Technical Database Services (United States)

The development of digital technology has brought about significant changes in people's transaction patterns, particularly with the increasing use of online platforms for buying and selling. This phenomenon provides convenience for consumers and merchants, but is also accompanied by a high rate of online fraud experienced by both consumers and merchants. This fraud can take the form of fraudulent transactions, goods not received, or identity fraud. Low public legal literacy regarding rights and obligations in digital transactions is one of the main causes of weak legal protection for fraud victims. Many consumers and merchants are unaware of their rights and the legal procedures to take when experiencing fraud. This community service activity aims to increase the legal understanding of the public, especially online merchants and consumers, regarding digital consumer protection and legal fraud handling mechanisms. The activity implementation methods included interactive legal counseling, distribution of closed-ended questionnaires, case study simulations, Q&A discussions on legal issues, and light consultations. A total of 60 respondents, consisting of 30 merchants and 30 consumers, participated in the closed-ended questionnaire. The results showed that the majority of participants did not fully understand their rights in digital transactions and were unaware of the legal procedures to take when experiencing fraud. Despite this, participant interest in legal education was very high, with over 75% stating they wanted further legal assistance. This activity is expected to provide the public with better insight into their rights and obligations in digital transactions, as well as raise awareness of the importance of legal protection in the digital age. This is expected to make the public more vigilant and protected from potential online fraud in the future. Furthermore, increased legal literacy is expected to create a safer and more trustworthy digital transaction ecosystem for all parties.

Abstract We study gradient flow on the multi‐index regression problem for high‐dimensional Gaussian data. Multi‐index functions consist of a composition of an unknown low‐rank linear projection and an arbitrary unknown, low‐dimensional link function. As such, they constitute a natural template for feature learning in neural networks. We consider a two‐timescale algorithm, whereby the low‐dimensional link function is learnt with a non‐parametric model infinitely faster than the subspace parametrizing the low‐rank projection. By appropriately exploiting the matrix semigroup structure arising over the subspace correlation matrices, we establish global convergence of the resulting Grassmannian gradient flow dynamics, and provide a quantitative description of its associated “saddle‐to‐saddle” dynamics. Notably, the timescales associated with each saddle can be explicitly characterized in terms of an appropriate Hermite decomposition of the target link function.

Efficient cloud resource allocation through timeseries forecasting is critical in cloud data centers, as accurate demand prediction enables optimal utilization of computing resources and cost reduction. Traditional methods based on conventional machine learning or statistical analysis often fall short in capturing complex temporal patterns, resulting in low prediction accuracy and inefficient resource use. To address these challenges, this study introduces a novel Cycle-Consistent Adversarial Adaptation Network with Geyser Inspired Algorithm (CCAAN-GIA), leveraging historical workload data from Bitbrain's cloud data center, specifically from virtual machines in the fast Storage and Rnd datasets. The raw data undergoes pre-processing; including handling of missing values, feature averaging, and min-max normalization to prepare it for time-series forecasting using the CCAAN framework. The model's predictive performance is further enhanced by tuning hyper parameters with the Geyser Inspired Algorithm (GIA). Additionally, to optimize resource distribution and minimize operational costs, the Cat Optimization Algorithm (COA) is employed, ensuring effective resource management. Experimental results demonstrate the superiority of the proposed approach, achieving 99.5% accuracy with significantly lower error metrics-MAE of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{0. 0 2}$</tex>, RMSE of 11.2, and MAPE of 0.46-outperforming traditional forecasting models. This confirms the method's potential as a robust solution for intelligent and cost-efficient cloud resource allocation.

Enterprise Resource Planning (ERP) systems serve as the digital backbone of mission-critical operations across diverse industries.As organizations increasingly demand 24/7 system availability, traditional patching methods that require downtime have become a significant operational challenge.Oracle E-Business Suite (EBS) 12.2.xintroduces a transformative solution in the form of the Online Patching feature powered by the ADOP utility, which leverages Edition-Based Redefinition (EBR) and dual file systems to enable near-zero-downtime application updates.This paper presents a comprehensive technical and strategic analysis of the ADOP architecture, breaking down its multi-phase lifecycle-prepare, apply, finalize, cutover, and cleanup-while assessing its operational and business impact.Through illustrative diagrams, data-driven comparisons, and enterprise case studies, we demonstrate how online patching improves resilience, availability, and compliance, while significantly reducing patching effort and risk.The paper also explores best practices, challenges, and implementation strategies for organizations planning or managing Oracle EBS 12.2.xenvironments.

Online Analytical Processing (OLAP) is crucial for modern organizations, enabling data-driven decision-making from evergrowing datasets.The increasing volume and complexity of this data present significant challenges to timely and efficient analysis.This paper investigates Amazon Redshift, a highly scalable and performant data warehouse service offered by AWS, as a solution to these challenges.We will examine Redshift's architecture, focusing on its parallel processing capabilities, columnar storage and optimization techniques.Furthermore, we will explore best practices for scaling Redshift deployments to handle heavy analytical workloads and discuss the impact of key design choices, such as data distribution strategies and ETL processes, on overall performance.The research will contribute to a better understanding of how Redshift can effectively address the analytical needs of large organizations.

Background:Nonalcoholic fatty liver disease (NAFLD) is a common chronic liver disease that imposes a huge economic burden on global public health. And the gut-liver axis theory supports the therapeutic role of intestinal flora in the development and progression of NAFLD. To this end, we designed bioinformatics study on the relationship between intestinal flora disorder and NAFLD, to explore the possible molecular mechanism of intestinal flora interfering with NAFLD.Methods:Differentially expressed genes for NAFLD were obtained from the GEO database. And the disease genes for NAFLD and intestinal flora disorder were obtained from the disease databases. The protein-protein interaction network was established by string 11.0 database and visualized by Cytoscape 3.7.2 software. Cytoscape plug-in MCODE and cytoHubba were used to screen the potential genes of intestinal flora disorder and NAFLD, to obtain potential targets for intestinal flora to interfere in the occurrence and process of NAFLD. Enrichment analysis of potential targets was carried out using R 4.0.2 software.Results:The results showed that 7 targets might be the key genes for intestinal flora to interfere with NAFLD. CCL2, IL6, IL1B, and FOS are mainly related to the occurrence and development mechanism of NAFLD, while PTGS2, SPINK1, and C5AR1 are mainly related to the intervention of intestinal flora in the occurrence and development of NAFLD. The gene function is mainly reflected in basic biological processes, including the regulation of metabolic process, epithelial development, and immune influence. The pathway is mainly related to signal transduction, immune regulation, and physiological metabolism. The TNF signaling pathway, AGE-RAGE signaling pathway in diabetic activity, and NF-Kappa B signaling pathways are important pathways for intestinal flora to interfere with NAFLD. According to the analysis results, there is a certain correlation between intestinal flora disorder and NAFLD.Conclusion:It is speculated that the mechanism by which intestinal flora may interfere with the occurrence and development of NAFLD is mainly related to inflammatory response and insulin resistance. Nevertheless, further research is needed to explore the specific molecular mechanisms.

Modern enterprises rely on large scale database systems, distributed storage engines, and big data processing platforms to sustain transaction processing, analytics, and real time decision workloads that are critical to business continuity. As these environments expand across cloud regions, hybrid infrastructures, and highly dynamic workloads, traditional manual incident response approaches struggle to keep pace with the volume, velocity, and complexity of operational events, leading to delayed detection and prolonged recovery times. The integration of machine learning techniques with automated observability pipelines, telemetry aggregation, and control plane automation enables the emergence of self-healing infrastructures that continuously analyze logs, metrics, traces, and system signals to identify anomalies, correlate symptoms, and infer probable root causes with minimal human intervention. Intelligent remediation workflows can trigger actions such as workload rebalancing, failover orchestration, resource scaling, configuration correction, or query throttling, forming closed loop feedback systems that stabilize operations. This paper examines how advanced log analytics, adaptive feedback control mechanisms, and distributed stream processing frameworks support machine learning augmented incident response in database environments, illustrating through architectural patterns and practical scenarios how organizations can enhance operational resilience, minimize service disruptions, and maintain performance guarantees by adopting proactive and autonomous recovery strategies.

Abstract The present study has investigated the role of meteorology and air quality for recovering from the COVID-19 pandemic in India. Using Pearson’s correlation method, we look into if there is any significant association occurs between the district level recovery case counts and different remote sensing based environmental variables. Among weather parameters, only precipitation and air temperature found to be significantly correlated with recovery situation. However, all the pollutants’ concentration was negatively correlated with count of recovery cases. It depicts that air quality might has greater importance in recovery from this disease. During late monsoon onwards, recovery rate was getting more than the infections which indicate that lesser temperature and good rainfall could help the air to be freshen. Through air pollution was greater during winter and post monsoon than the summer season in India the higher recovery rate was counted during post-monsoon and winter which suggest that patients may require lesser temperate ambient for better recovery. Spatial patterns also suggest that north-eastern hilly region followed by districts located in the northern mountain had better recovery where the pollutants’ concentration was also quite lower during the study period. Therefore, improving air quality with proper preventive precaution could help to combat the pandemic situation in India.

Infrastructure as Code (IaC) is transforming the way the IT systems of the enterprise are put into place and managed. The presented paper is a quantitative study of the effects of IaC. IT specialists were interviewed and the data was compared between the traditional systems and IaC-based systems. The analysis indicates IaC saves deployment time, costs, percentage better resource utilization, and reliability of the system. It also minimizes configuration error source and manual efforts. There are still certain challenges such as security issues and some requirement of appropriate abilities. The research demonstrates that IaC can be successfully used as the means of enhancing IT functioning and accelerating and streamlining the delivery of services in the contemporary enterprises