Effect of microporosity on tensile properties of as-cast AZ91D magnesium alloy

In the present study, the effect of microporosity on the tensile properties of as-cast AZ91D magnesium alloy was investigated through experimental observation and numerical prediction. The test specimens were fabricated by die-casting and gravity-casting. For gravity-casting, the inoculation and use of various metallic moulds were applied to obtain a wide range of microporosity. The deficiency of the interdendritic feeding of the liquid phase acted as a dominant mechanism on the formation of the micropores in the Mg?Al-alloys, rather than the evolution of hydrogen gas. Although tensile strength and elongation has a nonlinear and very intensive dependence upon microporosity, the yield strength appeared to have a linear relationship with microporosity. However, it was possible to quantitatively estimate the linear contribution of microporosity on the individual tensile property for a range of microporosity, which was below about 1%. The numerical prediction suggests that the effect of microporosity on fractured strength and elongation decreased as the strain hardening exponent increased. Furthermore, the shape and distribution of micropores may play a more dominant role than local plastic deformation on the tensile behavior of AZ91D alloy.     

Computation of the average power pattern of a reflector antennawith random surface errors and misalignment errors

To specify manufacturing tolerances of a reflector antenna, various errors such as random surface errors and misalignment errors must be considered at one time because superposition of the effects of those errors may not hold. Based on the Rahmat-Samii's formulation (1983), a method for computing efficiently the average power pattern of a reflector antenna with those errors is presented. Simulation results show that superposition of the effects of errors does not generally hold and demonstrate how those errors degrade the peak-gain and sidelobe levels     

Exploratory analysis of brain connectivity with ICA.

Covariance-based methods of exploration of functional connectivity of the brain from functional magnetic resonance imaging (fMRI) experiments, such as principal component analysis (PCA) and structural equation modeling (SEM), require a priori knowledge such as an anatomical model to infer functional connectivity. In this research, a hybrid method, combining independent component analysis (ICA) and SEM, which is capable of deriving functional connectivity in an exploratory manner without the need of a prior model is introduced. The spatial ICA (SICA) derives independent neural systems or sources involved in task-related brain activation, while an automated method based on the SEM finds the structure of the connectivity among the elements in independent neural systems. Unlike second-order approaches used in earlier studies, the task-related neural systems derived from the ICA provide brain connectivity in the complete statistical sense. The use and efficacy of this approach is illustrated on two fMRI datasets obtained from a visual task and a language reading task.     

A QoS management framework for distributed multimedia systems

This paper proposes a high‐performance connection management architecture to design a common QoS framework applied to an ATM network based on the Open Distributed Processing (ODP) concept. We design the QoS framework in accordance from the RM‐ODP information and computational viewpoints. Copyright © 2003 John Wiley & Sons, Ltd.     

Construction of efficient error-limiting variable length codes

A novel method for generating an optimal binary prefix condition code is described. The proposed code generation method generates optimal variable length codes that have short average error spans compared to the existing self-synchronising codes. A technique for calculating the expected error spans of variable length codes are also presented     

PBURC: a patterns-based,unsupervised requirements clustering framework for distributed agile software development

Agile software development methodologies are increasingly adopted by organizations because they focus on the client’s needs, thus safeguarding business value for the final product. At the same time, as the economy and society move toward globalization, more organizations shift to distributed development of software projects. From this perspective, while adopting agile techniques seems beneficial, there are still a number of challenges that need to be addressed; among these notable is the effective cooperation between the stakeholders and the geographically distributed development team. In addition, data collection and validation for requirements engineering demands efficient processing techniques in order to handle the volume of data as well as to manage different inconsistencies, when the data are collected using online tools. In this paper, we present “PBURC,” a patterns-based, unsupervised requirements clustering framework, which makes use of machine-learning methods for requirements validation, being able to overcome data inconsistencies and effectively determine appropriate requirements clusters for optimal definition of software development sprints.     

Highly Exposed ?NH2 Edge on Fragmented g-C3N4 Framework with Integrated Molybdenum Atoms for Catalytic CO2 Cycloaddition: DFT and Techno-Economic Assessment

This study focuses on the applicability of single-atom Mo-doped graphitic carbon nitride (GCN) nanosheets which are specifically engineered with high surface area (exfoliated GCN),  NH₂ rich edges, and maximum utilization of isolated atomic Mo for propylene carbonate (PC) production through CO₂ cycloaddition of propylene oxide (PO). Various operational parameters are optimized, for example, temperature (130 °C), pressure (20 bar), catalyst (Mo₂GCN), and catalyst mass (0.1 g). Under optimal conditions, 2% Mo-doped GCN (Mo₂GCN) has the highest catalytic performance, especially the turnover frequency (TOF) obtained, 36.4 h⁻¹ is higher than most reported studies. DFT simulations prove the catalytic performance of Mo₂GCN significantly decreases the activation energy barrier for PO ring-opening from 50–60 to 4.903 kcal mol⁻¹. Coexistence of Lewis acid/base group improves the CO₂ cycloaddition performance by the formation of coordination bond between electron-deficient Mo atom with O atom of PO, while  NH₂ surface group disrupts the stability of CO₂ bond by donating electrons into its low-level empty orbital. Steady-state process simulation of the industrial-scale consumes 4.4 ton h⁻¹ of CO₂ with PC production of 10.2 ton h⁻¹. Techno-economic assessment profit from Mo₂GCN is estimated to be 60.39 million USD year⁻¹ at a catalyst loss rate of 0.01 wt% h⁻¹.     

The VEGF Inhibitor Soluble Fms-like Tyrosine Kinase 1 Does Not Promote AKI-to-CKD Transition

(1) Background: Soluble Fms-like tyrosine kinase 1 (sFLT1) is an endogenous VEGF inhibitor. sFLT1 has been described as an anti-inflammatory treatment for diabetic nephropathy and heart fibrosis. However, sFLT1 has also been related to peritubular capillary (PTC) loss, which promotes fibrogenesis. Here, we studied whether transfection with sFlt1 aggravates experimental AKI-to-CKD transition and whether sFLT1 is increased in human kidney fibrosis. (2) Methods: Mice were transfected via electroporation with sFlt1. After confirming transfection efficacy, mice underwent unilateral ischemia/reperfusion injury (IRI) and were sacrificed 28 days later. Kidney histology and RNA were analyzed to study renal fibrosis, PTC damage and inflammation. Renal sFLT1 mRNA expression was measured in CKD biopsies and control kidney tissue. (3) Results: sFlt1 transfection did not aggravate renal fibrosis, PTC loss or macrophage recruitment in IRI mice. In contrast, higher transfection efficiency was correlated with reduced expression of pro-fibrotic and pro-inflammatory markers. In the human samples, sFLT1 mRNA levels were similar in CKD and control kidneys and were not correlated with interstitial fibrosis or PTC loss. (4) Conclusion: As we previously found that sFLT1 has therapeutic potential in diabetic nephropathy, our findings indicate that sFLT1 can be administered at a dose that is therapeutically effective in reducing inflammation, without promoting maladaptive kidney damage.     

Dynamic routing in reentrant flexible manufacturing systems

Consider a flexible manufacturing system (FMS), with several parallel production lines. Each line is statistically balanced. Due to process time and yield variations, some workstations may be temporarily starved of parts during the FMS operation, while others may have too many. The purpose of the dynamic routing algorithm described here is to achieve real-time load balancing in a stochastic processing environment and thus to increase the performance of the system in throughput, workload balance and reduced work-in-process queues. We formulate the problem and develop an optimal stationary policy (for two lines that have a material handling transport between them) based on the input buffer state of each station.