Sharif Digital Repository

In this paper, we investigate the recovery of a sparse weight vector (parameters vector) from a set of noisy linear combinations. However, only partial information about the matrix representing the linear combinations is available. Assuming a low-rank structure for the matrix, one natural solution would be to first apply a matrix completion to the data, and then to solve the resulting compressed sensing problem. In big data applications such as massive MIMO and medical data, the matrix completion step imposes a huge computational burden. Here, we propose to reduce the computational cost of the completion task by ignoring the columns corresponding to zero elements in the sparse vector. To... 

Knowing the metal content of electronic waste is essential to evaluate metal recovery. Lack of a standard method for digestion of precious metals from electronic waste has resulted in difficulty in comparison to the efficiency of recovery. In this study, different precious metal digestion methods and economic value of precious metals from different types of waste printed circuit boards in different fraction sizes, including computer printed circuit boards, mobile phone printed circuit boards, television printed circuit boards, fax machine printed circuit boards, copy machine printed circuit boards, and central processing unit were examined. The optimal digestion method using aqua regia,... 

In this paper, the seismic behavior of dual structural systems in forms of steel moment-resisting frames accompanied with reinforced concrete shear walls and steel moment-resisting frames accompanied with concentrically braced frames, have been studied. The nonlinear behavior of the mentioned structural systems has been evaluated as, in earthquakes, structures usually enter into an inelastic behavior stage and, hence, the applied energy to the structures will be dissipated. As a result, some parameters such as ductility factor of structure (μ), over-strength factor (Rs) and response modification factor (R) for the mentioned structures have been under assessment. To achieve these objectives,... 

The effects of severe shot-peening process and formation of a nanostructured surface layer on mechanical properties of A356.0 alloy were investigated in this paper. X-ray diffraction analyses revealed that the average size of near-surface grains in severe shot-peened specimens is 75.8 nm. Three types of disk-shaped specimens, non-treated, conventionally shot-peened, and severely shot-peened were subjected to pin-on-disk wear test in the dry condition, in different loading and sliding speeds. Shot-peening process increases both hardness and roughness of the surface, and these two factors have, respectively, positive and negative effects on wear resistance. However, because of high-density... 

Inference and Estimation in Missing Information (MI) scenarios are important topics in Statistical Learning Theory and Machine Learning (ML). In ML literature, attempts have been made to enhance prediction through precise feature selection methods. In sparse linear models, LASSO is well-known in extracting the desired support of the signal and resisting against noisy systems. When sparse models are also suffering from MI, the sparse recovery and inference of the missing models are taken into account simultaneously. In this paper, we will introduce an approach which enjoys sparse regression and covariance matrix estimation to improve matrix completion accuracy, and as a result enhancing... 

Background: Dynamic knee valgus (DKV) is a common lower extremity movement disorder among females. This study aimed to investigate kinematic couplings between lower extremity joints in female junior athletes with DKV during single and double-leg landing and gait. Methods: Twenty-six physically active female junior athletes (10–14 years old) with DKV were recruited. Kinematic couplings between rearfoot, tibia, knee, and hip were extracted using eight Vicon motion capture cameras and two force plates. Zero-lag cross-correlation coefficient and vector coding were used to calculate kinematic couplings between joints during physical tasks. Paired t-test and Wilcoxon tests were run to find... 

In this study, a hybrid magnetic-DNA directed immobilisation approach is presented to enhance protein capture and detection on a microfluidic platform. DNA-modified magnetic nanoparticles are added in a solution to capture fluorescently labelled immunocomplexes to be detected optically. A magnetic set-up composed of cubic permanent magnets and a microchannel was designed and implemented based on finite element analysis results to efficiently concentrate the nanoparticles only over a defined area of the microchannel as the sensing zone. This in turn, led to the fluorescence emission localisation and the searching area reduction. Also, compared to processes in which the immunocomplex is formed... 

In this paper, we propose two new algorithms for transductive multi-label learning from missing data. In transductive matrix completion (MC), the challenge is prediction while the data matrix is partially observed. The joint MC and prediction tasks are addressed simultaneously to enhance accuracy in comparison with separate tackling of each. In this setting, the labels to be predicted are modeled as missing entries inside a stacked matrix along the feature-instance data. Assuming the data matrix is of low rank, we propose a new recommendation method for transductive MC by posing the problem as a minimization of the smoothed rank function with non-affine constraints, rather than its convex... 

The present study is aimed at the development of a novel approach based on the magnetic improvement of DNA-directed antibody immobilization to prepare a highly efficient sensing platform. Magnetic nanoparticle substrates with high surface area capture the dual DNA-conjugated antibodies in a solution. This allows overcoming the typical mass transport limitation of the surface-based antibody immobilization. Antibody-magnetic nanoparticle conjugation is based on a robust hybridization between a DNA tether (attached to the antibody) and its complementary sequence (immobilized on the nanoparticle). Conventional antibody immobilization for the detection of proteins is often insignificant for the... 

One of the main issues of wastewater treatment is the silica deposition in steam turbines. Evaporation of silica with the steam in adequate concentration is one of the main sources of scale formation in steam turbines. In this study, the authors introduce the utilization of a genetic-based approach—gene expression programming (GEP)—for solubility prognostication of the silica in superheated steam of boilers with respect to water silica content and pressure. The result of GEP mathematical approach is a new algebraic formula to achieve our goals. Developed model predicts the silica solubility in the range of 0.8–22.1 MPa and 1–500 mg/kg for pressure and boiler water silica content,... 

Economic production and on-time ordering are among the most important topics related to production and inventory control issues. An economical production needs a comprehensive and precise planning to be implemented in all production stages. To have a controlled and comprehensive planning system, economic order quantity (EOQ) or economic production quantity (EPQ) models are usually used in various production-inventory environments to minimize costs, avoid delays in orders, and achieve high performance. To meet the demands, sometimes a multiperiod production-inventory planning that involves several products requires outsourcing. In this paper, a production-procurement plan that integrates EOQ... 

Various EEG features have been used in depth of anesthesia (DOA) studies. The objective of this study was to And the excellent features or combination of them than can discriminate between different anesthesia states. Conducting a clinical study on 22 patients we could define 4 distinct anesthetic states: awake, moderate, general anesthesia, and isoelectric. We examined features that have been used in earlier studies using single-channel EEG signal processing method. The maximum accuracy (99.02%) achieved using approximate entropy as the feature. Some other features could well discriminate a particular state of anesthesia. We could completely classify the patterns by means of 3 features and... 

In the present study, a prediction model was derived for the effective angle of shearing resistance (φ′) of soils using a novel hybrid method coupling genetic programming (GP) and orthogonal least squares algorithm (OLS). The proposed nonlinear model relates φ′ to the basic soil physical properties. A comprehensive experimental database of consolidated-drained triaxial tests was used to develop the model. Traditional GP and least square regression analyses were performed to benchmark the GP/OLS model against classical approaches. Validity of the model was verified using a part of laboratory data that were not involved in the calibration process. The statistical measures of correlation... 

In this paper, we will investigate the efficacy of IMAT (Iterative Method of Adaptive Thresholding) in recovering the sparse signal (parameters) for linear models with random missing data. Sparse recovery rises in compressed sensing and machine learning problems and has various applications necessitating viable reconstruction methods specifically when we work with big data. This paper will mainly focus on comparing the power of Iterative Method of Adaptive Thresholding (IMAT) in reconstruction of the desired sparse signal with that of LASSO. Additionally, we will assume the model has random missing information. Missing data has been recently of interest in big data and machine learning...