Wigner–Yanase skew information and entanglement generation in quantum measurement

The first step of quantum measurement procedure is known as premeasurement, during which correlation is established between the system and the measurement apparatus. Such correlation may be classical or nonclassical in nature. One compelling nonclassical correlation is entanglement, a useful resource for various quantum information theoretic protocols. Quantifying the amount of entanglement, generated during quantum measurement, therefore, seeks importance from practical ground, and this is the central issue of the present paper. Interestingly, for a two-level quantum system, we obtain that the amount of entanglement, measured in term of negativity, generated in premeasurement process can be quantified by two factors: skew information, which quantifies the uncertainty in the measurement of an observable not commuting with some conserved quantity of the system, and mixedness parameter of the system’s initial state.     

Decision‐theoretic rough sets under Pythagorean fuzzy information

Decision‐theoretic rough sets (DTRSs), which provide a classical model of three‐way decisions (3WDs), play an important role in risk decision‐making problems. The risk is associated with the loss function of DTRSs, which is evaluated by the decision makers. As a new extension of fuzzy sets, Pythagorean fuzzy sets can handle uncertain information more flexibly than intuitionistic fuzzy sets in the process of decision making and it gives a new measure for the determination of loss functions of DTRSs. More specifically, we take into account the loss functions of DTRSs with Pythagorean fuzzy numbers and propose a Pythagorean fuzzy decision‐theoretic rough set (PFDTRS) model. Some properties of the expected losses are carefully investigated. Then we further design three approaches for deriving 3WDs with the PFDTRS model. The group decision making (GDM) based on the PFDTRS model is also discussed. It provides a novel interpretation for the determination of loss functions. With the aid of the Pythagorean fuzz weighted averaging operator, we aggregate the loss functions, as suggested by the all experts, which support a coherent way of designing information granules in the presence of numerics. An algorithm for 3WDs in GDM based on the PFDTRS model is designed. Then, an example is presented to elaborate on 3WDs with the PFDTRS model.     

Predictions of High Strain Rate Failure Modes in Layered Aluminum Composites

A dislocation density-based crystalline plasticity formulation, specialized finite-element techniques, and rational crystallographic orientation relations were used to predict and characterize the failure modes associated with the high strain rate behavior of aluminum layered composites. Two alloy layers, a high strength alloy, aluminum 2195, and an aluminum alloy 2139, with high toughness, were modeled with representative microstructures that included precipitates, dispersed particles, and different grain boundary distributions. Different layer arrangements were investigated for high strain rate applications and the optimal arrangement was with the high toughness 2139 layer on the bottom, which provided extensive shear strain localization, and the high strength 2195 layer on the top for high strength resistance The layer thickness of the bottom high toughness layer also affected the bending behavior of the roll-bonded interface and the potential delamination of the layers. Shear strain localization, dynamic cracking, and delamination are the mutually competing failure mechanisms for the layered metallic composite, and control of these failure modes can be used to optimize behavior for high strain rate applications.     

Pythagorean linguistic preference relations and their applications to group decision making using group recommendations based on consistency matrices and feedback mechanism

In this paper, we introduce a new type of fuzzy set, called Pythagorean linguistic sets (PLSs), to address the preferred and nonpreferred degrees of linguistic variables. Moreover, it allows decision makers to offer effectively handle uncertain information more flexible than intuitionistic linguistic sets (ILSs) when one compares two alternatives in the process of decision making. Some of the fundamental operational laws, score, accuracy, and aggregation operators are defined, and their properties are investigated. Preference relation (PR) is a useful and efficient tool for decision making that only requires the decision makers to compare two alternatives at one time. Taking the advantages of PLSs and PRs, this paper also introduces Pythagorean linguistic preference relations (PLPRs) and studies their application. We propose an approach for group decision making using group recommendations based on consistency matrices and feedback mechanism. First, the proposed method constructs the collective consistency matrix, the weight collective PRs, and the group collective PRs. Then, it constructs a consensus relation for each expert and determines the group consensus degree (GCD) for all experts. If the GCD is smaller than a predefined threshold value, then a feedback mechanism is activated to update the PLPRs. Finally, after the GCD is greater than or equal to the predefined threshold value, we calculate the arithmetic mathematical average values of the updated group collective PR to select the most appropriate alternative.     

Efficient computation of cross-sections from human brain model by geometric processing

In this paper, an efficient method for computing the cross-sections of the internal structure from a 3D human brain model has been proposed. It can extract image slices from the brain model in sagittal, coronal, and axial views used for computed tomography and ultrasonography. A doubly connected edge list (DCEL) has been used for speeding up the computation during geometric processing, since the DCEL captures the topological relationship among vertices, edges, and faces of the triangulated surface. For a sectional plane, image slices are computed quite efficiently using the information of geometric coherence from the previous sectional plane with the help of DCEL. The optimal distance between two successive sectional planes is determined from the frequency distribution (Poisson distribution) of the edge lengths in the model. It reduces computational overhead without compromising on the quality of output, as demonstrated by experimental results.     

Phenyliodine Diacetate‐Mediated Intramolecular C(sp2)?H Amidation for 1,2‐Disubstituted Benzimidazole Synthesis under Metal‐Free Conditions

A transition metal‐free, hypervalent iodine(III) reagent [phenyliodine diacetate (PIDA)]‐mediated C(sp²) H amidation in trifluoroethanol (TFE) has been developed. The intramolecular coupling methodology presented here provides a direct access to 1,2‐disubstituted multifunctional benzimidazoles in good to excellent yields. The reactions were performed in the open air and at ambient temperature, and were found to be eco‐friendly and atom‐economical.

     

Stabilized gold clusters: from isolation toward controlled synthesis

Bare metal clusters with fewer than ～100 atoms exhibit intrinsically unique and size-specific properties, making them promising functional units or building blocks for novel materials. To utilize such clusters in functional materials, they need to be stabilized against coalescence by employing organic ligands, polymers, and solid materials. To realize rational development of cluster-based materials, it is essential to clarify how the stability and nature of clusters are modified by interactions with stabilizers by characterizing isolated clusters. The next stage is to design on-demand function by intentionally controlling the structural parameters of cluster-based materials; such parameters include the size, composition, and atomic arrangement of clusters and the interfacial structure between clusters and stabilizers. This review summarizes the current state of the art of isolation of gold clusters stabilized in various environments and surveys ongoing efforts to precisely control the structural parameters with atomic level accuracy.     

Influence of a few important parameters on the rheological behaviour of silicon carbide nanoparticles dispersed aqueous suspension

In this study, investigations have been carried out on the effect of a few important parameters such as molecular weight and concentration of dispersant (polyethyleneimine) as well as that of pH on the rheological behaviour of aqueous suspension comprising silicon carbide (SiC) nanoparticles. In addition to this, the effect of particle size and addition of finer SiC particles on the rheological behaviour of SiC suspension have been examined. It is observed that viscosity of suspension increases with the increase in molecular weight of dispersant and decreases as the concentration of dispersant increases. Further, it is noticed that viscosity of the suspension progressively increases below the pH ≈?7.6 and above the pH ≈?9.3, indicating that minimum viscosity i.e. maximum stabilization of suspension is obtained within the pH range of ≈ 7.6–9.3. It has been observed that variation of SiC particle size from submicrometer to nanometer range and addition of nanometric SiC powder in SiC suspension containing coarser particles increase the viscosity significantly.     

Adapting the Network Interface for High-Performance Computing: The CNI Approach

As the prices of commodity workstations go down, clusters of workstations have started to emerge as a viable economic solution for scalable computing. Recent advances in networking technology have made it possible to obtain high-bandwidth connections between applications. However, the interconnect latency between workstation nodes in a cluster remains a serious concern and can prove to be the limiting factor in workstation performance. In this paper, we present the CNI orcluster network interface that achieves the twin goals of low latency and high bandwidth. In addition, CNI efficiently supports multiple programming paradigms for programming generality. This is done by functionally coupling the network interface more closely to the CPU without violating the constraints of a standard workstation architecture, CNI results in performance gains for applications, substantially reducing communication overhead and delay.