Sharif Digital Repository

The immense space of composition-processing parameters leads to numerous trial-and-error experimental works for engineering of novel bulk metallic glasses (BMGs). To tackle this challenging problem, it is required to consider specific guidelines which are able to restrict the productive alloying compositions. In this work, a correlation-based neural network (CBNN) approach was developed, based on a dataset of 7950 alloying compositions, to design potential new MGs through prediction of casting ability, reduced glass transition (Trg) and critical thickness (Dmax). This approach involves individual and mutual characteristics of contributory factors to improve the prediction accuracy. To... 

Atomic force microscopy (AFM) is an efficient tool for studying the structural heterogeneity in metallic glasses (MGs). However, time-consuming analysis and limitations in the scanning process are downsides of this experiment. To tackle these problems, a machine learning (ML) model was developed to predict the distribution of energy dissipation on the MG surface with the increase in number of AFM scanning. The results indicated that it was possible to accurately predict the energy of scanning points, leading to a timesaving and reliable study. Moreover, characterization of structural heterogeneity shows that the viscoelastic response of each nanoscale region under sequences of AFM scans... 

Compressive strength, a crucial mechanical property of cement mortars, is typically measured destructively. However, there is a need to evaluate the strength of unique cement-based samples at various ages without causing damage. In this paper, a predictive framework using a genetic algorithm (GA) is proposed for estimating the compressive strength of ordinary cement-based mortars based on their dynamic elastic modulus, measured non-destructively using the impulse excitation technique. By combining the Popovics model (PM) and the Lydon-Balendran model (LBM), the static elastic modulus of samples was calculated using constant coefficients, representing an equivalent compressive strength. A GA... 

This study tries to establish a powerful machine learning (ML) model for predicting the compressive strength of cement-based mortars by using dynamic elasticity data. The ML model was developed on the basis of Bayesian theorem, leading to a decrease in the overfitting problem compared with other conventional neural networks. Moreover, for the first time, the empirical equations were embedded in the ML model, enhancing the correlation between dynamic elasticity and compressive strength in the cement-based mortars. The results showed that the ML model efficiently predicted the compressive strength with determination coefficient (R2) of 95.2% and root mean square error (RMSE) of 0.0488 for... 

There exists an extensive range of applications for elevated gain DC/DC converters, as numerous low-voltage resources are exploited for power supply. Therefore, this study introduces a groundbreaking magnetically coupled DC/DC converter specifically designed for resources with low voltage, including micro PV or fuel cell systems. By enduring low current and voltage stresses, the power devices in this converter ensure remarkable efficiency while maintaining proven voltage ratio capability. The operational principles of the converter are thoroughly discussed and supported by the implementation of a 200W-400V prototype. In order to confirm the effectiveness of the converter, a range of... 

The quantity and variety of parameters involved in the failure evolutions in solder joints under a thermo-mechanical process directs the reliability assessment of electronic devices to be frustratingly slow and expensive. To tackle this challenge, we develop a novel machine learning framework for reliability assessment of solder joints in electronic systems; we propose a correlation-driven neural network model that predicts the useful lifetime based on the materials properties, device configuration, and thermal cycling variations. The results indicate a high accuracy of the prediction model in the shortest possible time. A case study will evaluate the role of solder material and the joint... 

Costly and time-consuming approaches for solder joint lifetime estimation in electronic systems along with the limited availability and incoherency of data challenge the reliability considerations to be among the primary design criteria of electronic devices. In this paper, an iterative machine learning framework is designed to predict the useful lifetime of the solder joint using a set of self-healing data that reinforces the machine learning predictive model with thermal loading specifications, material properties, and geometry of the solder joint. The self-healing dataset is iteratively injected through a correlation-driven neural network to fulfill the data diversity. Outcomes show a... 

Costly and time-consuming approaches for solder joint lifetime estimation in electronic systems along with the limited availability and incoherency of data challenge the reliability considerations to be among the primary design criteria of electronic devices. In this article, an iterative machine learning framework is designed to predict the useful lifetime of the solder joint using a set of self-healing data that reinforce the machine learning predictive model with thermal loading specifications, material properties, and geometry of the solder joint. The self-healing dataset is iteratively injected through a correlation-driven neural network (CDNN) to fulfill the data diversity. Outcomes... 

Depending on the application in which power electronic converters (PECs) are deployed, failure processes may endanger the desirable performance of PECs. This paper offers holistic insights on reliability modeling of PECs considering dependencies in two simultaneous failure processes, namely gradual wearing-out degradation and vibration sudden degradation. While sudden and gradual degradation processes may individually affect the useful lifetime of PECs, their mutual interdependencies could significantly accelerate the aging mechanisms. A new analytical model for reliability assessment of PECs is proposed that can capture such mutual interdependence of simultaneous failure processes. The... 

In this paper, having investigated the behavior of GPGPU applications, we present an effcient L2 cache architecture for GPUs based on STT-RAM technology. With the increase of processing cores count, larger on-chip memories are required. Due to its high density and low power characteristics, STT-RAM technology can be utilized in GPUs where numerous cores leave a limited area for on-chip memory banks. They have however two important issues, high energy and latency of write operations, that have to be addressed. Low data retention time STT-RAMs can reduce the energy and delay of write operations. However, employing STT-RAMs with low retention time in GPUs requires a thorough investigation on... 

Future GPUs should have larger L2 caches based on the current trends in VLSI technology and GPU architectures toward increase of processing core count. Larger L2 caches inevitably have proportionally larger power consumption. In this article, having investigated the behavior of GPGPU applications, we present an efficient L2 cache architecture for GPUs based on STT-RAM technology. Due to its high-density and low-power characteristics, STT-RAM technology can be utilized in GPUs where numerous cores leave a limited area for on-chip memory banks. They have, however, two important issues, high energy and latency of write operations, that have to be addressed. Low retention time STT-RAMs can... 

In the present work we obtained a series of NIR luminescent platinum(II) complexes with a pincer N^N^C ligand based on the conjugated {benzoimidazo[1,2-a]pyrazine} system with the [Pt(N^N^C)L]n+ structural motif (L = phosphine, alkynyl or pyridine-type ligands). We have also synthesized two complexes with bidentate phosphines that demonstrate different types of coordination: 1) as chelating ligand (in case of 1,2-bis(diphenylphosphino)benzene), that led to de-coordination of pyridine ring of N^N^C ligand and formation of a [Pt(N^C)dppb]+ complex; 2) as a bridging ligand (in case of bis(diphenylphosphino)methane) between two {Pt(N^N^C)} fragments in a dimeric complex of type... 

The combined action of ultraviolet irradiation and microwave heating onto acetonitrile solution of [Re((Formula presented.))(CO)3(NCMe)]OTf ((Formula presented.) =phenantroline and neocuproine) afforded cis,trans-Re((Formula presented.))(CO)2(NCMe)2]+ acetonitrile derivatives. Substitution of relatively labile NCMe with a series of aromatic N-donor ligands (pyridine, pyrazine, 4,4’-bipyridine, N-methyl-4,4’-bipyridine) gave a novel family of the diimine cis,trans-[Re((Formula presented.))(CO)2(L)2]+ complexes. Photophysical studies of the obtained compounds in solution revealed unusually high absorption across the visible region and NIR phosphorescence with emission band maxima ranging from... 

A Deza graph with parameters (n, k, b, a) is a k-regular graph with n vertices such that any two of its vertices have b or a common neighbours, where b ≥ a. In this paper we investigate spectra of Deza graphs. In particular, using the eigenvalues of a Deza graph we determine the eigenvalues of its children. Divisible design graphs are significant cases of Deza graphs. Sufficient conditions for Deza graphs to be divisible design graphs are given, a few families of divisible design graphs are presented and their properties are studied. Our special attention goes to the invertibility of the adjacency matrices of Deza graphs. © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group  

A Deza graph with parameters (Formula presented.) is a k-regular graph with n vertices such that any two of its vertices have b or a common neighbours, where (Formula presented.). In this paper we investigate spectra of Deza graphs. In particular, using the eigenvalues of a Deza graph we determine the eigenvalues of its children. Divisible design graphs are significant cases of Deza graphs. Sufficient conditions for Deza graphs to be divisible design graphs are given, a few families of divisible design graphs are presented and their properties are studied. Our special attention goes to the invertibility of the adjacency matrices of Deza graphs. © 2020 Informa UK Limited, trading as Taylor &... 

The influence of the addition of the nonionic surfactants C12DMPO, C14DMPO, C10OH, and C10EO5 at concentrations between 10-5 and 10-1 mmol/L to solutions of β-casein (BCS) and β-lactoglobulin (BLG) at a fixed concentration of 10-5 mmol/L on the dilational surface rheology is studied. A maximum in the viscoelasticity modulus |E| occurs at very low surfactant concentrations (10-4 to 10-3 mmol/L) for mixtures of BCS with C12DMPO and C14DMPO and for mixtures of BLG with C10EO5, while for mixture of BCS with C10EO5 the value of |E| only slightly increased. The |E| values... 

The aim of this work was to study the treatment of strong beet sugar wastewater by an upflow anaerobic fixed bed (UAFB) at pilot plant scale. Three fixed bed bioreactors (each 60 L) were filled with standard industrial packing, inoculated with anaerobic culture (chicken manure, cow manure, anaerobic sludge digested from domestic wastewater) and operated at 32-34 °C with 20 h hydraulic retention time (HRT) and influent COD ranging between 2000-8000 mg/L. Under these conditions the maximum efficiency of organic content reduction in the reactor ranged from 75% to 93%. The reactor filled with standard pall rings made of polypropylene with an effective surface area of 206 m2/m3 performed best in... 

Let two coordinate systems, in possession of Alice and Bob, be related to each other by an unknown rotation R∈ SO (3). Alice is to send identical states | ψ⟩ to Bob who will make measurements on the received state and will determine the rotation R. The task of Bob is to estimate these parameters of the rotation R by the best possible measurements. Based on the quantum Fisher information, we show that Greenberger–Horne–Zeilinger (GHZ) states are near optimal states for this task. We show concrete measurements which will allow Bob to determine the rotation R. This shows that GHZ states, as superposition of macroscopically distinct states, are useful in yet another context in quantum...