A novel method for clustering cellular data to improve classification

Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.

Synthetic data as an investigative tool in hypertension and renal diseases research

There is a growing body of clinical research on the utility of synthetic data derivatives,an emerging research tool in medicine.In nephrology,clinicians can use machine learning and artificial intelligence as powerful aids in their clinical decision-making while also preserving patient privacy.This is especially important given the epidemiology of chronic kidney disease,renal oncology,and hypertension worldwide.However,there remains a need to create a framework for guidance regarding how to better utilize synthetic data as a practical application in this research.

Investigation on welding procedures for in-service pipelines

Some research results are given in this paper about burnthrough and hydrogen cracking with a flowing chamber and a loop. Many factors including plate thickness, running rate, heat input and so forth have been studied. By experiments it can be found that occurrence of hydrogen cracking can be effectively reduced by properly increasing heat input and using the tempering bead technique.

Study on the microstructure and hardness of in-service welded joint of X70 pipeline steel

Hydrogen induced cracking （HIC） is one of the main problems of in-service welding onto active pipeline. Microstructure and hardness of welded joint have a vital effect on hydrogen induced cracking. The microstructure and hardness of welded joint of XTO pipeline steel were studied using simulation in-service welding device. The results show that the main microstructures of in-service welded seam are grain boundary ferrite , intracrystalline acicular ferrite , as well as small amount of widmanztatten structure. The main microstructures of coarse grain heat-affected zone （CGHAZ） are coarse granular bainite, lath ferrite and martensite. Metastable phases such as martensite and lath ferrite are found in CGHAZ because of the too quick cooling velocity a＇nd the hardness of the CGHAZ is high.

FAILURE ANALYSIS AND INSPECTION OF CRACKING OF IN-SERVICE CATALYTIC REGENERATOR

The special subject 'research on life prediction technology of important in-service pressure' mainly analyzes the failure mechanism of large-sized important and criticalin-service pressure vessels under the action of working medium and investigates safety assessmentand life prediction technology with a view to enhance the operation reliability of in-servicepressure vessels in China. Based on a series of accident investigation and test & measuringresearch, the cause of cracking of catalytic regenerator is analyzed and the in-line non-destructiveexamination method and failure prevention measures for the cracking of catalytic regenerator areproposed.

基于re3data的中英科学数据仓储平台对比研究

以re3data为数据获取源,选取中英两国406个科学数据仓储为研究对象,从分布特征、责任类型、仓储许可、技术标准及质量标准等5个方面、11个指标对两国科学数据仓储的建设情况进行对比分析,试图为我国数据仓储的可持续发展提出建议:广泛联结国内外异质机构,推进多学科领域的交流与合作,有效扩充仓储许可权限与类型,优化技术标准的应用现况,提高元数据使用的灵活性。

Influence of cooling conditions on X70 pipeline steel in-service welding HAZ

The chamber device was designed and set up to simulate the in-service welding. The results show ： the t8/5 , t8/3 and inner wall peak temperature Tp decrease with the cooling rate increases. The welding energy is carried off by flowing medium, the cooling rate increases, and many unbalanced microstructures such as granular bainite, martensite and M-A generate ; it worsens the properties of HAZ. Under air-cooling, the cooling rate is slow, the austenite grain grows obviously, the lath ferrite crosses the whole austenite, and it causes the hardness value is also big. The change of HAZ width is not obvious with the increase of cooling rate; and burn-through is not susceptible to the cooling rate. The quench microstructures increase and the hydrogen does not outflow from the HAZ easily when increase the cooling rate, so the susceptibility of hydrogen cracking increases.

Non-destructive Testing Robots （NDTBOTs） for In-Service Storage Tank Inspection

Petrochemical storage tanks are generally inspected when the tank is offline mostly to assess the extent of underside corrosion on the tank floor. Emptying, cleaning and opening a tank for inspection take many months and are very expensive. Inspection costs can be reduced significantly by inserting robots through manholes on the tank roof to pertbrm non-destructive testing （NDT）. The challenge is to develop robots that can operate safely in explosive and hazardous environments and measure the thickness of floor plates using ultrasound sensors. This paper reports on the development of a small and inexpensive prototype robot （NDTBOT） which is designed to be intrinsically safe for zone zero operation. The robot ＂hops＂ across the floor to make measurements, without any external moving parts. The paper describes the design, experimental testing of the NDTBOT and presents results of steel plate thickness measurements made under water.

Numerical simulation of mechanical properties of straight pipe and elbow during in-service welding

Elbows are the most vulnerable parts in pipeline network systems. The residual stress for in-service welding repair has significant impacts on the mechanical properties of straight pipes and elbows. In this paper,the thermal elastic-plastic finite element method is employed to investigate the mechanical field during the in-service welding. The prediction of residual stress and deformation in the straight pipe and elbow is performed based on the validation of the numerical models. And the effects of the curvature radius and defects on the elbow are investigated. The results show that the residual stress distribution is uneven along various directions after welding. And the mechanical properties of the elbow decrease when the curvature radius is small. Compared to the intact elbow,the residual stress of the elbow with defects concentrates in the defective area. The depth of defect is the main factors affecting the mechanical properties of the elbow. A systematic analysis of the mechanical properties of straight pipes and elbows is proposed to provide guidance to the in-service welding.

Data Secure Storage Mechanism for IIoT Based on Blockchain

With the development of Industry 4.0 and big data technology,the Industrial Internet of Things(IIoT)is hampered by inherent issues such as privacy,security,and fault tolerance,which pose certain challenges to the rapid development of IIoT.Blockchain technology has immutability,decentralization,and autonomy,which can greatly improve the inherent defects of the IIoT.In the traditional blockchain,data is stored in a Merkle tree.As data continues to grow,the scale of proofs used to validate it grows,threatening the efficiency,security,and reliability of blockchain-based IIoT.Accordingly,this paper first analyzes the inefficiency of the traditional blockchain structure in verifying the integrity and correctness of data.To solve this problem,a new Vector Commitment(VC)structure,Partition Vector Commitment(PVC),is proposed by improving the traditional VC structure.Secondly,this paper uses PVC instead of the Merkle tree to store big data generated by IIoT.PVC can improve the efficiency of traditional VC in the process of commitment and opening.Finally,this paper uses PVC to build a blockchain-based IIoT data security storage mechanism and carries out a comparative analysis of experiments.This mechanism can greatly reduce communication loss and maximize the rational use of storage space,which is of great significance for maintaining the security and stability of blockchain-based IIoT.

Numerical simulation of stress and deformation of in-service welding onto gas pipeline

SYSWELD was used to simulate in-service welding process of gas pipeline of X70 pipeline steel. Welding thermal cycle, stress and deformation of in-service welded joint were studied. The results show that peak temperature of coarse grain heat-affected zone （CGHAZ） of in-service welding onto gas pipeline is the same with routine welding, but ts/5, ts/3 and ts/1 decrease at certain degree. For the zone near welded seam, axial stress and hoop stress in the inner pipe wall are compressive stress when welding source passes through the cross-section that is studied, but residual axial stress and residual hoop stress after welded are all tensile stress. Transient deformation and residual deformation are all convex deformation compared with the original pipe diameter size. Deformation achieves maximum when welding thermal source passes through the cross-section that is studied and then decreases during the cooling process after welding.

Hadoop-based secure storage solution for big data in cloud computing environment

In order to address the problems of the single encryption algorithm,such as low encryption efficiency and unreliable metadata for static data storage of big data platforms in the cloud computing environment,we propose a Hadoop based big data secure storage scheme.Firstly,in order to disperse the NameNode service from a single server to multiple servers,we combine HDFS federation and HDFS high-availability mechanisms,and use the Zookeeper distributed coordination mechanism to coordinate each node to achieve dual-channel storage.Then,we improve the ECC encryption algorithm for the encryption of ordinary data,and adopt a homomorphic encryption algorithm to encrypt data that needs to be calculated.To accelerate the encryption,we adopt the dualthread encryption mode.Finally,the HDFS control module is designed to combine the encryption algorithm with the storage model.Experimental results show that the proposed solution solves the problem of a single point of failure of metadata,performs well in terms of metadata reliability,and can realize the fault tolerance of the server.The improved encryption algorithm integrates the dual-channel storage mode,and the encryption storage efficiency improves by 27.6% on average.

Defect Detection Model Using Time Series Data Augmentation and Transformation

Time-series data provide important information in many fields,and their processing and analysis have been the focus of much research.However,detecting anomalies is very difficult due to data imbalance,temporal dependence,and noise.Therefore,methodologies for data augmentation and conversion of time series data into images for analysis have been studied.This paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to noise.The method of data augmentation is set as the addition of noise.It involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the model.In addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into images.It enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the data.For anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat maps.It allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies occur.The performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to images.Additionally,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training time.The proposed method can provide an important springboard for research in the field of anomaly detection using time series data.Besides,it helps solve problems such as analyzing complex patterns in data lightweight.

Influence of pipe parameters and heat input on thermal cycle of in-service welding onto active gas pipeline

The software of SYSWELD was used to build model and simulate thermal cycle of in-service welding onto active gas pipeline. Influence of pipe diameter, wall thickness and heat input on thermal cycle was studied. The results show that t8/5 , t8/3 and peak temperature of inner surface decrease when wall thickness increases from 5 mm to 12 mm. But t8/1 will increases with the increase of wall thickness and will decrease after the wall thickness is larger than 7 mm. Pipe diameter has little influence on thermal cycle and that influence can be ignored when pipe diameter is greater than 273 mm. t8/5 , t8/3 , t8/1 and peak temperature of inner surface will increase with the increase of heat input.

Enhanced prediction of anisotropic deformation behavior using machine learning with data augmentation

Mg alloys possess an inherent plastic anisotropy owing to the selective activation of deformation mechanisms depending on the loading condition.This characteristic results in a diverse range of flow curves that vary with a deformation condition.This study proposes a novel approach for accurately predicting an anisotropic deformation behavior of wrought Mg alloys using machine learning(ML)with data augmentation.The developed model combines four key strategies from data science:learning the entire flow curves,generative adversarial networks(GAN),algorithm-driven hyperparameter tuning,and gated recurrent unit(GRU)architecture.The proposed model,namely GAN-aided GRU,was extensively evaluated for various predictive scenarios,such as interpolation,extrapolation,and a limited dataset size.The model exhibited significant predictability and improved generalizability for estimating the anisotropic compressive behavior of ZK60 Mg alloys under 11 annealing conditions and for three loading directions.The GAN-aided GRU results were superior to those of previous ML models and constitutive equations.The superior performance was attributed to hyperparameter optimization,GAN-based data augmentation,and the inherent predictivity of the GRU for extrapolation.As a first attempt to employ ML techniques other than artificial neural networks,this study proposes a novel perspective on predicting the anisotropic deformation behaviors of wrought Mg alloys.

Effect of In-Service Training Program on the Practice of Healthcare Workers toward Malaria Prevention and Treatment Guidelines during Pregnancy in Health Facilities in Jowhar District, Somalia

Malaria is generally considered a major public health problem in Somalia. Providing early diagnosis and effective treatment is the key element of malaria control strategies in malaria-endemic countries, including Somalia. This required to advocate and ensure health worker’s adherence to the national malaria guidelines at all levels of health care service. A well-designed in-service training program may improve the level of health worker’s adherence to national malaria treatment guidelines, although results have been inconsistent. This is an interventional health facility-based pre and post comparative study aimed to assess the effect of an in-service training program on the practice of healthcare workers toward malaria prevention and treatment guidelines, during in pregnancy in health facilities in Jowhar district, Middle Shabelle region of Somalia. The study was implemented in three phases: pre-intervention phase, intervention phase and post-intervention phase. The sample size consisted of (n = 150) health workers who were selected from ten public health facilities using proportional to size sampling;the data collection adopted in this research is composed of a structured interview questionnaire and observational checklist. Data was analyzed through the application of descriptive statistical analysis that includes frequency and percentage and the Chi-square (x2) test was used to test the associations among variables using SPSS software version 25. The study showed that the level of health workers’ awareness of the national malaria guidelines in the treatment and prevention of malaria in pregnancy was found to be good before the intervention 89 (59.3%) and this proportion increased to 150 (100%) post-intervention of the training program. A significance difference has been observed between health workers’ awareness and their adherence to the malarial guidelines at pre-test and post-test with a p-value 0.000. The proportion of health workers who attended previous training on national malaria guidelines in the treatment and prevention of malaria in pregnancy increased from 46 (30.7%) at the pre-test to 150 (100%) after the post-test. A significant difference was observed in the training status among different categories of health worker and their adherence to the guidelines during the pre- and post-intervention of the training program, with a p-value of 0.000. The result showed that health workers were adhering to the guidelines at the pre-test 33 (22%), this increased after the post-test to 87 (58%). The knowledge of the need to adhere led to an increase in the adherence rate after the training program intervention. The study reveals that inadequate awareness was most reason for the non-adherence in the majority of the health workers as indicated by 89 (59.3%) at the pre-test and 56 (37.3%) in the post-test. However, difference was not significant between the availability of anti-malaria drugs in the facilities and the health workers’ adherence to the guidelines p-value 0.355 at the pretest and p-value 0.258 at post-test. The study concluded that the in-service training program significantly improved health workers’ knowledge and practice to the national malaria guidelines in the treatment, and prevention of malaria in pregnancy. The researcher recommends that the national malaria control programme (NMCP) of the Federal Ministry of Health should provide continuous regular in-service training to frontline healthcare workers at (facility and Community-based) to upgrade their skills and knowledge towards the malaria guidelines, disseminate job aids to the health facilities and undertake regular monitoring to ensure effective implementation of the national malaria treatment guidelines in the treatment and prevention of malaria in pregnancy in achieving desired proper case-management practices of malaria in pregnancy at all levels of health care service.

Reliability evaluation of IGBT power module on electric vehicle using big data

There are challenges to the reliability evaluation for insulated gate bipolar transistors(IGBT)on electric vehicles,such as junction temperature measurement,computational and storage resources.In this paper,a junction temperature estimation approach based on neural network without additional cost is proposed and the lifetime calculation for IGBT using electric vehicle big data is performed.The direct current(DC)voltage,operation current,switching frequency,negative thermal coefficient thermistor(NTC)temperature and IGBT lifetime are inputs.And the junction temperature(T_(j))is output.With the rain flow counting method,the classified irregular temperatures are brought into the life model for the failure cycles.The fatigue accumulation method is then used to calculate the IGBT lifetime.To solve the limited computational and storage resources of electric vehicle controllers,the operation of IGBT lifetime calculation is running on a big data platform.The lifetime is then transmitted wirelessly to electric vehicles as input for neural network.Thus the junction temperature of IGBT under long-term operating conditions can be accurately estimated.A test platform of the motor controller combined with the vehicle big data server is built for the IGBT accelerated aging test.Subsequently,the IGBT lifetime predictions are derived from the junction temperature estimation by the neural network method and the thermal network method.The experiment shows that the lifetime prediction based on a neural network with big data demonstrates a higher accuracy than that of the thermal network,which improves the reliability evaluation of system.

Detection of Turbulence Anomalies Using a Symbolic Classifier Algorithm in Airborne Quick Access Record(QAR)Data Analysis

As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The eddy dissipation rate(EDR)has been established as the standard metric for quantifying turbulence in civil aviation.This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder(QAR)data.The detection of atmospheric turbulence is approached as an anomaly detection problem.Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events.Moreover,comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available.In summary,the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data,comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms.This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.

Spatiotemporal deformation characteristics of Outang landslide and identification of triggering factors using data mining

Since the impoundment of Three Gorges Reservoir(TGR)in 2003,numerous slopes have experienced noticeable movement or destabilization owing to reservoir level changes and seasonal rainfall.One case is the Outang landslide,a large-scale and active landslide,on the south bank of the Yangtze River.The latest monitoring data and site investigations available are analyzed to establish spatial and temporal landslide deformation characteristics.Data mining technology,including the two-step clustering and Apriori algorithm,is then used to identify the dominant triggers of landslide movement.In the data mining process,the two-step clustering method clusters the candidate triggers and displacement rate into several groups,and the Apriori algorithm generates correlation criteria for the cause-and-effect.The analysis considers multiple locations of the landslide and incorporates two types of time scales:longterm deformation on a monthly basis and short-term deformation on a daily basis.This analysis shows that the deformations of the Outang landslide are driven by both rainfall and reservoir water while its deformation varies spatiotemporally mainly due to the difference in local responses to hydrological factors.The data mining results reveal different dominant triggering factors depending on the monitoring frequency:the monthly and bi-monthly cumulative rainfall control the monthly deformation,and the 10-d cumulative rainfall and the 5-d cumulative drop of water level in the reservoir dominate the daily deformation of the landslide.It is concluded that the spatiotemporal deformation pattern and data mining rules associated with precipitation and reservoir water level have the potential to be broadly implemented for improving landslide prevention and control in the dam reservoirs and other landslideprone areas.

Benchmark experiment on slab^(238)U with D-T neutrons for validation of evaluated nuclear data

A benchmark experiment on^(238)U slab samples was conducted using a deuterium-tritium neutron source at the China Institute of Atomic Energy.The leakage neutron spectra within energy levels of 0.8-16 MeV at 60°and 120°were measured using the time-of-flight method.The samples were prepared as rectangular slabs with a 30 cm square base and thicknesses of 3,6,and 9 cm.The leakage neutron spectra were also calculated using the MCNP-4C program based on the latest evaluated files of^(238)U evaluated neutron data from CENDL-3.2,ENDF/B-Ⅷ.0,JENDL-5.0,and JEFF-3.3.Based on the comparison,the deficiencies and improvements in^(238)U evaluated nuclear data were analyzed.The results showed the following.(1)The calculated results for CENDL-3.2 significantly overestimated the measurements in the energy interval of elastic scattering at 60°and 120°.(2)The calculated results of CENDL-3.2 overestimated the measurements in the energy interval of inelastic scattering at 120°.(3)The calculated results for CENDL-3.2 significantly overestimated the measurements in the 3-8.5 MeV energy interval at 60°and 120°.(4)The calculated results with JENDL-5.0 were generally consistent with the measurement results.