Electro-Mechanical Fatigue Behavior of a Quasi-Isotropic Laminate with an Embedded Piezoelectric Actuator

This study primarily investigated the electro-mechanical fatigue behavior of the embedded piezoelectric actuators in graphite/epoxy laminate with a lay-up of 0 | ± 45 | 90s. A secondary focus was the investigation of the mechanical fatigue effects of the 0 | 0 | ± 45 | 0 | 0 | 90s laminate with embedded PZT under tensile loading. All the fatigue tests were conducted with a triangular loading waveform which had a frequency of 10 Hz and with R=0. 1. In the electro-mechanical testing, the embedded actuator was excited by a -10 V to -100 V or a 10 V to 100 V voltage input, which resulted in either in-phase or out-of-phase electrically induced strain waveform with respect to the mechanical loading or strain. It was found that the embedded PZTs performed very well during the out-of-phase electro-mechanical and low stress fatigue conditions when the applied strain was within the operating range of PZT. Beyond the upper strain limit, the voltage output of the PZT was primarily influenced by the mechanical fatigue loading. Results from the high stress fatigue tests showed that the embedded piezoelectric actuators did not have significant effect on the tensile strength of the laminates

The Major Cause of Earthquake Disasters: Shear Bandings

In the last two decades, due to disasters happening around the world have been recorded precisely. People have begun to understand that earthquakes fall under several categories. Most of the earthquake-induced catastrophes, including fallen bridges, building collapses, soil liquefaction, and landslides, can only appear in shear banding zones induced by tectonic earthquakes. It is important to mention that tectonic earthquakes are different from other earthquakes because, in addition to the seismic vibration effect present in all earthquakes, tectonic earthquakes have a shear banding effect. In a tectonic earthquake, the shear banding energy can be more than 90% of the total earthquake energy, and the primary cause of earthquake disasters is the presence of the shear banding. In the past, the cause of earthquake disasters has been generally identified by structure dynamics researchers, without any proof, as the insufficiency of seismic-vibration resistant forces. Therefore, the modification of building codes and specifications has only focused on increasing these resistance forces. However, this type of specification modification cannot guarantee that an earthquake-resistant design structure would not fail due to shear banding. Thus, it is the objective of this study to present appropriate earthquake disaster prevention methods for a tectonic earthquake

Ensemble of expanded ensembles: A generalized ensemble approach with enhanced flexibility and parallelizability

Over the past decade, alchemical free energy methods like Hamiltonian replica exchange (HREX) and expanded ensemble (EXE) have gained popularity for the computation of solvation free energies and binding free energies. These methods connect the end states of interest via nonphysical pathways defined by states with different modified Hamiltonians. However, there exist systems where traversing all alchemical intermediate states is challenging, even if alchemical biases (e.g., in EXE) or coordinate exchanges (e.g., in HREX) are applied. This issue is exacerbated when the state space is multidimensional, which can require extensive communications between hundreds of cores that current parallelization schemes do not fully support. To address this challenge, we present the method of ensemble of expanded ensembles (EEXE), which integrates the principles of EXE and HREX. Specifically, the EEXE method periodically exchanges coordinates of EXE replicas sampling different ranges of states and allows combining weights across replicas. With the solvation free energy calculation of anthracene, we show that the EEXE method achieves accuracy akin to the EXE and HREX methods in free energy calculations, while offering higher flexibility in parameter specification. Additionally, its parallelizability opens the door to wider applications, such as estimating free energy profiles of serial mutations. Importantly, extensions to the EEXE approach can be done asynchronously, allowing looser communications between larger numbers of loosely coupled processors, such as when using cloud computing, than methods such as replica exchange. They also allow adaptive changes to the parameters of ensembles in response to data collected. All algorithms for the EEXE method are available in the Python package ensemble_md, which offers an interface for EEXE simulation management without modifying the source code in GROMACS

Plasticity Model Required to Prevent Geotechnical Failures in Tectonic Earthquakes

Although geotechnical engineering design must meet seismic design specifications, many geotechnical failures, including foundations, retaining walls, and slopes, have nevertheless occurred during tectonic earthquakes. The evaluation of the ultimate bearing capacity of the foundation, the active and passive earth pressure of the retaining wall, and the safety factor of the slope all need to use the shear failure band and soil plasticity models at the same time. In view of this, it is first proven that shear failure bands can only appear in the strain softening model. Secondly, it is shown through case studies that traditional evaluation methods for determining the foundation ultimate bearing capacity, the active earth pressure of the retaining wall, and the safety factor of slope stability all adopt both the shear failure band and the perfectly plastic soil model. Since the perfectly plastic soil model is incompatible with shear failure bands, this results in a large number of foundations, retaining walls, and slopes failing during tectonic earthquakes. Based on the research results, it is suggested that a soil strain softening model compatible with shear failure bands be adopted in the analysis of geotechnical engineering projects so as to ensure safety during tectonic earthquakes

Seismic Conditions Required to Cause Structural Failures in Tectonic Earthquakes

It is found that the failure of the structure conforming to the current seismic design code can only occur in the shear band of the tectonic earthquake, and with the increase in the amount of shear banding, the boundary conditions of the structure gradually deviate from the original design ones; therefore, the seismic insufficiency of structures therefore continues to increase. With the increase of the seismic insufficiency of the structure, the structure will appear more and more serious damage such as cracking, tilting and subsidence, and collapse. Based on the findings of this study, the author suggests that the main task of the seismic design of structures is to prevent the shear banding of tectonic earthquakes from extending to each element of the structure, rather than continuously increasing the level of vibration fortification of each structural element. Only in this way can it be ensured that the structures complying with the seismic design specifications will not be damaged such as cracks, tilting and subsidence, and collapse due to the deviation of the boundary conditions from the original design ones in shear banding

Fair Robust Active Learning by Joint Inconsistency

Fair Active Learning (FAL) utilized active learning techniques to achieve high model performance with limited data and to reach fairness between sensitive groups (e.g., genders). However, the impact of the adversarial attack, which is vital for various safety-critical machine learning applications, is not yet addressed in FAL. Observing this, we introduce a novel task, Fair Robust Active Learning (FRAL), integrating conventional FAL and adversarial robustness. FRAL requires ML models to leverage active learning techniques to jointly achieve equalized performance on benign data and equalized robustness against adversarial attacks between groups. In this new task, previous FAL methods generally face the problem of unbearable computational burden and ineffectiveness. Therefore, we develop a simple yet effective FRAL strategy by Joint INconsistency (JIN). To efficiently find samples that can boost the performance and robustness of disadvantaged groups for labeling, our method exploits the prediction inconsistency between benign and adversarial samples as well as between standard and robust models. Extensive experiments under diverse datasets and sensitive groups demonstrate that our method not only achieves fairer performance on benign samples but also obtains fairer robustness under white-box PGD attacks compared with existing active learning and FAL baselines. We are optimistic that FRAL would pave a new path for developing safe and robust ML research and applications such as facial attribute recognition in biometrics systems.Comment: 11 pages, 3 figure

Determination of Sea Level Height Variation by Dynamics Crossover Adjustment

Abstract. The satellite radar altimeter surveys global mean sea level height continuously. The crossover point adjustment is based on the ascending-tracks and the descending-tracks of satellite orbit. According to the dynamic model of the altimetry satellite motion, the method of dynamic crossover point adjustment is introduced in this paper. It is the fact that the altimetry data sets in the crossover points are just as the repeated observations which constructs the basis of the crossover adjustment. Assume that 0 h is a sea level height variation, h is the undulation of the sea level height in the crossovers of the ascending-tracks and the descending-tracks. The sea level height variation 0 h can be gained by least-squares adjustment procedure. By establishing the dynamic model of crossover adjustment, the orbit errors could be reduced or weaken, thus improves the accuracy of the sea level height variation determined

A quantitative analysis of monochromaticity in genetic interaction networks

<p>Abstract</p> <p>Background</p> <p>A genetic interaction refers to the deviation of phenotypes from the expected when perturbing two genes simultaneously. Studying genetic interactions help clarify relationships between genes, such as compensation and masking, and identify gene groups of functional modules. Recently, several genome-scale experiments for measuring quantitative (positive and negative) genetic interactions have been conducted. The results revealed that genes in the same module usually interact with each other in a consistent way (pure positive or negative); this phenomenon was designated as monochromaticity. Monochromaticity might be the underlying principle that can be utilized to unveil the modularity of cellular networks. However, no appropriate quantitative measurement for this phenomenon has been proposed.</p> <p>Results</p> <p>In this study, we propose the monochromatic index (MCI), which is able to quantitatively evaluate the monochromaticity of potential functional modules of genes, and the MCI was used to study genetic landscapes in different cellular subsystems. We demonstrated that MCI not only amend the deficiencies of MP-score but also properly incorporate the background effect. The results showed that not only within-complex but also between-complex connections present significant monochromatic tendency. Furthermore, we also found that significantly higher proportion of protein complexes are connected by negative genetic interactions in metabolic network, while transcription and translation system adopts relatively even number of positive and negative genetic interactions to link protein complexes.</p> <p>Conclusion</p> <p>In summary, we demonstrate that MCI improves deficiencies suffered by MP-score, and can be used to evaluate monochromaticity in a quantitative manner. In addition, it also helps to unveil features of genetic landscapes in different cellular subsystems. Moreover, MCI can be easily applied to data produced by different types of genetic interaction methodologies such as Synthetic Genetic Array (SGA), and epistatic miniarray profile (E-MAP).</p

The association of dimethylarginine dimethylaminohydrolase 1 gene polymorphism with type 2 diabetes: a cohort study

<p>Abstract</p> <p>Background</p> <p>Elevated plasma levels of asymmetric dimethylarginine (ADMA) has been reported to be associated with insulin resistance and micro/macrovascular diabetic complications, and may predict cardiovascular events in type 2 diabetic patients. Dimethylarginine dimethylaminohydrolase 1 (DDAH1) is the major enzyme eliminating ADMA in humans, but the effect of genetic variations in <it>DDAH1 </it>on type 2 diabetes and its long-term outcome are unknown.</p> <p>Methods</p> <p>From July 2006 to June 2009, we assessed the association between polymorphisms in <it>DDAH1 </it>and type 2 diabetes in 814 consecutive unrelated subjects, including 309 type 2 diabetic patients and 505 non-diabetic individuals. Six single nucleotide polymorphisms (SNPs) in <it>DDAH1</it>, rs233112, rs1498373, rs1498374, rs587843, rs1403956, and rs1241321 were analyzed. Plasma ADMA levels were determined by high performance liquid chromatography. Insulin sensitivity was assessed by the homeostasis model assessment of insulin resistance (HOMA-IR).</p> <p>Results</p> <p>Among the 6 SNPs, only rs1241321 was significantly associated with a decreased risk of type 2 diabetes (AA <it>vs </it>GG+AG, OR = 0.64, 95% CI 0.47-0.86, p = 0.004). The association remained unchanged after adjustment for plasma ADMA level. The fasting plasma glucose and log HOMA-IR tended to be lower in subjects carrying the homozygous AA genotype of rs1241321 compared with the GG+AG genotypes. Over a median follow-up period of 28.2 months, there were 44 all-cause mortality and 50 major adverse cardiovascular events (MACE, including cardiovascular death, non-fatal myocardial infarction and stroke). Compared with the GG and AG genotypes, the AA genotype of rs1241321 was associated with reduced risk of MACE (HR = 0.31, 95% CI: 0.11-0.90, p = 0.03) and all-cause mortality (HR = 0.18, 95% CI: 0.04-0.80, p = 0.02) only in subgroup with type 2 diabetes. One common haplotype (GGCAGC) was found to be significantly associated with a decreased risk of type 2 diabetes (OR = 0.67, 95% CI = 0.46-0.98, p = 0.04).</p> <p>Conclusions</p> <p>Our results provide the first evidence that SNP rs1241321 in <it>DDAH1 </it>is associated with type 2 diabetes and its long-term outcome.</p