Optimal Design of Cellular Material Systems for Crashworthiness

This work proposes a new method to design crashworthiness structures that made of functionally graded cellular (porous) material. The proposed method consists of three stages: The first stage is to generate a conceptual design using a topology optimization algorithm so that a variable density is distributed within the structure minimizing its compliance. The second stage is to cluster the variable density using a machine-learning algorithm to reduce the dimension of the design space. The third stage is to maximize structural crashworthiness indicators (e.g., internal energy absorption) and minimize mass using a metamodel-based multi-objective genetic algorithm. The final structure is synthesized by optimally selecting cellular material phases from a predefined material library. In this work, the Hashin-Shtrikman bounds are derived for the two-phase cellular material, and the structure performances are compared to the optimized structures derived by our proposed framework. In comparison to traditional structures that made of a single cellular phase, the results demonstrate the improved performance when multiple cellular phases are used

How dark the sky: the JWST backgrounds

We describe the sources of stray light and thermal background that affect JWST observations; report actual backgrounds as measured from commissioning and early science observations; compare those background levels to pre-launch predictions; estimate the impact of the backgrounds on science performance; and explore how the backgrounds probe the achieved configuration of the deployed observatory. We find the observatory is limited by the irreducible astrophysical backgrounds, rather than scattered stray light and thermal self-emission, for all wavelengths $\lambda < 12.5$ micron, thus meeting the level 1 requirement. This result was not assured given the open architecture and thermal challenges of JWST, and is the result of meticulous attention to stray light and thermal issues in the design, construction, integration, and test phases. From background considerations alone, JWST will require less integration time in the near-infrared compared to a system that just met the stray light requirements; as such, JWST will be even more powerful than expected for deep imaging at 1--5 micron. In the mid-infrared, the measured thermal backgrounds closely match pre-launch predictions. The background near 10 micron is slightly higher than predicted before launch, but the impact on observations is mitigated by the excellent throughput of MIRI, such that instrument sensitivity will be as good as expected pre-launch. These measured background levels are fully compatible with JWST's science goals and the Cycle 1 science program currently underway.Comment: Submitted to the "JWST Overview" special issue of PAS

In Vitro and In Vivo Investigation of the Efficacy of Arylimidamide DB1831 and Its Mesylated Salt Form - DB1965 - against Trypanosoma cruzi Infection

Chagas disease is caused by infection with the intracellular protozoan parasite Trypanosoma cruzi. At present, nifurtimox and benznidazole, both compounds developed empirically over four decades ago, represent the chemotherapeutic arsenal for treating this highly neglected disease. However, both drugs present variable efficacy depending on the geographical area and the occurrence of natural resistance, and are poorly effective against the later chronic stage. As a part of a search for new therapeutic opportunities to treat chagasic patients, pre-clinical studies were performed to characterize the activity of a novel arylimidamide (AIA - DB1831 (hydrochloride salt) and DB1965 (mesylate salt)) against T.cruzi. These AIAs displayed a high trypanocidal effect in vitro against both relevant forms in mammalian hosts, exhibiting a high selectivity index and a very high efficacy (IC50 value/48 h of 5–40 nM) against intracellular parasites. DB1965 shows high activity in vivo in acute experimental models (mouse) of T.cruzi, showing a similar effect to benznidazole (Bz) when compared under a scheme of 10 daily consecutive doses with 12.5 mg/kg. Although no parasitological cure was observed after treating with 20 daily consecutive doses, a combined dosage of DB1965 (5 mg/kg) with Bz (50 mg/kg) resulted in parasitaemia clearance and 100% animal survival. In summary, our present data confirmed that aryimidamides represent promising new chemical entities against T.cruzi in therapeutic schemes using the AIA alone or in combination with other drugs, like benznidazole

Meta-cognitive Recurrent Recursive Kernel OS-ELM for concept drift handling

In this paper, a Meta-cognitive Recurrent Recursive Kernel Online Sequential Extreme Learning Machine with Drift Detector Mechanism (meta-RRKOS-ELM-DDM) is proposed. It combines the strengths of Recurrent Kernel Online Sequential Extreme Learning Machine with a new modified Drift Detector Mechanism (DDM) and Approximate Linear Dependency Kernel Filter (ALD) in solving concept drift problems and reducing complex computations in the learning. The recursive kernel method successfully replaces the normal kernel method in Recurrent Kernel Online Sequential Extreme Learning Machine with DDM (RKOS-ELM-DDM) and generates a fixed reservoir with optimized information in enhancing the forecasting performance. Meta-cognitive learning strategy decides when the incoming data needs to be updated, retrained, or discarded during learning and automatically finding ALD threshold that reduces the learning time of prediction model. In the experiment, six synthetic and three real-world time series datasets are used to evaluate the ability of recursive kernel method, the performance of concept drift detectors, and meta-cognitive learning strategy in time series prediction. Experimental results indicate the meta-RRKOS-ELM with DDM has superior prediction ability in the different predicting horizons as compared with other algorithms

Optimized Solid-State Synthesis of Sr₂Fe_1.5Mo_0.5O_6−δ Perovskite: Implications for Efficient Synthesis of Mo-Containing SOFC Electrodes

Sr2Fe1.5Mo0.5O6−δ (SFMO) perovskite has been considered as a promising anode candidate for solid oxide fuel cells. However, the significant inconsistency in the conductivity properties of SFMO perovskite has been reported in the literature through various synthesis procedures, highlighting the necessity of a standard and unified synthesis process. In this work, we propose an optimized solid-state synthesis process of SFMO perovskite based on the thermal properties of the precursors. Our TG analysis indicates that the evaporation of MoO3 during sintering over 752 °C may affect the synthesis of the expected SFMO perovskite. The presence of Fe2O3 has a trap effect on MoO3, based on the TG analysis of the binary mixture. A cubically structured SFMO perovskite without a secondary phase is obtained from the as-proposed stepwise sintering program while an impurity phase of SrMoO4 is observed when adopting a direct sintering program. The as-synthesized SFMO perovskite exhibits high stability in a reducing atmosphere, which is attributed to the self-adjustment of the overall valence states of molybdenum ions and iron ions. Many pure cubically structured perovskites have been successfully synthesized using the as-proposed solid-state synthesis process, suggesting its universality for the synthesis of other Mo-containing SOFC perovskite electrodes

VHF Speech Enhancement Based on Transformer

To solve the poor quality of Very high frequency (VHF) speech communication in the navigation field, a VHF speech enhancement model based on an improved transformer (VHFSE) is proposed in this paper. The long-term and short-term noise are the reasons for the poor quality of VHF voice communication. VHFSE can reduce these two aspects of noise. We select the Two-stage Transformer based Neural Network (TSTNN) as the baseline. The Transformer structure pays attention to global information and parallel computing, which can reduce the long-term noise. In order to strengthen the ability of the model to reduce short-term noise, we add CNN module to the transformer according to the ability of revolutionary neural networks (CNN) to extract local information. Meanwhile, to improve the real-time performance, this study employs the lightweight convolution module (Depthwise Separable Convolution) to efficiency of VHF speech communication. Experimental results show that the proposed model VHFSE obtains the highest PESQ and STOI values than other compared modules. Besides, we apply the self-built dataset in our proposed model. The spectrum diagram shows that our model has the best enhancement effect on navigation VHF speech

An Efficient and Fast Model Reduced Kernel KNN for Human Activity Recognition

With accumulation of data and development of artificial intelligence, human activity recognition attracts lots of attention from researchers. Many classic machine learning algorithms, such as artificial neural network, feed forward neural network, K-nearest neighbors, and support vector machine, achieve good performance for detecting human activity. However, these algorithms have their own limitations and their prediction accuracy still has space to improve. In this study, we focus on K-nearest neighbors (KNN) and solve its limitations. Firstly, kernel method is employed in model KNN, which transforms the input features to be the high-dimensional features. The proposed model KNN with kernel (K-KNN) improves the accuracy of classification. Secondly, a novel reduced kernel method is proposed and used in model K-KNN, which is named as Reduced Kernel KNN (RK-KNN). It reduces the processing time and enhances the classification performance. Moreover, this study proposes an approach of defining number of K neighbors, which reduces the parameter dependency problem. Based on the experimental works, the proposed RK-KNN obtains the best performance in benchmarks and human activity datasets compared with other models. It has super classification ability in human activity recognition. The accuracy of human activity data is 91.60% for HAPT and 92.67% for Smartphone, respectively. Averagely, compared with the conventional KNN, the proposed model RK-KNN increases the accuracy by 1.82% and decreases standard deviation by 0.27. The small gap of processing time between KNN and RK-KNN in all datasets is only 1.26 seconds

N-(2,6-Dimethoxypyridin-3-yl)-9-methyl-9H-carbazole-3-sulfonamide

In the title compound, C20H19N3O4S, a novel tubulin ligand active against human cancer, the dihedral angle between the pyridine ring and the carbazole ring system is 42.87&#8197;(10)&#176;. In the crystal, the molecules are held together by N&#8212;H...O and C&#8212;H...O hydrogen bonds into layers, which are assembled into a three-dimensional network via &#960;&#8211;&#960; stacking interactions between inversion-related pyridine rings, with centroid&#8211;centroid distances of 3.5101&#8197;(12)&#8197;&#197;

Easy to Remember, Easy to Forget? The Memorability of Creative Advertisements

Previous studies have revealed that creative advertisements are recognized and recalled better than their less creative counterparts. Remembering and forgetting are two sides of the same coin of memory, denoting memory’s storage and elimination functions, respectively, which can both potentially impact advertising effectiveness. To date, there appear to have been no published studies examining the memorability of creative advertisements from the perspective of forgetting. Therefore, this issue was investigated using an intentional forgetting paradigm in which participants were cued either to remember or forget individual advertisements. The results showed that recognition hit rate and recognition latency were better for creative advertisements than for standard advertisements in both the remember and forget conditions. Furthermore, an advertising effectiveness analysis indicated that advertisements rated as more creative were also more easily remembered. There was additionally an effect of creativity category on intentional forgetting, with a higher hit rate and shorter recognition latency for creative advertisements. These results indicate that creative advertisements are easy to remember, but hard to forget, even when an instruction to forget is given. The findings provide further evidence that creative advertisements are more memorable and confirm the value of creativity in advertising