Plastic behaviour of perforated sheets under biaxial stress state

A yield criterion of sheets with a uniform triangular pattern of round holes under biaxial loading has been proposed in terms of apparent stresses. The yield criterion was confirmed by finite element analysis results in the whole range of ligament efficiency of the perforated sheet. In order to analyse stretching of a perforated sheet for dot-type shadow mask by the finite element method, a yield function which can incorporate the apparent yield stress and the apparent plastic contraction ratio has been obtained. The agreement between the measured and calculated results of stretch forming of the perforated sheet was satisfactory.     

Multifunctional Ir–Ru alloy catalysts for reversal-tolerant anodes of polymer electrolyte membrane fuel cells

To address the problem of fuel starvation in fuel-cell electric vehicles,which causes cell voltage reversal and results in cell failure when repeated continuously,we developed a reversal-tolerant anode(RTA) to promote water oxidation in preference to carbon corrosion.Graphitized carbon-supported Ir-Ru alloys with different compositions are employed as RTA catalysts in an acidic polyol solution and are shown to exhibit composition-dependent average crystallite sizes of <5.33 nm.The adopted approach allows the generation of relatively well-dispersed Ir-Ru alloy nanoparticles on the carbon support without severe agglomeration.The activity of IrRu₂/C for the hydrogen oxidation reaction is 1.10 times that of the stateof-the-art Pt/C catalyst.Cell reversal testing by simulation of fuel starvation reveals that the durability of IrRu₂/C(~7 h) significantly exceeds that of the conventional Pt/C catalyst(~10 min) and is the highest value reported so far.Thus,the developed Ir-Ru alloy catalyst can be used to fabricate practical RTAs and replace Pt catalysts in the anodes of polymer electrolyte membrane fuel cells.     

Ni@Ru and NiCo@Ru Core–Shell Hexagonal Nanosandwiches with a Compositionally Tunable Core and a Regioselectively Grown Shell

The development of highly active electrocatalysts is crucial for the advancement of renewable energy conversion devices. The design of core–shell nanoparticle catalysts represents a promising approach to boost catalytic activity as well as save the use of expensive precious metals. Here, a simple, one‐step synthetic route is reported to prepare hexagonal nanosandwich‐shaped Ni@Ru core–shell nanoparticles (Ni@Ru HNS), in which Ru shell layers are overgrown in a regioselective manner on the top and bottom, and around the center section of a hexagonal Ni nanoplate core. Notably, the synthesis can be extended to NiCo@Ru core–shell nanoparticles with tunable core compositions (Ni₃Co_x@Ru HNS). Core–shell HNS structures show superior electrocatalytic activity for the oxygen evolution reaction (OER) to a commercial RuO₂ black catalyst, with their OER activity being dependent on their core compositions. The observed trend in OER activity is correlated to the population of Ru oxide (Ru⁴⁺) species, which can be modulated by the core compositions.     

HARQ Optimization for PDCP Duplication-Based 5G URLLC Dual Connectivity

Packet duplication (PD) with dual connectivity (DC) was newly introduced in the 5G New Radio (NR) specifications to meet the stringent ultra reliable low latency communication (URLLC) requirements. PD technology uses duplicated packets in the packet data convergence protocol (PDCP) layer that are transmitted via two different access nodes (ANs) to the user equipment (UE) in order to enhance the reliability performance. However, PD can result in unnecessary retransmissions in the lower layers since the hybrid automatic retransmission request (HARQ) operation is unaware of the transmission success achieved through the alternate DC link to the UE. To overcome this issue, in this paper, a novel duplication-aware retransmission optimization (DRO) scheme is proposed to reduce the resource usage induced by unnecessary HARQ retransmissions. The proposed DRO scheme can minimize the average channel use while satisfying the URLLC requirements. The proposed DRO scheme derives the optimal HARQ retransmission attempts for different ANs by solving a nonlinear integer programming (NLIP) problem. The performance of the proposed DRO scheme was evaluated using MATLAB simulation and is compared to the existing 5G HARQ support schemes. The simulations results show that the proposed DRO scheme can provide a 14.71% and 15.11% reduced average channel use gain compared to the selective data duplication upon failure (SDUF) scheme and latency-aware dynamic multi-connectivity algorithm (LADMA) scheme, respectively, which are the existing 5G PD schemes that use HARQ.     

Egocentric visual scene description based on human-object interaction and deep spatial relations among objects

Multimedia Tools and Applications - Visual Scene interpretation is one of the major areas of research in the&nbsp;recent past. Recognition of human object interaction is a fundamental step...     

A transfer cost-sensitive boosting approach for cross-project defect prediction

Software defect prediction has been regarded as one of the crucial tasks to improve software quality by effectively allocating valuable resources to fault-prone modules. It is necessary to have a sufficient set of historical data for building a predictor. Without a set of sufficient historical data within a company, cross-project defect prediction (CPDP) can be employed where data from other companies are used to build predictors. In such cases, a transfer learning technique, which extracts common knowledge from source projects and transfers it to a target project, can be used to enhance the prediction performance. There exists the class imbalance problem, which causes difficulties for the learner to predict defects. The main impacts of imbalanced data under cross-project settings have not been investigated in depth. We propose a transfer cost-sensitive boosting method that considers both knowledge transfer and class imbalance for CPDP when given a small amount of labeled target data. The proposed approach performs boosting that assigns weights to the training instances with consideration of both distributional characteristics and the class imbalance. Through comparative experiments with the transfer learning and the class imbalance learning techniques, we show that the proposed model provides significantly higher defect detection accuracy while retaining better overall performance. As a result, a combination of transfer learning and class imbalance learning is highly effective for improving the prediction performance under cross-project settings. The proposed approach will help to design an effective prediction model for CPDP. The improved defect prediction performance could help to direct software quality assurance activities and reduce costs. Consequently, the quality of software can be managed effectively.     

Cobalt Assisted Synthesis of IrCu Hollow Octahedral Nanocages as Highly Active Electrocatalysts toward Oxygen Evolution Reaction

Development of oxygen evolution reaction (OER) catalysts with reduced precious metal content while enhancing catalytic performance has been of pivotal importance in cost‐effective design of acid polymer electrolyte membrane water electrolyzers. Hollow multimetallic nanostructures with well‐defined facets are ideally suited for saving the usage of expensive precious metals as well as boosting catalytic performances; however, Ir‐based hollow nanocatalysts have rarely been reported. Here, a very simple synthetic scheme is reported for the preparation of hollow octahedral nanocages of Co‐doped IrCu alloy with readily tunable morphology and size. The Co‐doped IrCu octahedral nanocages show excellent electrocatalytic activity and long‐term durability for OER in acidic media. Notably, their OER activity represents one of the best performances among Ir‐based acidic OER catalysts.