Data Exchange System Research Articles

Adaptive Cooperative Ship Identification for Coastal Zones Based on the Very High Frequency Data Exchange System

Adaptive Cooperative Ship Identification for Coastal Zones Based on the Very High Frequency Data Exchange System

Effectiveness of organizational and legal measures and new practices to against HIV infection in the Novosibirsk region

The purpose of the study: to study the effectiveness of organizational and legal measures and new practices to against HIV infection introduced in the Novosibirsk region in 2019–2022.Materials and methods. The study was conducted on the basis of the State Medical Institution of the NGO «City Infectious Clinical Hospital No. 1», a division of the Center for the Prevention and Control of AIDS.Results. By Order of the Ministry of Health of the Novosibirsk Region No. 3429 of 10/15/2019, mandatory HIV screening was provided for all hospitalized with unknown HIV status during the year, which significantly increased the coverage of HIV screening. In 2019–2022, an AIDS Center information system was introduced for all patients with HIV infection admitted to inpatient treatment: 6,938 notifications were sent to the AIDS Center. Of the 2,717 patients who required measures to increase the level of adherence and involvement in ART, 18.9% of patients resumed ART after the measures taken; 36.5% started taking ART for the first time. The order of the State Medical Institution of the Infectious Clinical Hospital № 247 «On the organization of family support in the department «Center for the Prevention and Control of AIDS» dated 02.08.2021 provides the principle of monitoring the whole family with one doctor, which improves the adherence of children and parents to ART. For 4 months of functioning of family support, the share of parents receiving ART and having a high commitment increased from 15.5% to 77.3%. Among 45 children who had a previously determined level of HIV RNA viral load on the background of ART, 20% (9 abs.) registered an undetectable level of viral load for 3 months, and 13.3% (6 abs.) – for 2 months.Conclusion. The organizational and legal measures implemented in 2019–2022 in the Novosibirsk region and new practices to combat HIV infection have shown high efficiency: the coverage of dispensary observation of patients with HIV infection increased from 79.3% to 86.5%, and the coverage of antiretroviral therapy – from 74.5% to 85.2%.

A COMPREHENSIVE APPROACH TO ENHANCING THE RESILIENCE OF INFORMATION SYSTEMS: FROM MATHEMATICAL MODELING TO RISK MANAGEMENT STRATEGIES

This article examines the issue of enhancing the reliability of information systems. As a solution to this problem, an approach to assessing resilience to failures in data exchange systems is presented, based on the use of mathematical analysis methods for systems capable of operating at three levels. The developed mathematical model is represented in the form of a graph of states and failure probabilities, as well as a complex of differential equations for its description. The solution of the proposed model allows determining the probabilities of failures at different stages of the information systems' operation. This, in turn, contributes to the effective identification and elimination of malfunctions and equipment failures.

Life-cycle optimal design and energy benefits of centralized cooling systems for data centers concerning progressive loading

Cooling of data centers requires a significant amount of energy, comparable to the energy consumption of the servers themselves. The current design of the centralized cooling systems for data centers is based on ideal IT loading conditions (i.e., 100 % loading). However, such conventional design often results in significant oversized cooling systems and leads to substantial energy waste, since most data centers operate at part load in their lifespan. To address this issue, this study proposes an optimal design for centralized cooling systems with multiple chillers under progressive loading. The optimization problem, aimed at minimizing life-cycle cost, is formulated adopting SLSQP (Sequential Least Squares Programming) algorithm. A cooling system model is developed using the manufacturer's performance data of cooling equipment. The optimal designs in different climate zones are identified according to energy performance under full-range loads and ambient temperatures. Furthermore, this study comprehensively analyzes and compares free cooling hours, cooling energy, and life-cycle cost of the optimized designs with conventional designs. The results show that the optimized cooling systems could operate more energy-efficiently, despite decreased free cooling hours (13–860). Significant cooling energy savings over the lifespan could be achieved, i.e., 4–22 %, corresponding to the PUE reductions of 0.02–0.11, depending on climate conditions and control strategies.

Non-fungible tokens (NFTs) in healthcare: a thematic analysis and research agenda.

In the big data era, where corporations commodify health data, non-fungible tokens (NFTs) present a transformative avenue for patient empowerment and control. NFTs are unique digital assets on the blockchain, representing ownership of digital objects, including health data. By minting their data as NFTs, patients can track access, monetize its use, and build secure, private health information systems. However, research on NFTs in healthcare is in its infancy, warranting a comprehensive review. This study conducted a systematic literature review and thematic analysis of NFTs in healthcare to identify use cases, design models, and key challenges. Five multidisciplinary research databases (Scopus, Web of Science, Google Scholar, IEEE Explore, Elsevier Science Direct) were searched. The approach involved four stages: paper collection, inclusion/exclusion criteria application, screening, full-text reading, and quality assessment. A classification and coding framework was employed. Thematic analysis followed six steps: data familiarization, initial code generation, theme searching, theme review, theme definition/naming, and report production. Analysis of 19 selected papers revealed three primary use cases: patient-centric data management, supply chain management for data provenance, and digital twin development. Notably, most solutions were prototypes or frameworks without real-world implementations. Four overarching themes emerged: data governance (ownership, tracking, privacy), data monetization (commercialization, incentivization, sharing), data protection, and data storage. The focus lies on user-controlled, private, and secure health data solutions. Additionally, data commodification is explored, with mechanisms proposed to incentivize data maintenance and sharing. NFTs are also suggested for tracking medical products in supply chains, ensuring data integrity and provenance. Ethereum and similar platforms dominate NFT minting, while compact NFT storage options are being explored for faster data access. NFTs offer significant potential for secure, traceable, decentralized healthcare data exchange systems. However, challenges exist, including dependence on blockchain, interoperability issues, and associated costs. The review identified research gaps, such as developing dual ownership models and data pricing strategies. Building an open standard for interoperability and adoption is crucial. The scalability, security, and privacy of NFT-backed healthcare applications require further investigation. Thus, this study proposes a research agenda for adopting NFTs in healthcare, focusing on governance, storage models, and perceptions.

Addendum To: Automation for structured cabling system in data centers using Building Information Modelling

Addendum To: Automation for structured cabling system in data centers using Building Information Modelling

Implementasi Sistem Informasi Puskesmas (SIP) di Puskesmas Baki Sukoharjo

The Primary Healthcare Information System is an arrangement that provides information to assist the decision-making process in implementing Puskesmas management in achieving its activity facilities. According to the Regulation of the Minister of Health of the Republic of Indonesia Number 31 of 2019 article 2, it aims to: 1) Realize the implementation of an integrated Primary Healthcare information system. 2) Ensuring the availability of quality, sustainable, and easily accessible data and information. 3) Improve the quality of health development in its working areas through strengthening the management of Puskesmas. Based on the above background, the researchers are interested in knowing how to implement the Public Health Center Information System at the Baki Sukoharjo Primary Healthcare. This type of research is a descriptive analytic study with a qualitative approach with the aim of getting a picture or description of an actual situation. Primary Healthcare Baki started using SIMPUS Webbased in 2006 which was carried out by medical record personnel, to open the program the Puskesmas had to enter the specified website and then enter an id and password, this program was used to manage patient data and was integrated with P-Care. Optimizing the use of the SIMPUS application and planning the development of the SIMPUS according to the needs of the Puskesmas and the standard for reporting the Primary Healthcare information system Keyword: SIP, Primary Healthcare, SIMPUS

Lake Environmental Data Harvester (LED) for Alpine Lake Monitoring with Autonomous Surface Vehicles (ASVs)

This article introduces the Lake Environmental Data Harvester (LED) System, a robotic platform designed for the development of an innovative solution for monitoring remote alpine lakes. LED is intended as the first step in creating portable robotic tools that are lightweight, cost-effective, and highly reliable for monitoring remote water bodies. The LED system is based on the Shallow-Water Autonomous Multipurpose Platform (SWAMP), a groundbreaking Autonomous Surface Vehicle (ASV) originally designed for monitoring wetlands. The objective of LED is to achieve the comprehensive monitoring of remote lakes by outfitting the SWAMP with a suite of sensors, integrating an IoT infrastructure, and adhering to FAIR principles for structured data management. SWAMP’s modular design and open architecture facilitate the easy integration of payloads, while its compact size and construction with a reduced weight ensure portability. Equipped with four azimuth thrusters and a flexible hull structure, SWAMP offers a high degree of maneuverability and position-keeping ability for precise surveys in the shallow waters that are typical of remote lakes. In this project, SWAMP was equipped with a suite of sensors, including a single-beam dual-frequency echosounder, water-quality sensors, a winch for sensor deployment, and AirQino, a low-cost air quality analysis system, along with an RTK-GNSS (Global Navigation Satellite System) receiver for precise positioning. Utilizing commercial off-the-shelf (COTS) components, a Multipurpose Data-Acquisition System forms the basis for an Internet of Things (IoT) infrastructure, enabling data acquisition, storage, and long-range communication. This data-centric system design ensures that acquired variables from both sensors and the robotic platform are structured and managed according to the FAIR principles.

Analysis Of Epuskesmas Adoption In Tasikmalaya Regency Using Utaut Model

The 4.0 Revolution has rendered information technology an indispensable necessity in human life. Technological progress, particularly in healthcare, offers significant conveniences. The healthcare sector's technological evolution is marked by the digital transformation of Community Health Center (Puskesmas) information systems, with ePuskesmas becoming one such system. ePuskesmas serves as an online and integrated Puskesmas information system following the standards set by the Ministry of Health of the Republic of Indonesia through the Puskesmas Information System (SIP). Despite not being universally implemented, particularly in Tasikmalaya District, this study uses the UTAUT model. Variables include Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Behavioral Intention, and Use Behavior. This study uses a quantitative method by distributing online questionnaires to ePuskesmas users in 40 Puskesmas in Tasikmalaya District. All collected data met validity and reliability criteria and were assessed using SmartPLS 4.0 software. Hypotheses were tested using Partial Least Square Structural Equation Modeling (PLS-SEM), revealing that Performance Expectancy and Effort Expectancy significantly and positively influence Behavioral Intention. Behavioral Intention and Facilitating Conditions significantly and positively impact the Use Behavior of ePuskesmas.

Control modeling and optimization of network information security system based on deep learning data interaction

In order to solve the problem of poor network security protection performance of traditional network information security protection systems, the author proposes a network information security system control modeling based on deep learning data interaction. Adopt CY8C2453 type processor to improve system data exchange and data processing speed, enhance system stability through peripheral backplane, and evaluate network information security, at the same time, the authority management mechanism is set, and the general strategy form of the network attack and defense game is defined as a triple, calculate the attacked path in a certain network area and the defense strategy that the defender should choose, so as to complete the network information security protection based on the attack-defense game model. Experimental results show that: The number of attacked information of the system is 58 less than that of the traditional system; In the webpage attack, the number of attacked information of the designed system is 82 less than that of the traditional system; In the data interception attack, the number of attacked information of the designed system is 77 less than that of the traditional system. ConclusionUnder different attack behaviors, the number of successful attacks on the network information security protection system based on the attack-defense game model designed this time is less than the number of successful attacks on the traditional system, which improves the security of network information.

Global optimization strategy of prosumer data center system operation based on multi-agent deep reinforcement learning

The escalating issues of high energy consumption and carbon emissions in data centers (DCs) necessitate the optimization of system operations. However, early optimization strategies were overly simplistic and lacked automated updating and iterative capabilities. With the evolution of artificial intelligence (AI), researchers have applied deep reinforcement learning (DRL) algorithms to system operations. However, the optimization focus has been limited to the internal systems, lacking global optimization. In this paper, a global optimization control strategy based on the Dueling double-deep Q network (D3QN) and value decomposition network (VDN) algorithms is proposed to make the DCs system operate more closely with the upstream, midstream, and downstream. By adjusting battery charging/discharging capacity, computational workload, and waste heat utilization heating temperature global synergistic optimization is achieved. Compared with without optimization, renewable energy waste, operation cost, total electricity consumption, and grid electricity consumption are reduced by 18.37%, 9.78%, 4.01%, and 29.74%, respectively. Additionally, a detailed comparison between non-algorithmic optimization and algorithmic optimization is provided, offering valuable insights for substantial energy savings and emissions reduction in DCs. The results demonstrate the importance of fully exploring the interactive potential between upstream energy supply, midstream computational workload, and downstream waste heat recovery to achieve synergistic global optimization of “computing power", “thermal energy" and “electrical energy" for the sustainable and green development of DCs or other prosumer buildings.

Variational quantum circuit learning-enabled robust optimization for AI data center energy control and decarbonization

As the demand for artificial intelligence (AI) models and applications continues to grow, data centers that handle AI workloads are experiencing a rise in energy consumption and associated carbon footprint. This work proposes a variational quantum computing-based robust optimization (VQC-RO) framework for control and energy management in large-scale data centers to address the computational challenges and overcome limitations of conventional model-based and model-free strategies. The VQC-RO framework integrates variational quantum circuits (VQCs) with classical optimization to enable efficient and uncertainty-aware control of energy systems in AI data centers. Quantum algorithms executed on noisy intermediate-scale quantum (NISQ) devices are used for value function estimation trained with Q-learning, leading to the formulation of a robust optimization problem with uncertain coefficients. The quantum computing-based robust control strategy is designed to address uncertainties associated with weather conditions and renewable energy generation while optimizing energy consumption in AI data centers. This work also outlines the computational experiments conducted at various AI data center locations in the United States to analyze the reduction in power consumption and carbon emission levels associated with the proposed quantum computing-based robust control framework. This work contributes a novel approach to energy-efficient and sustainable data center operation, promising to reduce carbon emissions and energy consumption in large-scale data centers handling AI workloads by 9.8 % and 12.5 %, respectively.

Evaluation of energy performance and ecological benefit of free-cooling system for data centers in worldwide climates

Water-side economizer (WSE) is an effective free-cooling solution aiming to enhance the natural cooling duration and in turn reduce the cooling-energy consumption of data centers. However, the system's performance is constrained by the limited understanding of its control mechanisms and the influence of local climatic conditions on its free-cooling capabilities. To overcome these limitations, a thermodynamic model of the WSE system with control strategies is provided and its adaptability performance in diverse worldwide locations is systematically evaluated. Modelica-based models of chiller plants and control strategies are firstly developed, and then the energy consumption and ecological benefit of the WSE system are investigated via large-scale simulations in worldwide climates. The performance indicators and application potential of the system are demonstrated through visualization maps. The analysis reveals significant energy savings potential of the WSE system, with the capacity to reduce annual cooling energy consumption of data centers by 4.4–33.7 % in hot to marine zones, and even by a remarkable 62.6 % in plateaus and cold zones. Furthermore, it is observed that every 1 °C increase in outdoor air temperature can reduce energy efficiency ratio by 1.3–8.7 % while simultaneously increase PUE by 0.3–1.0 %. The ecological benefit results revealed that the WSE system can effectively harness larger quantities of stable free-cooling resources in mixed and cool climate zones, exceeding its capabilities in hot and warm regions. These results offer valuable insights for the impact of climate on the energy and ecological benefits of WSE system for data center, which can be helpful to guide the design or retrofit of such cooling systems in specific locations, presenting an immense opportunity to maximize cooling-energy savings.

Ant Mill: an adversarial traffic pattern for low-diameter direct networks

Since today’s HPC and data center systems can comprise hundreds of thousands of servers and beyond, it is crucial to equip them with a network that provides high performance. New topologies proposed to achieve such performance need to be evaluated under different traffic conditions, aiming to closely replicate real-world scenarios. While most optimizations should be guided by common traffic patterns, it is essential to ensure that no pathological traffic pattern can compromise the entire system. Determining synthetic adversarial traffic patterns for a network typically relies on a thorough understanding of its topology and routing. In this paper, we address the problem of identifying a generic adversarial traffic pattern for low-diameter direct interconnection networks. We first focus on Random Regular Graphs (RRGs), which represent a typical case for these networks. Moreover, RRGs have been proposed as topologies for interconnection networks due to their superior scalability and expandability, among other advantages. We introduce Ant Mill, an adversarial traffic pattern for RRGs when using routes of minimal length. Secondly, we demonstrate that the Ant Mill traffic pattern is also adversarial in other low-diameter direct interconnection networks such as Slimfly, Dragonfly, and Projective networks. Ant Mill is thoroughly motivated and evaluated, enabling future studies of low-diameter direct interconnection networks to leverage its findings.

Study of nucleate boiling growth regime on thin surfaces.

In a context of energy transition, heat exchanges are a key for energy sobriety. Among those exchanges, nucleate boiling is the preferred heat transfer method for high heat flux. Vaporisation plays an important role in the nuclear industry, but also in thermal machines such as cooling systems in data centers. In spite of decades studying boiling thermodynamics, modelling heat transfers in the nucleate boiling is still a major difficulty because of its numerous parameters. In the 1960s, scientists developed promising models called heat flux partitioning models. These models have been improved over the years, but they need closure laws about bubbles dynamics and nucleation site density. For the purpose of improving those models, we took part in an international collaboration (ANR TraThI) to study interface heat transfer. Project focuses on boiling with a controlled nucleation site density with a strong emphasis on studying isolated bubbles in water pool boiling, and later addressing multiple bubble interactions in pool and flow boiling. This work focuses on the bubble growth regimes on thin metallic foil observed in a water pool boiling experiment, with a distinction between microlayer and contact line growth regime. Pulsed nanosecond laser was used to create active nucleation site, while high-speed infrared thermography and shadowgraphy were implemented to record transient wall temperatures and bubble dynamics, respectively. This work shows that our experimental configuration induced two different bubble growth regimes, depending on the imposed heat flux and the recent past at the nucleation site and its vicinity. Study provides a framework for further in-depth investigations with different experimental configurations.

End to End secure data exchange in value chains with dynamic policy updates

Data exchange among value chain partners offers a competitive advantage, but it also exposes sensitive data to increasing risks of compromise. To protect this data, securing it during storage and transmission is essential, allowing only the data producer and final consumer to access or modify it. This can be particularly challenging for Industrial Internet of Things (IIoT) devices, which often have limited processing and memory capabilities. To address these challenges, End-to-End (E2E) security mechanisms are needed to prevent data breaches resulting from value chain attacks. Additionally, value chain environments are dynamic and involve multiple data generation and consumption entities, requiring the ability to update access policies as needed. This paper presents a Ciphertext-Policy Attribute-Based Encryption (CP-ABE) data exchange system that provides E2E security for value chains, considering the most relevant security and industrial requirements. The proposed solution can protect data according to access policies while also allowing those policies to be updated without breaking E2E security or overloading field devices. The experimental evaluation demonstrates the feasibility of the proposed solution for IIoT platforms.

A smart and novel approach for managing incast and in-network congestion through adaptive routing

High-Performance Computing and Datacenter systems, with numerous endnodes, demand an efficient interconnection network to prevent performance bottlenecks. Fat-Tree topologies are preferred for their high bisection bandwidth and multiple shortest-path routes. While existing adaptive routing excels in light or in-network congestion, it struggles with incast congestion. This paper proposes a new technique, called Congestion-Aware Adaptive Routing (SCAR), which addresses both in-network and incast congestion. SCAR limits adaptivity for incast congestion, using deterministic routing, while employing adaptive routing for non-congesting flows. It also resolves in-network congestion by routing traffic flows through alternative routes. Simulation experiments on large Fat-Trees using synthetic and trace-based traffic patterns modeling realistic applications demonstrate SCAR’s immediate reaction on mitigating in-network congestion, and a reasonable delay during incast situations, while other state-of-the-art solutions are not able to cope with incast and in-network situations at the same time.

Analysis of Competency, Integrity, And Capability on The Performance Of Information System Based Budget Planning

Budget planning in the Regional Government financial system currently centers on an information system that is directly connected to the central information system in Jakarta. The accuracy of planning depends on the ability of the planning apparatus so that the circulation of money transfers from the Center to the Regions runs smoothly. The research aims to examine the competency, integrity and capability factors of budget planners in terms of using the available information systems properly so that the financial transfer process for the Region runs smoothly. Qualitative descriptive research. Data was obtained from structured, repeated and in-depth interviews with selected respondents who had been determined. The interview results were compiled using a weighting technique to obtain numerical figures on a scale of 1-10. The number of respondents was 115 people from budget planners and officials at the Regional Apparatus Organization of Fakfak Regency. Based on the weight results, a gap analysis can be carried out which is displayed using spider web analysis. The research results show that the competency, integrity and capability of budget planners must be continuously improved through education and training. This is based on the many technical changes in the use of information systems which budget planners must know and master at all times

Колекция с българска детска реч в термините на корпусната лингвистика

The proposed article focuses on the application possibilities of corpus linguistics to the research of the child-adult speech interaction, and more specifically – the CHILDES automated computerized system for language data exchange, freely ac-cessible online at https://childes.talkbank.org. The main aim of the report is to present the first Bulgarian corpus, Bulgarian La-bLing Corpus (https://childes.talkbank.org/access/Slavic/Bulgarian/LabLing.html), where the linguistic resources are transcribed and annotated according to the terms of the platform, as well as to point out the advantages of the latter. Those include the conditions provided by CHILDES for improved precision in the collecting, transcribing and coding of the data, along with the automation of the analysis of large quantities of spoken material, which considerably enlarges the empirical basis on which new theories are built.

Green Data Center : Reviews And Challenges.

Data is undoubtedly the currency of the 21st century. The data that we are providing to or retrieving from the users has a very important role to play in the evolution of services in both the computing and non-computing spheres. Various internet services, such as web hosting, e-commerce, and Social Networking, find their roots in the Data centers. With the ever-increasing concerns about carbon emissions, reliance on non-renewable energy resources, and energy consumption by the cooling systems in conventional Data centers, it is imperative to shift our focus to newer, greener, and cleaner technologies that will aim to reduce the overall carbon footprint, technologies that will reduce energy consumption, and the integration of renewable energy resources into the existing setup. There are several studies that have highlighted challenges regarding the adoption of technologies in the hardware and software domains and the standardization of performance metrics, which are generally used to study trends in energy consumption and net emissions and, therefore, assess the sustainability and efficiency of Data center operations. This shift to Green Data centers will certainly involve both standardized performance metrics, effective strategies to reduce energy consumption, and the integration of renewable energy resources to reduce the carbon footprint. Furthermore, we are going to discuss data center network architecture that will make our future Green Data centers scalable, fault-tolerant, agile, and energy efficient. This paper reviews various strategies for designing an efficient data center, ways to incorporate renewable energy resources, standardization and benchmarking of performance metrics, and optimizing the cooling systems to improve energy efficiency. Finally, we are going to outline the future directions and opportunities for further research and innovation.