Interoperability Issues Research Articles

Integracja systemów płatniczych w IoE oraz metaverse – wyzwania i przyszłość e-commerce w wirtualnym świecie

In the era of the burgeoning metaverse, integration of payment systems is becoming a key element to enable seamless and secure transactions in virtual environments. This paper focuses on an analysis of current payment solutions in the metaverse, as well as their challenges and future prospects. The first part of the paper discusses existing payment systems available in the metaverse, including cryptocurrencies, tokens and traditional payment methods, highlighting their impact on transaction dynamics and the perception of value in the virtual world. The main challenges of integrating these systems are then presented, such as data security, identity verification, complex processes for exchanging currencies (crypto and traditional), and interoperability issues between different virtual environments. In the final part of the paper we focus on the future of e-commerce in the metaverse, considering what technological innovations may affect the shape and nature of commerce in the virtual world, and what opportunities lie ahead for entrepreneurs and developers in terms of creating new business models. The conclusions of this publication highlight the importance of proper integration of payment systems in the metaverse for achieving sustainable and balanced e-commerce development in virtual environments, as well as underscoring the need for further research and innovation in this field to meet users’ growing expectations and provide them with secure and efficient payment methods.

Creating occupant-centered digital twins using the Occupant Feedback Ontology implemented in a smartwatch app

Occupant feedback enables building managers to improve occupants’ health, comfort, and satisfaction. However, acquiring continuous occupant feedback and integrating this feedback with other building information is challenging. This paper presents a scalable method to acquire continuous occupant feedback and directly integrate this with other building information. Semantic web technologies were applied to solve data interoperability issues. The Occupant Feedback Ontology was developed to describe feedback semantically. Next to this, a smartwatch app – Mintal – was developed to acquire continuous feedback on indoor environmental quality. The app gathers location, medical information, and answers on short micro surveys. Mintal applied the Occupant Feedback Ontology to directly integrate the feedback with linked building data. A case study was performed to evaluate this method. A semantic digital twin was created by integrating linked building data, sensor data, and occupant feedback. Results from SPARQL queries gave more insight into an occupant’s perceived comfort levels in the Open Flat. The case study shows how integrating feedback with building information allows for more occupant-centric decision support tools. The approach presented in this paper can be used in a wide range of use cases, both within and without the architecture, building, and construction domain.

FFO: Forest Fire Ontology and Reasoning System for Enhanced Alert and Management Services

Forest fires or wildfires pose a serious threat to property, lives, and the environment. Early detection and mitigation of such emergencies, therefore, play an important role in reducing the severity of the impact caused by wildfire. Unfortunately, there is often an improper or delayed mechanism for forest fire detection which leads to destruction and losses. These anomalies in detection can be due to defects in sensors or a lack of proper information interoperability among the sensors deployed in forests. This paper presents a lightweight ontological framework to address these challenges. Interoperability issues are caused due to heterogeneity in technologies used and heterogeneous data created by different sensors. Therefore, through the proposed Forest Fire Detection and Management Ontology (FFO), we introduce a standardized model to share and reuse knowledge and data across different sensors. The proposed ontology is validated using semantic reasoning and query processing. The reasoning and querying processes are performed on real-time data gathered from experiments conducted in a forest and stored as RDF triples based on the design of the ontology. The outcomes of queries and inferences from reasoning demonstrate that FFO is feasible for the early detection of wildfire and facilitates efficient process management subsequent to detection.

Is Healthcare Facing A Silent Epidemic: The Rise Of Cyberattacks?

Undesirable consequences, such as the potential harm due to enhanced reliance on electronic platforms, should not be underestimated regardless of the healthcare industry's spectacular growth and the repeatedly applied digital transformation during the COVID-19 pandemic. Healthcare organizations, storing endless documents with very sensitive patient data, have their allure increased among cyber-crooks. The article underlines the fact that hackers find these deficiencies quite appealing, particularly when looking at the slow and unwilling pace of technology adoption and revamping of old systems. Stringent data protection regulations like the Health Insurance Portability and Accountability Act (HIPAA) are catalyzing, cyber-badey organizations into pursuing healthcare systems as their targets for cyber-crime. Healthcare organizations are weakened by the absence of standardized practices for cybersecurity, adoption issues for interoperability, and deficiency in staff training. The article puts the weight on prevention actions as network patching, employee training, and advanced cyber threat detection technologies are vital to protect patient information and provide the high-security standards that correspond the evolving and sophisticated threats. KEYWORDS: Electronic platforms, Healthcare industry, Data protection regulations, Cyber-crime, COVID-19 pandemic.

Semantic Interoperability of Electronic Health Records: Systematic Review of Alternative Approaches for Enhancing Patient Information Availability.

Semantic interoperability facilitates the exchange of and access to health data that are being documented in electronic health records (EHRs) with various semantic features. The main goals of semantic interoperability development entail patient data availability and use in diverse EHRs without a loss of meaning. Internationally, current initiatives aim to enhance semantic development of EHR data and, consequently, the availability of patient data. Interoperability between health information systems is among the core goals of the European Health Data Space regulation proposal and the World Health Organization's Global Strategy on Digital Health 2020-2025. To achieve integrated health data ecosystems, stakeholders need to overcome challenges of implementing semantic interoperability elements. To research the available scientific evidence on semantic interoperability development, we defined the following research questions: What are the key elements of and approaches for building semantic interoperability integrated in EHRs? What kinds of goals are driving the development? and What kinds of clinical benefits are perceived following this development? Our research questions focused on key aspects and approaches for semantic interoperability and on possible clinical and semantic benefits of these choices in the context of EHRs. Therefore, we performed a systematic literature review in PubMed by defining our study framework based on previous research. Our analysis consisted of 14 studies where data models, ontologies, terminologies, classifications, and standards were applied for building interoperability. All articles reported clinical benefits of the selected approach to enhancing semantic interoperability. We identified 3 main categories: increasing the availability of data for clinicians (n=6, 43%), increasing the quality of care (n=4, 29%), and enhancing clinical data use and reuse for varied purposes (n=4, 29%). Regarding semantic development goals, data harmonization and developing semantic interoperability between different EHRs was the largest category (n=8, 57%). Enhancing health data quality through standardization (n=5, 36%) and developing EHR-integrated tools based on interoperable data (n=1, 7%) were the other identified categories. The results were closely coupled with the need to build usable and computable data out of heterogeneous medical information that is accessible through various EHRs and databases (eg, registers). When heading toward semantic harmonization of clinical data, more experiences and analyses are needed to assess how applicable the chosen solutions are for semantic interoperability of health care data. Instead of promoting a single approach, semantic interoperability should be assessed through several levels of semantic requirements A dual model or multimodel approach is possibly usable to address different semantic interoperability issues during development. The objectives of semantic interoperability are to be achieved in diffuse and disconnected clinical care environments. Therefore, approaches for enhancing clinical data availability should be well prepared, thought out, and justified to meet economically sustainable and long-term outcomes.

Exploring the Pathways of Diabetes Foot Complications Treatment and Investigating Experiences From Frontline Health Care Professionals: Protocol for a Mixed Methods Study.

Diabetes affects more than 4.3 million individuals in the United Kingdom, with 19% to 34% developing diabetes-related foot ulceration (DFU) during their lifespan, which can lead to an amputation. In the United Kingdom, every week, approximately 169 people have an amputation due to diabetes. Preventing first-ever ulcers is the most effective strategy to reduce the occurrence of diabetes-related amputations, but research in this space is lacking. This protocol seeks to document the experiences and perspectives of frontline health care professionals who work with people who have diabetes and diabetes-related foot problems. Special attention is given to their perceptions of barriers to effective care, their views about barriers to effective and inclusive engagement with people with diabetes, and their experience with the first-ever DFU. Another aspect of the study is the focus on whether clinical management is affected by data sharing, data availability, and interoperability issues. This is a mixed methods explanatory protocol, which is sequential, and its purpose is to use the qualitative data to explain the initial quantitative data collected through a survey of frontline health care professionals. Data analysis of quantitative data will be completed first and then synthesized with the qualitative data analysis. Qualitative data will be analyzed using the framework method. This study will use joint displays to integrate the data. Ethical approval has been granted by the ethics committee of Staffordshire University. The quantitative data collection started in March 2023 and will close in May 2024. The qualitative interviews commenced in November 2023 with volunteer participants who initially completed the survey. This study's survey focuses on data interoperability and the interviews focus more on the perspectives and experiences of clinicians and their perceived barriers for the effective management of diabetes foot ulcers. Including a geographically relevant and diverse cohort of health care professionals that spans a wide range of roles and care settings involved in diabetes-related foot care is very important for the successful application of this protocol. Special care is given to advertise and promote participation as widely as possible. The qualitative part of this protocol is also limited to 30-40 interview participants, as it is not realistic to interview higher numbers, due to time and resource constraints. DERR1-10.2196/54852.

Supply Chain 5.0: A Comprehensive Literature Review on Implications, Applications and Challenges

Supply Chain 5.0 represents a paradigm shift in logistics and operations, integrating advanced digital technologies such as artificial intelligence, Internet of Things (IoT), blockchain, and analytics. This paper explores the transformative potential of Supply Chain 5.0 in optimizing efficiency, flexibility, and responsiveness within supply chain management practices. Through an analysis of current literature and industry trends, we elucidate how Supply Chain 5.0 enables real-time data insights, predictive analytics, and automated decision-making to revolutionize traditional supply chain models. However, challenges including data security, interoperability issues, and workforce training remain significant barriers to adoption. This study underscores the importance of addressing these challenges to fully realize the benefits of Supply Chain 5.0 in driving competitiveness, resilience, and sustainability in the global marketplace.

Emerging Technologies for Automation in Environmental Sensing: Review

This article explores the impact of automation on environmental sensing, focusing on advanced technologies that revolutionize data collection analysis and monitoring. The International Union of Pure and Applied Chemistry (IUPAC) defines automation as integrating hardware and software components into modern analytical systems. Advancements in electronics, computer science, and robotics drive the evolution of automated sensing systems, overcoming traditional limitations in manual data collection. Environmental sensor networks (ESNs) address challenges in weather constraints and cost considerations, providing high-quality time-series data, although issues in interoperability, calibration, communication, and longevity persist. Unmanned Aerial Systems (UASs), particularly unmanned aerial vehicles (UAVs), play an important role in environmental monitoring due to their versatility and cost-effectiveness. Despite challenges in regulatory compliance and technical limitations, UAVs offer detailed spatial and temporal information. Pollution monitoring faces challenges related to high costs and maintenance requirements, prompting the exploration of cost-efficient alternatives. Smart agriculture encounters hurdle in data integration, interoperability, device durability in adverse weather conditions, and cybersecurity threats, necessitating privacy-preserving techniques and federated learning approaches. Financial barriers, including hardware costs and ongoing maintenance, impede the widespread adoption of smart technology in agriculture. Integrating robotics, notably underwater vehicles, proves indispensable in various environmental monitoring applications, providing accurate data in challenging conditions. This review details the significant role of transfer learning and edge computing, which are integral components of robotics and wireless monitoring frameworks. These advancements aid in overcoming challenges in environmental sensing, underscoring the ongoing necessity for research and innovation to enhance monitoring solutions. Some state-of-the-art frameworks and datasets are analyzed to provide a comprehensive review on the basic steps involved in the automation of environmental sensing applications.

A review of distributed ledger technologies application in medical systems interoperability

This study of the literature delves into the complex area of medical systems interoperability, focusing on mitigating variables that impact security and data transfer at the structural and semantic levels. In the era of digital healthcare, the secure sharing of medical data is crucial, and this study looks at how Distributed Ledger Technologies (DLTs) can play a major role in addressing these challenges. Complex interoperability issues that come from differences in communication protocols, data formats, established data structures, data models, and data meaning and codification methodologies face the healthcare industry. These problems typically impede the seamless transmission of electronic medical records between healthcare systems. Because of their decentralized structure and cryptographic foundation, DLTs offer a workable solution to these issues. By critically evaluating previous research and case studies, DLTs may be able to lessen these interoperability issues, as this literature review illustrates. Since DLTs provide an immutable and secure platform for the transmission of medical data, guaranteeing data integrity and confidentiality, they are a natural fit for the sensitive nature of healthcare data. Their importance in creating safe communication protocols, enhancing the meaning of data, and defining models and formats for data is emphasized in this review. A comprehensive architecture for DLT interoperability in healthcare is also recommended by the research. This framework encourages the development of DLT integration, shared data models, standardized data formats, and governance and policy. By implementing this strategy and strengthening secure medical data sharing, healthcare organizations and governments may increase the efficiency, precision, and speed of healthcare delivery. The crucial role that DLTs play in removing the structural and semantic barriers to safe medical systems interoperability is highlighted in the conclusion of this literature review. By adopting DLTs, the healthcare sector may usher in a new era of standardized, safe, and efficient medical data transmission, which will ultimately benefit both patients and healthcare providers. This study shows how distributed ledger technologies (DLTs) have the potential to revolutionize the healthcare industry by enabling the secure and meaningful exchange of medical data between different systems, thereby improving patient care and healthcare outcomes.

Internet of Medical Things and the Evolution of Healthcare 4.0: Exploring Recent Trends

Enhanced patient care and remote health monitoring have always been important issues. Internet of Medical Things (IoMT) is a subsection of Healthcare 4.0 that uses recent technologies like mobile computing, medical sensors, and cloud computing to track patients' medical information in real-time. These data are stored in a cloud computing framework that may be accessed and analyzed by healthcare experts. IoMT and Healthcare 4.0 have immense potential for revolutionizing patient care and diagnostics, despite facing numerous complex challenges. This paper thoroughly analyzes technical, structural, and regulatory obstacles encountered by the healthcare sector. Challenges in IoMT implementation include cost considerations, network stress, interoperability issues, ethical limitations, policy intricacies, security concerns, and vulnerabilities jeopardizing patient privacy. However, amidst these challenges, the study highlights the prospective long-term benefits, including diminished medical costs and enhanced patient care. In this study, we have portrayed a comprehensive exploration of the field of IoMT and different related technologies from more than 100 papers to represent the transformation and growth in this decade. We have illustrated some of the significant findings of applications and innovations in the domain of IoMT. This paper delves into IoMT's application in dementia detection and care, improved data management, fortified cybersecurity measures, and modernizing existing healthcare systems. The study also offers valuable insights into potential mitigation strategies, offered by ongoing research and innovation to address emerging trends and challenges, propelling the trajectory of Healthcare 4.0 towards an optimized and transformative future for patient well-being. Hence future research needs to integrate more prudent technologies addressing challenges including security, privacy, interoperability, and implementation costs.

THE IMPACT OF SMART GRIDS ON ENERGY EFFICIENCY: A COMPREHENSIVE REVIEW

Smart grids have emerged as a key technology in the quest for energy efficiency and sustainability. This review provides a comprehensive analysis of the impact of smart grids on energy efficiency, highlighting key findings, challenges, and future directions. Smart grids leverage advanced sensing, communication, and control technologies to optimize the generation, distribution, and consumption of electricity. By enabling two-way communication between utilities and consumers, smart grids improve grid reliability, reduce energy losses, and facilitate the integration of renewable energy sources. One of the key findings of this review is the significant impact of smart grids on energy efficiency. Studies have shown that smart grids can reduce energy consumption by enabling real-time monitoring and control of energy usage. This not only helps consumers reduce their electricity bills but also reduces the overall carbon footprint of the electricity sector. However, the deployment of smart grids faces several challenges, including high upfront costs, interoperability issues, and data privacy concerns. Addressing these challenges will be crucial for the widespread adoption of smart grid technologies. Looking ahead, the future of smart grids holds great promise for further improving energy efficiency. Emerging trends such as the Internet of Things (IoT) integration, artificial intelligence (AI) algorithms, and distributed energy resources (DERs) management are expected to drive further innovation in smart grid technologies. In conclusion, smart grids have a transformative impact on energy efficiency, offering significant benefits for consumers, utilities, and the environment. However, addressing challenges such as cost and interoperability will be crucial for realizing the full potential of smart grids. By embracing emerging trends and technologies, stakeholders can further enhance the efficiency and sustainability of the electricity sector.
 Keywords: Impact, Smart Grid, Energy, Efficiency, Review.

Internet of Things (IoT) of Smart Homes: Privacy and Security

The Internet of Things (IoT) constitutes a sophisticated network that interconnects devices, optimizing functionality across various domains of human activity. Recent literature projections anticipate a significant increase, with estimates exceeding 50 billion connected devices by 2025. Despite its transformative potential, the IoT landscape confronts formidable privacy and security challenges, encompassing intricate issues such as data acquisition, anonymization, retention, sharing practices, and behavioural profiling. Effectively addressing these challenges mandates the development of scalable solutions, innovative management strategies, and adaptable policy frameworks. In this paper, we conduct an exhaustive examination of major IoT applications, alongside associated privacy and security concerns. We systematically categorize prevalent privacy, security, and interoperability issues within the context of the IoT layered architecture. The review highlights current research initiatives focused on developing energy-efficient devices, optimizing microprocessors, and fostering interdisciplinary collaborations to address the challenges in the IoT landscape. To efficaciously manage risks in this dynamic landscape, stakeholders must implement comprehensive strategies that span stringent data protection legislation, extensive user education initiatives, and the deployment of robust authorization and authentication frameworks. This paper aims to empower industry leaders, policymakers, and researchers by providing actionable solutions, not just insights, to navigate the complexities of the IoT landscape effectively. Future research initiatives should prioritize the fortification of security measures for large-scale IoT deployments, the formulation of user-centric privacy solutions, and the standardization of interoperability protocols. By establishing a robust foundational framework, our paper endeavours to spearhead the discourse on IoT applications, privacy paradigms, and security frameworks, paving the way towards a resilient and interconnected future.

Beyond Automation: Exploring the Synergy of Cloud, AI, Machine Learning, and IoT for Intelligent Systems

In the rapidly evolving landscape of Industry 4.0 and beyond, the amalgamation of Cloud Computing, Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) has emerged as a transformative force capable of elevating traditional automation to intelligent systems. This research paper delves into the profound potential of synergizing these advanced technologies, aiming to surpass the limitations of rule-based automation and foster a new era of adaptability, efficiency, and innovation.
 The study begins by articulating the escalating demand for intelligent systems that can dynamically respond to complex and ever-changing environments. The integration of Cloud, AI, ML, and IoT is posited as a solution to the constraints of conventional automation, offering the ability to process vast datasets, make informed decisions, and continuously learn from interactions.
 A comprehensive review of existing approaches and related works forms the foundation of this research. The analysis encompasses diverse applications, ranging from smart manufacturing to healthcare, showcasing the ways in which individual technologies have been leveraged. By scrutinizing these approaches, the study aims to distill the strengths and weaknesses, paving the way for a novel methodology that harnesses their collective power.
 Identifying the limitations of current approaches, such as scalability challenges, real-time processing bottlenecks, and interoperability issues, serves as a critical precursor to the proposed methodology. The paper presents a holistic strategy that intricately weaves together Cloud, AI, ML, and IoT into a unified framework. The architectural design, data flow, and interaction mechanisms are elucidated to demonstrate how this synergy can overcome existing challenges, providing adaptability and innovation in diverse domains.
 Empirical results derived from the implementation of the proposed methodology are presented and rigorously analyzed in the Results and Discussion section. Performance metrics, efficiency gains, and the impact on decision-making processes are thoroughly examined. Real-world case studies exemplify the effectiveness of the integrated approach, offering tangible evidence of its potential applications.
 Concluding remarks encapsulate the key findings, emphasizing the significance of the research in shaping the trajectory of intelligent systems. The broader implications of the proposed methodology across various industries are discussed, and avenues for future work are suggested. As technologies continue to evolve, the proposed methodology serves as a foundation for ongoing exploration, adaptation, and integration with emerging technologies.
 In essence, this research paper offers a detailed exploration into the synergy of Cloud, AI, ML, and IoT, paving the way for a new era of intelligent systems that transcend the limitations of traditional automation, fostering adaptability, efficiency, and innovation in an ever-changing technological landscape.

The Communication Conundrum: A Pilot Cross-Sectional Descriptive Examination of Family Nurse Perspectives Surrounding Patient Information Exchange During Interfacility Patient Transfers in Montana.

Background: Interfacility patient transfers are fraught with issues such as missed or ineffective communication in Montana given wide geographic distance between facilities and variance in resources. Inaccurate, absent, or delayed patient details may negatively affect patient outcomes and further result in duplicative testing and medication errors. Objective: The objective of this study was to describe the process of patient information communication during interfacility transfers as perceived by nurses practicing in Montana. Methods: The study design was a pilot cross-sectional descriptive approach. An online Qualtrics survey included demographic questions, two exploratory communication competence instruments, and four open-ended questions regarding communicating interfacility transfer patient information. Results: A total of 33 nurses completed the study, with the majority practicing at a critical access hospital (n = 15, 47%). Communication competence mean scores increased with dyad conversations, and a lack of standardized handoff tools was noted as a challenge. Nurses identified the following as barriers in the interfacility transfer handoff: incivility, amount of paperwork, interoperability issues, incomplete or outdated information, time, and resources. Implications for Practice: There is wide variability in current communication practices, ranging from verbal to electronic document transfers. The rural healthcare space is prime to continue examinations surrounding workflow optimization, accuracy, and consistency in shared information exchange at the time of interfacility transfer. There is an opportunity for potential training and education surrounding effective communication, interpersonal behaviors that support cross-organizational interactions, and the development of a standardized handoff tool contextual for interfacility transfer patients.

A Novel Asymmetrical Quadrupolar Coil for Interoperability of Unipolar, Bipolar, and Quadrupolar Coils in Electric Vehicle Wireless Charging Systems

The unipolar, bipolar, and quadrupolar coils used in an electric vehicle wireless charging system are not interoperable with each other. Thus, there would be interoperability issues when the transmitting and receiving coils are not the same. To solve this issue, this letter proposes an asymmetrical quadrupolar coil that can be interoperable with the four conventional coils at the same position for efficient wireless power transfer, namely the unipolar coil, the bipolar coil along two perpendicular directions, and the quadrupolar coil. Large coupling coefficients can be achieved for the proposed coil with the four conventional coils when perfectly aligned. The misalignment performance with the four conventional coils is also investigated. An experimental prototype is developed to compare the conventional symmetrical quadrupolar coil and the proposed asymmetrical quadrupolar coil for validation of interoperability. The proposed asymmetrical quadrupolar coil achieves a maximum coupling coefficient of over 0.10 and a maximum dc-dc efficiency of over 85% with the four conventional coils.

Multi-domain data integration and management for enhancing service-oriented digital twin for infrastructure operation and maintenance

The evolution from Building Information Modelling (BIM) to Digital Twins (DT) marks a shift from static models to dynamic data-centric representations. Using DT can improve Infrastructure Operation and Maintenance (IOM) through prompt and comprehensive data, especially by providing services and value-added features. However, developing this service-oriented digital twin (SoDT) for IOM faces challenges due to data heterogeneity, fragmentation, and interoperability issues across various systems used by different asset authorities. This paper develops a service-oriented digital twin framework for IOM (SoDT-IOM), which focuses on multi-domain data integration and IOM service-oriented design. Service-oriented DT components and data integration mechanisms are developed, and an individual-multiple interacted data repository for IOM services is designed. Two-stage industrial interviews are conducted, and two use cases are illustrated to evaluate the functionality and efficacy of the framework. The evaluation results reveal that implementing a SoDT-IOM framework encounters challenges related to cost and data security; however, it is appreciated for its capacity to improve decision-making and collaboration with linked, accurate and timely data. This paper proposes further design requirements and implementation recommendations for SoDT of asset management from both technical and managerial perspectives.

Deep learning and capsule endoscopy: Automatic multi-brand and multi-device panendoscopic detection of vascular lesions.

Background and study aims Capsule endoscopy (CE) is commonly used as the initial exam for suspected mid-gastrointestinal bleeding after normal upper and lower endoscopy. Although the assessment of the small bowel is the primary focus of CE, detecting upstream or downstream vascular lesions may also be clinically significant. This study aimed to develop and test a convolutional neural network (CNN)-based model for panendoscopic automatic detection of vascular lesions during CE. Patients and methods A multicentric AI model development study was based on 1022 CE exams. Our group used 34655 frames from seven types of CE devices, of which 11091 were considered to have vascular lesions (angiectasia or varices) after triple validation. We divided data into a training and a validation set, and the latter was used to evaluate the model's performance. At the time of division, all frames from a given patient were assigned to the same dataset. Our primary outcome measures were sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and an area under the precision-recall curve (AUC-PR). Results Sensitivity and specificity were 86.4% and 98.3%, respectively. PPV was 95.2%, while the NPV was 95.0%. Overall accuracy was 95.0%. The AUC-PR value was 0.96. The CNN processed 115 frames per second. Conclusions This is the first proof-of-concept artificial intelligence deep learning model developed for pan-endoscopic automatic detection of vascular lesions during CE. The diagnostic performance of this CNN in multi-brand devices addresses an essential issue of technological interoperability, allowing it to be replicated in multiple technological settings.

Interoperability for intelligent traffic management systems in smart cities

Intelligent traffic management systems (ITMS) are essential for safe and livable smart cities. However, achieving seamless interoperability between diverse devices and services is challenging due to the lack of universal open standards. This study examines different types of interoperability (syntactic, semantic, network, middleware, and security) and their relationships with ITMS in the smart city context. By discussing requirements, challenges, and potential standards, this research provides a comprehensive understanding of interoperability issues in ITMS. It highlights the importance of standardization and collaboration among stakeholders, including policymakers, urban planners, and technology providers, to achieve interoperability. Addressing these challenges can optimize ITMS performance and contribute to smarter, sustainable cities. The study categorically examines challenges and potential standards, offering a framework for future research and practice. By advancing our understanding of ITMS interoperability, this research facilitates improved traffic management and smarter city development, enhancing urban residents’ quality of life. It makes a significant contribution to the field by emphasizing the critical role of interoperability in effective traffic management systems and the advancement of smart cities. By addressing interoperability challenges, we can create safer, more efficient, and sustainable transportation networks, fostering the development of livable cities.

The role of technology in supply chain risk management: Innovations and challenges in logistics

The review delves into the pivotal role of technology in revolutionizing supply chain risk management practices within the logistics sector. The paper explores the myriad of risks faced by modern supply chains, ranging from natural disasters and geopolitical tensions to cyber threats and disruptions in global trade patterns. It investigates how technological innovations such as blockchain, Internet of Things (IoT), artificial intelligence (AI), and predictive analytics are reshaping traditional risk management approaches by providing real-time visibility, data-driven insights, and proactive mitigation strategies. Through a comprehensive analysis, the study examines the transformative potential of these technologies in enhancing supply chain resilience, agility, and responsiveness to unforeseen disruptions. It highlights the benefits of leveraging blockchain technology for secure and transparent supply chain transactions, IoT sensors for real-time monitoring of goods in transit, AI algorithms for predictive risk modeling, and predictive analytics for identifying and mitigating potential disruptions before they escalate. Furthermore, the paper delves into the challenges and complexities associated with adopting and integrating these technologies into existing supply chain processes, including interoperability issues, data privacy concerns, and the need for specialized expertise. By providing practical insights, case studies, and best practices, the study aims to empower logistics professionals, policymakers, and industry stakeholders to harness the full potential of technology-driven solutions in managing supply chain risks effectively and ensuring business continuity in an increasingly uncertain and volatile global environment.

Blockchain Technology Implementation in Supply Chain Management: A Literature Review

This paper aims to comprehensively review the main benefits, limitations, and challenges associated with the uptake of Blockchain technology in supply chain management (SCM). The study utilizes the literature review method, examining articles published from 2016 to 2022 and exploring the factors influencing the adoption and implementation of Blockchain in SCM. Multiple scholarly insights have shown no more hacking or cherry picking of options given for presenting data with high confidence and, therefore, reliability, as well as an ability to see everything in a highly limited way. An SCM looking at these multi-party evolutionary models shows that for Blockchain, the key advantages center on significantly increased security, confidentiality, traceability, transparency, data accuracy, privacy, efficiency, accountability, and trust. In contrast, the review has also detailed several limitations and challenges that must be overcome by firms and industries looking to adopt Blockchain within their SCM processes, which include problems with scaling, lower performance as a result of interoperability issues, legal and regulatory matters, very high initial implementation and ongoing maintenance costs, a struggle with standardization of the technology and the needed surveillance of this space, avoidance of technology adoption because of trust, the substantial energy used, low awareness, complexity of integrating with existing systems, being in its last mile in partnerships, and privacy. Even with this potential to transform and be disrupted across industry and its sectors, there are still very significant challenges for Blockchain. For those looking to actually utilize SCM and other sectors, understanding this from a variety of perspectives is of great interest to understand if the technology is fit for that sector and what strategies or networks need to be established or linked into with existing firms. Regardless, if these challenges can be overcome and responded to, then the greater protection and efficiency of current and future SCM processes will not only occur, but it can be the first significant node for broader industry-wide innovation and gains in efficiency.