Data Exchange Research Articles

Design and implementation of a scalable and high-throughput EEG acquisition and analysis system

Recent advances in neuroscience, neuromorphic intelligence, and brain–computer interface (BCI) technologies have created a need for fast, efficient, and convenient electroencephalogram (EEG) data acquisition systems. However, the existing equipment was limited in its flexibility, restricting non-invasive studies to research or medical settings. To address this issue, low-cost, compact EEG acquisition devices have been developed, allowing for frequent and flexible brain data acquisition in various scenarios. This paper introduces a scalable and high-throughput EEG signal acquisition and analysis system based on field-programmable gate array (FPGA) technology. The proposed system offers electrode scalability, on-chip computing, and optional wireless functionality extension. These features are achieved through the design of a highly scalable underlying EEG acquisition module and an FPGA central module that enables software-defined high-throughput expansion and high-speed data exchange between software and hardware. The paper presents two implementation cases that demonstrate the potential of the proposed system. The first case introduces a wearable wireless EEG system, enabling the deployment of effective and user-friendly steady-state visual evoked potential (SSVEP)-BCI applications in consumer-grade scenarios. The second case integrates an FPGA central module with multiple basic EEG acquisition modules to construct a high-throughput BCI system for cost-effective and real-time EEG data acquisition and processing. This configuration allows for flexible deployment in research and clinical applications, including attention index, SSVEP, motor imagery (MI), and emotion recognition. This combination further demonstrates the potential of scalable EEG systems and emphasizes the need for further integration or chipization. These implementations validate the feasibility of compact and efficient EEG devices and highlight the promising applications of scalable BCI system in various fields.

The telemedical platform MyaLink for remote monitoring in myasthenia gravis – rationale and protocol for a proof of concept study

Rationale: Myasthenia gravis (MG) is a rare, chronic neurological disorder leading to fluctuating muscle weakness and potentially life-threatening crises. Patients often require life-long specialized treatment, but timely interventions are frequently hindered by the limited availability of specialists. Telemedical solutions at specialized centers enabling patient-physician interaction hold promise in bridging this gap, but are not yet available for MG. We developed ‘MyaLink,’ a remote monitoring platform tailored for MG, and outline the study design assessing the platform and clinical outcomes regarding telemedical intervention. Additionally, we present study results on care-related aspects in MG prior to telemedical intervention to identify challenges in the current care provision process. Design: The platform comprises a patient app and a physician portal, enabling systematic symptom monitoring using data from patient-reported outcome measures (PROMs), coupled devices and a communication module. The randomized controlled study included 45 study participants (SP) over a 12-weeks period, including a group receiving standard care (15 MG patients) and a group with additional telemedical treatment (30 MG patients) including assessment of PROMs, wearable data collection and telemedical check-ups. Questions regarding care-related aspects were assessed at baseline visit. Results: Many SP (N = 33, 73.3%) communicate with the physician managing their MG via email. 73.3% (N = 33) of SP identify areas for improvement in their MG care including symptom monitoring (N = 23, 69.7%), specialist appointment availability (N = 22, 66.7%), medication (N = 22, 66.7%) and specialist accessibility (N = 20, 60.6%). Additionally, 73.3% (N = 33) reported that the effort required to manage their MG was high. Conclusion: Our results emphasize the high demand of affected MG patients for continuous telemedicine services. MyaLink can provide such a service through personalized support based on the exchange of health data. Telemedicine solutions such as MyaLink promise to improve myasthenia care by providing accessible, patient-centred care that enables early detection of worsening symptoms and non-response to treatment. Trial registration: The study was registered under DRKS00029907 on August 19, 2022.

Blockchain Interoperability Frameworks: A Comparative Analysis of Cross-Chain Solutions for DLT Innovation

This article comprehensively analyzes blockchain interoperability as a crucial frontier in distributed ledger technology (DLT) innovation. Through systematic examination of current interoperability solutions, technical challenges, and implementation frameworks, the study provides insights into the evolving landscape of cross-chain communication and its implications for industry adoption. The article evaluates prominent interoperability frameworks, including the Interledger Protocol, Cosmos Network, Polkadot, and Canton Network, analyzing their architectural approaches, security mechanisms, and practical applications. Key findings reveal significant progress in addressing fundamental challenges such as protocol standardization, consensus mechanism compatibility, and scalability constraints while highlighting remaining cross-chain security and governance obstacles. The article demonstrates substantial benefits of blockchain interoperability across various sectors, with notable implementations in supply chain management reducing documentation errors by 65%, cross-border financial services decreasing settlement times from days to minutes, and healthcare data exchange improving information accessibility by 75%. Through examination of technical requirements, governance models, and risk mitigation strategies, this research establishes a comprehensive framework for implementing interoperable blockchain solutions while maintaining security and regulatory compliance. The article contributes to the growing knowledge in DLT innovation and provides practical insights for stakeholders working towards achieving seamless blockchain interoperability.

FHIR - Overdue Standard for Radiology Data Warehouses.

In radiology, technological progress has led to an enormous increase in data volumes. To effectively use these data during diagnostics or subsequent clinical evaluations, they have to be aggregated at a central location and be meaningfully retrievable in context. Radiology data warehouses undertake this task: they integrate diverse data sources, enable patient-specific and examination-specific evaluations, and thus offer numerous benefits in patient care, education, and clinical research.The international standard Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) is particularly suitable for the implementation of such a data warehouse. FHIR allows for easy and fast data access, supports modern web-based frontends, and offers high interoperability due to the integration of medical ontologies such as SNOMED-CT or RadLex. Furthermore, FHIR has a robust data security concept. Because of these properties, FHIR has been selected by the Medical Informatics Initiative (MII) as the data standard for the core data set and is intended to be promoted as an international standard in the European Health Data Space (EHDS).Implementing the FHIR standard in radiology data warehouses is therefore a logical and sensible step towards data-driven medicine. · A data warehouse is essential for data-driven medicine, clinical care, and research purposes.. · Data warehouses enable efficient integration of AI results and structured report templates.. · Fast Healthcare Interoperability Resources (FHIR) is a suitable standard for a data warehouse.. · FHIR provides an interoperable data standard, supported by proven web technologies.. · FHIR improves semantic consistency and facilitates secure data exchange.. · Arnold P, Pinto dos Santos D, Bamberg F et al. FHIR - Overdue Standard for Radiology Data Warehouses. Fortschr Röntgenstr 2024; DOI 10.1055/a-2462-2351.

Reconfigurable Intelligent Surface-Aided Security Enhancement for Vehicle-to-Vehicle Visible Light Communications

Vehicle-to-vehicle (V2V) visible light communication (VLC) systems are increasingly being deployed for real-time data exchange in intelligent transportation systems (ITS). However, these systems are highly vulnerable to eavesdropping, especially in scenarios such as road intersections where signals may be exposed to unauthorized receivers. To address these security challenges, we propose a novel reconfigurable intelligent surface (RIS)-assisted security enhancement scheme for V2V VLC networks. The proposed scheme leverages RIS to improve the reception of legitimate signals at the destination vehicle while simultaneously introducing artificial noise (AN) to interfere with potential eavesdroppers. Optimization problems are formulated to maximize the SINR of the destination vehicle and simultaneously minimize the worst-case SINR of eavesdroppers. The simulation results demonstrate that the proposed scheme achieves a notable improvement in the system’s secrecy rate by 1.64 bit/s/Hz and enhances the overall security performance, offering a robust solution to the security challenges in V2V VLC systems.

From Reality to Virtuality: Revolutionizing Livestock Farming Through Digital Twins

The impacts of climate change on agricultural production are becoming more severe, leading to increased food insecurity. Adopting more progressive methodologies, like smart farming instead of conventional methods, is essential for enhancing production. Consequently, livestock production is swiftly evolving towards smart farming systems, propelled by rapid advancements in technology such as cloud computing, the Internet of Things, big data, machine learning, augmented reality, and robotics. A Digital Twin (DT), an aspect of cutting-edge digital agriculture technology, represents a virtual replica or model of any physical entity (physical twin) linked through real-time data exchange. A DT conceptually mirrors the state of its physical counterpart in real time and vice versa. DT adoption in the livestock sector remains in its early stages, revealing a knowledge gap in fully implementing DTs within livestock systems. DTs in livestock hold considerable promise for improving animal health, welfare, and productivity. This research provides an overview of the current landscape of digital transformation in the livestock sector, emphasizing applications in animal monitoring, environmental management, precision agriculture, and supply chain optimization. Our findings highlight the need for high-quality data, comprehensive data privacy measures, and integration across varied data sources to ensure accurate and effective DT implementation. Similarly, the study outlines their possible applications and effects on livestock and the challenges and limitations, including concerns about data privacy, the necessity for high-quality data to ensure accurate simulations and predictions, and the intricacies involved in integrating various data sources. Finally, the paper delves into the possibilities of digital twins in livestock, emphasizing potential paths for future research and progress.

Federated learning for enhanced dose-volume parameter prediction with decentralized data.

The widespread adoption of knowledge-based planning in radiation oncology clinics is hindered by the lack of data and the difficulty associated with sharing medical data. This study aims to assess the feasibility of mitigating this challenge through federated learning(FL): a centralized model trained with distributed datasets, while keeping data localized and private. This concept was tested using 273 prostate 45Gy plans. The cases were split into a training set with 220 cases and a validation set with 53 cases. The training set was further separated into 10 subsets to simulate treatment plans from different clinics. A gradient-boosting model was used to predict bladder and rectum V30Gy, V35Gy, and V40Gy. The Federated Averaging algorithm was employed to aggregate the individual model weights from distributed datasets. Grid search with five-fold in-training-set cross-validation was implemented to tune model hyperparameters. Additionally, we evaluated the robustness of the FL approach by varying the distribution of the training set data in several scenarios, including different number of sites and imbalanced data across sites. The mean absolute error (MAE) for the FL model (4.7%±2.9%) is significantly lower than individual models trained separately (6.5%±4.9%, p<0.001) and similar to a traditional centralized model (4.4%±2.8%, p=0.14). The federated model is robust to the number of subsets, showing MAE of 4.7%±3.2%, 4.8%±3.1%, 4.8%±2.9%, 4.5%±2.8%, 4.9%±3.3%, and 4.8%±3.1% for 5, 10, 15, 20, 25, and 30 subsets, respectively. For the two imbalanced datasets, the FL model achieves MAEs of 4.5%±2.9% and 5.6%±4.0%, non-inferior to the balanced data model. For all bladder and rectum metrics, the FL model significantly outperforms 36.7% of individual models. This study demonstrates the potential advantages of implementing a federated model over training individual models: the proposed FL approach achieves similar prediction accuracy as a conventional model without requiring centralized data storage. Even when local models struggle to produce accurate predictions due to data scarcity, the federated model consistently maintains high performance.

Federated learning merges with v2x privacy protection

Vehicle-to-Everything (V2X) communication systems are poised to revolutionize road safety, traffic efficiency, and driver experience through real-time data exchange among vehicles and infrastructure. However, ensuring the privacy and security of sensitive vehicular data remains a critical challenge. Federated Learning (FL) emerges as a promising paradigm to address these concerns by enabling collaborative model training without centralized data aggregation. This paper explores the integration of FL techniques into V2X environments, where vehicles act as decentralized participants in model training processes. We discuss the unique challenges and opportunities presented by V2X scenarios, such as intermittent connectivity and diverse data distributions across vehicles. Through a detailed review of existing FL methodologies and their adaptation to V2X settings, this paper proposes practical solutions for privacy-preserving model aggregation and efficient learning across dynamic vehicular networks. Case studies and simulation results illustrate the feasibility and benefits of FL in enhancing V2X applications while safeguarding user privacy. This study contributes to the growing field of secure and collaborative machine learning in connected vehicle environments, paving the way for safer and smarter transportation systems.

Exchange of occupational health assessment summaries based on the EN 13606 standard

The globalization of world economy stimulates large number of people to search abroad for better employment opportunities. This is the reason to consider the exchange of occupational health assessment summary (OHAS) content from cross-border view point. It is a problem of larger social-significance than the cross-border exchange for International Patient Summary (IPS). While the IPS dataset is well studied in the framework of an international standard, the OHAS remains rather insufficiently explored in the existing literature. This paper proposes a novelty systematic approach to provide a practicable solution to this problem by ensuring semantic interoperability in the exchange of OHAS. It starts by presenting a description of the use case for cross-border exchange. Next, the OHAS dataset is defined on the basis of exploring common data requirements the national legislation of EU countries. The final step of this approach is the design of an EN~13606 archetype of OHAS satisfying all the requirements for semantic interoperability in the exchange of clinical data. Further on, the static, non-volatile and reusable information model of OHAS is used to create EN~13606 instances that are valid with respect to the Reference model and the datatypes of this standard. Finally, this paper demonstrates the software implementation of basic activities in the OHAS use case in a fully functional web application, where the EN~13606 instances are managed by means of a native XML database.

Unveiling the National Platform for Health and Insurance Exchange Services: Data Exchange Era within in the Healthcare System in Saudi Arabia

Unveiling the National Platform for Health and Insurance Exchange Services: Data Exchange Era within in the Healthcare System in Saudi Arabia

Pheno-Ranker: a toolkit for comparison of phenotypic data stored in GA4GH standards and beyond

BackgroundPhenotypic data comparison is essential for disease association studies, patient stratification, and genotype–phenotype correlation analysis. To support these efforts, the Global Alliance for Genomics and Health (GA4GH) established Phenopackets v2 and Beacon v2 standards for storing, sharing, and discovering genomic and phenotypic data. These standards provide a consistent framework for organizing biological data, simplifying their transformation into computer-friendly formats. However, matching participants using GA4GH-based formats remains challenging, as current methods are not fully compatible, limiting their effectiveness.ResultsHere, we introduce Pheno-Ranker, an open-source software toolkit for individual-level comparison of phenotypic data. As input, it accepts JSON/YAML data exchange formats from Beacon v2 and Phenopackets v2 data models, as well as any data structure encoded in JSON, YAML, or CSV formats. Internally, the hierarchical data structure is flattened to one dimension and then transformed through one-hot encoding. This allows for efficient pairwise (all-to-all) comparisons within cohorts or for matching of a patient’s profile in cohorts. Users have the flexibility to refine their comparisons by including or excluding terms, applying weights to variables, and obtaining statistical significance through Z-scores and p-values. The output consists of text files, which can be further analyzed using unsupervised learning techniques, such as clustering or multidimensional scaling (MDS), and with graph analytics. Pheno-Ranker’s performance has been validated with simulated and synthetic data, showing its accuracy, robustness, and efficiency across various health data scenarios. A real data use case from the PRECISESADS study highlights its practical utility in clinical research.ConclusionsPheno-Ranker is a user-friendly, lightweight software for semantic similarity analysis of phenotypic data in Beacon v2 and Phenopackets v2 formats, extendable to other data types. It enables the comparison of a wide range of variables beyond HPO or OMIM terms while preserving full context. The software is designed as a command-line tool with additional utilities for CSV import, data simulation, summary statistics plotting, and QR code generation. For interactive analysis, it also includes a web-based user interface built with R Shiny. Links to the online documentation, including a Google Colab tutorial, and the tool’s source code are available on the project home page: https://github.com/CNAG-Biomedical-Informatics/pheno-ranker.

Blockchain empowered dynamic access control for secure data sharing in collaborative emergency management

In the realm of emergency management involving multi-party collaboration, securely sharing data among multiple entities poses a significant challenge. Traditional centralized data platforms struggle with fine-grained access control for data from different sources and are susceptible to single-point failures and risks of forged authorization. This paper delves into the prospective application of blockchain technology for decentralized data governance. We propose a dynamic access control technology for multi-party collaborative emergency response, which combines blockchain and attribute-based access control technologies to enable dynamic access control in multi-party data sharing scenarios. A data sharing system for collaborative emergency management built with Hyperledger Fabric has been developed and subjected to a series of evaluation tests. The experimental results reveal that our blockchain-based system not only significantly enhances data security and privacy by leveraging decentralized control but also improves the efficiency and reliability of data sharing in emergency situations. Specifically, our findings demonstrate the system's ability to dynamically adapt access control policies based on changing emergency scenarios, effectively mitigating risks associated with single-point failures and unauthorized access. These outcomes underscore the viability and reliability of our approach, providing a robust framework for secure, scalable, and flexible data sharing in the demanding field of emergency management.

Perspectives of Clients and Health Care Professionals on the Opportunities for Digital Health Interventions in Cerebrovascular Disease Care: Qualitative Descriptive Study.

Cerebrovascular diseases (CVDs) are a major and potentially increasing burden to public health. Digital health interventions (DHIs) could support access to and provision of high-quality health care (eg, outcomes, safety, and satisfaction), but the design and development of digital solutions and technologies lack the assessment of user needs. Research is needed to identify opportunities to address health system challenges and improve CVD care with primary users of services as the key informants of everyday requirements. This study aims to identify opportunities for DHIs from clients' and health care professionals' perspectives to address health system challenges and improve CVD care. This study used a qualitative, descriptive approach. Semistructured, in-person interviews were conducted with 22 clients and 26 health care professionals in a single tertiary-level hospital in Finland between August 2021 and March 2022. The data were analyzed using a deductive and inductive content analysis. Identified opportunities for DHIs in CVD care were organized according to clients, health care professionals, and data services and classified into 14 main categories and 27 generic categories, with 126 subcategories of requirements. DHIs for clients could support the long-term management of health and life changes brought on by CVD. They could provide access to personal health data and offer health information, support, and communication possibilities for clients and their caregivers. Health care professionals would benefit from access to relevant patient data, along with systems and tools that support competence and decision-making. Intersectoral and professional collaboration could be promoted with digital platforms and care pathways. DHIs for data services could enhance care planning and coordination with novel predictive data and interoperable systems for data exchange. The combined study of client and health care professional perspectives identified several opportunities and requirements for DHIs that related to the information, availability, quality, acceptability, utilization, efficiency, and accountability challenges of health systems. These findings provide valuable social insights into digital transformation and the emerging design, development, and use of user-centered technologies and applications to address challenges and improve CVD care and health care.

Fiducial Reference Measurements for Air Quality Monitoring Using Ground-Based MAX-DOAS Instruments (FRM4DOAS)

The UV–Visible Working Group of the Network for the Detection of Atmospheric Composition Changes (NDACC) focuses on the monitoring of air-quality-related stratospheric and tropospheric trace gases in support of trend analysis, satellite validation and model studies. Tropospheric measurements are based on MAX-DOAS-type instruments that progressively emerged in the years 2010 onward. In the interest of improving the overall consistency of the NDACC MAX-DOAS network and facilitating its further extension to the benefit of satellite validation, the ESA initiated, in late 2016, the FRM4DOAS project, which aimed to set up the first centralised data processing system for MAX-DOAS-type instruments. Developed by a consortium of European scientists with proven expertise in measurements, data extraction algorithms and software design specialities, the system has now reached pre-operational status and has demonstrated its ability to deliver a set of quality-controlled atmospheric composition data products with a latency of one day. The processing system has been designed using a highly modular approach, making it easy to integrate new tools or processing updates. It incorporates advanced algorithms selected by community consensus for the retrieval of total ozone, lower tropospheric and stratospheric NO2 vertical profiles and formaldehyde profiles. The ozone and NO2 products are currently generated from a total of 22 stations and delivered daily to the NDACC rapid delivery (RD) repository, with an additional mirroring to the ESA Validation Data Centre (EVDC). Although it is still operated in a pre-operational/demonstrational mode, FRM4DOAS was already used for several validation and science studies, and it was also deployed in support of field campaigns for the validation of the TROPOMI and GEMS satellite missions. It recently went through a CEOS-FRM self-assessment process aiming at assessing the level of maturity of the service in terms of instrumentation, operations, data sampling, metrology and verification. Based on this evaluation, it falls under class C, which is a good rating but also implies that further improvements are needed to reach full compliance with FRM standards, i.e., class A.

STC-GraphFormer: Graph Spatial-Temporal Correlation Transformer for In-vehicle Network Intrusion Detection System

The integration of several developing technologies and their applications with Internet of Vehicles (IoVs) techniques has been improved. Utilizing these emerging technologies renders the in-vehicle network more susceptible to intrusions. Furthermore, the utilization of Electronic Control Units (ECUs) in current vehicles has experienced a significant increase, establishing the Controller Area Network (CAN) as the widely used standard in the automotive field. The CAN protocol provides an efficient and broadcast-based protocol for facilitating serial data exchange between ECUs. However, it lacks provisions for security measures such as authentication and encryption. The attackers have exploited these weaknesses to launch various attacks on CAN-based IVN. This paper proposes STC-GraphFormer, an innovative spatial-temporal model that utilizes a Graph Convolutional Network (GCN) and a transformer. The spatial GCN layers are utilized to construct and acquire local spatial features, while the temporal transformer layers are employed to capture the long-term global temporal dependencies. By employing this integrated approach, STC-GraphFormer can learn complex spatial-temporal correlations within the IVN data, enabling it to detect and classify malicious intrusions. The proposed STC-GraphFormer has been validated using five real in-vehicle CAN datasets that cover a wide range of attacks that have not been previously investigated together. The finding results indicate that the STC-GraphFormer is more efficient than the SOTA approaches. It demonstrates excellent performance, with Car-hacking (0.99983), IVN intrusion detection (0.9991), CAN Dataset for intrusion detection “OTIDS” (0.9992), CAR hacking: attack & defense challenge (0.9901), and Survival analysis (0.9982), with a minimal false alarm rate and the highest achievable F1 scores for various types of attacks.

The European Commission’s Challenge to Consent or Pay: Demystifying the Digital Markets Act?

The European Union’s (EU’s) Digital Markets Act (DMA) was hailed as a new era of digital regulation following a decade of debates on the flexibility of traditional competition law. Article 5(2) DMA requires gatekeepers to offer users a choice to opt-out of the data exchanges across a gatekeeper’s services. Half a year after becoming applicable, its fitness for purpose and theoretical underpinnings are in the spotlight as the European Commission (EC) investigates Meta’s consent or pay solution on the assumption that such a model is not compliant. This paper considers whether this assumption is supported by an interpretation of Article 5(2) DMA and assesses the risks posed by the EC’s position to the integrity of the DMA and its aim to revolutionize competition law.

Modeling of the intelligent interface of interaction between a pharmacy and a children's clinic

Introduction. Economic barriers, uneven territorial distribution of pharmacy organizations, and insufficient efficiency of drugs supply chains hinder the accessibility of drugs and the timely receipt of pharmaceutical care (PC) by patients worldwide. The digital transformation in healthcare, accelerated by the COVID-19 crisis, necessitates the adaptation of PC practices to meet modern patient needs. Objective of the study. To develop and validate a procedure for the interaction between a clinic and a pharmacy to facilitate data exchange through an interface, enabling the pharmacy to receive data for forming an assortment of drugs adapted to the characteristics and behavioral trends of the main target audience. Material and methods. The study utilized anonymized data on drugs prescriptions by physicians from the medical information system of a network of medical organizations in Moscow for the period from January 2018 to December 2023. Data preprocessing was conducted, followed by the training of a machine learning model using the LightGBM algorithm. The predictive performance of the model was assessed using MAE and RMSE metrics. Results. An analytical interface for the interaction between the clinic and the pharmacy was developed, incorporating a predictive model for forming the drugs assortment. The model effectively accounts for seasonal trends, patient demographic characteristics, and other key factors influencing drugs demand. The average MAE and RMSE values were 1.27 and 1.68, respectively, indicating high model accuracy. Conclusion. Implementing the developed interface allows the pharmacy to form drugs assortment tailored to the real needs of patients, contributing to optimized inventory management, reduced risk of shortages and overstocking, enhanced accessibility of PC for children, and increased economic efficiency of the pharmacy. The integration of big data technologies and machine learning opens new prospects for the personalization of medical and pharmaceutical care.

Overview of C-NAT and Its Reinforcement for IoT Data Exchange Platform

Overview of C-NAT and Its Reinforcement for IoT Data Exchange Platform

API C4E Augmentation: AI-Powered Agent(AIPA) Framework

Abstract: In today’s rapidly evolving digital landscape, organizations increasingly rely on Application Programming Interface known as API for seamless integration and data exchange. API providers comprising consumers, developers, and project teams face several challenges in discovering, developing and mapping APIs in compliance with organizational enterprise architecture principles. This paper aims to address these challenges by proposing an AI-Powered Agent (AIPA) Framework that leverages Generative AI and AI Code Assistant tools to enhance API governance and streamline the API development cycle (API Management, develop API, testing, security, deployment and monitoring). The proposed framework facilitates API discovery through an interactive chat interface that summarizes available APIs based on the use case. Additionally, it aims to automate key aspects of API development, including compliance checks, security protocols, and document generation thereby significantly reducing effort(s), human error(s), and improving productivity and efficiency. Establishing an API Center for Enablement (C4E) ensures consistent adoption of best practices across the organization. By integrating AI-driven solution with API governance, this paper outlines a pathway for organizations to improve API quality, security, and usability while empowering to adapt to digital disruptions. The findings suggest that a comprehensive, AI-enhanced governance framework enhances operational and development efficiency and fosters innovation.

A Survey on Modern Online Ticket Booking Systems

This study examines developments in the design and operation of online movie ticket booking systems and provides a thorough overview of the integration of secure QR code technologies, with a focus on visual cryptography. Although they make data access and exchange more efficient, QR codes are susceptible to risks such data tampering, counterfeiting, and unauthorized access. The use of visual cryptography to safeguard QR code data by splitting the code into discrete, unintelligible "shares" that must be combined in order to reconstruct the original data is the main topic of this survey. This study also examines advancements in ticketing platforms, particularly the move to frameworks like Node.js and MongoDB, which improve system scalability and performance. The findings show that data security, dependability, and user experience are much enhanced by contemporary encryption and backend developments in reservation systems.