Folding model study of the elastic α+α\alpha + \alpha scattering at low energies

The folding model analysis of the elastic $\alpha + \alpha$ scattering at the incident energies below the reaction threshold of 34.7 MeV (in the lab system) has been done using the well-tested density dependent versions of the M3Y interaction and realistic choices for the $^4$He density. Because the absorption is negligible at the energies below the reaction threshold, we were able to probe the $\alpha + \alpha$ optical potential at low energies quite unambiguously and found that the $\alpha + \alpha$ overlap density used to construct the density dependence of the M3Y interaction is strongly distorted by the Pauli blocking. This result gives possible explanation of a long-standing inconsistency of the double-folding model in its study of the elastic $\alpha + \alpha$ and $\alpha$-nucleus scattering at low energies using the same realistic density dependent M3Y interaction

Zero-Knowledge Password Policy Check from Lattices

Passwords are ubiquitous and most commonly used to authenticate users when logging into online services. Using high entropy passwords is critical to prevent unauthorized access and password policies emerged to enforce this requirement on passwords. However, with current methods of password storage, poor practices and server breaches have leaked many passwords to the public. To protect one's sensitive information in case of such events, passwords should be hidden from servers. Verifier-based password authenticated key exchange, proposed by Bellovin and Merrit (IEEE S\&P, 1992), allows authenticated secure channels to be established with a hash of a password (verifier). Unfortunately, this restricts password policies as passwords cannot be checked from their verifier. To address this issue, Kiefer and Manulis (ESORICS 2014) proposed zero-knowledge password policy check (ZKPPC). A ZKPPC protocol allows users to prove in zero knowledge that a hash of the user's password satisfies the password policy required by the server. Unfortunately, their proposal is not quantum resistant with the use of discrete logarithm-based cryptographic tools and there are currently no other viable alternatives. In this work, we construct the first post-quantum ZKPPC using lattice-based tools. To this end, we introduce a new randomised password hashing scheme for ASCII-based passwords and design an accompanying zero-knowledge protocol for policy compliance. Interestingly, our proposal does not follow the framework established by Kiefer and Manulis and offers an alternate construction without homomorphic commitments. Although our protocol is not ready to be used in practice, we think it is an important first step towards a quantum-resistant privacy-preserving password-based authentication and key exchange system

Position control for haptic device based on discrete-time proportional integral derivative controller

Haptic devices had known as advanced technology with the goal is creating the experiences of touch by applying forces and motions to the operator based on force feedback. Especially in unmanned aerial vehicle (UAV) applications, the position of the end-effector Falcon haptic sets the velocity command for the UAV. And the operator can feel the experience vibration of the vehicle as to the acceleration or collision with other objects through a forces feedback to the haptic device. In some emergency cases, the haptic can report to the user the dangerous situation of the UAV by changing the position of the end-effector which is be obtained by changing the angle of the motor using the inverse kinematic equation. But this solution may not accurate due to the disturbance of the system. Therefore, we proposed a position controller for the haptic based on a discrete-time proportional integral derivative (PID) controller. A Novint Falcon haptic is used to demonstrate our proposal. From hardware parameters, a Jacobian matrix is calculated, which combines with the force output from the PID controller to make the torque for the motors of the haptic. The experiment was shown that the PID has high accuracy and a small error position

Investigation of bond performance of reinforced fly ash-based Geopolymer concrete using experiments and numerical analysis

This study evaluates the bond performance of reinforced fly ash-based geopolymer concrete by using experiments and numerical analysis. Three types of mixture proportions along with two types of reinforcement diameter, (d12, ribbed bar) and (d14, smooth bar) mm, were selected for experimental work. The bond behaviour of reinforced geopolymer concrete is determined using the pullout test, and Finite Element Analysis (FEA). The test data indicated that the bond strength of reinforced fly ash-based geopolymer concrete increases with the increase in compressive strength. The concrete cover to diameter ratio (c/db) increases from 4.86 to 5.75 and the bond strength of all three groups of samples also increases. Besides, the bond stress-slip curves obtained by the ABAQUS software closely match the results from experimental works. Furthermore, the parametric analyses show that when the compressive strength of geopolymer concreteincreases, the bond strength of reinforced fly ash-based geopolymer concrete increases. These results are consistent with the test data

Approximation solution for steel concrete beam accounting high-order shear deformation using trigonometric-series

Steel concrete beams have a reasonable structure in terms of using material and high load carrying capacity. This paper deals with an approximate solution based on a trigonometric series for the static of steel concrete beams. The displacement field is based on the higher-order theory using Reddy’s hypothesis. The governing equations are derived from variation principles. An approximate solution based on the representation of displacement fields by trigonometric series is developed to solve the static problem of steel concrete beams. In order to verify the accuracy of the present approximate solution, numerical results are compared with those of exact solutions using classical beam theory. The displacements and nominal stress distributions in the depth direction are obtained with various high of beams. The present approximate approach can accurately predict the displacements and stresses of steel concrete beams

Approximation solution for steel concrete beam accounting high-order shear deformation using trigonometric-series

Steel concrete beams have a reasonable structure in terms of using material and high load carrying capacity. This paper deals with an approximate solution based on a trigonometric series for the static of steel concrete beams. The displacement field is based on the higher-order theory using Reddy’s hypothesis. The governing equations are derived from variation principles. An approximate solution based on the representation of displacement fields by trigonometric series is developed to solve the static problem of steel concrete beams. In order to verify the accuracy of the present approximate solution, numerical results are compared with those of exact solutions using classical beam theory. The displacements and nominal stress distributions in the depth direction are obtained with various high of beams. The present approximate approach can accurately predict the displacements and stresses of steel concrete beams

A Machine Learning-Assisted Numerical Predictor for Compressive Strength of Geopolymer Concrete Based on Experimental Data and Sensitivity Analysis

Geopolymer concrete offers a favourable alternative to conventional Portland concrete due to its reduced embodied carbon dioxide (CO2) content. Engineering properties of geopolymer concrete, such as compressive strength, are commonly characterised based on experimental practices requiring large volumes of raw materials, time for sample preparation, and costly equipment. To help address this inefficiency, this study proposes machine learning-assisted numerical methods to predict compressive strength of fly ash-based geopolymer (FAGP) concrete. Methods assessed included artificial neural network (ANN), deep neural network (DNN), and deep residual network (ResNet), based on experimentally collected data. Performance of the proposed approaches were evaluated using various statistical measures including R-squared (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE). Sensitivity analysis was carried out to identify effects of the following six input variables on the compressive strength of FAGP concrete: sodium hydroxide/sodium silicate ratio, fly ash/aggregate ratio, alkali activator/fly ash ratio, concentration of sodium hydroxide, curing time, and temperature. Fly ash/aggregate ratio was found to significantly affect compressive strength of FAGP concrete. Results obtained indicate that the proposed approaches offer reliable methods for FAGP design and optimisation. Of note was ResNet, which demonstrated the highest R2 and lowest RMSE and MAPE values

POPULATION DIVERSITY OF XANTHOMONAS ORYZAE PV. ORYZAE CAUSING BACTERIAL LEAF BLIGHT IN RICE FIELDS OF CAN THO

Bacterial leaf blight (BB) caused by Xanthomonas oryzae pv. oryzae (Xoo) is a destructive disease in rice fields. Can Tho is one of the most important rice-growing areas in the Mekong Delta, which is vulnerable to climate change, making the disease more damaging in this region. Deployment of resistance genes is considered an economic and eco-friendly approach to control the disease. However, Xoo exists in different races with diverse reactions on different resistance genes. Thus, for effective management of BB, it is essential to understand the diversity of contemporary Xoo population to deploy appropriate resistance genes in rice fields. This study aims at assessing the Xoo population diversity (race composition) in rice fields of Can Tho using pathogenicity reactions on the near-isogenic lines (pathotypes) in combination with insertion sequence-PCR technique using J3 primer (genotypes). Among 132 isolates obtained from BB-infected leaf samples collected from six rice-growing areas of Can Tho, 126 isolates were identified as Xoo using PCR with the specific primers XOO290F/R. The contemporary Xoo population in Can Tho was composed of four races including two classic standard races (5 and 7) and two newly emerged ones (5* and 5**) of which races 5 and 5* were the most predominant. Seven haplotypes were identified in the four races and haplotypes I and III were predominant, accounting for 50.79% and 40.48%, respectively. The combination of the pathotypic and genotypic analyses showed genetic variations in races 5 and 5*. These results could be used for deployment of appropriate BB resistance cultivars in rice fields of Can Tho

From Selective Deep Convolutional Features to Compact Binary Representations for Image Retrieval

In the large-scale image retrieval task, the two most important requirements are the discriminability of image representations and the efficiency in computation and storage of representations. Regarding the former requirement, Convolutional Neural Network is proven to be a very powerful tool to extract highly discriminative local descriptors for effective image search. Additionally, to further improve the discriminative power of the descriptors, recent works adopt fine-tuned strategies. In this article, taking a different approach, we propose a novel, computationally efficient, and competitive framework. Specifically, we first propose various strategies to compute masks, namely, SIFT-masks , SUM-mask , and MAX-mask , to select a representative subset of local convolutional features and eliminate redundant features. Our in-depth analyses demonstrate that proposed masking schemes are effective to address the burstiness drawback and improve retrieval accuracy. Second, we propose to employ recent embedding and aggregating methods that can significantly boost the feature discriminability. Regarding the computation and storage efficiency, we include a hashing module to produce very compact binary image representations. Extensive experiments on six image retrieval benchmarks demonstrate that our proposed framework achieves the state-of-the-art retrieval performances. </jats:p