A vibration fatigue analysis model considering interaction effects

In the typical vibration fatigue problem, the excited structure is usually subjected to variable amplitude loading in the fatigue weakness area, which is of the random nature. The fatigue life estimation under vibratory loads is generally studied in the frequency domain, which does not take into account the sequence or memory effect. Due to high nonlinearity of the fatigue damage evolution, it is difficult but useful to analyze the fatigue process under the vibration loading in the time-domain. In this paper a time-based method is proposed to analyze the fatigue crack growth behavior under variable amplitude loading caused by vibration. This approach is based on our proposed equivalent plastic zone concept and the generalized crack closure model, which is derived from plasticity in the vicinity of crack tip (such as monotonic and reverse plastic zone). The memory or loading interaction effect on the fatigue crack growth can be accounted for well by using the proposed method. A set of experimental data in Al 7075-T6 under “Christmas Tree” loading, a typical vibration loads condition, are used to validate the method. And then additional experimental data in Al 2024-T3 under block loading are also employed for model validation. The predictions have a very good agreement with the testing data, which are also compared with those of NASGRO equation. Finally, some conclusions are given based on our current investigation

Recent Advances in Heterogeneous Catalytic Hydrogenation of CO2 to Methane

With the accelerating industrialization, urbanization process, and continuously upgrading of consumption structures, the CO2 from combustion of coal, oil, natural gas, and other hydrocarbon fuels is unbelievably increased over the past decade. As an important carbon resource, CO2 gained more and more attention because of its converting properties to lower hydrocarbon, such as methane, methanol, and formic acid. Among them, CO2 methanation is considered to be an extremely efficient method due to its high CO2 conversion and CH4 selectivity. However, the CO2 methanation process requires high reaction temperatures (300–400°C), which limits the theoretical yield of methane. Thus, it is desirable to find a new strategy for the efficient conversion of CO2 to methane at relatively low reaction temperature, and the key issue is using the catalysts in the process. The advances in the noble metal catalysts, Ni-based catalysts, and Co-based catalysts, for catalytic hydrogenation CO2 to methane are reviewed in this paper, and the effects of the supports and the addition of second metal on CO2 methanation as well as the reaction mechanisms are focused

Comparison of lignocellulose composition in four major species of Miscanthus

Miscanthus is a perennial grass rich in lignocellulose that has attracted interest as a non-food crop for renewable bioenergy with major environmental and economic benefits for China. The lignocellulose composition of whole stems of four major species of Miscanthus was assessed. The average values of total moisture content (TMC) (61.90%) and hemicelluloses (34.86%) were the highest while cellulose (32.71%) and acid detergent lignin (ADL) (8.90%) were the lowest in Miscanthus floridulus. On the contrary, the contents of cellulose (42.11%) and ADL (13.64%) were the highest and total ash (TA) (2.89%) was the lowest in Miscanthus lutarioriparius. The Shannon–Weaver diversity indices of components for the four species showed that hemicellulose content (H’= 2.00±0.11) was the most variable trait followed by cellulose (H’= 1.84±0.07), then ADL (H’= 1.84±0.07). The variational range of each component was relatively higher in Miscanthus sacchariflorus. In M. lutarioriparius, the diversity indices of each component were moderate. The diversity of cellulose was the highest and hemicellulose, ADL, TA and TMC were low in Miscanthus sinensis. By correlation analysis, neutral detergent fiber (NDF) significantly and positively correlated with ADF, cellulose and ADL at P<0.01 as well as the relationship of cellulose and ADL in the four species. Hemicellulose showed significant (P<0.01) but negative correlation with cellulose and ADL in M. floridulus, M. lutarioriparius and M. sacchariflorus. By principal component analysis (PCA), the components ADF and cellulose were the PC1 that were considered the foremost for the evaluation and selection of resource in the four species. The conclusions show that lignocellulose composition contents of Miscanthus culms were different. M. floridulus was more fit to ethanol fermentation. Though the components contents in M. sinensis and M. sacchariflorus were moderate, the range of choice was large. It provided a possible means to screen the appropriate materials according to different utilization. M. lutarioriparius had more superiorities relatively. So the four species of Miscanthus were appropriate for extension as excellent herbaceous energy plants, though, reasonable species choice should be employed according to the conversion approach and the growth characteristics, productivity levels and biomass quality characteristics of these tall grasses.Keywords: Miscanthus, bioenergy, lignocellulose compositions, detergent fiber, diversity analysis, PC

Beyond the Obvious: Evaluating the Reasoning Ability In Real-life Scenarios of Language Models on Life Scapes Reasoning Benchmark~(LSR-Benchmark)

This paper introduces the Life Scapes Reasoning Benchmark (LSR-Benchmark), a novel dataset targeting real-life scenario reasoning, aiming to close the gap in artificial neural networks' ability to reason in everyday contexts. In contrast to domain knowledge reasoning datasets, LSR-Benchmark comprises free-text formatted questions with rich information on real-life scenarios, human behaviors, and character roles. The dataset consists of 2,162 questions collected from open-source online sources and is manually annotated to improve its quality. Experiments are conducted using state-of-the-art language models, such as gpt3.5-turbo and instruction fine-tuned llama models, to test the performance in LSR-Benchmark. The results reveal that humans outperform these models significantly, indicating a persisting challenge for machine learning models in comprehending daily human life

Antisense oligonucleotide targeting Livin induces apoptosis of human bladder cancer cell via a mechanism involving caspase 3

BACKGROUND AND AIM: in recent years, Livin, a new member of IAPs family, is found to be a key molecule in cancers. Researchers consider Livin may become a new target for tumor therapy; however, the role of it in bladder cancer is still unclear. The purpose of this article is to investigate Antisense Oligonucleotide (ASODN) of Livin on treating bladder cancer cell and underlying mechanisms. METHODS: Phosphorathioate modifying was used to synthesize antisense oligonucleotides targeting Livin, followed by transfection into human bladder cancer cell 5637. After transfection, Livin mRNA and protein level, cell proliferation and apoptosis changes, caspase3 level and its effect on human bladder cancer transplantable tumor in nude mice were measured. RESULT: results showed Livin ASODN effectively inhibited Livin expression and tumor cell proliferation, and these effects probably through enhanced caspase3 activity and apoptosis of tumor cells. In nude mice transplantable tumor model, Livin expressions were inhibited meanwhile caspase3 expression was increased. Tumor growth slowed down and apoptosis was enhanced. CONCLUSION: Our data suggest that Livin plays an important role in inhibiting apoptosis of bladder cancer cells. Livin ASODN may promote cell apoptosis, inhibit bladder cancer growth, and become one of the methods of gene therapy for bladder cancer

Urban Traffic Signals Timing at Four-Phase Signalized Intersection Based on Optimized Two-Stage Fuzzy Control Scheme

This paper proposes a signal timing scheme through a two-stage fuzzy logic controller. The controller first determines the signal phase and then adjusts the green time. At the first stage, the adaptive membership function of vehicle arrival rate is improved to adapt to the changing traffic flow. In addition to arrival rate and queue vehicles, a specific phase order rule is considered to avoid disordered phase selection in fuzzy control. At the second stage, the green time detection module decides whether to extend the current green time or switch phases every few seconds and the vehicle arrival rate is not required as the input to controller in real-time detection. Differential evolution algorithm with low space complexity and fast convergence is applied to optimize the fuzzy rules for avoiding artificial uncertainty. Simulation experiments are designed to compare traditional fuzzy controller, fixed-time controller, and fuzzy controller without flow prediction. Results show that the current proposed method in this paper can reduce vehicle delay significantly