Dynamic analysis of the investment decision of electric vehicle charging facilities and the promotion effect measurement for electric vehicles

This paper aims to analyze the deep reason why there exists hesitation when investors decide whether invest in EV charging facilities (ECFs). To this end, a series of theoretic models are built and derived, and some enlightening results are got. The main results confirm that charging facility investors are insufficiently motivated to follow a moderately aggressive investment strategy in the early stages of EV development. For stimulating ECFs’ investment, the marginal conditions in which the investors choose active or conservative investment strategies to lay out charging facilities are analyzed, and the effects under different ECFs investment strategies are quantized in terms of driving the market development of EVs. Based on the findings, relevant policy suggestions are proposed. Finally, to verify the gained results, a case study in the context of China is given

Online decentralized tracking for nonlinear time-varying optimal power flow of coupled transmission-distribution grids

The coordinated alternating current optimal power flow (ACOPF) for coupled transmission-distribution grids has become crucial to handle problems related to high penetration of renewable energy sources (RESs). However, obtaining all system details and solving ACOPF centrally is not feasible because of privacy concerns. Intermittent RESs and uncontrollable loads can swiftly change the operating condition of the power grid. Existing decentralized optimization methods can seldom track the optimal solutions of time-varying ACOPFs. Here, we propose an online decentralized optimization method to track the time-varying ACOPF of coupled transmission-distribution grids. First, the time-varying ACOPF problem is converted to a dynamic system based on Karush-Kuhn-Tucker conditions from the control perspective. Second, a prediction term denoted by the partial derivative with respect to time is developed to improve the tracking accuracy of the dynamic system. Third, a decentralized implementation for solving the dynamic system is designed based on only a few information exchanges with respect to boundary variables. Moreover, the proposed algorithm can be used to directly address nonlinear power flow equations without relying on convex relaxations or linearization techniques. Numerical test results reveal the effectiveness and fast-tracking performance of the proposed algorithm.Comment: 18 pages with 15 figure

Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach

With the growing need to comply with privacy regulations and respond to user data deletion requests, integrating machine unlearning into IoT-based federated learning has become imperative. Traditional unlearning methods, however, often lack verifiable mechanisms, leading to challenges in establishing trust. This paper delves into the innovative integration of blockchain technology with federated learning to surmount these obstacles. Blockchain fortifies the unlearning process through its inherent qualities of immutability, transparency, and robust security. It facilitates verifiable certification, harmonizes security with privacy, and sustains system efficiency. We introduce a framework that melds blockchain with federated learning, thereby ensuring an immutable record of unlearning requests and actions. This strategy not only bolsters the trustworthiness and integrity of the federated learning model but also adeptly addresses efficiency and security challenges typical in IoT environments. Our key contributions encompass a certification mechanism for the unlearning process, the enhancement of data security and privacy, and the optimization of data management to ensure system responsiveness in IoT scenarios.Comment: 13 pages, 25 figure

Federated TrustChain: Blockchain-Enhanced LLM Training and Unlearning

The development of Large Language Models (LLMs) faces a significant challenge: the exhausting of publicly available fresh data. This is because training a LLM needs a large demanding of new data. Federated learning emerges as a promising solution, enabling collaborative model to contribute their private data to LLM global model. However, integrating federated learning with LLMs introduces new challenges, including the lack of transparency and the need for effective unlearning mechanisms. Transparency is essential to ensuring trust and fairness among participants, while accountability is crucial for deterring malicious behaviour and enabling corrective actions when necessary. To address these challenges, we propose a novel blockchain-based federated learning framework for LLMs that enhances transparency, accountability, and unlearning capabilities. Our framework leverages blockchain technology to create a tamper-proof record of each model's contributions and introduces an innovative unlearning function that seamlessly integrates with the federated learning mechanism. We investigate the impact of Low-Rank Adaptation (LoRA) hyperparameters on unlearning performance and integrate Hyperledger Fabric to ensure the security, transparency, and verifiability of the unlearning process. Through comprehensive experiments and analysis, we showcase the effectiveness of our proposed framework in achieving highly effective unlearning in LLMs trained using federated learning. Our findings highlight the feasibility of integrating blockchain technology into federated learning frameworks for LLMs.Comment: 16 pages, 7 figures

QUEEN: Query Unlearning against Model Extraction

Model extraction attacks currently pose a non-negligible threat to the security and privacy of deep learning models. By querying the model with a small dataset and usingthe query results as the ground-truth labels, an adversary can steal a piracy model with performance comparable to the original model. Two key issues that cause the threat are, on the one hand, accurate and unlimited queries can be obtained by the adversary; on the other hand, the adversary can aggregate the query results to train the model step by step. The existing defenses usually employ model watermarking or fingerprinting to protect the ownership. However, these methods cannot proactively prevent the violation from happening. To mitigate the threat, we propose QUEEN (QUEry unlEarNing) that proactively launches counterattacks on potential model extraction attacks from the very beginning. To limit the potential threat, QUEEN has sensitivity measurement and outputs perturbation that prevents the adversary from training a piracy model with high performance. In sensitivity measurement, QUEEN measures the single query sensitivity by its distance from the center of its cluster in the feature space. To reduce the learning accuracy of attacks, for the highly sensitive query batch, QUEEN applies query unlearning, which is implemented by gradient reverse to perturb the softmax output such that the piracy model will generate reverse gradients to worsen its performance unconsciously. Experiments show that QUEEN outperforms the state-of-the-art defenses against various model extraction attacks with a relatively low cost to the model accuracy. The artifact is publicly available at https://anonymous.4open.science/r/queen implementation-5408/

A clinical study on the application of three-dimensionally printed splints combined with finite element analysis in paediatric distal radius fractures

PurposeThis single-centre randomised clinical trial assessed the clinical efficacy and patient satisfaction of 3D-printed splints optimised via finite element analysis (FEA) for pediatric distal radius fractures.MethodsThis retrospective study included 56 children diagnosed with forearm fractures at our hospital between August 2023 and August 2024. Those who underwent traditional U-shaped forearm plaster immobilisation were compared with those who received a customised 3D-printed splint. FEA was conducted based on the biomechanical characteristics of the forearm; the splint structure was optimised based on the analysis results and created via 3D printing. Outcomes were evaluated using the Patient Satisfaction Questionnaire and Wong-Baker Faces Pain Scale–Revised. Forearm function was evaluated using the Mayo Wrist Score and radiological outcomes. A power calculation was not performed due to the exploratory scope and resource limitations of this preliminary study.ResultsThe treatment costs significantly differed between the two groups (p = 0.001). In the Patient Satisfaction Questionnaire, the hot and sweaty item showed no significant difference (p = 0.089), whereas the last week's comfort (p = 0.001), first applied comfort (p = 0.004), weight (p = 0.001), itchiness (p = 0.015), smell (p = 0.003), and overall satisfaction items significantly differed between the two groups (p = 0.004). A comparison of the Mayo Wrist Score showed a statistically significant difference between the two groups after external fixation removal (p = 0.044). There were no significant differences between the two groups in terms of the palmar tilt angle (p = 0.196), ulnar deviation angle (p = 0.460), or height of the radial styloid (p = 0.111).ConclusionBoth 3D-printed splint and plaster cast fixation methods can effectively treat distal radial fractures in children, but the 3D-printed splint showed superior patient acceptance

Comparative effectiveness and safety of laser, needle, and “quick fenestrater” in in situ fenestration during thoracic endovascular aortic repair

BackgroundSpecial instruments are needed for the revascularization of aortic branches in in situ fenestration during thoracic endovascular aortic repair (TEVAR). This prospective study compared the effectiveness and safety of three currently used fenestraters: laser, needle, and Quick Fenestrater (QF).MethodsIn all, 101 patients who underwent TEVAR for aortic disease (dissection, n = 62; aneurysm, n = 16, or ulcer, n = 23) were enrolled. All patients were randomly assigned to three groups: 34 were assigned to laser fenestration, 36 to needle fenestration, and 31 to QF fenestration. The epidemiological data, treatment, imaging findings, and follow-up outcomes were analyzed using data from the medical records.ResultsThe technical success rates of the laser, needle, and QF fenestration groups were 94.1%, 94.4%, and 100% (p > 0.05). After correction of mixed factors such as age and gender, it was showed the average operative time (Laser group: 130.01 ± 9.36 min/ Needle group: 149.80 ± 10.18 min vs. QF group: 101.10 ± 6.75 min, p < 0.001), fluoroscopy time (Laser group: 30.16 ± 9.81 min/ Needle group: 40.20 ± 9.91 min vs. QF group: 19.91 ± 5.42 min, p < 0.001), fenestration time (Laser group 5.50 ± 3.10 min / Needle group 3.50 ± 1.50 min vs. QF group 0.67 ± 0.06 min, p < 0.001), and guide wire passage time after fenestration (Laser group 5.10 ± 1.70 min / Needle group 4.28 ± 1.60 min vs. QF group 0.07 ± 0.01 min, p < 0.001) were all shorter with QF fenestration than with the other two tools. The overall perioperative complication rates of the laser, needle, and QF fenestration groups were 5.9%, 5.6%, and 0% (p > 0.05): One case of sheath thermal injury and one case of vertebral artery ischemia occurred in the laser fenestration group; one case each of access site hematoma and brachial artery thrombosis were reported in the needle fenestration group. 89 (88.1%, 89/101) patients were followed for a median of 12.6 ± 1.6 months. The overall postoperative complication rates of the laser, needle, and QF fenestration groups were 3.3%, 6.5%, and 0% (p > 0.05): In the laser fenestration group, there was one death due to postoperative ST-segment elevation myocardial infarction; in the needle fenestration group, one patient developed occlusion of the bridge stent; no complications occurred in the QF group.ConclusionAll three fenestration methods were effective in reconstructing supra-arch artery during TEVAR. QF fenestration required less contrast agent, with a shorter surgery duration and fewer complications than laser and needle fenestration

Differential Mechanisms of Septic Human Pulmonary Microvascular Endothelial Cell Barrier Dysfunction Depending on the Presence of Neutrophils

Sepsis is characterized by injury of pulmonary microvascular endothelial cells (PMVEC) leading to barrier dysfunction. Multiple mechanisms promote septic PMVEC barrier dysfunction, including interaction with circulating leukocytes and PMVEC apoptotic death. Our previous work demonstrated a strong correlation between septic neutrophil (PMN)-dependent PMVEC apoptosis and pulmonary microvascular albumin leak in septic mice in vivo; however, this remains uncertain in human PMVEC. Thus, we hypothesize that human PMVEC apoptosis is required for loss of PMVEC barrier function under septic conditions in vitro. To assess this hypothesis, human PMVECs cultured alone or in coculture with PMN were stimulated with PBS or cytomix (equimolar interferon γ, tumor necrosis factor α, and interleukin 1β) in the absence or presence of a pan-caspase inhibitor, Q-VD, or specific caspase inhibitors. PMVEC barrier function was assessed by transendothelial electrical resistance (TEER), as well as fluoroisothiocyanate-labeled dextran and Evans blue-labeled albumin flux across PMVEC monolayers. PMVEC apoptosis was identified by (1) loss of cell membrane polarity (Annexin V), (2) caspase activation (FLICA), and (3) DNA fragmentation [terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL)]. Septic stimulation of human PMVECs cultured alone resulted in loss of barrier function (decreased TEER and increased macromolecular flux) associated with increased apoptosis (increased Annexin V, FLICA, and TUNEL staining). In addition, treatment of septic PMVEC cultured alone with Q-VD decreased PMVEC apoptosis and prevented septic PMVEC barrier dysfunction. In septic PMN–PMVEC cocultures, there was greater trans-PMVEC macromolecular flux (both dextran and albumin) vs. PMVEC cultured alone. PMN presence also augmented septic PMVEC caspase activation (FLICA staining) vs. PMVEC cultured alone but did not affect septic PMVEC apoptosis. Importantly, pan-caspase inhibition (Q-VD treatment) completely attenuated septic PMN-dependent PMVEC barrier dysfunction. Moreover, inhibition of caspase 3, 8, or 9 in PMN–PMVEC cocultures also reduced septic PMVEC barrier dysfunction whereas inhibition of caspase 1 had no effect. Our data demonstrate that human PMVEC barrier dysfunction under septic conditions in vitro (cytomix stimulation) is clearly caspase-dependent, but the mechanism differs depending on the presence of PMN. In isolated PMVEC, apoptosis contributes to septic barrier dysfunction, whereas PMN presence enhances caspase-dependent septic PMVEC barrier dysfunction independently of PMVEC apoptosis

Medical Economic Consequences, Predictors, and Outcomes of Immediate Atrial Fibrillation Recurrence after Radiofrequency Ablation

Background and aims: Immediate recurrence (Im-Recurr), a type of atrial fibrillation (AF) recurrence occurring during the blanking period after radiofrequency catheter ablation (RFCA), has received little attention. Therefore, this study was aimed at exploring the clinical significance of Im-Recurr in patients with AF after RFCA. Methods: This study retrospectively included patients with AF who underwent RFCA at our center. Regression, propensity score matching (PSM), and survival curve analyses were conducted to investigate the effects of Im-Recurr on costs, hospitalization durations, AF recurrence rates, and predictors of Im-Recurr. Results: A total of 898 patients were included, among whom 128 developed Im-Recurr after RFCA. Multiple linear regression analysis revealed that Im-Recurr correlated with greater cost, hospitalization duration, and hospitalization duration after ablation. Logistic regression and PSM analyses indicated that intraoperative electric cardioversion (IEC) was an independent predictor of Im-Recurr. The follow-up results suggested a significantly higher 1-year cumulative AF recurrence rate in the Im-Recurr group than the control group. Conclusions: Im-Recurr significantly increases the cost and length of hospitalization for patients with AF undergoing RFCA and is associated with an elevated 1-year cumulative AF recurrence rate. IEC serves as an independent predictor of Im-Recurr. Registration number: ChiCTR2200065235