Dislocation-mediated plasticity in silicon during nanometric cutting : a molecular dynamics simulation study materials science in semiconductor processing

The nucleation and propagation of dislocations and its consequence on the defect structure in silicon during nanometric cutting are not well known, although the amorphization and high pressure phase transformation studies on silicon have remained at the epicentre of research across various disparate disciplines for over a decade. This paper proposes a new mechanism of crystal plasticity identified by a fully automated dislocation extraction algorithm in molecular dynamics simulations of nanometric cutting of silicon for different cutting planes/directions at a wide range of temperatures (300 K-1500 K). Alongside amorphization of silicon, our simulations revealed nanoscale stochastic nucleation of dislocations and stacking faults, which serve as mediators of microscopic plasticity during various contact loading operations and manufacturing processes of silicon. Of interest is that, irrespective of the cutting temperature, the stacking faults, which were not formed for both the (010)[100] and (111)[1 ̅10] crystal setups, were generated with three atomic layers in the (110)[001 ̅] cutting

Tetra­aqua­bis­[4-(4H-1,2,4-triazol-4-yl)benzoato-κN 1]copper(II) dihydrate

In the title compound, [Cu(C9H6N3O2)2(H2O)4]·2H2O, the CuII atom lies on an inversion center and is six-coordinated by two N atoms from two 4-(1,2,4-triazol-4-yl)benzoate ligands and four water mol­ecules in a distorted octa­hedral geometry. In the crystal, inter­molecular O—H⋯O hydrogen bonds lead to a three-dimensional supra­molecular network. Intra­molecular O—H⋯N hydrogen bonds and π–π inter­actions between the benzene rings and between the benzene and triazole rings [centroid–centroid distances = 3.657 (1) and 3.752 (1) Å] are observed

Diaqua­bis­[4-(4H-1,2,4-triazol-4-yl)benzoato-κ2 O,O′]nickel(II)

In the title compound, [Ni(C9H6N3O2)2(H2O)2], the NiII atom lies on a twofold rotation axis and is six-coordinated by two bidentate chelating 4-(1,2,4-triazol-4-yl)benzoate ligands and two water mol­ecules in a distorted octa­hedral geometry. Inter­molecular O—H⋯N hydrogen bonds link the complex mol­ecules into a two-dimensional network parallel to (010)

Meta-learning based infrared ship object detection model for generalization to unknown domains

Infrared images exhibit considerable variations in probability distributions, stemming from the utilization of distinct infrared sensors and the influence of diverse environmental conditions. The variations pose great challenges for deep learning models to detect ship objects and adapt to unseen maritime environments. To address the domain shift problem, we propose an end-to-end infrared ship object detection model based on meta-learning neural network to improve domain adaptation for target domain where data is not available at training phase. Different from existing domain generalization methods, the novelty of our model lies in the effective exploitation of meta-learning and domain adaptation, ensuring that the extracted domain-independent features are meaningful and domain-invariant at the semantic level. Firstly, a double gradient-based meta-learning algorithm is designed to solve the common optimal descent direction between different domains through two gradient updates in the inner and outer loops. The algorithm enables extraction of domain-invariant features from the pseudo-source and pseudo-target domain data. Secondly, a domain discriminator with dynamic-weighted gradient reversal layer (DWGRL) is designed to accurately classify domain-invariant features and provide additional global supervision information. Finally, a multi-scale feature aggregation method is proposed to improve the extraction of multi-scale domain-invariant features. It can effectively fuse local features at different scales and global features of targets. Extensive experimental results conducted in real nighttime water surface scenes demonstrate that the proposed model achieves very high detection accuracy on target domain data, even no target domain data was used during the training phase. Compared to the existing methods, our method not only improves the detection accuracy of infrared ships by 18%, but also exhibits the smallest standard deviation with a value of 0.93, indicating its superior generalization performance

Naja naja atra

Systemic lupus erythematosus (SLE) is an autoimmune disease and effective therapy for this pathology is currently unavailable. We previously reported that oral administration of Naja naja atra venom (NNAV) had anti-inflammatory and immune regulatory actions. We speculated that NNAV may have therapeutic effects in MRL/lpr SLE mice. Twelve-week-old MRL/lpr mice received oral administration of NNAV (20, 40, and 80 μg/kg) or Tripterygium wilfordii polyglycosidium (10 mg/kg) daily for 16 weeks. The effects of NNAV on SLE manifestations, including skin erythema, proteinuria, and anxiety-like behaviors, were assessed with visual inspection and Multistix 8 SG strips and open field test, respectively. The pathology of spleen and kidney was examined with H&E staining. The changes in autoimmune antibodies and cytokines were determined with ELISA kits. The results showed that NNAV protected against the manifestation of SLE, including skin erythema and proteinuria. In addition, although no apparent histological change was found in liver and heart in MRL/lpr SLE mice, NNAV reduced the levels of glutamate pyruvate transaminase and creatine kinase. Furthermore, NNAV increased serum C3 and reduced concentrations of circulating globulin, anti-dsDNA antibody, and inflammatory cytokines IL-6 and TNF-α. NNAV also reduced lymphadenopathy and renal injury. These results suggest that NNAV may have therapeutic values in the treatment of SLE by inhibiting autoimmune responses

Topology Optimization for FDM Parts Considering the Hybrid Deposition Path Pattern

Based on a solid orthotropic material with penalization (SOMP) and a double smoothing and projection (DSP) approach, this work proposes a methodology to find an optimal structure design which takes the hybrid deposition path (HDP) pattern and the anisotropic material properties into consideration. The optimized structure consists of a boundary layer and a substrate. The substrate domain is assumed to be filled with unidirectional zig-zag deposition paths and customized infill patterns, while the boundary is made by the contour offset deposition paths. This HDP is the most commonly employed path pattern for the fused deposition modeling (FDM) process. A critical derivative of the sensitivity analysis is presented in this paper, which ensures the optimality of the final design solutions. The effectiveness of the proposed method is validated through several 2D numerical examples

The pth moment exponential ultimate boundedness of impulsive stochastic differential systems

10.1016/j.aml.2014.10.018Applied Mathematics Letters4222-2

Boundedness and stability analysis for impulsive stochastic differential equations driven by G-Brownian motion

10.1080/00207179.2017.1364426International Journal of Control9203642-65