Search Results (1,222)

Two-dimensional (2D) material terahertz (THz) detectors offer a promising platform for compact, room-temperature detection, yet their performance is fundamentally constrained by weak absorption in atomically thin layers. Here, we demonstrate a graphene plasmon polariton atomic cavity (PPAC) THz detector in which intrinsic graphene plasmon absorption is enhanced through vertical cavity-assisted field redistribution. By incorporating a metallic back reflector beneath a silicon substrate of designed thickness, a Fabry–Pérot (FP) interference cavity is formed that positions the standing-wave antinode near the graphene plasmonic layer. Electromagnetic simulations reveal that the Fabry–Pérot cavity itself primarily redistributes the vertical electromagnetic field, thereby enhancing the local in-plane driving field responsible for intrinsic graphene plasmon excitation. Experimental measurements at the optimized cavity condition confirm a pronounced increase in plasmon-induced photothermoelectric response, consistent with the predicted absorption enhancement. As a result, the detector exhibits an approximately 30-fold increase in responsivity compared with the corresponding structure without the cavity, while maintaining a fast response time below 130 μs. The detector further enables discrimination of concealed polar and nonpolar liquids through continuous-wave THz imaging at 2.52 THz, achieving a discrimination speed 30-fold faster than that of conventional time-domain spectroscopy. This result highlights the potential of cavity-enhanced intrinsic plasmon absorption for compact, high-sensitivity, and high-speed THz photodetection. Full article

Mycotoxin contamination of cereal crops poses escalating food safety risks across the Central and Balkan European (CBE) corridor under climate change, yet no PRISMA 2020-compliant synthesis of remote sensing (RS) and machine learning (ML) evidence for this region exists. We conducted an AI-augmented systematic review applying a four-stage automated pipeline—PICO domain scoring, SBERT semantic deduplication, and Thompson-sampling reinforcement learning—to 36,038 corpus records (2010–2025), yielding 156 included studies (inter-rater κ = 0.81 (95% CI: 0.74–0.88)). Logistic growth modelling identified a 56-fold corpus expansion with inflection at t₀ = 2024.8 (R² = 0.981). Satellite multispectral imaging dominated the literature (91.7% of studies); random forest and gradient boosting models achieved R² = 0.74–0.80 for aflatoxin B1 and deoxynivalenol prediction in CBE maize and wheat when integrating vegetation indices, land surface temperature, and precipitation covariates. Deep learning surpassed classical ML in annual study count from 2021, reaching ~60% relative share by 2025, though the performance advantage narrows at field scale relative to laboratory hyperspectral benchmarks (98–99% accuracy). A five-percentage-point CBE–global performance gap is largely consistent with differences in sample size and multi-toxin design scope rather than algorithmic access. The country × mycotoxin gap matrix identifies zero eligible studies for four CBE nations and for T-2/HT-2 toxins across the Balkan states. Climate-driven satellite mycotoxin prediction emerges as the field’s active research frontier. Full article

Multispectral temperature measurement technology based on blackbody radiation theory has been widely applied in the field of non-contact temperature measurement. However, its applicability is limited by the single-point measurement mode. To address this limitation, this study developed a spectral fusion temperature measurement device and proposed a new method for reconstructing the two-dimensional temperature field of deflagration fireballs by fusing spectral and imaging data. The device adopts a CCD sensor and a fiber optic spectrometer placed in parallel with parallel optical axes. To ensure the accuracy of the CCD’s response characteristics at different distances, the photo-response non-uniformity (PRNU) calculation method was used for precision validation. In this study, spectral and imaging data of deflagration fireballs were obtained through experiments. Spectral data of consecutive frames at 189 ms, 192 ms, 195 ms, and 198 ms were extracted and analyzed, confirming that the temperature range at the four time points is 1050 K to 1800 K. The proposed method generates temperature elements with equal temperature intervals and their probabilities within the temperature range, and calculates the theoretical radiation spectrum of each element. Then, least squares optimization fitting is performed on the experimentally measured spectra to obtain the optimal probabilities of the temperature elements in the temperature field. By combining these optimal probabilities with CCD grayscale images, the 2D temperature distribution of the deflagration fireball was reconstructed. Results show that: the PRNU value of the device at a distance of 9 m is less than 2.2% through experimental verification; fused images of the temperature field spectra of four consecutive frames of the deflagration fireball were obtained using the proposed method. The average temperatures reconstructed by the proposed method at 189 ms, 192 ms, 195 ms, and 198 ms were 1382 K, 1373 K, 1366 K, and 1357 K, respectively, while the corresponding temperatures obtained by conventional spectral inversion were 1430 K, 1422 K, 1414 K, and 1406 K. The relative errors were 3.2%, 3.4%, 3.3%, and 3.4%, respectively, with an average relative error of approximately 3.3%. Full article

Global climate change and soil salinization pose challenges to licorice cultivation. Evaluating seed vigor based on the dynamic changes in radicle morphology is crucial for screening and cultivating licorice varieties that are tolerant to low temperatures and salts. Traditional manual measurement of licorice radicle characteristics suffers from issues such as high cost, long time consumption, and large errors. The YOLOv11 instance segmentation model in the field of deep learning offers advantages including a simple architecture, strong lightweight properties, and a unified detection-segmentation framework. Therefore, this study selected the YOLOv11 model to build a deep learning framework and used the continuous time-series crop growth vitality monitoring system to collect full-time-series images of 18 groups of licorice seeds germinating under different temperature and salt stress conditions. The YOLOv11-seg model was improved by adding a Spatial Strip Attention mechanism (SSA) to enhance the spatial correlation of radicle features, replacing ordinary convolutions with a Multi-scale Edge Detail Enhancement Module (MEEM) to optimize multi-scale feature extraction capabilities, and embedding a Normalized Weighted Distance (NWD) loss function to strengthen the segmentation ability for tiny targets. The YOLOv11-LicoSeg model was constructed for segmenting and extracting licorice radicle features and calculating root length. The experimental results showed that the mAP50 of the model’s detection reached 97.4%, mAP50–95 reached 81.7%, the mAP50 of the segmentation mask reached 97.0%, and mAP50–95 reached 78.2%. Compared with the unimproved YOLOv11-seg, the mAP50 of detection increased by 0.7%, mAP50–95 increased by 1.3%, the mAP50 of segmentation increased by 0.7%, and mAP50–95 increased by 0.8%. The linear regression coefficient between manual measurement and machine-vision measurement was 0.94218, and the goodness of fit R² was 0.94408. Using this model and the monitoring system, the morphological evolution of the licorice radicle contour characteristics over the germination time was obtained. The study indicated that the growth of licorice radicles was optimal under salt stress of 1200 µs/cm and 1800 µs/cm. YOLOv11-LicoSeg accurately segmented licorice radicles and calculated radicle length, with the performance to segment 100 licorice radicle images within 7 s. After deployment, it significantly reduced the labor cost and time consumption for acquiring licorice radicle phenotypes. In conclusion, YOLOv11-LicoSeg provides a rapid and accurate method for variety screening in licorice breeding and cultivation. Full article

Pixelated detectors based on inorganic scintillation materials are widely used in radiation detection systems for medical imaging and many other fields of science and technology. A substantial application is X-ray scanning using flat-panel detectors (FPDs) for both fluorography and mammography. In this article, the detection properties of the monolithic planar ceramic scintillation elements are reported for the first time. A high-light yield (Gd,Y)₃Al₂Ga₃O₁₂:Ce,Mg garnet-type scintillation material was used to form square-shaped pixels, while a material of similar composition was used as a substrate. Green bodies were successfully fabricated by a digital light processing (DLP) 3D printing method. Subsequent debinding and pressureless high-temperature sintering resulted in composite elements consisting of two layers with different chemical compositions. The lower bulk layer consisted of transparent, non-luminescent garnet, whereas the upper pixelated layer, with pixel dimensions of 230 × 230 µm, was made of scintillation material. The spatial resolution of the matrices under UV light and alpha-particle excitation was evaluated. It was confirmed that the spatial resolution of the matrices produced by the developed technology is approximately 0.4 times the pixel size. The proven ability of the integrated technology of inorganic scintillation matrix production opens the way for future improvement in spatial resolution through optimizing the printed pixel dimensions. Full article

Accurate pest recognition is an important foundation for intelligent plant protection, precision pesticide application, and sustainable agricultural management. However, in real field environments, pest targets are often small in scale, severely occluded, and embedded in complex backgrounds, which limits the performance of existing supervised learning methods under low-annotation and cross-scenario conditions. To address these issues, a multimodal self-supervised pretraining framework is proposed for pest recognition, in which field pest images and environmental sensor data are integrated to construct pest representations with environmental awareness. In this framework, image features, including pest morphology, leaf texture, and damaged regions, are first extracted through a visual encoding branch, while temporal variation features of ecological factors, including temperature, humidity, illumination, soil moisture, rainfall, and wind speed, are modeled through an environmental encoding branch. On this basis, a cross-modal contrastive consistency module is designed to align visual and environmental representations, a temporal consistency self-supervised module is introduced to characterize the continuous evolutionary relationship between pest occurrence and environmental changes, and a multimodal collaborative representation fusion module is constructed to adaptively integrate information from different modalities. The experimental results show that the proposed method achieves favorable performance in the pest recognition task, with Accuracy, Precision, Recall, and F1-score reaching $94.37\%$, $93.96\%$, $93.42\%$, and $93.69\%$, respectively, outperforming ConvNeXtV2-T, ViT-B/16, Swin-T, SimCLR, MAE, and the conventional Image + Sensor fusion method. The ablation experiments further show that, after removing the cross-modal contrastive consistency module, the temporal consistency self-supervised module, and the multimodal collaborative fusion module, the F1-score decreases to $91.00\%$, $91.36\%$, and $90.49\%$, respectively, thereby demonstrating the contribution of each module. This study provides a viable multimodal self-supervised learning approach for AI-driven intelligent pest recognition, early warning, and precision control in agriculture. Full article

To explore the fracture performance and crack propagation mechanism of basalt fiber (BF)-reinforced asphalt mixtures and overcome the limitations of single-factor performance evaluations, this study systematically investigates the effects of aggregate gradation, material scale and test conditions on fracture behavior. The semi-circular bending (SCB) test was integrated with digital image correlation (DIC) technology to synchronously obtain macroscopic fracture parameters and full-field displacement/strain fields. The findings showed that fine aggregate particle size could better utilize the bridging effect of BFs, increasing fracture energy by 25.8% versus 15.9% for the coarse aggregate particle size. A consistent enhancement in fracture performance is observed between the asphalt mixture and the asphalt mortar after BF incorporation. Under the same test conditions, the addition of fibers increased the fracture energy by 25.8% for the mixture and by 28.4% for the mortar, while fracture toughness increased by 6.9% and 8.3%, respectively. The lower loading rate reduces the reinforcement effect due to viscoelastic stress relaxation, while low temperatures enhance the relative crack resistance efficiency of BFs. The incorporation of fibers increases the crack tortuosity coefficient by a range of 4–14%, leading to greater energy dissipation. However, low temperatures absolutely dominate the crack morphology. This study provides an experimental reference for the differentiated design of BF-reinforced asphalt mixtures under different gradation types and climatic conditions. Full article

Temperature is a physical quantity that represents the degree of heat or cold of an object and has significant application value across various fields. Traditional contact temperature measurement technologies, such as thermocouples and infrared thermometers, suffer from limitations like poor environmental adaptability and low spatial resolution, which makes it difficult to meet the temperature measurement requirements for micro-/nano-devices and extreme environments. In recent years, non-contact optical temperature measurement technology based on the luminescence characteristics of rare earth ions has garnered widespread attention due to its high sensitivity, strong interference resistance, and good environmental adaptability. In addition to inorganic luminescent materials, lanthanide-based molecular and coordination-complex thermometers have also become an important branch of this field; however, this paper focuses on inorganic rare earth luminescence thermometry. This paper provides a systematic review of the mechanisms of temperature measurement using rare earth ion luminescence, including single-energy-level luminescence intensity measurement and luminescence intensity ratio measurement based on thermally coupled levels (TCLs) and non-thermally coupled levels (NTCLs). It analyzes the principles of various technologies, performance parameters (such as absolute sensitivity S_a, relative sensitivity S_r, and temperature resolution δT), and their application progress in fields such as biomedical imaging, high-temperature aerospace environments, and the integration of micro-/nano-devices. Special attention is paid to emerging research directions, including Stark sublevel engineering for enhanced sensitivity, negative thermal expansion (NTE) host design for anti-thermal quenching, multi-modal collaborative thermometry, and artificial intelligence (AI)-assisted material design and data processing. The article also discusses the challenges currently faced by the technology, such as high-temperature fluorescence quenching and signal interference, and looks forward to future development directions, including artificial intelligence-assisted material design and multi-modal cooperative temperature measurement, aiming to provide a reference for the research and application of rare earth luminescence temperature sensing technology. Full article

The Schlieren technique is a powerful tool used in its early years for flow visualization. However, over time, the optical system has been modified for measuring temperature and velocity fields in fluid flows. A tool utilized for measuring velocity fields in Schlieren images is the optical flow method. A requirement for successfully applying optical flow to Schlieren images is that the images contain sufficient contrast to satisfy the mathematical defining condition. On the other hand, a common Schlieren technique uses a single knife-edge cutoff to create contrast images, allowing refractive-index gradients to be imaged in only one direction. However, fluid movement occurs in both directions, leaving the flow information incomplete to study. In this work, we use a bidirectional Schlieren method to calculate velocity fields of a low-speed unstable fluid flow. The Schlieren images are processed using the continuous wavelet transform to enhance their contrast for optical flow applications. To generate the flow at low speed, we use a flapping plate at a fixed frequency. The results show that the continuous wavelet transform improves the image characteristics of Schlieren images for optical flow applications, and the bidirectional Schlieren method provides more information about the behavior of the unstable flow than the classical Schlieren system. On the other hand, to validate some experimental results, we compared them with a numerical simulation in ANSYS Fluent 2024r. The comparison results are in good agreement. Full article

The transition toward smart grids and Industry 4.0 demands a fundamental shift in maintenance strategies, as manual inspection methods are increasingly being supplanted by automated monitoring systems. Among the advanced technologies for smart inspection, infrared imaging has advantages including non-contact operation, intuitive visualization, and predictive capabilities, which has become a cornerstone for autonomous inspection of critical power infrastructure. This review provides recent advancements in infrared imaging, with a specific focus on automated power system inspection. The discussion starts with an overview of the fundamental principles and system architectures, emphasizing the pivotal role of infrared detectors. A detailed analysis traces the technological evolution from traditional photon detectors to current uncooled microbolometers, and critically assesses emerging low-dimensional materials. The analysis highlights inherent performance trade-offs among sensitivity, operating temperature, and fabrication cost. Subsequently, the review explores advanced signal processing algorithms, such as real-time non-uniformity correction and adaptive noise suppression, which are typically implemented on FPGA platforms. Advanced optical configurations—encompassing computational imaging, lensless designs, and scattering suppression methods—are also discussed, demonstrating how their convergence enhances image fidelity and operational reliability in complex field environments. Representative application paradigms are surveyed, including drone-based transmission line inspections, patrol robots in substations, and fault diagnosis in photovoltaic plants; for each, operational efficacy and economic benefits are assessed. Despite considerable progress, several challenges persist, notably the performance–stability–cost trilemma in novel detector development, the substantial computational demands of end-to-end optimized systems, and a lack of standardization. Finally, the review outlines future research directions, such as high-performance uncooled arrays, AI-driven co-design of optics and algorithms, and the development of standardized, low-cost, intelligent inspection platforms. Full article

The thermal shock resistance (TSR) of refractories is a critical determinant of the service life and operational safety of high-temperature industrial equipment in metallurgy, building materials, and chemical engineering. This paper systematically reviews the state-of-the-art research on the evaluation of TSR for refractories. On the theoretical level, the evolutionary logic from classical thermoelastic theory to energy-based damage theory, brittleness evaluation criteria, and the dimensional analysis-based R_Π theory is delineated, with a comparative analysis of the applicability of various criteria in dense versus porous material systems. Regarding evaluation methodologies, the strengths and limitations of conventional thermal cycling tests, splitting tests (notably Brazilian and wedge splitting), and specialized techniques such as ultrasonic pulsing and nano-indentation are scrutinized. Furthermore, the application of non-destructive monitoring technologies, such as Digital Image Correlation (DIC) and Acoustic Emission (AE), for in-situ damage capture is discussed. Additionally, the potential of machine learning in performance prediction and inverse material design is explored. Finally, it is posited that future research should focus on promoting the development of multiscale, standardized, and intelligent evaluation frameworks to meet the requirements of harsh operating environments in emerging fields such as green metallurgy. Full article

Real-time imaging processing for microwave interferometric radiometer (MIR) has great potential in various application fields, such as onboard data processing, onboard information fusion, and alternative visual applications. The primary challenge lies in the computational complexity of the entire processing chain, including both visibility function preprocessing and brightness temperature (TB) reconstruction. In this study, the real-time estimation of the normalized threshold level is identified as the key step for enabling real-time imaging of three-level quantized MIR systems. Three algorithms—Acklam’s algorithm (AKA), polynomial fitting algorithm (PFA), and Taylor expansion algorithm (TEA)—are proposed and evaluated. The PFA provides a favorable balance between estimation accuracy and computational efficiency. Leveraging the proposed algorithms, this paper further establishes an onboard real-time processing framework for three-level quantization MIRs, enabling real-time TB imaging and radio frequency interference (RFI) localization. A real-time imaging experiment was carried out with a 15-element, 50 GHz one-dimensional MIR system, which demonstrates real-time imaging of fast-moving vehicles on the expressway with greatly reduced computational latency (an imaging time of 570.9 μs for 159 baselines). A further flight experiment employing an L-band system verifies the feasibility of onboard RFI localization, and the proposed real-time RFI localization method shows an average angular deviation of 0.32° with respect to an offline MUSIC estimator, corresponding to 2.1% of the nominal spatial resolution. Full article

Coral reefs in the Gulf of Eilat maintain a high diversity of ~100 stony coral species. Despite intense competition for a limited substrate, this raises fundamental questions about spatial organization and mechanisms of coexistence. This study combines deep learning species classification with spatial point-pattern analysis to quantify the frequency of intragenus versus intergenus competitive contacts among four dominant coral genera, Acropora, Favia, Platygyra, and Stylophora, across 12 standardized transects at four reef sites. The ResNet-50 convolutional neural network achieved 92.3% test accuracy for genus-level identification in field imagery of 1100 test images, enabling automated detection of 487 coral–coral competitive pairs exhibiting direct physical contact. Intragenus pairs comprised only 18.3% (89/487) of contacts, significantly below the 50% expected under spatial randomness (z = −14.0, p < 0.0001) with pair correlation functions g(r) > 1 at sub-meter scales indicating conspecific clustering. Genus-specific pair frequencies correlated strongly with relative abundance and spatial coverage (r = 1), with ecological traits explaining dominance patterns: fast-growing, competitive Acropora generated high contact rates, while stress-tolerant Favia and Platygyra prevailed through longevity and defensive competition. These findings demonstrate that intergeneric competition dominates despite local congeneric aggregation, maintaining diversity through niche partitioning rather than intransitive networks, even as coral cover declines amid rising temperatures above 0.05 °C yr⁻¹ and historical eutrophication. The deep learning workflow provides a scalable baseline for monitoring anthropogenic impacts on coral competition dynamics. Full article

Grassland fire is one of the major disasters threatening regional ecological security. Its occurrence, development, and spread are closely related to the spatial distribution and coverage of surface vegetation and snow cover across grassland areas. As the primary combustible fuel source, higher vegetation coverage increases fuel load and continuity, thereby directly determining grassland fire danger levels and accelerating fire spread velocity. In contrast, snow cover imposes an indirect regulatory effect on the spatiotemporal pattern of fire danger factors: it lowers surface temperature, raises near-surface humidity, and restricts the germination and growth of herbaceous vegetation in cold seasons, which effectively reduces available combustible materials and weakens regional fire hazard conditions. Therefore, accurately obtaining the coverage status of vegetation (direct combustible fuel factor) and snow cover (indirect fire-regulating factor) in complex grassland scenarios is the essential premise for reliable grassland fire danger monitoring, early warning, disaster prevention and control, and regional ecological management. Aiming at the practical problems in complex grassland scenarios (such as undulating terrain, uneven vegetation growth, large differences in snow depth, and complex lighting conditions), including difficulty in extracting vegetation and snow-covered areas, blurred and confusing boundaries, and low accuracy in coverage calculation, which seriously restrict the technical bottleneck of precise monitoring of grassland fire danger factors, this study takes near-ground images collected by grassland fire danger factor monitoring stations as the core data source, and proposes an improved UNet image segmentation model combined with image segmentation technology and deep learning methods to realize precise extraction of vegetation and snow-covered areas and efficient calculation of coverage in complex scenarios. To improve the model’s feature extraction ability, boundary localization accuracy, and reduce model parameters and computational overhead, the CBAM-ASPP (Convolutional Block Attention Module—Atrous Spatial Pyramid Pooling) module is integrated at the end of the encoding path. The attention mechanism is used to enhance the weight of key features, and the multi-scale receptive field of atrous spatial pyramid pooling is utilized to strengthen the model’s ability to fuse features of vegetation and snow areas of different scales. The residual attention mechanism is introduced in the upsampling stage to effectively alleviate the gradient disappearance problem, improve the model’s ability to accurately locate the boundaries of vegetation and snow areas, and reduce segmentation errors. In the training process, a dynamically weighted hybrid loss function is adopted to dynamically adjust the weights according to the segmentation difficulty of different types of samples during training, optimize the model training effect, and improve the segmentation accuracy and generalization ability. Experiments were conducted using near-ground images of typical complex grassland scenarios as the dataset, and the performance of the proposed model was verified through comparative experiments. The results show that in the vegetation segmentation task, the mean Intersection over Union (mIoU) of the model reaches 84.70%, and the accuracy rate is 91.28%, which are 1.48 and 1.58 percentage points higher than those of the standard UNet model, respectively. In the snow segmentation task, the mIoU of the model reaches 92.74%, and the accuracy rate is 94.19%, which are 2.39 and 2.36 percentage points higher than those of the standard UNet model, respectively. At the same time, the number of parameters of the model is reduced by 12.85% compared with the standard UNet. Also, its comprehensive performance is significantly better than that of mainstream image segmentation models such as FCN, SegNet, and DeepLabv3+. Based on the standardized time-series data retrieved by the optimized segmentation model, this study further constructs a Grassland Fire Risk Index (GFRI) using the Analytic Hierarchy Process (AHP). Pearson correlation verification confirms that the GFRI has an extremely significant positive correlation with historical fire frequency, accurately capturing the seasonal dynamic rhythm of regional grassland fire occurrence. This integrated framework of intelligent segmentation and fire risk quantification provides a reliable technical solution for grassland fire factor monitoring, dynamic fire risk assessment, early warning systems, and refined regional ecological management. Full article

Cairo’s Islamic architectural heritage represents one of the world’s most significant concentrations of pre-industrial environmental ingenuity. For over a millennium, an integrated suite of passive climate-control systems—the Mašrabiya latticework screen, the open courtyard (Ṣaḥn), the wind-scoop (Malqaf), and stalactite vaulting (Muqarnas)—has moderated temperature, humidity, and airflow with remarkable effectiveness. Today, these inherited solutions are under unprecedented stress from urban densification, chronic particulate pollution, climate-driven temperature rise, and growing visitor footfall. This study investigates indoor environmental quality (IEQ) in six Fatimid- and Mamlūk-era buildings in Historic Cairo through the integrated IQAD-IAH framework, combining IoT field monitoring (January–December 2023) of temperature, relative humidity, CO₂, and PM2.5 with CNN-based deterioration image analysis and Random Forest predictive modeling. Results document critical summer thermal buffering failures reaching 28% of occupied hours above the ASHRAE 55 adaptive comfort limit; hygrothermal stress cycles exceeding the EN 15757 ±10% RH safe threshold for up to 38% of annual hours; and PM2.5 courtyard concentrations of 40–61 µg/m³ under normal conditions, surging to 180–320 µg/m³ during Ḫamāsῑn-seasonal wind events. Machine-learning projections indicate all three principal passive elements will cross the critical deterioration threshold of 70/100 under RCP 8.5 before 2050. A precautionary intervention window is identified between 2025 and 2032. Evidence-based management recommendations compatible with UNESCO World Heritage obligations are presented. Full article