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Broadband metamaterial absorbers in the terahertz (THz) range are of great importance because of their applications, especially in the field of THz detection and imaging. Characteristics of an ideal absorber include wide bandwidth and insensitivity to polarization and incidence angle. Here we propose a new structure of an ultra-broadband THz absorber by adding an excess dielectric layer on a metamaterial structure with two stacked chromium square resonators. The excess layer besides protecting chromium resonator from oxidation creates an extra Fabry–Perot cavity that extends the bandwidth of the structure. The relative bandwidth of this structure for 90% absorption exceeds 150% and covers frequencies ranging from 0.52 to 3.66 THz, which is an advantage over the works reported in the literature review. The number of resonating layers is decreased related to the earlier works too. The proposed structure is not sensitive to the polarization of the incident radiation, at the same time its absorption is almost insensitive to the incident angle too. For both TE and TM polarization the absorption stays more than 80% up to 60° of the incident angles. Furthermore, the insensitivity of the proposed device to misalignment between layers is evaluated for misalignment up to $28~\mu \text{m}$ . The results show that the structure is insensitive to misalignment which makes its fabrication easy. We have simulated the proposed structure using the numerical finite element method (FEM). Also, we have extracted a circuit model for the suggested absorber which is in accordance to the numerical model and can be used to reduce the designing process time. Correspondingly, we efficiently explain the absorption mechanism using the circuit model and magnetic field analysis in the structure. We have shown that the broad bandwidth of the structure is the result of coupling between various modes of Fabry–Perot (FP) cavity resonances and surface plasmons (SPs). The FP resonances are coupled through the gap resonators between the different layers. This means absorbers are more compact than the convectional $\text{n}\lambda $ /4 wave plate absorbers.

Data mining and in particular forecasting tools and techniques are being increasingly exploited by businesses to predict customer behavior and to formulate effective marketing programs. Conventionally, customer segmentation approaches are utilized when dealing with a large population of customers. Inspired by this idea, a new methodology is proposed in this study to perform segment-level customer behavior forecasting. To keep the dynamic nature of customer behavior, customer behavior is represented as a time series. Therefore, customer behavior forecasting is changed into a time series forecasting problem. The proposed methodology contains two main components i.e. clustering and forecasting. In the clustering phase, time series are clustered using time series clustering algorithms, and then, in the forecasting phase, the behavior of each segment is predicted via time series forecasting techniques. The main objective is to predict future behavior at segment level. The forecasting component also consists of a combined method exploiting the concept of forecast fusion. The combined method employs a pool of forecasters both from traditional time series forecasting and computational intelligence methods. To test the usefulness of the proposed method, a case study is carried out using the data of customers’ point of sale (POS) in a bank. The results of the experiments demonstrate that the combined method outperforms all other individual forecasters in terms of symmetric mean absolute percentage error (SMAPE). The proposed methodology can be correspondingly applied in other areas and applications of time series forecasting.

Purpose The purpose of this paper is to propose a new methodology that handles the issue of the dynamic behavior of customers over time. Design/methodology/approach A new methodology is presented based on time series clustering to extract dominant behavioral patterns of customers over time. This methodology is implemented using bank customers’ transactions data which are in the form of time series data. The data include the recency (R), frequency (F) and monetary (M) attributes of businesses that are using the point-of-sale (POS) data of a bank. This data were obtained from the data analysis department of the bank. Findings After carrying out an empirical study on the acquired transaction data of 2,531 business customers that are using POS devices of the bank, the dominant trends of behavior are discovered using the proposed methodology. The obtained trends were analyzed from the marketing viewpoint. Based on the analysis of the monetary attribute, customers were divided into four main segments, including high-value growing customers, middle-value growing customers, prone to churn and churners. For each resulted group of customers with a distinctive trend, effective and practical marketing recommendations were devised to improve the bank relationship with that group. The prone-to-churn segment contains most of the customers; therefore, the bank should conduct interesting promotions to retain this segment. Practical implications The discovered trends of customer behavior and proposed marketing recommendations can be helpful for banks in devising segment-specific marketing strategies as they illustrate the dynamic behavior of customers over time. The obtained trends are visualized so that they can be easily interpreted and used by banks. This paper contributes to the literature on customer relationship management (CRM) as the proposed methodology can be effectively applied to different businesses to reveal trends in customer behavior. Originality/value In the current business condition, customer behavior is changing continually over time and customers are churning due to the reduced switching costs. Therefore, choosing an effective customer segmentation methodology which can consider the dynamic behaviors of customers is essential for every business. This paper proposes a new methodology to capture customer dynamic behavior using time series clustering on time-ordered data. This is an improvement over previous studies, in which static segmentation approaches have often been adopted. To the best of the authors’ knowledge, this is the first study that combines the recency, frequency, and monetary model and time series clustering to reveal trends in customer behavior.

Financial market analysis, especially the prediction of movements of stock prices, is a challenging problem. The nature of financial time-series data, being non-stationary and nonlinear, is the main cause of these challenges. Deep learning models have led to significant performance improvements in many problems coming from different domains, including prediction problems of financial time-series data. Although the prediction performance is the main goal of such models, dealing with ultra high-frequency data sets restrictions in terms of the number of model parameters and its inference speed. The Temporal Attention-Augmented Bilinear network was recently proposed as an efficient and high-performing model for Limit Order Book time-series forecasting. In this paper, we propose a low-rank tensor approximation of the model to further reduce the number of trainable parameters and increase its speed.

Forecasting future behavior of customers has significant importance in businesses. Consequently, data mining and prediction tools are increasingly utilized by firms to predict customer behavior and to devise effective marketing programs. When dealing with multiple time series data, we encounter with the problem that how to use those time series to forecast the behavior of all customers more accurately. In this study we proposed a methodology to create customer segments based on past data, create Segment-Wise forecasts and then discover the future behavior of each segment. The proposed methodology utilizes existing data mining and prediction tools including time series clustering and forecasting, but combines them in a unique way that results in higher level models in terms of accuracy than baseline model. The proposed methodology has substantial application in marketing for any firm in any domain where there is a need to forecast future behavior of different customer group in an effective manner.

Many genes induce angiogenesis in tumors, and among them, Notch family genes have received particular attention due to their extensive network of connections with other genes active in this function. Suppression of angiogenic signaling has been studied in various cancers, confirming Notch's fundamental and extensive role. According to studies, four Notch genes work independently with many genes such as vascular endothelial growth factor, phosphatase and tensin homolog, Phosphoinositide 3-kinase/Akt, and matrix metalloproteinases, and so many other genes, as well as proteins (such as hypoxia-inducible factor-1 alpha) significantly affect tumor angiogenesis. Notch1 regular activity in a healthy person causes angiogenesis in body tissues, controlled by normal Notch2 activity. However, in many cases of glioblastoma, whether on patients or tumor xenografts or in vivo models, a mutation in one of these two essential genes or at least one of the genes and proteins that affected by them can cause better angiogenesis in hypoxic conditions and lead to become an invasive tumor. In this review, we examined the contrasting activity of Notch1 and Notch2 and the signaling cascade that each generates in the angiogenesis of glioblastoma, the most invasive cancer of the central nervous system. [GMJ.2021;10:e2091]

User-Generated-Content (UGC) in the form of online reviews can be an invaluable source of information for both customers and businesses. Sentiment analysis and opinion mining tools and techniques have been proposed in the literature to extract knowledge from online reviews. Aspect-based opinion mining which has gained growing attention mainly has two tasks including aspect extraction and sentiment polarity detection. Once an aspect-based opinion mining task has been accomplished; a bag of sentiments will be achieved. In many cases, it is necessary to obtain an overall sentiment about a typical aspect. In this study, we have proposed a sentiment aggregation system based on weighted selective aggregated majority OWA (WSAM-OWA). WSAM-OWA considers both the majority and the degree of importance of information source in the process of aggregation. The proposed system exploits the helpfulness rating of reviews in determining the reliability and credibility of each sentiment. A case study was conducted to illustrates the usefulness of the proposed system. The results of this study demonstrated that the proposed sentiment aggregation system could be incorporated in opinion mining systems.

In a business environment with strict competition among firms, accurate demand forecasting is not straightforward. In this paper, a forecasting method is proposed, which has a strong capability of predicting highly fluctuating demand data. Therefore, in this paper we propose a demand forecasting method based on multi-layer LSTM networks. The proposed method automatically selects the best forecasting model by considering different combinations of LSTM hyperparameters for a given time series using the grid search method. It has the ability to capture nonlinear patterns in time series data, while considering the inherent characteristics of non-stationary time series data. The proposed method is compared with some well-known time series forecasting techniques from both statistical and computational intelligence methods using demand data of a furniture company. These methods include autoregressive integrated moving average (ARIMA), exponential smoothing (ETS), artificial neural network (ANN), K-nearest neighbors (KNN), recurrent neural network (RNN), support vector machines (SVM) and single layer LSTM. The experimental results indicate that the proposed method is superior among the tested methods in terms of performance measures.

In today’s fast moving world of marketing from product-orientation to customer-orientation, the management of customer treatment can be seen as a key to achieve revenue growth and profitability. Knowledge of customer behavior can help marketing managers re-evaluate their strategies with the customers and plan to improve and expand their application of the most effective strategies. B2B or business customers are more complex, their buying process is more complicated and their sales value is greater. The business marketers usually prefer to cooperate with fewer but larger buyers than the final consumer marketer. As a business transaction requires more decision makings and more professional buying effort than the consumer market does, the efficient relationship with business customers is of paramount importance. Most customer segmentation approaches based on customer value fail to account for the factor of time and the trend of value changes in their analysis. In this article, we classify customers based on their value using the RFM model and K-means clustering method. Then, an assessment of changes over several periods of time is carried out. The originality of this research lies in its incorporation of time and trend of customer value changes in improving the accuracy of predictions based on the past behavior of customers. For this purpose, we used the POS customer transactions.

Using first-principles density functional theory calculations, the mechanical and electronic properties of the three main (β12, χ3, and striped) phases of single-layer borophene sheets are calculated under in-plane uniaxial/biaxial strain, including the harmonic strain-energy regions of β12, χ3, and striped phases over the strain ranges of −3.5%–3.5%, −4.5%–4.5%, and −2.5%–2.5%, respectively, along the x direction (the direction of the highest bond orientation). We introduce a method by which the nonlinear behavior of these and any other two-dimensional materials can be investigated even above their ultimate strains, beyond which no-uniform plastic deformation occurs. Defining an appropriate deformation, and utilizing both continuum modeling and special equations based on the density functional theory, a method of computing second-, third-, and fourth-order elastic constants of the three different phases of borophene is presented that utilizes rectangular unit cells, which can substitute for any two-dimensional unit cell. Using this new method, 4 independent second-order, 6 third-order, and 9 fourth-order elastic constants are calculated, which is the complete set of elastic constants for two-dimensional structures. The electronic band structure of borophene shows anisotropic electronic behavior. Despite the metallic character of borophene sheets, applying directional strain based on deformation matrices creates a bandgap in some regions of the Brillouin zones, opening up the possibility of mechanical control of electronic properties.

A new structure is suggested for simultaneous microwave chirped pulse generation and array antenna beam steering. It is based on using a multi-channel flber Bragg grating in a photonic microwave delay-line fllter. The paper presents a feasibility study of the idea, discussing the main performance parameters of both signal generation and beam steering functions. Speciflcally, it focuses on the efiects of wavelength tuning, resolution and accuracy. The study shows that custom ofi-the-shelf components could be used to implement an all optical system capable of generating chirped pulses while steering the radiation pattern of a small sized antenna array. The advantages of the structure for avoiding single sideband modulation di-culties and also for the compensation of the multichannel flber Bragg grating inaccuracies are also discussed.

A semi-analytical formulation is presented to quantify the effects of group delay ripple on the performance of an optical beam-former. For delay elements, implemented with commercially available multi-channel fiber Bragg gratings, an impulse response is introduced and calculated for the group delay ripple. To do this, the transfer function of the fiber Bragg grating is parted into two dispersive and non-dispersive sections. This will also ease the demonstration of the fiber Bragg grating as a true time delay element. The formulation is applied to an ultra-wideband pulsed array of antennas. As an example the array factor and electric field patterns of a four element antenna array is derived, using certain available measured data. The distortion in the temporal-angular radiation patterns due to the non-ideal true time delay behavior may be predicted using the presented work. It also facilitates to distinguish between the effects of long and short ripples. This new mathematical investigation is applicable to any linearly chirped fiber Bragg grating other than multi-channel ones. It also includes corrections to previously published formulations.

Next generation wideband and ultra-wideband radar antenna arrays need efficient true-time delay networks for array control. Optical beam-forming networks have been considered as a promising solution in such arrays. Performance analysis of optical beam-formers is thus quiet necessary. In this paper signal to noise ratio and dynamic range of simple optical beam-forming structures are investigated with a custom microwave CAD tool. Two structures with optical and RF power combining methods are discussed. With the proposed devices' parameters, the optical combining method shows better performance for low and moderate optical powers.

Next generation wideband and ultra-wideband radar antenna arrays need efficient true-time delay networks for array control. Following the great interest for optical control of antenna array beam steering, performance analysis of optical beam-formers is a must. In this paper signal to noise ratio and dynamic range of simple optical beam-forming structures with components off the shelf are investigated theoretically. Especially the effects of certain power combining components such as arrayed wave guide gratings, broadband passive optical combiners and microwave combiners inspected. It turns up that an efficient microwave combiner would surpass the optical combiners in both SNR and dynamic range.

We use the 3D vectorial Gaussian beam tracing method to model millimeter wave propagation. We simulate the propagation of Gaussian beams in a large scale environment at 62.4 GHz. First, a known far field pattern of the transmitter antenna is expanded into a set of Gaussian beams. We chose Gabor expansion based method to expanding radiating field into Gaussian beams. Then, a Gaussian beam tracing model considering reflections and transmission is used to represent urban microcell propagation. We use a phase matching method to find reflected Gaussian beam The results of the Gaussian beam tracing simulations including line-of-sight (LOS) and non-line-of-sight (NLOS) propagation are compared with the results of the ray tracing method. The results are in good agreement with results of ray tracing technique on the same geometry. Overall coverage map shows the dominant effect of shadowing in the millimeter wave band. The tracing technique is faster and more promising compared to exhausted ray tracing methods.

Point matching technique is applied to a pyramidal horn antenna to expand its aperture field in a wide angular range in terms of Gaussian beams using the Jacobi iterative method. The obtained far field pattern is compared with the results of Gabor's Gaussian beam expansion. Convergence, speed, the impact of initial condition and the selection of sample points is discussed. The implemented point matching scheme is almost as fast and accurate as the Gabor's expansion. It can even be faster and more accurate in some cases especially when the Gaussian beams are neither wide-waisted nor narrow-waisted.

The 3D vectorial Gaussian beam tracing method is used to model a short 62.4-GHz link in a simple urban environment. First the Gabor expansion method with a wide-waisted window function is utilized to expand the known far field pattern of the transmitter antenna into a set of Gaussian beams and then these beams are propagated in space until they impinge on a building wall or ground, on which a phase matching method is used to find the reflected Gaussian beams. We will also concisely introduce a new method to consider 3D GB diffraction when incident on a wedge. Overall coverage map shows the dominant effect of shadowing in the millimeter wave band. Received mean signal power in a line of sight route is produced to be compared with results from an exhaustive ray tracing method done by other authors.

Limitations imposed by the non-linearity of optical links, in the front end of a receiving array antenna, on the performance of a pulse compression radar are studied. Particularly linear frequency-modulated waveforms backscattered from a pair of targets are inspected. Inter-modulation and gain compression by noise are presented as two remarkable phenomena. For an interconnection of two dual-drive Mach-Zehnder modulators, the sensitivity of the compressed echo at the output of a matched filter receiver to certain tunable parameters is studied numerically. Finally, certain values of DC bias and phase shift between the modulators- electrodes are recommended.