Search

Oil-water two-phase flow atoilfield wellheads is a common occurrence in distribution. This paper proposes using a heat transfer method to accurately measure and monitorthe total fluid volume at the wellhead. To achieve this, thermal sensors of types PT1000 and PT20, which are suitable for measuring oil-water mixed flow, are first designed. The feasibility and linearity of the sensors are simulated and calculated. Secondly, the heat transfercoefficient is computed using experimental methods and a functional relationship between the heat transfer coefficient and the flow rate is derived. Finally, the calculated results demonstrate the feasibility of using the heattransfer method to measure oil-water two-phase flow. If the water-liquid ratio (WLR) is known, the flow rate can be calculated through the functional relationship between the heat transfer coefficient and the flow rate. This method can improve the accuracy of estimating the flow rate. [ABSTRACT FROM AUTHOR]

In the context of global warming, rising air temperatures are increasing evapotranspiration ( ET c ) in all agricultural crops, including rice, a staple food worldwide. Simultaneously, the occurrence of droughts is reducing water availability, affecting traditional irrigation methods for rice cultivation (flood irrigation). The objective of this study was to determine ET c (water use) and yield performance in rice crop under different irrigation regimes: treatments with continuous flood irrigation (CF) and irrigations with alternating wetting and drying ( A W D 5 , A W D 10 , and A W D 20 ) in an experimental area in INIA–Vista Florida. Water balance, rice physiological data, and yield were measured in the field, and local weather data and thermal and multispectral images were collected with a meteorological station and a UAV (a total of 13 flights). ET c values obtained by applying the METRICTM (Mapping Evapotranspiration at High Resolution using Internalized Calibration) energy balance model ranged from 2.4 to 8.9 mm d−1 for the AWD and CF irrigation regimes. In addition, ET c was estimated by a water balance using the AquaCrop model, previously parameterized with RGB image data and field weather data, soil, irrigation water, and crops, obtaining values between 4.3 and 7.1 mm d−1 for the AWD and CF irrigation regimes. The results indicated that AWD irrigation allows for water savings of 27 to 28%, although it entails a yield reduction of from 2 to 15%, which translates into an increase in water use efficiency (WUE) of from 18 to 36%, allowing for optimizing water use and improving irrigation management. [ABSTRACT FROM AUTHOR]

As the complexity and integration of electronic devices increase, understanding and mitigating side-channel vulnerabilities will remain a critical area of cybersecurity research. The new and intriguing software-based thermal side-channel attacks and countermeasures use thermal emissions from a device to extract or defend sensitive information, by reading information from the built-in thermal sensors via software. This work extends the Hot-n-Cold anomaly detection technique, applying it in circumstances much closer to the real-world computational environments by detecting irregularities in the Linux command behavior through CPU temperature monitoring. The novelty of this approach lies in the introduction of five types of noise across the CPU, including moving files, performing extended math computations, playing songs, and browsing the web while the attack detector is running. We employed Hot-n-Cold to monitor core temperatures on three types of CPUs utilizing two commonly used Linux terminal commands, ls and chmod. The results show a high correlation, approaching 0.96, between the original Linux command and a crafted command, augmented with vulnerable system calls. Additionally, a Machine Learning algorithm was used to classify whether a thermal trace is augmented or not, with an accuracy of up to 88%. This research demonstrates the potential for detecting attacks through thermal sensors even when there are different types of noise in the CPU, simulating a real-world scenario. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Within the study of the urban heat island (UHI) in Echirolles and Grenoble (France, the eastern part of the alpine arc), two temperature measurement networks have been deployed. The aim is to measure the temperature gradients associated with the UHI in summer. A total of 62 measurement points has been installed in the various neighborhoods on 3-meter-high streetlights, starting in summer 2019. The preliminary classification of the different neighborhood typologies according to ``Local Climate Zone'' guided the choice of location for the temperature sensors. These urban observations respond to a dual challenge: firstly, to observe temperature located in complex topographical situations with valleys, and secondly, to observe the urban climate in neighborhoods where social considerations are important. Municipalities of Echirolles and Grenoble were involved in the investigation. The ADEME-funded (The French Agency for Ecological Transition) CASSANDRE research program analyzes and processes these observations to study the vulnerability of inhabitants to heat waves and more generally to summer heat stress.

In the context of global warming, rising air temperatures are increasing evapotranspiration (ETc) in all agricultural crops, including rice, a staple food worldwide. Simultaneously, the occurrence of droughts is reducing water availability, affecting traditional irrigation methods for rice cultivation (flood irrigation). The objective of this study was to determine ETc (water use) and yield performance in rice crop under different irrigation regimes: treatments with continuous flood irrigation (CF) and irrigations with alternating wetting and drying (AWD5, AWD10, and AWD20) in an experimental area in INIA–Vista Florida. Water balance, rice physiological data, and yield were measured in the field, and local weather data and thermal and multispectral images were collected with a meteorological station and a UAV (a total of 13 flights). ETc values obtained by applying the METRICTM (Mapping Evapotranspiration at High Resolution using Internalized Calibration) energy balance model ranged from 2.4 to 8.9 mm d−1 for the AWD and CF irrigation regimes. In addition, ETc was estimated by a water balance using the AquaCrop model, previously parameterized with RGB image data and field weather data, soil, irrigation water, and crops, obtaining values between 4.3 and 7.1 mm d−1 for the AWD and CF irrigation regimes. The results indicated that AWD irrigation allows for water savings of 27 to 28%, although it entails a yield reduction of from 2 to 15%, which translates into an increase in water use efficiency (WUE) of from 18 to 36%, allowing for optimizing water use and improving irrigation management.

In the face of the climate change crisis, the increase in air temperature negatively impacts rice crop productivity due to stress from water scarcity. The objective of this study was to determine the rice crop water stress index (CWSI) and stomatal conductance (Gs) under different irrigation regimes, specifically continuous flood irrigation treatments (CF) and irrigations with alternating wetting and drying (AWD) at water levels of 5 cm, 10 cm, and 20 cm below the soil surface ( AWD 5 ,   AWD 10 , and AWD 20 ) in an experimental area of INIA-Vista Florida and in six commercial areas of the Lambayeque region using thermal images captured with thermal sensors. The results indicated that AWD irrigation generated more water stress, with CWSI values between 0.4 and 1.0. Despite this, the yields were similar in CF and AWD 20.   In the commercial areas, CWSI values between 0.38 and 0.51 were obtained, with Santa Julia having the highest values. Furthermore, a strong Pearson correlation (R) of 0.91 was established between the CWSI and Gs, representing a reference scale based on Gs values for evaluating water stress levels. [ABSTRACT FROM AUTHOR]

Flexible thermal sensors have attracted immense interest as an alternative to conventional rigid sensors in personal healthcare monitoring, biomedical applications, and human‐machine interaction. However, most metal‐based flexible thermal sensors exhibit low sensitivity, primarily resulting from electron scattering due to manufacturing defects. The heat‐intolerant polymer substrate hinders the pathway to improve the sensitivity of metal thin films through high‐temperature annealing. Herein, a recrystallization‐induced laser lift‐off strategy is proposed to overcome the contradiction between the flexible substrate and high‐temperature annealing. Systematically theoretical and experimental studies reveal the mechanism of thermal‐induced recrystallization in increasing the temperature coefficient of resistance (TCR) and the separation of sensors from rigid substrates. Valuable insights are acquired in the modulation of interface adhesion by altering the surface roughness of nickel. A critical temperature is provided to guide the detachment of the sensor. The fabricated thin‐film flexible thermal sensor achieves a TCR of 6.2‰°C−1, approaching the TCR limit of bulk material. This strategy opens a new general route for fabricating high‐temperature annealed sensors on flexible substrates. [ABSTRACT FROM AUTHOR]

As the complexity and integration of electronic devices increase, understanding and mitigating side-channel vulnerabilities will remain a critical area of cybersecurity research. The new and intriguing software-based thermal side-channel attacks and countermeasures use thermal emissions from a device to extract or defend sensitive information, by reading information from the built-in thermal sensors via software. This work extends the Hot-n-Cold anomaly detection technique, applying it in circumstances much closer to the real-world computational environments by detecting irregularities in the Linux command behavior through CPU temperature monitoring. The novelty of this approach lies in the introduction of five types of noise across the CPU, including moving files, performing extended math computations, playing songs, and browsing the web while the attack detector is running. We employed Hot-n-Cold to monitor core temperatures on three types of CPUs utilizing two commonly used Linux terminal commands, ls and chmod. The results show a high correlation, approaching 0.96, between the original Linux command and a crafted command, augmented with vulnerable system calls. Additionally, a Machine Learning algorithm was used to classify whether a thermal trace is augmented or not, with an accuracy of up to 88%. This research demonstrates the potential for detecting attacks through thermal sensors even when there are different types of noise in the CPU, simulating a real-world scenario.

The peculiarities of the influence of cytokines and metabolites of the systemic inflammatory reaction and stress-implementing and nutritional factors contributing to the transformation of the phenotype of the resident intestinal microflora with an increase in its virulence are described. From the perspective of expression of genes and conformations of proteins and phospholipids, the influence of temperature as a signaling factor in increasing the virulence of the intestinal microbiome is considered. Evolutionarily formed mechanisms of expression of the maximum pathogenic phenotype of microorganisms and, thus, achieving an increase in their biomass and maximum dissemination through the microorganism compartments increase the probability of the transmission of commensals to another biotope, i.e., increases the probability of their survival after the death of the host organism. To prevent bacterial translocation after the relief of critical conditions, early enteral administration of β-glucans in food mixtures, iron excretion, and relief of inorganic phosphate deficiency, including by induction of alkaline phosphatase synthesis, are substantiated. [ABSTRACT FROM AUTHOR]

A ratiometric fiber optic temperature sensor based on a highly coupled seven-core fiber (SCF) is proposed and experimentally demonstrated. A theoretical analysis of the SCF's sinusoidal spectral response in transmission configuration is presented. The proposed sensor comprises two SCF devices exhibiting anti-phase transmission spectra. Simple fabrication of the devices is shown by just splicing a segment of a 2 cm long SCF between two single-mode fibers (SMFs). The sensor proved to be robust against light source fluctuations, as a standard deviation of 0.2% was registered in the ratiometric measurements when the light source varied by 12%. Its low-cost detection system (two photodetectors) and the range of temperature detection (25 °C to 400 °C) make it a very attractive and promising device for real industrial applications. [ABSTRACT FROM AUTHOR]

In real-world scenarios, the information quality provided by RGB and thermal (RGB-T) sensors often varies across samples. This variation will negatively impact the performance of semantic segmentation models in utilizing complementary information from RGB-T modalities, resulting in a decrease in accuracy and fusion credibility. Dynamically estimating the uncertainty of each modality for different samples could help the model perceive such information quality variation and then provide guidance for a reliable fusion. With this in mind, we propose a novel uncertainty-guided trustworthy fusion network (UTFNet) for RGB-T semantic segmentation. Specifically, we design an uncertainty estimation and evidential fusion (UEEF) module to quantify the uncertainty of each modality and then utilize the uncertainty to guide the information fusion. In the UEEF module, we introduce the Dirichlet distribution to model the distribution of the predicted probabilities, parameterized with evidence from each modality and then integrate them with the Dempster–Shafer theory (DST). Moreover, illumination evidence gathering (IEG) and multiscale evidence gathering (MEG) modules by considering illumination and target multiscale information, respectively, are designed to gather more reliable evidence. In the IEG module, we calculate the illumination probability and model it as the illumination evidence. The MEG module can collect evidence for each modality across multiple scales. Both qualitative and quantitative results demonstrate the effectiveness of our proposed model in accuracy, robustness, and trustworthiness. The code will be accessible at https://github.com/KustTeamWQW/UTFNet. [ABSTRACT FROM AUTHOR]

The main factors contributing to the occurrence of an earthquake are under the crust. Also; due to the lack of access to direct measurements of these factors and the parameters involved in the occurrence of an earthquake, the main goal of researchers is to study the earthquake occurrence through its precursors. Currently, monitoring and identifying some of these precursors are made possible by geomatics technologies. It is an undeniable fact that the behavioral variations of the precursors don't follow a common pattern in all earthquakes. Also, the variations of the precursors show peculiar behaviors in each region. So, it seems infeasible to provide an accurate prediction based on the analysis of the behavioral variations of a single precursor. Unlike previous studies, this study doesn't have a single-parametrical orientation toward an earthquake prediction process. Accordingly, this study aims to extract the trend of variations in crustal deformation anomalies and thermal anomalies before the earthquake to analyze them through an integrated process based on data mining methods. As a result, the tests of earthquake predictions for 17 cases have shown that the proposed method can make a reliable prediction of the probable time and magnitude range of oblique-thrust earthquakes with a magnitude greater than 5.5. Moreover, the proposed method has been able to accurately estimate the occurrence of the 26th November 2019 Albania earthquake (Mw = 6.4) as well as 21th September 2019 Albania earthquake (Mw = 5.6) before they happen. [ABSTRACT FROM AUTHOR]

The rapid expansion of the applications of infrared (IR) sensing in the commercial market has driven the need to develop new materials and detector designs for enhanced performance. In this work, we describe the design of a microbolometer that uses two cavities to suspend two layers (sensing and absorber). Here, we implemented the finite element method (FEM) from COMSOL Multiphysics to design the microbolometer. We varied the layout, thickness, and dimensions (width and length) of different layers one at a time to study the heat transfer effect for obtaining the maximum figure of merit. This work reports the design, simulation, and performance analysis of the figure of merit of a microbolometer that uses GexSiySnzOr thin films as the sensing layer. From our design, we obtained an effective thermal conductance of 1.0135 × 10 − 7   W / K , a time constant of 11   m s , responsivity of 5.040 × 10 5   V / W , and detectivity of 9.357 × 10 7   c m − H z 1 / 2 / W considering a 2   μ A bias current. [ABSTRACT FROM AUTHOR]

In the face of the climate change crisis, the increase in air temperature negatively impacts rice crop productivity due to stress from water scarcity. The objective of this study was to determine the rice crop water stress index (CWSI) and stomatal conductance (Gs) under different irrigation regimes, specifically continuous flood irrigation treatments (CF) and irrigations with alternating wetting and drying (AWD) at water levels of 5 cm, 10 cm, and 20 cm below the soil surface (AWD5, AWD10, and AWD20) in an experimental area of INIA-Vista Florida and in six commercial areas of the Lambayeque region using thermal images captured with thermal sensors. The results indicated that AWD irrigation generated more water stress, with CWSI values between 0.4 and 1.0. Despite this, the yields were similar in CF and AWD20. In the commercial areas, CWSI values between 0.38 and 0.51 were obtained, with Santa Julia having the highest values. Furthermore, a strong Pearson correlation (R) of 0.91 was established between the CWSI and Gs, representing a reference scale based on Gs values for evaluating water stress levels.

Power plants, electric generators, high-frequency controllers, battery storage, and control units are essential in current transportation and energy distribution networks. To improve the performance and guarantee the endurance of such systems, it is critical to control their operational temperature within certain regimes. Under standard working conditions, those elements become heat sources either during their entire operational envelope or during given phases of it. Consequently, in order to maintain a reasonable working temperature, active cooling is required. The refrigeration may consist of the activation of internal cooling systems relying on fluid circulation or air suction and circulation pulled from the environment. However, in both scenarios pulling surrounding air or making use of coolant pumps increases the power demand. The augmented power demand has a direct impact on the power plant or electric generator autonomy, while instigating higher power demand and substandard performance from the power electronics and batteries' compounds. In this manuscript, we present a methodology to efficiently estimate the heat flux load generated by internal heat sources. By accurately and inexpensively computing the heat flux, it is possible to identify the coolant requirements to optimize the use of the available resources. Based on local thermal measurements fed into a Kriging interpolator, we can accurately compute the heat flux minimizing the number of sensors required. Considering the need for effective thermal load description toward efficient cooling scheduling. This manuscript presents a procedure based on temperature distribution reconstruction via a Kriging interpolator to monitor the surface temperature using a minimal number of sensors. The sensors are allocated by means of a global optimization that minimizes the reconstruction error. The surface temperature distribution is then fed into a heat conduction solver that processes the heat flux of the proposed casing, providing an affordable and efficient way of controlling the thermal load. Conjugate URANS simulations are used to simulate the performance of an aluminum casing and demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present the sensor placement strategies that currently determine the thermal monitoring of the phase conductors of high-voltage power lines. In addition to reviewing the international literature, a new sensor placement concept is presented based on a strategy centered on the following question: What are the chances of thermal overload if devices are only placed in certain tension sections? In this new concept, the number and installation location of the sensors are determined in three steps, and a new type of tension-section-ranking constant is introduced that is universal in space and time. The simulations based on this new concept show that the data-sampling frequency and the type of thermal constraint influence the number of sensors. The paper's main finding is that there are cases when only a distributed sensor placement strategy can result in safe and reliable operation. However, due to requiring a large number of sensors, this solution means additional expenses. In the last section, the paper presents different possibilities to reduce costs and introduces the concept of low-cost sensor applications. These devices can result in more flexible network operation and more reliable systems in the future. [ABSTRACT FROM AUTHOR]

Aviation reports indicate that between the years of 1988 and 2019 there were 292 human fatalities and 327 injuries that had been reported due to wildlife strikes with airplanes. To minimize these numbers a new approach to airport Wildlife Hazard Management (WHM) is presented in the following article. The proposed solution is based on the data fusion of thermal and vision streams which are used to improve the reliability and adaptability of the real-time WHM system. The system is designed to operate in all environmental conditions and provides an advance information of the fauna presence at the airport's runway. The proposed sensor fusion approach was designed and developed using user driven design methodology. Moreover, the developed system has been validated in real case scenarios and previously installed at an airport. Performed tests proved detection capabilities during day and night of dog sized animals up to 300 meters. Moreover, by using machine learning algorithms during daylight the system was able to classify person sized objects with over 90% efficiency up to 300 meters and dog sized objects up to 200 meters. The overall threat level accuracy based on the three safety zones, was 94%.

This paper presents an inverse thermal modeling technique to determine the core losses from the temperature rise inside the transformer core. For this purpose, initially, a customized printed circuit board (PCB) with thermal sensors is used to measure the temperature rise. Afterward, a 3D magneto-thermal forward model is developed to validate the temperature rise. The accuracy of the forward model is checked by comparing the simulated core losses and temperature rise of the transformer with experimental measurements for different supply conditions. The results show that the forward model can accurately estimate the core losses with a maximum relative error of less than 2.7% and predict the temperature rise in the core with a maximum relative error of less than 6.2%. Lastly, after ensuring the accuracy of the forward model, an inverse modeling technique is applied to the 3D thermal model to predict the core losses of the transformer directly from the measured temperature rise. The accuracy of the inverse model in estimating the core losses is checked by comparing the results with experimental measurements. The novel approach for the PCB design besides the inverse model shows that the technique can be applied to estimate the core losses directly from the measured temperature rise inside the core with a relative error of 2.7% compared to experiments.

Simple Summary: A total of 251 putative TRPs from saurian are divided into 2 groups, belonging to 6 TRPs subfamilies, excluding the TRPN subfamily. The most conserved proteins of TRP box 1 are located in motif 1, and those of TRP box 2 are located in motif 10. The TRPA and TRPV in saurian tend to be one cluster, as a sister cluster with TRPC, and the TRPM is a root of group I. TRPM, TRPV, and TRPP are clustered into two clades, and TRPP is organized into TRP PKD1-like and PKD2-like. Segmental duplications mainly occur in the TRPM subfamily, and the tandem duplications only occur in the TRPV subfamily. Fifteen sites were under positive selection for TRPA1 and TRPV2 genes. The branch model revealed that positive selection fit the data better than the null model for the genes TRPC5 and TRPV3. The transient receptor potential plays a critical role in the sensory nervous systems of vertebrates in response to various mechanisms and stimuli, such as environmental temperature. We studied the physiological adaptive evolution of the TRP gene in the saurian family and performed a comprehensive analysis to identify the evolution of the thermo-TRPs channels. All 251 putative TRPs were divided into 6 subfamilies, except TRPN, from the 8 saurian genomes. Multiple characteristics of these genes were analyzed. The results showed that the most conserved proteins of TRP box 1 were located in motif 1, and those of TRP box 2 were located in motif 10. The TRPA and TRPV in saurian tend to be one cluster, as a sister cluster with TRPC, and the TRPM is the root of group I. The TRPM, TRPV, and TRPP were clustered into two clades, and TRPP were organized into TRP PKD1-like and PKD2-like. Segmental duplications mainly occurred in the TRPM subfamily, and tandem duplications only occurred in the TRPV subfamily. There were 15 sites to be under positive selection for TRPA1 and TRPV2 genes. In summary, gene structure, chromosomal location, gene duplication, synteny analysis, and selective pressure at the molecular level provided some new evidence for genetic adaptation to the environment. This result provides a basis for identifying and classifying TRP genes and contributes to further elucidating their potential function in thermal sensors. [ABSTRACT FROM AUTHOR]

Depth maps acquired with ToF cameras have a limited accuracy due to the high noise level and to the multi-path interference. Deep networks can be used for refining ToF depth, but their training requires real world acquisitions with ground truth, which is complex and expensive to collect. A possible workaround is to train networks on synthetic data, but the domain shift between the real and synthetic data reduces the performances. In this paper, we propose three approaches to perform unsupervised domain adaptation of a depth denoising network from synthetic to real data. These approaches are respectively acting at the input, at the feature and at the output level of the network. The first approach uses domain translation networks to transform labeled synthetic ToF data into a representation closer to real data, that is then used to train the denoiser. The second approach tries to align the network internal features related to synthetic and real data. The third approach uses an adversarial loss, implemented with a discriminator trained to recognize the ground truth statistic, to train the denoiser on unlabeled real data. Experimental results show that the considered approaches are able to outperform other state-of-the-art techniques and achieve superior denoising performances. [ABSTRACT FROM AUTHOR]

In this article, an array thermal camera equipment is developed to capture multiple infrared images with spatial and parallax information. Based on the captured images, an end-to-end method called spatial–parallax prior network (SPPN) is proposed. Specifically, we design a spatial–parallax prior block with two symmetric branches to extract spatial and parallax features in an interactive guidance manner. Then, to effectively integrate spatial and parallax features, we introduce a channel attention mechanism to enable the network to focus on and fuse the most useful information adaptively. In this way, spatial and parallax information can be fully utilized without any explicit alignment operation. Finally, considering the scarcity and poor quality of infrared training data, we leverage transfer learning to better train the network. Extensive experimental results demonstrate that the proposed SPPN consistently outperforms the current state-of-the-art methods, providing a highly effective and scalable solution for the improvement of infrared image quality. [ABSTRACT FROM AUTHOR]

A ratiometric fiber optic temperature sensor based on a highly coupled seven-core fiber (SCF) is proposed and experimentally demonstrated. A theoretical analysis of the SCF’s sinusoidal spectral response in transmission configuration is presented. The proposed sensor comprises two SCF devices exhibiting anti-phase transmission spectra. Simple fabrication of the devices is shown by just splicing a segment of a 2 cm long SCF between two single-mode fibers (SMFs). The sensor proved to be robust against light source fluctuations, as a standard deviation of 0.2% was registered in the ratiometric measurements when the light source varied by 12%. Its low-cost detection system (two photodetectors) and the range of temperature detection (25 °C to 400 °C) make it a very attractive and promising device for real industrial applications.

This paper reported the thermal and light sensing properties of graphene oxide (GO) and reduced graphene oxide (RGO) films obtained by spin coating on soda lime glass. GO showed a higher thermal sensing response of 9.0 compared to RGO which had a maximum thermal sensing response of 2.2 at 623 K. The thermal conductivity of GO and RGO increased with temperature although RGO typified better thermal conductivity. For GO, a negative temperature coefficient of resistance (NTC) of 0.0032153 /K was obtained which is higher than values obtained in literatures while RGO had a NTC value of 0.00201/K. For the photo sensing, the peak photo resistance of GO corresponds to an energy band gap of 3.5 eV while the peak photo resistance of RGO corresponds to a decreased energy band gap of 2.76 eV. It was also confirmed that lower bandgap favored improved photosensing function. RGO film showed intrinsic photoconductivity as the photoconductivity increased at longer wavelengths. The photo sensing of GO and RGO were dominated by inter-band optical transitions in the UV to mid infrared region. The obtained NTC values for GO and RGO and the photoconductivity of the materials can promote further utilization in robotics and optical applications. [ABSTRACT FROM AUTHOR]

Environmental temperature is a critical factor for all forms of life, and thermal tolerance defines the habitats utilized by a species. Moreover, the evolutionary tuning of thermal perception can also play a key role in habitat selection. Yet, the relative importance of thermal tolerance and perception in environmental adaptation remains poorly understood. Thermal conditions experienced by anuran tadpoles differ among species due to the variation in breeding seasons and water environments selected by parental frogs. In the present study, heat tolerance and avoidance temperatures were compared in tadpoles from five anuran species that spatially and temporally inhabit different thermal niches. These two parameters were positively correlated with each other and were consistent with the thermal conditions of habitats. The species difference in avoidance temperature was 2.6 times larger than that in heat tolerance, suggesting the importance of heat avoidance responses in habitat selection. In addition, the avoidance temperature increased after warm acclimation, especially in the species frequently exposed to heat in their habitats. Characterization of the heat-sensing transient receptor potential ankyrin 1 (TRPA1) ion channel revealed an amphibian-specific alternatively spliced variant containing a single valine insertion relative to the canonical alternative spliced variant of TRPA1, and this novel variant altered the response to thermal stimuli. The two alternatively spliced variants of TRPA1 exhibited different thermal responses in a species-specific manner, which are likely to be associated with a difference in avoidance temperatures among species. Together, our findings suggest that the functional change in TRPA1 plays a crucial role in thermal adaptation processes. [ABSTRACT FROM AUTHOR]

Interturn short-circuit faults are one of the most (if not the most) harmful electrical machine failures, that if not detected and mitigated at a very incipient stage of development may involve serious consequences, both in terms of repair costs and safety. This article proposes a novel interturn fault diagnostic technique for synchronous reluctance machines based on the Luenberger state observer and current's second-order harmonic. The superiority of the proposed approach over the conventional model-based techniques is the ability to evaluate the severity of the fault as well as the efficiency in detecting faults in early stages and under various operating conditions. The effectiveness of the proposed method is demonstrated and validated through several experimental tests. [ABSTRACT FROM AUTHOR]

Thermography is a useful imaging technique as it works well in poor visibility conditions. High-resolution thermal imaging sensors are usually expensive and this limits the general applicability of such imaging systems. Many thermal cameras are accompanied by a high-resolution visible-range camera, which can be used as a guide to super-resolve the low-resolution thermal images. However, the thermal and visible images form a stereo pair and the difference in their spectral range makes it very challenging to pixel-wise align the two images. The existing guided super-resolution (GSR) methods are based on aligned image pairs and hence are not appropriate for this task. In this paper, we attempt to remove the necessity of pixel-to-pixel alignment for GSR by proposing two models: the first one employs a correlation-based feature-alignment loss to reduce the misalignment in the feature-space itself and the second model includes a misalignment-map estimation block as a part of an end-to-end framework that adequately aligns the input images for performing guided super-resolution. We conduct multiple experiments to compare our methods with existing state-of-the-art single and guided super-resolution techniques and show that our models are better suited for the task of unaligned guided super-resolution from very low-resolution thermal images. [ABSTRACT FROM AUTHOR]

Thermal sensors play an important role in intelligent transportation system. This paper studies the problem of RGB and thermal (RGBT) tracking in challenging situations by leveraging multimodal data. A RGBT object tracking method is proposed in correlation filter tracking framework based on short term historical information. Given the initial object bounding box, hierarchical convolutional neural network (CNN) is employed to extract features. The target is tracked for RGB and thermal modalities separately. Then the backward tracking is implemented in the two modalities. The difference between each pair is computed, which is an indicator of the tracking quality in each modality. Considering the temporal continuity of sequence frames, we also incorporate the history data into the weights computation to achieve a robust fusion of different source data. Experiments on three RGBT datasets show the proposed method achieves comparable results to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a salience-aware face presentation attack detection (SAFPAD) approach, which takes advantage of deep reinforcement learning to exploit the salient local part information in face images. Most existing deep face presentation attack detection approaches extract features from the entire image or several fixed regions. However, the discriminative information beneficial for presentation attack detection is unevenly distributed in the image due to the illumination and presentation attack instrument variation, so treating all regions equally fails to highlight the most discriminative information which is important for more accurate and robust face presentation attack detection. To address this, we propose to identify the discriminative salient parts using deep reinforcement learning and focus on them to alleviate the adverse effects of redundant information in the face images. We fuse the high-level features and the local features which guide the policy network to exploit discriminative patches and assist the classification network to predict more accurate results. We jointly train the SAFPAD model with deep reinforcement learning to generate salient locations. Extensive experiments on five public datasets demonstrate that our approach achieves very competitive performance due to the concentrated employment of salient local information. [ABSTRACT FROM AUTHOR]

Recent outbreaks of infectious diseases such as Covid-19 that have fever as one of the symptoms drive the search for systems to track people with fever quickly and non-contact, also known as sanitary barriers. The use of non-contact infrared-based instruments, especially the infrared thermal imager, has widely spread. However, the screening process has presented low performance. This article addresses the choice of regions of interest on the human face for the analysis of the individual's fever, deals with the temperature thresholds used for this analysis, as well as the way to issue the recommendation to screen the person or not. The data collection and statistical analysis of temperatures of 198 volunteers allowed us to study and define the most appropriate face regions as targets for these barriers, as well as the temperature thresholds to be used for screening for each of these regions. Besides, the paper presents a probabilistic method based on the metrological quality of the sanitary barrier to the emission of recommendation for screening potentially febrile people. The developed method was tested in feverish and non-febrile volunteers, showing complete assertiveness in the tested cases.

We introduce and demonstrate new measurement and modeling techniques to fully resolve the spatial variation in shortwave and longwave radiant heat transfer in the outdoor environment. We demonstrate for the first time a way to directly resolve the shortwave radiant heat transfer from terrestrial reflected and diffuse sky components along with the standard direct solar radiation using an adapted thermopile array and ray-tracing modeling techniques validated by 6-direction net radiometer. Radiant heat transfer is a major component of heat experienced in cities. It has significant spatial variability that is most easily noticed as one moves between shade and direct solar exposure. But even on a cloudy and warm day the invisible longwave infrared thermal radiation from warm surfaces makes up a larger fraction of heat experienced than that caused by convection with surrounding air. Under warm or hot climate conditions in cities, radiant heat transfer generally accounts for the majority of heat transfer to people. Both the shortwave (visible/solar) and the longwave (infrared/thermal) have significant spatial variation. We demonstrate sensor methods and data analysis techniques to resolve how these radiant fluxes can change the heat experienced by >1 kWm−2 across small distances. The intense solar shortwave radiation is easily recognized outdoors, but longwave is often considered negligible. Longwave radiation from heat stored in urban surfaces is more insidious as it can cause changes invisible to the eye. We show how it changes heat experienced by >200 Wm−2. These variations are very common and also occur at the scale of a few meters.

• Thermal array sensors accurately recognize OWTPF flow patterns. • Use AOK-TFR, URP to realize the distinction of flow patterns. • First application of SSA-SVM for flow patterns segmentation. To understand the flow characteristics and kinetic modelling of oil–water two-phase flow(OWTPF), this paper employs thermal array sensors to identify the flow pattern (FP) in OWTPF horizontal pipelines. Firstly, the thermal array sensor installation was constructed. Secondly, the time-varying characteristics of multi-feature signals were qualitatively analyzed using the adaptive optimal kernel time–frequency representation (AOK-TFR) method and unthresholded recurrence plot (URP) method. Finally, the sparrow search algorithm-support vector machine (SSA-SVM) machine learning algorithm is used for quantitative prediction of multi-feature classification, which leads to high prediction accuracy. The experiment proves that the correct rate of the thermal method in recognizing six types of OWTPF is above 91%, the correct rate of five types of flow is above 97.33%. This paper adopts the thermal method to identify the multiphase FP, which is a major innovation in this paper and also lays the foundation for recognizing the flow characteristics of multiphase flow. [ABSTRACT FROM AUTHOR]

The rapid expansion of the applications of infrared (IR) sensing in the commercial market has driven the need to develop new materials and detector designs for enhanced performance. In this work, we describe the design of a microbolometer that uses two cavities to suspend two layers (sensing and absorber). Here, we implemented the finite element method (FEM) from COMSOL Multiphysics to design the microbolometer. We varied the layout, thickness, and dimensions (width and length) of different layers one at a time to study the heat transfer effect for obtaining the maximum figure of merit. This work reports the design, simulation, and performance analysis of the figure of merit of a microbolometer that uses GexSiySnzOr thin films as the sensing layer. From our design, we obtained an effective thermal conductance of 1.0135×10−7 W/K, a time constant of 11 ms, responsivity of 5.040×105 V/W, and detectivity of 9.357×107 cm−Hz1/2/W considering a 2 μA bias current.

Many scientists have been investigating Land Surface Temperature (LST) because of its relevance in water management science due to its direct influence on the hydrological water cycle. This effect stems from being one of the most significant variables influencing evapotranspiration. One of the most important reasons for the evapotranspiration retrieved from MODIS data's limited suitability for scheduling and planning irrigation schemes is the lack of spatial resolution. As a result, high-resolution LST is required for estimating evapotranspiration. The goal of this study is to improve the resolution of the available LST data, to improve evapotranspiration (ETa) estimation using statistical downscaling with Normalized Difference Vegetation Index (NDVI) as a predictor. The DisTrad (Disaggregation of Radiometric Surface Temperature) method was used for the LST downscaling procedure, which is based on aggregating the NDVI map to the LST map resolution and then calculating the coefficient of variation of the native NDVI map within the aggregated pixel and classifying the aggregated map into three classes: NDVI < 0.2 for the bare soil, 0.2 ≤ NDVI ≤ 0.5 for the partial vegetation, and NDVI > 0.5 for the full vegetation. DisTrad uses 25% of the pixels with the lowest coefficient of variation from each class to calculate the regression coefficients. In this work, adjustments to the DisTrad method were implemented to enhance downscaling LST and to examine the impacts of that alteration on the evapotranspiration estimation. The linear regression model was tested as an alternative to the original second-order polynomial. In using 10% of the pixels instead of the originally proposed 25% with the lowest coefficient of variation values, it is assumed that a group of pixels with a lower coefficient of variation represents a more homogeneous area, thus it gives more accurate values. The downscaled LST map retrieval was validated using Landsat 8 thermal maps (100 m). Applying the modified DisTrad approach to disaggregate Landsat LST to 30 m (NDVI resolution) yielded an R2 of 0.72 for the 10%, 0.74 for the 25% and 0.61 for the second-order polynomial lowest coefficient of variation compared to native LST Landsat, which means that 10% can be used as an alternative. Applying the downscaled LST map to estimate ETa yielded R2 0.84 in both cases, compared to ETa yielded from the native Landsat LST. These results prove that using the robust linear regression provided better results than using polynomial regression. With the downscaled Land Surface Temperature data, it was possible to create detailed ETa maps of the small agricultural fields in the test area. [ABSTRACT FROM AUTHOR]

Aviation reports indicate that between 1988 and 2019 there were 292 human deaths and 327 injuries that had been reported from wildlife strikes with airplanes. To minimize these numbers, a new approach to airport Wildlife Hazard Management (WHM) is presented in the following article. The proposed solution is based on the data fusion of thermal and vision streams, which are used to improve the reliability and adaptability of the real-time WHM system. The system is designed to operate under all environmental conditions and provides advance information on the fauna presence on the airport runway. The proposed sensor fusion approach was designed and developed using user-driven design methodology. Moreover, the developed system has been validated in real-case scenarios and previously installed at an airport. Performed tests proved detection capabilities during day and night of dog-sized animals up to 300 meters. Moreover, by using machine learning algorithms during daylight, the system was able to classify person-sized objects with over 90 % efficiency up to 300 meters and dog-sized objects up to 200 meters. The general accuracy of the threat level based on the three safety zones was 94 %. [ABSTRACT FROM AUTHOR]

The use of thermal sensors is increasing in response to dealing with the problems of the visible light spectrum. These sensors measure the temperature of the object and convert it to some readable output. There is a diverse range of temperature sensors, and different sensors are used for different purposes. The choice of the sensor depends on the cost of the sensor, resolution, and level of accuracy. For instance, an IR camera combined with the RGB sensor can produce better human activity recognition. However, increase the cost of the machine. For some applications, a high resolution is not required and a low-cost sensor can satisfy the need. In this survey, we discuss the employment of thermal sensors in HVAC systems, vehicle, and manufacturing industries as they are heavily used in these industries. We reported the types of available thermal sensors and the sensors commonly used in each industry. This is followed by a comprehensive review of the application-specific methods. In the end, we may say that the selection of the thermal sensor has much importance as well as the choice of the suitable algorithms according to the given conditions to avail the maximum accuracy in our results. [ABSTRACT FROM AUTHOR]

Universities worldwide are experiencing a surge in enrollments, therefore campus estate managers are seeking continuous data on attendance patterns to optimize the usage of classroom space. As a result, there is an increasing trend to measure classroom attendance by employing various sensing technologies, among which pervasive WiFi infrastructure is seen as a low-cost method. In a dense campus environment, the number of connected WiFi users does not well estimate room occupancy since connection counts are polluted by adjoining rooms, outdoor walkways, and network load balancing. This paper develops machine learning-based models, including unsupervised clustering and a combination of classification and regression algorithms, to infer classroom occupancy from WiFi sensing infrastructure. Our contributions are three-fold: (1) We analyze metadata from a dense and dynamic wireless network comprising of thousands of access points (APs) to draw insights into coverage of APs, the behavior of WiFi-connected users, and challenges of estimating room occupancy; (2) We propose a method to automatically map APs to classrooms and evaluate K-means, Expectation-Maximization (EM-GMM) and Hierarchical Clustering (HC) algorithms; and (3) We model classroom occupancy and evaluate varying algorithms, namely Logistic Regression, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Linear Regression (LR) and Support Vector Regression (SVR). We achieve 84.6% accuracy in mapping APs to classrooms, while our estimation for room occupancy (with symmetric Mean Absolute Percentage Error (sMAPE) of 13.10%) is comparable to beam counter sensors. [ABSTRACT FROM AUTHOR]

We develop a wavelength-multiplexed system of optical fiber-based photonic-crystal microphones for acoustic source localization. Our microphones use $392 \mu \text{m}$ wide and 450 nm thick photonic-crystal silicon diaphragms that we fabricate on the wafer scale and mount to an optical fiber with a simplified groove-based alignment. Our microphones have a bandwidth of acoustic sensitivity from 150 Hz to 50 kHz and an average minimum detectable pressure of $2 \mu Pa/ \sqrt {Hz}$ on resonance. The consistency of our microphone fabrication and assembly allows us to interrogate the pressure-sensitive Fabry-Pérot cavities using wavelength-sliced channels from the spontaneous emission of a C-band optical amplifier. Using our compact wavelength-multiplexed sensor system composed of standard fiber-optic communications hardware, we can localize acoustic sources in the environment to within 5 cm by a time-difference of arrival method. [ABSTRACT FROM AUTHOR]

This article introduces a closed-loop control strategy for maintaining consistency of liquid temperature in commercial drop-on-demand (DoD) inkjet printing. No additional sensors or additional actuators are installed in the printhead while achieving consistency in liquid temperature. To this end, this article presents a novel in situ sensing-actuation policy at every individual liquid nozzle, where the jetting mechanism has three distinct roles. It is used for jetting liquid droplet onto the print media based on the print job. It is used as a soft sensor to estimate the real-time liquid temperature of the jetting nozzle. While not jetting liquid, it is used as a heating actuator to minimize the gradient of liquid temperature among nozzles. The soft sensing-based in situ controller is implemented in an experimentally validated digital twin that models the thermofluidic processes of the printhead. The digital twin is scalable and flexible to incorporate an arbitrary number of liquid nozzles, making the control strategy applicable for future designs of the printhead. [ABSTRACT FROM AUTHOR]

Spatiotemporal modeling is critical to the simulation, optimization, and control of the thermal process of a lithium-ion battery, which is a typical kind of distributed parameter system (DPS). Data-driven spatiotemporal modeling methods are of practical interest to construct an analytical model of the thermal process of a lithium-ion battery, since they only need some sampled data rather than the structure descriptions or parameters of a DPS. How to sample data optimally for data-driven spatiotemporal modeling is still an open question. In this article, with the aim of minimizing the spatiotemporal modeling error, we propose a novel evolutionary algorithm to optimally place sensors for data sampling. First, an objective function that can quantify both the spatial error and the temporal error is designed. Additionally, a novel differential evolution algorithm with two kinds of encoding mechanisms (called DETEM) is proposed to optimize the objective function. Numerical simulations and experimental studies have shown that the proposed method is competitive. Besides, both the objective function and DETEM are critical to the proposed method. In summary, the proposed method provides an effective way to obtain the optimal sensor placement for spatiotemporal modeling of the thermal process of a lithium-ion battery. [ABSTRACT FROM AUTHOR]

There has been increasing interest in face recognition in the thermal infrared spectrum. A critical step in this process is face landmark detection. However, landmark detection in the thermal spectrum presents a unique set of challenges compared to in the visible spectrum: inherently lower spatial resolution due to longer wavelength, differences in phenomenology, and limited availability of labeled thermal face imagery for algorithm development and training. Thermal infrared imaging does have the advantage of being able to passively acquire facial heat signatures without the need for active or ambient illumination in low light and nighttime environments. In such scenarios, thermal imaging must operate by itself without corresponding/paired visible imagery. Mindful of this constraint, we propose visible-to-thermal parameter transfer learning using a coupled convolutional network architecture as a means to leverage visible face data when training a model for thermal-only face landmark detection. This differentiates our approach from models trained either solely on thermal images or models which require a fusion of visible and thermal images at test time. In this work, we implement and analyze four types of parameter transfer learning methods in the context of thermal face landmark detection: Siamese (shared) layers, Linear Layer Regularization (LLR), Linear Kernel Regularization (LKR), and Residual Parameter Transformations (RPT). These transfer learning approaches are compared against a baseline version of the network and an Active Appearance Model (AAM), both of which are trained only on thermal data. We achieve a 6.5% - 9.5% improvement on the DEVCOM ARL Multi-modal Thermal Face Dataset and a 4% improvement on the RWTH Aachen University Thermal Face Dataset over the baseline model. We show that LLR, LKR, and RPT all result in improved thermal face landmark detection performance compared to the baseline and AAM, demonstrating that transfer learning leveraging visible spectrum data improves thermal face landmarking.