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To identify the intrinsic active sites in oxides or oxide supported catalysts is a research frontier in the fields of heterogeneous catalysis and material science. In particular, the role of oxygen vacancies on the redox properties of oxide catalysts is still not fully understood. Herein, some relevant research dealing with M1–O–M2 or M1–□–M2 linkages as active sites in mixed oxides, in oxide supported single‐atom catalysts, and at metal/oxide interfaces of oxide supported nanometal catalysts for various reaction systems is reviewed. It is found that the catalytic activity of these oxides not only depends on the amounts of oxygen vacancies and metastable cations but also shows a significant influence from the local environment of the active sites, in particular, the symmetry of the oxygen vacancies. Based on the recent progress in the relevant fields, an “asymmetric oxygen vacancy site” is introduced, which indicates an oxygen vacancy with an asymmetric coordination of cations, making oxygen “easy come, easy go,” i.e., more reactive in redox reactions. The establishment of this new mechanism would shed light on the future investigation of the intrinsic active sites in oxide and oxide supported catalysts. Asymmetric oxygen vacancies in metal oxide catalysts act as intrinsic catalytic active sites for redox reactions by making oxygen species easy come, easy go. Some relevant work focused on the active sites with asymmetric bonding situations in doped oxides, in oxide stabilized single‐atom catalysts, and at metal/oxide interface is reviewed.

Despite the extensive application of porous nanostructures as oxygen electrocatalysts, it is challenging to synthesize single-metal state materials with porous structures, especially the ultrasmall ones due to the uniform diffusion of the same metal. Herein, we pioneer demonstrate a new size effect-based controllable synthesis strategy for the homogeneous Co nanokarstcaves assisted by Co-CN hybrids (CCHs). The preferential migration of cobalt atoms on the surface of small size zeolitic imidazolate framework (ZIF) with high surface energy during pyrolysis is the key factor for the formation of nanokarstcave structure. Furthermore, graphene can act as a diffusion barrier to prevent the agglomeration of nanoparticles in the synthesis process, which also plays an important role in the formation of porous nanostructures. In alkali media, CCHs achieve overpotential of 287 mV (@10 mA·cm −2 ) for oxygen evolution reaction (OER) and a half wave potential of 0.86 V (vs. RHE) for oxygen reduction reaction (ORR).

The multi-target assignment (MTA) problem, a crucial challenge in command control, mission planning, and a fundamental research focus in military operations, has garnered significant attention over the years. Extensively studied across various domains such as land, sea, air, space, and electronics, the MTA problem has led to the emergence of numerous models and algorithms. To delve deeper into this field, this paper starts by conducting a bibliometric analysis on 463 Scopus database papers using CiteSpace software. The analysis includes examining keyword clustering, co-occurrence, and burst, with visual representations of the results. Following this, the paper provides an overview of current classification and modeling techniques for addressing the MTA problem, distinguishing between static multi-target assignment (SMTA) and dynamic multi-target assignment (DMTA). Subsequently, existing solution algorithms for the MTA problem are reviewed, generally falling into three categories: exact algorithms, heuristic algorithms, and machine learning algorithms. Finally, a development framework is proposed based on the \"HIGH\" model (high-speed, integrated, great, harmonious) to guide future research and intelligent weapon system development concerning the MTA problem. This framework emphasizes application scenarios, modeling mechanisms, solution algorithms, and system efficiency to offer a roadmap for future exploration in this area.

A novel Z-scheme ZnFe 2 O 4 /BiVO 4 heterojunction photocatalyst was successfully synthesized using a convenient solvothermal method and applied in the visible light photocatalytic degradation of ciprofloxacin, which is a typical antibiotic contaminant in wastewater. The heterostructure of as-synthesized catalysts was confirmed using X-ray diffraction, scanning electron microscopy, transmission electron microscopy and X-ray photoelectron spectroscopy characterizations. Compared with the single-phase counterparts, ZnFe 2 O 4 /BiVO 4 demonstrated considerably enhanced photogenerated charge separation efficiencies because of the Z-scheme transfer mechanism of electrons between the composite photocatalysts. Consequently, the 30% ZnFe 2 O 4 /BiVO 4 catalyst afforded a degradation rate of up to 97% of 20 mg/L ciprofloxacin under 30 min of visible light irradiation with a total organic carbon removal rate of 50%, which is an excellent activity compared with ever reported BiVO 4 -based catalysts. In addition, the liquid chromatography-mass spectrometry and quantitative structure-activity relationships model analyses demonstrated that the toxicity of the intermediates was lower than that of the parent ciprofloxacin. Moreover, the as-synthesized ZnFe 2 O 4 /BiVO 4 heterojunctions were quite stable and could be reused at least four times. This study thus provides a promising Z-scheme heterojunction photocatalyst for the efficient removal and detoxication of antibiotic pollutants from wastewater.

The pandemic of \"Corona Virus Disease 2019\" (COVID-19) has changed the lives of people. There have been changes in common outpatient and emergency cases in otolaryngology, so an analysis of data pertaining to this was completed. This study is to evaluate the impact of viral infection disease in otolaryngological common disease. This study uses the data of common diseases in the outpatient and emergency department during the \"COVID-19\" pandemic (from February to April 2020) and the same period in the past 3 years from the Department of Otolaryngology. During the \"COVID-19\" period compared with the same period last year, the ranking of cases by diseases has changed. Diseases such as chronic pharyngitis, allergic rhinitis, sudden deafness, and tinnitus increased, meanwhile acute pharyngitis and acute laryngopharyngitis decreased (𝑝 < 0.05). The viral infection has impacted the mental behaviors of people, therefore mental-related disease cases of the department of Otolaryngology have increased indirectly. This study provides real data to illustrate mental-related diseases. It also provides experience and shows the importance of keeping and maintaining mental health.

Variable-thrust liquid rocket engines are essential for precision landing in deep-space exploration, reusable launch vehicle recovery, high-accuracy orbital maneuvers, and emergency obstacle evasions of space drones. However, with the increasingly complex space missions, challenges remain with the development of different technical schemes. In view of these issues, this paper systematically reviews the technology’s evolution through mechanical throttling, electromechanical precision regulation, and commercial space-driven deep throttling. Then, the development of key variable thrust technologies for liquid rocket engines is summarized from the perspective of thrust regulation and control strategy. For instance, thrust regulation requires synergistic flow control devices and adjustable pintle injectors to dynamically match flow rates with injection pressure drops, ensuring combustion stability across wide thrust ranges—particularly under extreme conditions during space drones’ high-maneuver orbital adjustments—though pintle injector optimization for such scenarios remains challenging. System control must address strong multivariable coupling, response delays, and high-disturbance environments, as well as bottlenecks in sensor reliability and nonlinear modeling. Furthermore, prospects are made in response to the research progress, and breakthroughs are required in cryogenic wide-range flow regulation for liquid oxygen-methane propellants, combustion stability during deep throttling, and AI-based intelligent control to support space drones’ autonomous orbital transfer, rapid reusability, and on-demand trajectory correction in complex deep-space missions.

In the realm of automated apple picking operations, the real-time monitoring of apple maturity and diameter characteristics is of paramount importance. Given the constraints associated with feature detection of apples in automated harvesting, this study proposes a machine vision-based methodology for the accurate identification of Fuji apples’ maturity and diameter. Firstly, maturity level detection employed an improved YOLOv5s object detection model. The feature fusion section of the YOLOv5s network was optimized by introducing the cross-level partial network module VoVGSCSP and lightweight convolution GSConv. This optimization aimed to improve the model’s multiscale feature information fusion ability while accelerating inference speed and reducing parameter count. Within the enhanced feature fusion network, a dual attention mechanism combining channel and spatial attention (GAM) was introduced to refine the color and texture feature information of apples and to increase spatial position feature weights. In terms of diameter determination, the contours of apples are obtained by integrating the dual features of color and depth images within the target boxes acquired using the maturity detection model. Subsequently, the actual area of the apple contour is determined by calculating the conversion relationship between pixel area and real area at the current depth value, thereby obtaining the diameter of the apples. Experimental results showed that the improved YOLOv5s model achieved an average maturity level detection precision of 98.7%. Particularly noteworthy was the detection accuracy for low maturity apples, reaching 97.4%, surpassing Faster R-CNN, Mask R-CNN, YOLOv7, and YOLOv5s models by 6.6%, 5.5%, 10.1%, and 11.0% with a real-time detection frame rate of 155 FPS. Diameter detection achieved a success rate of 93.3% with a real-time detection frame rate of 56 FPS and an average diameter deviation of 0.878 mm for 10 apple targets across three trials. Finally, the proposed method achieved an average precision of 98.7% for online detection of apple maturity level and 93.3% for fruit diameter features. The overall real-time inference speed was approximately 56 frames per second. These findings indicated that the method met the requirements of real-time mechanical harvesting operations, offering practical importance for the advancement of the apple industry.

Background Petrositis is a rare and fatal complication associated with otitis media. It is most likely caused by bacterial infections, but in some cases it is caused by fungal infections. Case study The case in this report is associated with fungal petrositis. The clinical symptoms are: ear pain from chronic otitis media, severe headache, peripheral facial palsy and diplopia. The case was finally confirmed through imaging of middle ear, bacterial culture, pathology, and blood Metagenomic next-generation sequencing (mNGS) test. The patient was treated with sensitive antifungal drugs. Conclusion Drug treatment is conservative but efficient method in this case. mNGS can provide pathogenic reference, when antibiotic is not efficient enough for fungal infections or drug-resistant fungal infections cases. This allows we to adjust drug use for the treatment.

Annexin A1 (ANXA1) is a protein known to have multiple roles in the regulation of inflammatory responses. In this study, we find that after oxygen glucose deprivation/reoxygenation (ODG/R) injury, activated PKC phosphorylated ANXA1 at the serine 27 residue (p27S-ANXA1), and promoted the translocation of p27S-ANXA1 to the nucleus of BV-2 microglial cells. This in turn induced BV-2 microglial cells to produce large amounts of pro-inflammatory cytokines. The phenomenon could be mimicked by either transfecting a mutant form of ANXA1 with its serine 27 residue converted to aspartic acid, S27D, or by using the PKC agonist, phorbol 12-myristate 13-acetate (PMA) in these microglial cells. In contrast, transfecting cells with an ANXA1 S27A mutant (serine 27 converted to alanine) or treating the cells with the PKC antagonist, GF103209X (GF) reversed this effet. Our study demonstrates that ANXA1 can be phosphorylated by PKC and is subsequently translocated to the nucleus of BV-2 microglial cells after OGD/R, resulting in the induction of pro-inflammatory cytokines.

Accurate detection of Jinxiu Malus fruits in unstructured orchard environments is hampered by frequent overlap, occlusion, and variable illumination. To address these challenges, we propose YOLOv8-MSP-PD (YOLOv8 with Multi-Scale Pyramid Fusion and Proportional Distance IoU), a lightweight model built on an enhanced YOLOv8 architecture. We replace the backbone with MobileNetV4, incorporating unified inverted bottleneck (UIB) modules and depth-wise separable convolutions for efficient feature extraction. We introduce a spatial pyramid pooling fast cross-stage partial connections (SPPFCSPC) module for multi-scale feature fusion and a modified proportional distance IoU (MPD-IoU) loss to optimize bounding-box regression. Finally, layer-adaptive magnitude pruning (LAMP) combined with knowledge distillation compresses the model while retaining performance. On our custom Jinxiu Malus dataset, YOLOv8-MSP-PD achieves a mean average precision (mAP) of 92.2% (1.6% gain over baseline), reduces floating-point operations (FLOPs) by 59.9%, and shrinks to 2.2 MB. Five-fold cross-validation confirms stability, and comparisons with Faster R-CNN and SSD demonstrate superior accuracy and efficiency. This work offers a practical vision solution for agricultural robots and guidance for lightweight detection in precision agriculture.