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Fireproof sealant is a common sealing material and has a wide range of applications, which has the dual performance of sealing and fireproof. To figure out its thermal degradation behavior, the thermogravimetric experiments combined with Fourier transform infrared technology were conducted at multiple heating rates. The results showed that the pyrolysis process could be separated into three stages and the explicit reaction equations were revealed corresponding to each stage: First, ammonium polyphosphate decomposed to produce a large amount of inorganic acid, along with a small amount of ammonia and water. Then, a large amount of ammonia gas was produced from melamine decomposition, and the substrate including acrylic resin began to react. Furthermore, the remaining base materials and intermediate carbonized materials continued to be decomposed to produce large amounts of carbon dioxide, water and residue. The experimental results indicated that the various components of fireproof sealant could cooperate closely and played a synergistic role in flame retardant, which could be an important reference value for guiding the improvement of fire sealing materials. [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]

Machine vision and near-infrared light technology are widely used in fruits and vegetable grading, as an important means of agricultural non-destructive testing. The characteristics of fruits and vegetables can easily be automatically distinguished by these two technologies, such as appearance, shape, color and texture. Nondestructive testing is reasonably used for image processing and pattern recognition, and can meet the identification and grading of single features and fusion features in production. Through the summary and analysis of the fruits and vegetable grading technology in the past five years, the results show that the accuracy of machine vision for fruits and vegetable size grading is 70–99.8%, the accuracy of external defect grading is 88–95%, and the accuracy of NIR and hyperspectral internal detection grading is 80.56–100%. Comprehensive research on multi-feature fusion technology in the future can provide comprehensive guidance for the construction of automatic integrated grading of fruits and vegetables, which is the main research direction of fruits and vegetable grading in the future. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department 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.)

With the rapid development of deep learning-based detection algorithms, deep learning is widely used in the field of infrared small target detection. However, well-designed adversarial samples can fool human visual perception, directly causing a serious decline in the detection quality of the recognition model. In this paper, an adversarial defense technology for small infrared targets is proposed to improve model robustness. The adversarial samples with strong migration can not only improve the generalization of defense technology, but also save the training cost. Therefore, this study adopts the concept of maximizing multidimensional feature distortion, applying noise to clean samples to serve as subsequent training samples. On this basis, this study proposes an inverse perturbation elimination method based on Generative Adversarial Networks (GAN) to realize the adversarial defense, and design the generator and discriminator for infrared small targets, aiming to make both of them compete with each other to continuously improve the performance of the model, find out the commonalities and differences between the adversarial samples and the original samples. Through experimental verification, our defense algorithm is not only able to cope with multiple attacks but also performs well on different recognition models compared to commonly used defense algorithms, making it a plug-and-play efficient adversarial defense technique. [ABSTRACT FROM AUTHOR]

Fresh onions are dehydrated to increase their shelf-life. Primarily, open dehydration techniques like solar dehydration come with the problem of contamination through natural air convection. A solar conduction dryer that uses conduction, convection, and radiation for dehydration of food samples is exploited in this study. The food samples are often contaminated by Bacillus and Aspergillus species spores. As a remedy, pulsed light treatment as a non-thermal technology and infrared treatment as a thermal technology are studied and compared. Bacillus cereus and Aspergillus niger spores are chosen as a representative of bacterial and fungal contamination in onions. Dehydrated onion shreds with varying water activities (0.4, 0.5, 0.6) were treated. The spore inactivation was best described by Weibull model as compared with first-order model. Scanning electron microscopy images of the microbial cells showed surface distortions on the bacterial and fungal spores. The effect of the treatment technologies on the colour, flavour (thiosulphinate and pyruvic acid concentration), total phenolic and flavonoid content, and ascorbic acid concentration are compared. Overall, pulsed light treatment showed promising inactivation with a maximum log reduction of 4.5 log B. cereus spores·g−1 and 3.1 log A. niger spores·g−1 at 2.131 J·cm−2 in samples with water activity 0.6. The inactivation rate increased with an increase in water activity. The colour was better retained in pulsed light treated samples. The thiosulphinate content (9.24 μmol·g−1), total phenolics (0.268 mg GAE·g−1), and flavonoid content (0.344 mg QE·g−1) in the sample were improved upon pulsed light exposure. [ABSTRACT FROM AUTHOR]

We present a new selective Metamaterial Perfect Absorber (MPA) consisting of zinc oxide embedded silver (Ag@ZnO), designed for applications in infrared stealth technology. The numerical simulation included a wide frequency range from 1 to 1000 THz and shows that the design MPA structure presented two absorption peaks at the desired wavelengths of 1.7 µm and 6.5 µm. The absorptivity of both peaks reached approximately 93.1% and 93.5%. The first peak at 1.7 µm decreases the scattering of IR laser beams from the surface of the MPA structure and also lowers the infrared tracks that could direct laser-guided devices to its specific target. On the other hand, the second peak reduces the surface heat wave. The suggested MPA (Ag@ZnO) structure is activated by a plane wave using a full wave vector and a broad frequency domain solution. In the framework of computer simulation technology (CST) Microwave Studio, uses both Finite-Difference-Time-Domain (FDTD) and Finite-Element-Method (FEM) techniques to predict the optical behavior of the proposed MPA structure. Both peaks achieved a high value of absorptivity due to the simultaneous excitation of the electric and magnetic dipole at resonance wavelength. [ABSTRACT FROM AUTHOR]

The global trends of urbanization and industrialization have given rise to critical environmental and air pollution issues that often receive insufficient attention. Among the myriad pollution sources, volatile organic compounds (VOCs) stand out as a primary cluster, posing a significant threat to human society. Addressing VOCs emissions requires an effective mitigation action plan, placing technological development, especially in detection, at the forefront. Photonic sensing technologies rooted in the infrared (IR) light and matter interaction mechanism offer nondestructive, fast-response, sensitive, and selective chemical measurements, making them a promising solution for VOC detection. Recent strides in nanofabrication processes have facilitated the development of miniaturized photonic devices and thus sparked growing interest in the creation of low-cost, highly selective, sensitive, and fast-response IR optical sensors for VOC detection. This review work thus serves a timely need to provide the community a comprehensive understanding of the state of the art in this field and illuminate the path forward in addressing the pressing issue of VOC pollution. [ABSTRACT FROM AUTHOR]

In the dairy industry, traditional heat treatments are known for their high water and energy consumption, and more economically and environmentally friendly solutions are being sought. Infrared (IR) technology offers advantages in energy efficiency and environmental sustainability; however, its effectiveness in milk processing, particularly in pathogen inactivation, remains relatively unexplored. In this study, homogenized raw milk was subjected to IR treatment, and its impact on Listeria monocytogenes, Salmonella spp., and Enterobacteriaceae was assessed. Results indicate that IR treatment effectively reduces the microbial load, achieving levels of inactivation comparable to conventional pasteurization methods (around 6 Log10 CFU/mL). Moreover, the treatment maintains milk pH levels, suggesting minimal alteration to its composition. Further research is needed to explore the full extent of IR treatment on milk sanitation efficacy, deeply exploring IR technology to fully assess its applicability and integration into dairy processing practices. Despite regulatory challenges, the Wir System Milk shows promise as a cost-effective and eco-friendly alternative for raw milk treatment. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Nanchang University (Engineering & Technology) is the property of Nanchang University 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.)

With the widespread application of infrared technology in military, security, medical, and other fields, the demand for high-definition infrared images has been increasing. However, the complexity of the noise introduced during the imaging process and high acquisition costs limit the scope of research on super-resolution algorithms for infrared images, particularly when compared to the visible light domain. Furthermore, the lack of high-quality infrared image datasets poses challenges in algorithm design and evaluation. To address these challenges, this paper proposes an optimized super-resolution algorithm for infrared images. Firstly, we construct an infrared image super-resolution dataset, which serves as a robust foundation for algorithm design and rigorous evaluation. Secondly, in the degradation process, we introduce a gate mechanism and random shuffle to enrich the degradation space and more comprehensively simulate the real-world degradation of infrared images. We train an RRDBNet super-resolution generator integrating the aforementioned degradation model. Additionally, we incorporate spatially correlative loss to leverage spatial–structural information, thereby enhancing detail preservation and reconstruction in the super-resolution algorithm. Through experiments and evaluations, our method achieved considerable performance improvements in the infrared image super-resolution task. Compared to traditional methods, our method was able to better restore the details and clarity of infrared images. [ABSTRACT FROM AUTHOR]

[Display omitted] Flexible thermal-responsive encryption devices are widely employed in information encryption and anti-counterfeiting due to their cost-effectiveness and dynamic data encryption and decryption capabilities. However, most current devices are limited to a single layer of encryption, resulting in restricted decryption methods and storage capacity, as well as reliance on external heating. In this study, we integrate multiple layers of encryption within a single device and introduce self-heating thermochromic technology along with infrared thermal imaging encryption to establish a novel concept of a multilayer flexible encryption system. By combining infrared encryption and thermochromic encryption in three-dimensional space enhances the difficulty level for decryption while achieving high storage capacity for information. The internally integrated conductive heating layer within the multilayer structure facilitates rapid and adjustable heating for thermochromic patterns, eliminating the need for external heat sources. Furthermore, we employ a low-cost customizable multi-material integrated 3D printing process for manufacturing multilayer flexible encryption devices. This research presents an innovative solution for designing and fabricating high-density multilevel flexible encryption devices. [ABSTRACT FROM AUTHOR]

• A heat-dynamic network model is developed for FPSCs with reasonable accuracy. • A miniature FPSC prototype is developed for HOT infrared detector. • The non-ideal heat exchange is dominant loss in the expander of miniature FPSCs. High Operating Temperature (HOT) infrared detectors with a miniature Stirling cryocooler, operating at 150 K and above, will become smaller size and more popular in applications with the development of infrared detector technology. In this paper a miniature free-piston Stirling cryocooler (FPSC) is developed for the application of HOT infrared detectors. Allowing for the active properties of the regenerator, actual thermal process in each chamber of the Stirling cryocooler, and the heat-dynamic coupling, the two-port network model is derived for each component of the FPSC, and then a heat-dynamics network analysis model is established for the entire cryocooler. Meanwhile, the performance prediction with the experimental verification is performed for the cryocooler prototype developed. Then analyses of energy losses are carried out, and the studied results shows that main loss for the expander of miniature cryocooler is ascribed to the non-ideal heat transfer of the regenerator. For the miniature cryocooler prototype developed, the cooling capacity of 1.35 W@150 K is achieved, at room temperature of 23 °C and the input power of 10 W AC. The relative Carnot efficiency of the prototype is 13.5 %, and the maximum input power is 20 W AC and the weigh is 200 g. The achieved performance is quite appropriate for the application of high operating temperature infrared detectors. [ABSTRACT FROM AUTHOR]

Given the broad application of infrared technology across diverse fields, there is an increasing emphasis on investigating super-resolution techniques for infrared images within the realm of deep learning. Despite the impressive results of current Transformer-based methods in image super-resolution tasks, their reliance on the self-attention mechanism intrinsic to the Transformer architecture results in images being treated as one-dimensional sequences, thereby neglecting their inherent two-dimensional structure. Moreover, infrared images exhibit a uniform pixel distribution and a limited gradient range, posing challenges for the model to capture effective feature information. Consequently, we suggest a potent Transformer model, termed Large Kernel Transformer (LKFormer), to address this issue. Specifically, we have designed a Large Kernel Residual Depth-wise Convolutional Attention (LKRDA) module with linear complexity. This mainly employs depth-wise convolution with large kernels to execute non-local feature modeling, thereby substituting the standard self-attention layer. Additionally, we have devised a novel feed-forward network structure called Gated-Pixel Feed-Forward Network (GPFN) to augment the LKFormer's capacity to manage the information flow within the network. Comprehensive experimental results reveal that our method surpasses the most advanced techniques available, using fewer parameters and yielding considerably superior performance. [ABSTRACT FROM AUTHOR]

The concentration of carbon dioxide (CO2) is an important indicator for coal mine safety. Real-time monitoring of CO2 concentration is of great importance for taking actions in advance to avoid the occurrence of potential accidents. To address the issues of poor portability and high cost associated with existing coal mine CO2 detection equipment, this paper develops a miniaturized CO2 detection system based on non-dispersive infrared (NDIR) technology. This sensor integrates an infrared light source and a dual-channel pyroelectric detector into a reflective gas chamber, thereby achieving an extended optical path and higher system sensitivity within limited space. Meanwhile, the noise interference was greatly mitigated by using hardware and software filtering techniques. Based on principle analysis, the Lambert–Beer law was parametrically corrected, and then, a model relationship between the dual-channel voltage ratio and concentration was established. In addition, temperature compensation for zero and span values was introduced to improve the adaptability of the detection results to temperature changes. Testing results indicate that the developed detection system can realize CO2 measurement in the concentration range of 0 to 50 000 ppm within a temperature range of 0–40 °C, with a maximum detection error of less than 0.12% and a repeatability deviation of less than 1.04%. During a stability test for 12 h, the maximum concentration drift is 0.07%, indicating that the developed system meets the requirements for monitoring CO2 safety in coal mines. [ABSTRACT FROM AUTHOR]

In order to explore the influence of the infrared stealth technology on the performance of the integrated helicopter/engine system, an integrated modeling method of helicopter/engine/infrared suppressor is proposed. Firstly, based on the power calculation model of the helicopter, combined with the high-precision turboshaft engine component-level model, an integrated simulation platform is built, which takes into account the nonlinear characteristics. Then, the aerodynamic characteristics of infrared suppressors under different engine operation states are studied by CFD numerical computation method, and the infrared radiation characteristics are obtained through combining the positive and negative ray tracing method and narrow band model method. Ultimately, the utilization of the power turbine outlet stagnation pressure is employed as the pivotal interface linking the turboshaft engine and the infrared suppressor in order to formulate an integrated model encompassing the helicopter, engine, and infrared suppressor subsystems. The simulation results demonstrate that compared with the conventional exhaust system, the application of the infrared suppressor greatly enhances the stealth performance of the helicopter, but also results in the unexpected decrease in engine output power. Moreover, the specific fuel consumption of the turboshaft engine increase and the compressor surge margin decreases in case of the consistent flight condition. [ABSTRACT FROM AUTHOR]

During aerial combat, when an aircraft is facing an infrared air-to-air missile strike, infrared baiting technology is an important means of penetration, and the strategy of effective delivery of infrared bait is critical. To address this issue, this study proposes an improved deep deterministic policy gradient (DDPG) algorithm-based intelligent bait-dropping control method. Firstly, by modeling the relative motion between aircraft, bait, and incoming missiles, the Markov decision process of aircraft-bait-missile infrared effect was constructed with visual distance and line of sight angle as states. Then, the DDPG algorithm was improved by means of pre-training and classification sampling. Significantly, the infrared bait-dropping decision network was trained through interaction with the environment and iterative learning, which led to the development of the bait-dropping strategy. Finally, the corresponding environment was transferred to the Nvidia Jetson TX2 embedded platform for comparative testing. The simulation results showed that the convergence speed of this method was 46.3% faster than the traditional DDPG algorithm. More importantly, it was able to generate an effective bait-throwing strategy, enabling the aircraft to successfully evade the attack of the incoming missile. The strategy instruction generation time is only about 2.5 ms, giving it the ability to make online decisions. [ABSTRACT FROM AUTHOR]

Sophisticated infrared detection technology, operating through atmospheric transmission windows (usually between 3 and 5 μm and 8–13 μm), can detect an object by capturing its emitted thermal radiation, posing a threat to the survival of targeted objects. As per Wien's displacement law, the shift of peak wavelength towards shorter wavelengths as blackbody temperature rises, underscores the significance of the 3–5 μm range for ultra-high temperature objects (e.g., at 400 °C), emphasizing the crucial need to control this radiation for the objects' viability. Additionally, effective heat management is essential for ensuring the consistent operation of these ultrahot entities. In this study, based on a database with high-temperature resist materials, we introduced a material-informatics-based framework aimed at achieving the inverse design of simultaneous thermal camouflage (low emittance in the 3–5 μm range) and radiative cooling (high emittance in the non-atmospheric window 5–8 μm range) tailored for ultrahigh-temperature objects. Utilizing the transfer matrix method to calculate spectral properties and employing the particle swarm optimization algorithm, two optimized multilayer structures with desired spectral characteristics are obtained. The resulted structures demonstrate effective infrared camouflage at temperatures up to 250 °C and 500 °C, achieving reductions of 86.7 % and 63.7 % in the infrared signal, respectively. At equivalent heating power densities applied to the structure and aluminum, structure 1 demonstrates a temperature reduction of 29.4 °C at 0.75 W/cm2, while structure 2 attains a temperature reduction of 57.5 °C at 1.50 W/cm2 compared to aluminum, showcasing enhanced radiative cooling effects. This approach paves the way for attenuating infrared signals from ultrahigh-temperature objects and effectively managing their thermal conditions. [ABSTRACT FROM AUTHOR]

Background Aims Methods Results Conclusion Noninvasive monopolar radiofrequency (NMRF) is widely used for dermal and subdermal volumetric heating, yet detailed research on its effects on dermal temperature is scarce.This study evaluates the impact of NMRF on dermal temperature and its potential for dermal remodeling using a porcine model.Noninvasive monopolar radiofrequency was applied to porcine skin with temperature monitoring via optic fiber technology and forward‐looking infrared thermal imaging. Safety was evaluated using nitro blue tetrazolium chloride assessments, and effectiveness was determined through histological examinations before and after treatment.Noninvasive monopolar radiofrequency treatment in a porcine model achieved effective dermal remodeling with no thermal damage, recording peak temperatures of 50°C, 60°C, and 70°C. Histological analysis showed increased collagen density, indicating successful tissue remodeling.Noninvasive monopolar radiofrequency is effective in delivering controlled dermal heating and enhancing collagen synthesis, promoting safe and efficient skin tightening and dermal remodeling in a porcine model. It presents a viable option for skin rejuvenation therapies. [ABSTRACT FROM AUTHOR]

Aerosol infrared stealth technology is a highly effective method to reduce the intensity of infrared radiation by releasing aerosol particles around the hot exhaust plume. This paper uses a Computational Fluid Dynamics (CFD) two-phase flow model to simulate the exhaust plume fields of three kinds of engine nozzles containing aerosol particles. The Planck-weighted narrow spectral band gas model and the Reverse Monte Carlo method are used for infrared radiation transfer calculations to analyze the influencing factors and laws for the suppression of the infrared radiation properties of exhaust plumes by four typical aerosol particles. The simulation calculation results show that the radiation suppression efficiency of aerosol particles on the exhaust plume reaches its maximum value at a detection angle (ϕ) of 0° and decreases with increasing ϕ, reaching its minimum value at 90°. Reducing the aerosol particle size and increasing the aerosol mass flux can enhance the suppression effect. In the exhaust plume studied in this paper, the radiation suppression effect is best when the particle size is 1 μm and the mass flux is 0.08 kg/s. In addition, the inhibition of aerosol particles varies among different materials, with graphite having the best inhibition effect, followed by H2O, MgO, and SiO2. Solid particles will increase the radiation intensity and change the spectral radiation characteristics of the exhaust plume at detection angles close to the vertical nozzle axis due to the scattering effect. Finally, this paper analyzed the suppression effects of three standard nozzle configurations under the same aerosol particle condition and found that the S-bend nozzle provides better suppression. [ABSTRACT FROM AUTHOR]

The ongoing advancements and growing adoption of infrared detection technology have spurred a tremendous amount of interest in thermal camouflage technology. Various approaches are employed to develop infrared camouflage materials capable of manipulating emissivity or surface temperature. However, the range of thermal radiation regulation implemented by these materials is still somewhat limited. In this paper, a combined emissivity and temperature regulation strategy that integrates a thermoelectric device (TED) and a thermochromic structure is proposed. By utilizing this strategy, it becomes possible to simultaneously control the surface temperature and the emissivity without needing additional complex excitation. As a concept demonstration, large tunabilities of 0.38 for long‐wave infrared (8–14 µm) emittance and 87 °C for surface temperature are observed, resulting in a prominent tunability of the thermal radiation temperature that is 15.4 °C greater than that of a conventional TED with constant emissivity. This work aims to introduce a new design paradigm for future thermal radiation management and camouflage techniques. [ABSTRACT FROM AUTHOR]

In modern war of “find and destroy,” hiding the target is one of the essential means to improve battlefield survivability of personnel, weapons, and equipment. Infrared stealth technology started to emerge when hiding in the visible light band could no longer meet the protection requirements. With the rapid development of infrared detection technology, infrared stealth materials are gradually diversified. At present, infrared stealth technology no longer focuses only on low infrared emissivity, but from multiple perspectives to achieve target stealth, such as the tunability of optical properties, good thermal management, and multi‐spectral compatibility stealth. At the same time, the infrared stealth of the target is gradually shifted from passive to active, emphasizing dynamic integration of the target and the environment. This review summarizes research progress, development, and application of infrared stealth materials. Camouflage dye and infrared stealth coating are classified as traditional technologies, however new developments and novel processes from literature can be constantly retrieved over the years. In addition to dyeing/coating technologies, this review also highlights advanced infrared stealth materials such as thermal insulation materials, phase change materials, electrochromic materials, metamaterials, and bionic materials. Outlines of the advanced technologies are mainly focused on those published in recent years. Challenges and prospects for the future development of infrared stealth materials are presented at the end of the review. [ABSTRACT FROM AUTHOR]

This study proposes an improved YOLOv4 algorithm based on mixed domain attention mechanism to design an intelligent substation inspection system. The proposed method combines improvement strategies such as lightweight, depthwise separable convolution, and mixed attention mechanism. The experimental results showed that the identification accuracy of the proposed model was only reduced by 0.2% for test samples at different positions, promoting the accuracy of intelligent inspection to reach 97.5%. The mIoU, mAP, detection speed, and recognition accuracy of the model constructed by the research were 78.34%, 95.12%, 62.05 frames per second, and 95.12%, respectively. Therefore, the proposed model could comprehensively enhance the information expression and recognition accuracy of the system, while promoting intelligent inspection to achieve high accuracy. [ABSTRACT FROM AUTHOR]

Cutting-edge mid-wavelength infrared (MWIR) sensing technologies leverage infrared photodetectors, memory units, and computing units to enhance machine vision. Real-time processing and decision-making challenges emerge with the increasing number of intelligent pixels. However, current operations are limited to in-sensor computing capabilities for near-infrared technology, and high-performance MWIR detectors for multi-state switching functions are lacking. Here, we demonstrate a non-volatile MoS2/black phosphorus (BP) heterojunction MWIR photovoltaic detector featuring a semi-floating gate structure design, integrating near- to mid-infrared photodetection, memory and computing (PMC) functionalities. The PMC device exhibits the property of being able to store a stable responsivity, which varies linearly with the stored conductance state. Significantly, device weights (stable responsivity) can be programmed with power consumption as low as 1.8 fJ, and the blackbody peak responsivity can reach 1.68 A/W for the MWIR band. In the simulation of Faster Region with convolution neural network (CNN) based on the FLIR dataset, the PMC hardware responsivity weights can reach 89% mean Average Precision index of the feature extraction network software weights. This MWIR photovoltaic detector, with its versatile functionalities, holds significant promise for applications in advanced infrared object detection and recognition systems. In-sensor computing architectures can provide energy-efficient multifunctional capabilities, but their application to the mid-infrared range is challenging. Here, the authors report the realization of non-volatile MoS2/black phosphorus photovoltaic detectors, integrating near- to mid-infrared photodetection, memory and computing functionalities. [ABSTRACT FROM AUTHOR]

The integration of visual algorithms with infrared imaging technology has become an effective tool for industrial gas leak detection. However, existing research has mostly focused on simple scenarios where a gas plume is clearly visible, with limited studies on detecting gas in complex scenes where target contours are blurred and contrast is low. This paper uses a cooled mid-wave infrared (MWIR) system to provide high sensitivity and fast response imaging and proposes the MWIRGas-YOLO network for detecting gas leaks in mid-wave infrared imaging. This network effectively detects low-contrast gas leakage and segments the gas plume within the scene. In MWIRGas-YOLO, it utilizes the global attention mechanism (GAM) to fully focus on gas plume targets during feature fusion, adds a small target detection layer to enhance information on small-sized targets, and employs transfer learning of similar features from visible light smoke to provide the model with prior knowledge of infrared gas features. Using a cooled mid-wave infrared imager to collect gas leak images, the experimental results show that the proposed algorithm significantly improves the performance over the original model. The segment mean average precision reached 96.1% (mAP50) and 47.6% (mAP50:95), respectively, outperforming the other mainstream algorithms. This can provide an effective reference for research on infrared imaging for gas leak detection. [ABSTRACT FROM AUTHOR]

Increasing concerns about air quality due to fossil fuel combustion, especially nitrogen oxides (NOx) from marine and diesel engines, necessitate advanced monitoring systems due to the significant health and environmental impacts of nitrogen dioxide (NO2). In this study, a gas detection system based on the principle of the non-dispersive infrared (NDIR) technique is proposed. Firstly, the pyroelectric detector was developed by employing an ultra-thin LiTaO3 (LT) layer as the sensitive element, integrated with nanoscale carbon material prepared by wafer-level graphics technology as the infrared absorption layer. Then, the sensor was hermetically sealed using inert gas through energy storage welding technology, exhibiting a high detectivity (D*) value of 4.19 × 108 cm·√Hz/W. Subsequently, a NO2 gas sensor was engineered based on the NDIR principle employing a Micro Electro Mechanical System (MEMS) infrared (IR) emitter, featuring a light path chamber length of 1.5 m, along with integrated signal processing and software calibration algorithms. This gas sensor was capable of detecting NO2 concentrations within the range of 0–500 ppm. Initial tests indicated that the gas sensor exhibited a full-scale relative error of less than 0.46%, a limit of 2.8 ppm, a linearity of −1.09%, a repeatability of 0.47% at a concentration of 500 ppm, and a stability of 2% at a concentration of 500 ppm. The developed gas sensor demonstrated significant potential for application in areas such as industrial monitoring and analytical instrumentation. [ABSTRACT FROM AUTHOR]

Infrared thermography technology, leveraging its unique ability to capture temperature features, has significantly improved the precision of high-temperature target localization. However, infrared imaging technology is limited by issues such as low image contrast, difficulty in distinguishing object categories, and limited image clarity. To enable intelligent detection of high-temperature objects that may cause fires in warehouses, this paper proposes an innovative method that integrates deep learning image segmentation with infrared and visible light image technology. We developed a new image segmentation model based on improved Fully Convolutional Networks and Deconvolutional Networks, introducing a batch normalization layer to accelerate convergence and employing the PReLU activation function to prevent neuron death, thereby enhancing convergence speed and accuracy. Through a feature dynamic image registration method combining a joint model and a cross-modulation strategy, we achieved efficient image fusion. In addition, a game theory-based strategy was adopted to correct localization results, ensuring accuracy. Experimental results demonstrate that the improved model achieves localization accuracy and precision rates of up to 89.30% and 88.00%, respectively, in real-world warehouse heat source scenarios, representing a significant improvement of 9.90% and 2.85% compared to the pre-improvement model, fully validating its advancement and effectiveness. [ABSTRACT FROM AUTHOR]

The rapid development of flexible conductive materials has promoted the research on conductive polymer (CP)‐based materials. However, CPs have relatively low thermal conductivity (TC), which can result in severe heat dissipation problems for device applications. In this work, core–sheath structured polyaniline@boron nitride (BN@PANI) composites were prepared by in‐situ growth strategy, in which PANI nanowire arrays were uniformly anchored to the surface of BN microrods. Thanks to the particular structure, the as‐prepared BN@PANI composite shows excellent thermal management capabilities. Specifically, the TC of the BN@PANI composite is 4.32 W/m·K at low BN loading of 10 wt%, which is almost 17.8 times that of pure PANI. Moreover, the excellent heat dissipation performance is intuitively presented through infrared imaging technology. Impressively, the PANI nanowire arrays are vertically arranged on the BN surface to form a conductive pathway, which results in conductivity comparable to pure PANI. This work broadens the ideas for preparing high‐performance CP‐based composites by using insulating fillers. Highlights: Core–sheath structured BN@PANI composites were prepared by an in‐situ growth strategy.The thermal conductivity of composite was 4.32 W/m·K at 10 wt% BN loading.The introduction of BN had little effect on the conductivity of PANI.The BN@PANI composites possessed excellent thermal management capability.This work contributes to understanding heat transport in conductive polymers. [ABSTRACT FROM AUTHOR]

Photodetectors operating across the near‐ to short‐wave infrared (NIR–SWIR, λ = 0.9–1.8 µm) underpin modern science, technology, and society. Organic photodiodes (OPDs) based on bulk‐heterojunction (BHJ) active layers overcome critical manufacturing and operating drawbacks inherent to crystalline inorganic semiconductors, offering the potential for low‐cost, uncooled, mechanically compliant, and ubiquitous infrared technologies. A constraining feature of these narrow bandgap materials systems is the high noise current under an applied bias, resulting in specific detectivities (D*, the figure of merit for detector sensitivity) that are too low for practical utilization. Here, this study demonstrates that incorporating wide‐bandgap insulating polymers within the BHJ suppresses noise by diluting the transport and trapping sites as determined using capacitance‐frequency analysis. The resulting D* of NIR–SWIR OPDs operating from 600–1400 nm under an applied bias of −2 V is improved by two orders of magnitude, from 108 to 1010 Jones (cm Hz1/2 W−1), when incorporating polysulfone within the blends. This broadly applicable strategy can reduce noise in IR‐OPDs enabling their practical operation and the realization of emerging technologies. [ABSTRACT FROM AUTHOR]

Obtaining thermal infrared images with prominent details, high contrast, and minimal background noise has always been a focal point of infrared technology research. To address issues such as the blurriness of details and low contrast in thermal infrared images, an enhancement algorithm for thermal infrared images based on multi-scale guided filtering is proposed. This algorithm fully leverages the excellent edge-preserving characteristics of guided filtering and the multi-scale nature of the edge details in thermal infrared images. It uses multi-scale guided filtering to decompose each thermal infrared image into multiple scales of detail layers and a base layer. Then, CLAHE is employed to compress the grayscale and enhance the contrast of the base layer image. Then, detail-enhancement processing of the multi-scale detail layers is performed. Finally, the base layer and the multi-scale detail layers are linearly fused to obtain an enhanced thermal infrared image. Our experimental results indicate that, compared to other methods, the proposed method can effectively enhance image contrast and enrich image details, and has higher image quality and stronger scene adaptability. [ABSTRACT FROM AUTHOR]

This paper mainly introduces related research of the full screen technology for face recognition under OLED screen. By analyzing and studying the influence of infrared light irradiation on OLED screen, especially the influence of infrared light irradiation on TFT, the relevant mechanism analysis is completed, and the corresponding optimization solution is proposed, so as to realize face recognition under the screen, achieve the perfect technical combination of full screen and infrared face recognition, and realize true full screen display. [ABSTRACT FROM AUTHOR]

Microbolometers have emerged as a cost‐effective alternative to cooled infrared photon detectors, albeit with certain trade‐offs in terms of responsivity (Rλ$R_{\lambda}$), detectivity (D*$D_{\*}$), and response time (tr$t_{\text{r}}$). The research in this field is driven by the potential applications in night vision devices, military surveillance, and autonomous vehicles, leading to a growing interest in exploring new materials to bridge the performance gap between cooled photon detectors and uncooled bolometers. This study focuses on the optoelectronic and bolometric characteristics of CuFeSe2$\left(\text{CuFeSe}\right)_{2}$ nanocrystals (NCs) in a colloidal solution. These NCs exhibit a significant change in resistivity (ρ) when subjected to temperature variations ranging from 170 to 400 K. Specifically, the temperature coefficient of resistance (TCR), α, is 1.9% per Kelvin for a room temperature resistivity of 505 Ω cm. Furthermore, the responsivity of CuFeSe2$\left(\text{CuFeSe}\right)_{2}$ NCs is reported to be 0.101 A W−1, and the Hall mobility of the colloidal solution is determined as 0.1 cm2 V s−1$0 \text{.1} \textrm{ } \left(\text{cm}\right)^{2} \textrm{ } \text{V} \textrm{ } \left(\text{s}\right)^{- 1}$. Finally, a comprehensive comparison is conducted between the performance metrics of established bolometer materials, such as VOx and a‐Si, and those of colloidal CuFeSe2$\left(\text{CuFeSe}\right)_{2}$ NCs. Based on the results, colloidal CuFeSe2$\left(\text{CuFeSe}\right)_{2}$ NCs are a promising option for future bolometer technology. [ABSTRACT FROM AUTHOR]

Objectives: To summarize development processes and research hotspots of infrared imaging technology research on acupuncture and to provide new insights for researchers in future studies. Methods: Publications regarding infrared imaging technology in acupuncture from 2008 to 2023 were downloaded from the Web of Science Core Collection (WoSCC). VOSviewer 1.6.19, CiteSpace 6.2.R4, Scimago Graphica, and Microsoft Excel software were used for bibliometric analyses. The main analyses include collaboration analyses between countries, institutions, authors, and journals, as well as analyses on keywords and references. Results: A total of 346 publications were retrieved from 2008 to 2023. The quantity of yearly publications increased steadily, with some fluctuations over the past 15 years. "Evidence-Based Complementary and Alternative Medicine" and "American Journal of Chinese Medicine" were the top-cited journals in frequency and centrality. China has the largest number of publications, with the Shanghai University of Traditional Chinese Medicine being the most prolific institution. Among authors, Litscher Gerhard from Austria (currently Swiss University of Traditional Chinese Medicine, Switzerland) in Europe, was the most published and most cited author. The article published by Rojas RF was the most discussed among the cited references. Common keywords included "Acupuncture," "Near infrared spectroscopy," and "Temperature," among others. Explore the relationship between acupoints and temperature through infrared thermography technology (IRT), evaluate pain objectively by functional near-infrared spectroscopy (fNIRS), and explore acupuncture for functional connectivity between brain regions were the hotspots and frontier trends in this field. Conclusion: This study is the first to use bibliometric methods to explore the hotspots and cutting-edge issues in the application of infrared imaging technology in the field of acupuncture. It offers a fresh perspective on infrared imaging technology research on acupuncture and gives scholars useful data to determine the field's hotspots, present state of affairs, and frontier trends. [ABSTRACT FROM AUTHOR]

Film cooling technology is of great significance to enhance the performance of aero-engines and extend service life. With the increasing requirements for film cooling efficiency, researchers and engineers have carried out a lot of work on the precision and digital measurement of cooling holes. Based on the above, this paper outlines the importance and principles of film cooling technology and reviews the evolution of cooling holes. Also, this paper details the traditional measurement methods of the cooling hole used in current engineering scenarios with their limitations and categorizes digital measurement methods into five main types, including probing measurement technology, optical measurement technology, infrared imaging technology, computer tomography (CT) scanning technology, and composite measurement technology. The five types of methods and integrated automated measurement platforms are also analyzed. Finally, through a generalize and analysis of cooling hole measurement methods, this paper points out technical challenges and future trends, providing a reference and guidance for forward researches. [ABSTRACT FROM AUTHOR]

Spectral data of the outer wall and inner capsule of Pericarpium Citri Reticulatae Viride from different regions (Anhui, Guangdong, and Sichuan) were collected with portable near infrared spectrometer. Multiple interferences in the spectra were eliminated using single and combined pretreatment methods. Pattern recognition methods such as principal component analysis (PCA), cluster independent soft pattern classification (SIMCA) and Fisher linear discriminant analysis (FLDA) were used to establish traceability models of Pericarpium Citri Reticulatae Viride origin. The results showed that spectral pretreatment could eliminate the interferences of baseline drift, background, and spectral peak overlap to a certain extent. However, the traceability analysis of the origin couldn't be achieved. Among the three pattern recognition methods, accurate traceability analysis of Pericarpium Citri Reticulatae Virid couldn't be achieved with PCA method. The whole identification rate of SIMCA model with the outer wall and inner capsule original spectra were 99.14% and 98.28%, respectively. The whole identification rates of FLDA models were both 99.57%, better than that of SIMCA models. The identification rates of SIMCA and FLDA models with appropriate spectral pretreatment were 100%. Therefore, portable near infrared spectroscopy technology combined with the supervised pattern recognition methods can achieve non-destructive traceability analysis of Pericarpium Citri Reticulatae Viride origin, expanding a new way for the traceability analysis of food and drug homologous substances origin. [ABSTRACT FROM AUTHOR]

Pulse Detonation Engine has been a point of interest in the propulsion industry for some time and the interest has been rising due to its better output and results. But any system can perform its task efficiently based on the efficiency of its control system. In this paper we have presented a comparative study between multiple control systems- one based on Bluetooth technology, the other based on infrared sensor technology and one based on wired electrical system. For the wireless system, the signal received from either of the media is passed to the various sensors and systems connected through an Arduino board that further controls the solenoid valves and ignition system. Primarily, a control system circuit is developed using Arduino board and different sensors to connect the fuel supply and ignition system. In the next stage, Bluetooth sensors are connected using an android app and then an infrared sensor based system is integrated with the Arduino to control the engine and the performance of the two systems are compared. Whereas for wired system, every sub system is controlled through optical wires including the solenoid valves, the injection system and the sensors. [ABSTRACT FROM AUTHOR]

We report on developing a wearable infrared (IR) display based on stretchable conductive fibers fabricated through an expansion–contraction process. The expansion process creates a gap between the strands of spandex fibers. This is achieved by immersing the fibers in a solvent where carbon nanotubes (CNTs) are dispersed, thereby embedding the CNTs. Contraction is achieved through a drying process, which removes the gap between the strands of the spandex fibers. This ensures that the CNTs remain embedded, even after repeated stretching. The CNT-embedded spandex fibers are arranged into a 5 × 7 pixel array. The intensity of the IR rays emitted from the fibers can be controlled by adjusting their temperature, which is achieved by varying the driving voltage. Full-color IR images and displays of letters and numbers are realized through precise control of the IR light intensity. The wearable IR display developed in this study opens up exciting possibilities for integration into advanced systems such as military identification, artificial intelligence robots, autonomous driving, and aerospace industry applications. [ABSTRACT FROM AUTHOR]

With the continuous progress of modern competitive sports, the training intensity of table tennis is gradually increasing, which has led to an increase in the proportion of injuries among table tennis players. Therefore, monitoring the muscle characteristics of table tennis players is crucial. Therefore, a table tennis muscle monitoring system was designed using wearable sensors based on infrared spectroscopy imaging. Firstly, this article explores the technology of infrared spectral imaging. According to the grating diffraction theory, light of different wavelengths will be guided to various parts of the convex grating, thereby achieving the goal of light splitting. Infrared spectral imaging has the characteristics of large aperture, excellent imaging quality, simple and compact structure, bent spectral lines, and small color difference. Secondly, this article studies wearable sensor technology, which can accurately collect various muscle indicators and surrounding environmental information of athletes in real-time and transmit them to the system. This device has the advantages of lightweight and portability, excellent electrical characteristics, and high integration. Finally, through the study of wearable sensor technology for infrared spectroscopy imaging, a table tennis muscle monitoring system was designed. This system can effectively reduce the risk of sports injury by monitoring the muscles of table tennis players. [ABSTRACT FROM AUTHOR]

This study investigates the complex diffraction patterns arising from aperture arrays in Under Display Infrared (UDIR) sensors for facial recognition applications. Utilizing simulations and experimental data, we analyze diffraction effects based on aperture shapes and open area ratios. Our findings reveal that the diffraction issues are too complex to describe using only simple parameters like Transmittance. Our study emphasizes the need for sophisticated methodologies to accurately assess the performance impact of diffraction in UDIR applications. [ABSTRACT FROM AUTHOR]

Drying is an important unit operation in the turmeric processing chain. This research investigates the kinetics of drying turmeric slices using infrared dryer. The drying was performed at temperatures of 50, 60, and 70°C for slice thickness of 2, 4, and 6 mm. The drying temperature 70°C and slice thickness of 2 mm showed the highest drying rate. The highest curcumin and oleoresin yield with color retention was observed at the drying temperature of 60°C at 2-mm slice thickness. Thirteen thin-layer drying models were employed to evaluate the experimental drying kinetics of turmeric slices. The model Verma et al. showed the best fit with an R2 value of 0.99 with the least RMSE and chi-squared values as 0.0193 and 3.73E04, respectively. The activation energy for the turmeric slices of thickness 2, 4, and 6 mm ranged between 37.31 and 52.95, 48.78 and 57.51, and 46.79 and 57.37 kJ/mol for 50, 60, and 70°C, respectively. Practical applications This research offers significant potential for improving the efficiency and quality of turmeric drying in industrial settings. The infrared drying enables accelerated drying of turmeric. The optimized infrared drying conditions (60°C and 2-mm slice thickness) can significantly reduce drying time compared to conventional methods. This translates to increased throughput and minimized postharvest losses, especially in largescale turmeric processing facilities. The preservation of curcumin, oleoresin, and color at the optimized conditions ensures the production of high-value turmeric products with superior nutritional and sensory attributes. This can lead to increased consumer appeal, brand differentiation, and potentially higher market prices for turmeric-based foods, supplements, and nutraceuticals. The infrared drying technology can be prescribed to the industries over the existing industrial drying techniques. The costeffectiveness of the method, coupled with its potential for quality improvements and reduced drying time, makes it a viable option for turmeric processors seeking to enhance their operations. [ABSTRACT FROM AUTHOR]

Infrared (IR) technology offers a promising solution for reducing microbiological loads in various food types while preserving their quality traits, such as flavour. However, research on IR's application in complex matrices is limited. Therefore, our preliminary study aimed to evaluate its effectiveness in sanitizing bovine raw milk. We assessed the bacterial count before and after IR treatment by comparing volatile organic compound profiles via headspace extraction and GC/MS analysis. Our findings showed that higher energy levels led to a greater bacterial reduction. IR85 was the most effective in reducing Coliforms and Enterobacteriaceae in non-homogenised samples, with a reduction ranging from −1.01 to >−2.99 and from −1.66 to −3.09 Log CFU/mL, respectively. IR60 and 70 showed no efficacy, while IR80 had intermediate but still satisfactory effect. IR85 notably affected volatile compounds, particularly increasing hexanal (from 0.08 to 4.21 ng g−1) and dimethyl sulphone (from 10.76 to 26.40 ng g−1), while IR80 better preserved the aroma profile. As a result, only IR80 was tested with homogenised raw milk, demonstrating significant bacterial reduction (from >2.39 to 3.06 Log CFU/mL for Coliforms and from 1.90 to >2.45 Log CFU/mL for Enterobacteriaceae) and maintaining the aroma profile quality. [ABSTRACT FROM AUTHOR]

To detect the debonding defect of glass fiber reinforced polymer (GFRP) laminate, the lock-in infrared thermography non-destructive testing system is built and the systematic testing research are conducted, and the effect of different geometrical parameters of debonding defects on the testing results are analyzed. Algorithms such as inter frame differential-multi frame cumulative average method, polynomial fitting-correlation coefficient method, and time-difference contrast method are used to process the image sequence, and signal-to-noise ratio and information entropy are defined as the parameters for evaluating the performance of algorithms. [ABSTRACT FROM AUTHOR]

The past decade has been a boom in lunar exploration. China, India, Japan and other countries have successfully landed landers or rovers on the lunar surface (Wu et al., 2014, 2018, 2020; Prasad et al., 2023). Future missions to explore the Moon are focusing on the lunar south pole (Peña-Asensio et al., 2024). The solar altitude angle at the lunar south pole is extremely low, resulting in low solar irradiance and large areas often in dim light or shadows. The permanently shadowed regions (PSRs) at the lunar south pole are also likely containing substantial amounts of water ice (Li et al., 2018). Future lunar robots exploring the lunar south pole will need to operate in low light or shadowed regions, making camera sensors sensitive to the dim-light environments necessary for these robots. Common night vision sensors usually use near-infrared cameras. However, sensors based on passive infrared technology have image resolution limited by several factors, including the intensity of infrared radiation emitted by the object, the sensitivity of the camera, and the performance of the optical system. For instance, thermal imagers typically have a resolution of 388*284 pixels only. We have developed a dim-light adaptive camera (DimCam) that is ultra-sensitive to the varying illumination conditions driven by AI to achieve high-definition imaging of 1080P or above, for future lunar robots operating in shadows or dim-light regions. The DimCam integrates two starlight-level ultra-sensitive imaging sensors connected by a rigid base to provide stereo vision in low illumination environment. An AI edge computing unit is embedded inside the DimCam to adaptively denoise and enhance image quality. The AI module uses an end-to-end image denoising network to identify and remove noises in the images more accurately by utilizing depth information from the stereo sensors. Compared with traditional monocular denoising algorithms, the denoising network based on stereo vision can significantly improve denoising effects and efficiency by enhancing the signal-to-noise ratio of the data input in the front end. The superposition of overlapping scenes can be regarded as a delayed exposure. Concurrently, the residual analysis of the aligned images aids in noise identification. In addition, for pixels obscured by noise, more accurate pixel values can be restored through interpolation or replacement using depth information obtained from the stereo sensors. Subsequently, a pre-trained lightweight deep network modified from Zero-DCE (Guo et al., 2020) is used for image quality enhancement in terms of brightness and contrast, providing high-quality images even in low-light environments for subsequent applications, such as positioning and navigation of robots, 3D mapping of the surrounding environment, and autonomous driven. We have tested the DimCam in a simulated environment in the laboratory, and the results show that the DimCam has promising performances and great potential for various applications. [ABSTRACT FROM AUTHOR]

Infrared polarization imaging holds significant promise for enhancing target recognition in both civil and defense applications. The Division of Focal Plane (DoFP) scheme has emerged as a leading technology in the field of infrared polarization imaging due to its compact design and absence of moving parts. However, traditional DoFP solutions primarily rely on micro-polarizer arrays, necessitating precise alignment with the focal plane array and leading to challenges in alignment and the introduction of optical crosstalk. Recent research has sought to augment the performance of infrared detectors and enable polarization and spectral selection by integrating metamaterial absorbers with the pixels of the detector. Nevertheless, the results reported so far exhibit shortcomings, including low polarization absorption rates and inadequate polarization extinction ratios. Furthermore, there is a need for a comprehensive figure of merit to systematically assess the performance of polarization-selective thermal detectors. In this study, we employ the particle swarm optimization algorithm to present a multilayer, multi-sized metamaterial absorber capable of achieving a remarkable polarization-selective absorption rate of up to 87.2% across the 8–14 μm spectral range. Moreover, we attain a polarization extinction ratio of 38.51. To elucidate and predict the resonant wavelengths of the structure, we propose a modified equivalent circuit model. Our analysis employs optical impedance matching to unveil the underlying mechanisms responsible for the high absorption. We also introduce a comprehensive figure of merit to assess the efficacy of infrared polarization detection through the integration of metamaterials with microbolometers. Finally, drawing on the proposed figure of merit, we suggest future directions for improving integrated metamaterial absorber designs, with the potential to advance practical mid-infrared polarization imaging technologies. [ABSTRACT FROM AUTHOR]

This study explores the existing literature on specific energy consumption (SEC) use for paddy drying and consolidates all relevant data for comparisons across technologies. Energy consumption data for a range of drying technologies are consolidated from published literature and normalized to enable comparison. A large proportion of the source data are generated from operational performance in industrial or laboratory settings, while the remainder is derived from computer simulations. The SEC of paddy drying is driven primarily by technology type; however, operational factors (such as the system size, temperature, and airflow) and external factors (such as the local climate and paddy moisture content) also heavily influence system energy use. The results of our analysis show that the industrial drying technologies explored in this study have an average SEC of 5.57 ± 2.21 MJ/kg, significantly lower than the 20.87 ± 14.97 MJ/kg observed in a laboratory setting, which can potentially be attributed to differences in processing capacity. Multi-stage drying typically has higher energy efficiency when tempering stages are incorporated. The self-circulating design of some drying systems may provide additional opportunities for heat exchange, leading to efficient drying performance without the need for a separate tempering stage. Beyond traditional methods, we have observed a notable shift towards solar-assisted and infrared drying technologies in laboratory settings, reflecting an increasing interest in sustainable and efficient drying solutions. In summary, this review consolidates SEC data for rice drying technologies, analyzes the energy intensity and performance of each drying technology, and identifies data gaps that might be addressed in future research. [ABSTRACT FROM AUTHOR]

The primary objective of this research was to utilise near-infrared reflectance spectroscopy as a swift, non-destructive method for identifying chlorogenic acid in whole coffee beans. Additionally, this investigation explored the efficacy of different spectral improvement techniques alongside partial least square regression to construct predictive models. NIR spectral data was gleaned from whole coffee beans spanning a wavelength range of 1000–2500 nm, while the chlorogenic acid content was ascertained via high-performance liquid chromatography procedures. Our findings revealed that the highest coefficient of determination reached for chlorogenic acid was 0.97, and the root mean square error for calibration was 0.31% when using the multiplicative scatter correction method. Furthermore, upon testing the model using an external validation dataset, a determination coefficient of 0.91 and a ratio error to range index of 11.56 with a root mean square prediction error at 0.51% was attained. From these results, it can be inferred that the near-infrared technology, coupled with an effective spectral enhancement process, can facilitate quick, non-invasive determination of chlorogenic acid in whole coffee beans. [ABSTRACT FROM AUTHOR]

Multispectral pedestrian detection technology leverages infrared images to provide reliable information for visible light images, demonstrating significant advantages in low-light conditions and background occlusion scenarios. However, while continuously improving cross-modal feature extraction and fusion, ensuring the model's detection speed is also a challenging issue. We have devised a deep learning network model for cross-modal pedestrian detectionbasedonResnet50, aiming to focus on more reliable features and enhance the model's detection efficiency. This model employs a spatial attention mechanism to reweight the input visible light and infrared image data, enhancing the model's focus on different spatial positions and sharing the weighted feature data across different modalities, thereby reducing the interference of multi-modal features. Subsequently, lightweight modules with depth wise separable convolution are incorporated to reduce the model's parameter count and computational load through channel-wise and point-wise convolutions. The network model algorithm proposed in this paper was experimentally validated on the publicly available KAIST dataset and compared with other existing methods. The experimental results demonstrate that our approach achieves favorable performance in various complex environments, affirming the effectiveness of the multispectral pedestrian detection technology proposed in this paper. [ABSTRACT FROM AUTHOR]

This article discusses the use of hyperspectral imaging technology for the detection of methane emissions. Methane is a potent greenhouse gas that contributes to climate change, and reducing its emissions can have positive impacts. The article highlights that traditional gas sniffing techniques and optical gas imagers have limitations in accurately identifying and quantifying methane emissions. Aerial surveys using hyperspectral cameras, such as the Hyper-Cam Airborne Mini, provide a cost-effective solution for detecting methane leaks in the oil and gas industry. This technology can also detect emissions of other gaseous substances. [Extracted from the article]