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In order to realize the biochemical tail water of pig wastewater resource utilization, this study took the A2O process effluent from the sewage treatment station of a pig enterprise in Jingzhou City as the sample. After coagulation pretreatment, the effluent entered the biofilter aerated with magnetic ceramics and ordinary ceramics as the fillers, respectively, to explore the pollutant removal effect of the device under the action of magnetic field, as well as the influence of magnetic field on the specific aerobic rate and nitrification rate of microorganisms. The experimental results showed that the mechanical properties of magnetic ceramic particles prepared under the conditions of m (sludge): m (kaolin): m (magnetic powder)=3∶1∶4, preheating temperature of 300 ℃, and calcination temperature of 1 000 ℃ were the best. Under the same conditions, the magnetic field could shorten the start-up time of biofilter by increasing the specific oxygen consumption rate and nitrification rate of microorganisms, and the removal effect of pollutants in the stable stage was better than that of ordinary biofilter. When the magnetization of the ceramides was 2.8 mT, HRT was 8 h, and DO was 4-5 mg/L, the system worked best. The removal rates of COD and NH4+-N were 76.1% and 93.8%, respectively. The dissolved microbial by-products and the ferritic acid-like substances in the effluent almost disappeared, and only a small amount of humic acids with complex molecular structure and stable chemical properties remained, which met the class Ⅰ standard in Integrated Wastewater Discharge Standard (GB 8978-1996).

The clinical adoption of small interfering RNAs (siRNAs) has prompted the development of various computational strategies for siRNA design, from traditional data analysis to advanced machine learning techniques. However, previous studies have inadequately considered the full complexity of the siRNA silencing mechanism, neglecting critical elements such as siRNA positioning on mRNA, RNA base-pairing probabilities, and RNA–AGO2 interactions, thereby limiting the insight and accuracy of existing models. Here, we introduce siRNADiscovery , a Graph Neural Network (GNN) framework that leverages both non-empirical and empirical rule-based features of siRNA and mRNA to effectively capture the complex dynamics of gene silencing. On multiple internal datasets, siRNADiscovery achieves state-of-the-art performance. Significantly, siRNADiscovery also outperforms existing methodologies in in vitro studies and on an externally validated dataset. Additionally, we develop a new data-splitting methodology that addresses the data leakage issue, a frequently overlooked problem in previous studies, ensuring the robustness and stability of our model under various experimental settings. Through rigorous testing, siRNADiscovery has demonstrated remarkable predictive accuracy and robustness, making significant contributions to the field of gene silencing. Furthermore, our approach to redefining data-splitting standards aims to set new benchmarks for future research in the domain of predictive biological modeling for siRNA. [ABSTRACT FROM AUTHOR]

BackgroundNuclear reactor structural material activation analysis is an important part of nuclear reactor radiation safety analysis. The traditional cell-based activation analysis method can not describe the non-uniform spatial activation effect inside the structural material cells, resulting in a large deviation between the activation source and the dose results, so it is necessary to carry out the high-resolution activation analysis method for nuclear reactor structural materials.PurposeThis study aims to develop a high-resolution activation analysis method for nuclear reactor structural materials based on mesh activation analysis, and establish an integrated mesh-based activation analysis code (MCFisMA).MethodsFirst of all, based on Rigorous 2-step method (R2S), Monte Carlo particle transport program MCNP and activation calculation program FISPACT, the mesh-based activation method was used to divide the structural material model into fine spatial mesh, and the activation calculation of each mesh was carried out in turn to realize the precise activation calculation analysis of nuclear reactor structural materials. Then, the MCFisMA was implemented by using the neutron transport method, material activation method and activation-induced dose coupled together. In addition, NUREG/CR-6115 PWR model was used to study the application of high-resolution activation analysis of structural materials.Results & ConclusionsThe correctness and reliability of the proposed method and MCFisMA program is verified with the ITER shutdown dose rate (SDR) benchmark. The method presented in this paper has theoretical significance for improving the calculation accuracy of the activation source term and decay gamma dose field of nuclear reactor structural materials. It provides theoretical and data support for reactor shielding design, overhaul and refueling plan and decommissioning strategy.

The lightweight design of object detection models plays an important role in environments with limited computing resources and storage space. To further compress the size of the object detection model and improve its detection accuracy, a higher performance lightweight object detection model named Lite-YOLOX is proposed, which improves the structure of the feature pyramid, the structure of the decoupling head, and the loss function based on the YOLOX-Tiny model. Firstly, to further compress the size of the original model, the structure of the feature pyramid and decoupled head are redesigned to make the neck and head parts of the model lighter. Then, to improve the detection accuracy of the model, the EIoU loss function which is more sensitive to the position of the ground truth box is designed to optimize the proposed model. Finally, the validation experiments are performed on the Pascal VOC and safety helmet wearing dataset. The experimental results show that compared with YOLOX-Tiny, Lite-YOLOX reduces the parameters by 40%, the floating point of operations by 37.5%, and the mAP50 increases by 3.2 and 3.1 percentage points. On the NVIDIA Jetson Xavier NX, the frames per second (FPS) is increased from 51 to 59, and the real-time performance is significantly improved. [ABSTRACT FROM AUTHOR]

Ag/tungsten disulfide (WS2)–polyvinylpyrrolidone (PVP)/Cu memristors based on monolayer WS2 nanosheets and polyvinylpyrrolidone (PVP) nanocomposites were fabricated, and the influence of PVP content on the switching behaviors was investigated. The results indicate that the WS2–PVP based memristors show write-once read-many times (WORM) memory behavior. Remarkable resistive switching results such as a low operating voltage (VSET < 1 V), a high switching ratio (>103), good endurance (>100 cycles), and data retention time (>200 s) are obtained. With the increase in the PVP content, the device VSET gradually increases, and the switching ratio first slightly increases and then remarkably decreases. The double logarithm I–V curves verify that the switching mechanism of the devices is the trap-controlled space charge limited current mechanism, which is explained with the energy band diagram. [ABSTRACT FROM AUTHOR]