52 research outputs found
Deep learning driven real time topology optimisation based on initial stress learning
Topology optimisation can facilitate engineers in proposing efficient and novel conceptual design schemes, but the traditional FEM based optimization demands significant computing power and makes the real time optimization impossible. Based on the convolutional neural network (CNN) method, a new deep learning approximate algorithm for real time topology optimisation is proposed. The algorithm learns from the initial stress (LIS), which is defined as the major principal stress matrix obtained from finite element analysis in the first iteration of classical topology optimisation. The initial major principal stress matrix of the structure is used to replace the load cases and boundary conditions of the structure as independent variables, which can produce topological prediction results with high accuracy based on a relatively small number of samples. Compared with the traditional topology optimisation method, the new method can produce a similar result in real time without repeated iterations. A classic short cantilever problem was used as an example, and the optimized topology of the cantilever structure is predicted successfully by the established approximate algorithm. By comparing the prediction results to the structural optimisation results obtained by the classical topology optimisation method, it is discovered that the two results are highly approximate, which verifies the validity of the established algorithm. Furthermore, a new algorithm evaluation method is proposed to evaluate the effects of using different methods to select samples on the prediction performance of the optimized topology, and the results were promising and concluded in the end
Correlations among the plasma concentrations of first-line anti-tuberculosis drugs and the physiological parameters influencing concentrations
Background: The plasma concentrations of the four most commonly used first-line anti-tuberculosis (TB) drugs, isoniazid (INH), rifampicin (RMP), ethambutol (EMB), and pyrazinamide (PZA), are often not within the therapeutic range. Insufficient drug exposure could lead to drug resistance and treatment failure, while excessive drug levels may lead to adverse reactions. The purpose of this study was to identify the physiological parameters influencing anti-TB drug concentrations.Methods: A retrospective cohort study was conducted. The 2-h plasma concentrations of the four drugs were measured by using the high-performance liquid chromatography-tandem mass spectrometry method.Results: A total of 317 patients were included in the study. The proportions of patients with INH, RMP, EMB, and PZA concentrations within the therapeutic range were 24.3%, 31.5%, 27.8%, and 18.6%, respectively. There were positive associations between the concentrations of INH and PZA and RMP and EMB, but negative associations were observed between the concentrations of INH and RMP, INH and EMB, RMP and PZA, and EMB and PZA. In the multivariate analysis, the influencing factors of the INH concentration were the PZA concentration, total bile acid (TBA), serum potassium, dose, direct bilirubin, prealbumin (PA), and albumin; those of the RMP concentration were PZA and EMB concentrations, weight, α-l-fucosidase (AFU), drinking, and dose; those of the EMB concentration were the RMP and PZA concentrations, creatinine, TBA and indirect bilirubin; and those of the PZA concentration were INH, RMP and EMB concentrations, sex, weight, uric acid and drinking.Conclusion: The complex correlations between the concentrations of the four first-line anti-TB drugs lead to a major challenge in dose adjustment to maintain all drugs within the therapeutic window. Levels of TBA, PA, AFU, and serum potassium should also be considered when adjusting the dose of the four drugs
KwaiYiiMath: Technical Report
Recent advancements in large language models (LLMs) have demonstrated
remarkable abilities in handling a variety of natural language processing (NLP)
downstream tasks, even on mathematical tasks requiring multi-step reasoning. In
this report, we introduce the KwaiYiiMath which enhances the mathematical
reasoning abilities of KwaiYiiBase1, by applying Supervised Fine-Tuning (SFT)
and Reinforced Learning from Human Feedback (RLHF), including on both English
and Chinese mathematical tasks. Meanwhile, we also constructed a small-scale
Chinese primary school mathematics test set (named KMath), consisting of 188
examples to evaluate the correctness of the problem-solving process generated
by the models. Empirical studies demonstrate that KwaiYiiMath can achieve
state-of-the-art (SOTA) performance on GSM8k, CMath, and KMath compared with
the similar size models, respectively.Comment: technical report. arXiv admin note: text overlap with
arXiv:2306.16636 by other author
Ancient Genomes Reveal the Evolutionary History and Origin of Cashmere-Producing Goats in China
Goats are one of the most widespread farmed animals across the world; however, their migration route to East Asia and local evolutionary history remain poorly understood. Here, we sequenced 27 ancient Chinese goat genomes dating from the Late Neolithic period to the Iron Age. We found close genetic affinities between ancient and modern Chinese goats, demonstrating their genetic continuity. We found that Chinese goats originated from the eastern regions around the Fertile Crescent, and we estimated that the ancestors of Chinese goats diverged from this population in the Chalcolithic period. Modern Chinese goats were divided into a northern and a southern group, coinciding with the most prominent climatic division in China, and two genes related to hair follicle development, FGF5 and EDA2R, were highly divergent between these populations. We identified a likely causal de novo deletion near FGF5 in northern Chinese goats that increased to high frequency over time, whereas EDA2R harbored standing variation dating to the Neolithic. Our findings add to our understanding of the genetic composition and local evolutionary process of Chinese goats
Magnetic Control of Valley Pseudospin in Monolayer WSe2
Local energy extrema of the bands in momentum space, or valleys, can endow
electrons in solids with pseudo-spin in addition to real spin. In transition
metal dichalcogenides this valley pseudo-spin, like real spin, is associated
with a magnetic moment which underlies the valley-dependent circular dichroism
that allows optical generation of valley polarization, intervalley quantum
coherence, and the valley Hall effect. However, magnetic manipulation of valley
pseudospin via this magnetic moment, analogous to what is possible with real
spin, has not been shown before. Here we report observation of the valley
Zeeman splitting and magnetic tuning of polarization and coherence of the
excitonic valley pseudospin, by performing polarization-resolved
magneto-photoluminescence on monolayer WSe2. Our measurements reveal both the
atomic orbital and lattice contributions to the valley orbital magnetic moment;
demonstrate the deviation of the band edges in the valleys from an exact
massive Dirac fermion model; and reveal a striking difference between the
magnetic responses of neutral and charged valley excitons which is explained by
renormalization of the excitonic spectrum due to strong exchange interactions
MicroRNAs in methamphetamine-induced neurotoxicity and addiction.
Methamphetamine (METH) abuse remains a significant public health concern globally owing to its strong addictive properties. Prolonged abuse of the drug causes irreversible damage to the central nervous system. To date, no efficient pharmacological interventions are available, primarily due to the unclear mechanisms underlying METH action in the brain. Recently, microRNAs (miRNAs) have been identified to play critical roles in various cellular processes. The expression levels of some miRNAs are altered after METH administration, which may influence the transcription of target genes to regulate METH toxicity or addiction. This review summarizes the miRNAs in the context of METH use, discussing their role in the reward effect and neurotoxic sequelae. Better understanding of the molecular mechanisms involved in METH would be helpful for the development of new therapeutic strategies in reducing the harm of the drug
Concurrent multi-scale optimization design of composite frame structures with maximum fundamental frequency
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