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Background: DNA-binding proteins (DNA-BPs) are the proteins that bind and interact with DNA. DNA-BPs regulate and affect numerous biological processes, such as, transcription and DNA replication, repair, and organization of the chromosomal DNA. Very few proteins, however, are DNA-binding in nature. Therefore, it is necessary to develop an efficient predictor for identifying DNA-BPs. Result: In this work, we have proposed new benchmark datasets for the DNA-binding protein prediction problem. We discovered several quality concerns with the widely used benchmark datasets, PDB1075 (for training) and PDB186 (for independent testing), which necessitated the preparation of new benchmark datasets. Our proposed datasets UNIPROT1424 and UNIPROT356 can be used for model training and independent testing respectively. We have retrained selected state-of-the-art DNA-BP predictors in the new dataset and reported their performance results. We also trained a novel predictor using the new benchmark dataset. We extracted features from various feature categories, then used a Random Forest classifier and Recursive Feature Elimination with Cross-validation (RFECV) to select the optimal set of 452 features. We then proposed a stacking ensemble architecture as our final prediction model. Named Stacking Ensemble Model for DNA-binding Protein Prediction, or StackDPP in short, our model achieved 0.92, 0.92 and 0.93 accuracy in 10-fold cross-validation, jackknife and independent testing respectively. Conclusion: StackDPP has performed very well in cross-validation testing and has outperformed all the state-of-the-art prediction models in independent testing. Its performance scores in cross-validation testing generalized very well in the independent test set. The source code of the model is publicly available at https://github.com/HasibAhmed1624/StackDPP. Therefore, we expect this generalized model can be adopted by researchers and practitioners to identify novel DNA-binding proteins. [ABSTRACT FROM AUTHOR]

Motivation Spatial domain identification is a very important problem in the field of spatial transcriptomics. The state-of-the-art solutions to this problem focus on unsupervised methods, as there is lack of data for a supervised learning formulation. The results obtained from these methods highlight significant opportunities for improvement. Results In this article, we propose a potential avenue for enhancement through the development of a semi-supervised convolutional neural network based approach. Named "ScribbleDom", our method leverages human expert's input as a form of semi-supervision, thereby seamlessly combines the cognitive abilities of human experts with the computational power of machines. ScribbleDom incorporates a loss function that integrates two crucial components: similarity in gene expression profiles and adherence to the valuable input of a human annotator through scribbles on histology images, providing prior knowledge about spot labels. The spatial continuity of the tissue domains is taken into account by extracting information on the spot microenvironment through convolution filters of varying sizes, in the form of "Inception" blocks. By leveraging this semi-supervised approach, ScribbleDom significantly improves the quality of spatial domains, yielding superior results both quantitatively and qualitatively. Our experiments on several benchmark datasets demonstrate the clear edge of ScribbleDom over state-of-the-art methods—between 1.82% to 169.38% improvements in adjusted Rand index for 9 of the 12 human dorsolateral prefrontal cortex samples, and 15.54% improvement in the melanoma cancer dataset. Notably, when the expert input is absent, ScribbleDom can still operate, in a fully unsupervised manner like the state-of-the-art methods, and produces results that remain competitive. Availability and implementation Source code is available at Github (https://github.com/1alnoman/ScribbleDom) and Zenodo (https://zenodo.org/badge/latestdoi/681572669). [ABSTRACT FROM AUTHOR]

Motivation Spatial transcriptomics (ST) can reveal the existence and extent of spatial variation of gene expression in complex tissues. Such analyses could help identify spatially localized processes underlying a tissue's function. Existing tools to detect spatially variable genes assume a constant noise variance across spatial locations. This assumption might miss important biological signals when the variance can change across locations. Results In this article, we propose NoVaTeST , a framework to identify genes with location-dependent noise variance in ST data. NoVaTeST models gene expression as a function of spatial location and allows the noise to vary spatially. NoVaTeST then statistically compares this model to one with constant noise and detects genes showing significant spatial noise variation. We refer to these genes as "noisy genes." In tumor samples, the noisy genes detected by NoVaTeST are largely independent of the spatially variable genes detected by existing tools that assume constant noise, and provide important biological insights into tumor microenvironments. Availability and implementation An implementation of the NoVaTeST framework in Python along with instructions for running the pipeline is available at https://github.com/abidabrar-bracu/NoVaTeST. [ABSTRACT FROM AUTHOR]

Motivation Protein structure provides insight into how proteins interact with one another as well as their functions in living organisms. Protein backbone torsion angles (⁠ ϕ and ψ ⁠) prediction is a key sub-problem in predicting protein structures. However, reliable determination of backbone torsion angles using conventional experimental methods is slow and expensive. Therefore, considerable effort is being put into developing computational methods for predicting backbone angles. Results We present SAINT-Angle, a highly accurate method for predicting protein backbone torsion angles using a self-attention-based deep learning network called SAINT, which was previously developed for the protein secondary structure prediction. We extended and improved the existing SAINT architecture as well as used transfer learning to predict backbone angles. We compared the performance of SAINT-Angle with the state-of-the-art methods through an extensive evaluation study on a collection of benchmark datasets, namely, TEST2016, TEST2018, TEST2020-HQ, CAMEO and CASP. The experimental results suggest that our proposed self-attention-based network, together with transfer learning, has achieved notable improvements over the best alternate methods. Availability and implementation SAINT-Angle is freely available as an open-source project at https://github.com/bayzidlab/SAINT-Angle. Supplementary information Supplementary data are available at Bioinformatics Advances online. [ABSTRACT FROM AUTHOR]

The size of transistors has drastically reduced over the years. Interconnects have likewise also been scaled down. Today, conventional copper (Cu)-based interconnects face a significant impediment to further scaling since their electrical conductivity decreases at smaller dimensions, which also worsens the signal delay and energy consumption. As a result, alternative scalable materials such as semi-metals and 2D materials were being investigated as potential Cu replacements. In this paper, we experimentally showed that CoPt can provide better resistivity than Cu at thin dimensions and proposed hybrid poly-Si with a CoPt coating for local routing in standard cells for compactness. We evaluated the performance gain for DRAM/eDRAM, and area vs. performance trade-off for D-Flip-Flop (DFF) using hybrid poly-Si with a thin film of CoPt. We gained up to a 3-fold reduction in delay and a 15.6% reduction in cell area with the proposed hybrid interconnect. We also studied the system-level interconnect design using NbAs, a topological semi-metal with high electron mobility at the nanoscale, and demonstrated its advantages over Cu in terms of resistivity, propagation delay, and slew rate. Our simulations revealed that NbAs could reduce the propagation delay by up to 35.88%. We further evaluated the potential system-level performance gain for NbAs-based interconnects in cache memories and observed an instructions per cycle (IPC) improvement of up to 23.8%. [ABSTRACT FROM AUTHOR]

Lysine succinylation is a kind of post-translational modification (PTM) that plays a crucial role in regulating the cellular processes. Aberrant succinylation may cause inflammation, cancers, metabolism diseases and nervous system diseases. The experimental methods to detect succinylation sites are time-consuming and costly. This thus calls for computational models with high efficacy, and attention has been given in the literature to develop such models, albeit with only moderate success in the context of different evaluation metrics. One crucial aspect in this context is the biochemical and physicochemical properties of amino acids, which appear to be useful as features for such computational predictors. However, some of the existing computational models did not use the biochemical and physicochemical properties of amino acids. In contrast, some others used them without considering the inter-dependency among the properties. The combinations of biochemical and physicochemical properties derived through our optimization process achieve better results than the results achieved by combining all the properties. We propose three deep learning architectures: CNN+Bi-LSTM (CBL), Bi-LSTM+CNN (BLC) and their combination (CBL_BLC). We find that CBL_BLC outperforms the other two. Ensembling of different models successfully improves the results. Notably, tuning the threshold of the ensemble classifiers further improves the results. Upon comparing our work with other existing works on two datasets, we successfully achieve better sensitivity and specificity by varying the threshold value. [ABSTRACT FROM AUTHOR]

Customer churn is one of the most critical issues faced by the telecommunication industry (TCI). Researchers and analysts leverage customer relationship management (CRM) data through the use of various machine learning models and data transformation methods to identify the customers who are likely to churn. While several studies have been conducted in the customer churn prediction (CCP) context in TCI, a review of performance of the various models stemming from these studies show a clear room for improvement. Therefore, to improve the accuracy of customer churn prediction in the telecommunication industry, we have investigated several machine learning models, as well as, data transformation methods. To optimize the prediction models, feature selection has been performed using univariate technique and the best hyperparameters have been selected using the grid search method. Subsequently, experiments have been conducted on several publicly available TCI datasets to assess the performance of our models in terms of the widely used evaluation metrics, such as AUC, precision, recall, and F-measure. Through a rigorous experimental study, we have demonstrated the benefit of applying data transformation methods as well as feature selection while training an optimized CCP model. Our proposed technique improved the prediction performance by up to 26.2% and 17% in terms of AUC and F-measure, respectively. [ABSTRACT FROM AUTHOR]

In Bangladesh, groundwater is the main source of both drinking water and irrigation. Suction lift pumps and force mode of operation are the predominant technologies for groundwater abstraction in Bangladesh. For a sustainable usage policy, it is thus important to identify which technology would be more appropriate for which area in Bangladesh. With that aim in mind, this paper proposes a methodology that leverages the power of machine learning that can potentially learn intricate relationships between the (annual maximum) groundwater level (GWL) and the relevant hydrogeological factors (HGFs). A number of machine learning algorithms—both classification and regression models—was trained. Our classification models were trained as a binary classifier to predict the abstraction technology of a particular point. Notably, our best classification model was based on the Random Forest algorithm, which achieved an accuracy of 91% and an excellent value of 96% for the area under receiver operating characteristics curve, indicating its strong discriminant capability. We also identified (elevation derived from) digital elevation model, specific yield and lithology as the three most important HGFs for GWL in Bangladesh. On the other hand, to predict the actual (annual maximum) GWL, we employed a two-stage approach, where we first employed the above-mentioned classification model to identify the suitable abstraction technology for the point of interest and subsequently predict the actual GWL using the appropriate Random Forest regressor. This also had a reasonable accuracy (minimum absolute error was less than 1 for suction mode and less than 5 for the force mode). Finally, using our prediction models, we prepared groundwater (technology) maps for the whole Bangladesh. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic is taking a toll on the social, economic, and psychological well-being of people. During this pandemic period, people have utilized social media platforms (e.g., Twitter) to communicate with each other and share their concerns and updates. In this study, we analyzed nearly 25M COVID-19 related tweets generated from 20 different countries and 28 states of USA over a month. We leveraged sentiment analysis and topic modeling over this collection and clustered different geolocations based on their sentiment. Our analysis identified 3 geo-clusters (country- and US state-based) based on public sentiment and discovered 15 topics that could be summarized under three main themes: government actions, medical issues, and people's mood during the home quarantine. The proposed computational pipeline has adequately captured the Twitter population's emotion and sentiment, which could be linked to government/policy makers' decisions and actions (or lack thereof). We believe that our analysis pipeline could be instrumental for the policymakers in sensing the public emotion/support with respect to the interventions/actions taken, for example, by the government instrumentality. [ABSTRACT FROM AUTHOR]

Based on the modified harmonic balance method, an analytical method has been developed for handling the forced Van der Pol vibration equation. Usually, a system of nonlinear algebraic equations arises within the unfamiliar coefficients in several harmonics terms and the frequency of the forcing term. A numerical technique has been applied to handle those nonlinear algebraic equations in the classical harmonic balance method. In our study, a system of linear algebraic equations is calculated with the aid of a single nonlinear one. The solutions attained by the suggested scheme have been likened to the results acquired by the well-known Runge-Kutta method, and these results display very nice harmony with the result obtained by the mentioned method. Also, it is noticed that the proposed technique is straightforward and gives the desired results in the whole solution domain. [ABSTRACT FROM AUTHOR]

Motivation Species tree estimation from genes sampled from throughout the whole genome is complicated due to the gene tree–species tree discordance. Incomplete lineage sorting (ILS) is one of the most frequent causes for this discordance, where alleles can coexist in populations for periods that may span several speciation events. Quartet-based summary methods for estimating species trees from a collection of gene trees are becoming popular due to their high accuracy and statistical guarantee under ILS. Generating quartets with appropriate weights, where weights correspond to the relative importance of quartets, and subsequently amalgamating the weighted quartets to infer a single coherent species tree can allow for a statistically consistent way of estimating species trees. However, handling weighted quartets is challenging. Results We propose wQFM, a highly accurate method for species tree estimation from multi-locus data, by extending the quartet FM (QFM) algorithm to a weighted setting. wQFM was assessed on a collection of simulated and real biological datasets, including the avian phylogenomic dataset, which is one of the largest phylogenomic datasets to date. We compared wQFM with wQMC, which is the best alternate method for weighted quartet amalgamation, and with ASTRAL, which is one of the most accurate and widely used coalescent-based species tree estimation methods. Our results suggest that wQFM matches or improves upon the accuracy of wQMC and ASTRAL. Availability and implementation Datasets studied in this article and wQFM (in open-source form) are available at https://github.com/Mahim1997/wQFM-2020. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Cognitive radio network (CRN) is considered to be the future technology for the fifth generation of cellular wireless standards (5G). CRN is based on a complex spectrum allocation system, and licensed primary users (PUs) or unlicensed secondary users (SUs) are allowed to share the spectrum, provided that they do not cause significant interference. In cognitive radio, it is important to control the transmission power of SUs so that the interference should not be harmful to the quality of service of PUs. In this paper, the authors addressed the impact of system imperfections on the overall performance of multi-carrier code division multiple access (MC-CDMA)-based CRN system. A new expression of the signal-to-interference-noise ratio (SINR) for MC-CDMA-based CRN system over a Rayleigh fading channel without any power control condition among the PUs and SUs is derived and investigated. The performance of MC-CDMA-based CRN system over the frequency selective multi-path fading channel is examined by varying the number of users, the signal-to-noise-ratio (SNR) per bit, number of fading path and number of subcarriers, respectively. From the simulation results, we have seen that the SINR performance is affected by these parameters. The analysis presented in this paper shows the impact of lack of a controlling mechanism on transmitted signal power affecting the between PUs and SUs ultimately system performance. The result of the analysis will provide relevant information to design the physical layer protocol for high speed CRN system for 5G. [ABSTRACT FROM AUTHOR]

Both the internet-connected devices, i.e. IoT and Cognitive Radio Network (CRN) are considered to be the future technologies for the fifth generation of cellular wireless standards (5G). On the one hand, Internet of Things (IoT) focuses primarily on how to allow general objects to see, hear, and smell their own physical environment and make them connected to share the observations. On the other hand, a CRN is based on a complex spectrum allocation system, and licenced primary users (PUs) or unlicenced secondary users (SUs) are allowed to share the spectrum, provided they do not cause significant interference. The IoTs are meaningless if IoT objects are not equipped with cognitive radio capability. In cognitive radio, it is important to control the transmission power of SUs so that the interference should not be harmful to the quality of service of PUs. In this paper, the authors addressed the effects of imperfect power control between primary users (PUs) and the secondary users (SUs) of an IoT-based CRN. The effect of the co-channel interference (CCI) and adjacent channel interferences (ACIs) occurring in CRN using MC-CDMA system is also analysed. A new expression of the signal-to-interference-noise ratio (SINR) for CRN-based MC-CDMA system over a Nakagami- m fading channel with imperfect power control condition is derived and investigated. The performance of IoT-based CRN using MC-CDMA system over the frequency selective multipath fading channel is examined with varying the number of users, the SINR per bit, number of fading path and number of sub-carriers. From the simulation results, we have seen that the SINR performance is affected by these parameters. The result of the analysis will provide relevant information to design the physical layer protocol for high-speed IoT-based CRN system for 5G. [ABSTRACT FROM AUTHOR]

Background: Segmentation of nuclei in cervical cytology pap smear images is a crucial stage in automated cervical cancer screening. The task itself is challenging due to the presence of cervical cells with spurious edges, overlapping cells, neutrophils, and artifacts. Methods: After the initial preprocessing steps of adaptive thresholding, in our approach, the image passes through a convolution filter to filter out some noise. Then, contours from the resultant image are filtered by their distinctive contour properties followed by a nucleus size recovery procedure based on contour average intensity value. Results: We evaluate our method on a public (benchmark) dataset collected from ISBI and also a private real dataset. The results show that our algorithm outperforms other state-of-the-art methods in nucleus segmentation on the ISBI dataset with a precision of 0.978 and recall of 0.933. A promising precision of 0.770 and a formidable recall of 0.886 on the private real dataset indicate that our algorithm can effectively detect and segment nuclei on real cervical cytology images. Tuning various parameters, the precision could be increased to as high as 0.949 with an acceptable decrease of recall to 0.759. Our method also managed an Aggregated Jaccard Index of 0.681 outperforming other state-of-the-art methods on the real dataset. Conclusion: We have proposed a contour property-based approach for segmentation of nuclei. Our algorithm has several tunable parameters and is flexible enough to adapt to real practical scenarios and requirements. [ABSTRACT FROM AUTHOR]

Motivation Protein structures provide basic insight into how they can interact with other proteins, their functions and biological roles in an organism. Experimental methods (e.g. X-ray crystallography and nuclear magnetic resonance spectroscopy) for predicting the secondary structure (SS) of proteins are very expensive and time consuming. Therefore, developing efficient computational approaches for predicting the SS of protein is of utmost importance. Advances in developing highly accurate SS prediction methods have mostly been focused on 3-class (Q3) structure prediction. However, 8-class (Q8) resolution of SS contains more useful information and is much more challenging than the Q3 prediction. Results We present SAINT, a highly accurate method for Q8 structure prediction, which incorporates self-attention mechanism (a concept from natural language processing) with the Deep Inception-Inside-Inception network in order to effectively capture both the short- and long-range interactions among the amino acid residues. SAINT offers a more interpretable framework than the typical black-box deep neural network methods. Through an extensive evaluation study, we report the performance of SAINT in comparison with the existing best methods on a collection of benchmark datasets, namely, TEST2016, TEST2018, CASP12 and CASP13. Our results suggest that self-attention mechanism improves the prediction accuracy and outperforms the existing best alternate methods. SAINT is the first of its kind and offers the best known Q8 accuracy. Thus, we believe SAINT represents a major step toward the accurate and reliable prediction of SSs of proteins. Availability and implementation SAINT is freely available as an open-source project at https://github.com/SAINTProtein/SAINT. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the impact of system imperfections on the overall performance of the MC-CDMA based Ultra-Wide-Band (UWB) system. A new expression of the signal-to-interference-noise ratio (SINR) for UWB Multicarrier Code division multiple access (MC-CDMA) system over a Rayleigh fading channel with imperfect power control condition are derived and investigated. The perfor- mance of MC-CDMA based UWB system over the frequency-selective multipath fading channel is examined with varying the number of users, K, the signal-to-interference-noise-ratio (SINR) per bit, Eb/N0. From the simulation results, we have seen that the SINR performance is affected by these parameters. The result of the analysis will pro- vide relevant information to design the physical layer protocol for high speed UWB 4G communications system. [ABSTRACT FROM AUTHOR]

An enhanced optical fiber-based surface plasmon resonance (SPR) biosensor is proposed utilizing a two-dimensional phosphorene heterostructure as an interacting layer with the analyte. This is the first model so far of a phosphorene-based fiber-optic SPR biosensor. High sensitivity and figure of merit (FOM), the two desirable parameters, are analyzed to investigate sensor performance. The analysis indicates that the proposed sensor provides significantly higher sensitivity in comparison to conventional fiber-optic SPR sensors. Numerical study shows that on using 10 layers of phosphorene on the Ag layer, the fiber-optic biosensor exhibits extremely high sensitivity of 3725 nm/RIU with a FOM of 61 RIU-1. FOM reaches its maximum value of 64 RIU-1 with six layers of phosphorene. Effects of the number of heterostructure layers and light wavelength on sensitivity are also analyzed. The results will open a new way for using phosphorene heterostructure in an optical fiber-based SPR sensor for biosensing applications. [ABSTRACT FROM AUTHOR]

Motivation Researchers and practitioners use a number of popular sequence comparison tools that use many alignment-based techniques. Due to high time and space complexity and length-related restrictions, researchers often seek alignment-free tools. Recently, some interesting ideas, namely, Minimal Absent Words (MAW) and Relative Absent Words (RAW), have received much interest among the scientific community as distance measures that can give us alignment-free alternatives. This drives us to structure a framework for analysing biological sequences in an alignment-free manner. Results In this application note, we present Alignment-free Dissimilarity Analysis & Comparison Tool (ADACT), a simple web-based tool that computes the analogy among sequences using a varied number of indexes through the distance matrix, species relation list and phylogenetic tree. This tool basically combines absent word (MAW or RAW) computation, dissimilarity measures, species relationship and thus brings all required software in one platform for the ease of researchers and practitioners alike in the field of bioinformatics. We have also developed a restful API. Availability and implementation ADACT has been hosted at http://research.buet.ac.bd/ADACT/. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

In this paper, we propose techniques for dynamically adjusting heartbeat or keep-alive interval of long-lived TCP connections, particularly the ones that are used in push notification service in mobile platforms. When a device connects to a server using TCP, often times the connection is established through some sort of middle-box, such as NAT, proxy, firewall, and so on. When such a connection is idle for a long time, it may get torn down due to binding timeout of the middle-box. To keep the connection alive, the client device needs to send keep-alive packets through the connection when it is otherwise idle. To reduce resource consumption, the keep-alive packet should preferably be sent at the farthest possible time within the binding timeout. Due to varied settings of different network equipments, the binding timeout will not be identical in different networks. Hence, the heartbeat rate used in different networks should be changed dynamically. We propose a set of iterative probing techniques, namely binary, exponential, and composite search, that detect the middle-box binding timeout with varying degree of accuracy; and in the process, keeps improving the keep-alive interval used by the client device. We also analytically derive performance bounds of these techniques. To the best of our knowledge, ours is the first work that systematically studies several techniques to dynamically improve keep-alive interval. To this end, we run experiments in simulation as well as make a real implementation on android to demonstrate the proof-of-concept of the proposed schemes. [ABSTRACT FROM AUTHOR]

An improved performance of graphene-coated surface plasmon resonance (SPR) biosensor using tungsten disulfide (WS2) for sensing deoxyribonucleicacid (DNA) hybridization is investigated. This biosensor structure consisted of prism (SF10 glass), gold (Au), WS2, graphene, and sensing medium. Highly enhanced overall performances are achieved using a thin layer of WS2 between metal and graphene layer and are provided in terms of sensitivity, detection accuracy (DA), and quality factor (QF). Concurrent improvement of all performance parameters is depicted by adding a WS2 layer instead of another graphene layer with the existing graphene layer. This overcomes the limitation of graphene-only sensors where addition of a graphene layer increases the sensitivity but decreases the DA and QF. Analysis of Au thickness effect and limit of detection are also investigated. Numerical study demonstrates that the deviation of SPR angle for mismatched DNA strands is relatively insignificant while that for complementary DNA strands is noticeably reckonable. Thus, the proposed biosensor offers a window for detecting DNA hybridization. [ABSTRACT FROM AUTHOR]

In this article, an analytical approach, namely, multi-level residue harmonic balance is introduced and developed for the nonlinear free vibration analysis of axially loaded beams with an internal hinge. The main advantage of this method is that only one set of nonlinear algebraic equations is required to be solved for obtaining the zero level solution while the high accuracy of the higher level solutions can be obtained by solving a set of linear equations. The new approximate analytical solution method is developed for solving the governing differential equations. The accuracy and efficiency of the proposed method are verified by a numerical method. In the comparison, the results obtained from the proposed method well agree with those from other methods. The effects of vibration amplitude, axial force, and hinge location on the fundamental frequencies of various beam cases are investigated. The optimum and worst hinge locations are also studied. [ABSTRACT FROM PUBLISHER]

This paper presents a highly sensitive photonic crystal fiber (PCF) refractive index sensor based on the surface plasmon resonance (SPR) effect operating in the telecommunication wavelengths. Gold is used as the plasmonic material due to its chemical stability and titanium dioxide (TiO2) is used to shift the resonance wavelength in the telecommunication bands. Both materials are deposited sequentially on the PCF surface, which is comparatively easy to fabricate. Numerical investigations show that the proposed sensor exhibits very high wavelength sensitivity of 10;800 nm∕RIU and amplitude sensitivity of 514 RIU−1 in the sensing range between 1.46 and 1.48. Moreover, it exhibits maximum sensor resolution of 9.25 × 10−6 RIU and high linearity over a wide sensing range. The proposed sensor can be practically realized due to its simple and straightforward structure. [ABSTRACT FROM AUTHOR]