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Sire is a Python/C++ library that is used both to prototype new algorithms and as an interoperability engine for exchanging information between molecular simulation programs. It provides a collection of file parsers and information converters that together make it easier to combine and leverage the functionality of many other programs and libraries. This empowers researchers to use sire to write a single script that can, for example, load a molecule from a PDBx/mmCIF file via Gemmi, perform SMARTS searches via RDKit, parameterize molecules using BioSimSpace, run GPU-accelerated molecular dynamics via OpenMM, and then display the resulting dynamics trajectory in a NGLView Jupyter notebook 3D molecular viewer. This functionality is built on by BioSimSpace, which uses sire's molecular information engine to interconvert with programs such as GROMACS, NAMD, Amber, and AmberTools for automated molecular parameterization and the running of molecular dynamics, metadynamics, and alchemical free energy workflows. Sire comes complete with a powerful molecular information search engine, plus trajectory loading and editing, analysis, and energy evaluation engines. This, when combined with an in-built computer algebra system, gives substantial flexibility to researchers to load, search for, edit, and combine molecular information from multiple sources and use that to drive novel algorithms by combining functionality from other programs. Sire is open source (GPL3) and is available via conda and at a free Jupyter notebook server at https://try.openbiosim.org. Sire is supported by the not-for-profit OpenBioSim community interest company. [ABSTRACT FROM AUTHOR]

In the era of industry 4.0 and the widespread use of digital devices, the number of cyber attacks poses an escalating and diverse threat, jeopardizing users' online activities. Intrusion detection systems (IDS) emerge as pivotal solutions, playing a crucial role in detecting anomalous signals within network systems. To counter novel attack patterns, IDS systems require periodic rule updates for effective identification of unusual signals. Typically, these policies are updated based on rule‐based or deep learning algorithms to enhance detection performance. However, the insufficient number of labeled samples remains a challenge for real‐world deployment. In this article, an automated labeling method is presented that has shown high effectiveness, requiring minimal hardware resources, and applicable to IDS systems. Additionally, the approach utilizes transfer learning combined with attention mechanisms to boost the efficiency of abnormal signal detection. The results from the approach are compared with those of a reference model, illustrating an overall improvement of nearly 10% in our model's performance compared to the reference model. This underscores the effectiveness of automating rule adjustments for IDS, contributing significantly to reducing associated financial costs. The research addresses the challenges in deploying IDS in real‐world scenarios and provides a valuable contribution to enhancing cyber threat detection capabilities. A preprint has previously been published [11]. [ABSTRACT FROM AUTHOR]

The Open-Radio Access Network (O-RAN) alliance is leading the evolution of telecommunications towards a greater intelligence, openness, virtualization, and interoperability within mobile networks. The O-RAN standard incorporates of many components the Open-Central Unit (O-CU) and Open-Distributed Unit (O-DU), network slicing and heterogeneous base stations (BS). Together, these innovations have given rise to a three-tiered user association (UA) relationship in a type of network called heterogeneous network (HetNet) with network slicing-enabled. There is an absence of efficient UA schemes for achieving fair resource allocation in such network scenario. Hence, this study formulates the fairness-aware UA problem as a utility-based combinatorial optimization problem, which is computationally hard to solve. Hence, an efficient Binary Particle Swarm Optimization (BPSO)-based UA scheme is proposed to solve the problem. Through simulations of an O-RAN based HetNet with network slicing-enabled, performance of the proposed BPSO-UA scheme is compared against two other baseline UA schemes. Results demonstrate the effectiveness of the proposed BPSO-UA scheme in achieving high fairness through equitable network slicing resource allocation, thereby leading to higher user connectivity rate and comparable average spectral efficiency. This innovative approach sheds light on the potential of metaheuristic algorithms in tackling intricate UA challenges, offering valuable insights for the future design and optimization of mobile networks. [ABSTRACT FROM AUTHOR]

Abstract In the era of industry 4.0 and the widespread use of digital devices, the number of cyber attacks poses an escalating and diverse threat, jeopardizing users' online activities. Intrusion detection systems (IDS) emerge as pivotal solutions, playing a crucial role in detecting anomalous signals within network systems. To counter novel attack patterns, IDS systems require periodic rule updates for effective identification of unusual signals. Typically, these policies are updated based on rule‐based or deep learning algorithms to enhance detection performance. However, the insufficient number of labeled samples remains a challenge for real‐world deployment. In this article, an automated labeling method is presented that has shown high effectiveness, requiring minimal hardware resources, and applicable to IDS systems. Additionally, the approach utilizes transfer learning combined with attention mechanisms to boost the efficiency of abnormal signal detection. The results from the approach are compared with those of a reference model, illustrating an overall improvement of nearly 10% in our model's performance compared to the reference model. This underscores the effectiveness of automating rule adjustments for IDS, contributing significantly to reducing associated financial costs. The research addresses the challenges in deploying IDS in real‐world scenarios and provides a valuable contribution to enhancing cyber threat detection capabilities. A preprint has previously been published [11].

In most cases, the utilization of the costly payload onboard Low Earth Orbit (LEO) satellites is restricted by the limited throughput of the payload data downlink to the ground station during the visibility window. The usefulness of these data in critical applications reduces due to the large latency of the process. Different techniques involving efficient modulation schemes, increased power within the allowed level and frequency band, and capacity enhancement using close-by satellites have been studied and implemented with their relative merits and limitations in an attempt to reduce the latency of the data to the user. The present study proposes a constellation of low-cost data relay satellites placed in the same orbital plane along with the main satellite, increasing directly the effective visibility window. As a result, the utility of the main satellite is also increased by the same factor. A detailed analysis of the constellation and configuration of the relay satellites is presented in this paper. [ABSTRACT FROM AUTHOR]

The current report is the result of the recent EFSA‐EBTC initiative to advance the use of automation tools in systematic reviews. The working group members identified the need for data exchange standards to facilitate transferring data between SR automation tools without loss or changes in data as a first priority. Closely related objectives identified were the concurrent development of data guidance and data content standards, and a strategy for governance of the resulting data standards, including their maintenance and regular updates. These conversations led up to the organization of workshops to initiate the development of data standards, as described in the current report. The EFSA‐EBTC collaboration organized a four half‐day workshop for an extensive group of stakeholders actively involved in the development or use of SR automation tools. The main objectives were 1) to generate a common understanding of the needs and hurdles for data standards in SR automation, 2) to collate viewpoints from all relevant stakeholders on how such standards may be developed, and 3) to kick‐off the development process. Three preparatory workshops preceding the final workshop were organized to clarify the scope and objectives of the main workshop. This external scientific report contains the summary of the preparatory and main workshops, as well as the proposed strategy for the process of how these data standards could be developed. The recommended next steps for development focus on further refining the aims, analysis of the landscape, interoperability with existing standards, setting up the infrastructure (e.g., establishing a governance structure and working groups containing all essential stakeholders), and the process of actual data standard development, launching and monitoring adoption of the standards. [ABSTRACT FROM AUTHOR]

Current risk assessment practices for chemicals primarily focus on individual substances or predefined mixtures. However, people are exposed daily to multiple chemicals from diverse sources. The Monte Carlo Risk Assessment (MCRA) platform, an open web‐based tool, facilitates cumulative risk assessment (CRA) of chemicals. It is developed by Wageningen University & Research (WUR, Biometris) for the Dutch Institute for Public Health and the Environment (RIVM). The a (EFSA) and RIVM have initiated a Framework Partnership Agreement (FPA) to enhance mixture risk assessment (MRA) capabilities in MCRA. This report describes the collaborative efforts of EFSA and RIVM to make the MCRA platform more interoperable and accessible, focusing on data connectivity and security. Emphasis is placed on integrating relevant data used in Standard Regulatory Actions (SRA) implemented in MCRA, ensuring up‐to‐date and relevant information. RIVM's security measures, including a security quick‐scan and subsequent risk management plan, ensure a secure information and communications technology (ICT) and data hosting environment, especially for privacy sensitive data (e.g., food consumption data). A workflow for data exchange from EFSA to MCRA is proposed, focusing on standardisation and automation to minimise manual operations. This workflow builds on EFSA's established yearly pesticide data collection and reporting cycle. Additionally, efforts to align MCRA with other modelling software are explored. Ensuring Open MCRA's accessibility, various user groups are defined, enhancing the platform's usability for diverse stakeholders. [ABSTRACT FROM AUTHOR]

This paper presents a constant-envelope modulation scheme, based on a synthesized binary offset carrier (SBOC), for a global navigation satellite system (GNSS) that combines three signals in a nonlinear fashion with unequal amplitudes. The proposed SBOC modulation meets the power spectral density criteria of multiplexed binary offset carrier (MBOC) modulation used in the L1 frequency band (1575.42 MHz) open civilian service interoperable signals for GNSS. This SBOC modulation also allows for the selection of an arbitrary power-sharing ratio between the data and pilot signals. This approach provides better performance than various MBOC(6, 1, 1/11) modulations for narrowband receivers. [ABSTRACT FROM AUTHOR]

The digital transformation of architecture, engineering, and construction (AEC) is being hailed as a panacea for the sectors' problems, with major actors in government and business promoting it as a benevolent harbinger of greater efficiency, productivity, better quality, and environmental performance. However, advancing digitalization in AEC is intertwined with a distinct political-economic logic that involves new constraints, dependencies, and a reconfiguration of power relations. Specifically, Building Information Modeling (BIM) as a digital convergence of three-dimensional building models, databases, and interfaces for multidisciplinary collaboration operates as a runway technology for digitalization and an entry point for platformization impelled by global software giants, such as this article's empirical case of software company Autodesk and its BIM product portfolio shows. BIM's linkage of governmental policy strategies with the techno-economic platform logic of software subscription models, open application programming interfaces, and cloud-enabled collaboration ensures management and control of construction data and software interoperability. At the same time, BIM's techno-economic and political configuration facilitates a near-monopolistic structure in the global architecture and construction software market. It thus grants one hegemonic company immense power to define what formats, standards, and data for building models and design processes are being used in creating the built environment. The expansive, intersecting logics of assetization, platformization, and datafication have been taking hold in the AEC sector, raising questions about their stratifying effects in the built environment and society. [ABSTRACT FROM AUTHOR]

This research paper explores the evolution of voting systems, assessing vulnerabilities in traditional methods and investigating blockchain technology as a solution. Traditional voting systems encounter challenges concerning security, transparency, and accessibility, prompting a transition towards blockchain-based online voting. Blockchain, originating from cryptocurrencies, decentralizes control, thereby enhancing transparency, integrity, and security in elections. Real-world case studies from Estonia and West Virginia offer insights into implementation and successes. Ethical considerations, including privacy and consent, alongside societal impacts like inclusivity and democratization, are scrutinized. The proposed blockchain-based voting system prioritizes decentralization, tamper resistance, and interoperability. Security measures incorporate cryptographic techniques and multi-factor authentication. Transparency is assured through a publicly accessible blockchain ledger, facilitating an auditable voting process. The paper addresses challenges like voter accessibility and election anonymity, proposing mitigation strategies. Future directions entail integrating blockchain with AI and machine learning for interoperability and standardization. Collaboration among policymakers, technologists, and researchers is pivotal for advocating secure, transparent, and accessible blockchain-based voting systems, with the goal of fostering globally participatory elections. [ABSTRACT FROM AUTHOR]

The traditional manual recording of vaccination records in Germany faced challenges during the COVID-19 pandemic, prompting the introduction of a COVID smartphone app with QR codes. However, this solution brought new challenges, emphasizing the need for a centrally managed European digital vaccination record for efficiency and validity. This study assesses the feasibility of using the HL7 FHIR standard in the healthcare industry for implementing a digital vaccination pass management and monitoring system. The system aims to offer convenience and improved efficiency for both patients and healthcare providers while promoting interoperability with other healthcare systems. To this end, we developed a prototype using modern technologies, such as React, Quarkus, and Keycloak. Results indicate potential benefits for patients and healthcare providers, offering access to immunization records, personalized recommendations, and streamlined management. However, integrating nuanced vaccination processes into the standardized FHIR system requires custom extensions, which might hinder interoperability. Manual data entry and the integration of an identity provider present further obstacles in industry scenarios. Despite these challenges, this study suggests that implementing HL7 FHIR can enhance efficiency, data accessibility, and accuracy in the vaccination process, supporting broader digitization efforts in the German healthcare system and beyond. [ABSTRACT FROM AUTHOR]

Interoperability is a central problem in digitization and System of Systems (SoS) engineering, which concerns the capacity of systems to exchange information and cooperate. The task to dynamically establish interoperability between heterogeneous cyber-physical systems (CPSs) at run-time is a challenging problem. Different aspects of the interoperability problem have been studied in fields such as SoS, neural translation, and agent-based systems, but there are no unifying solutions beyond domain-specific standardization efforts. The problem is complicated by the uncertain and variable relations between physical processes and human-centric symbols, which result from, e.g., latent physical degrees of freedom, maintenance, re-configurations, and software updates. Therefore, we surveyed the literature for concepts and methods needed to automatically establish SoSs with purposeful CPS communication, focusing on machine learning and connecting approaches that are not integrated in the present literature. Here, we summarize recent developments relevant to the dynamic interoperability problem, such as representation learning for ontology alignment and inference on heterogeneous linked data; neural networks for transcoding of text and code; concept learning-based reasoning; and emergent communication. We find that there has been a recent interest in deep learning approaches to establishing communication under different assumptions about the environment, language, and nature of the communicating entities. Furthermore, we present examples of architectures and discuss open problems associated with artificial intelligence (AI)-enabled solutions in relation to SoS interoperability requirements. Although these developments open new avenues for research, there are still no examples that bridge the concepts necessary to establish dynamic interoperability in complex SoSs, and realistic testbeds are needed. [ABSTRACT FROM AUTHOR]

Determining the precise location of a ship navigating on a lake is of utmost importance for various logistical and safety reasons. However, this task can become particularly challenging when operating in regions with limited or no access to the Internet. Fortunately, advancements in technology, specifically the Internet of Things (IoT) utilizing Low Power Wide Area Network (LPWAN) technology, have offered a robust solution to address this issue. LPWAN technology enables long-range data transmission without being reliant on traditional internet connectivity. In the context of our research, a LoRaWAN end node is employed equipped with LGT92, along with three gateways sourced from two different vendors, to effectively track the real-time position of a ship. To gauge the effectiveness of our system, let's conduct experiments and obtained significant results. One noteworthy finding was that the communication range between one of the gateways and the LoRaWAN end node extended to 2.5 kilometers when utilizing the RAK 7243 gateway. In contrast, the Dragino LG308 gateway impressively stretched this range to 5.7 kilometers. All three gateways to broaden the tracking coverage area and ensure comprehensive monitoring are strategically deployed. Our research's primary focus was tracking a ship's journey spanning 16 kilometers from Balige to Onanrunggu port. Throughout our experimentation, it is confirmed that the ship's precise coordinates, including latitude and longitude, were accurately transmitted from the end node on the ship to the designated gateway. This crucial position data was then seamlessly transmitted to the cloud. This real-time tracking capability proved invaluable, allowing for constant oversight and decision-making support during the ship's journey. [ABSTRACT FROM AUTHOR]

In current building design practices, the operation and performance of heating, ventilation and air conditioning (HVAC) systems are rarely documented in detail. This is mainly caused by the manual burden of generating detailed HVAC simulation models. To lower the barrier for detailed HVAC simulations, this paper presents an automated toolchain that generates and simulates models in Modelica language utilizing building information structured in a web-based common data environment. This approach differs from previous approaches by its focus on HVAC systems and integration with a common data environment over file-based BIM, which increases interoperability and allows users to run simulation studies in the cloud. The tool successfully generated and simulated a model of the HVAC systems in a small building under ideal and faulty operation of the heating system and thus demonstrated a fully interoperable data exchange between a common data environment and a simulation environment while showcasing the potential of analyzing HVAC systems with Modelica. [ABSTRACT FROM AUTHOR]

A new community of research infrastructures has joined forces to provide scientists with seamless access to the plethora of data, services and tools in biodiversity research. New levels of technological innovation and interoperability between infrastructures foster unprecedented access to biodiversity data across all data domains and the entire research lifecycle, thus advancing open science practices and strengthening Europe's position in the global biodiversity research landscape. This policy brief highlights the potential benefits derived from enhanced connectivity and interoperability among various types of biodiversity data, fostering innovation and advancements in biodiversity science, monitoring, conservation, and policy development. [ABSTRACT FROM AUTHOR]

This article summarises the aims, methods, information architecture, outputs, and innovations of the Australian Cultural Data Engine (ACD-Engine), a project that harnesses leading cultural databases to build bridges to research, industry, and government. The project investigates digital heritage collections, data ontologies, and interoperability, building an information architecture to enhance the open sharing of Australian cultural data. Working with a cross-disciplinary team, the ACD-Engine establishes conceptual and technical frameworks for better understanding the platforms and uses of cultural data across a range of national and international contexts. This new cyber-infrastructure advances cultural data aggregation and interoperability whilst prioritising data quality and domain distinctiveness to answer new research questions across disciplines. As such, the ACD-Engine provides a novel approach to data management and data modelling in the arts and humanities that has significant implications for digital collections, digital humanities, and data analytics. Cultural databases are complicated beasts: rich in their contents and yet often idiosyncratic, siloed, and precarious. This article outlines an innovative workflow and information architecture designed to harness the interoperability of digital resources/records for cultural analytics research without obliterating distinctive domain knowledges. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive survey of the integration of graph neural networks (GNN) and deep reinforcement learning (DRL) in end-to-end (E2E) networking solutions. We delve into the fundamentals of GNN, its variants, and the state-of-the-art applications in communication networking, which reveal the potential to revolutionize access, transport, and core network management policies. This paper further explores DRL capabilities, its variants, and the trending applications in E2E networking, particularly in enhancing dynamic network (re)configurations and resource management. By fusing GNN with DRL, we spotlight novel approaches, ranging from radio access networks to core management and orchestration, across E2E network layers. Deployment scenarios in smart transportation, smart factory, and smart grids demonstrate the practical implications of our survey topic. Lastly, we point out potential challenges and future research directions, including the critical aspects for modelling explainability, the reduction in overhead consumption, interoperability with existing schemes, and the importance of reproducibility. Our survey aims to serve as a roadmap for future developments in E2E networking, guiding through the current landscape, challenges, and prospective breakthroughs in the algorithm modelling toward network automation using GNN and DRL. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0, this paper explores the vital role of advanced technologies, including Cyber–Physical Systems (CPS), Big Data, Internet of Things (IoT), digital twins, and Artificial Intelligence (AI), in enhancing data valorization and management within industries. These technologies are integral to addressing the challenges of producing highly customized products in mass, necessitating the complete digitization and integration of information technology (IT) and operational technology (OT) for flexible and automated manufacturing processes. The paper emphasizes the importance of interoperability through Service-Oriented Architectures (SOA), Manufacturing-as-a-Service (MaaS), and Resource-as-a-Service (RaaS) to achieve seamless integration across systems, which is critical for the Industry 4.0 vision of a fully interconnected, autonomous industry. Furthermore, it discusses the evolution towards Supply Chain 4.0, highlighting the need for Transportation Management Systems (TMS) enhanced by GPS and real-time data for efficient logistics. A guideline for implementing CPS within Industry 4.0 environments is provided, focusing on a case study of real-time data acquisition from logistics vehicles using CPS devices. The study proposes a CPS architecture and a generic platform for asset tracking to address integration challenges efficiently and facilitate the easy incorporation of new components and applications. Preliminary tests indicate the platform's real-time performance is satisfactory, with negligible delay under test conditions, showcasing its potential for logistics applications and beyond. [ABSTRACT FROM AUTHOR]

Collaborative Enterprise (CE) comprises of organizations that adopt digital platforms to achieve shared goals. In CE the prospect of Distributed Ledger Technologies (DLT) such as blockchain is reliant on its capability to integrate with other systems to improve organizational operations. But the inability for different blockchains to communicate with one another is an inherent issue as it puts a strain on the mainstream deployment of blockchains in CE. Therefore, this study presents a standardized architecture to support DLT interoperability and intraoperability within CE. A structural review was conducted after which design science research methodology was adopted to validate the architecture. [ABSTRACT FROM AUTHOR]

The utilization of virtualization technology, particularly Docker containers, has increased significantly in recent years. As Docker provides a lightweight and efficient virtualization environment for software packages, ensuring its security becomes crucial. This paper performs a security analysis of Docker with two perspectives: the security within Docker and its relationship to the security features of the Linux kernel. Resources that are isolated, controlled, and limited are all examined in the Docker internal security. Linux Cgroup is used by Docker to manage computer resources, while Linux Namespace is used to securely isolate running environments. In this paper discussed how to separate resources such as filesystems, networks, devices, and processes, as well as how to isolate inter-process communication. Docker's interactions with Linux kernel security characteristics such as SELinux, AppArmor, Seccomp, and Linux functions were also discussed. These capabilities boost host system security by deploying Docker containers. AppArmor maintains security policies, SELinux offers further permission checks, and the Linux function limits container rights. Network-based assaults are defended against with the aid of Seccomp and the network framework. Additionally, the study makes recommendations for possible enhancements to improve Docker's security. This involves configuring Docker to deactivate specific functions within containers to thwart possible breaches and enhancing interoperability with Linux kernel security mechanisms. [ABSTRACT FROM AUTHOR]

With the ever-increasing population and historic highest energy demand, the energy efficiency of buildings is becoming crucial. Architectural firms are moving from traditional Computer-Aided Design (CAD) to BIM. However, nearly 40% of the energy consumption is due to buildings. Therefore, there is a need to integrate BIM with Building Energy Modeling (BEM), which presents an innovative opportunity to demonstrate the potential of BIM to minimize energy consumption by integrating building information software with data from existing energy-efficient building automation systems (EBAS). BEM is a form of computational analysis that can be used in conjunction with BIM or Computer-Aided Engineering (CAE) systems. In this paper, an attempt has been made to explore the existing literature on BIM and BEM and identify the effect of the integration of BEM in BIM in the design phase of the project. A recent survey from the last ten years (2012 to 2023) was carried out on Google Scholar, Web of Science, Science Direct, and Scopus databases. Inclusion/exclusion criteria were applied, and papers were scrutinized. From the results, it can be observed that the convergence of BIM and BEM is found to be useful in practical applications; however, projects with short life cycles might not be suitable for this solution. Challenges exist in the interoperability tools which have restrictions on data exchange. Binary translation is found to be the most suitable candidate for data exchange. The analysis further showed that the most used program for integrating BIM/BEM is Green Building Studio developed by Autodesk to improve construction and operational efficiencies. [ABSTRACT FROM AUTHOR]

This study investigates web 3.0 heterogeneous computing with webGL, webGPU, and IPFS. The primary focus is on the benefits of utilising these technologies to enhance the functionality and performance of web 3.0 applications. The study investigates web 3.0 as it currently exists and the constraints that developers face due to graphic, computational, and storage capabilities. According to the findings, incorporating webGL and webGPU can considerably increase user experience, speed, efficacy, and decentralization. Finally, this study summarizes the importance of continuing research in this subject, particularly with relation to platform interoperability and the future prospects of heterogeneous computing on web 3.0 via graphical APIs. [ABSTRACT FROM AUTHOR]

Semantic interoperability establishes intercommunications and enables data sharing across disparate systems. In this study, we propose an ostensive information architecture for healthcare information systems to decrease ambiguity caused by using signs in different contexts for different purposes. The ostensive information architecture adopts a consensus-based approach initiated from the perspective of information systems re-design and can be applied to other domains where information exchange is required between heterogeneous systems. Driven by the issues in FHIR (Fast Health Interoperability Resources) implementation, an ostensive approach that supplements the current lexical approach in semantic exchange is proposed. A Semantic Engine with an FHIR knowledge graph as the core is constructed using Neo4j to provide semantic interpretation and examples. The MIMIC III (Medical Information Mart for Intensive Care) datasets and diabetes datasets have been employed to demonstrate the effectiveness of the proposed information architecture. We further discuss the benefits of the separation of semantic interpretation and data storage from the perspective of information system design, and the semantic reasoning towards patient-centric care underpinned by the Semantic Engine. [ABSTRACT FROM AUTHOR]

The growing prominence of Industry 4.0 necessitates the increased utilization and complexity of Cyber-Physical Production Systems (CPPS). However, challenges persist in the systematic application of the RAMI 4.0 reference architecture for CPPS and the lack of standardization. To address these issues, we propose a novel process-based methodology leveraging RAMI 4.0 for analyzing, evaluating, and enhancing system interoperability within CPPS. By examining two exemplary CPPS in this study, we demonstrate the significance of minimizing interfaces and adaptations. The results reveal a reduction in required hardware, interfaces, information, and protocols between the systems. Our findings contribute to the advancement of standardized approaches for achieving holistic interoperability among individual systems within the RAMI 4.0 framework. [ABSTRACT FROM AUTHOR]

Metaproteomics is an increasingly popular methodology that provides information regarding the metabolic functions of specific microbial taxa and has potential for contributing to ocean ecology and biogeochemical studies. A blinded multi-laboratory intercomparison was conducted to assess comparability and reproducibility of taxonomic and functional results and their sensitivity to methodological variables. Euphotic zone samples from the Bermuda Atlantic Time-Series Study in the North Atlantic Ocean collected by in situ pumps and the AUV Clio were distributed with a paired metagenome, and one-dimensional liquid chromatographic data dependent acquisition mass spectrometry analyses was stipulated. Analysis of mass spectra from seven laboratories through a common informatic pipeline identified a shared set of 1056 proteins from 1395 shared peptides constituents. Quantitative analyses showed good reproducibility: pairwise regressions of spectral counts between laboratories yielded R2 values ranging from 0.43 to 0.83, and a Sørensen similarity analysis of the top 1,000 proteins revealed 70–80 % similarity between laboratory groups. Taxonomic and functional assignments showed good coherence between technical replicates and different laboratories. An informatic intercomparison study, involving 10 laboratories using 8 software packages successfully identified thousands of peptides within the complex metaproteomic datasets, demonstrating the utility of these software tools for ocean metaproteomic research. Future efforts could examine reproducibility in deeper metaproteomes, examine accuracy in targeted absolute quantitation analyses, and develop standards for data output formats to improve data interoperability. Together, these results demonstrate the reproducibility of metaproteomic analyses and their suitability for microbial oceanography research including integration into global scale ocean surveys and ocean biogeochemical models. [ABSTRACT FROM AUTHOR]

Metaproteomics is an increasingly popular methodology that provides information regarding the metabolic functions of specific microbial taxa and has potential for contributing to ocean ecology and biogeochemical studies. A blinded multi-laboratory intercomparison was conducted to assess comparability and reproducibility of taxonomic and functional results and their sensitivity to methodological variables. Euphotic zone samples from the Bermuda Atlantic Time-Series Study in the North Atlantic Ocean collected by in situ pumps and the AUV Clio were distributed with a paired metagenome, and one-dimensional liquid chromatographic data dependent acquisition mass spectrometry analyses was stipulated. Analysis of mass spectra from seven laboratories through a common informatic pipeline identified a shared set of 1056 proteins from 1395 shared peptides constituents. Quantitative analyses showed good reproducibility: pairwise regressions of spectral counts between laboratories yielded R² values ranging from 0.43 to 0.83, and a Sørensen similarity analysis of the top 1,000 proteins revealed 70-80% similarity between laboratory groups. Taxonomic and functional assignments showed good coherence between technical replicates and different laboratories. An informatic intercomparison study, involving 10 laboratories using 8 software packages successfully identified thousands of peptides within the complex metaproteomic datasets, demonstrating the utility of these software tools for ocean metaproteomic research. Future efforts could examine reproducibility in deeper metaproteomes, examine accuracy in targeted absolute quantitation analyses, and develop standards for data output formats to improve data interoperability. Together, these results demonstrate the reproducibility of metaproteomic analyses and their suitability for microbial oceanography research including integration into global scale ocean surveys and ocean biogeochemical models. [ABSTRACT FROM AUTHOR]

In an age of burgeoning open data sources, ranging from authoritative platforms to crowd-sourced contributions, the need for data integration is paramount - only with integrated, combined, data can today's complex problems be addressed. However, assessing the quality of data and its potential for integration/interoperability is complex. FAIR (findable, accessible, interoperable and re-useable) approaches go some way to help, but most repositories still only offer text-based search results and require the user to download the data and assess its quality and fitness-for-purpose manually. This paper examines whether exploiting geospatial approaches - specifically, understanding whether a dataset can be mapped and hence integrated with other datasets - could address these issues, in particular for non-expert users. We explore challenges related to open data for education and environmental sustainability and introduce a novel, visual map-ability rating to assist data users in rapidly understanding the data quality and interoperability potential of data they wish to use. This rating system has been developed by researchers from outside the location science domain, to better reflect what is important to the wider community, and derives from a review of 104 open datasets. As well as providing useful insight to data users, the ratings can be used to guide data publishers as to how to improve their data offering. [ABSTRACT FROM AUTHOR]

With the advent of the Internet of Everything (IoE), the concept of fully interconnected systems has become a reality, and the need for seamless communication and interoperability among different industrial systems has become more pressing than ever before. To address the challenges posed by massive data traffic, we demonstrate the potentials of semantic information processing in industrial manufacturing processes and then propose a brief framework of semantic processing and communication system for industrial network. In particular, the scheme is featured with task-orientation and collaborative processing. To illustrate its applicability, we provide examples of time series and images, as typical industrial data sources, for practical tasks, such as lifecycle estimation and surface defect detection. Simulation results show that semantic information processing achieves a more efficient way of information processing and exchanging, compared to conventional methods, which is crucial for handling the demands of future interconnected industrial networks. [ABSTRACT FROM AUTHOR]

The digitalization of urban mechanisms relies on the integration of digital terrain and 3D city models. However, a systematic examination of this topic within the context of integrated smart cities or digital twins has been lacking, hindering reproducibility and interpretation of results. To address this gap, this study conducts a systematic literature review to explore the role of digital terrain and 3D city models in supporting urban decision-making for integrated smart cities or digital twins. By following a review protocol, research questions were formulated, and systematic search strategies were implemented involving reputable databases such as Scopus, Web of Science, Science Direct, Emerald, Taylor Francis, Springer Link, and Sage Journals. The review process included identification, screening, eligibility assessment, quality appraisal, data extraction, and thematic analysis. The thematic analysis identified eight main themes and 22 sub-themes, shedding light on various aspects of digital terrain and 3D city models in urban decision-making, including (1) data integration and interoperability, (2) integrated visualization; (3) environmental simulation; (4) digital twins application; (5) smart cities application; (6) semantic enrichment; (7) applied planning; and (8) urban planning. This systematic review identifies gaps in the field and provides directions for future studies. [ABSTRACT FROM AUTHOR]

With the constant growth of software-defined radio (SDR) technologies in fields related to wireless communications, the need for efficient ways of testing and evaluating the physical-layer (PHY) protocols developed for these technologies in real-life traffic scenarios has become more critical. This paper proposes a software testbed that enhances the creation of network environments that allow GNU radio applications to be fed with test traffic in a simple way and through an interoperable interface. This makes the use of any traffic generator possible—existing ones or one that is custom-built—to evaluate a GNU radio application. In addition, this paper proposes an efficient way to collect PHY-specific monitoring data to improve the performance of the critical components of the message delivery path by employing the protocol buffers library. This study considers the entire testing and evaluation ecosystem and demonstrates how PHY-specific monitoring information is collected, handled, stored, and processed as time series to allow complex visualization and real-time monitoring. [ABSTRACT FROM AUTHOR]

Confronted by the development of various services, such as cloud computing, fifth-generation mobile communications, telemedicine, and high-definition video transmission, as well as the increase in fixedline network traffic, international standardization organizations and forums have been expanding the specifications of fixed-line transmission networks and optical components and ensuring interoperability. This article provides examples of NTT's activities in the International Telecommunication Union - Telecommunication Standardization Sector (ITU-T), International Electrotechnical Commission (IEC), Broadband Forum (BBF), and OpenROADM Multi-Source Agreement. [ABSTRACT FROM AUTHOR]

This article explores the practice of secondary qualitative data analysis (SDA) in engineering education research. SDA involves reusing existing qualitative data from previous studies, offering advantages such as reduced publication time and participant burden, as well as increased equity in the field. However, ethical challenges and ensuring quality remain important considerations. The authors propose the SHARE principles for qualitative data sharing and secondary analysis, emphasizing collaborative relationships, contextual understanding, responsible data reuse, and expanding capacity and ownership. The document aims to promote conversation and opportunities in the field of engineering education research through SDA. [Extracted from the article]

Technology standards are critical in creating a common interface, allowing interoperability between individual components, devices and networks. Technology standards often incorporate many essential patented inventions, which are referred to as standard-essential patents (SEPs). Owners of declared SEPs provide commitments to standard setting organizations (SSOs) that they will license their SEPs on fair, reasonable and non-discriminatory (FRAND) terms to companies that practice the standards. However, it is sometimes disputed whether or not these royalty rates are FRAND; in these cases, the burden of determining a FRAND royalty falls to courts. It is important for parties involved in SEP litigation to be knowledgeable of the appropriate economic tools and models to address the various issues arising in these unique and challenging patent disputes. This article discusses how various approaches to patent valuation have been applied in prominent SEP cases over the last decade. [ABSTRACT FROM AUTHOR]

Hospital information system, as an integrated system consists of complex, diverse, and heterogeneous information systems, which supports all clinical, administrative, and financial activities of patients; therefore, it faces interoperability problems, which is a critical feature of data sharing and integration. In this regard, a reference architecture will be presented for the integration of different systems in the hospital which comprises system, information and software architecture. This architecture emphasizes interoperability with eight layers of user interface and application, services, data collection and storage, integration, external application systems, communication and information infrastructure, management, and security/privacy, based on service orientation. It will be evaluated by ATAM method, based on ten scenarios. It shows the presented architecture will satisfy all functional and non-functional requirements such as interoperability between different information systems, reduction of information redundancy and development cost and time, scalability and accessibility. [ABSTRACT FROM AUTHOR]

AI development in biotechnology relies on high-quality data to train and validate algorithms. The FAIR principles (Findable, Accessible, Interoperable, and Reusable) and regulatory frameworks such as the In Vitro Diagnostic Regulation (IVDR) and the Medical Device Regulation (MDR) specify requirements on specimen and data provenance to ensure the quality and traceability of data used in AI development. In this paper, a framework is presented for recording and publishing provenance information to meet these requirements. The framework is based on the use of standardized models and protocols, such as the W3C PROV model and the ISO 23494 series, to capture and record provenance information at various stages of the data generation and analysis process. The framework and use case illustrate the role of provenance information in supporting the development of high-quality AI algorithms in biotechnology. Finally, the principles of the framework are illustrated in a simple computational pathology use case, showing how specimen and data provenance can be used in the development and documentation of an AI algorithm. The use case demonstrates the importance of managing and integrating distributed provenance information and highlights the complex task of considering factors such as semantic interoperability, confidentiality, and the verification of authenticity and integrity. • AI development in Biotechnology needs high quality data. • The FAIR principles, biobanking standards, IVDR and MDR define requirements on specimen and data provenance. • A framework to record and publish provenance information is presented. • A use case in computational pathology illustrates our approach. [ABSTRACT FROM AUTHOR]

The importance of improving the FAIRness (findability, accessibility, interoperability, reusability) of research data is undeniable, especially in the face of large, complex datasets currently being produced by omics technologies. Facilitating the integration of a dataset with other types of data increases the likelihood of reuse, and the potential of answering novel research questions. Ontologies are a useful tool for semantically tagging datasets as adding relevant metadata increases the understanding of how data was produced and increases its interoperability. Ontologies provide concepts for a particular domain as well as the relationships between concepts. By tagging data with ontology terms, data becomes both human- and machine- interpretable, allowing for increased reuse and interoperability. However, the task of identifying ontologies relevant to a particular research domain or technology is challenging, especially within the diverse realm of fundamental plant research. In this review, we outline the ontologies most relevant to the fundamental plant sciences and how they can be used to annotate data related to plant-specific experiments within metadata frameworks, such as Investigation-Study-Assay (ISA). We also outline repositories and platforms most useful for identifying applicable ontologies or finding ontology terms. [ABSTRACT FROM AUTHOR]

The main objective of this review is to summarize and thoroughly investigate the most popular and promising BIM (building information modeling) and BEM (building energy modeling) interoperability strategies employed in the last years (2004–2023), highlighting pros and cons of each strategy and trying to understand the reason for the still limited BIM–BEM interaction. This review summarizes the main countries, areas, modeling tools, and interoperability strategies, with the advantages and disadvantages of each one. The methodology is based on the PRISMA protocol, and two databases were used for the research: Scopus and Google Scholar. A total of 532 publications were selected and 100 papers were deemed useful for the purposes of this review. The main findings led to the identification of four different interoperability strategies between BIM and BEM tools: (1) real-time connection; (2) standardized exchange formats and middleware corrective tools; (3) adherence to model view definitions; (4) proprietary tool-chain. These strategies were found to be characterized by different degrees of complexity, time required for information exchange, proprietary or opensource software, ability to reduce information loss, and detailed energy results. The results of this study showed that, to date, there is no better interoperability strategy, and that further efforts are needed so that interoperability between the two tools can become commonplace. [ABSTRACT FROM AUTHOR]

As cloud computing continues to evolve, the imperative for secure and interoperable legal document management becomes increasingly critical. This research paper introduces a blockchain-backed verification system designed to enhance security, interoperability, and trust in managing legal documents across multi-cloud environments. We present a decentralized framework that leverages blockchain technology for authenticating documents, ensuring their integrity and authenticity across different cloud platforms. Our multi-cloud framework facilitates seamless interoperability and flexible workflow management, while smart-contract-driven verification processes streamline validation and minimize security vulnerabilities. The implementation of this system has led to a notable improvement in document security, with a 100% reduction in security breach incidents and a 90% decrease in unauthorized access attempts. Audit processes have been optimized as well, achieving a 100% complete audit trail and reducing audit times by 80%. Moreover, the system has significantly enhanced user trust, demonstrated by a 50% increase in user satisfaction and a 300% rise in adoption rates. These advancements establish a resilient infrastructure for legal document management, promising a higher degree of security, operational efficiency, and trust, which are pivotal for the widespread acceptance and integration of cloud-based services in the legal domain. [ABSTRACT FROM AUTHOR]