Numerical Simulation on Jet Formation of Shaped Charge with Different Liner Materials

In this paper, the effect of liner material of the shaped charge on jet formation and its penetration capability is investigated by experimental and numerical methods. Liner materials investigated in this paper are copper, steel, and aluminium, respectively. Pulse X-ray photographic technology to shoot the formation of jet is employed to obtain the tip velocity and the diameter of jet. A two-dimensional multi-material code is designed to simulate the entire process from jet formation to penetrating a target. A markers on cell lines method is utilised to treat the multi-material interface. The results show that aluminium jet has the highest velocity with the poorest penetration capability. Copper jet has the strongest penetration capability with a velocity higher than that of steel jet, but lower than that of aluminium jet. The simulated results agree with the experimental results very well. It also indicates that the code developed can not only address large distortion problems but also track the variation of multi-material interfaces. It is favourable to simulate the explosive loading on thin-wall structure such as shaped charge. It is proved that authors’ method is feasible and reliable for optimising the structure of shaped charge jet to dramatically improve its tip velocity and penetration capability, and provides an important theoretic basis for designing high explosive anti-tank warhead.Defence Science Journal, Vol. 65, No. 4, July 2015, pp. 279-286, DOI: http://dx.doi.org/10.14429/dsj.65.864

Multiple bombesin-like peptides with opposite functions from skin of Odorrana grahami

AbstractBombesin-like peptides (BLPs) are a family of neuroendocrinic peptides that mediate a variety of biological activities. Three mature BLPs from the skin secretions of the frog Odorrana grahami were purified. Several bombesin-like peptide cDNA sequences encoding precursors of BLPs were identified from the skin cDNA library of O. grahami. This is the maximal diversity of BLPs ever found in animals. Five mature BLPs (B1–B5) based on the amino acid sequences derived from the cDNA cloning were synthesized. In the in vitro myotropic contraction experiment, all synthesized BLPs displayed a stimulating effect toward rat stomach strips, except B4 and B5 which showed the opposite effect, suggesting that certain BLPs may act as antagonists of bombesin receptors while most other BLPs act as agonists. This finding will facilitate the finding of novel bombesin receptors and novel ligands of bombesin receptors. The diversity of amphibian BLPs and their precursors were also analyzed and results suggest that amphibian BLPs and corresponding precursors of various sizes and processing patterns can be used as markers of taxonomic and molecular phylogenetics. The remarkable similarity of preproregions gives rise to very different BLPs and 3′-terminal regions in distantly related frog species, suggesting that the corresponding genes form a multigene family originating from a common ancestor. The diversification of BLP loci could thus be part of an evolutionary strategy developed by amphibian species as a result of shifts to novel ecological niches when environmental factors change rapidly

Fault Identification of Rotating Machinery Based on Dynamic Feature Reconstruction Signal Graph

To improve the performance in identifying the faults under strong noise for rotating machinery, this paper presents a dynamic feature reconstruction signal graph method, which plays the key role of the proposed end-to-end fault diagnosis model. Specifically, the original mechanical signal is first decomposed by wavelet packet decomposition (WPD) to obtain multiple subbands including coefficient matrix. Then, with originally defined two feature extraction factors MDD and DDD, a dynamic feature selection method based on L2 energy norm (DFSL) is proposed, which can dynamically select the feature coefficient matrix of WPD based on the difference in the distribution of norm energy, enabling each sub-signal to take adaptive signal reconstruction. Next the coefficient matrices of the optimal feature sub-bands are reconstructed and reorganized to obtain the feature signal graphs. Finally, deep features are extracted from the feature signal graphs by 2D-Convolutional neural network (2D-CNN). Experimental results on a public data platform of a bearing and our laboratory platform of robot grinding show that this method is better than the existing methods under different noise intensities

Energy efficiency, energy conservation and determinants in the agricultural sector in emerging economies

Improving energy efficiency and conservation is integral to sustain agricultural growth in emerging economies. This paper investigates the energy efficiency and energy-saving potential of the agricultural sector of 27 emerging economies using a stochastic frontier approach and Shephard distance function, and their determinants are examined using the Tobit quantile regression model. Results revealed that energy efficiency in the agricultural sector fluctuated during the period from 1998 to 2017. The median average energy efficiency was estimated at 0.74, and the cumulative energy-saving potential was estimated at 542.80 million tons of oil equivalent (Mtoe), which can be achieved by eliminating energy inefficiency alone. Differences exist in energy efficiency and energy-saving potential across continents, with higher potential in Asia and lower potential in Europe. Economic structure, urbanization and GDP per capita have negative influences on agricultural energy efficiency. Energy mix and pesticide use are significant drivers of energy efficiency, while the ratio of agricultural land that has varied influences different quantiles. Policy implications include optimization of the energy mix, economic structure and pesticide use

Development of a Non-invasive Deep Brain Stimulator With Precise Positioning and Real-Time Monitoring of Bioimpedance

Methods by which to achieve non-invasive deep brain stimulation via temporally interfering with electric fields have been proposed, but the precision of the positioning of the stimulation and the reliability and stability of the outputs require improvement. In this study, a temporally interfering electrical stimulator was developed based on a neuromodulation technique using the interference modulation waveform produced by several high-frequency electrical stimuli to treat neurodegenerative diseases. The device and auxiliary software constitute a non-invasive neuromodulation system. The technical problems related to the multichannel high-precision output of the device were solved by an analog phase accumulator and a special driving circuit to reduce crosstalk. The function of measuring bioimpedance in real time was integrated into the stimulator to improve effectiveness. Finite element simulation and phantom measurements were performed to find the functional relations among the target coordinates, current ratio, and electrode position in the simplified model. Then, an appropriate approach was proposed to find electrode configurations for desired target locations in a detailed and realistic mouse model. A mouse validation experiment was carried out under the guidance of a simulation, and the reliability and positioning accuracy of temporally interfering electric stimulators were verified. Stimulator improvement and precision positioning solutions promise opportunities for further studies of temporally interfering electrical stimulation

Potentially toxic elements in saltmarsh sediments and common reed (Phragmites australis) of Burullus coastal lagoon at North Nile Delta, Egypt: A survey and risk assessment

Burullus lagoon is the second largest lake in Egypt. However, there has never been a comprehensive survey which studied nineteen potentially toxic elements in sediments and plants and evaluated the associated potential risk. Thus, we aimed to study the total and potentially available content of As, Al, Cd, Co, Cr, Cu, Fe, Hg, Mn, Mo, Ni, Sb, Se, Sn, Tl, V, and Zn in the sediments and common reed (Phragmites australis) at thirty two sites along the entire lagoon and connected drains. Contamination Factor (CF), Pollution Load Index (PLI), Geo-accumulation Index (Igeo), and Enrichment Factor (EF) were calculated to assess the grade of contamination. Element accumulation factor (AF) and bio-concentration ratio (BCR) were also calculated. Aluminum showed the highest median (mg kg−1) total content (41,200), followed by Fe (30,300), Mn (704.7), V (82.0), Zn (75.5), Cr (51.2), Cu (47.8), Ni (44.3), As (31.9), Tl (24.6), Co (21.4), Se (20.3), Sb (17.6), Sn (15.6), Mo (11.3), and Hg (16.6 μg kg−1). Values of the EF, CF, and Igeo showed that the sediments were heavily contaminated with As, Sb, Se, Tl, Mo, Sn, Co, Ni, and Cu. The drained sediment had significantly higher values of total and potentially available element content than the lagoon sediments. Sediments of the middle and western area showed significantly higher contents of total and available elements than the eastern section. The BCR and AF values indicate that the studied plant is efficient in taking up high amounts of Zn, Fe, As, Sn, Tl, Ni, Mo, Mn; then Co, Cu, and V. The results exhibit a dramatic contamination at certain sites of the lagoon, and the studied PTEs have a predominant role in contamination-related ecological risk. Further investigations concerning redox-induced mobilization of PTEs in sediments, the risk of fish contamination and the potential health hazards are highly recommended

An improved model using convolutional sliding window-attention network for motor imagery EEG classification

IntroductionThe classification model of motor imagery-based electroencephalogram (MI-EEG) is a new human-computer interface pattern and a new neural rehabilitation assessment method for diseases such as Parkinson's and stroke. However, existing MI-EEG models often suffer from insufficient richness of spatiotemporal feature extraction, learning ability, and dynamic selection ability.MethodsTo solve these problems, this work proposed a convolutional sliding window-attention network (CSANet) model composed of novel spatiotemporal convolution, sliding window, and two-stage attention blocks.ResultsThe model outperformed existing state-of-the-art (SOTA) models in within- and between-individual classification tasks on commonly used MI-EEG datasets BCI-2a and Physionet MI-EEG, with classification accuracies improved by 4.22 and 2.02%, respectively.DiscussionThe experimental results also demonstrated that the proposed type token, sliding window, and local and global multi-head self-attention mechanisms can significantly improve the model's ability to construct, learn, and adaptively select multi-scale spatiotemporal features in MI-EEG signals, and accurately identify electroencephalogram signals in the unilateral motor area. This work provided a novel and accurate classification model for MI-EEG brain-computer interface tasks and proposed a feasible neural rehabilitation assessment scheme based on the model, which could promote the further development and application of MI-EEG methods in neural rehabilitation