Simulation of Shielding Materials against Galactic Cosmic Rays

Radiation is one of the most critical hazards for deep space missions. Among the sources of deep space radiation, the Galactic Cosmic Rays, which are composed of high energy ions travelling at relativistic speeds from outside the solar system, are especially difficult to shield. As spacefaring nations have progressed in their exploration activities, there has been increasing interest in longer and deeper space voyages. However, beyond Low Earth Orbit, without the protection afforded by the Earth's magnetic field, long space voyages have increased risks from radiation exposure. Hence more efficient shielding materials are necessary for solving this radiation issue. Space radiation shielding can be examined by either ground-based experiments or simulations. Deterministic or Monte Carlo approaches are the two computational methods to simulate the radiation transport problem. MULASSIS, a Monte Carlo code developed by QinetiQ, BIRA and ESA is based on Geant4 and is used in this thesis. A convergence study is first performed with aluminum as a shielding material in order to determine the number of primary particles to use in the MULASSIS simulations. Dose equivalent analysis is then performed for single shielding materials with updated radiation weighting factors recommended in ICRP 103, including aluminum, polyethylene, boron nitride infused with hydrogen and liquid hydrogen. Dose equivalent depth curves are plotted for each shielding material, and in addition for various multilayer combinations of aluminum and the other materials. Because the biological impact from secondary produced neutrons can be so harmful, a fluence analysis is performed for various elemental components of the GCR radiation spectrum for different shielding materials

Nodal Project Evaluation Applied to Large-Scale Renewable Energy Procurment: A case analysis of Massachusetts clean energy initiative

Abstract Evaluating a large number of renewable energy project proposals received in response to a single Request for Proposals (RFP) in a consistent manner independent of size and technology and fully cognizant of location and timing is a significant challenge. The current paper presents a methodology and set of tools for preparing a comparative quantitative evaluation of the economic and environmental benefits and costs of the renewable project proposals over a 25-year time horizon. The paper presents a case study of the large-scale renewable energy procurements undertaken in 2018 to comply with Massachusetts energy diversity and greenhouse gas (GHG) emission reduction goals mandated under its “Green Communities Act” of 2008 and Global Warming Solutions Act” of 2008. Section 83D of the Green Communities Act requires Massachusetts electric distribution companies (EDCs) to acquire 9,450 gigawatt hours per year of cost-effective renewable energy. The quantitative evaluation of each proposed renewable project is based on a scenario analysis approach in which a simulation modeling tool calculates energy costs and GHG emissions in the Northeast region (New England and New York) over the evaluation period for a “but for” case without any of the proposed renewable projects and for individual cases for each proposed renewable project. Working from a single database structure, the simulation modeling tool moves from a 30-year, annual resource adequacy module, to an hourly, nodal, 20-year plus SCUC / SCD, to a detailed capacity market valuation model. The simulation modeling system (ENELYTIX) operates with cloud-based technology utilizing user-friendly Excel interfacing with complex data / information transfer from an OLAP cube on the cloud to users’ workstations

Effect of Metformin on Lactate Metabolism in Normal Hepatocytes under High Glucose Stress in Vitro

Objective: To study the effect of metformin on lactate metabolism in hepatocytes in vitro under high glucose stress. Method: LO2 hepatocytes was cultured in vitro, hepatocytes were randomly divided into blank control group, 25 mmol/L glucose solution, 27 mmol/L glucose solution, 29 mmol/L glucose solution, 31 mmol/L glucose solution, 33 mmol/L glucose solution, 35 mmol/L glucose solution treatment group, after determining the optimal concentration as 31 mmol/L, use 30 mmol/L metformin solution, and then divided into blank control group, normal hepatocytes + the optimal concentration of glucose solution, normal hepatocytes + metformin solution , normal hepatocytes+. The optimal concentration of glucose solution normal hepatocytes + metformin solution, calculate the number of hepatocytes on cell count plate respectively in the 12 h, 24 h, 48 h, and use the lactic acid kit to determine the lactic acid value of the cell culture medium of normal liver cells + optimal concentration glucose solution and normal liver cells + optimal concentration glucose solution + metformin solution at 12 h, 24 h, and 48 h, respectively. Results: There was no significant change in the lactic acid concentration but significant increase in the number of surviving hepatocytes in the high-glycemic control group compared with that in the high-glycemic control group without metformin. Conclusions: Metformin has no significant effect on lactic acid metabolism of hepatocytes under high glucose stress in vitro, and has a protective effect on hepatocytes under high glucose stress. Based on this, it is preliminarily believed that metformin is not the direct factor leading to diabetic lactic acidosis

Deep learning models for cancer stem cell detection: a brief review

Cancer stem cells (CSCs), also known as tumor-initiating cells (TICs), are a subset of tumor cells that persist within tumors as a distinct population. They drive tumor initiation, relapse, and metastasis through self-renewal and differentiation into multiple cell types, similar to typical stem cell processes. Despite their importance, the morphological features of CSCs have been poorly understood. Recent advances in artificial intelligence (AI) technology have provided automated recognition of biological images of various stem cells, including CSCs, leading to a surge in deep learning research in this field. This mini-review explores the emerging trend of deep learning research in the field of CSCs. It introduces diverse convolutional neural network (CNN)-based deep learning models for stem cell research and discusses the application of deep learning for CSC research. Finally, it provides perspectives and limitations in the field of deep learning-based stem cell research

Evaluating Benefits of Rolling Horizon Model Predictive Control for Intraday Scheduling of a Natural Gas Pipeline Market

This paper analyzes a mechanism for clearing a physical market for intra-day schedules of receipts and deliveries of a natural gas pipeline. The Gas Balancing Market (GBM) is implemented to trade deviations from previously confirmed ratable nominations by solving a rolling horizon model predictive control (MPC) optimization formulation. The GBM mechanism operates by accepting quantity/price offers and bids from sellers and buyers of gas and producing an economically optimal schedule while guaranteeing its physical feasibility. The GBM’s solution engine is based on a strict mathematical representation of engineering factors of transient pipeline hydraulics and compressor station operations. The GBM’s settlement of cleared transactions is based on Locational Trade Values (LTVs) of natural gas that are fully consistent with the physics of energy flow. In this paper we provide numerical results of simulating a hypothetical GBM market operation using historical SCADA data for an actual pipeline system operation during the Polar Vortex period of February – March 2014. Based on these simulations, we quantify the potential deliverability and economic benefits of the GBM utilizing transient optimization of pipeline operations

RenderIH: A Large-scale Synthetic Dataset for 3D Interacting Hand Pose Estimation

The current interacting hand (IH) datasets are relatively simplistic in terms of background and texture, with hand joints being annotated by a machine annotator, which may result in inaccuracies, and the diversity of pose distribution is limited. However, the variability of background, pose distribution, and texture can greatly influence the generalization ability. Therefore, we present a large-scale synthetic dataset RenderIH for interacting hands with accurate and diverse pose annotations. The dataset contains 1M photo-realistic images with varied backgrounds, perspectives, and hand textures. To generate natural and diverse interacting poses, we propose a new pose optimization algorithm. Additionally, for better pose estimation accuracy, we introduce a transformer-based pose estimation network, TransHand, to leverage the correlation between interacting hands and verify the effectiveness of RenderIH in improving results. Our dataset is model-agnostic and can improve more accuracy of any hand pose estimation method in comparison to other real or synthetic datasets. Experiments have shown that pretraining on our synthetic data can significantly decrease the error from 6.76mm to 5.79mm, and our Transhand surpasses contemporary methods. Our dataset and code are available at https://github.com/adwardlee/RenderIH.Comment: Accepted by ICCV 202

Pharmacological effects and mechanisms of paeonol on antitumor and prevention of side effects of cancer therapy

Cancer represents one of the leading causes of mortality worldwide. Conventional clinical treatments include radiation therapy, chemotherapy, immunotherapy, and targeted therapy. However, these treatments have inherent limitations, such as multidrug resistance and the induction of short- and long-term multiple organ damage, ultimately leading to a significant decrease in cancer survivors’ quality of life and life expectancy. Paeonol, a nature active compound derived from the root bark of the medicinal plant Paeonia suffruticosa, exhibits various pharmacological activities. Extensive research has demonstrated that paeonol exhibits substantial anticancer effects in various cancer, both in vitro and in vivo. Its underlying mechanisms involve the induction of apoptosis, the inhibition of cell proliferation, invasion and migration, angiogenesis, cell cycle arrest, autophagy, regulating tumor immunity and enhanced radiosensitivity, as well as the modulation of multiple signaling pathways, such as the PI3K/AKT and NF-κB signaling pathways. Additionally, paeonol can prevent adverse effects on the heart, liver, and kidneys induced by anticancer therapy. Despite numerous studies exploring paeonol’s therapeutic potential in cancer, no specific reviews have been conducted. Therefore, this review provides a systematic summary and analysis of paeonol’s anticancer effects, prevention of side effects, and the underlying mechanisms involved. This review aims to establish a theoretical basis for the adjunctive strategy of paeonol in cancer treatment, ultimately improving the survival rate and enhancing the quality of life for cancer patients