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The effect of multi-pass MAG welding transfer modes on the sigma phase precipitation and corrosion performance of AISI 316L thick plate were investigated. The evolution of the microstructure was examined by optical and electron microscopy as well as ferritscope measurements and energy dispersive X-ray spectrometry. An electrochemical microcell was then used to characterize the electrochemical behaviour of the different weld regions. The fusion line was the most critical zone for pitting corrosion for all welding procedures, due to the sigma phase precipitation, alloy elements partitioning and galvanic coupling between base metal and weld metal. It was observed the formation of sigma phase after short-circuiting or spray-arc modes, with no evidence of it to the pulsed-arc, which obtained the best corrosion resistance performance. The results evidenced the selective corrosion around sigma phase due to the depletion in Cr and Mo, with subsequent pitting nucleation. A strong correlation between the MAG welding transfer modes and the sigma phase morphology was observed. The influence of weld parameters on microstructure evolution and corrosion resistance performance was discussed. The corrosion resistance performance of the MAG welding procedures was ranked as: pulsed-arc > short-circuit > spray-arc.

The kernel ridge regression (KRR) approach is extended to include the odd-even effects in nuclear mass predictions by remodulating the kernel function without introducing new weight parameters and inputs in the training network. By taking the WS4 mass model as an example, the mass for each nucleus in the nuclear chart is predicted with the extended KRR network, which is trained with the mass model residuals, i.e., deviations between experimental and calculated masses, of other nuclei with known masses. The resultant root-mean-square mass deviation from the available experimental data for the 2353 nuclei with Z≥8 and N≥8 can be reduced to 128 keV, which provides the most precise mass model from machine learning approaches so far. Moreover, the extended KRR approach can avoid the risk of worsening the mass predictions for nuclei at large extrapolation distances, and meanwhile, it provides a smooth extrapolation behavior with respect to the odd and even extrapolation distances.

In combination with genetic algorithm (GA), the prediction in abrasive water jet (AWJ) cutting depth and process parameters optimization are implemented aiming at the disadvantages of slow convergence rate and prone to trap in local optimal solution of BP neural network. Moreover, the prediction unit for AWJ cutting is developed to make the network model visible for convenience in guiding the operation during actual machining. The neural network, when combined with GA, makes full use of nonlinear mapping ability of neural network and global optimization capability of GA. Prior to further optimization of BP algorithm, this paper adopts GA to optimize the initial weight and threshold to the area near to global minimum point. The contrast between prediction and experimental results shows that the GA-BP neural network can implement the prediction of cutting depth and optimization of process parameters effectively.

Abrasive water jet (AWJ) is an advanced machining technology, which presents better cutting quality than traditional machining methods. Because the large deformation in finite element method (FEM) tends to produce mesh distortion, this paper enables simulation of AWJ cutting steel by adopting smoothed particle hydrodynamics (SPH) coupled FEM. Abrasive and water are modeled by SPH particles, target is modeled by FE. The distribution of two kinds of SPH particles is realized through modifying the keyword file and the impact velocity of SPH particles is obtained through computational fluid dynamics (CFD). Simulation results clearly show the change to kerf sectional shape. The relation curves between cutting depth and AWJ parameters, including pressure and transverse speed, are consistent with the experimental results, which proves the effectiveness of simulation method.

This article comprises three parts. The first part addresses the Nigerian Court system, detailing the hierarchy of courts that make up our legal system or as the topic suggests the traditional court system. The second predominantly dwells on the introduction of the Multi- Door court system into our judicial landscape, and the uniqueness of its operation. The third offers my analysis, based on my experience as a court administrator on whether, with the introduction of the Multi-Door system we have swam or sank, and my advice to other judicial systems that are yet to imbibe the ADR culture.

Reumatoid arthritis (RA) is an autoimmune disease which has been studied experimentally using a wide variety of animal models including collagen-induced arthritis (CIA). Using this CIA model we studied the therapeutic effects and mechanism of action of Ebosin, a novel exopolysaccharide produced by Streptomyces sp. 139, on arthritis. Ebosin at 200, 400 and 600 mg/kg/day was orally administered to rats respectively between day 10 and 30 after immunization with chicken type II collagen. With the treatment arthritic progression was remarkably suppressed. Levels of anti-type II collagen-specific antibody, IL-1β and TNF-α were significantly lower in the Ebosin-treated CIA rats compared with the untreated controls. In cultured fibroblast-like synoviocytes (FLS), remarkable suppression of IL-1β, TNF-α and IL-6 production was detected at both protein and mRNA levels after Ebosin administration. Ebosin also resulted in lower activities of IL-1β-converting enzyme and TNF-α-converting enzyme in FLS. Based on these results, it is concluded that development and progression of rat CIA can be significantly suppressed by orally-administrated Ebosin. The therapeutic effect may be attributed to its inhibition in the production of IL-1β, TNF-α and IL-6 in the CIA rats.