控制营养状态(CONUT)评分与脑出血后临床结局相关性研究

研究CONUT评分所反映的营养状态与脑出血后临床结局的关联。方法 选择我院在2019至2023年之间收治的脑出血住院患者。回顾性的收集病例表一般信息采用斯皮尔曼等级相关系数、多变量logistic分析确定各COUNT评分及其他指标与患者3个月预后结局的相关性。结果 本次研究共纳入576名患者,多变量 logistic 分析显示,年龄(OR 1.08,95% CI 1.06–1.10,P < 0.001),入院时NIHSS分数(OR 1.21,95% CI 1.17–1.25,P < 0.001),初始血肿量(OR 1.03,95% CI 1.01–1.05,P = 0.002),CONUT评分(OR 1.29,95% CI 1.11–1.49,P = 0.001)与不良结局独立相关。即使在添加吸入性肺炎和血肿扩张等指标后,较高的CONUT评分仍与不良结局相关(OR 1.28,95% CI 1.09–1.49,P = 0.002)。结论 CONUT评分与脑出血患者不良结局相关,可被认为是预测脑出血患者预后更简单、更客观的营养指标。营养不良与脑出血患者不良预后呈正相关。

Studies of an event-building algorithm of the readout system for the twin TPCs in HFRS

The High-energy Fragment Separator(HFRS),which is currently under construction,is a leading international radioactive beam device.Multiple sets of position-sensitive twin time projection chamber(TPC)detectors are distributed on HFRS for particle identification and beam monitoring.The twin TPCs'readout electronics system operates in a trigger-less mode due to its high counting rate,leading to a challenge of handling large amounts of data.To address this problem,we introduced an event-building algorithm.This algorithm employs a hierarchical processing strategy to compress data during transmission and aggregation.In addition,it reconstructs twin TPCs'events online and stores only the reconstructed particle information,which significantly reduces the burden on data transmission and storage resources.Simulation studies demonstrated that the algorithm accurately matches twin TPCs'events and reduces more than 98%of the data volume at a counting rate of 500 kHz/channel.

Deep Learning Based Efficient Crowd Counting System

Estimation of crowd count is becoming crucial nowadays,as it can help in security surveillance,crowd monitoring,and management for different events.It is challenging to determine the approximate crowd size from an image of the crowd’s density.Therefore in this research study,we proposed a multi-headed convolutional neural network architecture-based model for crowd counting,where we divided our proposed model into two main components:(i)the convolutional neural network,which extracts the feature across the whole image that is given to it as an input,and(ii)the multi-headed layers,which make it easier to evaluate density maps to estimate the number of people in the input image and determine their number in the crowd.We employed the available public benchmark crowd-counting datasets UCF CC 50 and ShanghaiTech parts A and B for model training and testing to validate the model’s performance.To analyze the results,we used two metrics Mean Absolute Error(MAE)and Mean Square Error(MSE),and compared the results of the proposed systems with the state-of-art models of crowd counting.The results show the superiority of the proposed system.

Lightweight Res-Connection Multi-Branch Network for Highly Accurate Crowd Counting and Localization

Crowd counting is a promising hotspot of computer vision involving crowd intelligence analysis,achieving tremendous success recently with the development of deep learning.However,there have been stillmany challenges including crowd multi-scale variations and high network complexity,etc.To tackle these issues,a lightweight Resconnection multi-branch network(LRMBNet)for highly accurate crowd counting and localization is proposed.Specifically,using improved ShuffleNet V2 as the backbone,a lightweight shallow extractor has been designed by employing the channel compression mechanism to reduce enormously the number of network parameters.A light multi-branch structure with different expansion rate convolutions is demonstrated to extract multi-scale features and enlarged receptive fields,where the information transmission and fusion of diverse scale features is enhanced via residual concatenation.In addition,a compound loss function is introduced for training themethod to improve global context information correlation.The proposed method is evaluated on the SHHA,SHHB,UCF-QNRF and UCF_CC_50 public datasets.The accuracy is better than those of many advanced approaches,while the number of parameters is smaller.The experimental results show that the proposed method achieves a good tradeoff between the complexity and accuracy of crowd counting,indicating a lightweight and high-precision method for crowd counting.

A multi-source information fusion layer counting method for penetration fuze based on TCN-LSTM

When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.

Predictive value of preoperative routine examination for the prognosis of patients with pT2N0M0 or pT3N0M0 colorectal cancer

BACKGROUND In recent years,the incidence of colorectal cancer(CRC)has been increasing.With the popularization of endoscopic technology,a number of early CRC has been diagnosed.However,despite current treatment methods,some patients with early CRC still experience postoperative recurrence and metastasis.AIM To search for indicators associated with early CRC recurrence and metastasis to identify high-risk populations.METHODS A total of 513 patients with pT2N0M0 or pT3N0M0 CRC were retrospectively enrolled in this study.Results of blood routine test,liver and kidney function tests and tumor markers were collected before surgery.Patients were followed up through disease-specific database and telephone interviews.Tumor recurrence,metastasis or death were used as the end point of study to find the risk factors and predictive value related to early CRC recurrence and metastasis.RESULTS We comprehensively compared the predictive value of preoperative blood routine,blood biochemistry and tumor markers for disease-free survival(DFS)and overall survival(OS)of CRC.Cox multivariate analysis demonstrated that low platelet count was significantly associated with poor DFS[hazard ratio(HR)=0.995,95% confidence interval(CI):0.991-0.999,P=0.015],while serum carcinoembryonic antigen(CEA)level(HR=1.008,95%CI:1.001-1.016,P=0.027)and serum total cholesterol level(HR=1.538,95%CI:1.026-2.305,P=0.037)were independent risk factors for OS.The cutoff value of serum CEA level for predicting OS was 2.74 ng/mL.Although the OS of CRC patients with serum CEA higher than the cutoff value was worse than those with lower CEA level,the difference between the two groups was not statistically significant(P=0.075).CONCLUSION For patients with T2N0M0 or T3N0M0 CRC,preoperative platelet count was a protective factor for DFS,while serum CEA level was an independent risk factor for OS.Given that these measures are easier to detect and more acceptable to patients,they may have broader applications.

Clinical comprehensive treatment protocol for managing diabetic foot ulcers:A retrospective cohort study

BACKGROUND Diabetic foot ulcers(DFUs)are a common complication of diabetes,often leading to severe infections,amputations,and reduced quality of life.The current standard treatment protocols for DFUs have limitations in promoting efficient wound healing and preventing complications.A comprehensive treatment approach targeting multiple aspects of wound care may offer improved outcomes for patients with DFUs.The hypothesis of this study is that a comprehensive treatment protocol for DFUs will result in faster wound healing,reduced amputation rates,and improved overall patient outcomes compared to standard treatment protocols.AIM To compare the efficacy and safety of a comprehensive treatment protocol for DFUs with those of the standard treatment protocol.METHODS This retrospective study included 62 patients with DFUs,enrolled between January 2022 and January 2024,randomly assigned to the experimental(n=32)or control(n=30)group.The experimental group received a comprehensive treatment comprising blood circulation improvement,debridement,vacuum sealing drainage,recombinant human epidermal growth factor and anti-inflammatory dressing,and skin grafting.The control group received standard treatment,which included wound cleaning and dressing,antibiotics administration,and surgical debridement or amputation,if necessary.Time taken to reduce the white blood cell count,number of dressing changes,wound healing rate and time,and amputation rate were assessed.RESULTS The experimental group exhibited significantly better outcomes than those of the control group in terms of the wound healing rate,wound healing time,and amputation rate.Additionally,the comprehensive treatment protocol was safe and well tolerated by the patients.CONCLUSION Comprehensive treatment for DFUs is more effective than standard treatment,promoting granulation tissue growth,shortening hospitalization time,reducing pain and amputation rate,improving wound healing,and enhancing quality of life.

Effect of bivalirudin on myocardial microcirculation and adverse events after interventional therapy in older patients with acute coronary syndrome

BACKGROUND Bivalirudin,a direct thrombin inhibitor,is used in anticoagulation therapies as a substitute for heparin,especially during cardiovascular procedures such as percutaneous coronary intervention.AIM To explore the effect of bivalirudin on myocardial microcirculation following an intervention and its influence on adverse cardiac events in elderly patients with acute coronary syndrome(ACS).METHODS In total,165 patients diagnosed with acute myocardial at our hospital between June 2020 and June 2022 were enrolled in this study.From June 2020 to June 2022,elderly patients with ACS with complete data were selected and treated with interventional therapy.The study cohort was randomly divided into a study group(n=80,administered bivalirudin)and a control group(n=85,administered unfractionated heparin).Over a 6-mo follow-up period,differences in emergency processing times,including coronary intervention,cardiac function indicators,occurrence of cardiovascular events,and recurrence rates,were analyzed.RESULTS Significant differences were observed between the study cohorts,with the observation group showing shorter emergency process times across all stages:Emergency classification;diagnostic testing;implementation of coronary intervention;and conclusion of emergency treatment(P<0.05).Furthermore,the left ventricular ejection fraction in the observation group was significantly higher(P<0.05),and the creatine kinase-MB and New York Heart Association scores were CONCLUSION In elderly patients receiving interventional therapy for ACS,bivalirudin administration led to increased activated clotting time achievement rates,enhanced myocardial reperfusion,and reduced incidence of bleeding complications and adverse cardiac events.

Predictive value of thrombocytopenia for bloodstream infection in patients with sepsis and septic shock

BACKGROUND Thrombocytopenia is common in patients with sepsis and septic shock.AIM To analyse the decrease in the number of platelets for predicting bloodstream infection in patients with sepsis and septic shock in the intensive care unit.METHODS A retrospective analysis of patients admitted with sepsis and septic shock in Xingtai People Hospital was revisited.Patient population characteristics and laboratory data were collected for analysis.RESULTS The study group consisted of 85(39%)inpatients with bloodstream infection,and the control group consisted of 133(61%)with negative results or contamination.The percentage decline in platelet counts(PPCs)in patients positive for pathogens[57.1(41.3-74.6)]was distinctly higher than that in the control group[18.2(5.1–43.1)](P<0.001),whereas the PPCs were not significantly different among those with gram-positive bacteraemia,gram-negative bacteraemia,and fungal infection.Using receiver operating characteristic curves,the area under the curve of the platelet drop rate was 0.839(95%CI:0.783-0.895).CONCLUSION The percentage decline in platelet counts is sensitive in predicting bloodstream infection in patients with sepsis and septic shock.However,it cannot identify gram-positive bacteraemia,gram-negative bacteraemia,and fungal infection.

Diagnostic significance of complete blood cell count and hemogramderived markers for neonatal sepsis at Southwest Public Hospitals,Ethiopia

BACKGROUND Neonatal sepsis is defined as an infection-related condition characterized by signs and symptoms of bacteremia within the first month of life.It is the leading cause of mortality and morbidity among newborns.While several studies have been conducted in other parts of world to assess the usefulness of complete blood count parameters and hemogram-derived markers as early screening tools for neonatal sepsis,the associations between sepsis and its complications with these blood parameters are still being investigated in our setting and are not yet part of routine practice.AIM To evaluate the diagnostic significance of complete blood cell count hemogramderived novel markers for neonatal sepsis among neonates attending public hospitals in the southwest region of Oromia,Ethiopia,through a case control study.METHODS A case control study was conducted from October 2021 to October 2023 Sociodemographic,clinical history,and laboratory test results data were collected using structured questionnaires.The collected data were entered into Epi-data 3.1 version and exported to SPSS-25 for analysis.Chi-square,independent sample ttest,and receiver operator characteristics curve of curve were used for analysis.A P-value of less than 0.05 was considered statistically significant.RESULTS In this study,significant increases were observed in the following values in the case group compared to the control group:In white blood cell(WBC)count,neutrophils,monocyte,mean platelet volume(MPV),neutrophils to lymphocyte ratio,monocyte to lymphocyte ratio(MLR),red blood cell width to platelet count ratio(RPR),red blood width coefficient variation,MPV to RPR,and platelet to lymphocyte ratio.Regarding MLR,a cut-off value of≥0.26 was found,with a sensitivity of 68%,a specificity of 95%,a positive predictive value(PPV)of 93.2%,and a negative predictive value(NPV)of 74.8%.The area under the curve(AUC)was 0.828(P<0.001).For WBC,a cutoff value of≥11.42 was identified,with a sensitivity of 55%,a specificity of 89%,a PPV of 83.3%,and a NPV of 66.4%.The AUC was 0.81(P<0.001).Neutrophils had a sensitivity of 67%,a specificity of 81%,a PPV of 77.9%,and a NPV of 71.1%.The AUC was 0.801,with a cut-off value of≥6.76(P=0.001).These results indicate that they were excellent predictors of neonatal sepsis diagnosis.CONCLUSION The findings of our study suggest that certain hematological parameters and hemogram-derived markers may have a potential role in the diagnosis of neonatal sepsis.

Single Tooth Segmentation on Panoramic X-Rays Using End-to-End Deep Neural Networks

In dentistry, panoramic X-ray images are extensively used by dentists for tooth structure analysis and disease diagnosis. However, the manual analysis of these images is time-consuming and prone to misdiagnosis or overlooked. While deep learning techniques have been employed to segment teeth in panoramic X-ray images, accurate segmentation of individual teeth remains an underexplored area. In this study, we propose an end-to-end deep learning method that effectively addresses this challenge by employing an improved combinatorial loss function to separate the boundaries of adjacent teeth, enabling precise segmentation of individual teeth in panoramic X-ray images. We validate the feasibility of our approach using a challenging dataset. By training our segmentation network on 115 panoramic X-ray images, we achieve an intersection over union (IoU) of 86.56% for tooth segmentation and an accuracy of 65.52% in tooth counting on 87 test set images. Experimental results demonstrate the significant improvement of our proposed method in single tooth segmentation compared to existing methods.

Improving landscape Characteristics in Port Said’s El Sallam Garden via Observational and Space Syntax Analysis

This paper provides a comprehensive examination of El Sallam Garden in Port Said City,concentrating on its landscape characteristics and potential for design enhancement.This study looks at how space syntax can be used to assess the impact of a tree planting design’s spatial configuration on an urban park’s visual fields.Trees play an important role in determining the spatial characteristics of an outdoor space.According to space syntax theory,an urban area is a collection of connected spaces that can be represented by a matrix of quantitative properties known as syntactic measures.Computer simulations can be used to measure the quantitative properties of these matrices.This study uses space syntax techniques to assess how tree configurations and garden area which can affect the social structures of small-scale gardens in Port Said.It also looks at how these techniques can be used to predict the social structures of four garden zones in El Sallam Garden.The study includes an observational and space syntax study through comparative analysis of four garden zones in El Sallam garden.The results of the study show that the area and planting configurations of the garden had a significant effect on the syntactic social and visual measures of the urban garden.The conclusions and recommendations can be a useful tool for landscape architects,urban planners,and legislators who want to enhance public areas and encourage social interaction in urban settings.

The Planck Constant and Its Relation to the Compton Frequency

The Planck constant is considered one of the most important universal constants of physics, and despite all we know much about it, the physical nature of it has not been fully understood. Further investigation and new perspectives on the Planck constant should therefore be of interest. We demonstrate that the Planck constant also can be directly linked to the Compton frequency of one, which again is divided by the Compton frequency in one kg. If this is right, it means also the Planck constant is linked to quantization of matter, not only energy. However, as we will show the frequency of one when expressed in relation to kg will be observational time dependent. This means the missing mass gap surprisingly both is equal to the Planck mass, which is larger than any known particle and also it is linked to a very small mass that again is equal to what has been suggested as the photon mass in the existing literature. This new view could be an important step forward in understanding the physical nature of the Planck constant as well as the mass gap and even the rest mass of a photon.

Efficient People Detection with Infrared Images

This work focuses on the problem of monitoring the coastline, which in Portugal’s case means monitoring 3007 kilometers, including 1793 maritime borders with the Atlantic Ocean to the south and west. The human burden on the coast becomes a problem, both because erosion makes the cliffs unstable and because pollution increases, making the fragile dune ecosystem difficult to preserve. It is becoming necessary to increase the control of access to beaches, even if it is not a popular measure for internal and external tourism. The methodology described can also be used to monitor maritime borders. The use of images acquired in the infrared range guarantees active surveillance both day and night, the main objective being to mimic the infrared cameras already installed in some critical areas along the coastline. Using a series of infrared photographs taken at low angles with a modified camera and appropriate filter, a recent deep learning algorithm with the right training can simultaneously detect and count whole people at close range and people almost completely submerged in the water, including partially visible targets, achieving a performance with F1 score of 0.945, with 97% of targets correctly identified. This implementation is possible with ordinary laptop computers and could contribute to more frequent and more extensive coverage in beach/border surveillance, using infrared cameras at regular intervals. It can be partially automated to send alerts to the authorities and/or the nearest lifeguards, thus increasing monitoring without relying on human resources.

Counting of alpha particle tracks on imaging plate based on a convolutional neural network

Imaging plates are widely used to detect alpha particles to track information,and the number of alpha particle tracks is affected by the overlapping and fading effects of the track information.In this study,an experiment and a simulation were used to calibrate the efficiency parameter of an imaging plate,which was used to calculate the grayscale.Images were created by using grayscale,which trained the convolutional neural network to count the alpha tracks.The results demonstrated that the trained convolutional neural network can evaluate the alpha track counts based on the source and background images with a wider linear range,which was unaffected by the overlapping effect.The alpha track counts were unaffected by the fading effect within 60 min,where the calibrated formula for the fading effect was analyzed for 132.7 min.The detection efficiency of the trained convolutional neural network for inhomogeneous ^(241)Am sources(2π emission)was 0.6050±0.0399,whereas the efficiency curve of the photo-stimulated luminescence method was lower than that of the trained convolutional neural network.

基于频繁项统计的锂动力电池SOH估计

快速而精确的估计电池健康状态(SOH)是保证动力电池系统安全的基础。纯电动汽车在运行过程中,传统的估计方法难以利用车载有限的计算资源,在线构建精确的SOH估计模型。针对该问题,提出一种利用短时监测数据在线构建电池健康指标(HI)的特征提取方法。该方法将不同电压区间内积累的电量看作频繁项,利用Lossy Counting算法构建概要数据结构对频繁项分布进行统计,基于频繁项分布规律的变化对电池健康状态进行表征。仿真和实验结果表明,该方法能利用车载计算资源,以较小的时间和空间复杂度提取电池健康状态指标。

Study on Licker-In and Flat Speeds of Carding Machine and Its Effects on Quality of Cotton Spinning Process

Spinning has a significant influence on all textile processes. Combinations of all the capital equipment display the process’ critical condition. By transforming unprocessed fibers into carded sliver and yarn, the carding machine serves a critical role in the textile industry. The carding machine’s licker-in and flat speeds are crucial operational factors that have a big influence on the finished goods’ quality. The purpose of this study is to examine the link between licker-in and flat speeds and how they affect the yarn and carded sliver quality. A thorough experimental examination on a carding machine was carried out to accomplish this. The carded sliver and yarn produced after experimenting with different licker-in and flat speed combinations were assessed for important quality factors including evenness, strength, and flaws. To account for changes in material qualities and machine settings, the study also took into consideration the impact of various fiber kinds and processing circumstances. The findings of the investigation showed a direct relationship between the quality of the carded sliver and yarn and the licker-in and flat speeds. Within a limited range, greater licker-in speeds were shown to increase carding efficiency and decrease fiber tangling. On the other hand, extremely high speeds led to more fiber breakage and neps. Higher flat speeds, on the other hand, helped to enhance fiber alignment, which increased the evenness and strength of the carded sliver and yarn. Additionally, it was discovered that the ideal blend of licker-in and flat rates varied based on the fiber type and processing circumstances. When being carded, various fibers displayed distinctive behaviors that necessitated adjusting the operating settings in order to provide the necessary quality results. The study also determined the crucial speed ratios between the licker-in and flat speeds that reduced fiber breakage and increased the caliber of the finished goods. The results of this study offer useful information for textile producers and process engineers to improve the quality of carded sliver and yarn while maximizing the performance of carding machines. Operators may choose machine settings and parameter adjustments wisely by knowing the impacts of licker-in and flat speeds, which will increase textile industry efficiency, productivity, and product quality.

RPNet: Rice plant counting after tillering stage based on plant attention and multiple supervision network

Rice is a major food crop and is planted worldwide. Climatic deterioration, population growth, farmland shrinkage, and other factors have necessitated the application of cutting-edge technology to achieve accurate and efficient rice production. In this study, we mainly focus on the precise counting of rice plants in paddy field and design a novel deep learning network, RPNet, consisting of four modules: feature encoder, attention block, initial density map generator, and attention map generator. Additionally, we propose a novel loss function called RPloss. This loss function considers the magnitude relationship between different sub-loss functions and ensures the validity of the designed network. To verify the proposed method, we conducted experiments on our recently presented URC dataset, which is an unmanned aerial vehicle dataset that is quite challenged at counting rice plants. For experimental comparison, we chose some popular or recently proposed counting methods, namely MCNN, CSRNet, SANet, TasselNetV2, and FIDTM. In the experiment, the mean absolute error(MAE), root mean squared error(RMSE), relative MAE(rMAE) and relative RMSE(rRMSE) of the proposed RPNet were 8.3, 11.2, 1.2% and 1.6%, respectively,for the URC dataset. RPNet surpasses state-of-the-art methods in plant counting. To verify the universality of the proposed method, we conducted experiments on the well-know MTC and WED datasets. The final results on these datasets showed that our network achieved the best results compared with excellent previous approaches. The experiments showed that the proposed RPNet can be utilized to count rice plants in paddy fields and replace traditional methods.

Distribution of chromophytic phytoplankton in the Western Subarctic Gyre of Pacific Ocean revealed by morphological observation and rbc L gene sequences

Western Subarctic Gyre(WSG),which possesses distinctive differences in oceanographic and biogeochemical processes,is situated in the northwest subarctic Pacific.The WSG is characterized by high nutrient and low chlorophyll.We carried out a field investigation in this area in summer 2020 and performed microscopic observation,cytometric counting,and RuBisCO large subunit(rbc L)gene analysis to understand the community structure and spatial distribution of chromophytic phytoplankton better.Microscopic method revealed that total phytoplankton(>10μm,including Bacillariophyta,Dinoflagellata,Ochrophyta,and Chlorophyta)abundances ranged(0.6×10^(3))-(167.4×10^(3))cells/L with an increasing trend from south to north.Dinoflagellates and Pennatae diatoms dominated the phytoplankton assemblages in the southern and northern stations,respectively.Major chromophytic phytoplankton groups derived from rbc L genes included Haptophyta,Ochrophyta,Bacillariophyta,as well as rarely occurring groups,such as Xanthophyta,Cyanobacteria,Dinoflagellata,Rhodophyta,and Cryptophyta.At the phylum level,Haptophyta was the most abundant phylum,accounting for approximately 30.80%of the total obtained operational taxonomic units in all samples.Ochrophyta and Bacillariophyta were the second and third most abundant phylum,and their relative abundance was 20.26% and 19.60%,respectively.Further,redundancy analysis showed that high proportion of diatoms(e.g.,microscopic and rbc L methods)was positively correlated with nutrients(e.g.,dissolved inorganic nitrogen(DIN),dissolved inorganic phosphorous,and dissolved silicate(DSi))and negatively correlated with temperature and salinity.The proportion of Ochrophyta,Rhodophyta,and Cyanobateria identified by rbc L genes was positively correlated with salinity and temperature and showed negative correlation to nutrients.This work is the first molecular study of phytoplankton accomplished in the WSG,and our results show some discrepancies between morphological observation and rbc L gene sequences,which highlight the necessity of combining the microscopic and molecular methods to reveal the diversity of phytoplankton in marine environment.

Large area^(3)He tube array detector with modular design for multi‑physics instrument at CSNS

The multi-physics instrument(MPI)is the first user cooperative instrument at the China Spallation Neutron Source(CSNS).It was designed to explore the structures of complex materials at multiple scales based on the neutron total scattering technique.This imposes the requirements for the detector,including a high detection efficiency to reduce the measurement time and a large solid angle coverage to cover a wide range of momentum transfers.To satisfy these demands,a large-area array of 3He-filled linear position-sensitive detectors(LPSDs)was constructed,each with a diameter of 1 inch and pressure of 20 atm.It uses an orbicular layout of the detector and an eight-pack module design for the arrangement of 3He LPSDs,covering a range of scattering angles from 3°to 170°with a total detector area of approximately 7 m2.The detector works in air,which is separated from the vacuum environment to facilitate installation and maintenance.The characteristics of the MPI detector were investigated through Monte Carlo(MC)simulations using Geant4 and experimental measurements.The results suggest that the detectors are highly efficient in the wavelength range of the MPI,and an efficiency over 25%is achievable for above 0.1 A neutrons.A minimal position resolution of 6.4 mm full width at half maximum(FWHM)along the tube length was achieved at a working voltage of 2200 V,and a deviation below 2 mm between the real and measured positions was attained in the beam experiment.The detector module exhibited good consistency and an excellent counting rate capacity of up to 80 kHz,which satisfied the requirements of experiments with a high event rate.Observations of its operation over the past year have shown that the detector works steadily in sample experiments,which allows the MPI to serve the user program successfully.