Tiande (China)

In an era marked by technological innovation, visual applications have become ubiquitous in everyday life. Harnessing the power of computer vision, these applications process and interpret video data from edge cameras, facilitating tasks such as object detection and vehicle counting. Yet, implementing complex deep learning models on cameras with limited computational capacity poses significant challenges. Furthermore, the bandwidth constraints and fluctuating nature of wide-area networks present substantial difficulties for video analysis systems dependent on cloud computing. This paper first characterizes the relationship between different parameter combinations (such as frame rate and resolution) and video analysis accuracy through offline analysis. It proposes a video stream analysis configuration selection scheme, SPStream, for slowly changing scenes, and a configuration file switching strategy, SPStream+, for rapidly changing scenes. These strategies use idle resources at the camera edge end to select the optimal configuration in real-time, adjust video encoding quality, and dynamically switch configuration files based on the changing states of object motion. Finally, a real-time video stream analysis system for vehicle counting and pedestrian detection suitable for both scenarios is designed, which saves bandwidth to the greatest extent while meeting the accuracy requirements of users and achieving high accuracy of video analysis.

In the process of in situ stress fracturing drilling in coal mines, obtaining downhole vibration data not only improves drilling efficiency but also plays a key role in ensuring operational safety. Nevertheless, the energy supply techniques used in current vibration detectors reduce operational performance and escalate excavation expenses. This research proposes a self-powered vibration sensor based on the triboelectric nanogenerator, designed for the operational environment of coal mine in situ stress fracturing drilling. It can simultaneously detect axial and lateral vibration frequencies, and the inclusion of redundant sensing units provides the sensor with high reliability. Experimental outcomes demonstrate that the device functions across a frequency span of 0 to 11 Hz, maintaining error margins for frequency and amplitude under 4%. Furthermore, it functions reliably in environments where temperatures are under 150 °C and humidity is under 90%, proving its strong resilience to environmental factors. In addition, the device possesses self-generating potential, achieving a maximum voltage of 68 V alongside an output current of 51 nA. When connected to a 6 × 107 Ω load, the maximum output power can reach 3.8 × 10-7 W. Unlike traditional subsurface oscillation detectors, the proposed unit combines self-generation capabilities with highly reliable measurement characteristics, making it more suitable for practical drilling needs.

IntroductionThe Qinghai–Tibet Plateau (QTP), one of the most extreme environments on Earth, provides a unique natural setting for exploring viral diversity and evolution under conditions of high altitude, hypoxia, and intense ultraviolet radiation. The yak (Bos grunniens), a key endemic ruminant species of the QTP, plays an essential ecological and economic role, yet its fecal virome remains poorly characterized.MethodsIn this study, we analyzed 43 yak fecal samples collected from Yushu, Qinghai Province, and constructed nine metagenomic libraries to investigate the composition, diversity, and phylogenetic characteristics of the yak fecal virome.ResultsMetagenomic sequencing generated approximately 463 million raw reads, of which 2.87 million were classified as viral. The viral reads in the sequenced libraries were primarily composed of single-stranded DNA viruses (92.46%), particularly members of Smacoviridae, Circoviridae, and Genomoviridae, whereas RNA viruses such as Picornaviridae accounted for a minor fraction (0.71%). Phylogenetic analyses revealed that several circular single-stranded DNA (CRESS-DNA) virus and picornavirus genomes share high similarity with known ruminant-associated viruses, while forming independent evolutionary clades, suggesting potential cross-species transmission among plateau animals. The large-scale divergence within Smacoviridae further reflects extensive lineage expansion under the plateau’s extreme environmental pressures.DiscussionCompared with our previous yak virome study, this work provides independent and complementary insights into the genomic and evolutionary characteristics of key viral taxa. Overall, our findings expand the genomic landscape of the yak fecal virome and highlight the Qinghai–Tibet Plateau as an important reservoir for exploring viral diversity, evolution, and host–environment interactions in extreme ecosystems.

In the paper, because the pumpkin pest leaf image acquisition process is susceptible to noise interference resulting in the loss of details of the picture, and the traditional feature extraction algorithm initial feature point selection accuracy is not high and so on, a research on image filtering algorithms has been carried out. Combined with the experimental objects and the characteristics of the three filtering algorithms, Gaussian filtering algorithm was selected for the image denoising work in this paper. The image data of greenhouse greenhouses were collected and organised, and the collected images of pumpkin wilt spot leaves were cropped in the same size, and the similar images were categorised. Aiming at the defect of the arbitrariness of artificially selected standard deviation in the traditional Gaussian filtering algorithm for image denoising, the design and experimental study of the improved adaptive Gaussian filtering algorithm was carried out. The comparison experiments of traditional algorithm, ordinary adaptive Gaussian filtering algorithm and improved adaptive Gaussian filtering algorithm were carried out with the images of pumpkin leaf pests and diseases as experimental objects. The experimental results show that the PSNR value of the improved adaptive algorithm can be improved by at least 12.8% than that of the traditional and ordinary adaptive Gaussian filtering algorithm.

Metal–air batteries offer higher theoretical energy densities and simpler system architectures compared with conventional fuel cells and other rechargeable batteries, making them highly attractive for next-generation energy storage applications. However, platinum dependency and sluggish oxygen reduction reaction (ORR) kinetics impede zinc–air fuel cell commercialization. Herein, we develop low-platinum FePt alloys anchored on tannic acid-etched ZIF-8-derived hollow mesoporous carbon nanoframeworks (FePt-HCNFs), leveraging nanoconfinement effects to prevent nanoparticle aggregation during synthesis. The FePt-HCNF catalyst achieves a 0.91 V half-wave potential (20 mV above Pt/C) and a 1.13 A mgPt –1 mass activity in acidic media while delivering a peak power density of 195.2 mW cm–2 in primary zinc–air batteries. Operational stability tests confirm the structural integrity of the FePt-HCNF catalyst, stemming from the optimized interplay between the engineered carbon framework and atomically dispersed active sites. This work demonstrates a rational design paradigm for durable, cost-effective platinum-based catalysts through atomic-scale support engineering, advancing sustainable energy conversion technologies.

ABSTRACTDisseminated tuberculosis (TB) is a severe form of TB associated with high mortality typically occurring in individuals with impaired host defenses. It results from the lymphohematogenous dissemination of Mycobacterium tuberculosis from the primary infection site. Diagnosis can be particularly challenging when pulmonary symptoms are absent. We present a case of disseminated TB in a 73‐year‐old woman who presented with nausea, poor appetite, and low‐grade fever. Imaging studies revealed multiple lesions in the lungs, sacroiliac joints, and brain. The nonspecific nature of her initial symptoms, which were inconsistent with the extent of disease involvement, posed significant diagnostic challenges. In this case, metagenomic next‐generation sequencing played a pivotal role in confirming the diagnosis of disseminated TB.

Angular Super-Oscillatory Metalens The concept of angular super-oscillatory metalens (ASOM) is proposed by combining the superoscillatory onto an angular metalens, which is the first exploration of angular superoscillatory phenomena for sub-diffractive focusing of vortex beams along angular direction. The determined mapping from angular position to topological charge enables determining large-capacity orbital angular modes with a snapshot measurement. Compared to the traditional angular metalens, the sorting resolution is improved by more than three times, which holds substantial promise for high-dimensional optical and quantum systems. For further details, see article number 2300009 by Xiangang Luo and co-workers.