A note on dissipative particle dynamics (DPD) modelling of simple fluids

In this paper, we show that a Dissipative Particle Dynamics (DPD) model of a viscous Newtonian fluid may actually produce a linear viscoelastic fluid. We demonstrate that a single set of DPD particles can be used to model a linear viscoelastic fluid with its physical parameters, namely the dynamical viscosity and the relaxation time in its memory kernel, determined from the DPD system at equilibrium. The emphasis of this study is placed on (i) the estimation of the linear viscoelastic effect from the standard parameter choice; and (ii) the investigation of the dependence of the DPD transport properties on the length and time scales, which are introduced from the physical phenomenon under examination. Transverse-current auto-correlation functions (TCAF) in Fourier space are employed to study the effects of the length scale, while analytic expressions of the shear stress in a simple small amplitude oscillatory shear flow are utilised to study the effects of the time scale. A direct mechanism for imposing the particle diffusion time and fluid viscosity in the hydrodynamic limit on the DPD system is also proposed

Half-duplex energy harvesting relay network over different fading environment: System performance with effect of hardware impairment

In this paper, we introduce a half-duplex (HD) energy harvesting (EH) relay network over the different fading environment with the effect of hardware impairment (HI). The model system was investigated with the amplify-and-forward (AF) and the power splitting (PS) protocols. The system performance analysis in term of the outage probability (OP), achievable throughput (AT), and bit error rate (BER) were demonstrated with the closed-form expressions. In addition, the power splitting (PS) factor was investigated. We verified the analytical analysis by Monte Carlo simulation with all primary parameters. From the results, we can state that the analytical and simulation results match well with each other.Web of Science911art. no. Unsp 228

DoubleEcho: Mitigating Context-Manipulation Attacks in Copresence Verification

Copresence verification based on context can improve usability and strengthen security of many authentication and access control systems. By sensing and comparing their surroundings, two or more devices can tell whether they are copresent and use this information to make access control decisions. To the best of our knowledge, all context-based copresence verification mechanisms to date are susceptible to context-manipulation attacks. In such attacks, a distributed adversary replicates the same context at the (different) locations of the victim devices, and induces them to believe that they are copresent. In this paper we propose DoubleEcho, a context-based copresence verification technique that leverages acoustic Room Impulse Response (RIR) to mitigate context-manipulation attacks. In DoubleEcho, one device emits a wide-band audible chirp and all participating devices record reflections of the chirp from the surrounding environment. Since RIR is, by its very nature, dependent on the physical surroundings, it constitutes a unique location signature that is hard for an adversary to replicate. We evaluate DoubleEcho by collecting RIR data with various mobile devices and in a range of different locations. We show that DoubleEcho mitigates context-manipulation attacks whereas all other approaches to date are entirely vulnerable to such attacks. DoubleEcho detects copresence (or lack thereof) in roughly 2 seconds and works on commodity devices

Stability of twin circular tunnels in cohesive-frictional soil using the node-based smoothed finite element method (NS-FEM)

This paper presents an upper bound limit analysis procedure using the node-based smoothed finite element method (NS-FEM) and second order cone programming (SOCP) to evaluate the stability of twin circular tunnels in cohesive-frictional soils subjected to surcharge loading. At first stage, kinematically admissible displacement fields of the tunnel problems are approximated by NS-FEM using triangular elements (NS-FEM-T3). Next, commercial software Mosek is employed to deal with the optimization problems, which are formulated as second order cone. Collapse loads as well as failure mechanisms of plane strain tunnels are obtained directly by solving the optimization problems. For twin circular tunnels, the distance between centers of two parallel tunnels is the major parameter used to determine the stability. In this study, the effects of mechanical soil properties and the ratio of tunnel diameter and the depth to the tunnel stability are investigated. Numerical results are verified with those available to demonstrate the accuracy of the proposed method

Energy harvesting-based spectrum access with incremental cooperation, relay selection and hardware noises

In this paper, we propose an energy harvesting (EH)-based spectrum access model in cognitive radio (CR) network. In the proposed scheme, one of available secondary transmitters (STs) helps a primary transmitter (PT) forward primary signals to a primary receiver (PR). Via the cooperation, the selected ST finds opportunities to access licensed bands to transmit secondary signals to its intended secondary receiver (SR). Secondary users are assumed to be mobile, hence, optimization of energy consumption for these users is interested. The EH STs have to harvest energy from the PT's radio-frequency (RF) signals to serve the PTPR communication as well as to transmit their signals. The proposed scheme employs incremental relaying technique in which the PR only requires the assistance from the STs when the transmission between PT and PR is not successful. Moreover, we also investigate impact of hardware impairments on performance of the primary and secondary networks. For performance evaluation, we derive exact and lower-bound expressions of outage probability (OP) over Rayleigh fading channel. Monte-Carlo simulations are performed to verify the theoretical results. The results present that the outage performance of both networks can be enhanced by increasing the number of the ST-SR pairs. In addition, the outage performance of both primary and secondary networks is severely degraded with the increasing of hardware impairment level. It is also shown that fraction of time used for EH and positions of the secondary users significantly impact on the system performance.Web of Science26125024

OmniShare : Encrypted Cloud Storage for the Multi-Device Era

Two attractive features of cloud storage services are (1) the automatic synchronization of files between multiple devices and (2) the possibility of sharing files with other users. However, many users are concerned about the security and privacy of data stored in the cloud. Client-side encryption is an effective safeguard, but it requires all client devices to have the decryption key. Current solutions derive these keys from user-chosen passwords, which are easily guessed. We present OmniShare, the first scheme to combine strong client-side encryption with intuitive key distribution mechanisms to enable access from multiple client devices and sharing between users. OmniShare uses a novel combination of out-of-band channels (including QR codes and ultrasonic communication), as well as the cloud storage service itself, to authenticate new devices. We describe the design and implementation of OmniShare and explain how we evaluated its security (using formal methods), its performance (benchmarks), and its usability (cognitive walkthrough).Two attractive features of cloud storage services are (1) the automatic synchronization of files between multiple devices and (2) the possibility of sharing files with other users. However, many users are concerned about the security and privacy of data stored in the cloud. Client-side encryption is an effective safeguard, but it requires all client devices to have the decryption key. Current solutions derive these keys from user-chosen passwords, which are easily guessed. We present OmniShare, the first scheme to combine strong client-side encryption with intuitive key distribution mechanisms to enable access from multiple client devices and sharing between users. OmniShare uses a novel combination of out-of-band channels (including QR codes and ultrasonic communication), as well as the cloud storage service itself, to authenticate new devices. We describe the design and implementation of OmniShare and explain how we evaluated its security (using formal methods), its performance (benchmarks), and its usability (cognitive walkthrough).Peer reviewe

UIT-Saviors at MEDVQA-GI 2023: Improving Multimodal Learning with Image Enhancement for Gastrointestinal Visual Question Answering

In recent years, artificial intelligence has played an important role in medicine and disease diagnosis, with many applications to be mentioned, one of which is Medical Visual Question Answering (MedVQA). By combining computer vision and natural language processing, MedVQA systems can assist experts in extracting relevant information from medical image based on a given question and providing precise diagnostic answers. The ImageCLEFmed-MEDVQA-GI-2023 challenge carried out visual question answering task in the gastrointestinal domain, which includes gastroscopy and colonoscopy images. Our team approached Task 1 of the challenge by proposing a multimodal learning method with image enhancement to improve the VQA performance on gastrointestinal images. The multimodal architecture is set up with BERT encoder and different pre-trained vision models based on convolutional neural network (CNN) and Transformer architecture for features extraction from question and endoscopy image. The result of this study highlights the dominance of Transformer-based vision models over the CNNs and demonstrates the effectiveness of the image enhancement process, with six out of the eight vision models achieving better F1-Score. Our best method, which takes advantages of BERT+BEiT fusion and image enhancement, achieves up to 87.25% accuracy and 91.85% F1-Score on the development test set, while also producing good result on the private test set with accuracy of 82.01%.Comment: ImageCLEF2023 published version: https://ceur-ws.org/Vol-3497/paper-129.pd

The Respiratory Syncytial Virus G Protein Conserved Domain Induces a Persistent and Protective Antibody Response in Rodents

Respiratory syncytial virus (RSV) is an important cause of severe upper and lower respiratory disease in infants and in the elderly. There are 2 main RSV subtypes A and B. A recombinant vaccine was designed based on the central domain of the RSV-A attachment G protein which we had previously named G2Na (aa130–230). Here we evaluated immunogenicity, persistence of antibody (Ab) response and protective efficacy induced in rodents by: (i) G2Na fused to DT (Diphtheria toxin) fragments in cotton rats. DT fusion did not potentiate neutralizing Ab responses against RSV-A or cross-reactivity to RSV-B. (ii) G2Nb (aa130–230 of the RSV-B G protein) either fused to, or admixed with G2Na. G2Nb did not induce RSV-B-reactive Ab responses. (iii) G2Na at low doses. Two injections of 3 µg G2Na in Alum were sufficient to induce protective immune responses in mouse lungs, preventing RSV-A and greatly reducing RSV-B infections. In cotton rats, G2Na-induced RSV-reactive Ab and protective immunity against RSV-A challenge that persisted for at least 24 weeks. (iv) injecting RSV primed mice with a single dose of G2Na/Alum or G2Na/PLGA [poly(D,L-lactide-co-glycolide]. Despite the presence of pre-existing RSV-specific Abs, these formulations effectively boosted anti-RSV Ab titres and increased Ab titres persisted for at least 21 weeks. Affinity maturation of these Abs increased from day 28 to day 148. These data indicate that G2Na has potential as a component of an RSV vaccine formulation

Deep Learning-Based Detector for OFDM-IM

This letter presents the first attempt of exploiting deep learning (DL) in the signal detection of orthogonal frequency division multiplexing with index modulation (OFDM-IM) systems. Particularly, we propose a novel DL-based detector termed as DeepIM, which employs a deep neural network with fully connected layers to recover data bits in an OFDM-IM system. To enhance the performance of DeepIM, the received signal and channel vectors are pre-processed based on the domain knowledge before entering the network. Using datasets collected by simulations, DeepIM is first trained offline to minimize the bit error rate (BER) and then the trained model is deployed for the online signal detection of OFDM-IM. Simulation results show that DeepIM can achieve a near-optimal BER with a lower runtime than existing hand-crafted detectors