Network Key Equipment Design Adapted to Underground Pipe Rack and Urban Energy Metering

Traditional energy automatic metering management scheme has solved the two level problem of monitoring and application, but there is shortcoming at the transport level. Along with the advancement of industrialization and informationization, a variety of sensing devices, interconnection and unified transport demand is becoming more and more urgent. Based on FPGA platform and multichannel parallel MBUS communication mode, fusion NB - IoT technology, this paper puts forward a suitable for underground pipe rack and the network of urban energy metering design method of key equipment, it is a good way to adapt to a variety of field and meet the requirements of multiple class sensor interconnectivity unified transmission, has the very good application value

Distributed secondary control of microgrids with unknown disturbances and non-linear dynamics

In this paper, the voltage and frequency regulation of microgrid with unknown disturbances and non-linear dynamics was studied. The disturbance observer was designed and the sliding mode control (SMC) method was used to realize the secondary regulation of voltage and frequency. First, a distributed secondary control protocol was designed to reduce the communication burden between generators and to solve voltage and frequency deviations. Second, a consensus protocol for secondary control of voltage and frequency was designed, based on the idea of multi-agent consensus, to indirectly ensure that the voltage and frequency to be adjusted reach the reference values when the consensus is realized. In addition, considering unknown disturbances in the microgrid, a sliding mode control strategy, based on a disturbance observer, was designed to overcome the influence of disturbances and to reduce chatter. This SMC scheme ensured finite time accessibility of the sliding mode surface. This design provides sufficient conditions for voltage and frequency regulation. The effectiveness of this design scheme was verified through simulation

MMBench: Is Your Multi-modal Model an All-around Player?

Large vision-language models have recently achieved remarkable progress, exhibiting great perception and reasoning abilities concerning visual information. However, how to effectively evaluate these large vision-language models remains a major obstacle, hindering future model development. Traditional benchmarks like VQAv2 or COCO Caption provide quantitative performance measurements but suffer from a lack of fine-grained ability assessment and non-robust evaluation metrics. Recent subjective benchmarks, such as OwlEval, offer comprehensive evaluations of a model's abilities by incorporating human labor, but they are not scalable and display significant bias. In response to these challenges, we propose MMBench, a novel multi-modality benchmark. MMBench methodically develops a comprehensive evaluation pipeline, primarily comprised of two elements. The first element is a meticulously curated dataset that surpasses existing similar benchmarks in terms of the number and variety of evaluation questions and abilities. The second element introduces a novel CircularEval strategy and incorporates the use of ChatGPT. This implementation is designed to convert free-form predictions into pre-defined choices, thereby facilitating a more robust evaluation of the model's predictions. MMBench is a systematically-designed objective benchmark for robustly evaluating the various abilities of vision-language models. We hope MMBench will assist the research community in better evaluating their models and encourage future advancements in this domain. Project page: https://opencompass.org.cn/mmbench

Detection and analysis of human papillomavirus (HPV) DNA in breast cancer patients by an effective method of HPV capture

Despite an increase in the number of molecular epidemiological studies conducted in recent years to evaluate the association between human papillomavirus (HPV) and the risk of breast carcinoma, these studies remain inconclusive. Here we aim to detect HPV DNA in various tissues from patients with breast carcinoma using the method of HPV capture combined with massive paralleled sequencing (MPS). To validate the confidence of our methods, 15 cervical cancer samples were tested by PCR and the new method. Results showed that there was 100% consistence between the two methods.DNA from peripheral blood, tumor tissue, adjacent lymph nodes and adjacent normal tissue were collected from seven malignant breast cancer patients, and HPV type 16(HPV16) was detected in 1/7, 1/7, 1/7and 1/7 of patients respectively. Peripheral blood, tumor tissue and adjacent normal tissue were also collected from two patients with benign breast tumor, and 1/2, 2/2 and 2/2 was detected to have HPV16 DNA respectively. MPS metrics including mapping ratio, coverage, depth and SNVs were provided to characterize HPV in samples. The average coverage was 69% and 61.2% for malignant and benign samples respectively. 126 SNVs were identified in all 9 samples. The maximum number of SNVs was located in the gene of E2 and E4 among all samples. Our study not only provided an efficient method to capture HPV DNA, but detected the SNVS, coverage, SNV type and depth. The finding has provided further clue of association between HPV16 and breast cancer

Mussel-Inspired Fabrication of PDA@PAN Electrospun Nanofibrous Membrane for Oil-in-Water Emulsion Separation

Emulsified oily wastewater threatens human health seriously, and traditional technologies are unable to separate emulsion containing small sized oil droplets. Currently, oil–water emulsions are usually separated by special wettability membranes, and researchers are devoted to developing membranes with excellent antifouling performance and high permeability. Herein, a novel, simple and low-cost method has been proposed for the separation of emulsion containing surfactants. Polyacrylonitrile (PAN) nanofibers were prepared via electrospinning and then coated by polydopamine (PDA) by using self-polymerization reactions in aqueous solutions. The morphology, structure and oil-in-water emulsion separation properties of the as-prepared PDA@PAN nanofibrous membrane were tested. The results show that PDA@PAN nanofibrous membrane has superhydrophilicity and almost no adhesion to crude oil in water, which exhibits excellent oil–water separation ability. The permeability and separation efficiency of n-hexane/water emulsion are up to 1570 Lm−2 h−1 bar−1 and 96.1%, respectively. Furthermore, after 10 cycles of separation, the permeability and separation efficiency values do not decrease significantly, indicating its good recycling performance. This research develops a new method for preparing oil–water separation membrane, which can be used for efficient oil-in-water emulsion separation

Comparative Study on Crack Initiation and Propagation of Glass under Thermal Loading

This paper explores the fracture process based on finite element simulation. Both probabilistic and deterministic methods are employed to model crack initiation, and several commonly used criteria are utilized to predict crack growth. It is concluded that the criteria of maximum tensile stress, maximum normal stress, and maximum Mises stress, as well as the Coulomb-Mohr criterion are able to predict the initiation of the first crack. The mixed-mode criteria based on the stress intensity factor (SIF), energy release rate, and the maximum principal stress, as well as the SIF-based maximum circumferential stress criterion are suitable to predict the crack propagation

Cooperative Neuro-Adaptive Fault-Tolerant Tracking Control for a Train Platoon Under Actuator Saturation

This study investigates the cooperative fault-tolerant tracking control problem of a train platoon in the presence of actuator failure, saturation limits, and unknown operating resistance. To construct a cooperative tracking control scheme, the proposed distributed controller is designed according to adjacent trains’ state information, including position, velocity, and acceleration. Radial basis function neural networks are introduced to tackle uncertain operational resistance, and the weight values of the neural network are updated online. Actuator failures with partial loss of effectiveness are estimated and compensated using adaptive methods. Another neuro-adaptive fault-tolerant control law is proposed to further solve the actuator saturation problem. The developed tracking controller remains fault-tolerant even under saturation limits while ensuring the stability of the closed-loop system. It is demonstrated using Lyapunov stability analysis that uniform ultimate boundedness is guaranteed for all signals of the closed-loop system. Numerical simulation results illustrate the effectiveness of the proposed control scheme

Optimization of Classification Track Assignment Considering Block Sequence at Train Marshaling Yard

An operational process at train marshaling yard is considered in this study. The inbound trains are decoupled and disassembled into individual railcars, which are then moved to a series of classification tracks, forming outbound trains after being assembled and coupled. We focus on the allocation plan of the classification tracks. Given are the disassembling and assembling sequence, the railcars connection plan, and a number of classification tracks. Output is the assignment of the railcars to the classification tracks. An integer programming model is proposed, aimed at reducing the number of coupling operations, as well as the number of dirty tracks which is related to the rehumping operation, and the order of the railcars on the outbound train must satisfy the block sequence. Tabu algorithm is designed to solve the problem, and the model is also tested by CPLEX in comparison. A numerical experiment based on a real-world case is analyzed, and the result can be reached within a reasonable amount of time. We also discussed a number of factors that may affect the track assignment and gave suggestions for the real-world case