Reliability analysis for systems with outsourced components

The current business model for many industrial firms is to function as system integrators, depending on numerous outsourced components from outside component suppliers. This practice has resulted in tremendous cost savings; it makes system reliability analysis, however, more challenging due to the limited component information available to system designers. The component information is often proprietary to component suppliers. Motivated by the need of system reliability prediction with outsourced components, this work aims to explore feasible ways to accurately predict the system reliability during the system design stage. Four methods are proposed. The first method reconstructs component reliability functions using limited reliability data with respect to component loads, and the system reliability is then estimated statistically. The second method applies two-class support vector machines (SVM) to approximate limit-state functions of outsourced components based on the categorical reliability dataset. With the integration of the obtained limit-state functions and those of in-house components, the joint probability density function of all the components is estimated, thereby leading to accurate system reliability prediction. The third method is an extension of the second one, and a one-class SVM is proposed to rebuild limit-state functions for outsourced components given only the failure dataset. The last method deals with the case where no reliability dataset is available. A partial safety factor method is developed, which enables component suppliers to provide sufficient information to system designers for accurate reliability analysis without revealing the proprietary design details. Both numerical examples and engineering applications demonstrate the accuracy and effectiveness of the proposed methods --Abstract, page iv

DRPN: Making CNN Dynamically Handle Scale Variation

Based on our observations of infrared targets, serious scale variation along within sequence frames has high-frequently occurred. In this paper, we propose a dynamic re-parameterization network (DRPN) to deal with the scale variation and balance the detection precision between small targets and large targets in infrared datasets. DRPN adopts the multiple branches with different sizes of convolution kernels and the dynamic convolution strategy. Multiple branches with different sizes of convolution kernels have different sizes of receptive fields. Dynamic convolution strategy makes DRPN adaptively weight multiple branches. DRPN can dynamically adjust the receptive field according to the scale variation of the target. Besides, in order to maintain effective inference in the test phase, the multi-branch structure is further converted to a single-branch structure via the re-parameterization technique after training. Extensive experiments on FLIR, KAIST, and InfraPlane datasets demonstrate the effectiveness of our proposed DRPN. The experimental results show that detectors using the proposed DRPN as the basic structure rather than SKNet or TridentNet obtained the best performances

Seeding Rate and Row-Spacing Effects on Seed Yield and Yield Components of \u3cem\u3eLeymus chinensis\u3c/em\u3e (Trin.) Tzvel.

Chinese sheepgrass (Leymus chinensis (Trin.) Tzvel.) is widely distributed in the eastern portion of the Inner Mongolian Plateau and the Songnen Grassland of China. This grass is highly salt, cold and drought tolerant and has been the major source of forage for cows and other ruminants in China (Gao et al. 2012). Seed yield of this grass is very low under native conditions because of the low heading percentage and percentage of seed set (Wang et al. 2010). The Hexi Corridor, located in China’s northwestern Gansu Province, is the seed production center of China because of its dry, sunny climate and favorable irrigation conditions. Our field study was conducted to determine the optimum seeding rate and row-spacing for seed production of Chinese sheepgrass in the Hexi Corridor, where this grass has not been previously grown

EvEval: A Comprehensive Evaluation of Event Semantics for Large Language Models

Events serve as fundamental units of occurrence within various contexts. The processing of event semantics in textual information forms the basis of numerous natural language processing (NLP) applications. Recent studies have begun leveraging large language models (LLMs) to address event semantic processing. However, the extent that LLMs can effectively tackle these challenges remains uncertain. Furthermore, the lack of a comprehensive evaluation framework for event semantic processing poses a significant challenge in evaluating these capabilities. In this paper, we propose an overarching framework for event semantic processing, encompassing understanding, reasoning, and prediction, along with their fine-grained aspects. To comprehensively evaluate the event semantic processing abilities of models, we introduce a novel benchmark called EVEVAL. We collect 8 datasets that cover all aspects of event semantic processing. Extensive experiments are conducted on EVEVAL, leading to several noteworthy findings based on the obtained results

Line identification of extreme ultraviolet spectra from aluminum ions in EAST Tokamak plasmas

Extreme ultraviolet (EUV) spectra emitted from aluminum in the 5-340 A wavelength range were observed in Experimental Advanced Superconducting Tokamak (EAST) discharges. Several spectral lines from aluminum ions with different degrees of ionization were successfully observed with sufficient spectral intensities and resolutions using three fast-time-response EUV spectrometers. The line identification uses three independent state-of-art computational codes for the atomic structure calculations, which provide the wavelengths and radiative transition probabilities rate coefficients. These programs are HULLAC (Hebrew University - Lawrence Livermore Atomic Code), AUTOSTRUCTURE, and FAC (Flexible Atomic Code). Using three different codes allows us to resolve some ambiguities in identifying certain spectral lines and assess the validity of the theoretical predictions

Compound dietary fiber and high-grade protein diet improves glycemic control and ameliorates diabetes and its comorbidities through remodeling the gut microbiota in mice

Dietary intervention with a low glycemic index and full nutritional support is emerging as an effective strategy for diabetes management. Here, we found that the treatment of a novel compound dietary fiber and high-grade protein diet (CFP) improved glycemic control and insulin resistance in streptozotocin-induced diabetic mice, with a similar effect to liraglutide. In addition, CFP treatment ameliorated diabetes-related metabolic syndromes, such as hyperlipidemia, hepatic lipid accumulation and adipogenesis, systemic inflammation, and diabetes-related kidney damage. These results were greatly associated with enhanced gut barrier function and altered gut microbiota composition and function, especially those bacteria, microbial functions, and metabolites related to amino acid metabolism. Importantly, no adverse effect of CFP was found in our study, and CFP exerted a wider arrange of protection against diabetes than liraglutide. Thereby, fortification with balanced dietary fiber and high-grade protein, like CFP, might be an effective strategy for the management and treatment of diabetes

Fabrication of CuO nanoparticle interlinked microsphere cages by solution method

Here we report a very simple method to convert conventional CuO powders to nanoparticle interlinked microsphere cages by solution method. CuO is dissolved into aqueous ammonia, and the solution is diluted by alcohol and dip coating onto a glass substrate. Drying at 80 °C, the nanostructures with bunchy nanoparticles of Cu(OH)2can be formed. After the substrate immerges into the solution and we vaporize the solution, hollow microspheres can be formed onto the substrate. There are three phases in the as-prepared samples, monoclinic tenorite CuO, orthorhombic Cu(OH)2, and monoclinic carbonatodiamminecopper(II) (Cu(NH3)2CO3). After annealing at 150 °C, the products convert to CuO completely. At annealing temperature above 350 °C, the hollow microspheres became nanoparticle interlinked cages