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Development of a Data Portal System for Governance in Berau

The development of a Data Portal System for Berau Governance addresses the need for an integrated platform to effectively manage and disseminate government data. This system aims to enhance transparency, accountability, and public access to diverse datasets in alignment with the One Data Indonesia initiative. The portal consolidates various data categories, including news, announcements, agendas, publications, downloads, infographics, and visualizations, into a single platform, streamlining data access for both government agencies and the public. The system was developed using the Rapid Application Development (RAD) methodology, enabling iterative prototyping and quick adjustments based on feedback from key stakeholders. This approach ensured the system could meet the dynamic requirements of the government while remaining adaptable to future needs. The Laravel framework was chosen for its flexibility, scalability, and security features, allowing the system to provide a responsive, user-friendly experience while maintaining high levels of data integrity.The Data Portal System significantly improves the local government’s ability to manage and share information, fostering greater civic engagement and enhancing public trust in governance processes. Furthermore, the system facilitates more efficient public service delivery by making key data accessible in a structured and intuitive format. Regular assessments and updates are recommended to ensure the system continues to align with evolving user needs and governmental policies, further advancing data governance in Berau Regency.

Evaluating rice crop phenology and crop yield in hilly region using satellite imagery and Google Earth Engine

Monitoring vegetation phenology is essential for understanding the impacts of climate change on agricultural production. This study leverages Sentinel-2 data to develop an algorithm in Google Earth Engine (GEE) for calculating phenological metrics of rice crop cultivated over the hilly area, allowing for high-resolution, efficient, and large-scale analysis without the need for data download. The study focuses on key metrics, including the start of the season and end of the season , length of growing season derived from various vegetation indices. The results demonstrate that NDVI-based phenological metrics closely align with the observed values at the experimental site, Malan. Moreover, the relationship of NDVI based length of growing season with the rice crop yield was found stronger with a R2 value of 0.68, depicting the capability of the satellite-based phenology metrics to estimate the rice crop yield in hilly region of Himachal Pradesh.

SecureCloud: A Shield Against Virtual Threats

Cloud computing is a constituent of today and tomorrow innovative information and computing technologies which provides useful and convenient services like storage and access etc. But it has important problems which aggravate with security aspects of a system with regards to the data confidentiality and integrity. These challenges are met in this paper by conducting a detailed theoretical study of literature on data security risks in single and multi-cloud models. Some of the threats include; break-ins, theft of information and loss of information. A comparison of extant literature on security measures like encryption and access control in the cloud based systems is done. It is shown that the solutions available currently have certain deficits, which contribute to the necessity of enhancing safety features. Thus, to fill these gaps, it is suggested to have one layer of protection against data access and another one in relation to data downloads. That way it is protective of the data, useful and clients’ information privacy will be well protected. Thus, the results of the study can be valuable for researchers and cloud service providers who are working on the improvement of the information security in cloud environments

Constructing and identifying an eighteen-gene tumor microenvironment prognostic model for non-small cell lung cancer

BackgroundThe tumor microenvironment (TME) plays a crucial role in tumorigenesis and tumor progression. This study aimed to identify novel TME-related biomarkers and develop a prognostic model for patients with non-small-cell lung cancer (NSCLC).MethodsAfter downloading and preprocessing data from The Cancer Genome Atlas (TCGA) data portal and Gene Expression Omnibus (GEO) datasets, we classified the molecular subtypes using the “NMF” R package. We performed survival analysis and quantified immune scores between clusters. A Cox proportional hazards model was then constructed, and its formula was produced. We assessed model performance and clinical utility. A prediction nomogram was also constructed and validated. Additionally, we explored the potential regulatory mechanisms of our TME gene signature using Gene Set Enrichment Analysis (GSEA).ResultsFrom data processing and univariate Cox regression analysis, 57 TME-related prognostic genes were identified, and two significantly distinct clusters were established. Using Cox regression and Lasso regression, an 18-gene TME-related prognostic model was developed. Patients were stratified into high- and low-risk groups based on the risk score, with survival analysis showing that the low-risk group had significantly better outcomes than the high-risk group (P < 0.01). ROC curve analysis demonstrated strong predictive performance, with 1-year, 3-year, and 5-year AUC values ranging from 0.654 to 0.702 across different cohorts. The model accurately predicted survival outcomes across subgroups with varying clinical features, and its predictive accuracy was validated through a nomogram.ConclusionsWe developed a prognostic model based on TME-related genes in NSCLC. Our 18-gene TME signature can effectively predict the prognosis of NSCLC with high accuracy.

Open Source Adoption for Digital Transformation and Data Management During the COVID-19 Crisis

With COVID-19-led business disruption and businesses expediting their move towards digital transformation, Industry experts observed a phenomenon of increased adoption of Open Source Software and database management systems to digitalize the business while keeping costs low. This research has studied this phenomenon using a three-step approach: using the collective intelligence of Twitter without the context of COVID-19, Twitter data with the context of COVID-19, and empirical validation with actual monthly downloads data of open source projects from SourceForge in Pre-COVID and COVID-19 time periods. The research finds that although the COVID-19 pandemic has triggered a digital transformation and the use of database management systems in many organizations, but there is no statistically significant increased use that can be attributed to a crisis response due to the pandemic.

Estimating Rainfall Anomalies with IMERG Satellite Data: Access via the IPE Web Application

This study assesses the possibilities of the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG-GPM) to estimate extreme rainfall anomalies. A web application, the IMERG Precipitation Extractor (IPE), was developed which allows for the querying, visualization, and downloading of time-series satellite precipitation data for points, watersheds, country extents, and digitized areas. The tool supports different temporal resolutions ranging from 30 min to 1 week and facilitates advanced analyses such as anomaly detection and storm tracking, an important component for climate change study. To validate the IMERG precipitation data for anomaly estimation over a 22-year period (2001 to 2022), the Rainfall Anomaly Index (RAI) was calculated and compared with RAI data from 2360 NOAA stations across the conterminous United States (CONUS), considering both dry and wet climate regions. In the dry region, the results showed an average correlation coefficient (CC) of 0.94, a percentage relative bias (PRB) of −22.32%, a root mean square error (RMSE) of 0.96, a mean bias ratio (MBR) of 0.74, a Nash–Sutcliffe Efficiency (NSE) of 0.80, and a Kling–Gupta Efficiency (KGE) of 0.52. In the wet region, the average CC of 0.93, PRB of 24.82%, RMSE of 0.96, MBR of 0.79, NSE of 0.80, and KGE of 0.18 were computed. Median RAI indices from both the IMERG and NOAA indicated an increase in rainfall intensity and frequency since 2010, highlighting growing concerns about climate change. The study suggests that IMERG data can serve as a valuable alternative for modeling extreme rainfall anomalies in data-scarce areas, noting its possibilities, limitations, and uncertainties. The IPE web application also offers a platform for extending research beyond CONUS and advocating for further global climate change studies.

EXPRESSO: a multi-omics database to explore multi-layered 3D genomic organization.

The three-dimensional (3D) organization of the human genome plays a crucial role in gene regulation. EXPloration of Regulatory Epigenome with Spatial and Sequence Observations (EXPRESSO) is a novel multi-omics database for exploration and visualization of multi-layered 3D genomic features across 46 different human tissues. Integrating 1360 3D genomic datasets (Hi-C, HiChIP, ChIA-PET) and 842 1D genomic and transcriptomic datasets (ChIP-seq, ATAC-seq, RNA-seq) from the same biosample, EXPRESSO provides a comprehensive resource for studying the interplay between 3D genome architecture and transcription regulation. This database offers diverse 3D genomic feature types (compartments, contact matrix, contact domains, stripes as diagonal lines extending from a genomic locus in contact matrix, chromatin loops, etc.) and user-friendly interface for both data exploration and download. Other key features include REpresentational State Transfer application programming interfaces for programmatic access, advanced visualization tools for 3D genomic features and web-based applications that correlate 3D genomic features with gene expression and epigenomic modifications. By providing extensive datasets and tools, EXPRESSO aims to deepen our understanding of 3D genomic architecture and its implications for human health and disease, serving as a vital resource for the research community. EXPRESSO is freely available at https://expresso.sustech.edu.cn.

A secure, privacy-preserving, and cost-efficient decentralized cloud storage framework using blockchain

Cloud services benefit countless users worldwide due to notable features, such as on-demand self-service, scalability, easy maintenance, etc. Secure storage and access to data in the cloud is critical. Cloud Identity and Access Management (IAM) service, which acts in a centralized way to provide access requests to the authenticated users. Controlled access sometimes fails to preserve the privacy of the sensitive information stored in the cloud due to several reasons, such as insider attacks, breaches of data security, or any other types of unauthorized access. This paper suggests a blockchain-assisted secure storage and access mechanism to secure sensitive data. Here blockchain is used as a trust management entity that verifies the identity of the user. Along with this it issues the Access Control Lists (ACLs) and identity token, and at the same time, it records all the interactions between the users and service providers. Data transmission is transparent since transactions are recorded. Importance is given to user privacy and decryption keys security. Linear(t,n) secret sharing scheme is used for key share generation and distribution. For experimentation, in MetaMask cryptocurrency wallet Goerli test network is used. Results reveal that our model consumes less cost to execute than other existing works. The total execution cost to upload and download a data file is 0.00281392 and 0.02455307 GoerliETH. Where the all verification operations such as identity token, ACL, access_log, and data integrity are executed in Zero gas value. The proposed model maintains a constant gas cost regardless of transaction volume, with costs of 33.04 ETH and 32.24 ETH for data upload and download. Moreover, we present a comparison of execution time performance in three different system configurations.

The Pico Balloon Archive: Establishing the First Super-Pressure Balloon Satellite Network for Atmospheric Research

Abstract We present the Pico Balloon Archive (PBA), a specialized platform for archiving, visualizing, and verifying data from pico balloons—lightweight, super-pressure balloons capable of operating in the upper troposphere and lower stratosphere for durations ranging from weeks to years. With a latitudinal range from 88.77°S to 89.60°N and data collection ongoing since 2021, the PBA stands as the most spatially extensive repository for super-pressure balloons to date. It offers a centralized, user-friendly online portal (http://picoballoonarchive.org) for data access, featuring a summary of flight details, downloadable raw data, and individual flight visualizations. In this study, we validate the PBA’s wind speed and direction calculations against the Integrated Global Radiosonde Archive (IGRA), showing strong correlations (r2 = 0.66 and 0.78 for wind speed and direction, respectively). We also highlight the PBA’s effectiveness in charting global atmospheric circulation and the capacity of certain balloons to traverse hemispheres, a feat made possible by unique stratospheric dynamics. Furthermore, we demonstrate the PBA’s effectiveness in validating numerical weather prediction (NWP) Lagrangian trajectories, quantifying errors based on model initialization latitude. This continuously updated super-pressure balloon network is poised to significantly aid the atmospheric science community, facilitating a deeper understanding of global atmospheric processes. Significance Statement The first ever super-pressure balloon network is discussed and validated using radiosondes and numerical weather prediction (NWP) models. The Pico Balloon Archive (PBA) is positioned to offer worldwide data, facilitating cost-effective and high-reward atmospheric research for numerous users.

Low‐cost animal tracking using Bluetooth low energy beacons on a crowd‐sourced network

Abstract Animal tracking has opened the door to address many fundamental questions in ecology and conservation. Whilst historically animals have been tracked as a means to understand their large‐scale movements, such as migration, there is now a greater focus on using tracking to study movements over smaller scales, individual variation in movement or how movements shape social network structure. With this shift in focus also comes different tracking needs, including the need to track larger numbers of individuals. Tracking studies all face some technological limitations. For example, GPS and other active tracking solutions can collect fine‐scale movement data, but have a high cost per tag, limiting the number of individuals that can be followed. They also have high low‐energy costs of data acquisition and download, limiting time periods over which data can be collected. Low‐energy passive (e.g. PIT) or active (e.g. reverse GPS) tags can overcome these limitations, but instead require animals to remain within a bounded study area or to come into close proximity to detectors. Here we describe one solution that can overcome many current limitations by employing the massive global network of personal mobile phones as gateways for tracking animals using Bluetooth low‐energy (BLE) beacons. In areas with medium to high density of people, these simple‐to‐make beacons can provide regular updates of position over long time periods (battery life 1–3 years). We describe how to use off‐the‐shelf components to produce BLE beacons that weigh c. 5–6 g and cost &lt;$7USD. Using field‐testing, we then show that beacons are capable of producing high‐frequency tracking data that can be used to build home ranges or to detect spatiotemporal co‐occurrences among individuals. BLE beacons are a low cost, low‐energy solution for studying organisms (e.g. birds, mammals and reptiles) living and moving in urban landscapes. Their low weight and small size makes them particularly well‐suited for tracking smaller species. When combined with fixed gateways, their use can also be extended to non‐urban habitats. Their high accessibility is likely to make them an attractive solution for many research projects.

SesamumGDB: a comprehensive platform for Sesamum genetics and genomics analysis.

Sesame (Sesamum indicum L., 2n = 26) is a crucial oilseed crop cultivated worldwide. The ancient evolutionary position of the Sesamum genus highlights its value for genomics and molecular genetics research among the angiosperms of other genera. However, Sesamum is considered a small orphan genus with only a few genomic databases for cultivated sesame to date. The urgent need to construct comprehensive, curated genome databases that include genus-specific gene resources for both cultivated and wild Sesamum species is being recognized. In response, we developed Sesamum Genomics Database (SesamumGDB), a user-friendly genomic database that integrates extensive genomic resources from two cultivated sesame varieties (S. indicum) and seven wild Sesamum species, covering all three chromosome groups (2n = 26, 32, and 64). This database showcases a total of 352 471 genes, including 6026 related to lipid metabolism and 17 625 transcription factors within Sesamum. Equipped with an array of bioinformatics tools such as BLAST (basic local alignment search tool) and JBrowse (the Javascript browser), SesamumGDB facilitates data downloading, screening, visualization, and analysis. As the first centralized Sesamum genome database, SesamumGDB offers extensive insights into the genomics and genetics of sesame, potentially enhancing the molecular breeding of sesame and other oilseed crops in the future. Database URL: http://www.sgbdb.com/sgdb/.

Overview and Methods for the Youth Risk Behavior Surveillance System - United States, 2023.

The Youth Risk Behavior Surveillance System (YRBSS) is a set of surveys that tracks a broad range of behaviors, experiences, and conditions that can lead to poor health among high school students. The system includes a nationally representative Youth Risk Behavior Survey (YRBS) and separate school-based YRBSs conducted by states, tribes, territories, and local school districts. For the 2023 national YRBS, CDC made changes to the sampling method, survey administration mode, and questionnaire. Specifically, the sampling design added an American Indian or Alaska Native (AI/AN) supplemental sample so that separate, precise estimates could be made for AI/AN high school students, in addition to the usual sample designed to provide nationally representative data for the population of students in grades 9-12. To decrease the time needed to collect and process data, CDC changed the survey administration mode from paper-and-pencil scannable booklets to a tablet-based electronic survey. To provide national data on topics of emerging interest, CDC added new questions to the questionnaire. These new questions assessed social media use, experiences of racism at school, adverse childhood experiences, transgender identity, consent for sexual contact, and unfair discipline at school. Public health practitioners and researchers can use YRBSS data to examine the prevalence of youth health behaviors, experiences, and conditions; monitor trends; and guide interventions. This overview report describes 2023 YRBSS survey methodology, including sampling, data collection, data processing, weighting, and data analyses. The 2023 YRBS participation map, survey response rates, and a detailed examination of student demographic characteristics are included in this report. During 2023, in addition to the national YRBS, 68 site-level surveys were administered to high school students in 39 states, three tribal governments, five territories, and 21 local school districts. These site-level surveys use site-specific questionnaires that are similar to the national YRBS questionnaire but are modified to meet sites' needs. This overview and methods report is one of 11 featured in this MMWR supplement, which reports results from the 2023 national YRBS but does not include data from the 68 site-level surveys. Each report is based on data collected using methods presented in this overview report. A full description of YRBSS results and downloadable data are available (https://www.cdc.gov/yrbs/index.html).

12420 Effects Of A Continuous Glucose Monitoring (CGM) Optimization Program On Prescribing Behavior And Glycemic Outcomes

Abstract Disclosure: A. Sterbenz: None. J. Fuentes Rosales: None. S. Abraham: None. P. Siroya: None. R. Middelbeek: None. J. Mitri: None. F. Rossitto: None. E. Cabana: None. A. Gharse: None. L. Collins: None. L. Aliaga: None. A. Millan Ferro: None. J. Cifuni: None. A. Schwartz: None. E. Kelley: None. C. Gomez: None. M. Aballay: None. Introduction: Continuous glucose monitoring (CGM) improves glucose control in patients with diabetes, as demonstrated in clinical trials. There is limited information available on the real-world application of CGM adoption in a resource-limited setting or safety net outpatient hospital setting. This intervention's aim was to assess the impact of a multifaceted approach on improving CGM utilization and on glycemic control among patients with diabetes. Methods: Wyckoff Heights Medical Center, located in Brooklyn, NY is a resource-limited setting that in partnership with the Joslin Diabetes Center, Boston, MA launched a CGM Clinical Care Optimization Program. This quality improvement (QI) initiative included establishing a diabetes technology team (DTT) which met weekly (n=24 sessions) to establish a highly specialized interprofessional group. The DTT activities included: 1) Establishing a CGM workflow; 2) Expanding Medical Assistant responsibilities related to diabetes technology and training; 3) Creating five bilingual (English-Spanish) patient handouts; 4) Building CGM vendor relationships; 5) Developing a CGM manual for data download, billing and coding, and education to increase CGM knowledge across the system. Data were collected prior to CGM initiation and at most recent visit. Results: The intervention occurred between February and November 2023. Prior to this intervention, 49 patients had a CGM prescription. After 9 months, CGM prescriptions were written for 333 patients (680% increase) with the following baseline characteristics (mean ± SD): age (61.2 ± 14.7 yrs.); 56% female; 44% male; 71% Latino; Race: 67% Other (63% Hispanic or Latino), 25% Black or African American, 4% White, Asian 2%, American Indian or Alaska Native 2%; 38% Spanish-speaking, 59% English-Speaking, 3% Other; 81% government insurance (63% Medicaid, 18% Medicare), 19% commercial insurance. The majority of patients had been diagnosed with type 2 diabetes (91%) and 9% had type 1 diabetes. Baseline HbA1C was 9.7 ± 1.5%. Of note, prior to CGM, 34% of patients had A1C&amp;gt;9%. The impact of CGM use was evaluated on 116 patients that met the following criteria: baseline A1C value within 60 days before or up-to 30 days after prescription, and a A1C &amp;gt;90 days after. After an average duration of 154 days of CGM use, HbA1c decreased significantly by -0.51% (p=0.001), demonstrating improved glycemic control post-CGM use. In those with A1c &amp;gt;9%, HbA1c decreased from 10.5% to 8.8% (p&amp;lt;0.01). Conclusion: Creating a CGM optimization program in a resource-limited setting is critical in improving patient self-awareness and provider insight in glycemic control in patients diagnosed with type 1 and type 2 diabetes. These data demonstrate the feasibility of implementing a CGM optimization program in a resource-limited setting, which is critically important in improving clinical care for people with diabetes. Presentation: 6/2/2024

HumanIslets.com: Improving accessibility, integration, and usability of human research islet data.

HumanIslets.com supports diabetes research by offering easy access to islet phenotyping data, analysis tools, and data download. It includes molecular omics, islet and cellular function assays, tissue processing metadata, and phenotypes from 547 donors. As it expands, the resource aims to improve human islet data quality, usability, and accessibility.

Toolkit to Examine Lifelike Language v.2.0: Optimizing Speech Biomarkers of Neurodegeneration

Plain Language SummaryNeurodegenerative disorders (NDs), such as Alzheimer’s and Parkinson’s disease, are a leading cause of disability, caregiver stress, and financial strain worldwide. The number of cases, now estimated at 60 million, will triple by 2050. Early detection is crucial to improve treatments, management, and financial planning. Unfortunately, standard diagnostic methods are costly, stressful, and often hard to access due to scheduling delays and availability issues. A promising alternative consists in digital speech analysis. This affordable, noninvasive approach can identify NDs based on individuals’ voice recordings and their transcriptions. In 2023, we launched the Toolkit to Examine Lifelike Language (TELL), an online app providing robust speech biomarkers for clinical and research purposes. This paper introduces TELL v.2.0, a novel version with improved data collection, encryption, processing, storing, download, and visualization features. First, we explain the app’s basic operations and its possibilities for online and offline data collection. Second, we describe its language survey, which covers questions about demographics as well as language history, usage, competence, and difficulties. Third, we describe TELL’s speech tests, which assess key clinical features. Fourth, we outline the app’s functions for analyzing, visualizing, and downloading data. We finish by discussing the main challenges and future opportunities for TELL and the speech biomarker field. With this effort, we hope to boost the use of digital speech markers in medical and research fields.

DRCTdb: disease-related cell type analysis to decode cell type effect and underlying regulatory mechanisms.

Understanding the molecular mechanisms underlying genetic diseases is challenging due to environmental and genetic factors. Genome-wide association studies (GWAS) have identified numerous genetic loci, but their functional implications are largely unknown. Single-cell multiomics sequencing has emerged as a powerful tool to study disease-specific cell types and their relationship with genetic variants. However, comprehensive databases for exploring these mechanisms across different tissues are lacking. We present the Disease-Related Cell Type database (DRCTdb), integrating GWAS and single-cell multiomics data to identify disease-related cell types and elucidate their regulatory mechanisms. DRCTdb contains well-processed data from 16 studies, covering 4 million cells within 28 tissues. Users can browse relationships and regulatory mechanisms between SNPs of 42 genetic diseases and cell types based on GWAS and single-cell data. DRCTdb also offers data downloads and is available at https://singlecellatlas.top/DRCTDB .

Processing and Visualization of Protec-Seq Data for the Generation of Calibrated, Genome-Wide Double Double-Strand Break(dDSB) Maps.

This chapter describes the computational pipeline for the processing and visualization of Protec-Seq data, a method for purification and genome-wide mapping of double-stranded DNA protected by a specific protein at both ends. In the published case, the protein of choice was Saccharomyces cerevisiae Spo11, a conserved topoisomerase-like enzyme that makes meiotic double-strand breaks (DSBs) to initiate homologous recombination, ensuring proper segregation of homologous chromosomes and fertility. The isolated DNA molecules were thus termed double DSB (dDSB) fragments and were found to represent 34 to several hundred base-pair long segments that are generated by Spo11 and are enriched at DSB hotspots, which are sites of topological stress. In order to allow quantitative comparisons between dDSB profiles across experiments, we implemented calibrated chromatin immunoprecipitation sequencing (ChIP-Seq) using the meiosis-competent yeast species Saccharomyces kudriavzevii as calibration strain. Here, we provide a detailed description of the computational methods for processing, analyzing, and visualizing Protec-Seq data, comprising the download of the raw data, the calibrated genome-wide alignments, and the scripted creation of either arc plots or Hi-C-style heatmaps for the illustration of chromosomal regions of interest. The workflow is based on Linux shell scripts (including wrappers for publicly available, open-source software) as well as R scripts and is highly customizable through its modular structure.

An interactive R-based custom quantification program for semi-quantitative analysis of triacylglycerols in bovine milk.

The R programming language, RStudio, and open-source software solutions for analysis of liquid chromatography-mass spectrometry (LC-MS) data have been used with user-written R-based custom quantification programs (CQP) for semi-quantification of triacylglycerols (TAGs) in bovine milk lipid extracts. Using the peak-finding capabilities of the package "xcms" in RStudio, peaks were integrated, and retention times aligned, normalized, and then used for semi-quantitative analysis of a custom set of four extraction internal standards (EISs) and 29 TAG regioisomers using the choice of four analytical internal standards (AISs). Alternating stereospecific numbering (sn) 1,3 TAG regioisomers (standards 1, 3, and 5 of six calibration standards) and sn-1,2 TAG regioisomers (standards 2, 4, and 6 of six standards) were used to make a set of six calibration standards, which were used for quantification using a linear fit model, polynomial fit model, power fit model, level-bracketed linear fit, replicate-bracketed polynomial fit, replicate-bracketed power fit, and replicate- and level-bracketed linear fit and response factors. For example, the linear fit for EIS1 gave an unacceptable coefficient of determination (CoD), r2 = 0.9616, whereas the polynomial fit gave r2 = 0.9908 and the power fit gave r2 = 0.9928, while the double-bracketed linear fit gave CoDs of r2 = 0.9960, 0.9848, and 0.9781 for the three brackets, yet gave the least % difference to known calibration concentrations. For unparalleled transparency, the CQP produced webpages that allowed every step in the data processing and quantification sequence to be verified and reproduced, and contained interactive figures. The data are publicly available using a digital object identifier (DOI). The R code can be downloaded and used with the downloadable data to reproduce the results, to modify the code and further customize the results, or to copy and paste and adapt the code to other quantification applications.

Application of intelligent algorithms in library resource malicious download detection system

The malicious download of library resources may lead to serious security risks and data leakage. To address this issue, this study proposes an intelligent algorithm based on Sliding Event Windows for detecting the malicious download behavior. This research method includes collecting and analyzing library resource download data and establishing a Sliding Event Window model for behavioral analysis. For each event window, this intelligent algorithm was utilized for feature extraction and behavior classification. Experimental results showed that window size and class radius had a large impact, while clustering radius had a small impact. The maximum topic cluster ratio affected the false alarm rate. In the ROC curve area comparison, the AUC values of the proposed method, RBM Method, a detection method based on IP request time interval, and a detection method based on IP request frequency were 0.904, 0.879, 0.841, and 0.797, respectively. The research confirmed that Sliding Event Windows can effectively improve the accuracy of malicious download detection for library resources, enhance the security of library resources, and protect user privacy. This study can further optimize resource utilization efficiency and promote scientific research and innovative development.

3-generation family histories of mental, neurologic, cardiometabolic, birth defect, asthma, allergy, and autoimmune conditions associated with autism: An open-source catalog of findings.

The relatively few conditions and family member types (e.g., sibling, parent) considered in investigations of family health history in autism spectrum disorder (ASD, or autism) limits understanding of the role of family history in autism etiology. For more comprehensive understanding and hypothesis-generation, we produced an open-source catalog of autism associations with family histories of mental, neurologic, cardiometabolic, birth defect, asthma, allergy, and autoimmune conditions. All live births in Denmark, 1980-2012, of Denmark-born parents (1,697,231 births), and their 3-generation family members were followed through April 10, 2017 for each of 90 diagnoses (including autism), emigration or death. Adjusted hazard ratios (aHR) were estimated via Cox regression for each diagnosis-family member type combination, adjusting for birth year, sex, birth weight, gestational age, parental ages at birth, and number of family member types of index person; aHRs also calculated for sex-specific co-occurrence of each disorder. We obtained 6462 individual family history aHRS across autism overall (26,840 autistic persons; 1.6% of births), by sex, and considering intellectual disability (ID); and 350 individual co-occurrence aHRS. Results are cataloged in interactive heat maps and down-loadable data files: https://ncrr-au.shinyapps.io/asd-riskatlas/ and interactive graphic summaries: https://public.tableau.com/app/profile/diana.schendel/viz/ASDPlots_16918786403110/e-Figure5. While primarily for reference material or use in other studies (e.g., meta-analyses), results revealed considerable breadth and variation in magnitude of familial health history associations with autism by type of condition, family member type, sex of the family member, side of the family, sex of the index person, and ID status, indicative of diverse genetic, familial, and nongenetic autism etiologic pathways. Careful attention to sources of autism likelihood in family health history, aided by our open data resource, may accelerate understanding of factors underlying neurodiversity.