Adaptive Interval Type-2 Fuzzy Logic Control of Marine Vessels

Ph.DDOCTOR OF PHILOSOPH

The anti-sepsis activity of the components of Huanglian Jiedu Decoction with high lipid A-binding affinity

Huanglian Jiedu Decoction (HJD), one of the classic recipes for relieving toxicity and fever, is a common method for treating sepsis in China. However, the effective components of HJD have not yet been identified. This experiment was carried out to elucidate the effective components of HJD against sepsis. Thus, seven fractions from HJD were tested using a biosensor to test their affinity for lipid A. The components obtained that had high lipid A-binding fractions were further separated, and their affinities to lipid A were assessed with the aid of a biosensor. The levels of LPS in the blood were measured, and pathology experiments were conducted. The LPS levels and mRNA expression analysis of TNF-α and IL-6 of the cell supernatant and animal tissue were evaluated to investigate the molecular mechanisms. Palmatine showed the highest affinity to lipid A and was evaluated by in vitro and in vivo experiments. The results of the in vitro and in vivo experiments indicated that the levels of LPS, TNF-α and IL-6 of the palmatine group were significantly lower than those of the sepsis model group (p \u3c 0.01). The group treated with palmatine showed strong neutralizing LPS activity in vivo. The palmatine group exhibited stronger protective activity on vital organs compared to the LPS-induced animal model. This verifies that HJD is a viable treatment option for sepsis given that there are multiple components in HJD that neutralize LPS, decrease the release of IL-6 and TNF-α induced by LPS, and protect vital organs

The Long-Term Effects of Bariatric Surgery on Type 2 Diabetes Remission, Microvascular and Macrovascular Complications, and Mortality: a Systematic Review and Meta-Analysis.

This systematic review aimed to evaluate the long-term (≥ 5 years) outcomes of bariatric surgery on diabetes remission, microvascular and macrovascular events, and mortality among type 2 diabetes (T2D) patients. Ten articles (one randomized controlled trial and nine cohorts) met the inclusion criteria and were included in this review. Pooled estimates of nine cohort studies showed that surgery significantly increased the diabetes remission (relative risk (RR) = 5.90; 95% CI 3.75-9.28), reduced the microvascular (RR = 0.37; 95% CI = 0.30-0.46) and macrovascular events (RR = 0.52; 95% CI 0.44-0.61), and mortality (RR = 0.21; 95% CI 0.20-0.21) as compared to non-surgical treatment. Available evidence suggests better remission and lower risks of microvascular and macrovascular disease and mortality in the surgery group as compared to non-surgical treatment group in T2D patients after at least 5 years of follow-up

DoseDiff: Distance-aware Diffusion Model for Dose Prediction in Radiotherapy

Treatment planning is a critical component of the radiotherapy workflow, typically carried out by a medical physicist using a time-consuming trial-and-error manner. Previous studies have proposed knowledge-based or deep learning-based methods for predicting dose distribution maps to assist medical physicists in improving the efficiency of treatment planning. However, these dose prediction methods usuallylack the effective utilization of distance information between surrounding tissues andtargets or organs-at-risk (OARs). Moreover, they are poor in maintaining the distribution characteristics of ray paths in the predicted dose distribution maps, resulting in a loss of valuable information obtained by medical physicists. In this paper, we propose a distance-aware diffusion model (DoseDiff) for precise prediction of dose distribution. We define dose prediction as a sequence of denoising steps, wherein the predicted dose distribution map is generated with the conditions of the CT image and signed distance maps (SDMs). The SDMs are obtained by a distance transformation from the masks of targets or OARs, which provide the distance information from each pixel in the image to the outline of the targets or OARs. Besides, we propose a multiencoder and multi-scale fusion network (MMFNet) that incorporates a multi-scale fusion and a transformer-based fusion module to enhance information fusion between the CT image and SDMs at the feature level. Our model was evaluated on two datasets collected from patients with breast cancer and nasopharyngeal cancer, respectively. The results demonstrate that our DoseDiff outperforms the state-of-the-art dose prediction methods in terms of both quantitative and visual quality

A Modeling Study of the Responses of Mesosphere and Lower Thermosphere Winds to Geomagnetic Storms at Middle Latitudes

Thermosphere Ionosphere Mesosphere Electrodynamics General Circulation Model (TIMEGCM) simulations are diagnostically analyzed to investigate the causes of mesosphere and lower thermosphere (MLT) wind changes at middle latitudes during the 17 April 2002 storm. In the early phase of the storm, middle‐latitude upper thermospheric wind changes are greater and occur earlier than MLT wind changes. The horizontal wind changes cause downward vertical wind changes, which are transmitted to the MLT region. Adiabatic heating and heat advection associated with downward vertical winds cause MLT temperature increases. The pressure gradient produced by these temperature changes and the Coriolis force then drive strong equatorward meridional wind changes at night, which expand toward lower latitudes. Momentum advection is minor. As the storm evolves, the enhanced MLT temperatures produce upward vertical winds. These upward winds then lead to a decreased temperature, which alters the MLT horizontal wind pattern and causes poleward wind disturbances at higher latitudes

Assessing Hidden Risks of LLMs: An Empirical Study on Robustness, Consistency, and Credibility

The recent popularity of large language models (LLMs) has brought a significant impact to boundless fields, particularly through their open-ended ecosystem such as the APIs, open-sourced models, and plugins. However, with their widespread deployment, there is a general lack of research that thoroughly discusses and analyzes the potential risks concealed. In that case, we intend to conduct a preliminary but pioneering study covering the robustness, consistency, and credibility of LLMs systems. With most of the related literature in the era of LLM uncharted, we propose an automated workflow that copes with an upscaled number of queries/responses. Overall, we conduct over a million queries to the mainstream LLMs including ChatGPT, LLaMA, and OPT. Core to our workflow consists of a data primitive, followed by an automated interpreter that evaluates these LLMs under different adversarial metrical systems. As a result, we draw several, and perhaps unfortunate, conclusions that are quite uncommon from this trendy community. Briefly, they are: (i)-the minor but inevitable error occurrence in the user-generated query input may, by chance, cause the LLM to respond unexpectedly; (ii)-LLMs possess poor consistency when processing semantically similar query input. In addition, as a side finding, we find that ChatGPT is still capable to yield the correct answer even when the input is polluted at an extreme level. While this phenomenon demonstrates the powerful memorization of the LLMs, it raises serious concerns about using such data for LLM-involved evaluation in academic development. To deal with it, we propose a novel index associated with a dataset that roughly decides the feasibility of using such data for LLM-involved evaluation. Extensive empirical studies are tagged to support the aforementioned claims

Bayesian updating for ground surface settlements of shield tunneling

Accurate prediction of ground surface settlements induced by shield construction is of great significance for ensuring the safety of shield construction. This paper proposes a ground surface settlement prediction method for shield tunneling based on Bayesian updating. The sequential observation data during the advance of excavation is utilized to update the key soil parameters, leading to a more accurate settlement prediction for the subsequent excavation stages. Response surfaces are constructed to replace the finite element model as the forward models for higher computational efficiency. A tunnel excavation project in Hangzhou, China, is selected to illustrate the effectiveness of the proposed method. The shield excavation face passes through four soil layers, and two soil parameters (i.e., Young’s modulus and friction angle) of these soil layers are selected as random variables to be updated. The results show that the soil parameters can be effectively updated based on the observation data at multiple points and various excavation stages. The predictions of ground surface settlements are improved by using the updated soil parameters. The prediction accuracy of the proposed method increases as more stages of observation data are sequentially obtained and incorporated