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In response to problems such as large target scale variations, strong background noise, and blurred features leading by low contrast in infrared target detection in near space environments, this paper proposes an efficient detection model, YOLO-MARS, which is based on YOLOv8. The model introduces a Space-to-Depth (SPD) convolution module into the backbone section, which retains the detailed features of smaller targets by downsampling operations without information loss, alleviating the loss of the target feature caused by traditional downsampling. The Grouped Multi-Head Self-Attention (GMHSA) module is added after the backbone’s SPPF module to improve cross-scale global modeling capabilities for target area feature responses while suppressing complex thermal noise background interference. In addition, a Light Adaptive Spatial Feature Fusion (LASFF) detector head is designed to mitigate the scale sensitivity issue of infrared targets (especially smaller targets) in the feature pyramid. It uses a shared weighting mechanism to achieve adaptive fusion of multi-scale features, reducing computational complexity while improving target localization and classification accuracy. To address the extreme scarcity of near space data, we integrated 284 near space images with the HIT-UAV dataset through physical equivalence analysis (atmospheric transmittance, contrast, and signal-to-noise ratio) to construct the NS-HIT dataset. The experimental results show that mAP@0.5 increases by 5.4% and the number of parameters only increase 10% using YOLO-MARS compared to YOLOv8. YOLO-MARS improves the accuracy of detection significantly while considering the requirements of model complexity, which provides an efficient and reliable solution for applications in near space infrared target detection.

In the field of high-altitude operations, the frequent occurrence of fall accidents is usually closely related to safety measures such as the incorrect use of safety locks and the wrong installation of safety belts. At present, the manual inspection method cannot achieve real-time monitoring of the safety status of the operators and is prone to serious consequences due to human negligence. This paper designs a new type of high-altitude operation safety device based on the STM32F103 microcontroller. This device integrates ultra-wideband (UWB) ranging technology, thin-film piezoresistive stress sensors, Beidou positioning, intelligent voice alarm, and intelligent safety lock. By fusing five modes, it realizes the functions of safety status detection and precise positioning. It can provide precise geographical coordinate positioning and vertical ground distance for the workers, ensuring the safety and standardization of the operation process. This safety device adopts multi-modal fusion high-altitude operation safety monitoring technology. The UWB module adopts a bidirectional ranging algorithm to achieve centimeter-level ranging accuracy. It can accurately determine dangerous heights of 2 m or more even in non-line-of-sight environments. The vertical ranging upper limit can reach 50 m, which can meet the maintenance height requirements of most transmission and distribution line towers. It uses a silicon carbide MEMS piezoresistive sensor innovatively, which is sensitive to stress detection and resistant to high temperatures and radiation. It builds a Beidou and Bluetooth cooperative positioning system, which can achieve centimeter-level positioning accuracy and an identification accuracy rate of over 99%. It can maintain meter-level positioning accuracy of geographical coordinates in complex environments. The development of this safety device can build a comprehensive and intelligent safety protection barrier for workers engaged in high-altitude operations.

Infrared small target detection is a critical task in remote sensing. However, it remains highly challenging due to low contrast, heavy background clutter, and large variations in target scale. Traditional convolutional networks are inadequate for joint modeling, as they cannot effectively capture both fine structural details and global contextual dependencies. To address these issues, we propose FSGPNet, a frequency–spatial domain jointly guided perceptual network that explicitly exploits complementary representations in both the frequency and spatial domains. Specifically, a Frequency–Spatial Enhancement Module (FSEM) is introduced to strengthen target details while suppressing background interference through high-frequency enhancement and Perona–Malik diffusion. To enhance global context modeling, we propose a Multi-Scale Global Perception (MSGP) module that integrates non-local attention with multi-scale dilated convolutions, enabling robust background modeling. Furthermore, a Gabor Transformer Attention Module (GTAM) is designed to achieve selective frequency–spatial feature aggregation via self-attention over multi-directional and multi-scale Gabor responses, effectively highlighting discriminative structures of various small targets. Extensive experiments are conducted on two benchmark datasets (IRSTD-1K and NUDT-SIRST) that cover typical remote sensing infrared scenarios. Quantitative and qualitative results demonstrate that FSGPNet consistently outperforms state-of-the-art methods across multiple evaluation metrics. These findings validate the effectiveness and robustness of the proposed FSGPNet for detecting small infrared targets in remote sensing applications.

We experimentally investigated the soliton collisions between soliton molecules and deuterogenic solitons spontaneously generated on the continuous wave (cw) noise background in an ultrafast erbium-doped fiber laser mode locked with MoS saturable absorber (SA). The dynamics of the soliton collisions were observed using the time-stretch dispersion Fourier transform technique. The noise-induced deuterogenic solitons first undergo spectral broadening and wavelength shifting, then collide successively with a soliton molecule and eventually vanish. Within the simple collision framework, the spectral-temporal dynamics of soliton collision would help to unveil the self-stabilization mechanism of the soliton molecules in consideration of dispersive wave shedding. This nonlinear dynamics is similar to the soliton rain, except that complex condensed soliton phase is substituted with a soliton molecule.

Accurate cloud detection is an important preprocessing step for subsequent remote sensing data processing. Traditional threshold cloud detection methods have a complex process and require a large number of threshold tests. In recent years, deep learning has been widely applied to cloud detection. However, annotating training datasets for deep learning models typically requires substantial human effort and time investment. Consequently, there are few existing manually annotated cloud detection datasets, and MODIS cloud detection datasets are particularly scarce. To overcome this limitation, we proposed a cloud detection method that combines radiative transfer simulations with deep learning. We first produced a simulated cloud detection dataset using a radiative transfer model and some existing remote sensing products, and then proposed a neural network for training the cloud detection model. Compared with other deep learning models for cloud detection, our method has achieved satisfactory results on the simulated dataset overall. Furthermore, we conducted cloud detection experiments on real satellite imagery. For comparative analysis, we trained other deep learning models on a real satellite image dataset and compared their performance with that of models trained on our simulated dataset. The cloud detection results on real satellite images demonstrate that the models trained on the simulated dataset we proposed achieve performance comparable to those trained on real remote sensing datasets. Specifically, for MODIS data, we compared our results with the official MODIS cloud mask product, MOD35. The results indicate that our method achieves lower false detection rates on mixed surfaces of snow and bare land.

The pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that the application of deep learning (DL) to 3D CT images could help identify COVID-19 infections. Using data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 pneumonia patients, COVIDNet achieved an accuracy rate of 94.3% and an area under the curve of 0.98. As of March 23, 2020, the COVIDNet system had been used 11,966 times with a sensitivity of 91.12% and a specificity of 88.50% in six hospitals with PCR confirmation. Application of DL to CT images may improve both efficiency and capacity of case detection and long-term surveillance.

Nitrogen (N) is one of the most important macronutrients for plant growth and development. However, the concentration and distribution of N varies in soil due to a variety of environmental factors. In response, higher plants have evolved a developmentally flexible root system to efficiently take up N under N-limited conditions. Over the past decade, significant progress has been made in understanding this form of plant ‘root-foraging’ behavior, which is controlled by both a local and a long-distance systemic nitrate signaling pathway. In this review, we focus on the key components of nitrate perception, signaling, and transduction and its role in lateral root development. We also highlight recent findings on the molecular mechanisms of the nitrate systemic signaling pathway, including small signaling peptides involved in long-distance shoot–root communication. Furthermore, we summarize the transcription factor networks responsible for nitrate-dependent lateral root and root hair development.

OsmiR535 belongs to the miR156/miR529/miR535 superfamily, a highly conserved miRNA family in plants. OsmiR535 is involved in regulating the cold-stress response, modulating plant development, and determining panicle architecture and grain length. However, the role that OsmiR535 plays in plant responses to drought and salinity are elusive. In the current study, molecular and genetic engineering techniques were used to elucidate the possible role of OsmiR535 in response to NaCl, PEG(Poly ethylene glycol), ABA(Abscisic acid), and dehydration stresses. Our results showed that OsmiR535 is induced under stressed conditions as compared to control. With transgenic and CRISPR/Cas9 knockout system techniques, our results verified that either inhibition or knockout of OsmiR535 in rice could enhance the tolerance of plants to NaCl, ABA, dehydration and PEG stresses. In addition, the overexpression of OsmiR535 significantly reduced the survival rate of rice seedlings during PEG and dehydration post-stress recovery. Our results demonstrated that OsmiR535 negatively regulates the stress response in rice. Moreover, our practical application of CRISPR/Cas9 mediated genome editing created a homozygous 5 bp deletion in the coding sequence of OsmiR535, demonstrating that OsmiR535 could be a useful genetic editing target for drought and salinity tolerance and a new marker for molecular breeding of Oryza sativa.

Common methods of ocean remote sensing and seafloor surveying are mainly carried out by airborne and spaceborne hyperspectral imagers. However, the water column hinders the propagation of sunlight to deeper areas, thus limiting the scope of observation. As an emerging technology, underwater hyperspectral imaging (UHI) is an extension of hyperspectral imaging technology in air conditions, and is undergoing rapid development for applications in shallow and deep-sea environments. It is a close-range, high-resolution approach for detecting and mapping the seafloor. In this paper, we focus on the concepts of UHI technology, covering imaging systems and the correction methods of eliminating the water column’s influence. The current applications of UHI, such as deep-sea mineral exploration, benthic habitat mapping, and underwater archaeology, are highlighted to show the potential of this technology. This review can provide an introduction and overview for those working in the field and offer a reference for those searching for literature on UHI technology.

Under a foggy environment, the air contains a large number of suspended particles, which lead to the loss of image information and decline of contrast collected by the vision system. This makes subsequent processing and analysis difficult. At the same time, the current stage of the defogging system has problems such as high hardware cost and poor real-time processing. In this article, an image defogging system is designed based on the ZYNQ platform. First of all, on the basis of the traditional dark-channel defogging algorithm, an algorithm for segmenting the sky is proposed, and in this way, the image distortion caused by the sky region is avoided, and the atmospheric light value and transmittance are estimated more accurately. Then color balancing is performed after image defogging to improve the quality of the final output image. The parallel computing advantage and logic resources of the PL (Programmable Logic) part (FPGA) of ZYNQ are fully utilized through instruction constraints and logic optimization. Finally, the visible light detector is used as the input to build a real-time video processing experiment platform. The experimental results show that the system has a good defogging effect and meet the real-time requirements.