Data Management System Research Articles

Quality of routine malaria data captured at primary health facilities in the Hohoe Municipality, Ghana.

Malaria is a major public health concern and requires quality data management system for effective preventive measures. The District Health Management Information System (DHMIS II) has been used to routinely capture data at health facilities. However, little is known about the quality of routine malaria data captured on the DHIMS II database in Community-Based Health Planning and Service (CHPS) compounds. The study therefore determined the quality of routine malaria data captured on the DHIMS II database in CHPS compounds in the Hohoe Municipality, Ghana. A retrospective cross-sectional analysis of Out Patient Department (OPD) malaria indicators was conducted using data from January 2018 to December 2022 at CHPS compounds in the Hohoe Municipality. Data were collected from three sources: the DHIMS II, monthly morbidity report forms, and consulting room registers. The study assessed three (3) malaria indicators: suspected malaria cases, tested malaria cases, and confirmed OPD malaria cases. A data validation tool was developed to determine the quality of malaria indicators measuring availability, completeness (percentage of missing data), and accuracy. The data was analysed descriptively using Microsoft excel. Out of the four (4) health facilities, 50% (2/4) met the suggested target of (≥ 90%) in 2018 and 2020 whiles all the (4) facilities met the recommended target in 2021 and 2022 for the availability of monthly OPD morbidity reports. For the availability of monthly data returns on anti-malarial, none of the facilities met the recommended target from 2018 to 2022. All 4 facilities met the recommended target in 2021 and 2022. For completeness of source data, 25% of the facility had complete data that met the required target in specific years (2021-2022). For accuracy, 50% of the facilities showed accurate reporting with a Good (± 5%) accuracy level. The remaining 50% underreported data, resulting in a Poor (± 11-20%) accuracy level. The study finds that while half of the facilities had reliable and complete malaria data in their source registers, there are inconsistencies with the DHIMS II database regarding the standards of data quality. Most facilities faced significant issues like unavailability of data, uncompleted data and underreporting of data, making it not-advisable to rely on DHIMS II for critical health decisions. Although, half of the facilities showed evidence of good data quality, there is still a need for improvement in the capturing of routine malaria data.

Blockchain Security Attacks, Difficulty, and Prevention

Blockchain technology is increasingly attracting young people because it is so well adapted to the digital age. A decentralised data management system is necessary for the blockchain idea in order to store and share data and transactions throughout the network. This study investigates various types of risks associated with blockchain technology. The research covers different aspects of blockchain, including the architecture, consensus mechanism, smart contracts, and underlying cryptographic algorithms. It also examines the risks associated with the adoption and implementation of blockchain in various industries, such as finance, healthcare, and supply chain management. Moreover, this study identifies several types of risks, including technical risks, such as scalability, interoperability, and security, as well as non-technical risks, such as regulatory compliance, legal liability, and governance issues. This study also discusses the potential impact of these risks on blockchain-based systems and the strategies that can be used to mitigate them.

Parallelized Event Data Management System Based on MT-SNiPER Framework and PODIO

Software framework serves as a skeleton for the offline data processing software for many high energy physics (HEP) experiments. The event data management, including the event data model (EDM), transient event store and data input/output, implements the core functionalities of the framework, and has a great impact on the performance of the entire offline software. Future HEP experiments are generating increasingly large amounts of data, bringing challenges to offline data processing. To address this issue, a common event data management system that supports efficient parallelized data processing applications has been developed based on Software for Non-collider Physics ExpeRiments (SNiPER) common software framework as well as PODIO, a common EDM toolkit for future HEP experiments. In this paper, the implementation of a parallelized event data management (PEDM) system is introduced, including the integration with MT-SNiPER and PODIO, as well as the implementation of GlobalStore to support multi-threaded event processing. Finally, the application and performance evaluation of the data management system in OSCAR (offline software of Super Tau Charm Facility) is presented.

Organization of the data monitoring process within the framework of clinical trials

This article explores innovative methodologies for organizing the data monitoring process in clinical trials, with a focus on traditional, remote, and risk-based approaches, as well as the integration of digital technologies. Through a comparative analysis of monitoring models, the study highlights the advantages of combining these methods in multicenter projects to enhance data accuracy, security, and compliance with modern regulatory standards. The findings demonstrate that digitalization and centralized systems significantly improve monitoring efficiency and risk management, offering actionable recommendations for clinical researchers and project managers. The materials of the article are based on a comparison of different monitoring models. Their application in multicenter projects is being considered, and their compliance with modern regulatory requirements is being assessed. Special attention is paid to the stages of planning, execution, completion of monitoring, as well as the introduction of information technologies. The possibilities of using data management systems. The results confirm that the combination of methods using elements of digitalization helps to increase the efficiency of data analysis, and minimizes the risks that arise in the process of processing large amounts of information. Ensuring data security and adapting monitoring to the specifics of specific projects is of particular importance. This work is of interest to researchers, clinical project managers, and specialists involved in the development of digital solutions. The proposed recommendations are aimed at introducing relevant approaches that meet the requirements of modern science. Data monitoring remains an important element of clinical research. To achieve these goals, it is necessary to use methods that combine traditional practices and innovative solutions aimed at improving management and data processing technologies.

Using a clinical decision support system to reduce excess driving pressure: the ALARM trial

BackgroundPatients at need for ventilation often are at risk of acute respiratory distress syndrome (ARDS). Although lung-protective ventilation strategies, including low driving pressure settings, are well known to improve outcomes, clinical practice often diverges from these strategies. A clinical decision support (CDS) system can improve adherence to current guidelines; moreover, the potential of a CDS to enhance adherence can possibly be further increased by combination with a nudge type intervention.MethodsA prospective cohort trial was conducted in patients at risk of ARDS admitted to an intensive care unit (ICU). Patients were assigned to control or intervention by their date of admission: First, the control group was included without changing anything in clinical practice. Next, the CDS was activated showing an alert in the patient data management system if driving pressure exceeded recommended values; additionally, data on the performance of the wards were sent to the healthcare professionals as the nudge intervention. The main hypothesis was that this combined intervention would lead to a significant decrease in excess driving pressure.ResultsThe 472 included patients (230 in the control group and 242 in the intervention group) consisted of 33% females. The median age was 64 years; median Sequential Organ Failure Assessment score was 8. There was a significant reduction in excess driving pressure in the augmented ventilation modes (0.28 ± 0.67 mbar vs. 0.14 ± 0.45 mbar, p = 0.012) but not the controlled mode (0.37 ± 0.83 mbar vs. 0.32 ± 0.8 mbar, p = 0.53). However, there was no significant difference between groups in mechanical power, the number of ventilator-free days, or the percentage of patients showing progression to ARDS. Although there was no difference in progression to ARDS, 28-day mortality was higher in the intervention group. Notably, the mean overall driving pressure across both groups was low (12.02 mbar ± 2.77).ConclusionsIn a population at risk of ARDS, a combined intervention of a clinical decision support system and a nudge intervention was shown to reduce the excessive driving pressure above 15 mbar in augmented but not in controlled modes of ventilation.

Fog assisted data size reduction based data management system for optimizing resources in medical IoT

Fog assisted data size reduction based data management system for optimizing resources in medical IoT

Core software for the super tau-charm facility

The Super Tau Charm Facility (STCF) is a new generation electron–positron collider, proposed to study various physics topics in the tau charm energy region. STCF will operate within a center-of-mass energy spanning 2 to 7[Formula: see text]GeV, with a peak luminosity of [Formula: see text] [Formula: see text]cm[Formula: see text]s[Formula: see text], which means STCF will produce two orders of magnitude more data compared to BESIII each year, posing great challenges for the data processing software. To support the intricate requirements from the offline data processing tasks, as well as to tackle this immense challenge caused by the huge data volume, the offline software for STCF (OSCAR) is designed and developed, based on the state-of-the-art technology and toolkits in the high energy physics community. This paper provides an overview of the design and implementation of OSCAR, highlighting its event data management system, parallel data processing capabilities leveraging SNiPER and TBB, and its geometry management framework powered by DD4hep. Presently, OSCAR is fully operational, effectively supporting the conceptual design of the STCF detector and exploring its vast physics potential.

A novel spherical fuzzy-based decision model for assessing data management maturity in governmental institutions

Abstract The capability of government institutions to manage data effectively is fundamental to their operational efficiency and innovation potential. Governments face unique challenges, including rapid data generation, evolving regulations, and demands for quality services and transparency. This necessitates a tailored approach to data governance, given the complexities of balancing public interests with data privacy. This study aims to establish a robust framework for evaluating the data management maturity of Government Entities by developing an evaluative metric that reflects their data management maturity. The research approach involved gathering and synthesizing dispersed principles from existing literature into a set of definitive criteria. The criteria were subjectively weighted by an expert panel (SME) to reflect the significance of each criterion in a government setting. For methodology, the study pioneers the hybridization of spherical fuzzy sets (SFSs) built on the criteria importance through intercriteria correlation (CRITIC) and the evaluation based on distance from average solution (EDAS) model. The criteria weighting was methodically calculated using the CRITIC method, and the subsequent evaluation of the alternatives was ascertained through EDAS. This combination of methodologies effectively reduced subjective bias, yielding a more reliable foundation for the rankings. A sensitivity analysis was conducted to confirm the robustness of the presented methodology when subjected to variations. To verify the validity of the developed method, we compared the SF- CRITIC and SF-EDAS approach with the SF-AHP and SF-EDAS, SF-CRITIC and SF-TOPSIS, the SF-CRITIC and SF-WPM, and the SF-CRITIC and SF-MARCOS. The results showcased a spectrum of maturity levels across the evaluated entities, pinpointing both commendable proficiencies and key areas for growth. This research presents a strategic asset for government bodies, aiding in the targeted enhancement of their data management systems. The broader implications of our findings serve as a strategic compass for governmental organizations, steering them toward achieving a higher echelon of data management sophistication.

A quick response data collection and management system for digital twin production line based on cloud-fog-edge computing collaboration

A quick response data collection and management system for digital twin production line based on cloud-fog-edge computing collaboration

Analysis of the Role of Regional Government in Optimizing the School Operational Assistance Program (BOSP) Policy in Surabaya as a Strategy for Improving Education Quality

The development of the implementation of the School Operational Assistance Policy (BOSP) in Surabaya faces various challenges that affect policy optimization. This study aims to analyze the role of local government in optimizing the BOSP policy using Edward III's policy implementation theory, which includes communication, bureaucratic structure, resources, and the disposition or attitudes of implementers. This research employs a qualitative method by collecting data through in-depth interviews with informants from the education office and educational institutions in Surabaya, as well as documentation studies related to the BOSP policy. The study results show that communication contributes 68% to the success of the BOSP policy implementation, although there are still shortcomings in responsiveness and socialization. Bureaucratic structure contributes 72%, with major challenges in fragmented coordination between units and complex decision-making procedures. Resources contribute the highest at 74%, but limitations in staff competence and technological infrastructure remain significant obstacles. The disposition or attitudes of implementers contribute 73% to success, though deficiencies in discipline, integrity, and initiative are still evident. These four aspects indicate that while most elements have performed well, there is still room for improvement requiring serious attention to enhance policy effectiveness.The conclusion of this study is that optimizing the BOSP policy in Surabaya requires strengthening the four analyzed aspects. To address communication shortcomings, more structured and responsive socialization mechanisms are needed. Bureaucratic fragmentation can be resolved by simplifying procedures and integrating data management systems. Human resource and technological limitations must be addressed through intensive training and infrastructure investment. Finally, implementer disposition can be improved through development programs and supervision. These strategic recommendations can support the Surabaya local government in optimizing the implementation of the BOSP policy in a transparent, accountable, and effective manner to achieve better educational goals.

MANAGEMENT ASPECTS OF THE INTEGRATION OF MEDICAL STATISTICS AND INFORMATION SYSTEMS FOR EFFECTIVE RESOURCE PLANNING IN THE HEALTHCARE SYSTEM

This article delves into the administrative aspects of integrating medical statistics and information systems, focusing on their role in enhancing the efficiency of resource planning within the healthcare system. It emphasizes the critical importance of medical statistics in the development and implementation of effective healthcare programs. These statistics provide essential data on the health status of the population, the distribution of diseases, the utilization of medical resources, and the effectiveness of healthcare services, all of which are crucial for making informed decisions at both the local and national levels. The article also identifies several barriers and challenges in the current medical data management processes, such as the lack of unified standards for data collection and the insufficient integration of information systems. These issues result in fragmented data and complicate the process of data analysis and interpretation, thereby hindering efficient decision-making and resource optimization. The study stresses the need to overcome these obstacles to ensure that medical statistics can be used more effectively to improve healthcare outcomes. In addition, the article explores the integration of various medical information systems, including the Ministry of Health's central database (CBD) and the medical institutions’ internal data management systems. It underlines the necessity for seamless data exchange between different levels of healthcare management to promote transparency, coordination, and accountability in healthcare services. Such integration is crucial for improving the overall quality of healthcare delivery and for making timely, evidence-based decisions. Furthermore, the paper addresses the need for modernizing reporting systems to ensure they align with the latest legal frameworks and to adapt to the evolving healthcare landscape. This is particularly important in the context of the ongoing war in Ukraine, where new challenges are emerging in healthcare management. The article highlights that the current systems of data reporting and collection often fail to meet the specific needs arising from the current socio-political situation, such as the monitoring of displaced persons and the need for rehabilitation services. By investigating these issues, the article aims to provide recommendations for enhancing the management of medical statistics and improving the overall functionality of healthcare systems. The ultimate goal is to create a more integrated and efficient healthcare system that can better respond to the challenges posed by both the ongoing conflict and the long-term health needs of the population.

Implementation of the Gender Mainstreaming Policy in Supporting Sustainable Development in Kotamobagu City

This research aims to describe and analyze the Implementation of Gender Mainstreaming (PUG) in Supporting Sustainable Development in Kotamobagu City. This research uses a descriptive qualitative approach, with 10 predetermined informants with data collection techniques namely observation, interviews and documentation with data analysis techniques through data reduction, data presentation and verification conclusion drawing. The results showed that the implementation of gender mainstreaming in Kotamobagu City faces significant challenges in various aspects that have an impact on its not optimal implementation. From the commitment sub-focus, limited understanding of the apparatus and inadequate budget allocations cause gender responsive policies and programs to be unevenly distributed at all levels of government, so that vulnerable groups such as people with disabilities and people with economic limitations have not been optimally accommodated. In the policy sub-focus, the lack of capacity of government officials and the unequal distribution of resources, especially in remote areas, further exacerbates the gap between policies and their implementation in the field. After that, in the resource sub-focus, limited time and budget for continuous training hinder the strengthening of apparatus competencies in integrating gender perspectives into the planning and implementation of development programs. In addition, the disaggregated data management sub-focus is still constrained by inadequate information systems and weak coordination between institutions, so that existing data has not been fully utilized to design gender-based policies. Therefore, it is necessary to strengthen commitment, increase human resource capacity, effective data management systems, and cross-sector coordination to support inclusive sustainable development in Kotamobagu City.

The AI revolution: Identifying the internal capabilities to AI-powered innovation among manufacturing small and medium enterprises

The ongoing fourth industrial revolution has undoubtedly had a significant impact on virtually all aspects of society and business, and in this disruptive digital landscape. This study investigates the critical internal capabilities that enable small and medium enterprises to effectively adopt AI-driven innovations in the South African context. By examining key factors such as AI readiness, organizational learning capacity, strategic flexibility, and data management capabilities, the research provides a comprehensive analysis of their impact on AI-powered innovation performance. Using structural equation modelling to analyse data from SME owners and managers, the findings reveal significant positive correlations between these internal capabilities and innovation success. The results highlight the importance of investing in technological infrastructure, fostering a learning-oriented culture, and enhancing data management systems to ensure sustained competitiveness in the rapidly evolving AI landscape. These insights are crucial for SMEs aiming to leverage AI technologies for business growth and for policymakers seeking to support technology-driven sector development in emerging markets.

Enterprise Microservices: Revolutionizing Telecom Network Architecture

This comprehensive article explores the transformative impact of enterprise microservices architecture in the telecommunications industry through the implementation of the UltraVailable Network Service (UVNS) project. The article examines how microservices address challenges related to critical network scalability, availability, and operational efficiency. The article demonstrates the significant advantages of microservices over traditional monolithic architectures through a detailed analysis of automated provisioning, billing systems, and advanced monitoring capabilities. The implementation showcases innovations in dynamic resource allocation, high availability features, and data management systems supported by artificial intelligence and machine learning technologies. The results reveal substantial improvements in service delivery, operational costs, and system performance, establishing a new benchmark for telecommunications infrastructure management.

Challenges and Countermeasures in Cross-Border Data Collection

In the context of globalization, cross-border data collection has become a crucial tool for countries combating transnational crime. China primarily employs international criminal judicial assistance to conduct cross-border data collection. While this approach respects national sovereignty, it faces practical challenges such as low efficiency and limited authority. In contrast, the United States and the European Union adopt a more flexible data controller model, achieving robust cross-border data collection capabilities. However, conflicts in data sovereignty and differences in legal systems hinder the direct application of these models in China. This paper employs a comparative research methodology to analyze the legislative frameworks and backgrounds of the U.S. and EU cross-border data collection approaches, identifying reasons these models are unsuitable for direct implementation in China. Building on insights from these legislative models, the paper proposes recommendations such as increasing the number of bilateral judicial assistance agreements and establishing a categorized data management system, aiming to provide a reference for China's future development in this field.

Development of a new data management system for the study of the gut microbiome of children who are small for their gestational age.

Microbiome studies aim to answer the following questions: which organisms are in the sample and what is their impact on the patient or the environment? To answer these questions, investigators have to perform comparative analyses on their classified sequences based on the collected metadata, such as treatment, condition of the patient, or the environment. The integrity of sequences, classifications, and metadata is paramount for the success of such studies. Still, the area of data management for the preliminary study results appears to be neglected. Here, we present the development of MetagenomicsDB (http://github.com/IOB-Muenster/MetagenomicsDB; accessed 2024/12/18), a central data management system for the study of the gut microbiome in children who are small for their gestational age (SGA). Our system provided more flexibility to conduct study-specific analyses and to integrate specific external resources than existing and necessarily more generic solutions. It supports short or long read data produced by virtually any sequencing instrument targeting (parts of) popular marker genes, such as the 16S rRNA gene and its variable regions. Classifications of these reads from the MetaG and Kraken 2 software are supported. The main goals of the system are to store the pre-computed study data securely under concurrent load and to make downstream analyses accessible to all researchers, regardless of programming proficiency. Thus, after initial plausibility checks on the input data to reduce human error, data are stored in a relational database and can be continuously updated over the whole life time of the study. We used a modular approach for MetagenomicsDB with comprehensive tests verifying the expected behavior and extensively described the underlying rational which allows users to adapt the system to their needs. We advocate the use of MetagenomicsDB as the backend for a graphical web interface. We showcase the potential of this approach at the example of our study on SGA children (http://www.bioinformatics.uni-muenster.de/tools/metagenomicsDB; accessed 2024/12/02). Without restrictions caused by the level of programming proficiency, our team members could explore the study data and optionally filter them using the graphical interface, before exporting the data in a format directly suitable for external normalization of read counts and statistical analyses. Study results could be conveniently and transparently shared with the public, as demonstrated here. Links to external resources facilitated literature search with regard to the SGA condition and assessments of the potential pathogenicity of taxa. Since different users will have different demands regarding features, data security, and web environments, we provide our implementation of the web interface as a visual example. By providing users with the MetagenomicsDB backend which constitutes the major part of the system, we ensure that custom development can be finished in a reasonable amount of time. We report our endeavors in order to motivate the application of data management systems at the scale of single studies in microbiome research.

Development of an autonomous Internet of Things system for distributed production of boxed flat sheet metal parts in railcar manufacturing

AbstractRailcar manufacturing takes place in a multi‐tier production environment, where scheduling and resource allocation are two of many critical components of operations management. In general, organizations harness the opportunities presented by enterprise information systems (EIS) with connected devices to enhance the efficiency of their supply chains. However, systematic vulnerabilities emerge in these EIS due to a lack of unified and synchronized data management systems across enterprises. Industry 4.0 (i4.0) prompted the adoption of cyber‐physical systems (CPS) in industry, creating new opportunities to address these vulnerabilities. A key challenge identified in planning and developing autonomous Internet of Things (IoT) system lies in integrating and managing data from multiple sources, which come in various formats, and is generated in different scales, using different platforms. Hence, the study centred on developing a cloud‐native autonomous manufacturing application, with bi‐directional data flow for production of boxed flat sheet metal parts in a distributed network of manufacturing suppliers. Serverless computing enables integration of information technology and operational technology (IT/OT) for real‐time control and monitoring of industrial Internet of Things operations (IIoT). An autonomous manufacturing system is developed for the closed‐loop manufacturing supply chain (SC), utilizing a system‐to‐system (SoS) approach.

A data management system for precision medicine.

Precision, or personalised medicine has advanced requirements for medical data management systems (MedDMSs). MedDMS for precision medicine should be able to process hundreds of parameters from multiple sites, be adaptable while remaining in sync at multiple locations, real-time syncing to analytics and be compliant with international privacy legislation. This paper describes the LogiqSuite software solution, aimed to support a precision medicine solution at the patient care (LogiqCare), research (LogiqScience) and data science (LogiqAnalytics) level. LogiqSuite is certified and compliant with international medical data and privacy legislations. This paper evaluates a MedDMS in five types of use cases for precision medicine, ranging from data collection to algorithm development and from implementation to integration with real-world data. The MedDMS is evaluated in seven precision medicine data science projects in prehospital triage, cardiovascular disease, pulmonology, and oncology. The P4O2 consortium uses the MedDMS as an electronic case report form (eCRF) that allows real-time data management and analytics in long covid and pulmonary diseases. In an acute myeloid leukaemia, study data from different sources were integrated to facilitate easy descriptive analytics for various research questions. In the AIDPATH project, LogiqCare is used to process patient data, while LogiqScience is used for pseudonymous CAR-T cell production for cancer treatment. In both these oncological projects the data in LogiqAnalytics is also used to facilitate machine learning to develop new prediction models for clinical-decision support (CDS). The MedDMS is also evaluated for real-time recording of CDS data from U-Prevent for cardiovascular risk management and from the Stroke Triage App for prehospital triage. The MedDMS is discussed in relation to other solutions for privacy-by-design, integrated data stewardship and real-time data analytics in precision medicine. LogiqSuite is used for multi-centre research study data registrations and monitoring, data analytics in interdisciplinary consortia, design of new machine learning / artificial intelligence (AI) algorithms, development of new or updated prediction models, integration of care with advanced therapy production, and real-world data monitoring in using CDS tools. The integrated MedDMS application supports data management for care and research in precision medicine.

The assessment of routine health information system performance towards improvement of quality of reproductive, maternal, newborn, child and adolescent health services in Ondo and Ekiti States, Nigeria.

Nigeria's reproductive, maternal, newborn, child, and adolescent health indicators have remained unsatisfactory in the face of poor-quality healthcare services. Nigeria initiated the reproductive, maternal, newborn, child, and adolescent, elderly + nutrition (RMNCAEH+N) quality of care (QoC) agenda to address the challenge. The health management information system (HMIS) is integral to the agenda but there is sparse evidence on its performance so far. This study assessed the performance of routine HMIS for RMNCAEH+N QoC in Ondo and Ekiti States. This paper described the review of health facility records and health facility survey components of a multi-component study which employed a mixed-method research design. Using the routine health information system performance diagnostic tool, service data captured for over one year were critically reviewed in randomly selected sample of 169 public health facilities (Ondo:117; Ekiti:52) and information was obtained from facility heads or designates. Performance of routine HMIS for RMNCAEH+N QoC in terms of data collection, data quality, and data use were analysed using univariate and bivariate statistics. Results show that 67.3% of health facilities in Ekiti and 88.9% of facilities in Ondo had all required HMIS tools for selected RMNCAEH+N services (p<0.001). Data accuracy was 70.1% for Ondo and 40.4% for Ekiti (p <0.001); 82.9% of facilities in Ondo and 44.2% in Ekiti had complete data (p <0.001); almost all facilities (Ondo: 99.1%; Ekiti: 96.2%, p = 0.224) demonstrated data consistency; and, 82.9% of facilities in Ondo and 94.2% of facilities in Ekiti demonstrated timeliness in data submission (p = 0.048). Also, 70.1% (Ondo) and 78% (Ekiti) of facilities had quality improvement (QI) teams (p = 0.338); 53.5% (Ondo) and 77.1% (Ekiti) of QI teams regularly extracted data, calculated, and visualised prioritized indicators (p = 0.007); while 72.1% (Ondo) and 79.2% (Ekiti) regularly reviewed data and used it to make QI decisions (p = 0.367). Routine RMNCAEH+N QoC data management system in Ondo and Ekiti States vary in terms of the status of reporting forms, data quality, and data use for decision-making, and there were specific performance gaps. The routine RMNCAEH+N QoC data management system in Ondo and Ekiti States needs improvement and findings from this study can serve as the basis for evidence-based advocacy for the required efforts and investment toward improved performance.

The development of gyrotron data management system for ECRH system based on InfluxDB

The development of gyrotron data management system for ECRH system based on InfluxDB