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"Bai, Jing"
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Dependability Analysis for the Blockchain Oracle System: A Quantitative Modeling Approach
Blockchain oracles, as data intermediaries between on-chain and off-chain environments, have opened up a wide range of application scenarios for blockchain technology. The dependability of a blockchain oracle system will affect the dependability of blockchain systems. However, the dynamic and heterogeneous nature of blockchain oracle systems poses challenges to assessing their dependability. Furthermore, how to comprehensively analyze the dependability of blockchain oracle systems from multiple dimensions of transient availability, steady-state availability, and reliability is also a challenge. In order to solve these challenges, this paper proposes three models based on a semi-Markov process (SMP): (1) the SMP model for steady-state availability analysis; (2) the hierarchical model for transient analysis; and (3) the SMP model with absorption states for reliability analysis. Then, we derive the formulas for calculating the dependability metrics, which can be used to evaluate the dependability of blockchain oracle systems composed of any number of oracle nodes. Finally, based on the comparative experiments to verify the approximate accuracy of the proposed model and formulas, we analyze the impact of system parameters and the number of oracle nodes on the dependability metrics. The experimental results reveal that the key factor affecting availability is the failure time and recovery time of the threshold oracle, while the key factor affecting MTTF is the failure time of the threshold oracle.
Journal Article
Molecular and functional imaging in cancer-targeted therapy: current applications and future directions
Targeted anticancer drugs block cancer cell growth by interfering with specific signaling pathways vital to carcinogenesis and tumor growth rather than harming all rapidly dividing cells as in cytotoxic chemotherapy. The Response Evaluation Criteria in Solid Tumor (RECIST) system has been used to assess tumor response to therapy via changes in the size of target lesions as measured by calipers, conventional anatomically based imaging modalities such as computed tomography (CT), and magnetic resonance imaging (MRI), and other imaging methods. However, RECIST is sometimes inaccurate in assessing the efficacy of targeted therapy drugs because of the poor correlation between tumor size and treatment-induced tumor necrosis or shrinkage. This approach might also result in delayed identification of response when the therapy does confer a reduction in tumor size. Innovative molecular imaging techniques have rapidly gained importance in the dawning era of targeted therapy as they can visualize, characterize, and quantify biological processes at the cellular, subcellular, or even molecular level rather than at the anatomical level. This review summarizes different targeted cell signaling pathways, various molecular imaging techniques, and developed probes. Moreover, the application of molecular imaging for evaluating treatment response and related clinical outcome is also systematically outlined. In the future, more attention should be paid to promoting the clinical translation of molecular imaging in evaluating the sensitivity to targeted therapy with biocompatible probes. In particular, multimodal imaging technologies incorporating advanced artificial intelligence should be developed to comprehensively and accurately assess cancer-targeted therapy, in addition to RECIST-based methods.
Journal Article
Research on Brand Design and Promotion Strategy of Health Food in Changbai Mountain Region based on the Big Data Analysis under the Background of Big Health Industry
With the continuous improvement of people's quality of life, sound health concept has also witnessed a certain development and promotion. At the same time, the big health industry has developed more rapidly. In this context, Changbai mountain regional health food needs to be designed and promoted to build its brand for gaining a larger market share. However, due to the low popularity of health food, the design and promotion of health food brand in Changbai mountain region need to grasp the right time and intensify the publicity to increase the brand influence and realize the improvement of economic benefits. Based on the dilemma in the brand design and promotion of health food in Changbai mountain region, this paper gives corresponding strategy analysis to help improve the brand awareness and occupy a larger market share based on the analysis of big data.
Journal Article
BMSC-derived exosomes promote osteoporosis alleviation via M2 macrophage polarization
Osteoporosis is characterized by reduced bone mass due to imbalanced bone metabolism. Exosomes derived from bone mesenchymal stem cells (BMSCs) have been shown to play roles in various diseases. This study aimed to clarify the regulatory function and molecular mechanism of BMSCs-derived exosomes in osteogenic differentiation and their potential therapeutic effects on osteoporosis. Exosomes were extracted from BMSCs. Bone marrow-derived macrophages (BMDMs) were cultured and internalized with BMSCs-derived exosomes. Real-time quantitative PCR was used to detect the expression of macrophage surface markers and tripartite motif (TRIM) family genes. BMDMs were co-cultured with human osteoblasts to assess osteogenic differentiation. Western blot was performed to analyze the ubiquitination of triggering receptor expressed on myeloid cell 1 (TREM1) mediated by TRIM25. An ovariectomized mice model was established to evaluate the role of TRIM25 and exosomes in osteoporosis. Exosomes were successfully isolated from BMSCs. BMSCs-derived exosomes upregulated TRIM25 expression, promoting M2 macrophage polarization and osteogenic differentiation. TRIM25 facilitated the ubiquitination and degradation of TREM1. Overexpression of TREM1 reversed the enhanced M2 macrophage polarization and osteogenic differentiation caused by TRIM25 overexpression. TRIM25 enhanced the protective effect of BMSCs-derived exosomes against bone loss in mice. These findings suggested that BMSCs-derived exosomes promoted osteogenic differentiation by regulating M2 macrophage polarization through TRIM25-mediated ubiquitination and degradation of TREM1. This mechanism might provide a novel approach for treating osteoporosis.
Journal Article
College Student Social Dynamic Analysis and Educational Mechanism Using Big Data Technology
With the advancement of the “big data” technology, college students inadvertently purchase personal advice while taking advantage of the exciting Internet to access information quickly and easily. In order to objectively achieve the real office of college students’ material enlightenment penetration in the mobile-friendly network, we choose the popular mobile social network, and we apply the natural clustering algorithm rules to segment the college students. Further, we identify college students, based on which we construct information leakage and apply the risk assessment design. The comprehensive entrepreneurial evaluation of the microblog platform combined with the user’s mobile complaints is utilized to conduct a psychological analysis on the key components and key communication channels of college students’ complaint leakage. We obtain ticket data using the social prospect method and refer to four dogmatic characteristic elements of query motivation. And we also collect dimensions through surrogate analysis. Based on the reference feature factor, four different user groups are rapidly moved. Dining, social, teaching and large users, and the model features of various usage profiles are described in combination with eight categories of user feature importance. The |objective is to improve college students’ awareness of social network and mobile partner network mobile information to a certain extent. We attempt to protect college students from fraud and installation plans, to standardize the management of online social platform of advertisements, and to progressively promote the disposal of network movable property due to security changes.
Journal Article
Chip-integrated metasurface full-Stokes polarimetric imaging sensor
Polarimetric imaging has a wide range of applications for uncovering features invisible to human eyes and conventional imaging sensors. Chip-integrated, fast, cost-effective, and accurate full-Stokes polarimetric imaging sensors are highly desirable in many applications, which, however, remain elusive due to fundamental material limitations. Here we present a chip-integrated
Meta
surface-based Full-Stokes
Polar
imetric
Im
aging sensor (MetaPolarIm) realized by integrating an ultrathin (~600 nm) metasurface polarization filter array (MPFA) onto a visible imaging sensor with CMOS compatible fabrication processes. The MPFA is featured with broadband dielectric-metal hybrid chiral metasurfaces and double-layer nanograting polarizers. This chip-integrated polarimetric imaging sensor enables single-shot full-Stokes imaging (speed limited by the CMOS imager) with the most compact form factor, records high measurement accuracy, dual-color operation (green and red) and a field of view up to 40 degrees. MetaPolarIm holds great promise to enable transformative applications in autonomous vision, industry inspection, space exploration, medical imaging and diagnosis.
We present a chip-integrated
Meta
surface-based Full-Stokes
Polar
imetric
Im
aging sensor (MetaPolarIm) with ultra-compactness, record high measurement accuracy, dual color operation, and a field of view up to 40 degrees.
Journal Article
SCNGO-CNN-LSTM-Based Voltage Sag Prediction Method for Power Systems
To achieve accurate voltage sag prediction and early warning, thereby improving power quality, a hybrid voltage sag prediction framework is proposed by integrating Kernel Entropy Component Analysis (KECA) with an improved Northern Goshawk Optimization (NGO) algorithm for hyperparameter tuning of a CNN-LSTM model. First, to address the limitations of the original NGO, such as proneness to falling into local optima and high randomness of the initial population distribution, a refraction-opposition-based learning mechanism is introduced to enhance population diversity and expand the search space. Furthermore, a sine–cosine strategy (SCA) with nonlinear weight coefficients is integrated into the exploration phase to dynamically adjust the search step size, optimizing the balance between global exploration and local exploitation, thereby boosting convergence speed and accuracy. The improved algorithm (SCNGO) is then utilized to optimize the hyperparameters of the CNN-LSTM model. Second, KECA is applied to voltage-sag-related data to extract key features and eliminate redundant information, and the resulting dimensionally reduced data are fed as input to the SCNGO-CNN-LSTM model to further improve prediction performance. Experimental results demonstrate that the SCNGO-CNN-LSTM model outperforms other comparative models significantly across multiple evaluation metrics. Compared with NGO-CNN-LSTM, GWO-CNN-LSTM, and the original CNN-LSTM, the proposed method achieves a mean squared error (MSE) reduction of 53.45%, 44.68%, and 66.76%, respectively. The corresponding root mean squared error (RMSE) is decreased by 25.33%, 18.61%, and 36.92%, while the mean absolute error (MAE) is reduced by 81.23%, 77.04%, and 86.06%, respectively. These results confirm that the proposed framework exhibits superior feature representation capability and significantly improves voltage sag prediction accuracy.
Journal Article
Virtual Synchronous Generator Multi-Parameter Cooperative Adaptive Control Based on a Fuzzy and Soft Actor–Critic Fusion Framework
To address the issue that distributed renewable energy grid-connected Virtual Synchronous Generator (VSG) systems are prone to significant power and frequency fluctuations under changing operating conditions, this paper proposes a multi-parameter coordinated control strategy for VSGs based on a fusion framework of fuzzy logic and the Soft Actor–Critic (SAC) algorithm, termed Improved SAC-based Virtual Synchronous Generator control (ISAC-VSG). First, the method uses fuzzy logic to map the frequency deviation and its rate of change into a five-dimensional membership vector, which characterizes the uncertainty and nonlinear features during the transient process, enabling segmented policy optimization for different transient regions. Second, a stage-based guidance mechanism is introduced into the reward function to balance the agent’s exploration and stability, thereby improving the reliability of the policy. Finally, the action space is expanded from inertia–damping to the coordinated regulation of inertia, damping, and active power droop coefficient, achieving multi-parameter dynamic optimization. MATLAB/Simulink R2022b simulation results indicate that, compared with the traditional SAC-VSG and DDPG-VSG method, the proposed strategy can reduce the maximum frequency overshoot by up to 29.6% and shorten the settling time by approximately 15.6% under typical operating conditions such as load step changes and grid phase disturbances. It demonstrates superior frequency oscillation suppression capability and system robustness, verifying the effectiveness and application potential of the proposed method in high-penetration renewable energy power systems.
Journal Article