Sharif Digital Repository

Titanium alloys, in particular Ti6Al4V, are increasingly used recently due to high strength-to-weight ratio, biocompatibility and robust mechanical-properties at high temperatures. However, Ti6Al4V have poor machinability because of their poor thermal conductivity and high reactivity. Usually in conventional grinding (CG) of these alloys, surface burning is unavoidable. Ultrasonic assisted grinding (UAG) is an efficient method for overcoming the poor machinability of such materials. In this research, effect of imposed vibration on grinding of Ti6Al4V is studied. Obtained results show forces and surface roughness are reduced 18% and 12% by UAG comparing to CG respectively. It also results in... 

Ultrasonic assisted machining (UAM) is an efficient nontraditional machining operation for brittle, hard-to-cut and poor-machinability materials. In UAM, high frequency oscillation in ultrasonic range at low amplitude is imposed on the workpiece or cutting tool. In most cases, the equipments that generates and transfers the vibration, have a complicated structure, and requires significant effort to achieve their optimum function. In this work, a mathematical model is developed and an optimization method is employed for design process. This makes it possible to achieve proper setup and reduce the amount of calculation. For this purpose, the combination of a two level full factorial design is... 

In this paper, we will propose a new methodology for performance modeling of IEEE 802.11p medium access control (MAC) sub-layer in broadcast mode. In most of the current works, it is assumed that the number of contending nodes is deterministic and constant. However, in vehicular ad hoc networks, we can observe a kind of uncertainty in number of contending vehicles, which try to get access to the shared wireless channel. This uncertainty can affect the performance analysis of MAC protocols in vehicular environments. In this paper, we will try to model the dynamicity of number of contending vehicles based on the distribution of vehicles' velocity. This model will be combined with the current... 

Text classification in imbalanced datasets poses a significant challenge in natural language processing (NLP) applications. In this paper, we address this issue by treating text data as graphs, with words as nodes, and employ two strategies for establishing connections between words within these graphs. The first strategy employs a fixed-size context window to connect words that lie within the same window, while the second strategy leverages word embedding vectors and a k-nearest neighbors (KNN) approach to connect each word to its k most similar neighbors. To further enhance the classification performance, we combine Graph Convolutional Neural Networks (GCNs) and Long Short-Term Memory... 

The rapid developments in wireless communications have introduced the vehicular ad hoc networks as an efficient tool to improve the safety of traffic through Dedicated Short Range Communications systems on highways. This paper addresses the broadcast mode of the 802.11p protocol and it quantitatively analyzes the impact of the highway vehicular traffic on its performance in terms of the packet delivery ratio and the packet transmission delay. To this aim, we propose to consider the number of the contending vehicles as a traffic-dependent stochastic counting process, which can be computed by analyzing the inter-vehicle spacing distribution in different traffic conditions. Based on the... 

It has been shown that a macroscopic system being in a high-temperature thermal coherent state can be, in principle, driven into a non-classical state by coupling to a microscopic system. Therefore, thermal coherent states do not truly represent the classical limit of quantum description. Here, we study the classical limit of quantum state of a more relevant macroscopic system, namely the pointer of a detector, after the phase-preserving linear amplification process. In particular, we examine to what extent it is possible to find the corresponding amplified state in a superposition state, by coupling the pointer to a qubit system. We demonstrate quantitatively that the amplification process... 

We address the performance of the 802.11p protocol in terms of the packet delivery ratio (PDR) and the packet reception ratio (PRR) for broadcasting safety messages in highway environments. Regarding the semi-sparse and intermediate traffic, we try to answer to questions such as what are the upper, dominant and lower bounds of PDR and PRR in each traffic condition? How they may deviate from their average values? and What fraction of vehicles may experience such bounds? To answer to such questions, we propose to consider the communication graph formed among vehicles as a special type of the complex networks and we apply the recently proposed obligatory attachment ruleto synthesize such... 

The drug discovery process faces challenges due to its length, cost, and experimental failures. Researchers are addressing these issues through 'drug repurposing,' seeking new applications for existing pharmaceuticals. To predict drug-target interactions, computational methods, like drug-target interaction prediction, utilize bipartite graphs and link prediction. In this paper, we propose BiProSimGCN, a framework based on convolutional graph neural networks, to predict drug-protein interactions. BiProSimGCN integrates data from the bipartite drug-protein network and the protein similarity network, aiming to reveal latent factors of drugs and proteins. The performance of BiProSimGCN is... 

In this study, we apply the monitoring master equation describing decoherence of internal states to an optically active molecule prepared in a coherent superposition of nondegenerate internal states interacting with thermal photons at low temperatures. We use vibrational Raman scattering theory up to the first chiral-sensitive contribution, i.e., the mixed electric-magnetic interaction, to obtain scattering amplitudes in terms of molecular polarizability tensors. The resulting density matrix is used to obtain elastic decoherence rates  

In the past few years, the study of complex networks has attracted the attention of researchers. Many real networks, ranging from technological networks such as the Internet to biological networks, have been considered as special types of complex networks. Through application of the network science, important structural properties of such networks have been analyzed and the mechanisms that form such characteristics have been introduced. In this paper, we address the structural characteristics of a technological network called Vehicular Ad hoc Networks (VANETs). Recent studies reveal that the communication graph of VANETs has some interesting characteristics including: the Gaussian degree... 

Preciseness and finished surface quality are the significant factors of final products, especially in a number of drilling processes. Burr is usually considered a negative outcome in assembly procedures. One way to reduce or remove burr and improve surface roughness in metal cutting is to employ ultrasonic vibration in the drilling process. In this paper, the effects of ultrasonic vibration on burr size reduction, drilling force and surface roughness with two different vibration systems are investigated. To this end, two vibration structures were built, one to excite the workpiece (the workpiece vibration system) and the other to vibrate the tool (the tool vibration system). Besides, the... 

Conventional noise reduction methods often fail to effectively handle high levels of noise, leading to artifacts and distortions. This paper proposes a Generative Adversarial Network (GAN) approach for noise reduction with low complexity. The proposed Semi Noise Reduction GAN (SNRGAN) effectively learns the underlying patterns of noise and generates denoised versions of noisy images, even with different noise levels. Training our model on three diverse datasets yielded admissible results, as evidenced by superior PSNR and NMSE scores. Furthermore, our model excelled in both subjective evaluations and objective metrics and its efficacy in handling elevated noise levels positions it as a... 

Identifying sub-networks showing similar properties, referred to as community detection, is a challenging task in network analysis. This challenge becomes even more formidable in bipartite networks. The focus of this study is the patients with problems in their Peripheral Nerve System. To this aim, we engaged the assistance of spinal specialty clinics in the collection of necessary Data. We employ the bipartite network to represent the relationship between the patients and their symptoms and disorders. The resulting bipartite network showcases unequally sized sets of nodes, making community detection more challenging. The principal purpose of this study is to develop a new, practically... 

We examine the olfactory discrimination of left- and right-handed enantiomers of chiral odorants based on the odorant-mediated electron transport from a donor to an acceptor of the olfactory receptors embodied in a biological environment. The chiral odorant is effectively described by an asymmetric double-well potential whose minima are associated to the left- and right-handed enantiomers. The introduced asymmetry is considered an overall measure of chiral interactions. The biological environment is conveniently modeled as a bath of harmonic oscillators. The resulting spin-boson model is adapted by a polaron transformation to derive the corresponding Born-Markov master equation with which we... 

We study the dynamics of a macroscopic superconducting qubit coupled to two independent non-stationary reservoirs by using time-dependent perturbation theory. We show that an equilibrium environment surpasses the coherent evolution of the macroscopic qubit completely. When the qubit couples to two different reservoirs, exemplifying a non-equilibrium environment, the short-time dynamics is affected by the interference between two reservoirs, implying the non-additivity of effects of two reservoirs. The non-additivity can be traced back to a non-Markovian effect, even though two reservoirs are independently assumed to be Markovian. Explicitly, the non-equilibrium environment intensifies both... 

Depth estimation is critical for autonomous vehicles (AVs) to perceive their surrounding environment. However, the majority of current approaches rely on costly sensors, making wide-scale deployment or integration with present-day transportation difficult. This issue highlights the camera as the most affordable and readily available sensor for AVs. To overcome this limitation, this paper uses monocular depth estimation as a low-cost, data-driven strategy for approximating depth from an RGB image. To achieve low complexity, we approximate the distance of vehicles within the frontal view in two stages: firstly, the YOLOv7 algorithm is utilized to detect vehicles and their front and rear... 

In order to improve titanium corrosion behavior, we can increase the thickness of oxide layer on titanium surface during anodizing process and by electrochemistry. In this research, self-color anodizing of Ti in sulfuric acid was done, and anodizing layers were created in different colors. The highest value of chromaticity was 37.8 for the anodized sample in 10 V, and the lowest value was 8.6 at 15 V. The oxide layer thickness was calculated by optical method (light refraction). The anodic film thickness increased by increasing the anodizing voltage. The highest thickness of anodic film was 190 nm in sulfuric acid solution for the anodized sample in 80 V. Corrosion resistance of anodized Ti...