-infected mouse model to synchronously monitor the bacterial load, temperature, and immune response using the second near-infrared (NIR-II) fluorescence imaging, infrared thermography, and immune response analysis for 2 weeks. Staphylococcus aureus (S. aureus) was proved successfully labeled with glucose-conjugated quantum dots in vitro and in subcutaneous-infected model. The bacterial load indicated by NIR-II fluorescence imaging underwent a sharp drop at 1 day postinfection. At the same time, the temperature gap detected through infrared thermography synchronously brought by infection reached lowest value. Meanwhile, the flow cytometry analysis demonstrated that immune response including macrophage, neutrophil, B lymphocyte, and T lymphocyte increased to the peak at 1 day postinfection. Moreover, both M1 macrophage and M2 macrophage in the blood have an obvious change at ~1 day postinfection, and the change was opposite. In summary, this study not only obtained real-time and long-time in vivo data on the bacterial load, temperature gap, and immune response in the mice model of S. aureus infection, but also found that 1 day postinfection was the key time point during immune response against S. aureus infection. Our study will contribute to synchronously and precisely studying the complicated complex dynamic relationship after bacterial infection at the animal level.

Temperature behavior of long-span cable-stayed bridges by using unified analysis

Varying temperatures significantly affect long-span cable-stayed bridges. However, previous studies on the temperature behavior of bridges were either limited by finite sensors, failing to capture the accurate relation between the temperature and responses, or constrained to a “divide-and-conquer” strategy, requiring considerable manual intervention. This study develops a unified approach to the investigation of thermal behaviors of cable-stayed bridges by integrating heat-transfer analysis and structural analysis based on the same refined global 3D finite element model. A navigation channel bridge of Hong Kong‒Zhuhai‒Macao Bridge is used as the testbed. First, the heat-transfer analysis is conducted based on thermal boundary conditions carefully determined from real-time ambient temperature, wind, and solar radiation, which are tailored for each surface to reflect the influence of the geometric configuration and temperature difference. Then, the detailed temperature distribution results are automatically converted to thermal loads, and thermal elements are changed to structural elements to calculate the temperature-induced responses. Results show that temperature has a significant effect on the structural responses of the bridge. The calculated temperatures and temperature-induced responses are in good agreement with their measured counterparts, verifying the effectiveness of the proposed unified approach to investigating the temperature behavior of cable-stayed bridges.

Kontrol Motor Berbasis PID untuk Pompa Air di Miniatur Rumah Kaca

Greenhouses are crucial in agriculture for enhancing crop yields by providing controlled environmental conditions. Efficient water management is a key challenge in conventional greenhouse systems, often leading to water wastage due to unmeasured soil moisture levels. This research aimed to design and implement a PID control system for water pumps in a miniature greenhouse to optimize water usage and maintain stable soil moisture and temperature levels. The methodology involved integrating PID control with sensors to monitor and adjust the water pump operation based on real-time soil moisture and temperature data. The study found that the PID-controlled system reduced the time to reach steady-state conditions from 650 ms to 600 ms, with no overshoot compared to systems without control. Additionally, the error in the PID system ranged from 0.89% to 2.32%. These findings suggest that PID control significantly enhances the efficiency and effectiveness of water usage in greenhouses, contributing to better crop management and resource conservation. The implications of this research are substantial for agricultural practices, as it demonstrates a viable solution for improving irrigation precision and overall greenhouse management.

Highly stable lead-free perovskite glass for real-time 3D surface temperature measurement across the wide temperature range

3D Optical temperature sensors are highly valuable in various fields such as industry, medicine, and aerospace, as they allow for real-time detection of temperature distribution on surfaces. However, existing temperature sensor materials face challenges in achieving accurate detection across a wide range of temperatures from low to high. To overcome these issues, Cs2Zn1-xEuxBr4 perovskite B2O3–BaF2-Ga2O3 (BBG) borate glass was prepared for non-contact 3D optical temperature sensing. Cs2Zn1-xEuxBr4 perovskite B2O3–BaF2-Ga2O3 (BBG) borate glass improves the stability, thus expanding the temperature measurement range. Utilizing the fluorescence intensity ratio (FIR) method, based on the 613 nm (Eu3+: 5D0→7F2) and 592 nm (Eu3+: 5D0→7F1) emission bands, a FIR benchmark database was constructed for the entire temperature range from 10 K to 640 K. Temperature calculation equations related to FIR were derived, with a maximal relative sensitivity of 0.08 % K−1 at 180 K. The method for measuring the surface temperature of 3D curved aircraft is based on the rare earth perovskite borate glass. The temperature measured with this method is close to the thermal imaging temperature and has a good temperature measuring effect. The temperature measurement range is wider and more advantageous than previously reported ranges. These results indicate that Cs2Zn1-xEuxBr4-BBG perovskite borate glass temperature sensors have broad application prospects in many applications.

Algorithm-driven estimation of household cooking activity and its impact on indoor PM2.5 assessments

BackgroundHousehold PM2.5 exposures have adverse health effects, and cooking behaviors are an important source of PM2.5 in the home. There is a need for accurate measures of cooking activity to better understand its associations with household PM2.5 since self-reported surveys are often subject to recall bias and misreporting of cooking events. ObjectiveIn this study, we aimed to address limitations associated with a self-reported cooking metric, by using temperature data to estimate cooking activity. MethodsWe developed an algorithm to identify cooking events at the 5-minute level using real-time temperature data measured near the stove and in the living room, across 148 households in Chelsea and Dorchester, MA. We compared the number of cooking events identified by this algorithm with cooking events self-reported by participants in daily activity logs and survey responses, and further assessed how these metrics differed with respect to their associations with occurrence of peak PM2.5, in mixed effects logistic regression models. ResultsWe found that 65 % of the cooking events identified by the algorithm were not reported by participants. Furthermore, households classified as frequent vs infrequent cooking households using the algorithm had a larger difference in indoor PM2.5 levels, compared to households classified by self-report. In mixed effects logistic regression models for elevated household PM2.5 levels, we observed much stronger associations between household PM2.5 and algorithm-derived cooking activity (OR: 2.85 [95 % CI: 2.76, 2.95]) as compared to the association between household PM2.5 and self-reported cooking activity (OR: 1.22 [95 % CI: 1.17, 1.27] for stove use and OR: 1.67 [95 % CI: 1.58, 1.76] for grill use/frying/broiling/sauteing). SignificanceOverall, the algorithm developed in this study presents a data-driven approach to collecting cooking activity data in U.S. households, that may be more indicative of actual cooking events and also more predictive of household PM2.5 in indoor environmental models.

Thermal transfer analysis of machines used for the treatment of textile materials

The present paper aims to introduce a comprehensive thermal analysis of the sizing-finishing machine (Squeezing Pader Machine) to identify the overheating areas to organise maintenance activities. As there is no universal application for checking the condition of a machine, specific methods must be used depending on the characteristics of the machines being monitored, as machine condition monitoring is very important in terms of productivity, economic benefits and maintenance. The thermal analysis of the equipment in this work paper, has an important role (for planning maintenance activities), because this machine ensures optimal loading with different material treatment solutions, intended for mattresses, according to customer requirements. In the present paper, the authors conducted research on the whole technological flow to plan predictive maintenance activities using online monitoring methods. The mathematical modelling of the data obtained from the thermal analysis of the engines carried out based on the polynomial regression is used to predict the evolution of the temperatures along the analysed lines outside the measurement interval for each engine analysed. In this sense, the authors propose online monitoring of the motors that have the role of stretching and transporting the material, which is not included in the real-time temperature monitoring in the machine software, as the rest of the motors are monitored. Regular monitoring of motors with the help of thermography can help identify potential problems before they become serious, allowing preventive maintenance to improve the equipment’s reliability and efficiency.

Monitoring and Analyzing Air Quality in Several Locations around Medan City Using IoT-Based Integrated Sensors

This research is motivated by the importance of monitoring air quality in industrial and non-industrial areas, especially in the city of Medan, Indonesia. Increasing growth of the industrial sector and use of transportation can increase air pollution. Air pollution caused by emissions of harmful gases such as NO2 and CO, as well as microscopic particles such as PM2.5 has a significant impact on human health, especially causing respiratory and heart disease and premature death. The rapid development of Internet of Things (IoT) technology can detect air quality and transmit sensor readings wirelessly via the internet to an Android or PC. This research aims to design an air quality monitoring tool related to the presence of dangerous gases NO2, CO and levels of microscopic particles PM2.5 with an integrated mics-6814 sensor and PMS5003 sensor based on IoT accurately and in real-time and analyze the results of air quality monitoring in research area. The research methodology involves measuring air quality parameters such as PM2.5, CO, NO2, temperature, and humidity using IoT-based integrated design tools. The research location consists of 3 points, namely around the industrial and residential areas of the Kawasan Industri Medan (KIM) Mabar, the Industrial Area in Tanjung Morawa, and the University of North Sumatra. Numerical data will be tabulated from the results of research carried out for 7 hours in 3 days based on the specified time. The results of this research show that the air quality monitoring system designed can measure temperature, humidity, PM2.5, NO2 and CO levels in real time and accurately. Air quality data collected at several research locations shows different trends. Overall, the air quality in the research area is in the healthy category, having no impact on the health of humans, animals and plants which is linked to Peraturan Menteri Lingkungan Hidup Dan Kehutanan Republik Indonesia. Suggestions for further research include selecting components that are more sensitive in detecting air quality and paying attention to environmental conditions which can reduce deviation values ​​from the sensor.

Self Operable E-health Diagnosis and Monitoring System with Gesture Control and Voice Assistance

In the present timeline, technology has advanced significantly, and the healthcare sector is constantly expanding. People in rural areas who cannot access corporate hospitals postpone their health check-ups due to the difficulty of traveling long distances. The readily available and expensive nature of diagnostic technologies have prevented many rural residents from seeking clinical examinations, even in cases when they have health issues. Corporate hospitals often make people to wait for long periods of time for health reports and causing delays. These issues highlighted the need for a device which aimed at developing an Arduino based health diagnosis system To resolve this issue, we have introduced a device that offers a comprehensive health diagnosis and monitoring system that uses Arduino technology to store and display vital health information. The system is designed to analyze electrocardiograms (ECG), photoplethysmograms (PPG), body temperature, heart rate and stress levels in real time and then process this information to create meaningful health status assessments and store them in the database for further health assessments. Furthermore, these data could be displayed through web server or an application. Additionally, this system provides voice assistance for the condition of patient when it is critical which helps in early detection of health conditions. Hand gestures are incorporated into this device, which makes it distinctive. Users can control the display of health parameters and easily move through the system's features with simple hand gestures. Users can switch between different types of modes in the system (such as on/off and different operation) by moving their hand in specified patterns. Thus, this makes the system user-friendly so that everyone may use it.

Enhancing resilience in urban utility tunnels power transmission systems: Analysing temperature distribution in near-wall cable fires for risk mitigation

Urban utility tunnels are integral to the underground power transmission systems of cities. Enhancing the understanding of cable fire hazards within these tunnels is pivotal for fostering risk-resilient underground environments. This study delves into the temperature distribution characteristics of near-wall cable fires in urban utility tunnels. Employing a reduced-scale utility tunnel model, a series of fire experiments were conducted, focusing on the safety monitoring of horizontally arranged cables. Cone calorimeter tests were initially performed to ascertain the heat release rate of cable fires. The study meticulously considered cable spatial position parameters, such as cable height and near-wall distance, as dependent variables, and monitored real-time temperature changes within the utility tunnel under various fire scenarios.The findings reveal that the cable’s proximity to the wall significantly influences the fire temperature field. For instance, when a fire near the sidewall (at 1 cm) is compared to one in the middle of the tunnel (at 20 cm), the ceiling maximum temperature is reduced by 38.9 %. The sidewall’s limiting effect diminishes fire energy release, moderates vertical temperature decay, and enhances longitudinal temperature attenuation. The study introduces dimensionless mathematical models that account for the sidewall effect, quantifying the characteristics of fire-induced smoke temperature distribution in utility tunnels, such as ceiling maximum temperature rise, vertical smoke temperature profile, and longitudinal smoke temperature decay. The models’ predicted values closely align with the experimental results.These insights are pivotal for integrating cable numbers and spatial positioning in scenario-based fire prevention and mitigation strategies. The study’s implications extend to the design and operation of urban utility tunnels, emphasizing the need for strategic cable placement and the potential for mathematical models to inform risk mitigation measures. The research contributes to the advancement of sustainable urban development by providing a framework for enhancing the safety and resilience of critical infrastructure against fire hazards.

Thermal-Sensitive Supramolecular Coordination Complex Formed by Orthogonal Metal Coordination and Host-Guest Interactions for an Electrical Thermometer.

Supramolecular coordination complexes (SCCs) are popular for their structural diversity and functional adaptability, which make them suitable for a wide range of applications. Photophysical and mechanical performance of SCCs are the most attractive characteristics, yet their ionically conductive behavior and potential in electrical sensing have been rarely investigated. This study reports a well-designed SCC that integrates orthogonal metal coordination and host-guest interactions to achieve sensitive electrical thermal sensing. Owing to the thermodynamic nature of the host-guest interaction, the SCC encounters thermally induced disassembly, leading to significantly enhanced ion mobility and thus allowing for the precise detection of minor temperature variation. The SCC-based thermometer is then fabricated with the assistance of 3D printing and demonstrates good accuracy and reliability in monitoring human skin temperature and real-time temperature changes of mouse during the whole anesthesia and recovery process. Our findings provide an innovative strategy for developing electrical thermometers and expand the current application scope of SCCs in electrical sensing.

Real-time pavement temperature prediction through ensemble machine learning

Pavement temperature is crucial for assessing asphalt pavement's performance and structural integrity due to its viscoelastic behavior. Currently, asphalt temperature prediction includes analytical, numerical, and statistical methods and relies on field investigation with intensive labor and time to hinder routine practice. Machine learning (ML) offers a promising solution for reliable and accurate real-time predictions by considering various dynamic environmental factors. This research employs four ensemble ML algorithms: eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Adaptive Boosting (AdaBoost) to predict pavement temperature at different depths. XGB consistently outperformed other ensemble models, boasting the highest average R2 value (97.92%) and lower MAE and RMSE than LGBM, RF, and AdaBoost. XGB also significantly outperformed other commonly used models (linear regression, SVR, ANN, LSTM, and GRU) with an average reduction in errors of 35.29%, 63.75%, 42.75%, and an increase in R2 by 4.39% for MAE, MSE, RMSE, and R2, respectively. The Shapley additive explanation (SHAP) was applied to interpret the underlying influencing factors and their interactions, and revealed atmospheric temperature as the most impactful feature, contributing over 40.58% among the significant features.Furthermore, the predictive performance of XGB was enhanced by incorporating Principal Component Analysis to reduce computational consumption with average reductions of 8.98%, 16.59%, 9.01%, and 7.89% in MAE, MSE, MAPE, and RMSE. XGB with PCA emerges as the most effective and efficient model for predicting pavement temperatures at various depths to facilitate timely decision-making for preventive maintenance and rehabilitation and prevent future catastrophic failures.