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A method is proposed to measure the in-plane thermal diffusivity of anisotropic solids along an arbitrary direction using laser-line lock-in thermography. In this method, the sample is heated by an intensity-modulated laser beam, which impinges on the sample surface with the shape of a narrow strip. The resulting temperature oscillations are recorded by a thermographic camera and a phase thermogram is obtained by lock-in processing the image sequence. The thermal diffusivity along the direction perpendicular to the laser line can be determined from the linear dependence of phase lag with the distance to the heating line using the slope method. Unlike laser-spot lock-in thermography, in which the slope method only provides the thermal diffusivity along the principal directions, in this configuration, the thermal diffusivity can be measured experimentally along any direction, just selecting the relative orientation of the sample with respect to the laser line. Moreover, this configuration allows averaging several parallel phase profiles leading to more reliable thermal diffusivity values. The experimental results are presented for a highly anisotropic material, namely, a unidirectional carbon fiber-reinforced polymer plate. [ABSTRACT FROM AUTHOR]

The objectives of this study were to develop a thermal image analysis method for assessing the surface temperature of stainless steel (30 cm × 30 cm) during pilot-scale superheated steam sanitation and evaluate the sanitation efficacy based on the inactivation of Enterococcus faecium. An infrared camera, calibrated to a root-mean-square error (RMSE) of 1.4 °C within a range of 25 °C and 250 °C, was utilized. The results showed that the surface temperature at the impingement point decreased linearly from 245.6 ± 3.2 to 157.6 ± 1.7 °C as the nozzle-to-surface distance was increased from 2 to 5 cm. Furthermore, at a 2 cm nozzle-to-surface distance, temperatures swiftly dropped from 245.6 ± 3.2 to 95.8 ± 6.0 °C as the radial distance increased from 0 to 10 cm. In the stagnation region (0–1 cm radial distance), where the steam jet directly contacts the surface, the time required to achieve a 3-log reduction of E. faecium was reduced from 3 to 1 min as the nozzle-to-surface distance decreased from 5 to 2 cm. The efficacy of superheated steam sanitation was further evaluated under practical sweeping conditions, demonstrating a 2.7 ± 0.4 log reduction of E. faecium on a 900 cm2 stainless steel surface within 10 min. This study thus highlights the potential use of thermal image analysis for optimizing superheated steam sanitation processes, particularly in dry food processing environments. [ABSTRACT FROM AUTHOR]

Background: The development of a standard motion capture system is needed since the measurement of temporomandibular disorders is time-consuming and costly using laboratory tools. Objective: The current study aimed to investigate the mandibular kinematic variables using a regular mobile phone and the motion analysis system. Material and Methods: In this quasi-laboratory and comparative study, ten healthy individuals participated, and three mobile cameras, nine red markers, and Kinovea software were also used to investigate the mandibular kinematic variables. Nine light reflective markers were used to check the sensitivity, accuracy, and reliability of the proposed system. The motion was analyzed using seven motion analysis infrared cameras and Qualisys Track Manager (QTM) software. Two other raters analyzed the kinematic variables obtained from the mobile to measure intra- and interrater reliability. Results: Pearson's correlation coefficient was obtained at 0.98, 0.75, 0.98, and 0.96, showing a high correlation. The accuracy and reliability validation tests showed an average error and an accuracy of 0.156 mm and 95%, respectively, with a mobile phone. The Intra Class Correlation coefficient showed a high internal correlation in the mentioned confidence interval (0.98 and 0.81, and 0.96 and 0.97). The intraclass correlation coefficient method also showed 97% inter-raster reliability. Conclusion: Mobile phones as a new system can evaluate the kinematic variables of mandibular disorders with appropriate accuracy and reliability. [ABSTRACT FROM AUTHOR]

Purpose: A prototype infrared camera – cone-beam computed tomography (CBCT) system for tracking in brachytherapy has recently been developed. We evaluated for the first time the corresponding tracking accuracy and uncertainties, and implemented a tracking-based prediction of needles on CBCT scans. Methods: A marker tool rigidly attached to needles was 3D printed. The precision and accuracy of tool tracking was then evaluated for both static and dynamic scenarios. Euclidean distances between the tracked and CBCT-derived markers were assessed as well. To implement needle tracking, ground truth models of the tool attached to 200 mm and 160 mm needles were matched to the tracked positions in order to project the needles into CBCT scans. Deviations between projected and actual needle tips were measured. Finally, we put our results into perspective with simulations of the system's tracking uncertainties. Results: For the stationary scenario and dynamic movements, we achieved tool-tracking precision and accuracy of 0.04 ± 0.06 mm and 0.16 ± 0.18 mm, respectively. The tracked marker positions differed by 0.52 ± 0.18 mm from the positions determined via CBCT. In addition, the predicted needle tips in air deviated from the actual tip positions by only 1.62 ± 0.68 mm (200 mm needle) and 1.49 ± 0.62 mm (160 mm needle). The simulated tracking uncertainties resulted in tip variations of 1.58 ± 0.91 mm and 1.31 ± 0.69 mm for the 200 mm and 160 mm needles, respectively. Conclusion: With the innovative system it was possible to achieve a high tracking and prediction accuracy of marker tool and needles. The system shows high potential for applicator tracking in brachytherapy. [ABSTRACT FROM AUTHOR]

In this article, the authors propose a general framework for checking the calibration of the A325sc camera and then minimizing the temperature measurement errors due to thermal drift. Thermal drift and non-uniformity affect the measurement accuracy of infrared bolometric cameras and remain a major problem for the reproducibility and repeatability of radiance quantification. Any thermal camera is pre-calibrated at the factory to correct for thermal drift. This pre-calibration was done for specific case temperatures, and variations in these temperatures are a major source of uncertainty. To improve the accuracy of the camera measurements, it's important to control the housing temperatures. To this end, a cold box was build up. The effectiveness of the thermal drift compensation was examined over the two ranges of the camera. In the [-20°C; 120°C] range, the thermal drift compensation is efficient up to 110°C. The range [0°C; 350°C] highlights two behaviors: for an emitting temperature within [65-225] °C, the thermal drift compensation is made difficult by the self-heating of the measurement chain due to the intensity of the source. Above 225°C, the self-heating of the optics is significant, as it becomes more absorbent. A correction of the thermosignal is suggested. [ABSTRACT FROM AUTHOR]

The first James Webb Space Telescope (JWST) Near InfraRed Camera imaging in the field of the galaxy cluster PLCK G165.7+67.0 (z = 0.35) uncovered a Type Ia supernova (SN Ia) at z = 1.78, called "SN H0pe." Three different images of this one SN were detected as a result of strong gravitational lensing, each one traversing a different path in spacetime, thereby inducing a relative delay in the arrival of each image. Follow-up JWST observations of all three SN images enabled photometric and rare spectroscopic measurements of the two relative time delays. Following strict blinding protocols which oversaw a live unblinding and regulated postunblinding changes, these two measured time delays were compared to the predictions of seven independently constructed cluster lens models to measure a value for the Hubble constant, H 0 = 71.8 + 9.2 − 8.1 km s−1 Mpc−1. The range of admissible H 0 values predicted across the lens models limits further precision, reflecting the well-known degeneracies between lens model constraints and time delays. It has long been theorized that a way forward is to leverage a standard candle, but this has not been realized until now. For the first time, the lens models are evaluated by their agreement with the SN absolute magnifications, breaking degeneracies and producing our best estimate, H 0 = 75.7 − 5.5 + 8.1 km s−1 Mpc−1. This is the first precise measurement of H 0 from a multiply imaged SN Ia and only the second from any multiply imaged SN. [ABSTRACT FROM AUTHOR]

This study compares the heat generated during bone drilling using different protocols and implant systems, first in vitro and then in vivo with an animal model. In the experimental phase, thermal data were collected using an infrared camera while preparing implant beds in bone similes. The heat generated by a one-drill protocol with a new-generation drill bit and the Straumann BLT sequential drilling protocol was evaluated. The experimental study was then replicated in an animal model to assess the impact of these protocols on early osseointegration, measured by bone-to-implant contact (BIC) at three weeks post-surgery for Straumann BLT SLActive and Medentika Quattrocone implants. The results showed the BLT sequential protocol generated significantly more heat during drilling in bone similes compared to the new-generation drill bit. In the animal model, a histological analysis revealed a trend favoring shorter drilling protocols, with reduced drilling times and a potential advantage for osseointegration, though the BIC differences were not statistically significant. These findings suggest that minimizing the number of drilling steps and thermal stress may enhance osseointegration more effectively than advanced implant surface treatments. This aligns with emerging views on the importance of optimized drilling protocols and designs to reduce heat generation and better preserve surrounding bone structure. [ABSTRACT FROM AUTHOR]

The loss and fragmentation of wildlife habitats is a major threat to their survival and expansion, and protected areas (PAs) are the main tool for conserving biodiversity and protecting habitats. However, most current studies focus on analyzing suitable habitats for species and rarely analyze the spatial and temporal changes in multi-species habitats in protected areas and the effectiveness of conservation. In this study, we analyzed changes in the suitable habitats of five focal mammal species before and after the incorporation of the Hainan Jianfengling protected area into China's national parks. We utilized the ensemble species distribution model (ESDM) to assess these changes, based on multi-species infrared camera monitoring data from 2015 to 2016 and 2020 to 2021. Furthermore, we evaluated differences in conservation effectiveness before and after the establishment of the national parks. The results showed that there were some differences in habitat changes among all the species included in this study, and all of them showed the phenomenon of the migration from suitable habitats to the central area. The environmental changes in and around the protected area suggest that the changes are closely related to the increase in anthropogenic activities around the protected area, and it is recommended that the protected area should be better managed at its edges to minimize the impact of anthropogenic disturbances on the species and their habitats. [ABSTRACT FROM AUTHOR]

Purpose: This study aim was to analyze the joint temperature of patients affected by bilateral knee osteoarthritis (OA) using infrared thermography to investigate whether thermographic imaging patterns are influenced by the severity of symptoms and joint degeneration. Methods: Sixty-sixpatients ranging from 43 to 78 years old (63.3 ± 8.8 years) with bilateral knee OA and one symptomatic knee were enrolled. Thermograms of the two knees were captured using a thermographic camera FLIR T1020 and analyzed with the ResearchIR software to calculate the temperature of the overall knee and the four regions of interest (ROIs): patella, suprapatellar, medial, and lateral areas. Results: The temperature of knees affected by OA was influenced by joint degeneration level and symptoms: patients with higher OA grade in the symptomatic knees presented higher total knee temperatures compared to the asymptomatic ones (p = 0.002), as well as in the patellar (p = 0.005), lateral (p = 0.002), and medial (p = 0.001) areas. On the other hand, patients with the same OA level in the two knees presented a higher temperature in the symptomatic knee only in the medial area (p = 0.037). Symptomatic knees demonstrated a different pattern compared to asymptomatic knees, with the medial area presenting the highest temperature changes (p = 0.020). Patients reporting prevalent pain in the lateral knee area presented higher differences in total knee temperature (0.7 ± 0.7 °C) than patients with pain in the medial area (0.1 ± 0.5 °C) (p = 0.023). Conclusion: The temperature of knees affected by OA is influenced by the degree of joint degeneration and by the presence of symptoms, with higher temperatures found in symptomatic joints, especially with prevalent lateral knee pain, and in more severe OA. Symptomatic knees demonstrated a different pattern compared to asymptomatic knees, with the medial area presenting the highest temperature changes. [ABSTRACT FROM AUTHOR]

As wild boar populations and their distribution ranges increase, human–wild boar conflicts have become increasingly prevalent in numerous regions across the globe. These conflicts have a profound impact on human livelihoods, resulting in significant economic losses. Understanding the habitat requirements and relative abundance of wild boars is crucial prior to implementing any conservation measures. However, studies on wild boar habitat and population in the central and eastern regions of the Tianshan Mountains in China are lacking. We assessed the activity patterns and relative abundance of wild boars in these areas and evaluated habitat suitability using a combination of camera trapping, line transects, species distribution modeling (maximum entropy model), and hierarchical abundance modeling (Bayesian N‐mixture model). We used 311 infrared cameras and 280 field‐based line transects to cover approximately 31,000 km² from September 2022 to May 2023 in the east‐central Tianshan Mountains. We used 240 wild boar distribution locations and 13 environmental predictors in the development of species distribution models. We also used species counts and associated environmental predictors in the N‐mixture model to estimate the relative abundance of wild boar. Wild boars were most active during crepuscular hours (1800), and relatively active in the diurnal period compared to the nocturnal period. The probability of wild boar occurrence increased with higher normalized difference vegetation index (NDVI), the minimum temperature of the coldest month, and annual temperatures below 39°C. Boars were most likely to be found in closed deciduous‐coniferous forests. The relative abundance of wild boars was positively affected by NDVI and negatively affected by the minimum temperature of the coldest month and temperature annual range. Based on our results, we suggest areas of management priority. In particular, extensive and intact habitat with substantial wild boar populations, such as the Banfanggou, the South Mountain of Urumqi, and the Hutubi, should be prioritized for long‐term wild boar population monitoring and management so the adverse impacts of increasing wild boar populations in the study region can be minimized. [ABSTRACT FROM AUTHOR]

This study evaluated the degree of conversion (DC), polymerization kinetics, and temperature of a new self-cure bulk-fill resin-based composite (Stela Automix, SDI). The study was divided into seven groups: (1) Stela Primer, (2) Stela Automix, (3) Stela Automix exposed to light for 20 s after 100 s, (4) Stela Primer with Stela Automix, (5) Stela Primer with Stela Automix and exposed to light for 20 s after 100 s, (6) Scotchbond Universal with Stela Automix, and (7) Scotchbond Universal with Stela Automix and exposed to light for 20 s after 100 s. The real-time reaction rates and DC at the bottom of 2 mm thick specimens at ∼32 °C were measured at 720 s after insertion using a spectrometer with an Attenuated Total Reflectance detector. The temperature of Stela, Stela exposed to light, Stela Primer with Stela, and Stela Primer with Stela exposed to light were measured by an infrared thermal camera in human molar teeth with Class I cavities. The temperature was recorded in real-time every 0.03 s for 720 s. The results were compared with ANOVA and Tukey's multiple comparison test (α = 0.05). The highest degree of conversion (DC) at the 2 mm depth was achieved when Stela Primer was combined with Stela (72.4 ± 3.5 % at 720 s). Stela could self-cure independently, but not as rapidly or effectively as when used with its Primer. The greatest temperature increase was for the light-cured Stela, followed by Stela Primer with Stela RBC exposed to light for 20 s. The Scotchbond Universal bonding system did not significantly increase the DC compared to Stela alone, except when exposed to light for 20 s, which slightly increased the final DC. Exposing Stela to light for 20 s did not improve the final DC of Stela as much as using the Stela Primer, but it did increase the temperature and prolonged the time taken to return to 37 °C. The Stela primer accelerates the degree of conversion of Stela. The reaction occurred rapidly and achieved a higher DC at the bottom of the specimens where the Stela was in contact with the Stela primer. Exposing Stela to light for 20 s is not recommended. Instead, Stela should be used with Stela Primer. [ABSTRACT FROM AUTHOR]

Fused Filament Fabrication (FFF) is the most used additive manufacturing (AM) technique. Understanding the behavior of the in situ temperature profile during the cooling stage is crucial to enhancing the mechanical properties of the parts manufactured by FFF since adhesion between printed layers is strongly related to the polymer cooling rate. However, only some studies analyze each layer in detail. For such analysis, infrared thermographic cameras can be used as a tool for non-contact temperature measurement. Numerous variables in constructing the part offer potential for such investigation. This study may lead to the enhancement of the part manufactured and improvements in the 3D printer itself. In addition, the polymer matrix and the manufacturing software can also be optimized. This work aims to systematically evaluate the temperature profile along the deposited layers during the fabrication of three-dimensional parts using poly(lactic acid) (PLA) filament. An infrared camera was used for real-time temperature measurements, and the data were processed with MATLAB® as a function of time and part length. A difference of up to 30 °C was observed between the edges, and non-uniform temperature profiles were also observed at the beginning, middle, and end of the part manufacturing. The highest temperatures were observed at the side where the print nozzle positions itself for the base to descend to the next layer. Several strategies are proposed to enhance the temperature distribution during the cooling process. [ABSTRACT FROM AUTHOR]

As the technology for mass identification of species is advancing rapidly, we developed a field sampling method that takes advantage of the emerging possibilities of combining sensor-based data with automated high-throughput data processing. This article describes the five field sampling methods used by the LIFEPLAN project to collect biodiversity data in a systematic manner, all over the world. These methods are designed for use by anyone with basic biology or ecology knowledge from the higher education or university level. We present the selection and characteristics of international sampling locations for urban and natural sites, as well as the nested scale design in the Nordic countries and Madagascar. We describe the steps to collect sequences of animal images (.jpg) from infrared triggered camera traps, audio data (.WAV) of environment sounds from audio recorders, invertebrate samples in ethanol from Malaise traps for DNA metabarcoding, as well as both soil samples and 24-hour air samples obtained from cyclone samplers for fungal DNA metabarcoding. To ensure the usability and consistency of the data for future use, we pay particular attention to the metadata collected. In specifying the current sampling protocols, we note that technology will continue to improve and evolve. Hardware will also change within a short time period, with the advantage of improving the equipment used for collecting samples. Thus, we present examples of the samples collected by each current sampling method, to be used as a baseline or in comparison with different equipment models. [ABSTRACT FROM AUTHOR]

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Person re-identification (ReID) is a significant issue in computer vision, aiming to match the same pedestrian across various cameras. Recent research on this issue has successfully reached a satisfactory performance level in the daytime scenario. However, the performance of ReID in low illumination scenarios suffers a dramatic degradation due to the insufficient personal information captured by visible cameras in low or no light scenarios. To address this challenge, the adoption of novel camera technologies for more effective pedestrian data is imperative. Owing to the constraints of distance and usage scenarios in deploying depth cameras, practical infrared cameras are frequently utilized, spurring research in visible infrared person re-identification (VI-ReID). Significant progress has been made in the field of VI-ReID in recent years, but only a few reviews have categorized this problem based on methodology. This article offers a novel perspective by classifying VI-ReID according to application scenarios. Following our categorization scheme, the main advancements in deep learning for VI-ReID over the past five years have been grouped into three categories as follows: retrieval of infrared images using visible images (V → IR), retrieval of visible images using infrared images (IR → V), and joint retrieval of infrared and visible images (V → IR and IR → V). Next, this survey outlines the available datasets and evaluation metrics. After that, we categorize diverse methodologies under the proposed classification and assess their performance metrics across common datasets. Finally, we present two prospective avenues for future research in this domain, informed by the current state of advancement. [ABSTRACT FROM AUTHOR]

Curl distortion has been a persistent challenge for vat photopolymerization‐based printing technology such as digital light processing (DLP), leading to structural deformation and print failures. This study presents a new approach to mitigate curling distortion and heat effects during DLP printing by dividing the printing layer image into sequential subimages, using a breadth‐first search algorithm. The progressive curing process, resembling a ripple pattern, results in a significant improvement in printing accuracy. The deviation is reduced tenfold when the layer image is divided into subimages with 10 pixels for a 32 mm diameter disc. Additionally, subdivision strategy helps to reduce the heat effect during photopolymerization, as monitored in situ by a long‐wave infrared camera. The successful reduction of residual stress using the subdivision strategy results in a 75% improvement in the mechanical performance of the printed products. The simple adoption of subdivision strategy in practical 3D printing applications is also demonstrated. For solid 3D printing structures, introducing intervals within the solid printing layers—such as using a grid structure instead of a fully solid one, can help to reduce curling and heat effects, thereby improving 3D printing accuracy. [ABSTRACT FROM AUTHOR]

The purpose of this study was to elucidate the effect of stroking ewes' body as a positive emotional stimulant on peripheral temperatures, facial action units and ear postures. Thirty-eight healthy ewes were included in the current study. The animals were stroked individually on neck, withers and forehead. Then the procedure was divided into three stages: pre-stroking, stroking and post-stroking with five minutes each. Peripheral temperatures were measured from eye, nose and ear using infrared thermal camera. Images were also captured to analyse facial units in studied sheep. For measuring head behaviours and ear postures, the ear change as a frequency per minute was measured at 30-second intervals for 15 respective minutes. During stroking, the temperature of eye, ear and nasal areas has significantly dropped (P<0.05), however it raised in eyes during the post-stroking period. Significant differences (P<0.05) were also found between pre-stroking and post-stroking phases in ear and nose temperatures. For facial units, significant differences (P<0.05) were found between prestroking, compared to stroking and post-stroking phases for all five facial units. In addition, the total facial action score was significantly (P<0.05) higher during the stroking and post-stroking phases, compared to the pre-stroking phase. Results obtained from ear positions showed that the time spent with ears in plane position had significantly decreased (P<0.05) in the stroking phase compared to pre- and post-stroking phases. In addition, ewes spent more time with their ears backward (P<0.05) during stroking in comparison to pre- and post-stroking phases. No significant differences were found in asymmetrical and forward ear postures. In comparison to the pre-stroking stage, the number of ear position change was not significantly increased during stroking, however, it increased considerably (P<0.05) in the post-stroking period. From this study, it was concluded that stroking ewes' body parts improved significantly the positive emotional state in healthy animals. [ABSTRACT FROM AUTHOR]

We present near-infrared high-dispersion spectroscopic observations of Europa using the Infrared Camera and Spectrograph (IRCS) onboard the Subaru Telescope, seeking direct evidence of water plumes on Europa and exploring spatial variations in plume activity. Using the high spectral/spatial resolution and sensitivity of Subaru/IRCS, our observations have enabled a spatially resolved search for water plumes on Europa. Within our detection limits and time of observation, we found no evidence for the presence of water emission. For a rotation temperature of 50 K, we derived an upper limit on the H |$_{2}$| O abundance of 9.46 |$\times$| 10 |$^{19}$| –5.92 |$\times$| 10 |$^{20}$| m |$^{-2}$| in each divided slit area and 4.61 |$\times$| 10 |$^{19}$| m |$^{-2}$| in the entire area covered by the slit. This upper limit lies below the inferred water abundance from previous UV observations by the Hubble Space Telescope, while being less sensitive by a factor of three compared to the Keck telescope and by one order of magnitude or more than the James Webb Space Telescope observations. Our results align with previous studies and demonstrate that using Subaru/IRCS is an effective strategy for searching for water plumes on Europa with high spatial resolution. Continued observations across different surface areas and orbital phases are essential to fully characterize Europa's plume activity and complement upcoming space missions. [ABSTRACT FROM AUTHOR]

This study aimed to determine whether canopy and air temperature difference (ΔT) as an existing simple normalizing index can be used to detect an increase in canopy temperature induced by soil drought in urban parks, regardless of the unique energy balance and three-dimensional (3D) structure of urban trees. Specifically, we used a thermal infrared camera to measure the canopy temperature of Zelkova serrata trees and compared the temporal variation of ΔT to that of environmental factors, including solar radiation, wind speed, vapor pressure deficit, and soil water content. Normalization based on a 3D energy-balance model was also performed and used for comparison with ΔT. To represent the 3D structure, a terrestrial light detection and ranging-derived 3D tree model was used as the input spatial data. The temporal variation in ΔT was similar to that of the index derived using the energy-balance model, which considered the 3D structure of trees and 3D radiative transfer, with a correlation coefficient of 0.85. In conclusion, the thermal-image-based ΔT performed comparably to an index based on the 3D energy-balance model and detected the increase in canopy temperature because of the reduction in soil water content for Z. serrata trees in an urban environment. [ABSTRACT FROM AUTHOR]

Western central Africa is atypical of the equatorial domain as the main dry season is cloudier than the rainy seasons. To understand this cloud cover's diurnal evolution, we set up an infrared camera and acquired measurements of the total cloud cover fraction (TCF) and cloud optical depth at Bambidie, Gabon (0°44′30.5″S, 12°58′12.4″E), from May to October 2022. Diurnal variations in TCF can be summarized into four types, mostly discretized through the timing and duration of clouds clearing in the afternoon [early afternoon clearing (EaC), late afternoon clearing (LaC), and clear night (CNi)], while one type [no clearing (NoC)] shows overcast conditions all day long. Meteorological measurements show that NoC days record 50 W m−2 less shortwave incoming surface radiation, resulting in daytime temperatures 1°C lower than the seasonal norm, but 20% more diffuse light and 0.5 mm day−1 less ETo. Conversely, EaC days record 50 W m−2 more shortwave incoming surface radiation, leading to temperatures 1.5°C higher than the seasonal norm, but 40% more direct light. The larger water demand (0.5 mm day−1 more ETo) is partly compensated by more frequent rainfall at nighttime. The satellite estimates of Satellite Application Facilities for supporting Nowcasting and very short-range Forecasting (SAFNWC) capture the TCF variations for most of the four types well. They confirm that TCF is dominated by very low and low clouds whose dissipation in the afternoon and evolution into fractional and cumuliform convective clouds explains the clearings on EaC and LaC days. Satellite estimates also show that the four types of days extracted at Bambidie are representative of a larger-scale cloud cover evolution in western central Africa, with a west–east gradient in the timing of afternoon cloud dissipation. [ABSTRACT FROM AUTHOR]

Simultaneous measurement of the temperature in different regions during machining operations presents many limitations. Currently, only orthogonal cutting using a infrared camera allows the simultaneous measurement of temperature in different regions. Additionally, temperature measurement in certain regions is a challenge, for instance, in the chip/tool interface and inside the chip. The application of advanced sensors and the adaptation of well-established techniques in regions of difficult access, such as the chip-tool interface and the chip itself, have been the subject of research to allow the better understanding of the heat generation and temperature evolution during machining operations. This work investigates the application of the inserted thermocouple method to measure the temperature inside the chip during its formation, together with the tool-workpiece thermocouple method to compare the effect of the cutting parameters on both the chip-tool interface and chip temperature. Orthogonal cutting of AISI 1020 steel was performed using cemented tungsten carbide bits. The findings indicated that both methods were able to assess the influence of the investigated parameters and that temperature presented the same behavior, in spite of the differences in absolute values (higher temperatures were recorded using the tool-workpiece thermocouple). Temperature increased with cutting speed, decreased with the elevation of the undeformed chip thickness and was not affected by width of cut. The highest temperature (668 °C) was observed at the tool-workpiece interface using a cutting speed of 120 m/min, undeformed chip thickness of 0.1 mm and width of cut of 1.5 mm. [ABSTRACT FROM AUTHOR]

Rectal thermometers are commonly used to assess animal health, but non-invasive methods like thermal imaging are gaining popularity for their ease. This study explores using infra-red thermography to measure eye temperatures as a proxy for body temperature in buffaloes. Among 280 adult female buffaloes, rectal temperature averaged 38.26 ± 0.38 °C, while eye temperature averaged 36.99 ± 0.47 °C. Statistical analysis showed a significant positive correlation (0.674, p < 0.001) between eye and rectal temperatures in adult buffaloes. Moderate correlations were also found between rectal temperature and ambient temperature (0.488, p < 0.001), as well as with ocular temperature (0.488, p < 0.001), while the correlation with humidity was low (0.176, p < 0.05). Regression modelling incorporating rectal and eye temperatures, ambient temperature, and humidity revealed that ocular temperature and ambient temperature significantly affect rectal temperature. The developed equation RT ̂ = 20.377 + 0.465(AET) + 0.024(AT) accurately predicts rectal temperature in buffaloes, with R2 of 51.6%, RMSE = 0.272, and AIC = 71.601 indicating satisfactory variability. These findings highlight the potential of thermal imaging as a diagnostic tool and provide insights into temperature measurement interrelationships. HIGHLIGHTS: The study highlights infra-red thermal imaging as a non-invasive technique for remotely measuring the core temperature of buffaloes. There is a significant and high correlation (p < 0.001) between rectal temperature (RT) and average eye temperature (AET) in buffaloes, indicating proportional shifts in AET with changes in RT. The relationship between rectal temperature (RT) and average eye temperature (AET) is quantified by the equation RT = 20.377 + 0.465(AET) + 0.024(AT), derived from a regression model with R2 of 51.6% and absence of autocorrelation (Durbin-Watson = 1.318), where AT represents ambient temperature. [ABSTRACT FROM AUTHOR]

With the extensive application of coal gasification technology, the production and storage of coal gasification slag continue to increase. Expanding the application scope of coal gasification slag is of positive significance for mitigating environmental pollution. Coal gasification slag has potential in the field of microwave-heated asphalt mixtures; however, there is limited research in this area. This study attempts to investigate the impact of utilizing coal gasification slags as fillers on the self-healing properties of asphalt mixtures. For this purpose, two different types of coal gasification slag in five ratios (within the range of <0.075 mm , 10%, 20%, 30%, 40%, and 50%) were used to replace limestone filler (LF) for preparing the Marshall test samples. Firstly, the micromorphology, chemical composition, and microwave absorption properties of LF, coarse slag (CS), and fine slag (FS) were characterized by scanning electron microscopy, X-ray fluorescence tests, and electromagnetic measurement. Afterward, an infrared camera examined the microwave-heating behaviors of different asphalt mixtures under microwave irradiation. Furthermore, semicircular bending fracture–healing–fracture tests were carried out to evaluate the self-healing properties of the asphalt test samples. Finally, the economical aspects were calculated by the cost analysis. Results indicated that CS and FS exhibited excellent microwave absorption performance. However, the excessive use of CS or FS could potentially weaken the thermal diffusion behavior of asphalt mixtures. Moreover, the microwave-heating capacity and self-healing properties of asphalt mixtures containing coal gasification slag were improved compared to typical specimens. It was possible to find that 50% CS or 40% FS could be considered as the suitable alternative because this rate obtained excellent microwave-heating efficiency, while significantly increasing the self-healing ratio. Moreover, the statistical results demonstrated significant differences in microwave-heating capacity and self-healing properties between CS and FS asphalt mixtures. And the results of cost analysis showed that the utilization process can lead to substantial economic advantages. In general, this study proves the feasibility of utilizing coal gasification slag as a microwave absorber to accelerate the efficiency of microwave healing of asphalt mixture. This finding provides a new methodology for consuming coal gasification slag, enabling the reuse of waste resources. [ABSTRACT FROM AUTHOR]

Non-linear-linear dynamics behaviour of the contact force is investigated for different cam angular speeds (N) and different internal dimensions of the follower guide (F.G.I.D.). The largest Lyapunov exponent parameter conception is used to detect whether the follower will detach from the cam or the follower will stay in permanent contact. Power spectrum analysis and Poincaré maps with a phase-plane diagram are added to detect the non-periodic motion of the contact force. The follower is moved with three degrees of freedom. Multi degrees of freedom (spring-damper-mass) systems are added at the end of the follower stem to suppress the non-linear dynamics behaviour of the contact force of the follower. SolidWorks software is used in the numerical simulation of the contact force. Contact force and follower displacement are determined at the different coefficient of restitution values. The friction and impact are considered between the cam and the follower and between the follower and its guide. The contact force is measured experimentally using OPTOTRACK/3020 by tracking the position of the follower through an infrared camera device. The value of the contact force has declined with the increase of cam angular speeds (N) and the internal dimension of the follower guide (F.G.I.D.). The peak of non-linear response of the follower displacement is reduced to 15%, 32%, 45%, and 62% after using multi degrees of freedom systems. Highlights: Multi degrees of freedom (spring-damper-mass) systems at the end of the follower stem are used to suppress the nonlinear dynamics phenomena of the contact force of the follower. The power density function with dominant frequency is calculated numerically for contact force and follower displacement using SolidWorks software. An OPTOTRAK/3020 with an infrared camera device is used to track the follower position and measure the contact force experimentally. A phase-plane diagram with Poincare maps, fast Fourier transform (FFT), and Lyapunov exponent conception are used to detect the non-periodic motion of the offset follower movement. Non-linear dynamic response of the follower movement is investigated at different follower guides' internal dimensions (F.G.I.D.), cam angular velocities (N), and coefficient of restitution. [ABSTRACT FROM AUTHOR]

Agricultural loss due to the overpopulation of Sika deer poses a significant challenge in Japan, leading to frequent human–wildlife conflicts. We conducted a study in Muroran, Hokkaido (42°22′56.1″ N–141°01′51.5″ E), with the objective of monitoring Sika deer and notifying farmers and locals. We deployed a Sika deer detection model (YOLOv8-nano) on a Raspberry Pi, integrated with an infrared camera that captured images only when a PIR sensor was triggered. To further understand the timing of Sika deer visits and potential correlations with environmental temperature and humidity, respective sensors were installed on Raspberry Pi and the data were analyzed using an ANOVA test. In addition, a buzzer was deployed to deter Sika deer from the study area. The buzzer was deactivated in the first 10 days after deployment and was activated in the following 20 days. The Sika deer detection model demonstrated excellent performance, with precision and recall values approaching 1, and a bounding box creation latency of 0.82 frames per second. Once a bounding box was established after Sika deer detection, alert notifications were automatically sent via email and the LINE messaging application, with an average notification time of 0.32 s. Regarding the buzzer's impact on Sika deer, 35% of the detected individuals reacted by standing upright with alert ears, while 65% immediately fled the area. Analysis revealed that the time of day for Sika deer visits was significantly correlated with humidity (F = 8.95, p < 0.05), but no significant association with temperature (F = 0.681, p > 0.05). These findings represent a significant step toward mitigating human–wildlife conflicts and reducing agricultural production losses through effective conservation measures. [ABSTRACT FROM AUTHOR]

Directed Energy Deposition Laser Beam (DED-LB) is a promising additive manufacturing technique that uses a laser source and a powder stream to build or repair metal components. Repair applications offer significant economic and environmental benefits but are more challenging to develop, especially for components that are difficult to process due to their intricate geometries and materials. Process conditions can change precipitously, and it is essential to implement monitoring systems that ensure high process stability and, consequently, superior end-product quality. In the present work, a mid-wave infrared coaxial camera was used to monitor the melt pool geometry. To simulate the challenging repair process conditions of the DED-LB process, experimental tests were carried out on substrates with different thicknesses. The stability of the deposition process on nickel-based superalloys was analyzed by means of MATLAB algorithms. Thus, the effect of open-loop and closed-loop monitoring with back control on laser power on the process conditions was assessed and quantified. Metallographic analysis of the produced samples was carried out to validate the analyses performed by the monitoring system. The occurrence of production defects (lack of fusion and porosity) related to parameters not directly controllable by monitoring systems, such as penetration depth and dilution, was determined. [ABSTRACT FROM AUTHOR]

Friction drilling is a non-conventional process that generates heat through the interaction between a rotating tool and a workpiece, forming a hole with a bushing. In this study, the effect of the preheating temperature, rotational speed, and feed rate on the induced temperature during the friction drilling of A356 aluminum alloy was investigated. This study aimed to analyze the influence of friction-drilling parameters on the thermal conditions in the induced bushing, where it focused on the relationship between preheating and the resulting heat generation. The analysis of variance (ANOVA) approach was carried out to optimize the friction-drilling parameters that contributed most to the induced temperature during the friction-drilling processing. Experiments were conducted at various preheating temperatures (100 °C, 150 °C, 200 °C), rotational speeds (2000 rpm, 3000 rpm, 4000 rpm), and feed rates (40 mm/min, 60 mm/min, 80 mm/min). The induced temperature during the process was recorded using an infrared camera, where the observed temperatures ranged from a minimum of 154.4 °C (at 2000 rpm, 60 mm/min, and 100 °C preheating) to a maximum of 366.8 °C (at 4000 rpm, 40 mm/min, and 200 °C preheating). The results show that preheating increased the peak temperature generated in the bushing during friction drilling, especially at lower rotational speeds. The rotational speed rise led to an increase in the induced temperature. However, the increase in the feed rate resulted in a decrease in the observed temperature. The findings provide insights into optimizing friction-drilling parameters for enhanced thermal management in A356 aluminum alloy. [ABSTRACT FROM AUTHOR]

The leakage of gas in the domestic sectors is a dangerous issue that causes various hazards. This is due to the damage in the pipelines that affects the overall functioning. Prior detection of leakage can help to avoid various dangerous consequences. The proposed system helps in the detection of gas leakage through artificial intelligence techniques evolving image processing. This includes infrared (IR) cameras that help to detect the leakage of gas. This helps in detecting the gas by analyzing and detecting the temperature of the leaking liquid with that of the surrounding temperature. This is a vision-based sensing system. To obtain accurate images, image enhancement processing with Gaussian filters is used. This helps to enhance the quality of the infrared images. Then the feature extraction phase is implemented to extract the most prominent features that aid to detect the leakage accurately. The proposed system is implemented using deep learning techniques with an optimization algorithm. This helps in obtaining accurate results that help to prevent leakages and protect from various constraints. [ABSTRACT FROM AUTHOR]

This study investigates the feasibility of using fiber Bragg grating (FBG) sensors for multipoint thermal monitoring of several power semiconductor devices (PSDs), such as insulated gate bipolar transistors (IGBTs), and rectifiers assembled on a common heatsink in a three-phase inverter. A novel approach is proposed to integrate FBG sensors beneath the baseplates of the IGBT modules, avoiding the need for invasive modifications to the device structure. By strategically positioning multiple FBG sensors, accurate temperature profiles of critical components can be obtained. The experimental results demonstrate the effectiveness of the proposed method, with the temperature measurements from FBG sensors closely matching those obtained using thermal infrared (IR) cameras within ±1.1 °C. This research highlights the potential of FBG sensors for reliable and precise thermal management in power electronic systems, contributing to improved performance and reliability. [ABSTRACT FROM AUTHOR]

Infrared cameras form images by capturing the thermal radiation emitted by objects in the infrared spectrum, making them complex sensors widely used in maritime surveillance. However, the broad spectral range of the infrared band makes it susceptible to environmental interference, which can reduce the contrast between the target and the background. As a result, detecting infrared targets in complex marine environments remains challenging. This paper presents a novel and enhanced detection model developed from the real-time detection transformer (RT-DETR), which is designated as MAFF-DETR. The model incorporates a novel backbone by integrating CSP and parallelized patch-aware attention to enhance sensitivity to infrared imagery. Additionally, a channel attention module is employed during feature selection, leveraging high-level features to filter low-level information and enabling efficient multi-level fusion. The model's target detection performance on resource-constrained devices is further enhanced by incorporating advanced techniques such as group convolution and ShuffleNetV2. The experimental results show that, although the enhanced RT-DETR algorithm still experiences missed detections under severe object occlusion, it has significantly improved overall performance, including a 1.7% increase in mAP, a reduction in 4.3 M parameters, and a 5.8 GFLOPs decrease in computational complexity. It can be widely applied to tasks such as coastline monitoring and maritime search and rescue. [ABSTRACT FROM AUTHOR]

Human motion classification is gaining more interest among researchers, and it is significant in various applications. Human motion classification and assessment play a significant role in health science and security. Technology-based human motion evaluation deploys motion sensors and infrared cameras for capturing essential portions of human motion and key facial elements. Nevertheless, the prime concern is providing effectual monitoring sensors amidst several stages with less privacy. To overcome this issue, we have developed a human motion categorization system called Deep Kronecker LeNet (DKLeNet), which uses a hybrid network.The system design of impulse radio Ultra-Wide Band (IR-UWB) through-wall radar (TWR) is devised, and the UWB radar acquires the signal. The acquired signal is passed through the gridding phase, and then the feature extraction unit is executed. A new module DKLeNet, which is tuned by Spotted Grey Wolf Optimizer (SGWO), wherein the layers of these networks are modified by applying the Fuzzy concept. In this model, the enhanced technique DKLeNet is unified by Deep Kronecker Network (DKN) and LeNet as well as the optimization modules SGWO is devised by Spotted Hyena Optimizer (SHO) and Grey Wolf Optimizer (GWO). The classified output of human motion is based on human walking, standing still, and empty. The analytic measures of DKLeNet_SGWO are Accuracy, True positive rate (TPR), True Negative rate (TNR), and Mean squared error (MSE) observed as 95.8%, 95.0%, 95.2%, and 38.5%, as well as the computational time observed less value in both training and testing data when compared to other modules with 4.099 min and 3.012 s. [ABSTRACT FROM AUTHOR]

We investigated tail-belting (TB), the newly-discovered freeze avoidance behavior among wild rodents. When temperatures dropped to -6 °C, wild mice (Apodemus agrarius and Apodemus flavicollis) were observed curling their tails inward and positioning it on the back. A literature search suggested TB had never been documented, presumably because rodents, especially in the laboratory, are seldomly assayed under cold stress. Due to the infrequent occurrence of the behavior, we used infrared and thermal cameras to confirm observations. We also collected tail-skin samples to investigate whether any physiological mechanisms might co-occur with TB. If such mechanisms were found, they could inform wider debate involving freeze protection among mammals, and could potentially lead to understanding mammal susceptibility or resilience to sudden temperature changes such as those associated with climate change. Lastly, we scored behaviors by bank voles (Myodes glareolus) which unexpectedly visited chambers. Across four winters, we observed TB in both Apodemus species during subzero conditions, but bank voles never performed the behavior. We also confirmed that TB occurs as an adaptive reflex which warms the tail. From tissue samples, we found that free amino acids, peptides, and glycoproteins were significantly higher during cold-stress. Thus, TB may have been accompanied by the expression of cold-protective proteins which ostensibly enable the peripheral body parts of mammals to survive temperatures well below 0 °C. These findings should inspire new dialogue regarding the role of lipids in tissues of peripheral organs in mammals. By extension, our findings may lead to the discovery of a putative cryoprotection mechanism among mammals. [ABSTRACT FROM AUTHOR]

Composite robots often encounter difficulties due to changes in illumination, external disturbances, reflective surface effects, and cumulative errors. These challenges significantly hinder their capabilities in environmental perception and the accuracy and reliability of pose estimation. We propose a nonlinear optimization approach to overcome these issues to develop an integrated localization and navigation framework, IIVL-LM (IMU, Infrared, Vision, and LiDAR Fusion for Localization and Mapping). This framework achieves tightly coupled integration at the data level using inputs from an IMU (Inertial Measurement Unit), an infrared camera, an RGB (Red, Green and Blue) camera, and LiDAR. We propose a real-time luminance calculation model and verify its conversion accuracy. Additionally, we designed a fast approximation method for the nonlinear weighted fusion of features from infrared and RGB frames based on luminance values. Finally, we optimize the VIO (Visual-Inertial Odometry) module in the R3LIVE++ (Robust, Real-time, Radiance Reconstruction with LiDAR-Inertial-Visual state Estimation) framework based on the infrared camera's capability to acquire depth information. In a controlled study, using a simulated indoor rescue scenario dataset, the IIVL-LM system demonstrated significant performance enhancements in challenging luminance conditions, particularly in low-light environments. Specifically, the average RMSE ATE (Root Mean Square Error of absolute trajectory Error) improved by 23% to 39%, with reductions from 0.006 to 0.013. At the same time, we conducted comparative experiments using the publicly available TUM-VI (Technical University of Munich Visual-Inertial Dataset) without the infrared image input. It was found that no leading results were achieved, which verifies the importance of infrared image fusion. By maintaining the active engagement of at least three sensors at all times, the IIVL-LM system significantly boosts its robustness in both unknown and expansive environments while ensuring high precision. This enhancement is particularly critical for applications in complex environments, such as indoor rescue operations. [ABSTRACT FROM AUTHOR]

The complex microstructure evolution and heterogeneities in metal additive manufacturing (AM) continue to delay the adoption of AM parts by additional industries. Achieving uniform and superior properties in AM parts requires better fundamental understanding of the microstructural evolution. A suitable pathway to gain such understanding is via in situ techniques such as high-speed X-ray imaging, high-resolution infrared cameras, or via synchrotron and neutron diffraction. However, these methods are complex and resource intensive. Modeling may be a more economical avenue, yet, to make these models more robust and reliable, data from in situ techniques are often required. We believe that in some cases, physical simulation methods originally developed for research on conventional processing such as forging, rolling, and welding may provide similar insights. This viewpoint article discusses existing experimental methods for tracking the microstructure evolution during AM in lab-scale settings, focusing on Ni-based superalloys as a case study. The proposed physical simulation methods include the Gleeble thermo-mechanical simulator, dilatometry, and the arc-melting heat treatment technique. These methods can also be integrated into various X-ray, synchrotron, and neutron diffraction set-ups. We discuss how insights derived from thermo-kinetic modeling can underpin the experimental observations from physical simulations. Last, in situ transmission electron microscopy is evaluated as a powerful method with unparalleled resolution for observing the microstructure evolution directly during simulated AM processes. We believe that these methods can be extended to other alloy systems, enhancing scientific understanding, and streamlining the efficient development of AM parts with superior and more uniform properties, promoting the more widespread adoption of AM. [ABSTRACT FROM AUTHOR]

Artificial light at night (ALAN) changes animal behavior in multiple invertebrates and vertebrates and can result in decreased fitness. However, ALAN effects have not been studied in European honey bees (Apis mellifera), an important pollinator in which foragers show strong circadian rhythmicity. Colonies can be exposed to ALAN in swarm clusters, when bees cluster outside the nest on hot days and evenings, and, in limited cases, when they build nests in the open. We captured and maintained foragers in incubated cages and subjected them to constant light (LL), constant dark (DD), or 12 h light:12 h dark (LD) cycle, and observed them with infrared cameras. After 79 h, there was a significant interaction of treatment and time because LL bees slept less. In detail, the bees maintained a regular sleep pattern for three days but LL bees showed a shift on the fourth day. LL bees had the largest sleep differences from LD controls, with trends of lengthened periods and increased phase misalignment from both LD and DD bees. LL bees also experienced significantly more disturbances from their nestmates and preferred to sleep in the lower portion of the cages, which had significantly lower light intensity. These findings suggest that ALAN can disrupt the sleep of honey bee foragers, which has implications for their behavior and overall colony health. [ABSTRACT FROM AUTHOR]

Corn is widely cultivated on a global scale. However, high temperatures during storage and transportation can lead to thermal damage to the kernels, negatively impacting their quality. Traditional methods for identifying heat-damaged grains primarily rely on manual inspection, which is characterized by low efficiency and accuracy. This study proposes a novel identification method that integrates laser ultrasonic signals with infrared image texture features. A pulsed laser stimulates the seeds to generate laser ultrasonic signals, while an infrared camera captures infrared images of the seeds. We extract time-domain, frequency-domain, and Hilbert-domain features from the laser ultrasonic signals, in addition to texture features from the infrared images. These features are combined using Canonical Correlation Analysis (CCA). Subsequently, the fused features are classified using a Backpropagation (BP) neural network, Support Vector Machine (SVM), and Particle Swarm Optimization–Support Vector Machine (PSO–SVM). The results indicate that the recognition rate achieved with the fused 'signal-image' features reaches 99.17%, providing a novel approach for detecting heat-damaged corn seeds. [ABSTRACT FROM AUTHOR]

This study presents a novel deep-learning framework for predicting the thermal appearance of building envelopes under varying weather conditions based on a new dataset collected using a thermal infrared camera at 10 min intervals over a one-and-a-half-year period. Unlike existing studies that rely on simulated data or physical models that do not always accurately reflect the complex heat transfer processes in real buildings, we have collected a large dataset showing how a building behaves under different climatic conditions. We propose a novel deep-learning approach that integrates weather data and thermal imagery to predict the temperature distribution on the building façade for the next 24 and 48 h. The model uses a state-of-the-art recurrent neural network architecture, PredRNN V2, with an action conditioning mechanism to incorporate weather forecasting data into the prediction process. We evaluate this approach in terms of average accuracy, prediction accuracy in specific regions, and visual-perceptual performance of the images. The proposed framework achieves a prediction accuracy of 1.5 °C (root mean square error—RMSE) for the 24 h prediction and 2.04 °C (RMSE) for the 48 h prediction, outperforming baseline models in terms of temperature prediction accuracy and structural similarity of the predicted images. [ABSTRACT FROM AUTHOR]

Effective detection techniques are important for wildlife monitoring and conservation applications and are especially helpful for species that live in complex environments, such as arboreal animals like koalas (Phascolarctos cinereus). The implementation of infrared cameras and drones has demonstrated encouraging outcomes, regardless of whether the detection was performed by human observers or automated algorithms. In the case of koala detection in eucalyptus plantations, there is a risk to spotters during forestry operations. In addition, fatigue and tedium associated with the difficult and repetitive task of checking every tree means automated detection options are particularly desirable. However, obtaining high detection rates with minimal false alarms remains a challenging task, particularly when there is low contrast between the animals and their surroundings. Koalas are also small and often partially or fully occluded by canopy, tree stems, or branches, or the background is highly complex. Biologically inspired vision systems are known for their superior ability in suppressing clutter and enhancing the contrast of dim objects of interest against their surroundings. This paper introduces a biologically inspired detection algorithm to locate koalas in eucalyptus plantations and evaluates its performance against ten other detection techniques, including both image processing and neural-network-based approaches. The nature of koala occlusion by canopy cover in these plantations was also examined using a combination of simulated and real data. The results show that the biologically inspired approach significantly outperformed the competing neural-network- and computer-vision-based approaches by over 27%. The analysis of simulated and real data shows that koala occlusion by tree stems and canopy can have a significant impact on the potential detection of koalas, with koalas being fully occluded in up to 40% of images in which koalas were known to be present. Our analysis shows the koala's heat signature is more likely to be occluded when it is close to the centre of the image (i.e., it is directly under a drone) and less likely to be occluded off the zenith. This has implications for flight considerations. This paper also describes a new accurate ground-truth dataset of aerial high-dynamic-range infrared imagery containing instances of koala heat signatures. This dataset is made publicly available to support the research community. [ABSTRACT FROM AUTHOR]

Nickel-titanium (NiTi)-based shape-memory alloys (SMAs) have various applications in biomedicine, actuators, aerospace technologies, and elastocaloric devices, due to their shape-memory and super-elastic effects. These effects are induced in SMAs by a reversible martensitic phase transformation. This transformation can be achieved by applying stress or temperature differences. It is extremely difficult to measure the temperature of NiTi elements with contact thermometers because they disturb the phase transformation phenomenon. An alternative approach is contactless infrared thermography for which accurate emissivity data are mandatory. To determine the temperature distribution in NiTi components with an infrared camera, a newly constructed apparatus is presented to measure the emissivity of metals. For this purpose, a state-of-the-art infrared camera was employed to analyze the emissivity behavior with temperature, especially during the phase transformation. The emissivity of the NiTi samples was systematically studied by comparing them with a reference-quality black body cavity (BBC) having an emissivity better than 0.995. Calibration measurements revealed that the maximum deviation of the BBC temperature measured with the infrared camera was only 0.15% (0.45 K) from its temperature measured with the built-in contact thermometers. The apparatus was validated by measuring the emissivity of a polished aluminum sample and found to be in good agreement with literature data, particularly for temperatures above 340 K. Finally, the emissivity of two rough and one polished NiTi samples was measured covering the temperature range from 313 K to 423 K with an expanded uncertainty of about 0.02 ( k = 2 ). The studied NiTi samples have an atomic percent composition of Ni 42.5 Ti 49.9 Cu 7.5 Cr 0.1 . We observed that the emissivity of NiTi varies between 0.17 and 0.31 depending on temperature. As the temperature rises, the emissivity increases rapidly during the phase transformation, and it decreases gradually beyond the austenite finish temperature. In addition, the emissivity of NiTi depends on the microstructure of the martensite and austenite phases and the surface roughness of the samples. [ABSTRACT FROM AUTHOR]

Despite the considerable success of deep learning methods in stereo matching for binocular images, the generalizability and robustness of these algorithms, particularly under challenging conditions such as occlusions or degraded infrared textures, remain uncertain. This paper presents a novel deep-learning-based depth optimization method that obviates the need for large infrared image datasets and adapts seamlessly to any specific infrared camera. Moreover, this adaptability extends to standard binocular images, allowing the method to work effectively on both infrared and visible light stereo images. We further investigate the role of infrared textures in a deep learning framework, demonstrating their continued utility for stereo matching even in complex lighting environments. To compute the matching cost volume, we apply the multi-scale census transform to the input stereo images. A stacked sand leak subnetwork is subsequently employed to address the matching task. Our approach substantially improves adversarial robustness while maintaining accuracy on comparison with state-of-the-art methods which decrease nearly a half in EPE for quantitative results on widely used autonomous driving datasets. Furthermore, the proposed method exhibits superior generalization capabilities, transitioning from simulated datasets to real-world datasets without the need for fine-tuning. [ABSTRACT FROM AUTHOR]

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Depending on the obturation technique, the tooth and surrounding tissues may heat up during root canal filling, particularly with warm methods. This study aimed to analyze the temperature increase in the periradicular and -apical region during various warm obturation techniques with a present simulated periodontal blood flow. Seventy-five extracted human teeth were shortened to 11 mm (cut-grinder Primus diamond cutting device; Walter Messner GmbH, Oststeinbek, Germany) and prepared using the ProTaper Gold system (Dentsply Sirona Inc., Charlotte, NC, USA) ISO size 40/.06. Specimens were prepared to ensure stable fluid circulation in an artificially created periodontal space, and the procedure was recorded with a thermal infrared camera (VarioCAM HD; InfraTec GmbH Infrarotsensorik und Messtechnik, Dresden, Germany). The following obturation methods were applied: I, cold single-cone obturation (control group); II, gutta-percha-coated rigid carrier technique (GuttaFusion); III, squirting technique (injection technique); IV, continuous wave technique; and V, Schilder technique. Statistical analysis was performed using the Kruskal–Wallis test, followed by the Mann–Whitney pairwise test using the sequential Bonferroni procedure for significant differences (p < 0.05). The Schilder technique with 0 mL/min showed the lowest temperature change, with a median of 0.00 °C (max. 0.00 °C, min. 0.00 °C, IQR 0.00 °C). In contrast, the continuous wave technique at a circulation rate of 2.6 mL/min exhibited the highest temperature change, with a median of 3.76 °C (max. 5.33 °C, min. 2.42 °C, IQR 1.46 °C). Although warm obturation techniques can increase surface temperature, none of the methods produced changes that were potentially damaging to the periodontium or surrounding bone. [ABSTRACT FROM AUTHOR]

ABSTRACT Genomic selection (GS) and genome‐wide association studies (GWAS) have not been investigated in Vietnamese dairy cattle, even for basic milk production traits, largely due to the scarcity of individual phenotype recording in smallholder dairy farms (SDFs). This study aimed to estimate heritability (h2) and test the applicability of GS and GWAS for milk production, body conformation and novel heat tolerance traits using single test day phenotypic data. Thirty‐two SDFs located in either the north (a lowland vs. a highland) or the south (a lowland vs. a highland) of Vietnam were each visited for an afternoon and the next morning to collect phenotype data of all lactating cows (n = 345). Tail hair from each cow was sampled for subsequent genotyping with a 50K SNP chip at that same visit. Milk production traits (single‐test day) were milk yield (MILK, kg/cow/day), energy corrected milk yield adjusted for body weight (ECMbw, kg/100 kg BW/day), fat (mFA, %), protein (mPR, %) and dry matter (mDM, %). Conformation traits were body weight (BW, kg) and body condition score (BCS, 1 = thin to 5 = obese). Heat tolerance traits were panting score (PS, 0 = normal to 4.5 = extremely heat‐stressed) and infrared temperatures (IRTs, °C) at 11 areas on the external body surface of the cow (inner vulval lip, outer vulval surface, inner tail base surface, ocular area, muzzle, armpit area, paralumbar fossa area, fore udder, rear udder, forehoof and hind hoof), assessed by an Infrared Camera. Univariate linear mixed models and a 10‐fold cross‐validation approach were applied for GS. Univariate single SNP mixed linear models were applied for the GWAS. Estimated h2 (using the genotype information to build relationships among animals) were moderate (0.20–0.37) for ECMbw, mFA, mPR, mRE, BW, BCS and IRT at rear udder; low (0.08–0.19) for PS and other IRTs; and very low (≤ 0.07) for MILK, ECM and mDM. Accuracy of genomic estimated breeding values (GEBVs) was low (≤ 0.12) for MILK, ECM, mDM and IRT at hind hoof; and moderate to high (0.32–0.46) for all other traits. The most significant regions on chromosomes (BTA) associated with milk production traits were 0.47–1.18 Mb on BTA14. Moderate to high h2 and moderate accuracies of GEBVs for mFA, mPR, ECMbw, BCS, BW, PS and IRTs at rear udder and outer vulval surface suggested that GS using single test day phenotypic data could be applied for these traits. However, a greater sample size is required to decrease the bias of GEBVs by GS and increase the power of detecting significant quantitative trait loci (QTLs) by GWAS. [ABSTRACT FROM AUTHOR]

Brown adipose tissue (BAT) plays an important role in energy metabolism because it uses fatty acids for thermogenesis during cold exposure. Preclinical studies found that boysenberry anthocyanins (BoyACs) activate BAT. Therefore, the aim of this preliminary study was to evaluate how BoyAC intake affects BAT in humans. We performed an open-label single-arm nonrandomized study in healthy volunteers. Before and after 4 weeks of daily consumption of 100 ml boysenberry juice (BoyJ) containing 61 mg of BoyACs, participants were assessed at 24 °C and then after 1 h of mild cold exposure (18 °C). An infrared thermography camera was used to measure skin surface temperatures in the supraclavicular BAT region (Tscv) and the non-BAT region of the upper chest (Tch). Energy metabolism was measured by indirect calorimetry. For each endpoint, we calculated Δ as the difference between values before and after cold exposure and compared the values before and after BoyJ intake. 10 volunteers participated (age: 36.1 ± 4.1, body mass index (BMI): 20.9 ± 0.6). After BoyJ intake, ΔTscv-ch was significantly higher (p = 0.029), but Δ energy expenditure, Δ fat oxidation, and Δ carbohydrate oxidation were not significantly different. We found a significant positive correlation between BMI and Δfat oxidation with BoyJ intake. The results indicate that 4 weeks of BoyJ intake activates cold-induced thermogenesis in the scv-BAT but does not have a significant effect on energy metabolism. BoyJ intake may increase fat oxidation during cold exposure in individuals with higher BMI. Trial registry number: UMIN000043476, 05/03/2021. [ABSTRACT FROM AUTHOR]

Introduction: The tennis serve is a complex motion with numerous rotations which are important to manage for performance. The main aim of this study was to investigate kinematic parameters, including the evolution of the center of gravity, and kinetic parameters correlated with racket velocity over all phases of the tennis serve. The secondary objective was to find out which of the correlated parameters differed between a slow and a fast serve. The advantage of such an approach would be to propose biomechanical parameters that coaches and teachers could use to optimize performance or learn how to serve. Methods: Quantitative analysis was carried out on 5 flat serves performed by four ranked players using an optoelectronic system (82 markers located on whole body and racket) composed of 10 infrared cameras (150 Hz) and two force platforms (750 Hz). Results: A descriptive statistical analysis highlighted 11 very large and almost perfect correlations with racket velocity: vertical ground reaction force of back foot in release backward, trunk axial rotation during loading phase, back and front knee flexions, dominant shoulder and hip mediolateral rotation during cocking phase, and center of gravity vertical velocity, dominant shoulder medial rotation velocity, dominant elbow flexion, trunk flexion/extension and axial rotation during acceleration phase. Differences were observed for some of the correlated parameters between slow and fast serve. Discussion: Consequently, all these correlated kinematic and kinetics parameters constitute information that coaches, instructors and athletes can use to improve, optimize or teach the tennis serve. [ABSTRACT FROM AUTHOR]

Introduction: Studying the behavioral responses and movement trajectories of insects under different stimuli is crucial for developing more effective biological control measures. Therefore, accurately obtaining the movement trajectories and behavioral parameters of insects in three-dimensional space is essential. Methods: This study used the litchi pest Thalassodes immissaria as the research object. A special binocular vision observation system was designed for nighttime movement. A thermal infrared camera was used for video recording of T. immissaria in a lightless environment. Moreover, a multi-object tracking method based on the YOLOX-GMM and SORT-Pest algorithms was proposed for tracking T. immissaria in thermal infrared images. By obtaining the central coordinates of the two T. immissaria in the video, target matching and 3D trajectory reconstruction in the parallel binocular system were achieved. Results: Error analysis of the T. immissaria detection and tracking model, as well as the 3D reconstruction model, showed that the average accuracy of T. immissaria detection reached 89.6%, tracking accuracy was 96.9%, and the average error of the reconstructed 3D spatial coordinates was 15 mm. Discussion: These results indicate that the method can accurately obtain the 3D trajectory and motion parameters of T. immissaria. Such data can greatly contribute to researchers' comprehensive understanding of insect behavioral patterns and habits, providing important support for more targeted control strategies. [ABSTRACT FROM AUTHOR]

Regular population monitoring of imperilled charismatic species such as large carnivores is critical for conservation. However, the role of monitoring in conservation is frequently diminished due to: 1) surveys being implemented in isolation, 2) limited on-ground-capacity leading to infrequent monitoring, and 3) inappropriate methods being applied. Wildlife monitoring is often resource-intensive and the utility and cost of different field protocols is rarely reported. In this study we deployed two standard field protocols aimed at collecting data on African lions within a spatial capture-recapture framework. For our first protocol, we trained Uganda Wildlife Authority rangers in search-encounter techniques, the industry gold standard for monitoring lions. The second protocol involved deploying 32 paired stations of state-of-the-art infra-red camera traps. During the search-encounter protocol, two rangers covered 2939 km in 76 days, recording 102 detections (30 individuals) in a ~ 256 km2 area. The resulting density estimates (13.91 lions/100 km2, posterior SD = 2.34) yielded acceptable precision. Conversely, 64 camera traps over 1601 trap nights yielded two usable lion detections. We argue that where wildlife tourism rangers exist, they could be a powerful addition to future lion and wildlife census attempts across the continent. Our results confirm that the current technology of store-bought infra-red camera traps is not suitable for individual identification of lions, and therefore cannot be applied to analytical models that require unambiguous individual identities. However, we encourage the continued testing and advancement of infra-red camera trap technology since in many instances, this may be preferable to white-flash camera traps, which can yield individual identities for lions. Our study also shows the immense importance of the Nile Delta for African lions in Uganda's Murchison Falls National Park, a protected area with both oil extraction and high rates of anthropogenic snaring pressure. A study on African lions in Uganda's Nile Delta shows wildlife tourism rangers, an often-forgotten group in the scientific process, deliver strong density estimates. The study also shows infrared camera traps are weak at detecting lions. [ABSTRACT FROM AUTHOR]