Data Exchange Research Articles

PyDwCA: A Tool for Integrating Biodiversity Data

The Darwin Core Archive (DwC-A) format, based on the Darwin Core standard (Wieczorek et al. 2012), facilitates the exchange, management, and integration of biodiversity data from multiple sources. This ability to collate biodiversity data allows datasets to be aggregated at community-supported infrastructures, merged in different combinations, meta-analyzed and submitted to public repositories (Baker et al. 2014). Thus, the DwC-As serve as unifying archives in concatenated collective efforts, such as biodiversity inventories at different spatial and taxonomic scales. Here we describe PyDwCA*1, 2, a Python library implemented to handle the "star scheme" of DwC-A. This new library reads compressed zip files containing the expected meta.xml and uses it to assign the core component and its extensions. It also provides Python classes to define the core, the extensions, and the metadata file for creating an archive and writing it into a compressed zip file. PyDwCA also implements functionality to select, filter and merge DwC-A files. We present this new tool in the context of the construction of the Chilean National Biodiversity Inventory (Fig. 1), but PyDwCA serves as a versatile technical solution applicable to different contexts in the field of biodiversity informatics (e.g., integration of datasets from biological collection and sampling events). To exemplify how PyDwCA works, we present the step-by-step integration of the Chilean Catalogue of Vascular Plants (Rodriguez et al. 2018) on a matrix provided by the Catalogue of Life (Banki 2024), filtered with the species with occurrences recorded for Chile in the Global Biodiversity Information Facility (GBIF) (GBIF.Org 2023).

A privacy-preserving traceability system for self-sovereign identity-based inter-organizational business processes

Blockchain is a potential technology for collaborating organizations, notably for executing their Inter-Organizational Business Processes (IOBPs). While Blockchain’s transparency and decentralized characteristics address the lack-of-trust issue in IOBPs, many existing Blockchain solutions share this data on the ledger, often at the expense of serious privacy concerns. Alternatively, Self-Sovereign Identity (SSI) systems are revolutionary Blockchain-based solutions that provide complete data control. Unlike traditional Blockchain solutions, many SSI systems do not record the exchange of transactional data between entities on the ledger in order to comply with privacy regulations. However, this can imply a gap in cases where legal traceability is required for audit purposes. To address traceability issues in SSI-based IOBP, this paper leverages Zero-Knowledge Proof (ZKP) and Fully Homomorphic Encryption (FHE) to provide an efficient privacy-preserving traceability solution. The purpose of this paper is to achieve traceability that strikes a balance between privacy and transparency. This paper also provides a proof-of-concept implementation and a comparative evaluation. The evaluation shows that the proposed ZKP approach provides better financial cost and performance results compared to traditional Blockchain-based traceability solutions.

Cyber-Physical Scheduling System for Multiobjective Scheduling Optimization of a Suspension Chain Workshop Using the Improved Non-Dominated Sorting Genetic Algorithm II

Cyber-Physical Systems (CPSs) offer significant potential to address the evolving demands of industrial development. In the Industry 4.0 era, a framework integrating sensing, data exchange, numerical analysis, and real-time feedback is essential for meeting modern industrial needs. However, implementing this integration presents challenges across multiple domains, particularly in digital analysis, information sensing, and data exchange during corporate transformation. Companies yet to undergo transformation face distinct challenges, including the risks and trial-and-error costs of adopting new technologies. This study focuses on a heavy-duty workpiece processing factory, with a specific emphasis on the painting process. The complexity of this process frequently results in congestion, which is approached as a multi-objective, multi-constraint optimization problem. This paper proposes the Improved Non-dominated Sorting Genetic Algorithm II (INSGA-II) to address the requirements of multi-objective optimization. The proposed approach uses multi-chromosome structures, listeners, and recursive backtracking initialization to optimize the search for solutions under constraints. This enables the factory to automatically streamline production lines based on workpiece processing sequences, leading to increased efficiency. Additionally, a CPS framework focused on simulation modeling has been designed. First, the INSGA-II algorithm processes order data to generate production schedules. Next, the data transmission formats and input-output interfaces are designed. Then, a simulation model is built using the algorithm’s results. These components collectively form the CPS framework for this study. The proposed method offers an automated digital solution through the algorithm, enabling verification of its feasibility via the simulation model. As a result, it significantly enhances decision-making speed, reliability, and equipment utilization.

A robust algorithm for authenticated health data access via blockchain and cloud computing.

In modern healthcare, providers increasingly use cloud services to store and share electronic medical records. However, traditional cloud hosting, which depends on intermediaries, poses risks to privacy and security, including inadequate control over access, data auditing, and tracking data origins. Additionally, current schemes face significant limitations such as scalability concerns, high computational overhead, practical implementation challenges, and issues with interoperability and data standardization. Unauthorized data access by cloud providers further exacerbates these concerns. Blockchain technology, known for its secure and decentralized nature, offers a solution by enabling secure data auditing in sharing systems. This research integrates blockchain into healthcare for efficient record management. We proposed a blockchain-based method for secure EHR management and integrated Ciphertext-Policy Attribute-Based Encryption (CP-ABE) for fine-grained access control. The proposed algorithm combines blockchain and smart contracts with a cloud-based healthcare Service Management System (SMS) to ensure secure and accessible EHRs. Smart contracts automate key management, encryption, and decryption processes, enhancing data security and integrity. The blockchain ledger authenticates data transactions, while the cloud provides scalability. The SMS manages access requests, enhancing resource allocation and response times. A dual authentication system confirms patient keys before granting data access, with failed attempts leading to access revocation and incident logging. Our analyses show that this algorithm significantly improves the security and efficiency of health data exchanges. By combining blockchain's decentralized structure with the cloud's scalability, this approach significantly improves EHR security protocols in modern healthcare setting.

Comparison of different group-level templates in gradient-based multimodal connectivity analysis

Abstract The study of large-scale brain connectivity is increasingly adopting unsupervised approaches that derive low-dimensional spatial representations from high-dimensional connectomes, referred to as gradient analysis. When translating this approach to study interindividual variations in connectivity, one technical issue pertains to the selection of an appropriate group-level template to which individual gradients are aligned. Here, we compared different group-level template construction strategies using functional and structural connectome data from neurotypical controls and individuals with autism spectrum disorder (ASD) to identify between-group differences. We studied multimodal magnetic resonance imaging data obtained from the Autism Brain Imaging Data Exchange (ABIDE) Initiative II and the Human Connectome Project (HCP). We designed six template construction strategies that varied in whether (1) they included typical controls in addition to ASD; or (2) they mapped from one dataset onto another. We found that aligning a combined subject template of the ASD and control subjects from the ABIDE Initiative onto the HCP template exhibited the most pronounced effect size. This strategy showed robust identification of ASD-related brain regions for both functional and structural gradients across different study settings. Replicating the findings on focal epilepsy demonstrated the generalizability of our approach. Our findings will contribute to improving gradient-based connectivity research.

Design of an FPGA-Based Controller for Fast Scanning Probe Microscopy.

Atomic-scale imaging using scanning probe microscopy is a pivotal method for investigating the morphology and physico-chemical properties of nanostructured surfaces. Time resolution represents a significant limitation of this technique, as typical image acquisition times are on the order of several seconds or even a few minutes, while dynamic processes-such as surface restructuring or particle sintering, to be observed upon external stimuli such as changes in gas atmosphere or electrochemical potential-often occur within timescales shorter than a second. In this article, we present a fully redesigned field programmable gate array (FPGA)-based instrument that can be integrated into most commercially available standard scanning probe microscopes. This instrument not only significantly accelerates the acquisition of atomic-scale images by orders of magnitude but also enables the tracking of moving features such as adatoms, vacancies, or clusters across the surface ("atom tracking") due to the parallel execution of sophisticated control and acquisition algorithms and the fast exchange of data with an external processor. Each of these measurement modes requires a complex series of operations within the FPGA that are explained in detail.

Design and demonstration of a roll-out membrane antenna based on thin-walled deployable composite booms

The thin-walled deployable composite boom (DCB) features the advantages of high deployment-to-package envelope ratio and elastic self-development, which is an enduring topic of interest in the field of lightweight aerospace structures. The structural design and performance demonstration was proposed herein for a roll-out membrane antenna (ROMA) based on DCBs. Firstly, a simplified analytical algorithm was formulated for approximate fundamental frequencies of the ROMA frame structure, and the two types of first-order torsional modal shapes of the frame structure was investigated with respect to configurational design parameters. In sequentially, the bending, swing and the two distinct torsional fundamental frequencies of the ROMA frame were estimated comparatively. Furthermore, the effects of the equilibrated tension from the membrane array on the bending fundamental frequency of the ROMA structure was investigated by assuming a simplified mass distribution model for the thin-film structures. Finally, The simplified analytical algorithm was used for structural design of an AIS/VDE (automatic identification system/VHF data exchange system) ROMA and its performance was validated through numerical simulations and ground tests. The successful deployment and operation of the AIS/VDE ROMA on the Pujiang-2 satellite further demonstrated the practical aerospace application significance of thin-walled DCBs.

Efficient Data Exchange between WebAssembly Modules

In the past two decades, there has been a noticeable decoupling of machines and operating systems. In this context, WebAssembly has emerged as a promising alternative to traditional virtual machines. Originally designed for execution in web browsers, it has expanded to executing code in restricted and secure environments, and it stands out for its rapid startup, small footprint, and portability. However, WebAssembly presents significant challenges in data transfer and the management of interactions with the module. Its specification requires each module to have its own memory, resulting in a “share-nothing” architecture. This restriction, combined with the limitations of importing and exporting functions that only handle numerical values, and the absence of an application binary interface (ABI) for sharing more complex data structures, leads to efficiency problems; these are exacerbated by the variety of programming languages that can be compiled and executed in the same environment. To address this inefficiency, the Karmem was designed and developed. It includes a new interface description language (IDL) aimed at facilitating the definition of data, functions, and relationships between modules. Additionally, a proprietary protocol—an optimized ABI for efficient data reading and minimal decoding cost—was created. A code generator capable of producing code for various programming languages was also conceived, ensuring harmonious interaction with the ABI and the foreign function interface. Finally, the compact runtime of Karmem, built atop a WebAssembly runtime, enables communication between modules and shared memory. Results of the experiments conducted show that Karmem represents an innovation in data communication for WASM in multiple environments and demonstrates the ability to overcome challenges of efficiency and interoperability.

A database of shake-table tests conducted on unreinforced masonry buildings

Experimental results obtained from shake-table tests on complete unreinforced masonry buildings (URM) play a crucial role in enhancing modeling techniques and reducing uncertainties in seismic assessments for this type of building. This paper presents an open-access database comprising 69 shake-table tests on URM buildings with a scale equal to or larger than 1:4 that have been documented in the literature. The database summarizes important geometric parameters, structural characteristics, and experimental results, including maximum shake-table accelerations, story drifts, and evolution of dynamic properties, as well as qualitative information such as observed failure modes and damage progression throughout the experiments. This database is meant to serve the research community as a resource for planning future experimental campaigns, benchmarking numerical models, validating analysis methods, and improving predictive capabilities. As an example application, we generate vulnerability functions (fragility curves) from the experimental data. Our findings reveal that buildings tested in experiments are less vulnerable compared to real buildings, particularly when severe damage is observed and when the ground motion intensity is low to moderate. The ultimate objective is to facilitate the exchange of experimental data and boost collaborative approaches in the seismic assessment of URM buildings.

A Cross-Layer Approach to Analyzing Energy Consumption and Lifetime of a Wireless Sensor Node

Several wireless communication technologies, including Wireless Sensor Networks (WSNs), are essential for Internet of Things (IoT) applications. WSNs employ a layered framework to govern data exchanges between sender and recipient, which facilitates the establishment of rules and standards. However, in this conventional framework, network data sharing is limited to directly stacked layers, allowing manufacturers to develop proprietary protocols while impeding WSN optimization, such as energy consumption minimization, due to non-directly stacked layer effects on network performance. A Cross-Layer (CL) framework addresses implementation, modeling, and design challenges in IoT systems by allowing unrestricted data and parameter sharing between non-stacked layers. This holistic approach captures system dynamics, enabling network design optimization to address IoT network challenges. This paper introduces a novel CL modeling methodology for wireless communication systems, which is applied in two case studies to develop models for estimating energy consumption metrics, including node and network lifetime. Each case study validates the resulting model through experimental tests, demonstrating high accuracy with less than 3% error.

Multi-Robot Path Planning Algorithm for Collaborative Mapping under Communication Constraints

In extensive outdoor collaborative exploration tasks, multiple robots require efficient path planning methods to ensure rapid and comprehensive map construction. However, current collaborative mapping algorithms often integrate poorly with path planning, especially under limited communication conditions. Such conditions can complicate data exchange, leading to inefficiencies and missed areas in real-world environments. This paper introduces a path planning approach specifically designed for distributed collaborative mapping tasks, aimed at enhancing map completeness, mapping efficiency, and communication robustness under communication constraints. We frame the entire task as a k-Chinese Postman Problem (k-CPP) and optimize it using a genetic algorithm (GA). This method fully leverages topology maps to efficiently plan subpaths for multiple robots, ensuring thorough coverage of the mapping area without the need for prior navigation maps. Additionally, we incorporate communication constraints into our path planning to ensure stable data exchange among robots in environments with only short-range communication capabilities. Field experiment results highlight the superior performance of our method in terms of stability, efficiency, and robust inter-robot communication.

ANALIZA STATYSTYK SIECI KOMPUTEROWYCH W CELU IDENTYFIKACJI PRZEPŁYWÓW INFORMACJI ZAKŁÓCAJĄCYCH STABILNOŚĆ W WOJSKOWYCH SIECIACH LOKALNYCH

The increasing complexity and scope of military computer networks necessitate robust methods to ensure network stability and security. This study presents a comprehensive analysis of computer network statistics in military local networks to develop a method for detecting information flows that disrupt stability. By leveraging advanced statistical techniques and machine learning algorithms, this research aims to enhance the cybersecurity posture of military local networks globally. Military networks are vital for communication, data exchange, and operational coordination. However, the dynamic nature of network traffic and the persistent threat of cyberattacks pose significant challenges to maintaining network stability. Traditional monitoring techniques often fail to meet the unique requirements of military networks, which demand high levels of security and rapid response capabilities. This study employs a multi-faceted approach to detect anomalies in network traffic, utilizing statistical methods such as Z-score analysis, Principal Component Analysis (PCA), and Autoregressive Integrated Moving Average (ARIMA) models. Machine learning techniques, including Support Vector Machines (SVM), Random Forests, Neural Networks, K-means clustering, and Reinforcement Learning, are also applied to identify patterns indicative of stability-disrupting information flows. The integration of statistical and machine learning methods forms a hybrid model that enhances anomaly detection, providing a robust framework for network security. The research problem is formulated as follows: does data collection include comprehensive network traffic data from various segments of military local area networks, including packet flows, transmission rates, and error rates over a specified period? Statistical analysis identifies patterns in the network traffic, which are then used to train machine learning models to classify normal and abnormal traffic. The research hypothesis states that machine learning models achieve high accuracy in detecting stability-disrupting information flows, with a precision rate exceeding 90%. The models identified several instances of stability-disrupting events, correlating these with known security incidents to validate the effectiveness of the detection method. This study underscores the importance of continuous monitoring and analysis of network statistics to ensure stability and security. The proposed method can be integrated with existing network monitoring and intrusion detection systems, providing a comprehensive approach to network security. Future research can build on these findings to develop more sophisticated models and explore additional factors influencing network stability, including the incorporation of advanced machine learning techniques, such as deep learning, and the exploration of other network metrics, like latency and packet loss. This comprehensive approach aims to enhance the security and operational reliability of military local networks.

CHAM-CLAS: A Certificateless Aggregate Signature Scheme with Chameleon Hashing-Based Identity Authentication for VANETs

Vehicular ad hoc networks (VANETs), which are the backbone of intelligent transportation systems (ITSs), facilitate critical data exchanges between vehicles. This necessitates secure transmission, which requires guarantees of message availability, integrity, source authenticity, and user privacy. Moreover, the traceability of network participants is essential as it deters malicious actors and allows lawful authorities to identify message senders for accountability. This introduces a challenge: balancing privacy with traceability. Conditional privacy-preserving authentication (CPPA) schemes are designed to mitigate this conflict. CPPA schemes utilize cryptographic protocols, including certificate-based schemes, group signatures, identity-based schemes, and certificateless schemes. Due to the critical time constraints in VANETs, efficient batch verification techniques are crucial. Combining certificateless schemes with batch verification leads to certificateless aggregate signature (CLAS) schemes. In this paper, cryptanalysis of Xiong’s CLAS scheme revealed its vulnerabilities to partial key replacement and identity replacement attacks, alongside mathematical errors in the batch verification process. Our proposed CLAS scheme remedies these issues by incorporating an identity authentication module that leverages chameleon hashing within elliptic curve cryptography (CHAM-CLAS). The signature and verification modules are also redesigned to address the identified vulnerabilities in Xiong’s scheme. Additionally, we implemented the small exponents test within the batch verification module to achieve Type III security. While this enhances security, it introduces a slight performance trade-off. Our scheme has been subjected to formal security and performance analyses to ensure robustness.

Securing Federated Learning: Approaches, Mechanisms and Opportunities

With the ability to analyze data, artificial intelligence technology and its offshoots have made difficult tasks easier. The tools of these technologies are now used in almost every aspect of life. For example, Machine Learning (ML), an offshoot of artificial intelligence, has become the focus of interest for researchers in industry, education, healthcare and other disciplines and has proven to be as efficient as, and in some cases better than, experts in answering various problems. However, the obstacles to ML’s progress are still being explored, and Federated Learning (FL) has been presented as a solution to the problems of privacy and confidentiality. In the FL approach, users do not disclose their data throughout the learning process, which improves privacy and security. In this article, we look at the security and privacy concepts of FL and the threats and attacks it faces. We also address the security measures used in FL aggregation procedures. In addition, we examine and discuss the use of homomorphic encryption to protect FL data exchange, as well as other security strategies. Finally, we discuss security and privacy concepts in FL and what additional improvements could be made in this context to increase the efficiency of FL algorithms.

EHR Interoperability Challenges Leveraging HL7 FHIR for Seamless Data Exchange in Healthcare

Electronic health records, often known as EHRs, have revolutionised the healthcare industry by making patient information more easily accessible and more efficient on the whole. There is still a huge obstacle to overcome in order to achieve seamless data transmission across different electronic health record systems. variances in data formats, terminologies, and standards often give rise to interoperability concerns. These variances impede the smooth flow of information across various systems and have the potential to cause problems. The Fast Healthcare Interoperability Resources (FHIR) standard, which is based on Health Level Seven (HL7), has emerged as a potentially useful option to overcome these difficulties. Through the provision of a uniform and standardized method for the sharing of healthcare data, FHIR is intended to facilitate the interchange of data in a manner that is both more efficient and trustworthy. The issues that are involved with electronic health record (EHR) interoperability are investigated in this study, as is the manner in which HL7 FHIR may ease the interchange of data in healthcare settings in a smooth manner. It starts out by providing an overview of the basic problems that exist in electronic health record (EHR) interoperability. These problems include data fragmentation, inconsistent data formats, and the absence of standardized protocols. HL7 FHIR is then presented in the article, along with a description of its fundamental principles. These concepts include the emphasis placed on interoperability, the simplicity of implementation, and the support for contemporary web technologies.

Research on information extension of mountainous rack railway engineering based on IFC standard

In response to the practical demands for data sharing and exchange in the field of rack railway systems engineering, as well as to address the gaps in the rack railway domain within the framework of the IFC4 standard, we extend and define the rack railway domain through entity extension and custom attribute sets. By utilizing the ongoing construction of the Dujiangyan to Mount Siguniang Railway as a case study, we validate the utility of this IFC extension and modeling approach. Leveraging IfcOpenShell, we incorporate the extended data content into the generated IFC file. We present a process for extension tailored to the characteristics of rack railway engineering. This study aims to provide broader information support for the digital construction of track structures in the design phase of rack railway engineering and to facilitate more efficient data exchange and sharing.

FedCPG: A class prototype guided personalized lightweight federated learning framework for cross-factory fault detection

Industrial equipment condition monitoring and fault detection are crucial to ensure the reliability of industrial production. Recently, data-driven fault detection methods have achieved significant success, but they all face challenges due to data fragmentation and limited fault detection capabilities. Although centralized data collection can improve detection accuracy, the conflicting interests brought by data privacy issues make data sharing between different devices impractical, thus forming the problem of industrial data silos. To address these challenges, this paper proposes a class prototype guided personalized lightweight federated learning framework(FedCPG). This framework decouples the local network, only uploading the backbone model to the server for model aggregation, while employing the head model for local personalized updates, thereby achieving efficient model aggregation. Furthermore, the framework incorporates prototype constraints to steer the local personalized update process, mitigating the effects of data heterogeneity. Finally, a lightweight feature extraction network is designed to reduce communication overhead. Multiple complex industrial data distribution scenarios were simulated on two benchmark industrial datasets. Extensive experiments have demonstrated that FedCPG can achieve an average detection accuracy of 95% in complex industrial scenarios, while simultaneously reducing memory usage and the number of parameters by 82%, surpassing existing methods in most average metrics. These findings offer novel perspectives on the application of personalized federated learning in industrial fault detection.

A Review of Physical and Logical Design in IoT: Comparative Analysis of Devices, Protocols, Communication Models, and APIs

The Internet of Things (IoT) has emerged as a transformative technology, enabling connectivity and data exchange among a vast network of devices. This paper presents a comprehensive comparative analysis of the physical and logical design elements of IoT systems, focusing on IoT devices, communication protocols, models, and APIs (REST and WebSocket). The research highlights the critical role that both physical and logical components play in ensuring efficient, secure, and scalable IoT deployments. By examining the integration of these aspects, the paper aims to provide insights for optimizing IoT architectures for various use cases, particularly in resource-constrained environments.

DeepASD: a deep adversarial-regularized graph learning method for ASD diagnosis with multimodal data

Autism Spectrum Disorder (ASD) is a prevalent neurological condition with multiple co-occurring comorbidities that seriously affect mental health. Precisely diagnosis of ASD is crucial to intervention and rehabilitation. A single modality may not fully reflect the complex mechanisms underlying ASD, and combining multiple modalities enables a more comprehensive understanding. Here, we propose, DeepASD, an end-to-end trainable regularized graph learning method for ASD prediction, which incorporates heterogeneous multimodal data and latent inter-patient relationships to better understand the pathogenesis of ASD. DeepASD first learns cross-modal feature representations through a multimodal adversarial-regularized encoder, and then constructs adaptive patient similarity networks by leveraging the representations of each modality. DeepASD exploits inter-patient relationships to boost the ASD diagnosis that is implemented by a classifier compositing of graph neural networks. We apply DeepASD to the benchmarking Autism Brain Imaging Data Exchange (ABIDE) data with four modalities. Experimental results show that the proposed DeepASD outperforms eight state-of-the-art baselines on the benchmarking ABIDE data, showing an improvement of 13.25% in accuracy, 7.69% in AUC-ROC, and 17.10% in specificity. DeepASD holds promise for a more comprehensive insight of the complex mechanisms of ASD, leading to improved diagnosis performance.

Systematic Review of IoT-Based Solutions for User Tracking: Towards Smarter Lifestyle, Wellness and Health Management.

The Internet of Things (IoT) base has grown to over 20 billion devices currently operational worldwide. As they greatly extend the applicability and use of biosensors, IoT developments are transformative. Recent studies show that IoT, coupled with advanced communication frameworks, such as machine-to-machine (M2M) interactions, can lead to (1) improved efficiency in data exchange, (2) accurate and timely health monitoring, and (3) enhanced user engagement and compliance through advancements in human-computer interaction. This systematic review of the 19 most relevant studies examines the potential of IoT in health and lifestyle management by conducting detailed analyses and quality assessments of each study. Findings indicate that IoT-based systems effectively monitor various health parameters using biosensors, facilitate real-time feedback, and support personalized health recommendations. Key limitations include small sample sizes, insufficient security measures, practical issues with wearable sensors, and reliance on internet connectivity in areas with poor network infrastructure. The reviewed studies demonstrated innovative applications of IoT, focusing on M2M interactions, edge devices, multimodality health monitoring, intelligent decision-making, and automated health management systems. These insights offer valuable recommendations for optimizing IoT technologies in health and wellness management.