Ruptured Tubo-Ovarian Abscess in a Postmenopausal Woman Presenting with Septic Shock: a Case Report and Literature Review

SummaryObjectiveTo report a case of a ruptured tubo-ovarian abscess which induced septic shock in a postmenopausal woman.Case ReportA 53-year-old postmenopausal woman was transferred to our emergency department for drowsiness, hypotension, and lower abdominal discomfort. Transabdominal sonography and computed tomography showed a large pelvic tumor over the left adnexa with some ascites. The uterus and other adnexa were unremarkable. Laboratory data, including blood count and electrolytes, showed leukocytosis and azotemia. Under suspicion of a ruptured adnexal tumor, laparotomy was performed and showed a large ruptured tuboovarian tumor arising from the left adnexa with intra-abdominal pus formation. Subtotal hysterectomy and bilateral salpingo-oophorectomy led to massive bleeding during manipulation of the left adnexa because of the necrotic change in the left infundibulopelvic vessels. Deep vein thrombosis and wound disruption occurred after the operation, but, fortunately, she recovered 1 month later.ConclusionTubo-ovarian abscesses in postmenopausal women are uncommon but should be kept in mind with a pelvic tumor accompanied by septic shock, as this may cause a terrible outcome and other sequelae

CFD Simulations to Study Parameters Affecting Gas Explosion Venting in Compressor Compartments

In this work, a series of vented explosions in a typical compressor compartment are simulated using FLACS code to analyze the explosion venting characteristics. The effects of relevant parameters on the pressure peaks (i.e., overpressure and negative pressure) are also numerically investigated, including vent area ratio of the compressor compartment, vent activation pressure, mass per unit area of vent panels, and volume blockage ratio of obstacles. In addition, the orthogonal experiment design and improved grey relational analysis are implemented to evaluate the impact degree of these relevant parameters. The results show that the pressure peaks decrease with the increase of vent area ratio. There is an approximately linearly increasing relationship between the pressure peaks and the vent activation pressure. The pressure peaks increase with the mass per unit area of vent panels. The pressure peaks increase with the volume blockage ratio of obstacles. Based on the grey relational grade values, the effects of these relevant parameters on the overpressure peak are ranked as follows: volume blockage ratio of obstacles > vent activation pressure > vent area ratio > mass per unit area of vent panels. These achievements provide effective guidance for the venting safety design of gas compressor compartments

On the Trustworthiness Landscape of State-of-the-art Generative Models: A Comprehensive Survey

Diffusion models and large language models have emerged as leading-edge generative models and have sparked a revolutionary impact on various aspects of human life. However, the practical implementation of these models has also exposed inherent risks, highlighting their dual nature and raising concerns regarding their trustworthiness. Despite the abundance of literature on this subject, a comprehensive survey specifically delving into the intersection of large-scale generative models and their trustworthiness remains largely absent. To bridge this gap, This paper investigates both the long-standing and emerging threats associated with these models across four fundamental dimensions: privacy, security, fairness, and responsibility. In this way, we construct an extensive map outlining the trustworthiness of these models, while also providing practical recommendations and identifying future directions. These efforts are crucial for promoting the trustworthy deployment of these models, ultimately benefiting society as a whole.Comment: draft versio

On the Robustness of Split Learning against Adversarial Attacks

Split learning enables collaborative deep learning model training while preserving data privacy and model security by avoiding direct sharing of raw data and model details (i.e., sever and clients only hold partial sub-networks and exchange intermediate computations). However, existing research has mainly focused on examining its reliability for privacy protection, with little investigation into model security. Specifically, by exploring full models, attackers can launch adversarial attacks, and split learning can mitigate this severe threat by only disclosing part of models to untrusted servers.This paper aims to evaluate the robustness of split learning against adversarial attacks, particularly in the most challenging setting where untrusted servers only have access to the intermediate layers of the model.Existing adversarial attacks mostly focus on the centralized setting instead of the collaborative setting, thus, to better evaluate the robustness of split learning, we develop a tailored attack called SPADV, which comprises two stages: 1) shadow model training that addresses the issue of lacking part of the model and 2) local adversarial attack that produces adversarial examples to evaluate.The first stage only requires a few unlabeled non-IID data, and, in the second stage, SPADV perturbs the intermediate output of natural samples to craft the adversarial ones. The overall cost of the proposed attack process is relatively low, yet the empirical attack effectiveness is significantly high, demonstrating the surprising vulnerability of split learning to adversarial attacks.Comment: accepted by ECAI 2023, camera-ready versio