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Compared with traditional physical commodities, data are intangible and easy to leak, and the related trading process has problems, such as complex participating roles, lengthy information flow, poor supervisory coverage and difficult information traceability. To handle these problems, we construct a distributed supervision model for data trading based on blockchain, and conduct multi-party hierarchical and multi-dimensional supervision of the whole process of data trading through collaborative supervision before the event, at present and after the event. First, the characteristics of information flow in the data trading process are analyzed, and the main subject and key supervision information in the data trading process are sorted out and refined. Secondly, combined with the actual business process of data trading supervision, a multi-channel structure of distributed supervision is proposed by adopting an access–verification–traceability strategy. Finally, under the logical framework of the supervision model, the on-chain hierarchical structure and the data hybrid storage method of “on-chain + off-chain” are designed, and multi-supervisor-oriented hierarchical supervision and post-event traceability are realized through smart contracts. The results show that the constructed blockchain-based distributed supervision model of data trading can effectively isolate and protect sensitive and private information between data trading, so as to realize the whole process, multi-subject and differentiated supervision of key information of data trading, and provide an effective and feasible method for the controllable and safe supervision of data trading.

A supervisory expert system based on fuzzy logic rules was developed for diagnosis and control of a laboratory- scale plant comprising anaerobic digestion and anoxic/aerobic modules for combined high rate biological N and C removal. The design and implementation of a computational environment in LabVIEW for data acquisition, plant operation and distributed equipment control is described. A step increase in ammonia concentration from 20 to 60 mg N/L was applied during a trial period of 73 h. Recycle flow rate from the aerobic to the anoxic module and bypass flow rate from the influent directly to the anoxic reactor were the output variables of the fuzzy system. They were automatically changed (from 34 to 111 L/day and from 8 to 13 L/day, respectively), when new plant conditions were recognised by the expert system. Denitrification efficiency higher than 85% was achieved 30 h after the disturbance and 15 h after the system response at an HRT as low as 1.5 h. Nitrification efficiency gradually increased from 12 to 50% at an HRT of 3 h. The system proved to react properly in order to set adequate operating conditions that led to timely and efficient recovery of N and C removal rates.

PurposeThe credit of agricultural product quality and safety reflects the ability of the main actors involved in the supply chain to provide reliable agricultural products to consumers. To fundamentally solve the problem of agricultural product quality and safety, it is worth studying how to make the credit awareness and integrity self-discipline of the supply chain agriculture-related subjects strengthened and the role and value of credit supervision given full play. Starting from the application of blockchain in the agricultural product supply chain, this paper aims to investigate the main factors affecting the credit regulation of agricultural product quality.Design/methodology/approachUsing the DEMATEL-ISM (decision-making trial and evaluation laboratory–interpretative structural modeling) method, we analyze the credit influencing factors of agricultural quality and safety empowered by blockchain technology, find the causal relationship between the crucial influencing factors and deeply explore the hierarchical transmission relationship between the influencing factors. Then, the path analysis in structural equation modeling is utilized to verify and measure the significance and effect value of the transmission relationship among the crucial influencing factors of credit regulation.FindingsThe results show that the quality and safety credit regulation of agricultural products is influenced by a combination of direct and deep influencing factors. Long-term stable cooperative relationship, Quality and safety credit evaluation, Supply chain risk control ability, Quality and safety testing, Constraints of the smart contract are the main influence path of blockchain embedded in agricultural product supply chain quality and safety credit supervision.Originality/valueCredit supervision is an important means to improve the ability and level of social governance and standardize the market order. From the perspective of blockchain embedded in the agricultural supply chain, the regulatory body is transformed from the product body to the supply chain body. Take the credit supervision of supply chain subjects as the basis of agricultural product quality supervision. With the help of blockchain technology to improve the effectiveness of agricultural product quality and safety credit supervision, credit supervision is used to constrain and incentivize the behavior of agricultural subjects.

Patient enrollment, data sharing, and data privacy are enormous medical challenges for clinical trial studies. In recent years, blockchain technology has drawn the attention of various researchers and institutes. As a new and innovative distributed ledger technology, blockchain can be critical to addressing these challenges, thus making clinical research transparent and building public trust fairly and openly. However, the existing literature lacks a comprehensive survey on the adoption of blockchain in clinical trials. To fill the research void, this paper presents a punctilious taxonomy of blockchain technology in clinical trials according to the literature. This taxonomy comprises decentralized scenarios, decentralized practices, blockchain types, deployment methods, and consensus algorithms. The results show that blockchain technology can cover all aspects of the clinical trial study in a decentralized, secure, transparent manner. Besides, some open research challenges of blockchain are categorized into three groups: technical challenges, security challenges, and organizational challenges. Moreover, some recent blockchain projects, micro applications in clinical trials, and several research areas or technologies for future research and development are discussed.

This work proposes a new methodology for the construction of deep learning-based prediction intervals (PIs) based on a selective jointly supervised cost function. The proposed method aims to preserve the advantages of the traditional joint supervision method, such as its interval robustness and compatibility with gradient-based optimizers, while improving the general convergence of the utilized training algorithms and reducing the computational costs incurred when using deep learning models. To test the capability of the proposed method to estimate uncertainties in complex, nonlinear dynamic systems, two prediction interval construction experiments were tested: one with an artificially generated dataset consisting of a modified Chen series and another using real electrical load data measured in the Huatacondo Microgrid in northern Chile. In these experiments, the proposed model was required to generate future predictions with accompanying prediction intervals, while its performance was measured according to its interval coverage, average width, average prediction error and computational cost of training. The proposal’s performance was compared with that of two other state-of-the-art interval models: the quality-driven method (Pearce et al. 2018), and the traditional joint supervision method (Cruz et al. 2018). The experimental results showed that the proposed selective joint supervision method incurred lower computational costs than the traditional joint supervision approach, with training times reduction magnitudes ranging from 15% to 85%. Additionally, the results showed that the proposed selective joint supervision method achieved better interval performance when using deep learning-based network architectures, showing up to a 6% prediction error decrease and up to a 15% overall interval width decrease.

The typical food traceability system’s data layer is made up of relational databases managed by core businesses, which cannot ensure data security. It is inefficient and requires a lot of upkeep. The food supply chain has numerous actors, making it difficult for consumers to safeguard their rights when purchasing food with quality issues. Due to the numerous organizations involved in the food supply chain, food safety monitoring and traceability have become challenging. The supply chain’s major organizations have control and administrative authority over the data under the current food traceability system, which is overly centralized for traceability information. The safety and dependability of food may be ensured by using the food traceability system to track food information. We can witness a series of detailed insights into food from the manufacturing source to the consumption terminal. A blockchain-based food tracking system is created as a solution to these issues. On the Ethereum platform, the system was created. It was also employed in the blockchain system, in addition to its features of decentralization, tamper-proof, and traceability. To implement the data update service and the food recall function, introduce the Food and Drug Administration node. Consumers have the option to not only enquire about food traceability throughout the manufacturing process but also to file complaints regarding the traceability system’s rights protection.

This work proposes a method for real-time supervision and predictive fault diagnosis applicable to solar panel strings in real-world installations. It is focused on the detection and parametric isolation of fault symptoms through the analysis of the Voc-Isc curves. The method performs early, systematic, online, automatic, permanent predictive supervision, and diagnosis of a high sampling frequency. It is based on the supervision of predictive electrical parameters easily accessible by the design of its architecture, whose detection and isolation precedes with an adequate margin of maneuver, to be able to alert and stop by means of automatic disconnection the degradation phenomenon and its cumulative effect causing the development of a future irrecoverable failure. Its architecture design is scalable and integrable in conventional photovoltaic installations. It emphasizes the use of low-cost technology such as the ESP8266 module, ASC712-5A, and FZ0430 sensors and relay modules. The method is based on data acquisition with the ESP8266 module, which is sent over the internet to the computer where a SCADA system (iFIX V6.5) is installed, using the Modbus TCP/IP and OPC communication protocols. Detection thresholds are initially obtained experimentally by applying inductive shading methods on specific solar panels.

This paper presents an innovative intelligent decision optimization model that integrates distributed blockchain technology with federated reinforcement learning to address critical challenges in ship traffic collaborative supervision. Traditional maritime traffic monitoring systems suffer from data silos, privacy concerns, and centralized decision-making bottlenecks that impede effective multi-jurisdictional coordination. The proposed framework employs a multi-layered architecture consisting of data layer, blockchain layer, federated learning layer, and decision layer to enable secure data sharing while preserving operational autonomy among maritime authorities. The distributed blockchain mechanism ensures data integrity and immutability through cryptographic protocols and smart contracts, while the federated reinforcement learning algorithm enables privacy-preserving collaborative model training without exposing sensitive commercial information. Experimental validation demonstrates superior performance with 93.6% decision accuracy, 520ms average response time, and 285 transactions per second throughput. Case studies involving emergency collision avoidance, abnormal behavior identification, and search-and-rescue coordination confirm the system’s practical effectiveness, achieving 40% reduction in incident response times and 60% enhancement in cross-agency collaboration efficiency. The research provides a robust foundation for next-generation maritime traffic management systems that require secure multi-party collaboration and intelligent decision optimization.

This paper describes a supervisor system for monitoring the operation of automated agricultural vehicles. The system analyses all of the information provided by the sensors and subsystems on the vehicles in real time and notifies the user when a failure or potentially dangerous situation is detected. In some situations, it is even able to execute a neutralising protocol to remedy the failure. The system is based on a distributed and multi-level architecture that divides the supervision into different subsystems, allowing for better management of the detection and repair of failures. The proposed supervision system was developed to perform well in several scenarios, such as spraying canopy treatments against insects and diseases and selective weed treatments, by either spraying herbicide or burning pests with a mechanical-thermal actuator. Results are presented for selective weed treatment by the spraying of herbicide. The system successfully supervised the task; it detected failures such as service disruptions, incorrect working speeds, incorrect implement states, and potential collisions. Moreover, the system was able to prevent collisions between vehicles by taking action to avoid intersecting trajectories. The results show that the proposed system is a highly useful tool for managing fleets of autonomous vehicles. In particular, it can be used to manage agricultural vehicles during treatment operations.

Distributed drive electric vehicles improve steering response and enhance overall vehicle stability by independently controlling each motor. This paper introduces a control framework based on Adaptive Model Predictive Control (AMPC) for coordinating handling stability, consisting of three layers: the dynamic supervision layer, online optimization layer, and low-level control layer. The dynamic supervision layer considers the yaw rate and maneuverability limits when establishing the β−β˙ phase plane stability boundary and designs variable weight factors based on this stability boundary. The online optimization layer constructs the target weight-adaptive AMPC strategy, which can adjust the control weights for maneuverability and lateral stability in real time based on the variable weight factors provided by the dynamic supervision layer. The low-level control layer precisely allocates the driver’s requested driving force and additional yaw moment by using torque distribution error and tire utilization as the cost function. Finally, experiments are conducted on a Simulink-CarSim co-simulation platform to assess the performance of AMPC. Simulation results show that, compared to the traditional MPC strategy, this control strategy not only enhances maneuverability under normal conditions but also improves lateral stability control under extreme conditions.