Iran University of Science and Technology

Detailed theoretical and experimental analyses are presented for the comparison of two simple, fast and reliable maximum power-point tracking (MPPT) techniques for photovoltaic (PV) systems: the voltage-based (VMPPT) and the current-based (CMPPT) approaches. A microprocessor-controlled tracker capable of online voltage and current measurements and programmed with VMPPT and CMPPT algorithms is constructed. The load of the solar system is either a water pump or resistance. "Simulink" facilities are used for simulation and modeling of the novel trackers. The main advantage of this new MPPT, compared with present trackers, is the elimination of reference (dummy) cells which results in a more efficient, less expensive, and more reliable PV system.

This paper presents a method for modeling and estimation of the state of charge (SOC) of lithium-ion (Li-Ion) batteries using neural networks (NNs) and the extended Kalman filter (EKF). The NN is trained offline using the data collected from the battery-charging process. This network finds the model needed in the state-space equations of the EKF, where the state variables are the battery terminal voltage at the previous sample and the SOC at the present sample. Furthermore, the covariance matrix for the process noise in the EKF is estimated adaptively. The proposed method is implemented on a Li-Ion battery to estimate online the actual SOC of the battery. Experimental results show a good estimation of the SOC and fast convergence of the EKF state variables.

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

Power-electronics-based microgrids (MGs) consist of a number of voltage source inverters (VSIs) operating in parallel. In this paper, the modeling, control design, and stability analysis of parallel-connected three-phase VSIs are derived. The proposed voltage and current inner control loops and the mathematical models of the VSIs are based on the stationary reference frame. A hierarchical control scheme for the paralleled VSI system is developed comprising two levels. The primary control includes the droop method and the virtual impedance loops, in order to share active and reactive powers. The secondary control restores the frequency and amplitude deviations produced by the primary control. Also, a synchronization algorithm is presented in order to connect the MG to the grid. Experimental results are provided to validate the performance and robustness of the parallel VSI system control architecture.

A historical overview of the activation and porosity of MOFs including strategies to design and preserve permanent porosity in MOFs.

The purpose of this paper is to revisit the maximum tensile stress (MTS) criterion to predict brittle fracture for mixed mode conditions. Earlier experimental results for brittle fracture of polymethylmethacrylate (PMMA) using angled cracked plates are also re‐examined. The role of the T ‐stress in brittle fracture for linear elastic materials is emphasized. The generalized MTS criterion is described in terms of mode I and II stress intensity factors, K I and K II and the T‐ stress (the stress parallel to the crack), and a fracture process zone, r c . The generalized MTS criterion is then compared with the earlier experimental results for PMMA subjected to mixed mode conditions. It is shown that brittle fracture can be controlled by a combination of singular stresses (characterized by K ) or non‐singular stress ( T ‐stress). The T ‐stress is also shown to have an influence on brittle fracture when the singular stress field is a result of mode II loading.

In this paper, we have trained several deep convolutional networks with introduced training techniques for classifying X-ray images into three classes: normal, pneumonia, and COVID-19, based on two open-source datasets. Our data contains 180 X-ray images that belong to persons infected with COVID-19, and we attempted to apply methods to achieve the best possible results. In this research, we introduce some training techniques that help the network learn better when we have an unbalanced dataset (fewer cases of COVID-19 along with more cases from other classes). We also propose a neural network that is a concatenation of the Xception and ResNet50V2 networks. This network achieved the best accuracy by utilizing multiple features extracted by two robust networks. For evaluating our network, we have tested it on 11302 images to report the actual accuracy achievable in real circumstances. The average accuracy of the proposed network for detecting COVID-19 cases is 99.50%, and the overall average accuracy for all classes is 91.4%.

Abstract It is predicted that the continuously increasing demand for the energy-critical element of lithium will soon exceed its availability, rendering it a geopolitically significant resource. The present work critically reviews recent reports on Li + selective membranes. Particular emphasis has been placed on the basic principles of the materials’ design for the development of membranes with nanochannels and nanopores with Li + selectivity. Fundamental and practical challenges, as well as prospects for the targeted design of Li + ion-selective membranes are also presented, with the goal of inspiring future critical research efforts in this scientifically and strategically important field.

Vehicular ad hoc network (VANET), a subclass of mobile ad hoc networks (MANETs), is a promising approach for future intelligent transportation system (ITS). These networks have no fixed infrastructure and instead rely on the vehicles themselves to provide network functionality. However, due to mobility constraints, driver behavior, and high mobility, VANETs exhibit characteristics that are dramatically different from many generic MANETs. This article provides a comprehensive study of challenges in these networks, which we concentrate on the problems and proposed solutions. Then we outline current state of the research and future perspectives. With this article, readers can have a more thorough understanding of vehicle ad hoc networking and the research trends in this area

Detailed theoretical and experimental analyses are presented for the comparison of two simple fast and reliable maximum power point tracking (MPPT) techniques for photovoltaic systems (PV): the voltage-based (VMPPT) and the current-based (CMPPT) approaches. A microprocessor-controlled tracker capable of online voltage and current measurements and programmed with both VMPPT and CMPPT algorithms is constructed. The load of the solar system is either a water pump or a resistance. Simulink facilities are used for simulation and modeling of the novel trackers. The main advantage of this new MPPT, as compared with present trackers, is the elimination of reference (dummy) cells, which results in a more efficient, less expensive, and more reliable PV system.

Convolutional neural networks (CNNs) have been the consensus for medical image segmentation tasks. However, they suffer from the limitation in modeling long-range dependencies and spatial correlations due to the nature of convolution operation. Although transformers were first developed to address this issue, they fail to capture low-level features. In contrast, it is demonstrated that both local and global features are crucial for dense prediction, such as segmenting in challenging contexts. In this paper, we propose HiFormer, a novel method that efficiently bridges a CNN and a transformer for medical image segmentation. Specifically, we design two multi-scale feature representations using the seminal Swin Transformer module and a CNN-based encoder. To secure a fine fusion of global and local features obtained from the two aforementioned representations, we propose a Double-Level Fusion (DLF) module in the skip connection of the encoder-decoder structure. Extensive experiments on various medical image segmentation datasets demonstrate the effectiveness of HiFormer over other CNN-based, transformer-based, and hybrid methods in terms of computational complexity, quantitative and qualitative results. Our code is publicly available at GitHub.

The concept of microgrid hierarchical control is presented recently. In this paper, a hierarchical scheme is proposed which includes primary and secondary control levels. The primary level comprises distributed generators (DGs) local controllers. The local controllers mainly consist of power, voltage and current controllers, and virtual impedance control loop. The central secondary controller is designed to manage the compensation of voltage unbalance at the point of common coupling (PCC) in an islanded microgrid. Unbalance compensation is achieved by sending proper control signals to the DGs local controllers. The design procedure of the control system is discussed in detail and the simulation results are presented. The results show the effectiveness of the proposed control structure in compensating the voltage unbalance.

The application of model predictive control in electrical drives has been studied extensively in the past decade. This article presents what the authors consider the most relevant contributions published in the last years, mainly focusing on three relevant issues: weighting factor calculation when multiple objectives are utilized in the cost function, current/torque harmonic distortion optimization when the power converter switching frequency is reduced, and robustness improvement under parameters uncertainties. Therefore, this article aims to enable readers to have a more precise overview while facilitating their future research work in this exciting area.

The unique properties of carbon nanotubes (CNTs) (such as their high surface to volume ratios, enhanced conductivity and strength, biocompatibility, ease of functionalization, optical properties, etc.) have led to their consideration to serve as novel drug and gene delivery carriers. CNTs are effectively taken up by many different cell types through several mechanisms. CNTs have acted as carriers of anticancer molecules (including docetaxel (DTX), doxorubicin (DOX), methotrexate (MTX), paclitaxel (PTX), and gemcitabine (GEM)), anti-inflammatory drugs, osteogenic dexamethasone (DEX) steroids, etc. In addition, the unique optical properties of CNTs have led to their use in a number of platforms for improved photo-therapy. Further, the easy surface functionalization of CNTs has prompted their use to deliver different genes, such as plasmid DNA (PDNA), micro-RNA (miRNA), and small interfering RNA (siRNA) as gene delivery vectors for various diseases such as cancers. However, despite all of these promises, the most important continuous concerns raised by scientists reside in CNT nanotoxicology and the environmental effects of CNTs, mostly because of their non-biodegradable state. Despite a lack of widespread FDA approval, CNTs have been studied for decades and plenty of in vivo and in vitro reports have been published, which are reviewed here. Lastly, this review covers the future research necessary for the field of CNT medicine to grow even further.

In this paper, an improved finite control set-model predictive control (FCS-MPC) with an optimized weighting factor is presented. The main goal of this paper is reducing the torque ripples when the FCS-MPC is implemented by means of the two-level inverter. For this purpose, the weighting factor is calculated via an optimization method. The optimization is based on dividing the control interval into two parts: active time for applying the active voltage vectors and zero time for applying the zero voltage. With this technique, the torque ripple is calculated as a function of weighting factor and it is optimized. The method is validated by simulations and experiments, using two-level inverter, at two speed regions (nominal speed and low speed). The results are compared with conventional FCS-MPC.

In this research a modeling technique for simulating the fatigue behaviour of laminated composite materials, with or without stress concentrations, called progressive fatigue damage modeling, is established. The model is capable of simulating the residual stiffness, residual strength and fatigue life of composite laminates with arbitrary geometry and stacking sequence under complicated fatigue loading conditions. The model is an integration of three major components: stress analysis, failure analysis, and material property degradation rules. A three-dimensional, nonlinear, finite element technique is developed for the stress analysis. By using a large number of elements near the edge of the stress concentration and at layer interfaces, the edge effect has been accounted for. Each element is considered to be an orthotropic material under multiaxial state of stress. Using the three-dimensional state of stress within each element, different failure modes of a unidirectional ply under multiaxial states of stress are detected by a set of fatigue failure criteria. An analytical technique, called the generalized residual material property degradation technique, is established to degrade the material properties of elements. This analytical technique is not restricted to the application of failure criteria to limited applied stress ratios. Based on the model, a computer code is developed that simulates cycle-by-cycle behaviour of composite laminates under fatigue loading.

Daunorubicin is a famous anthracycline anticancer chemotherapy drug with many side effects that is very important to measure in biological samples. A daunorubicin electrochemical biosensor was fabricated in this study using ds-DNA as the biorecognition element and glassy carbon electrode (GCE) amplified by Pt/SWCNTs as a sensor. The synthetization of Pt/SWCNTs was done by the polyol method, and their characterization was accomplished via XRD, EDS, and TEM methods. The results showed a diameter of about 5.0 nm for the Pt nanoparticle decorated at the surface of SWCNTs. The morphological and conductivity properties of Pt/SWCNTs/GCE were investigated by EIS and AFM methods, and the results confirmed that Pt/SWCNTs/GCE had a high surface area and high conductivity. ds-DNA/Pt/SWCNTs/GCE showed an oxidation signal relative to that of the guanine base at the potential of 847 mV and a positive shift after interaction with the daunorubicin anticancer drug. This point confirms the intercalation reaction between the guanine base in the ds-DNA structure and the drug that could be used as an analytical factor for the determination of daunorubicin. Furthermore, molecular docking study is used to predict the interaction site of daunorubicin with DNA. It is found that daunorubicin interacts with guanine bases of DNA via an intercalative mode. Kinetic investigation showed an association equilibrium constant (Ka) of about 5.044 × 103 M–1 between ds-DNA and daunorubicin. The differential pulse voltammetric results showed a linear dynamic range of 4.0 nM to 250.0 μM with a detection limit of 1.0 nM for determination of daunorubicin on the surface of ds-DNA/Pt/SWCNTs/GCE. Finally, ds-DNA/Pt/SWCNTs/GCE was successfully used for the determination of daunorubicin in injection samples with a recovery range of 98.27–10313%.

Resilience to disruptions and sustainability are both of paramount importance to supply chains. However, the interactions between the two have not been thoroughly explored in the academic literature. We attempt to contribute to this area by presenting a hybrid methodology for the design of a sustainable supply network that performs resiliently in the face of random disruptions. A stochastic bi-objective optimisation model is developed that utilises a fuzzy c-means clustering method to quantify and assess the sustainability performance of the suppliers. The proposed model determines outsourcing decisions and resilience strategies that minimise the expected total cost and maximise the overall sustainability performance in disruptions. Important managerial insights and practical implications are obtained from the model implementation in a case study of plastic pipe industry.

This paper proposes a fast and reliable method for anomaly detection and localization in video data showing crowded scenes. Time-efficient anomaly localization is an ongoing challenge and subject of this paper. We propose a cubicpatch- based method, characterised by a cascade of classifiers, which makes use of an advanced feature-learning approach. Our cascade of classifiers has two main stages. First, a light but deep 3D auto-encoder is used for early identification of "many" normal cubic patches. This deep network operates on small cubic patches as being the first stage, before carefully resizing remaining candidates of interest, and evaluating those at the second stage using a more complex and deeper 3D convolutional neural network (CNN). We divide the deep autoencoder and the CNN into multiple sub-stages which operate as cascaded classifiers. Shallow layers of the cascaded deep networks (designed as Gaussian classifiers, acting as weak single-class classifiers) detect "simple" normal patches such as background patches, and more complex normal patches are detected at deeper layers. It is shown that the proposed novel technique (a cascade of two cascaded classifiers) performs comparable to current top-performing detection and localization methods on standard benchmarks, but outperforms those in general with respect to required computation time.

Tissue engineering is a rapidly-growing approach to replace and repair damaged and defective tissues in the human body. Every year, a large number of people require bone replacements for skeletal defects caused by accident or disease that cannot heal on their own. In the last decades, tissue engineering of bone has attracted much attention from biomedical scientists in academic and commercial laboratories. A vast range of biocompatible advanced materials has been used to form scaffolds upon which new bone can form. Carbon nanomaterial-based scaffolds are a key example, with the advantages of being biologically compatible, mechanically stable, and commercially available. They show remarkable ability to affect bone tissue regeneration, efficient cell proliferation and osteogenic differentiation. Basically, scaffolds are templates for growth, proliferation, regeneration, adhesion, and differentiation processes of bone stem cells that play a truly critical role in bone tissue engineering. The appropriate scaffold should supply a microenvironment for bone cells that is most similar to natural bone in the human body. A variety of carbon nanomaterials, such as graphene oxide (GO), carbon nanotubes (CNTs), fullerenes, carbon dots (CDs), nanodiamonds and their derivatives that are able to act as scaffolds for bone tissue engineering, are covered in this review. Broadly, the ability of the family of carbon nanomaterial-based scaffolds and their critical role in bone tissue engineering research are discussed. The significant stimulating effects on cell growth, low cytotoxicity, efficient nutrient delivery in the scaffold microenvironment, suitable functionalized chemical structures to facilitate cell-cell communication, and improvement in cell spreading are the main advantages of carbon nanomaterial-based scaffolds for bone tissue engineering.