Showing papers by "Amirkabir University of Technology published in 2023"

Prediction of post-operative clinical parameters in posterior scoliosis surgery using an adaptive neuro-fuzzy interface system

Abstract: Abstract Background and Objective: Postoperative clinical indices should be estimated accurately in scoliosis correction surgeries, which have been analyzed in various studies such as experimental (in vitro or in vivo) trials through different modeling methods (finite element or multibody analysis). These costly and time-consuming methods can only be conducted on a large number of scoliotic patients. An adaptive neuro-fuzzy interface system (ANFIS) is used in this study to estimate the postoperative cobb and thoracic kyphosis angles in adolescent idiopathic scoliosis patients undergoing posterior scoliosis correction surgeries. Methods: Four groups of 55 patients with distinct preoperative clinical indices (thoracic cobb and pelvic incidence) were considered the ANFIS inputs, whereas postoperative thoracic cobb and kyphosis angles were used as the outputs. For robustness evaluation, the predicted values of postoperative angles were compared with measurements by calculating the root mean square errors and clinical correction deviation indices (the relative deviation of postoperative predicted angles from the real angles). Results: The least root mean square errors (3.0º and 6.3° for the main thoracic cobb and thoracic kyphosis estimations, respectively) were recorded in the group with the main thoracic cobb, pelvic incidence, thoracic kyphosis, and T1 spinopelvic inclination used as inputs. The clinical correction deviation indices were calculated 0.0086 and 0.0641 for cobb angles in two cases and 0.0534 and 0.2879 for thoracic kyphosis in two other cases. Conclusion: Greater differences between preoperative and postoperative cobb angles compared with those of thoracic kyphosis decreased the root-mean-square errors and clinical deviation indices but improved accuracy.

Prediction of post-operative clinical parameters in posterior scoliosis surgery through an adaptive neuro-fuzzy interface system

Abstract: Abstract Background and Objective: Postoperative clinical indices should be estimated accurately in scoliosis correction surgeries, which have been analyzed in various studies such as experimental (in vitro or in vivo) trials through different modeling methods (finite element or multibody analysis). These costly and time-consuming methods can only be conducted on a large number of scoliotic patients. An adaptive neuro-fuzzy interface system (ANFIS) is used in this study to estimate the postoperative cobb and thoracic kyphosis angles in adolescent idiopathic scoliosis patients undergoing posterior scoliosis correction surgeries. Methods: Four groups of 55 patients with distinct preoperative clinical indices (thoracic cobb and pelvic incidence) were considered the ANFIS inputs, whereas postoperative thoracic cobb and kyphosis angles were used as the outputs. For robustness evaluation, the predicted values of postoperative angles were compared with measurements by calculating the root mean square errors and clinical correction deviation indices (the relative deviation of postoperative predicted angles from the real angles). Results: The least root mean square errors (3.0º and 6.3° for the main thoracic cobb and thoracic kyphosis estimations, respectively) were recorded in the group with the main thoracic cobb, pelvic incidence, thoracic kyphosis, and T1 spinopelvic inclination used as inputs. The clinical correction deviation indices were calculated 0.0086 and 0.0641 for cobb angles in two cases and 0.0534 and 0.2879 for thoracic kyphosis in two other cases. Conclusion: Greater differences between preoperative and postoperative cobb angles compared with those of thoracic kyphosis decreased the root-mean-square errors and clinical deviation indices but improved accuracy.

Enhancing the efficiency and short-circuit current of silicon solar cells using MoO3 as emitter layers

Abstract: Molybdenum trioxide (MoO3) is an interesting material for optoelectronic applications due to their low-cost, simple processing and thickness-dependent band gap tunability. So far, however, few have been reported to date the impact of post-synthesis annealing on the electrical characteristics of the MoO3 in silicon heterojunctions solar cells. Here, we show that Ar annealing treatment can promote the crystallization of MoO3 nano structures (NSs) and enlarge the MoO3 NSs crystalline size. In this work, thermal evaporation technique is used to prepare MoO3 NSs to be used as emitter layers in silicon heterojunctions solar cell. Then, the as-synthesized sample is annealed under Ar gas flow at 600 ˚C to improve structural and optical properties of the sample. It is noted that by Ar treatment at 600 ˚C, we found its quantum efficiency (QE) enhances from 8.41% to 10.74%, showing a 27.70% increase due to a significant enhancement of short circuit-current (ISC). Furthermore, EQE of Ag/Ar treatment MoO3/n-type Si/Ag showed significant enhancement entire spectra range especially in visible region respect to the bare cell.

Spike time displacement-based error backpropagation in convolutional spiking neural networks

Abstract: In this paper, we introduce a supervised learning algorithm, which avoids backward recursive gradient computation, for training deep convolutional spiking neural networks (SNNs) with single-spike-based temporal coding. The algorithm employs a linear approximation to compute the derivative of the spike latency with respect to the membrane potential, and it uses spiking neurons with piecewise linear postsynaptic potential to reduce the computational cost and the complexity of neural processing. To evaluate the performance of the proposed algorithm in deep architectures, we employ it in convolutional SNNs for the image classification task. For two popular benchmarks of MNIST and Fashion-MNIST datasets, the network reaches accuracies of, respectively, 99.2 and $$92.8\%$$ . The trade-off between memory storage capacity and computational cost with accuracy is analyzed by applying two sets of weights: real-valued weights that are updated in the backward pass and their signs, binary weights, that are employed in the feedforward process. We evaluate the binary CSNN on two datasets of MNIST and Fashion-MNIST and obtain acceptable performance with a negligible accuracy drop with respect to real-valued weights (about 0.6 and $$0.8\%$$ drops, respectively).

A passive isolation of sensor faults from un-stealthy attacks in uncertain nonlinear systems

Abstract: In this paper, a novel nonlinear observer-based approach is established to cast light on the problem of isolating sensor faults from un-stealthy attacks for Lipschitz affine nonlinear systems subjected to unknown uncertainties and disturbances. A structure consisting of a multi-line transmission mechanism, a random number generator (RNG), and Dedicated Observer Scheme is developed to make the abnormal behaviors distinguishable. A group of two interlinked nonlinear Luenberger-Like observers is designed in nonlinear coordinates to detect abnormal behaviors. Two banks of detection observers and an incidence matrix are introduced to make the final decisions. Adaptive threshold techniques are exploited to guarantee the robustness against the model uncertainties and disturbances. Compared with the existing results, the proposed approach isolates the abnormal behaviors without the need for any redundant hardware components. Finally, the performance of the proposed approach is evaluated on a continuous stirred tank reactor (CSTR).

Deep learning-based method for segmenting epithelial layer of tubules in histopathological images of testicular tissue

Abstract: There is growing concern that male reproduction is affected by environmental chemicals. One way to determine the adverse effect of environmental pollutants is to use wild animals as monitors and evaluate testicular toxicity using histopathology. We propose an automated method to process histology images of testicular tissue.Testicular tissue consists of seminiferous tubules. Segmenting the epithelial layer of the seminiferous tubule is a prerequisite for developing automated methods to detect abnormalities in tissue. We suggest an encoder-decoder fully connected convolutional neural network model to segment the epithelial layer of the seminiferous tubules in histological images. The ResNet-34 is used in the feature encoder module, and the squeeze and excitation attention block is integrated into the encoding module improving the segmentation and localization of epithelium.We applied the proposed method for the two-class problem, where the epithelial layer of the tubule is the target class. The F -score and Intersection over Union of the proposed method are 0.85 and 0.92. Although the proposed method is trained on a limited training set, it performs well on an independent dataset and outperforms other state-of-the-art methods.The pretrained ResNet-34 in the encoder and attention block suggested in the decoder result in better segmentation and generalization. The proposed method can be applied to testicular tissue images from any mammalian species and can be used as the first part of a fully automated testicular tissue processing pipeline. The dataset and codes are publicly available on GitHub.

Rb atoms’ Polarization and its gradient estimation for nonresonant light by using Lambert function

Abstract: Abstract Polarization of alkali vapor is a key parameter in the atomic magnetometer's response to the applied magnetic field. In this paper, estimating polarization is used by studying the transmission behavior of the pump beam as a function of intensity. In our study, the transmission of circularly polarized light exhibits nontrivial features such as nonlinear absorption and relaxation. The advantages of this method are its simplicity for characterizing polarization and its accuracy. The intensity and wavelength of the pumping laser are considered for studying polarization changes and polarization gradient. This method could apply directly to the quantum computing in devices like memory, gate, clock, and for reference cell. Also, this method is fast, reliable and accurate and doesn't need on resonance detection which makes it more suitable for ZULF NMR detection.

Designing photoelectrode architecture modified with mesoporous MCM-41/CeO2 composites as specific scattering layers for dye-sensitized solar cells

Abstract: Depositing light scattering film on top of the photoactive layer of dye-sensitized solar cells (DSSCs) is one method to boost the device efficiency. Therefore, mesoporous MCM-41 and CeO2 were prepared and coated on TiO2 photoactive layer in both their pure and composite (1–4 wt% CeO2:MCM-41) forms by doctor blade technique. XRD, FESEM, EDS, TEM, BET, FTIR, DRS, and PL analyses were employed to characterize all compounds. Additionally, the power conversion efficiency (PCE), dye adsorption capacity, and charge transfer parameters of various DSSCs were measured. The data taken from J-V plots indicated that the highest conversion efficiency belonged to 2 wt% CeO2:MCM-41 when was used as the scattering layer which could boost the efficiency up to 7.42% with fill factor (FF), open-circuit voltage (Voc) and short-circuit current density (Jsc), respectively, equal to 0.48, 0.752 V, 20.55 mA.cm−2, confirming 78.8% greater efficiency than the bare TiO2 containing DSSC (PCE = 4.15%, FF = 0.39, Voc = 0.736 V, Jsc = 14.45 mA.cm−2). In fact, once 2 wt% CeO2:MCM-41 with large particles was applied, according to Mie's theory, prolonged the light path through incident light scattering, which resulted in more light harvesting by photoanode material, further dye molecules excitation, and improvement in the current density. Also, because of the insulating nature of silica material in 2 wt% CeO2:MCM-41 layer, it reduced the charge recombination process and accelerated the transfer of electrons from the TiO2 photoanode to the Pt counter electrode via the external circuit. Besides, high porosity and huge surface area (757.34 m2/g) of 2 wt% CeO2:MCM-41 promoted dye molecules to be adsorbed additionally which generated extra photoelectrons.

A semantic modular framework for events topic modeling in social media

Abstract: The advancement of social media contributes to the growing amount of content they share frequently. This framework provides a sophisticated place for people to report various real-life events. Detecting these events with the help of natural language processing has received researchers’ attention, and various algorithms have been developed for this goal. In this paper, we propose a Semantic Modular Model (SMM) consisting of 5 different modules, namely Distributional Denoising Autoencoder, Incremental Clustering, Semantic Denoising, Defragmentation, and Ranking and Processing. The proposed model aims to (1) cluster various documents and ignore the documents that might not contribute to the identification of events, (2) identify more important and descriptive keywords. Compared to the state-of-the-art methods, the results show that the proposed model has a higher performance in identifying events with lower ranks and extracting keywords for more important events in three English Twitter datasets: FACup, SuperTuesday, and USElection. The proposed method outperformed the best-reported results in the mean keyword-precision metric by 7.9%.

Design and analysis of active transtibial prosthesis function using 7-link inverse dynamic model of gait

Abstract: Abstract In today’s world, in order to increase the movement abilities of amputees, different types of passive prostheses are used according to the level of the person’s disability. Although these types of prostheses increase a person’s mobility; however, they still have a limited ability to help the amputee walk normally on uneven terrain, as there is no net stimulus input power for the target joint. These limitations have led to the growth of the use of active prostheses in recent years, which has led to a variety of prosthetic designs. The purpose of this research is, first of all, to provide a suitable design and control of active transtibial prosthesis close to the performance of lost limb of a healthy person, and second and more importantly, to develop a 7-link inverse dynamic model of human gait and use it to analyze of an amputee gait with the designed prosthesis. Winter’s reference data are used in the entire process of design and simulation of prosthesis performance. Also, it is assumed that the amputation occurred in only one leg of the person. Based on the obtained results, when an amputee with 57 kg weight and 1.55 cm height wears an active prosthesis designed with 0.5 kg extra weight, the amount of metabolic cost of amputee in the swing phase increases by about 20%. By using this obtained model, it will be possible to optimize different prosthesis designs for people with different weight and height conditions.

CALL Teacher Education Models, Methods, and Theoretical Groundings: A Systematic Review of the Studies in Under-Represented Contexts

Abstract: This chapter reports a systematic literature review of CALL teacher education research conducted in under-represented geographical contexts, namely Asia, the Middle East, and Africa over the past two decades. The main objective was twofold: first, to explore how CALL professional development (PD) is approached in these contexts with special attention to the educational and geographical contexts of the studies, their design features, and their study-related qualities; and second, to identify research gaps. Drawing on the PRISMA model as the main analysis procedure, 89 articles published in peer-reviewed scholarly journals were screened from a pool of 3495 works. Applying geographical context filtering, 12 papers were finally selected as eligible to be included in the study, of which 61.6% were conducted in the Middle East and 38.4% belonged to Eastern Asia. No accounts of CALL PD attempts in African educational settings were found in scholarly journals. Several design-, context-, and study-related categories were explored to understand the approaches toward CALL PD and the research gaps. The analyses revealed the dominance of qualitative studies and self-report data sources in the CALL PD studies. Drawing on the findings, some pedagogical implications are generated for future research.

Investigation of the source term and radiological consequences of small break LOCA with failure of the emergency core cooling system in Bushehr nuclear power plant

Abstract: In the present study, the in-containment source term derivation, environmental dispersion and deposition as well as radiological consequences of Bushehr Nuclear Power Plant (BNPP) due to Small Break Loss of Coolant Accident (SB-LOCA) have been studied. First, the calculation of in-containment release and distribution of the source terms due to the severe accident of SB-LOCA with failure of the active part of the Emergency Core Cooling System (ECCS) has been carried out. In this calculation, two scenarios, including with and without operator control actions, have been considered. Then, the environmental leakage of radionuclides as well as their dispersion and deposition considering two meteorological scenarios have been investigated. Consequently, public exposure has been calculated for each meteorological scenario and the obtained results have been reported and discussed. For two accidents with the same meteorological conditions, at a distance of 400 m from the release point, the received dose in the first 24 h is 18 and 342 mSv without and with operator action, respectively. The results show that due to the intervention of the operator and the use of emergency cooling supply systems, more water enters the reactor. Therefore, if the operator's actions do not prevent the core from melting, more source terms have entered the containment after the core melts and worse conditions than the baseline scenario without operator action occurred.

Extreme Multistability and Extreme Events in a Novel Chaotic Circuit with Hidden Attractors

Abstract: Extreme multistable systems can show vibrant dynamical properties and infinitely many coexisting attractors generated by changing the initial conditions while the system and its parameters remain unchanged. On the other hand, the frequency of extreme events in society is increasing which could have a catastrophic influence on human life worldwide. Thus, complex systems that can model such behaviors are very significant in order to avoid or control various extreme events. Also, hidden attractors are a crucial issue in nonlinear dynamics since they cannot be located and recognized with conventional methods. Hence, finding such systems is a vital task. This paper proposes a novel five-dimensional autonomous chaotic system with a line of equilibria, which generates hidden attractors. Furthermore, this system can exhibit extreme multistability and extreme events simultaneously. The fascinating features of this system are examined by dynamical analysis tools such as Poincaré sections, connecting curves, bifurcation diagrams, Lyapunov exponents spectra, and attraction basins. Moreover, the reliability of the introduced system is confirmed through analog electrical circuit design so that this chaotic circuit can be employed in many engineering fields.

Mathematical modeling of dispersed phase behavior of water-in-oil emulsions in electrostatic crude oil desalters

Abstract: The produced crude oil from reservoirs usually contains a considerable amount of water which is submitted to large shear rates through production process, and due to the existence of natural surface-active agents in crude oils, stable water-in-oil (W/O) emulsions are formed. Effectively purifying the emulsified crude oil through the electrostatic desalting process plays an important role in reducing its water and water-soluble salts contents, which otherwise exacerbate oil deterioration, equipment corrosion, and catalysts deactivation in subsequent units. The electrostatic desalting process has proved to be an efficient means of separation using electrodes subjected to a high voltage to enhance the coalescence of water droplets. In this study, a mathematical model was developed to simulate the W/O dispersed flow to study the evolution of droplet size distributions in crude oil desalters. The population balance approach was employed to describe the behavior of W/O emulsions in the continuous phase assuming that the process is controlled by two simultaneous physical phenomena; breakage and coalescence of droplets. Experimental results on the W/O system were utilized to validate the mathematical model and the employed numerical technique. The agreement between the developed model and experimental droplet volume size distributions was shown to be satisfactory, confirming the further applicability of the model. The present study can be helpful for optimizing crude oil desalting operating conditions, enhancing efficiency, and decreasing energy and chemical consumption.

Sudden-onset disaster resilience considering functionality improvement planning: An upstream oil and gas company

Abstract: The present paper presents a quantitative approach to evaluate organizational resilience in sudden-onset disasters considering preparedness actions (PAs). The resilience triangle concept is extended and gradual improvement of the level of functionality (LF) strategy is examined as PAs. Robustness and rapidity measures are considered as loss of LF and recovery time to indicate the overall disaster resilience. Besides, the resourcefulness and redundancy measures are considered pre-determined response plans and PAs aimed to improve disaster resilience. Two mathematical models are developed by applying these measures. Thereafter, graphical analytics are conducted to demonstrate the effectiveness of PAs and provide a better outlook for decision-makers. Then, mathematical analyses are conducted to show how the PAs affect resilience measures. It is mathematically proved that PAs exert considerable impacts on recovery time, loss of LF, and robustness. To illustrate the application of the proposed approach, it is applied to an upstream oil and gas company in the field of exploration and production. The results suggest that the approach is significantly effective in disaster response, planning, and mitigation.