Search Results (112)

Background: Ministerial Decree (D.M.) 77/2022 aims to reform Italy’s primary care system by establishing community health centres and integrating digital tools to address healthcare access disparities and workforce shortages. This review focuses on frailty assessment tools, digital health innovations, and workforce challenges in the Autonomous Province of Bolzano, South Tyrol, emphasising interprofessional trust and collaboration issues. Methods: Using a narrative custom review approach guided by the SANRA checklist, this study synthesised findings from PubMed, official health websites, and regional surveys on frailty, workforce dynamics, interprofessional collaboration, and digital infrastructure in South Tyrol. Results: General practitioners (GPs) exhibited high professional motivation but expressed concerns about autonomy and administrative burdens in collaborative care models. Trust issues between GPs and specialists hinder workforce cohesion and care coordination, highlighting the need for structured inter-professional communication. Frailty assessments, such as the PRISMA-7 tool, identify over 33% of community-dwelling individuals aged 75 years and older as frail, necessitating targeted interventions. Digital health adoption, particularly electronic health records and telemedicine, is slow because of workforce shortages and infrastructure limitations. Conclusions: The successful implementation of D.M. 77/2022 in South Tyrol requires addressing workforce challenges, improving interprofessional trust, expanding digital infrastructure, and integrating frailty assessment findings into care strategies. These measures are critical for achieving a more resilient, equitable, and effective primary healthcare system. Full article

Projects involve inter-organizational relationship cognition, which is central to collaborative engineering project value added. Interest in value added in the project lifecycle is mounting and gaining increasing attention in the research literature. However, little is known about how inter-organizational relationship cognition facilitates value added and how such cognition pushes a project toward higher end-states of value. The existing literature mainly analyzes and studies value added on functional analysis and cost control. There are predominantly static analyses of the factors that influence value added in studies. The guiding role of value added has not been adequately explored in the studies on the influencing factors of value added. Utilizing a combination of Structural Equation Modeling (SEM) and Fuzzy Cognitive Maps (FCMs), this study addresses how inter-organizational relationship cognition influences engineering project value added, identifying complex structures of interaction and cognition dynamics. Results indicate that: (1) A hybrid SEM–FCM method can be able to model dynamic interactions between inter-organizational relationship cognition and value added; (2) trust and shared vision have positive effects on in-role behavior and extra-role behavior. Shared vision has a negative effect on opportunistic behavior. In-role behavior and extra-role behavior have a positive impact on value added, while opportunistic behavior has a negative impact. Organizational behavior is an important mediating variable to explain the interaction between inter-organizational relationship cognitions and value added. This hybrid method explores the potential mechanisms of inter-organizational relationship cognition on project value added from novel perspectives on construction project management practices, proposing practical advice for further project management. Full article

Ensuring trust, security, and privacy among all participating parties in the process of sharing supervision data is crucial for engineering quality and safety. However, the current centralized architecture platforms that are commonly used for engineering supervision data have problems such as low data sharing and high centralization. A blockchain-based framework for the secure sharing of engineering supervision data is proposed by utilizing the tamper-proof, decentralized, and traceable characteristics of blockchain. The secure storage of supervision data is achieved by combining it with the IPFS (InterPlanetary File System), reducing the storage pressure of on-chain data. Additionally, a fast data retrieval framework is designed based on the storage characteristics of supervision data. Then, CP-ABE (Ciphertext Policy Attribute Based Encryption) is combined with a data storage framework to ensure the privacy, security, and reliability of supervisory data during the sharing process. Finally, smart contracts are designed under the designed framework to ensure the automatic and trustworthy execution of access control processes. The analysis and evaluation results of the security, encryption and decryption, and cost performance of the proposed blockchain framework show that the encryption and decryption time is completed within 0.1 s, the Gas cost is within the normal consumption range, and the time cost of smart contract invocation does not exceed 5 s, demonstrating good availability and reusability of the method proposed in this article. Full article

Due to the intricate terrain and restricted resources, hyperspectral image (HSI) datasets captured in real-world scenarios typically contain noisy labels, which may seriously affect the classification results. To address this issue, we work on a universal method that rectifies the labels first and then trains the classifier with corrected labels. In this study, we relax the common assumption that all training data are potentially corrupted and instead posit the presence of a small set of reliable data points within the training set. Under this framework, we propose a novel label-correction method named adaptive selective loss propagation algorithm (ASLPA). Firstly, the spectral–spatial information is extracted from the hyperspectral image and used to construct the inter-pixel transition probability matrix. Secondly, we construct the trusted set with the known clean data and estimate the proportion of accurate labels within the untrusted set. Then, we enlarge the trusted set according to the estimated proportion and identify an adaptive number of samples with lower loss values from the untrusted set to supplement the trusted set. Finally, we conduct label propagation based on the enlarged trusted set. This approach takes full advantage of label information from the trusted and untrusted sets, and moreover the exploitation on the untrusted set can adjust adaptively according to the estimated noise level. Experimental results on three widely used HSI datasets show that our proposed ASLPA method performs better than the state-of-the-art label-cleaning methods. Full article

Japanese firms are accelerating their engagement in horizontal collaboration through unprecedented inter-firm combinations that allow organizations to respond flexibly and quickly to changes in the external environment. However, existing research has not sufficiently examined trust formation and individual interaction processes in the initial stages of such inter-organizational collaboration. This study examines a newly established value-creation consortium led by the private sector that uses state-of-the-art artificial intelligence (AI) technology to solve social issues. We interviewed consortium members in different positions; the steps for coding and theorization (SCAT) were used to analyze individuals’ interactions in the initial stage of forming inter-organizational collaboration. The results showed that the members’ willingness to collaborate increased due to the leader exhibiting trustworthy behavior. Furthermore, uncertainty caused by AI’s technological specificity led to insecurity, creating role ambiguity and role conflicts, which leaders and members overcame to form interdependent relationships among individuals. The indication of such a process is a new finding, the practical implications of which are discussed. Full article

Frugal innovation offers cost-effective, demand-driven products for consumers in emerging markets. By leveraging this approach, enterprises can capitalize on opportunities, boost profits, and nurture beneficial consumer relationships. This study uses the transaction cost theory to explore how symbiotic relationships within environmental turbulence influence frugal innovation. The goal is to unravel the interconnected factors that drive high-performance frugal innovation, providing enterprises with a roadmap for seizing opportunities, enhancing profitability, and cultivating enduring consumer relationships. Data from 218 Chinese enterprises were collected through a survey and analyzed using fuzzy-set Qualitative Comparative Analysis (fsQCA) and Necessary Condition Analysis (NCA) methods. The research findings indicate that factors such as market dynamics, technological dynamics, collaboration with suppliers and customers, relationship governance, contract governance, and inter-organizational trust are not necessary prerequisites for high frugal innovation performance. Instead, the synergy among these factors is imperative for reaching elevated levels of frugal innovation performance. Furthermore, three distinct pathways to heightened frugal innovation performance were identified: a dual-drive mechanism involving membership relationships and governance mechanisms, a membership-driven approach in dynamic technological conditions, and a governance mechanism-driven approach in dynamic market conditions. This study offers valuable theoretical and practical insights for enterprises pursuing frugal innovation in turbulent environments, guiding them towards sustained growth and competitive advantages in new markets. Full article

Real-time electrical impedance tomography (EIT) data sharing is becoming increasingly necessary, due to the extensive use of EIT technology in various sectors, including material analysis, biomedicine, and industrial process monitoring. The prevalence of portable EIT equipment and remote imaging technology has led to a predominance of centralized storage, Internet protocol transmission, and certificates from certificate authorities (CA) in telemedicine data. This has resulted in compromised data security, network communication delays, high CA maintenance costs, increased risks of medical data privacy breaches, and low security. Therefore, this paper offers a consortia blockchain-based method for exchanging EIT data that addresses security and integrity concerns during data storage and exchange, while maintaining transparency and traceability. Proprietary re-encryption techniques are employed to guarantee traceability when exchanging anonymous data, enabling precise control over data access. This scheme serves to protect both data and identity privacy, as well as to trace the actual identity of potential malicious users, while also thwarting any coordinated efforts between partially trusted parties and data requesters seeking unauthorized access to confidential information. Additionally, a combination of blockchain and InterPlanetary File System (IPFS) distributed storage technology is utilized to ease the burden of EIT data storage. The feasibility and effectiveness of the proposed solution were validated through a series of experiments, demonstrating its ability to effectively prevent data tampering and misuse, reduce data management costs, and enhance the efficiency and quality of data sharing. Full article

This research presents an advanced methodology to enhance conflict measurement within the Dempster–Shafer framework, integrating linguistic preferences and trust relationships for improved sustainability decision-making. By developing a unique algorithm, we introduce a novel approach to quantify inter-expert similarity and establish consensus thresholds. Furthermore, our study innovates with a dual-path adjustment mechanism to effectively reconcile discrepancies in expert opinions. These methodological advancements enable a more accurate and nuanced representation of expert judgments, facilitating superior decision support in sustainability-oriented applications. Through rigorous numerical simulations and a detailed case study, we validate our approach’s efficacy in optimizing decision-making processes, underscoring its potential to significantly influence sustainable practices and policy formulation. Our contributions not only advance theoretical understanding but also offer practical tools for incorporating expert insights into the pursuit of sustainability goals, marking a significant leap forward in decision-making research. Full article

This work presents a novel trusted LC voltage-controlled oscillator (VCO) with an embedded compact analog Physically Unclonable Function (PUF) used for authentication. The trusted VCO is implemented in a 1P9M 65 nm standard CMOS process and consumes 1.75 mW. It exhibits a measured phase noise (PN) of −104.8 dBc/Hz @ 1 MHz and −132.2 dBc/Hz @ 10 MHz offset, resulting in Figures of Merit (FoMs) of 191.7 dBc/Hz and 199.1 dBc/Hz, respectively. With the measured frequency tuning range (TR) of ~5.5 GHz, the FoM with tuning (FoM_T) reaches 197.6 dBc/Hz and 205.0 dBc/Hz at 1 MHz and 10 MHz offset, respectively. The analog PUF consists of CMOS cross-coupled pairs in the main VCO to change analog characteristics. Benefiting from the impedance change and parasitic capacitance of the cross-coupled pairs, the AC and DC responses of the VCO are utilized for multiple responses for each input. The PUF consumes 0.83 pJ/bit when operating at 1.5 Gbps. The proposed PUF exhibits a measured Inter-Hamming Distance (HD) of 0.5058b and 0.4978b, with Intra-HD reaching 0.0055b and 0.0053b for the current consumption and f_osc, respectively. The autocorrelation function (ACF) of 0.0111 and 0.0110 is obtained for the current consumption and f_osc, respectively, at a 95% confidence level. Full article

With the advent of 6G Narrowband IoT (NB-IoT) technology, IoT security faces inevitable challenges due to the application requirements of Massive Machine-Type Communications (mMTCs). In response, a 6G base station (gNB) and User Equipment (UE) necessitate increased capacities to handle a larger number of connections while maintaining reasonable performance during operations. To address this developmental trend and overcome associated technological hurdles, this paper proposes a hardware-accelerated and software co-designed mechanism to support streaming data transmissions and secure zero-trust inter-endpoint communications. The proposed implementations aim to offload processing efforts from micro-processors and enhance global system operation performance by hardware and software co-design in endpoint communications. Experimental results demonstrate that the proposed secure mechanism based on the use of non-repeating keys and implemented in FPGA, can save 85.61%, 99.71%, and 95.68% of the micro-processor’s processing time in key block generations, non-repeating checks, and data block transfers, respectively. Full article

Companies have strengthened their long-term inter-organizational partnerships throughout the supply chain to neutralize competitive pressures and risks in uncertain environments. On this basis, this research aims to propose and test a model of partners’ behavior aimed at the maintenance of long-term collaboration. By using confirmatory factor analysis, structural equation modeling, and rival model testing, the theoretical model proposed attempts to identify, from a seller’s perspective, the critical variables of partners’ behavior. It also seeks to understand the effect of satisfaction between trust and commitment (as antecedents associated with relationship quality) and sales formalization, sales opportunism, and sales-specific assets (as postcendents linked to relationship efficiency). Our findings verify the nomological framework and demonstrate that the partnership quality variables affect relationship efficiency, through sales satisfaction. However, the results of our research cannot confirm the relationship between satisfaction and specific assets. This research is relevant as it deals with inter-organizational partnerships from a seller-oriented approach, and it is based on a combination of Relationship Marketing Theory and Transaction Cost Theory to demonstrate that the inter-organizational partnership quality variables exert a direct effect on the partnership efficiency variables. Full article

Federated learning is a privacy-preserving machine learning framework where multiple data owners collaborate to train a global model under the orchestra of a central server. The local training results from trainers should be submitted to the central server for model aggregation and update. Busy central server and malicious trainers can introduce the issues of a single point of failure and model poisoning attacks. To address the above issues, the trusty decentralized federated learning (called TrustDFL) framework has been proposed in this paper based on the zero-knowledge proof scheme, blockchain, and smart contracts, which provides enhanced security and higher efficiency for model aggregation. Specifically, Groth 16 is applied to generate the proof for the local model training, including the forward and backward propagation processes. The proofs are attached as the payloads to the transactions, which are broadcast into the blockchain network and executed by the miners. With the support of smart contracts, the contributions of the trainers could be verified automatically under the economic incentive, where the blockchain records all exchanged data as the trust anchor in multi-party scenarios. In addition, IPFS (InterPlanetary File System) is introduced to alleviate the storage and communication overhead brought by local and global models. The theoretical analysis and estimation results show that the TrustDFL efficiently avoids model poisoning attacks without leaking the local secrets, ensuring the global model’s accuracy to be trained. Full article

As an important part of the industrial internet, identity analysis data are growing with the expansion of the field involved in the industrial internet. The management of industrial internet identity analysis data faces many problems, such as complex types, a wide range of information, rapid growth, reduced security, etc. In view of the above problems, a trusted management model of industrial internet identity analysis data based on blockchain is first designed. Meanwhile, the identity analysis data information is analyzed and classified, and industrial data are divided into three levels according to the degree of privacy for hierarchical encryption. Secondly, the “on-chain + off-chain” storage model combining the blockchain main-slave chain and the off-chain database is designed to improve the efficiency of the whole model. Then, a collaborative consensus mechanism suitable for the main-slave multi-chain of the industrial internet is also designed, including slave-chain CIPBFT consensus, inter-chain cross-chain transmission protocol and main chain KZKP consensus. Finally, a prototype system is built to analyze the correctness, security, scalability and consensus efficiency of the model proposed in this study. The results show that the model proposed in this study can be applied to trusted management of data information for industrial internet identity analysis, and also provides an optimized solution for the same problem in fields of the industrial internet. Full article

In the era of deep learning as a service, ensuring that model services are sustainable is a key challenge. To achieve sustainability, the model services, including but not limited to storage and inference, must maintain model security while preserving system efficiency, and be applicable to all deep models. To address these issues, we propose a sub-network-based model storage and inference solution that integrates blockchain and IPFS, which includes a highly distributed storage method, a tamper-proof checking method, a double-attribute-based permission management method, and an automatic inference method. We also design a smart contract to deploy these methods in the blockchain. The storage method divides a deep model into intra-sub-network and inter-sub-network information. Sub-network files are stored in the IPFS, while their records in the blockchain are designed as a chained structure based on their encrypted address. Connections between sub-networks are represented as attributes of their records. This method enhances model security and improves storage and computational efficiency of the blockchain. The tamper-proof checking method is designed based on the chained structure of sub-network records and includes on-chain checking and IPFS-based checking stages. It efficiently and dynamically monitors model correctness. The permission management method restricts user permission based on the user role and the expiration time, further reducing the risk of model attacks and controlling system efficiency. The automatic inference method is designed based on the idea of preceding sub-network encrypted address lookup. It can distribute trusted off-chain computing resources to perform sub-network inference and use the IPFS to store model inputs and sub-network outputs, further alleviating the on-chain storage burden and computational load. This solution is not restricted to model architectures and division methods, or sub-network recording orders, making it highly applicable. In experiments and analyses, we present a use case in intelligent transportation and analyze the security, applicability, and system efficiency of the proposed solution, particularly focusing on the on-chain efficiency. The experimental results indicate that the proposed solution can balance security and system efficiency by controlling the number of sub-networks, thus it is a step towards sustainable model services for deep learning. Full article

The Fourth Industrial Revolution, also known as Industrial 4.0, has greatly accelerated inter-connectivity and smart automation in industrial control systems (ICSs), which has introduced new challenges to their security. With the fast growth of the Internet of Things and the advent of 5G/6G, the collaboration of Artificial Intelligence (Al) and the Internet of Things (loT) in ICSs has also introduced lots of security issues as it highly relies on advanced communication and networking techniques. Frequent ICS security incidents have demonstrated that attackers have the ability to stealthily breach the current system defenses and cause catastrophic effects to ICSs. Thankfully, trusted computing technology, which has been a popular research topic in the field of information security in recent years, offers distinct advantages when applied to ICSs. In this paper, we first analyze the vulnerabilities of ICSs and the limitations of existing protection technologies. Then, we introduce the concept of trusted computing and present a security framework for ICSs based on Trusted Computing 3.0. Finally, we discuss potential future research directions. Full article