Rate Of Data Research Articles

A STUDY ON BIOCHEMICAL PARAMETERS IN ASSESSING THE SEVERITY OF ACUTE ORGANOPHOSPHORUS POISONING

Objectives: Organophosphorus (OP) poisoning is an important global health issue with increasing mortality rate for every year particularly in developing countries since its overuse in agricultural activity. The present study was performed to analyze the clinical profile and biochemical parameters such as serum cholinesterase, serum creatinine phosphokinase (CPK), liver enzymes, serum amylase, lipase, serum k+, troponin, serum urea, and creatinine on day 1 for correlation with clinical severity of poisoning. Methods: A observational study was performed on 100 cases with OP poisoning at a tertiary care center. Peradeniya OP Poisoning Scale was applied to all cases at the time of admission and the severity was designated as mild, moderate, and severe taking into account 5 vital parameters such as miosis, fasciculations, respiration rate, bradycardia, and level of consciousness and data were statistically analyzed by SPSS. Results: Rise in clinical severity of poisoning and substantial reduction in the level of serum cholinesterase was observed. The mean serum CPK, troponin T (Trop T), amylase, lipase, bilirubin, aspartate aminotransferase, and alanine aminotransferase showed marked elevation according to the severity of OP poisoning. In addition, serum potassium levels showed decreased level as per the severity level. Conclusion: Initial serum cholinesterase and serum Trop T are strong predictors of the clinical severity of OP poisoning.

Crime of theft prediction using Machine Learning K-Nearest Neighbour Algorithm at Polresta Bandar Lampung

The era of the industrial revolution 4.0 is a time where cyber and physical technology collaborate. This study aims to predict the types of theft crimes that occur in the Bandar Lampung Police area with the K-Nearest Neighbor algorithm, evaluate the prediction results and profiling the prediction results carried out by Bandar Lampung Police investigators in efforts to prevent and handle criminal acts of theft in the jurisdiction of the Bandar Lampung Police Lampung. The approach was carried out using the quantitative method of the K-Nearest Neighbor algorithm using the Rapidminer application by utilizing 1671 police report data from the Bandar Lampung Police and a questionnaire survey method conducted on 49 police investigators from the Bandar Lampung Police. Data collection techniques are carried out in a valid and reliable manner as a support for predictive validity. Based on the results of the classification and questionnaire, it was found that the majority of victims of the crime of theft were adult men who did not have a job and lived in urban areas. It was found that the majority of thefts occurred in parking lots in urban areas on Monday morning where the perpetrators used tools and targeted moving objects by tampering with locks which caused losses of around 10-50 million rupiah. This type of theft is theft by weighting (CURAT) which applies to Article 363 of the Criminal Code. The prediction results show that the neighboring value (K) and the distribution ratio of training and testing data are K=3 and 7:3, respectively. Predictions using K values and data sharing ratios show a high level of accuracy, namely 99. 20%. The results of the questionnaire show results that are in line with the results of the classification with an accuracy rate of the actual data of 75. 7122%. So by increasing the understanding skills of Bandar Lampung Police investigators using technology to predict the crime of theft, the number of theft crimes can be reduced.

General Intelligent Dataset Description Method and Application

With the development of the Internet, the growth rate of big data has exceeded the ability of human beings to obtain information from data. In the near future, faced with the sea of data, seemingly logically correct algorithms may come to the opposite conclusion, which will be a “data disaster” era. Therefore, letting data “speak” and enable data intelligence is an important problem that must be solved in the field of big data research. In this paper, we propose a “data intelligence” framework based on Brooks’s Subsumption Architecture for robotics. In this framework, we define a new dataset description method and try to encapsulate the dataset described by this method into a software container. Then, we set up the corresponding operations on the dataset container. Finally, we identify an interaction model for a “dataset agent” based on academic work on agents in the field of artificial intelligence. And we designed and implemented some functions of an intelligent document manager, which proves that our design ideas and methods are feasible.

Improved Vehicle Longitudinal Velocity Estimation Using Accelerometer Based Intelligent Tire

ABSTRACT An intelligent tire–based algorithm was developed to reinforce the vehicle longitudinal velocity estimation, from the vehicle inertial measurement unit (IMU). A tire was instrumented using a triaxis accelerometer (intelligent tire) in an instrumented vehicle with an IMU, and a global positioning system (GPS) based speed sensor (VBOX) as the ground truth for vehicle velocity. A testing matrix was developed, including two tire inflation pressures, two normal loads, and variable speed between 4 m/s to 14 m/s. A signal processing algorithm was developed to analyze the data from the accelerometer. Variational mode decomposition and Hilbert spectrum analysis were used for extracting features from each tire revolution. Later, a machine learning algorithm was trained to estimate the velocity using the acceleration data from the intelligent tire. Because the sampling rates of the IMU data and the intelligent tire data were different, sensor fusion was implemented. This calculated velocity was then used to correct the IMU-based estimated velocity. This new velocity can be used to enhance the performance of all advanced chassis control systems, such as anti-lock braking system (ABS) and electronic stability program (ESP).

Integrating Coordinate Transformation and Random Sampling Into High-Capacity Reversible Data Hiding in Encrypted Polygonal Models

Reversible data hiding in encrypted media involves using algorithms to perform data encryption to improve the privacy of the original media and hide data for covert communication or access control. This study explores the feasibility of applying coordinate transformation and random sampling to enhance the embedding rate and total embedding capacity of separable reversible data hiding in encrypted polygonal models based on multiple most significant bit (multi-MSB) prediction and Huffman coding. We first transform each vertex coordinate value of the input model into a decimal value between 0 and 1 and then convert the value into numerous binary digits with a user-defined compression threshold. Thereafter, the random sampling concept is used to generate embeddable vertices with only partial neighboring vertices as a reference. Thus, the embedding rate and total embedding capacity can both be increased considerably. The multi-MSB prediction technique is adopted to obtain the embedding capacity of each embeddable vertex, each vertex coordinate value in the proposed study can have a respective embedding length. Finally, the auxiliary information compressed through Huffman coding and an encrypted secret message are embedded in the multi-MSB of each embeddable vertex coordinate value through bit substitution. The experimental results of this study indicate the feasibility of the proposed algorithm.

Modeling the survival of Campylobacter jejuni in raw milk considering the viable but non‐culturable cells (VBNC)

AbstractCampylobacter spp. cannot grow in raw milk, but it is able to transform into a viable but non‐culturable (VBNC) state enabling the survival in such harsh conditions. In this study, Campylobacter jejuni survival in raw milk was investigated taken into consideration colony‐forming units (CFUs) and VBNC cells. CFU from two different strains of C. jejuni (DSM 4688 and BfR‐CA‐18043) were enumerated at three temperatures (5°C, 8°C, and 12°C). In parallel, a viability real‐time PCR was conducted to quantify intact and putatively infectious units (IPIUs) (comprising CFU and VBNC bacteria). The data generated were used to model the viability of C. jejuni during raw milk storage. Here, a one‐step fitting approach was performed using parameter estimates from an intermediate two‐step fit as starting values to generate tertiary models. Different primary model equations (Trilinear and Weibull) were required to fit the CFU and the IPIU data. Strain‐specific linear secondary models were generated to analyze the effect of storage temperature on the maximum specific inactivation rate of the CFU data. The time of the first decimal reduction parameter of the IPIU models could be modeled by a strain‐independent linear secondary model. The developed tertiary models for CFU and IPIU differ significantly in their predictions, for example, for the time required for a one log10 reduction. Taken into consideration that VBNC could revert to a culturable state during the raw milk storage, our results underline the importance of considering IPIU and not only CFU to avoid underestimation of the survival of C. jejuni in raw milk.

A Data-Driven Model for Simulating Longitudinal Visual Field Tests in Glaucoma.

To develop a simulation model for glaucomatous longitudinal visual field (VF) tests with controlled progression rates. Longitudinal VF tests of 1008 eyes from 755 patients with glaucoma were used to learn the statistical characteristics of VF progression. The learned statistics and known anatomic correlations between VF test points were used to automatically generate progression patterns for baseline fields of patients with glaucoma. VF sequences were constructed by adding spatially correlated noise templates to the generated progression patterns. The two one-sided test (TOST) procedure was used to analyze the equivalence between simulated data and data from patients with glaucoma. VF progression detection rates in the simulated VF data were compared to those in patients with glaucoma using mean deviation (MD), cluster, and pointwise trend analysis. VF indices (MD, pattern standard deviation), MD linear regression slopes, and progression detection rates for the simulated and patients' data were practically equivalent (TOST P < 0.01). In patients with glaucoma, the detection rates in 7 years using MD, cluster, and pointwise trend analysis were 24.4%, 26.2%, and 38.4%, respectively. In the simulated data, the mean detection rates (95% confidence interval) for MD, cluster, and pointwise trend analysis were 24.7% (24.1%-25.2%), 24.9% (24.2%-25.5%), and 35.7% (34.9%-36.5%), respectively. A novel simulation model generates glaucomatous VF sequences that are practically equivalent to longitudinal VFs from patients with glaucoma. Simulated VF sequences with controlled progression rates can support the evaluation and optimization of methods to detect VF progression and can provide guidance for the interpretation of longitudinal VFs.

Fast inference of genetic recombination rates in biobank scale data.

Although rates of recombination events across the genome (genetic maps) are fundamental to genetic research, the majority of current studies only use one standard map. There is evidence suggesting population differences in genetic maps, and thus estimating population-specific maps, are of interest. Although the recent availability of biobank-scale data offers such opportunities, current methods are not efficient at leveraging very large sample sizes. The most accurate methods are still linkage disequilibrium (LD)-based methods that are only tractable for a few hundred samples. In this work, we propose a fast and memory-efficient method for estimating genetic maps from population genotyping data. Our method, FastRecomb, leverages the efficient positional Burrows-Wheeler transform (PBWT) data structure for counting IBD segment boundaries as potential recombination events. We used PBWT blocks to avoid redundant counting of pairwise matches. Moreover, we used a panel-smoothing technique to reduce the noise from errors and recent mutations. Using simulation, we found that FastRecomb achieves state-of-the-art performance at 10-kb resolution, in terms of correlation coefficients between the estimated map and the ground truth. This is mainly because FastRecomb can effectively take advantage of large panels comprising more than hundreds of thousands of haplotypes. At the same time, other methods lack the efficiency to handle such data. We believe further refinement of FastRecomb would deliver more accurate genetic maps for the genetics community.

Stock market optimization amidst the COVID-19 pandemic: Technical analysis, K-means algorithm, and mean-variance model (TAKMV) approach

The Philippine stock market, just like most of its neighbors in the region, was seriously impacted by the global pandemic COVID-19. Investors remain hopeful while continuing to seek great ones in the damaged market. This paper developed a methodology for portfolio selection and optimization with the use of technical analysis, machine learning techniques, and portfolio optimization model. The combined methods of technical analysis, K-means clustering algorithm, and mean-variance portfolio optimization model will result in the development of the proposed TAKMV method. The study aims to integrate these three important analyses to identify portfolio investments. This paper uses the average annual risk and annual rate of return data for the years 2018 and 2020 to form the clusters and assessed the stocks that correspond to the investor's technical strategy such as Moving Average Convergence/Divergence (MACD) and Hybrid MACD with Arnaud Legoux Moving Average (ALMA). This paper solved the risk minimization problem on selected shares of the companies, based on the mean-variance portfolio optimization model. There are 230 and 239 companies for 2018 and 2020, respectively, listed in Philippine Stock Market, and all simulations were performed in MATLAB environment platform. Results showed that MACD strategy dominates the MACD-ALMA strategy in terms of the number of assets with a positive annual rate of return. The MACD works efficiently in the pre-COVID-19 condition while MACD-ALMA works efficiently during-COVID-19 condition, regardless of the number of assets with a positive annual rate of return. The results also show that the maximum expected portfolio return (RP) can be achieved using the MACD and MACD-ALMA in the pre-and during-COVID-19 conditions, respectively. The MACD-ALMA shows an advantage during high-risk market conditions and can also provide maximum RP. The performance of the TAKMV method was validated by applying its results and comparing it to the next year's historical price. The 2018 results were compared to 2019 data and the 2020 results were compared to 2021 data. For consistency, the comparison was applied to the same company per portfolio. Simulation results show that the MACD strategy is more effective compared to MACD-ALMA.

Applicability of Deep Learning Algorithms for Predicting Indoor Temperatures: Towards the Development of Digital Twin HVAC Systems

The development of digital twins leads to the pathway toward intelligent buildings. Today, the overwhelming rate of data in buildings carries a high amount of information that can provide an opportunity for a digital representation of the buildings and energy optimization strategies in the Heating, Ventilation, and Air Conditioning (HVAC) systems. To implement a successful energy management strategy in a building, a data-driven approach should accurately forecast the HVAC features, in particular the indoor temperatures. Accurate predictions not only increase thermal comfort levels, but also play a crucial role in saving energy consumption. This study aims to investigate the capabilities of data-driven approaches and the development of a model for predicting indoor temperatures. A case study of an educational building is considered to forecast indoor temperatures using machine learning and deep learning algorithms. The algorithms’ performance is evaluated and compared. The important model parameters are sorted out before choosing the best architecture. Considering real data, prediction models are created for indoor temperatures. The results reveal that all the investigated models are successful in predicting indoor temperatures. Hence, the proposed deep neural network model obtained the highest accuracy with an average RMSE of 0.16 °C, which renders it the best candidate for the development of a digital twin.

A New Progressive EOFs Quality Control Method for Surface Pressure Data Based on the Barometric Height and Biweight Average Correction

When assimilating surface pressure data in synoptic-scale models, we find the utilization rate of surface pressure data in zones with complex terrains is not high. Therefore, it is particularly important and urgent to carry out quality control of surface pressure data. Numerical weather prediction model analysis and forecasting provide essential data that can be compared with surface observations. The main adverse effects on surface pressure quality control include elevation differences between the model terrain and the observation stations and continuous outliers with the same characteristics in the initiation stage of quality control. Therefore, we propose a progressive empirical orthogonal function (EOF) with simulated observation (EOFs) combining barometric height correction (BHC) and biweight average correction (BAC) methods for the quality control of surface pressure data in this study. From the quality control results of the surface pressure data in regions with complex topography in China during June–August 2013, it was found that the BHC method could effectively reduce the deviations caused by elevation differences between the model terrain and the observation stations, and the BAC method could obviously reduce systematic deviations due to physical processes and the parameterization schemes of the models. The BHC-BAC method integrated the advantages of both methods and had the best correction effect. When continuous outliers with the same characteristics occurred in the initiation stage of quality control, the progressive EOF method might unreasonably eliminate observations. However, the progressive EOFs method could effectively solve this problem and had better performance in data quality control. The progressive EOFs quality control method with the combined BHC-BAC method could obviously reject outliers. The observation increment (deviations between observations and background field) after quality control by the progressive EOFs method was the closest to normal distribution, satisfying the Gaussian distribution assumption of data assimilation.

Quality registry improves the data of chronic ulcers: Validation of Tampere Wound Registry.

Quality registries are potential tools for improving health care documentation, but the quality and completeness of each registry should be ensured. This study aimed to evaluate the completion rate (completeness) and accuracy of data, first contact-to-registration time (timeliness), and case coverage of the Tampere Wound Registry (TWR) to assess whether it can be reliably used in clinical practice and for research purposes. Data from all 923 patients registered in the TWR between 5 June 2018 and 31 December 2020 were included in the analysis of data completeness, while data accuracy, timeliness and case coverage were analysed in those registered during the year 2020. In all analyses values over 80% were considered good and values over 90% excellent. The study showed that the overall completeness of the TWR was 81% and overall accuracy was 93%. Timeliness achieved 86% within the first 24 h, and case coverage was found to be 91%. When completion of seven selected variables was compared between TWR and patient medical records, the TWR was found to be more complete in five out of seven variables. In conclusion, the TWR proved to be a reliable tool for health care documentation and an even more reliable data source than patient medical records.

Improving blood product management in placenta accreta patients with severe bleeding: institutional experience

BackgroundPlacenta accrete spectrum (PAS) is a significant risk factor for postpartum hemorrhage and effective blood product management is critical in ensuring patient safety. In PAS patients undergoing cesarean section (CS) blood transfusion management guided by the combined clinical experience of the anesthesiologist and surgeon with point-of-care coagulation testing appears safe and effective. We describe and evaluate our experience and identify potential areas for improvement with blood product management in this patient population. MethodsA retrospective chart review of peri-operative demographic, anesthetic, and obstetric data was conducted for all patients with PAS undergoing CS between 2012 and 2018 at our center. To facilitate a practical evaluation of blood product management, we divided patients into two groups based on the severity of bleeding. ResultsA total of 221 parturients with PAS underwent CS, with 133 in group 1 requiring excessive amounts of transfusion and 88 in group 2 requiring management similar to other uncomplicated CS cases. There were no deaths or instances of disseminated intravascular coagulation, and intensive care unit admission occurred in five cases (2.2%). Patients in group 1 had higher mean nadir values of intra-operative hemoglobin and platelet count. We observed a high rate of missing data for peri-operative measurement of lactate and fibrinogen, PAS grade documentation, and temperature monitoring. ConclusionGiven no significant morbidity or mortality, clinical judgment in experienced centers appears safe for the management of PAS patients undergoing CS. The adoption of an institutional protocol and point-of-care coagulation testing could decrease over-transfusion and associated complications.

An Example of (Too Much) Hyper-Parameter Tuning In Suicide Ideation Detection

This work starts with the TWISCO baseline, a benchmark of suicide-related content from Twitter. We find that hyper-parameter tuning can improve this baseline by 9%. We examined 576 combinations of hyper-parameters: learning rate, batch size, epochs and date range of training data. Reasonable settings of learning rate and batch size produce better results than poor settings. Date range is less conclusive. Balancing the date range of the training data to match the benchmark ought to improve performance, but the differences are relatively small. Optimal settings of learning rate and batch size are much better than poor settings, but optimal settings of date range are not that different from poor settings of date range. Finally, we end with concerns about reproducibility. Of the 576 experiments, 10% produced F1 performance above baseline. It is common practice in the literature to run many experiments and report the best, but doing so may be risky, especially given the sensitive nature of Suicide Ideation Detection.

17th International Conference on Malignant Lymphoma, Palazzo dei Congressi, Lugano, Switzerland, 13 - 17 June, 2023.

Introduction: Epcoritamab, an off-the-shelf, subcutaneous CD3xCD20 T-cell–engaging bispecific antibody that redirects T cells to eliminate malignant CD20+ B cells, showed single-agent efficacy in patients with relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL) with a manageable safety profile. The phase 1/2 EPCORE NHL-1 trial (NCT03625037) of epcoritamab in patients with R/R DLBCL who received ≥2 prior lines of systemic therapy showed high complete response (CR) and minimal residual disease rates. Methods: This matching-adjusted indirect comparison included published data on overall response rate (ORR), CR rate, and overall survival (OS) for axi-cel (ZUMA-1 trial, N = 101) and individual patient-level data (IPD) for epcoritamab (EPCORE NHL-1 trial). Patients in EPCORE NHL-1 with prior CAR T therapy were excluded as ZUMA-1 included only patients without prior CAR T therapy. Analyses were adjusted for imbalances in baseline characteristics between IPD from EPCORE NHL-1 and aggregate data from ZUMA-1. Propensity score weights were applied to estimate risk differences for ORR and CR rate; weighted Cox proportional-hazards models were used to estimate OS hazard ratio (HR). Subgroup analyses of CAR T-eligible patients assessed patients with more similar clinical characteristics. Results: Compared with the ZUMA-1 population, the majority of patients enrolled in EPCORE NHL-1 (N = 86) were men (61.6% vs. 23.8%) with a median age of 69.5 y (vs. 58.0 y), and were refractory to ≥2 consecutive lines of therapy (62.8% vs. 53.5%). Among the CAR T-adjusted matched population, response rates were not statistically different in those treated with epcoritamab versus axi-cel (ORR: 73.4% vs. 74.3%, respectively; difference in ORR: −0.8%; P = 0.927; CR rate: 48.5% vs. 54.5%; difference in CR rate, −6.0%; P = 0.583). In a subgroup of CAR T-naive patients that were CAR T eligible and treated with epcoritamab (n = 50), response rates also did not significantly differ from those in patients treated with axi-cel (ORR 72.7% vs. 74.3%, respectively; difference in ORR: −1.6%; P = 0.873; CR rate: 47.7% vs. 54.5%; difference in CR rate, −6.8%; P = 0.576). In all CAR T-naive patients, OS was not statistically different between those treated with epcoritamab versus axi-cel (HR: 0.695; 95% CI: 0.351, 1.376; P = 0.297). Likewise, the CAR T-eligible subpopulation treated with epcoritamab did not have statistically different OS versus axi-cel (HR: 0.708; 95% CI: 0.309, 1.626; P = 0.416). Conclusions: Findings from this cross-study comparison suggest that epcoritamab is a promising, emerging, novel therapy with no statistically significant difference in efficacy versus axi-cel in patients with highly refractory, hard-to-treat DLBCL. Additional analyses are needed to better understand the therapeutic potential of epcoritamab as an off-the shelf, subcutaneously delivered core therapy for these patients. Encore Abstract - previously submitted to EHA 2023 The research was funded by: Epcoritamab is jointly developed by Genmab A/S and AbbVie Inc.; AbbVie and Genmab are sponsoring this study. Keyword: Aggressive B-cell non-Hodgkin lymphoma Conflicts of interests pertinent to the abstract. C. Thieblemont Consultant or advisory role: AbbVie, BeiGene, BMS/Celgene, Roche, Janssen, Kite/Gilead, Novartis, Regeneron, and Takeda C. P. Fox Consultant or advisory role: GenMab and AbbVie Other remuneration: Steering Committee: GenMab A. Wang Consultant or advisory role: AbbVie, BeiGene, BMS/Celgene, Roche, Janssen, Kite/Gilead, Novartis, Regeneron, and Takeda K. Sail Employment or leadership position: AbbVie Stock ownership: AbbVie A. Alshreef Employment or leadership position: AbbVie Stock ownership: AbbVie M. Moran Employment or leadership position: AbbVie Stock ownership: AbbVie A. Mutebi Employment or leadership position: Genmab Stock ownership: Genmab J. Blaedel Employment or leadership position: Genmab Stock ownership: Genmab V. Chirikov Employment or leadership position: OPEN Health, which received funding support from AbbVie to conduct the research G. Salles Consultant or advisory role: AbbVie, Bayer, BeiGene, BMS/Celgene, Epizyme, Genentech/Roche, Genmab, Incyte, Janssen, Kite/Gilead, Loxo, Miltenyi, MorphoSys, Novartis, Rapt, Regeneron, and Takeda

Acute lymphocytic leukemia incidence and survivability trends in the United States over the last two decades: SEER data set from 2000-2019.

e19050 Background: The primary objective of this study is to evaluate how race, age at which patients were diagnosed, sex, and phenotype affect incidence and survivability for acute lymphocytic leukemia (ALL) patients in the United States. Keeping these factors in consideration this study was developed to see how existing cancer registry data can assist us in early detection of ALL in patients. Our hypothesis was that statistically significant correlations exist between race, age at which patients were diagnosed, sex, and phenotype of the ALL patients. Methods: The rate of incidence and survivability data were evaluated using SEER*Stat statistical software from National Cancer Institute. For incidence calculation we used the SEER 22 registry that covered approximately 47.99% of the U.S. population, while survival statistics were calculated using the SEER 17 registry that covered 26.5% of the U.S. population, both containing records from 2000 through 2019. In calculating the incidence statistics only the first primary tumor and reported diagnosis of the disease was accounted for. Results: There were statistically significant correlations between sex, race/ethnicity, age at which patients were diagnosed, phenotype of the ALL patient, and their rate of incidence. Analysis of All incidence data showed that the disease was more prevalent among Caucasian population. Among ALL patients, 59% patients were between 0 to 19 years of age when diagnosed, and 56% were male. ALL are mostly associated in patients with B-cell phenotype (73%). Evaluation of survivability data showed a slightly higher 5-year survival rate than 10-year survival rate. Conclusions: Survivability rates of African Americans patients were the lowest compared to Caucasian, Asian, Pacific Islanders, Alaskan Native, Native Americans and others. Survivability rates progressively decreased for older patients. Additionally, this study looked into the common treatment approaches that were administered in ALL patients which primarily included chemotherapy supplemented with some form of radiation therapy, as and when needed. The study showed chemotherapy was quite effective in improving patients’ survival probabilities, while the prognosis from the disease was not quite good for those who did not receive any treatment. This updated study on ALL incidence, survival rate, and common treatment options will further assist the health care professionals in effectively screening potential patients, their early detection, and in implementing a successful treatment regimen. This could potentially improve survival rates among those diagnosed at an older age who have a far greater mortality rate from ALL compared to younger patients.

ESnet/JLab FPGA Accelerated Transport

To increase the science rate for high data rates/volumes, Thomas Jefferson National Accelerator Facility (JLab) has partnered with Energy Sciences Network (ESnet) to define an edge to compute cluster traffic shaping / steering transport capability featuring data event aware network shaping and forwarding. The keystone of this ESnet+JLab FPGA Accelerated Transport (EJFAT) is the joint development of a dynamic compute work Load Balancer (LB) of UDP streamed data. The LB is a suite consisting of a Field Programmable Gate Array (FPGA) executing the dynamically configurable, low fixed latency LB <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data plane</i> featuring real-time packet redirection and high throughput, and a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">control plane</i> running on the FPGA host computer that monitors network and compute farm telemetry in order to make dynamic load-balancing decisions for destination compute host redirection / load balancing. The LB provides for three-tier horizontal scaling across LB suites, cluster compute hosts, and CPUs within a host. The LB effectively provides seamless integration of edge / cluster computing to support direct experimental data processing for immediate use by JLab science programs and others such as the Electron-Ion Collider (EIC) as well as data centers of the future requiring high throughput and low latency for both time-critical (e.g., data acquisition systems) and data-driven (data center) workflows.

Time Series Data Validity

As a key step of data preparation, it is always necessary to first assert the quality of data before conducting any data application. Given a set of constraints, the validity measure evaluates the degree of data meeting the constraints, e.g., whether the values are in the specified range or fluctuate drastically over time in a series. It is worth noting that simply counting all the data points in violation to the constraints may over claim the data validity issue. Following the minimum change criteria in data repairing, we propose to study the minimum number of data points that need to be changed in order to satisfy the constraints, or equivalently, the maximum rate of data that can be reserved without change, as the validity measure. To our best knowledge, this is the first study on defining and evaluating time series data validity. We devise algorithms for computing the validity measure in quadratic time and linear space. Remarkably, the validity measure has been deployed and included as a function in SQL statements, in Apache IoTDB, an open-source time series database. The algorithm fully adapts to the LSM-based storage of time series in multiple segments. Extensive experiments over 8 real-world datasets show up to 4 orders of magnitude improvement in time cost compared to the related method SCREEN.

Evaluation of the Effect of Virtual Simulation Teaching on Learning Behavior of College Students

As a new learning model that breaks the barriers of time and space, virtual simulation covers a variety of learning behaviors. To evaluate the effect of virtual simulation teaching on college students’ learning behavior, with the help of a course integration model which reduces the omissions in the process of data collection, the data collection process of college students on the online platform was constructed. The collected data before storage were preprocessed which reduces the repetition rate of the original data of learning behavior, and the correlation between the collected data and the actual learning behavior was analyzed. Results show that learners’ specific process of online learning can be summarized into four stages: login trajectory, core learning, social interaction, and learning evaluation. Then, the actual final examination scores of the college students who participated in the test are all above 90 points, and their academic achievements are significantly improved. The use of the virtual simulation teaching method also enhances the overall satisfaction of college students with experimental courses, and the accuracy of the analysis results of course results is 99%, with better results. Conclusions are helpful to build college students' active learning consciousness of virtual simulation teaching courses.

Single-cell RNA-seq data clustering by deep information fusion.

Determining cell types by single-cell transcriptomics data is fundamental for downstream analysis. However, cell clustering and data imputation still face the computation challenges, due to the high dropout rate, sparsity and dimensionality of single-cell data. Although some deep learning based solutions have been proposed to handle these challenges, they still can not leverage gene attribute information and cell topology in a sensible way to explore the consistent clustering. In this paper, we present scDeepFC, a deep information fusion-based single-cell data clustering method for cell clustering and data imputation. Specifically, scDeepFC uses a deep auto-encoder (DAE) network and a deep graph convolution network to embed high-dimensional gene attribute information and high-order cell-cell topological information into different low-dimensional representations, and then fuses them to generate a more comprehensive and accurate consensus representation via a deep information fusion network. In addition, scDeepFC integrates the zero-inflated negative binomial (ZINB) into DAE to model the dropout events. By jointly optimizing the ZINB loss and cell graph reconstruction loss, scDeepFC generates a salient embedding representation for clustering cells and imputing missing data. Extensive experiments on real single-cell datasets prove that scDeepFC outperforms other popular single-cell analysis methods. Both the gene attribute and cell topology information can improve the cell clustering.