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Ovarian cancer is one of the most common gynecological malignancies. Upon initial diagnosis, the majority of patients present with widespread metastatic growth within the peritoneal cavity. This metastatic growth occurs in stages, with the formation of a pre-metastatic niche occurring prior to macroscopic tumor cell invasion. Exosomes released by the primary ovarian tumor are small extracellular vesicles which prepare the distant tumor microenvironment for accelerated metastatic invasion. They regulate intercellular communication between tumor cells and normal stroma, cancer-associated fibroblasts, and local immune cells within the tumor microenvironment. In this review, we highlight the emerging roles of ovarian cancer exosomes as coordinators of pre-metastatic niche formation, biomarkers amenable to liquid biopsy, and targets of chemotherapy.

Traditional intellectual property transaction traceability has problems such as information asymmetry, traceability information storage methods relying on centralized databases, and easy tampering of transaction information, etc. A blockchain-based information security mechanism for intellectual property transaction traceability is proposed. Firstly, through the analysis of massive intellectual property transaction case information, the commonality and individuality data are studied, and the structure and scope of data collection requirements for traceability information are established; secondly, the traceability information structure is constructed based on the smart contract and PROV data origin model, the signature verification of traceability information is completed based on the BLS threshold signature of the Dynamic DKG protocol, and the signature process integrates the PROV model and constructs a chained signature structure. The multi-level traceability information verification strategy and process are developed to achieve the security protection of traceability information throughout the entire life cycle of intellectual property transactions.

Based on the practical Byzantine fault tolerance algorithm (PBFT), a grouped multilayer PBFT consensus algorithm (GM-PBFT) is proposed to be applied to digital asset transactions in view of the problems with excessive communication complexity and low consensus efficiency found in the current consensus mechanism for digital asset transactions. Firstly, the transaction nodes are grouped by type, and each group can handle different types of consensus requests at the same time, which improves the consensus efficiency as well as the accuracy of digital asset transactions. Second, the group develops techniques like validation, auditing, and re-election to enhance Byzantine fault tolerance by thwarting malicious node attacks. This supervisory mechanism is implemented through the Raft consensus algorithm. Finally, the consensus is stratified for the nodes in the group, and the consensus nodes in the upper layer recursively send consensus requests to the lower layer until the consensus request reaches the end layer to ensure the consistency of the block ledger in the group. Based on the results of the experiment, the approach may significantly outperform the PBFT consensus algorithm when it comes to accuracy, efficiency, and preserving the security and reliability of transactions in large-scale network node digital transaction situations.

In service-transaction scenarios, blockchain technology is widely used as an effective tool for establishing trust between service providers and consumers. The consensus algorithm is the core technology of blockchain. However, existing consensus algorithms, such as the practical Byzantine fault tolerance (PBFT) algorithm, still suffer from high resource consumption and latency. To solve this problem, in this study, we propose an improved PBFT blockchain consensus algorithm based on quality of service (QoS)-aware trust service evaluation for secure and efficient service transactions. The proposed algorithm, called the QoS-aware trust practical Byzantine fault tolerance (QTPBFT) algorithm, efficiently achieves consensus, significantly reduces resource consumption, and enhances consensus efficiency. QTPBFT incorporates a QoS-aware trust service global evaluation mechanism that implements service reliability ranking by conducting a dynamic evaluation according to the real-time performance of the services. To reduce the traffic of the blockchain, it uses a mechanism that selects nodes with higher values to form a consensus group that votes for consensus according to the global evaluation result of the trust service. A practical protocol is also constructed for the proposed algorithm. The results of extensive simulations and comparison with other schemes verify the efficacy and efficiency of the proposed scheme.

In view of the problems of false property rights and difficulties in identity authentication in intellectual property transactions, an identity authentication model for intellectual property transactions based on an alliance chain is proposed. Firstly, the two-factor identity authentication model’s roles, functions, and processes are constructed. Secondly, the two-factor authentication mechanism of ID password combined with physiological and property rights features is proposed, the identity identification generation method of fingerprint biometrics and intellectual property features is established, and the constraint compression strategy based on Poseidon hash is designed to reduce the workload of zero-knowledge proof algorithm and realize the consistency of property rights identity. Finally, the security and performance analysis of the authentication model is carried out, and the comparison and validation of related models are carried out, which shows that the model has good security and reliability.

•A dual-flip-angle blipped multi-gradient-echo (DFA-mGRE) sequence with a stack-of-stars (SOS) trajectory was developed to accelerate the acquisition for fast simultaneous three-dimensional (3D) multiparametric mapping of whole brain myelin water fraction (MWF), T1, proton density (PD), R2*, magnetic susceptibility (QSM), and B1 transmit field (B1+).•A novel joint-sparsity-constrained multicomponent T2*-T1 spectrum estimation (JMSE) algorithm was proposed to improve the quantification of MWF by correcting for the T1 saturation effect and B1+/B1− inhomogeneities.•By integrating the DFA-mGRE SOS sequence, the JMSE algorithm, the tissue-prior-based B1+ estimation algorithm, and the subspace-based reconstruction, our technique can provide robust multiparametric mapping of 3D whole brain MWF, T1, PD, R2*, QSM, and B1+ with a two-minute scan. A new MRI technique is presented for three-dimensional fast simultaneous whole brain mapping of myelin water fraction (MWF), T1, proton density (PD), R2*, magnetic susceptibility (QSM), and B1 transmit field (B1+). Phantom and human (N = 9) datasets were acquired using a dual-flip-angle blipped multi-gradient-echo (DFA-mGRE) sequence with a stack-of-stars (SOS) trajectory. Images were reconstructed using a subspace-based algorithm with a locally low-rank constraint. A novel joint-sparsity-constrained multicomponent T2*-T1 spectrum estimation (JMSE) algorithm is proposed to correct for the T1 saturation effect and B1+/B1− inhomogeneities in the quantification of MWF. A tissue-prior-based B1+ estimation algorithm was adapted for B1 correction in the mapping of T1 and PD. In the phantom study, measurements obtained at an acceleration factor (R) of 12 using prospectively under-sampled SOS showed good consistency (R2 > 0.997) with Cartesian reference for R2*/T1app/M0app. In the in vivo study, results of retrospectively under-sampled SOS with R = 6, 12, 18, showed good quality (structure similarity index measure > 0.95) compared with those of fully-sampled SOS. Besides, results of prospectively under-sampled SOS with R = 12 showed good consistency (intraclass correlation coefficient > 0.91) with Cartesian reference for T1/PD/B1+/MWF/QSM/R2*, and good reproducibility (coefficient of variation < 7.0 %) in the test-retest analysis for T1/PD/B1+/MWF/R2*. This study has demonstrated the feasibility of simultaneous whole brain multiparametric mapping with a two-minute scan using the DFA-mGRE SOS sequence, which may overcome a major obstacle for neurological applications of multiparametric MRI.

A service can be an intangible commodity in which no physical goods are transferred from the seller to the buyer. However, traditional trading platforms have many limitations in trading services due to dishonest buyers and brokers. In this paper, we propose a service trading ecosystem based on blockchain, named STEB, which combines blockchain, smart contract, encryption, and digital authentication techniques for service trading. In addition, a dual-chain architecture, which contains two types of blockchains, namely TraChain and SerChain, and a hierarchical encryption scheme of the data on the chain, are proposed to ensure the integrity of transaction data and fine-grained privacy protection of users. Furthermore, we describe a new set of smart contracts to ensure safe transactions for the entire service trading. Security analysis and simulation results confirm that the proposed STEB can achieve more efficient contract execution and enhance service transaction privacy.

A neural network-based global fast terminal sliding mode control method with non-linear differentiator (NNFTSMC) is proposed in this paper to design the dynamic control system for three-axis stabilized platform. The dynamic model of the three-axis stabilized platform is established with various uncertainties and unknown external disturbances. To overcome the external disturbance and reduce the output chatter of the classical sliding mode control (SMC) system, the improved global fast terminal sliding mode control method using the nonlinear differentiator and neural network techniques is proposed and implemented in the three-axis stabilized platform system. The global fast terminal sliding mode controller can make the controlled state approach to the sliding surface in a finite time. To eliminate the system output chatter, the nonlinear differentiator is employed to obtain the differentiation of the signal. The neural network is introduced to estimate the uncertainties disturbances to improve the stability and the robustness of the control system. The stability and the robustness of the proposed control method are analyzed using the Lyapunov theory. The performance of the proposed NNFTSMC method is verified and compared with the classical proportion-integral-differential (PID) controller, SMC controller and fast terminal sliding mode controller (FTSMC) through the computer simulation. Results validate the effectiveness and robustness of the proposed NNFTSMC method in presence of uncertainties and unknown external disturbances.

Traditional intellectual property authentication relies on centralized intermediaries, which makes it difficult to address issues such as forgery, lack of trust, and opaque information. Combined with the characteristics of blockchain, such as decentralization, tampering, and traceability, these challenges can be effectively dealt with. Aiming at the shortcomings of traditional consensus algorithms in intellectual property authentication, such as high communication overhead and low efficiency, the improved PBFT (Practical Byzantine Fault Tolerance) algorithm (MBFT algorithm) is proposed and combined with the distributed database IPFS (Inter Planetary File System) to alleviate the pressure of blockchain data storage and enhance operational efficiency. The algorithm first adopts the evaluation system in the hierarchical mechanism, invokes the Fibonacci series incremental law to update the Score value of the nodes and sort them, and divides the nodes into the classification consensus layer, the consensus confirmation layer, and the supervision layer. Secondly, the Maglev algorithm is used to generate a lookup table and design a classification consensus strategy, which is divided into four consensus groups based on the characteristics of intellectual property categories, namely, the patent authentication consensus group, the trademark authentication consensus group, the copyright authentication consensus group, and the other types of authentication consensus group. Then, the algorithm optimizes the consistency protocol, carries out PBFT consensus once in each of the classification consensus layers and consensus confirmation layers, according to the consensus situation, and realizes the nodes’ dynamic update to ensure the consensus’s accuracy and reliability. The experiments show that the MBFT algorithm performs better in terms of communication complexity and throughput. As the number and size of files increase, the execution time of IPFS progressively lengthens. However, the overall performance still meets the actual demand. Compared with the traditional PBFT, MBFT improves the communication complexity by about 50% or more, the throughput is about 3 times the traditional PBFT, and the scalability and response speed of the system are significantly improved when the number of nodes increases.

Aiming at the problems of significant communication overheads, the low reliability of primary nodes, and the insufficient dynamic adaptability of traditional consensus algorithms in intellectual property transaction scenarios, an Improved Practical Byzantine Fault Tolerant (IPBFT) algorithm based on the Chord algorithm and entropy weight method is proposed. Firstly, the Chord algorithm is employed to map nodes onto a hash ring, enabling dynamic grouping. Secondly, an entropy-based dynamic reputation model is constructed, quantifying the evaluation of node behaviors and calculating the overall reputation value. A three-level reputation classification mechanism is used to dynamically select primary and supervisory nodes, thereby reducing the probability of Byzantine nodes being elected. Then, a three-phase monitoring strategy for supervisory nodes is developed, which includes collection, review, and blackout. This improves the Raft consensus process, enhancing the detection and fault tolerance against malicious leaders. Finally, a grouped dual-layer consensus architecture is proposed. The lower layer uses an improved Raft algorithm for efficient consensus within groups, while the upper layer uses the PBFT algorithm for cross-group global consistency verification. Experimental findings demonstrate that the IPBFT algorithm is able to balance security, scalability, and consensus efficiency in a dynamic network environment, providing a better consensus solution for intellectual property transactions.