Analysis of nonlinear dynamic behavior and pull-in prediction of micro circular plate actuator

The dynamic behavior of micro circular plate electrostatic devices is not easily analyzed using traditional methods such as perturbation theory or Galerkin approach method due to the complexity of the interactions among the electrostatic coupling effect, the residual stress and the nonlinear electrostatic force. Accordingly, the present study proposes a approach for analyzing the dynamic response of such devices using a hybrid numerical scheme comprising the differential transformation method and the finite difference method. The feasibility of the proposed approach is demonstrated by modeling the dynamic response of a micro circular plate actuated by a DC voltage. The numerical results for the pull-in voltage are found to deviate by no more than 0.27 % from those derived in the literature using various computational methods. Thus, the basic validity of the hybrid numerical scheme is confirmed. Moreover, the effectiveness of a combined DC/AC loading scheme in driving the micro circular actuator is examined. It is shown that the use of an AC actuating voltage in addition to the DC driving voltage provides an effective means of tuning the dynamic response of the micro circular plate

Application of the Smart Material on the Overtopping Type Wave Energy Converter

This thesis presents a method of applying “Shape Memory Alloy” (SMA) on an overtopping wave energy converter (OWEC). A control system which can fit all sea states is necessary for OWEC to adapt to a mutative wave condition and achieve an optimal overtopping discharge rate. Among all the parameters affecting the overtopping discharge rate, the crest freeboard height is the most influential one. To change the crest freeboard height, commonly used old methods include installation of a hinge at the bottom and adjustment of the floating height of the entire device. Both of them will inevitably affect other parameters while changing the crest freeboard height. To fill this gap, the application of SMA springs, which can solely adjust the crest freeboard height, will benefit the optimization of the OWECs. In a laboratory test, a scaled down physical model is placed in a water tank. The entire model is set to be fixed in the water tank and there are two boards, which are connected by the SMA springs, represent as a ramp that waves need to overcome. The SMA springs are able to change their length by the temperature change. A LabVIEW program sent spectra-wave signals to the wave maker and a pumping system is used to calculate the mean overtopping discharge rate. The result of non-using SMA springs follows the rule of the overtopping discharge rate. But, there are some differences with the general formula of the OWEC which comes from the different experimental setups and the limitation of the water tank. However, this result is useful to become the reference of the result of using SMA springs which shows that there is no significant change at the mean overtopping discharge rate and the errors are acceptable. All of the results and comparisons indicate that the concept of applying the SMA springs on the OWEC is proven

Value of high-sensitivity C-reactive protein assays in predicting atrial fibrillation recurrence: a systematic review and meta-analysis

Objectives: We performed a systematic review and meta-analysis of studies on high-sensitivity C-reactive protein (hs-CRP) assays to see whether these tests are predictive of atrial fibrillation (AF) recurrence after cardioversion. Design: Systematic review and meta-analysis. Data sources PubMed, EMBASE and Cochrane databases as well as a hand search of the reference lists in the retrieved articles from inception to December 2013. Study eligibility criteria This review selected observational studies in which the measurements of serum CRP were used to predict AF recurrence. An hs-CRP assay was defined as any CRP test capable of measuring serum CRP to below 0.6 mg/dL. Primary and secondary outcome measures We summarised test performance characteristics with the use of forest plots, hierarchical summary receiver operating characteristic curves and bivariate random effects models. Meta-regression analysis was performed to explore the source of heterogeneity. Results: We included nine qualifying studies comprising a total of 347 patients with AF recurrence and 335 controls. A CRP level higher than the optimal cut-off point was an independent predictor of AF recurrence after cardioversion (summary adjusted OR: 3.33; 95% CI 2.10 to 5.28). The estimated pooled sensitivity and specificity for hs-CRP was 71.0% (95% CI 63% to 78%) and 72.0% (61% to 81%), respectively. Most studies used a CRP cut-off point of 1.9 mg/L to predict long-term AF recurrence (77% sensitivity, 65% specificity), and 3 mg/L to predict short-term AF recurrence (73% sensitivity, 71% specificity). Conclusions: hs-CRP assays are moderately accurate in predicting AF recurrence after successful cardioversion

Graphic-Card Cluster for Astrophysics (GraCCA) -- Performance Tests

In this paper, we describe the architecture and performance of the GraCCA system, a Graphic-Card Cluster for Astrophysics simulations. It consists of 16 nodes, with each node equipped with 2 modern graphic cards, the NVIDIA GeForce 8800 GTX. This computing cluster provides a theoretical performance of 16.2 TFLOPS. To demonstrate its performance in astrophysics computation, we have implemented a parallel direct N-body simulation program with shared time-step algorithm in this system. Our system achieves a measured performance of 7.1 TFLOPS and a parallel efficiency of 90% for simulating a globular cluster of 1024K particles. In comparing with the GRAPE-6A cluster at RIT (Rochester Institute of Technology), the GraCCA system achieves a more than twice higher measured speed and an even higher performance-per-dollar ratio. Moreover, our system can handle up to 320M particles and can serve as a general-purpose computing cluster for a wide range of astrophysics problems.Comment: Accepted for publication in New Astronom

Assessment of Chitosan-Affected Metabolic Response by Peroxisome Proliferator-Activated Receptor Bioluminescent Imaging-Guided Transcriptomic Analysis

Chitosan has been widely used in food industry as a weight-loss aid and a cholesterol-lowering agent. Previous studies have shown that chitosan affects metabolic responses and contributes to anti-diabetic, hypocholesteremic, and blood glucose-lowering effects; however, the in vivo targeting sites and mechanisms of chitosan remain to be clarified. In this study, we constructed transgenic mice, which carried the luciferase genes driven by peroxisome proliferator-activated receptor (PPAR), a key regulator of fatty acid and glucose metabolism. Bioluminescent imaging of PPAR transgenic mice was applied to report the organs that chitosan acted on, and gene expression profiles of chitosan-targeted organs were further analyzed to elucidate the mechanisms of chitosan. Bioluminescent imaging showed that constitutive PPAR activities were detected in brain and gastrointestinal tract. Administration of chitosan significantly activated the PPAR activities in brain and stomach. Microarray analysis of brain and stomach showed that several pathways involved in lipid and glucose metabolism were regulated by chitosan. Moreover, the expression levels of metabolism-associated genes like apolipoprotein B (apoB) and ghrelin genes were down-regulated by chitosan. In conclusion, these findings suggested the feasibility of PPAR bioluminescent imaging-guided transcriptomic analysis on the evaluation of chitosan-affected metabolic responses in vivo. Moreover, we newly identified that downregulated expression of apoB and ghrelin genes were novel mechanisms for chitosan-affected metabolic responses in vivo

Association Between Type I and II Diabetes With Gallbladder Stone Disease

Objective: To assess the association of type 1 diabetes (T1DM) and type 2 diabetes (T2DM) with the subsequent development of gallbladder stone disease (GSD).Setting: Cohort Study.Participants: We identified two study cohort groups to evaluate the association of T1DM and T2DM with the development of GSD. The first group comprised a T1DM cohort of 7015 patients aged ≤ 40 years and a non-diabetes cohort randomly matched with the study cohort (4:1). The second group comprised a T2DM cohort of 51,689 patients aged ≥20 years and a non-diabetes cohort randomly matched with the study cohort (1:1). All patients were studied from 1996 to the end of 2011 or withdrawal from the National Health Insurance program to determine the incidence of GSD.Results: Compared with patients without diabetes, those with T1DM had a decreased risk of GSD [adjusted hazard ratios (aHR) = 0.48, 95% confidence interval (CI) = 0.25–0.92]. Those with T2DM had an increased risk of GSD (aHR = 1.55, 95% CI = 1.41–1.69), after adjustment for age, sex, comorbidities, and number of parity. The relative risk of GSD in the T2DM cohort was higher than that in the non-diabetes cohort in each group of age, sex, and patients with or without comorbidity. However, the relative risk of GSD in the T1DM cohort was lower than that in the non-diabetes cohort only in the age group of 20–40 years.Conclusion: Our population-based cohort study reveals a strong association between T2DM and GSD. However, an inverse relationship exists between T1DM and GSD in patients aged 20–40 years

Linking Surface Facts to Large-Scale Knowledge Graphs

Open Information Extraction (OIE) methods extract facts from natural language text in the form of ("subject"; "relation"; "object") triples. These facts are, however, merely surface forms, the ambiguity of which impedes their downstream usage; e.g., the surface phrase "Michael Jordan" may refer to either the former basketball player or the university professor. Knowledge Graphs (KGs), on the other hand, contain facts in a canonical (i.e., unambiguous) form, but their coverage is limited by a static schema (i.e., a fixed set of entities and predicates). To bridge this gap, we need the best of both worlds: (i) high coverage of free-text OIEs, and (ii) semantic precision (i.e., monosemy) of KGs. In order to achieve this goal, we propose a new benchmark with novel evaluation protocols that can, for example, measure fact linking performance on a granular triple slot level, while also measuring if a system has the ability to recognize that a surface form has no match in the existing KG. Our extensive evaluation of several baselines show that detection of out-of-KG entities and predicates is more difficult than accurate linking to existing ones, thus calling for more research efforts on this difficult task. We publicly release all resources (data, benchmark and code) on https://github.com/nec-research/fact-linking

RegRNA: an integrated web server for identifying regulatory RNA motifs and elements

Numerous regulatory structural motifs have been identified as playing essential roles in transcriptional and post-transcriptional regulation of gene expression. RegRNA is an integrated web server for identifying the homologs of regulatory RNA motifs and elements against an input mRNA sequence. Both sequence homologs and structural homologs of regulatory RNA motifs can be recognized. The regulatory RNA motifs supported in RegRNA are categorized into several classes: (i) motifs in mRNA 5′-untranslated region (5′-UTR) and 3′-UTR; (ii) motifs involved in mRNA splicing; (iii) motifs involved in transcriptional regulation; (iv) riboswitches; (v) splicing donor/acceptor sites; (vi) inverted repeats; and (vii) miRNA target sites. The experimentally validated regulatory RNA motifs are extracted from literature survey and several regulatory RNA motif databases, such as UTRdb, TRANSFAC, alternative splicing database (ASD) and miRBase. A variety of computational programs are integrated for identifying the homologs of the regulatory RNA motifs. An intuitive user interface is designed to facilitate the comprehensive annotation of user-submitted mRNA sequences. The RegRNA web server is now available at

On the Limitations of Sociodemographic Adaptation with Transformers

Sociodemographic factors (e.g., gender or age) shape our language. Previous work showed that incorporating specific sociodemographic factors can consistently improve performance for various NLP tasks in traditional NLP models. We investigate whether these previous findings still hold with state-of-the-art pretrained Transformers. We use three common specialization methods proven effective for incorporating external knowledge into pretrained Transformers (e.g., domain-specific or geographic knowledge). We adapt the language representations for the sociodemographic dimensions of gender and age, using continuous language modeling and dynamic multi-task learning for adaptation, where we couple language modeling with the prediction of a sociodemographic class. Our results when employing a multilingual model show substantial performance gains across four languages (English, German, French, and Danish). These findings are in line with the results of previous work and hold promise for successful sociodemographic specialization. However, controlling for confounding factors like domain and language shows that, while sociodemographic adaptation does improve downstream performance, the gains do not always solely stem from sociodemographic knowledge. Our results indicate that sociodemographic specialization, while very important, is still an unresolved problem in NLP

Walking a Tightrope -- Evaluating Large Language Models in High-Risk Domains

High-risk domains pose unique challenges that require language models to provide accurate and safe responses. Despite the great success of large language models (LLMs), such as ChatGPT and its variants, their performance in high-risk domains remains unclear. Our study delves into an in-depth analysis of the performance of instruction-tuned LLMs, focusing on factual accuracy and safety adherence. To comprehensively assess the capabilities of LLMs, we conduct experiments on six NLP datasets including question answering and summarization tasks within two high-risk domains: legal and medical. Further qualitative analysis highlights the existing limitations inherent in current LLMs when evaluating in high-risk domains. This underscores the essential nature of not only improving LLM capabilities but also prioritizing the refinement of domain-specific metrics, and embracing a more human-centric approach to enhance safety and factual reliability. Our findings advance the field toward the concerns of properly evaluating LLMs in high-risk domains, aiming to steer the adaptability of LLMs in fulfilling societal obligations and aligning with forthcoming regulations, such as the EU AI Act.Comment: EMNLP 2023 Workshop on Benchmarking Generalisation in NLP (GenBench