Two-layer Stack Research Articles

Diabetes prediction model based on GA-XGBoost and stacking ensemble algorithm.

Diabetes, as an incurable lifelong chronic disease, has profound and far-reaching effects on patients. Given this, early intervention is particularly crucial, as it can not only significantly improve the prognosis of patients but also provide valuable reference information for clinical treatment. This study selected the BRFSS (Behavioral Risk Factor Surveillance System) dataset, which is publicly available on the Kaggle platform, as the research object, aiming to provide a scientific basis for the early diagnosis and treatment of diabetes through advanced machine learning techniques. Firstly, the dataset was balanced using various sampling methods; secondly, a Stacking model based on GA-XGBoost (XGBoost model optimized by genetic algorithm) was constructed for the risk prediction of diabetes; finally, the interpretability of the model was deeply analyzed using Shapley values. The results show: (1) Random oversampling, ADASYN, SMOTE, and SMOTEENN were used for data balance processing, among which SMOTEENN showed better efficiency and effect in dealing with data imbalance. (2) The GA-XGBoost model optimized the hyperparameters of the XGBoost model through a genetic algorithm to improve the model's predictive accuracy. Combined with the better-performing LightGBM model and random forest model, a two-layer Stacking model was constructed. This model not only outperforms single machine learning models in predictive effect but also provides a new idea and method in the field of model integration. (3) Shapley value analysis identified features that have a significant impact on the prediction of diabetes, such as age and body mass index. This analysis not only enhances the transparency of the model but also provides more precise treatment decision support for doctors and patients. In summary, this study has not only improved the accuracy of predicting the risk of diabetes by adopting advanced machine learning techniques and model integration strategies but also provided a powerful tool for the early diagnosis and personalized treatment of diabetes.

Predicting Pump Inspection Cycles for Oil Wells Based on Stacking Ensemble Models

Beam pumping is currently the broadly used method for oil extraction worldwide. A pumpjack shutdown can be incurred by failures from the load, corrosion, work intensity, and downhole working environment. In this study, the duration of uninterrupted pumpjack operation is defined as the pump inspection cycle. Accurate prediction of the pump inspection cycle can extend the lifespan, reduce unexpected pump accidents, and significantly enhance the production efficiency of the pumpjack. To enhance the prediction performance, this study proposes an improved two-layer stacking ensemble model, which combines the power of the random forests, light gradient boosting machine, support vector regression, and Adaptive Boosting approaches, for predicting the pump inspection cycle. A big pump-related oilfield data set is used to demonstrate the proposed two-layer stacking ensemble model can significantly enhance the prediction quality of the pump inspection cycle.

Stacked co-training for semi-supervised multi-label learning

Due to the difficulty of annotation, multi-label learning sometimes obtains a small amount of labeled data and a large amount of unlabeled data as supplements. To make up this issue, many algorithms extended the existing semi-supervised strategies in single-label patterns to multi-label applications, but failed to effectively consider the characteristics of semi-supervised multi-label learning. In this paper, a novel method named SCTML (Stacked Co-Training for Multi-Label learning) is proposed for semi-supervised multi-label learning. Through a two-layer stacking framework, SCTML learns label correlation in both base learners and meta learner, and effectively incorporates the semi-supervised assumptions of co-training, clustering and manifold. Extensive experiments demonstrate that the combination of multiple semi-supervised learning strategies effectively solves the semi-supervised multi-label learning problem.

Stacking Ensemble Learning with Regression Models for Predicting Damage from Terrorist Attacks

Terrorist attacks can cause unexpectedly enormous damage to lives and property. To prevent and mitigate damage from terrorist activities, governments and related organizations must have suitable measures and efficient tools to cope with terrorist attacks. This work proposed a new method based on stacking ensemble learning and regression for predicting damage from terrorist attacks. First, two-layer stacking classifiers were developed and used to indicate if a terrorist attack can cause deaths, injuries, and property damage. For fatal and injury attacks, regression models were utilized to forecast the number of deaths and injuries, respectively. Consequently, the proposed method can efficiently classify casualty terrorist attacks with an average area under precision-recall curves (AUPR) of 0.958. Furthermore, the stacking model can predict property damage attacks with an average AUPR of 0.910. In comparison with existing methods, the proposed method precisely estimates the number of fatalities and injuries with the lowest mean absolute errors of 1.22 and 2.32 for fatal and injury attacks, respectively. According to the superior performance shown, the stacking ensemble models with regression can be utilized as an efficient tool to support emergency prevention and management of terrorist attacks.

NiCuAg: An electrochemically-synthesised trimetallic stack for CO2 reduction

Electrochemical CO2 reduction is a promising technique for the production of desirable hydrocarbons without the need to resort to fossil resources. However, high overpotentials and poor selectivity remain a challenge for CO2 electro-reduction, especially for deep reduction by more than two electrons. One apparently attractive approach for breaking the scaling relations caused by simultaneous CO2 reduction pathways and for achieving deeper reduction is the use of multi-metallic electrodes, where several promising metal catalysts are present in close proximity. Herein, noting the activity shown by Ni, Cu and Ag for CO2 electroreduction when used individually, we set out to synthesise a tri-metallic “stack” catalyst, NiCuAg, and then to test this for electrochemical CO2 reduction. The stack architecture was successfully generated and the trimetallic NiCuAg system did show improved Faradaic efficiency for the reduction of CO2 to formic acid when compared to the bare Ni and bimetallic NiCu controls under some select conditions. However, the two-layer NiCu stack and bare Ni exhibited consistently higher Faradaic efficiencies than NiCuAg for deeper CO2 electroreduction to methanol and ethanol, indicating that the combination of three individually promising metals does not necessarily translate into superior catalytic performance for deep carbon dioxide reduction. [Display omitted]

Crash severity analysis: A data-enhanced double layer stacking model using semantic understanding

The crash severity analysis is of significant importance in traffic crash prevention and emergency resource allocation. A range of innovations offers potential traffic crash severity prediction models to improve road safety. However, the semantic information inherent in traffic crash data, which is crucial in enabling a deeper understanding of its underlying factors and impacts, has yet to be fully utilized. Moreover, traffic crash data are commonly characterized by a small sample size, which leads to sample imbalance problem resulting in prediction performance decline. To tackle these problems, we propose a semantic understanding-based data-enhanced double-layer stacking model, named EnLKtreeGBDT, for crash severity prediction. Specifically, to fully leverage the inherent semantic information within traffic crash data and analyze the factors influencing crashes, we design a semantic enhancement module for multi-dimensional feature extraction. This module aims to enhance the understanding of crash semantics and improve prediction accuracy. Then we introduce a data enhancement module that utilizes data denoising and migration techniques to address the challenge of data imbalance, reducing the prediction model's dependence on large sample crash data. Furthermore, we construct a two-layer stacking model that combines multiple linear and nonlinear classifiers. This model is designed to augment the capability of learning linear and nonlinear mixed relationships, thereby improving the accuracy of predicting the severity of crashes on complex urban roads. Experiments on historical datasets of UK road safety crashes validate the effectiveness of the proposed model, and superior performance of prediction precision is achieved compared with the state-of-the-arts. The ablation experiments on both semantic and data enhancement modules further confirm the indispensability of each module in the proposed model.

A multifaceted journey in coastal meteorological projections through multioutput regression: a two-layer stacking ensemble approach

A multifaceted journey in coastal meteorological projections through multioutput regression: a two-layer stacking ensemble approach

Soybean polypeptide content detection based on fusion of spectral and image information

Soybean polypeptide is a protein hydrolysate. It has multiple benefits, such as anticancer, antioxidant, etc. Addressing the limitations of traditional methods, such as time-consuming procedures and high costs, this study proposed a novel approach for the rapid, nondestructive detection of soybean polypeptide content. This approach utilizes the fusion of spectral and image information. First, seven preprocessing methods were applied to the spectral data. The results indicated that the model accuracy was highest after applying the standard normal variate preprocessing method. Second, a combined method, consisting of synergy interval partial least squares and iteratively retains informative variables, was proposed to extract characteristic variables. A two-layer Stacking framework ensemble learning model was constructed to compare the detection effects between single spectral input and fusion of spectral and image information input. The results showed that the fusion input model achieved the best performance. The coefficient of determination (R 2) of prediction of the fusion input model reached 0.952, an improvement of 0.061 compared to single spectral input. And root mean square error of prediction (RMSEP) of the fusion input model reached 1.803 × 10−4, a reduction of 7.43 × 10−5 compared to single spectral input. These results indicate that the fusion of spectral and image information can effectively improve the model’s accuracy, offering valuable technical support for soybean breeding and quality detection.

Strategy to improve synaptic behavior of ion-actuated synaptic transistors—the use of ion blocking layer to improve state retention

Synaptic transistors (STs) with a gate/electrolyte/channel stack, where mobile ions are electrically driven across the solid electrolyte, have been considered as analog weight elements for neuromorphic computing. The current (ID) between the source and drain in the ST is analogously updated by gate voltage (VG) pulses, enabling high pattern recognition accuracy in neuromorphic systems; however, the governing physical mechanisms of the ST are not fully understood yet. Our previous physics-based simulation study showed that ion movement in the electrolyte, rather than the electrochemical reactions that occur in the channel, plays an important role in switching. In this study, we experimentally explore the properties of the HfOx electrolyte and show that by tuning the density of oxygen vacancies, it can assume the dual role of electrolyte and channel. We demonstrate analog synaptic behavior using a novel ST with a two-layer stack of CuOx/HfOx, where the CuOx is the gate and Cu ion reservoir, and the HfOx is the electrolyte and channel. To improve state retention and linearity, we introduce a Cu ion transport barrier in the form of a dense and stoichiometric Al2O3 layer. The CuOx/Al2O3/HfOx exhibits excellent state retention and improved potentiation and depression response. Energy dispersive spectroscopy mapping following potentiation confirms the role of the Al2O3 layer in confining the Cu ions in the HfOx layer. We also show that a two-step programming scheme can further enhance synaptic response and demonstrate high recognition accuracy on the Fashion-MNIST dataset in simulation.

A two-layer Timepix3 stack for improved charged particle tracking and radiation field decomposition

Abstract We characterize a novel instrument designed for radiation field decomposition and particle trajectory reconstruction for application in harsh radiation environments. The device consists of two Timepix3 assemblies with 500 µm thick silicon sensors in a face-to-face geometry. These detectors are interleaved with a set of neutron converters: 6LiF for thermal neutrons, polyethylene (PE) for fast neutrons above 1 MeV, and PE with an additional aluminum recoil proton filter for neutrons above ∼4 MeV. Application of the coincidence and anticoincidence technique together with pattern recognition allows improved separation of charged and neutral particles, their discrimination against γ-rays and assessment of the overall directionality of the fast neutron field. The instrument's charged particle tracking and separation capabilities were studied at the Danish Center for Particle Therapy (DCPT), the Proton Synchrotron, and Super Proton Synchrotron with protons (50–240 MeV), pions (1–10 GeV/c and 180 GeV/c). After developing temporal and spatial coincidence assignment methodology, we determine the relative amount of coincident detections as a function of the impact angle, present the device's impact angle resolving power (both in coincidence and anticoicidence channels). The detector response to neutrons was studied at the Czech Metrology Institute (CMI), at n_ToF and the Los Alamos Neutron Science Center (LANSCE), covering the entire spectrum from thermal up to 600 MeV. The measured tracks were assigned to their corresponding neutron energy by application of the time of flight technique. We present the achieved neutron detection efficiency as a function of neutron kinetic energy and demonstrate how the ratio of events found below the different converters can be used to assess the hardness of the neutron spectrum. As an application, we determine the neutron content within a PMMA phantom just behind the Bragg-peak during clinical irradiation condition with protons of 160 MeV.

CTISL: a dynamic stacking multi-class classification approach for identifying cell types from single-cell RNA-seq data.

Effective identification of cell types is of critical importance in single-cell RNA-sequencing (scRNA-seq) data analysis. To date, many supervised machine learning-based predictors have been implemented to identify cell types from scRNA-seq datasets. Despite the technical advances of these state-of-the-art tools, most existing predictors were single classifiers, of which the performances can still be significantly improved. It is therefore highly desirable to employ the ensemble learning strategy to develop more accurate computational models for robust and comprehensive identification of cell types on scRNA-seq datasets. We propose a two-layer stacking model, termed CTISL (Cell Type Identification by Stacking ensemble Learning), which integrates multiple classifiers to identify cell types. In the first layer, given a reference scRNA-seq dataset with known cell types, CTISL dynamically combines multiple cell-type-specific classifiers (i.e. support-vector machine and logistic regression) as the base learners to deliver the outcomes for the input of a meta-classifier in the second layer. We conducted a total of 24 benchmarking experiments on 17 human and mouse scRNA-seq datasets to evaluate and compare the prediction performance of CTISL and other state-of-the-art predictors. The experiment results demonstrate that CTISL achieves superior or competitive performance compared to these state-of-the-art approaches. We anticipate that CTISL can serve as a useful and reliable tool for cost-effective identification of cell types from scRNA-seq datasets. The webserver and source code are freely available at http://bigdata.biocie.cn/CTISLweb/home and https://zenodo.org/records/10568906, respectively.

A two-layer stacking regression model for predicting yarn quality of small samples

In addressing the prevalent challenges associated with the limited generalization ability and weak adaptability to small samples observed in neural network models in contemporary yarn quality prediction research, this study proposes a two-layer stacking regression model. The initial layer of the stacking model integrates three base models: random forest, gradient boosting decision tree, and adaptive boosting. The secondary layer employs the linear regression algorithm. The efficacy of the stacking model was juxtaposed against the performance assessment of a multilayer perceptron neural network model, in addition to evaluations of the single random forest, gradient boosting decision tree, and adaptive boosting models, respectively. The generalization ability of the models was assessed through experiments that randomly partitioned the sample dataset based on 10 distinct random seeds. The adaptability to small samples of the models was evaluated through experiments spanning nine varying training set sample sizes. The experiment results underscore that the stacking model outperforms in aspects of prediction accuracy, generalization ability, and adaptability to small samples.

Ion Selectivity in Multilayered Stacked Nanoporous Graphene.

Nanoporous graphene is an ideal candidate for molecular filtration as it can potentially combine high permeability with high selectivity at molecular levels. To make use of graphene in filtration setups, the defects formed during its growth and during the transfer of graphene to the carrier support pose a challenge. These uncontrolled pores can be avoided by stacking graphene layers, and then, controlled pores can be initiated with oxygen plasma. Here, we show that two-layer stacks provide the best balance of defect coverage and high selectivity compared with other stacks. Using the electrical characterization of ionic solutions in the standard diffusion cell, we compare the ionic transport and ionic selectivity of up to three-layered stacks of graphene that have been plasma-treated. We find that there is a decrease in the ionic selectivity of a two-layered stack as one more layer of graphene is added. We provide a model for this occurrence. Our results will be helpful for making practical and high-performance filtration systems from two-dimensional materials.

Comparing the Performance of Machine Learning and Deep Learning Algorithms in Wastewater Treatment Process

This study assessed the performance of single and modified algorithms based on machine learning and deep learning for wastewater treatment process. More specifically, this study adopted support vector machine (SVM), random forest (RF), and artificial neural network (ANN) for machine learning as well as long short-term memory (LSTM) for deep learning. The performance of these (single) algorithms were compared with that of modified ones processed through hyperparameter tuning, ensemble learning (only for machine learning), and multi-layer stacking (i.e., two layers of LSTM units). The daily effluent of wastewater treatment process observed between 2017 and 2022 in the Cheong-Ju National Industrial Complex was used as input to all tested algorithms, which was evaluated with respect to mean squared error. For the model performance evaluation, discharge and biochemical oxygen demand are selected as dependent variables out of nine measured parameters. Results showed that the performance of any machine learning algorithms was superior to their competitor LSTM. This is mainly attributed to a small amount of input data provided to the LSTM algorithm and unstable effluent wastewater characteristics. Meanwhile, hyperparameter tuning improved the performance of all tested algorithms. However, ensemble learning for machine learning and two-layer stacking for LSTM generally resulted in performance degradation as compared to that of single algorithms, regardless of dependent variables. Therefore, this calls for a careful design and evaluation of modified algorithms, specifically for model architecture and performance improvement processes.

Electric field-induced anisotropy in the electronic properties of blue phosphorus/WSe2 heterojunction

In this study, first principle calculations was used to investigate the characteristics of a Blue P/WSe2 heterojunction. Among six potential stacking arrangements, the structure II was identified as the most energetically favorable. And interlayer spacing analyzing shown that a distance of 3.354 Å for the two-layer stack is the most stable configuration. The band structure results demonstrated that the heterojunction possesses an indirect bandgap of 1.12 eV utilizing HSE06 and 0.81 eV utilizing GGA. Upon accounting for spin-orbit effects based on GGA method, the indirect bandgap transforms into a direct bandgap of 1.34 eV. Notably, the valence band maximum (VBM) and conduction band minimum (CBM) mainly stem from P's p orbitals and W's d orbitals, respectively. Moreover, scrutiny revealed that W's d orbitals within WSe2 introduce a spin-orbit coupling-induced splitting of approximately 0.50 eV. By exploring the impact of an external electric field in various directions, strengths, and interlayer distances, it was found the electronic structure of the heterojunction can be manipulated. This capability leads to a broad spectrum of tunable bandgap values. Density of states and population analyses underscore the susceptibility of interactions between outer P and outer Se pz orbitals, to electric field influence. Our research findings have the potential implications for designing adaptable optical and electronic devices.

PACVP: Prediction of Anti-Coronavirus Peptides Using A Stacking Learning Strategy with Effective Feature Representation.

Due to the global outbreak of COVID-19 and its variants, antiviral peptides with anti-coronavirus activity (ACVPs) represent a promising new drug candidate for the treatment of coronavirus infection. At present, several computational tools have been developed to identify ACVPs, but the overall prediction performance is still not enough to meet the actual therapeutic application. In this study, we constructed an efficient and reliable prediction model PACVP (Prediction of Anti-CoronaVirus Peptides) for identifying ACVPs based on effective feature representation and a two-layer stacking learning framework. In the first layer, we use nine feature encoding methods with different feature representation angles to characterize the rich sequence information and fuse them into a feature matrix. Secondly, data normalization and unbalanced data processing are carried out. Next, 12 baseline models are constructed by combining three feature selection methods and four machine learning classification algorithms. In the second layer, we input the optimal probability features into the logistic regression algorithm (LR) to train the final model PACVP. The experiments show that PACVP achieves favorable prediction performance on independent test dataset, with ACC of 0.9208 and AUC of 0.9465. We hope that PACVP will become a useful method for identifying, annotating and characterizing novel ACVPs.

(Invited) Potential of Silicon Oxide Films for Low-Cost and High-Performance Resistive Switching Devices

Although resistance switching in transition-metal oxide (TMO) films has been widely studied [1], the physics and chemistry of resistance switching in non-transition-metal oxide (nTMO) films remains under-studied [2]. This must be corrected because inexpensive and chemically stable device configurations are required for the future Internet-of-Things society. It is easily anticipated that the switching electric field of silicon oxide films is apt to be higher than that of TMO films because the dielectric constant of silicon oxide films is smaller than those of the TMO films. However, the use of two-layer stacks like SiOx/hi-k oxide reduces the switching electric field [3]. Therefore, the study of silicon oxide films is still meaningful.Many scientists have recently investigated resistance switching in sputter-deposited silicon oxide films in detail [4,5] because this structure dispenses with the TMO (Fig. 1). Many papers addressed the role of silicon sub-oxide (SiOx) [6] because it is anticipated that the unstable bonds of non-stoichiometric silicon oxide can create degraded, but reversible, conductive paths inside the film. However, it is not yet clear how the SiOx region can trigger resistance switching, how important the SiOx region is, and whether the SiOx region is the only determiner of resistance switching [7] (Figs. 2, 3). The author demonstrated that hot-electron injection from the Si substrate had great potential in triggering resistance switching and lowering the switching voltage of sputter-deposited Si oxide films [5,8]; it was also mentioned that Si precipitates played an important role in realizing repeatable bipolar switching [9] (Fig. 2). Relating to this study, the author also proposed the physical and chemical structure of conductive filaments and their switching behavior based on an analysis of a possibly equivalent circuit model [10] (Fig. 4). However, it was not definitely elucidated why unipolar switching is not easily observed in sputter-deposited silicon oxide films, even though it is not a TMO.Recently, the author performed various Monte Carlo simulations to elucidate the physical and chemical parameters that rule the unipolar switching process in sputter-deposited silicon oxide films. Generations of simple bond breaking, oxygen vacancies, metallic Si sites, and E’’ centers were implemented in the simulation algorithm [8]. All-positive voltage stress mode for both the electroforming process and the reset process will not yield devices with stable, repeatable switching [11]. On the other hand, the all-negative stress mode results in stable, repeatable switching because the recovery of the internal degradation of the Si oxide film is not completed [11] (Fig. 5). This difference stems from the physical asymmetry of the electrode materials (Fig. 1).Though some may consider that silicon oxide films are not preferable to ReRAM devices from the chemical points of view, the theoretical analysis provided by the author in this paper suggests that silicon oxide films can be applied to the ReRAM device.[1] Y. Tokura, Physics Today, vol. 56, pp. 50-55, 2003.[2] T. Yanagida, et al., Sci. Rep., vol. 3, No.1657, pp.1-6, 2013.[3] P. Broqvist and A. Pasquarello, Appl. Phys. Lett., vol. 91, 192905, 2007.[4] J. Yao, et al., Appl. Phys. Lett, vol. 93, pp. 253101-1-253101-3, 2008.[5] R. Yamaguchi, S. Sato, and Y. Omura, Jpn. J. Appl. Phys., vol. 56, pp. 041301-1-041301-6, 2017.[6] A. Mehonic, et al., J. Appl. Phys., vol. 111, pp. 074507-1-074507-9, 2012.[7] Y. Wang, et al., Appl. Phys. Lett., vol.102, pp. 042103-1-042103-5, 2013.[8] Y. Omura, Ind. J. Electrical Eng. & Comput. Sci., vol. 24, pp. 1367-1378, 2021.[9] Y. Omura, R. Yamaguchi, and S. Sato, IEEE Trans. Device Reliab. and Mat. Vol. 17, pp. 561-567 (2017).[10] Y. Omura, ECS J. Solid State Sci. and Technol., vol. 10, pp. 124006-1-124006-10, 2021.[11] Y. Omura, Materials Today Proc., vol. 20, pp. 273-282, 2020; the advanced study will be published in the ECS J. Solid State Sci. and Technol. Figure 1

A framework combined stacking ensemble algorithm to classify crop in complex agricultural landscape of high altitude regions with Gaofen-6 imagery and elevation data

Mapping crop distribution using satellite technology is an effective approach for gaining information about food production over broad, regional scales. However, crop classification in high altitude regions from satellite platforms remains challenging, due to the spatial heterogeneity caused by the complex planting patterns. Moreover, the frequent cloud cover makes it difficult to collect time-series imagery for these regions. Thus, this study used a mosaic of single images of Gaofen-6 data to map the crop distribution in high altitude regions of Xining City and Haidong City prefectures of Qinghai Province, China. To improve the accuracy of the crop classification, random forest-recursive feature elimination (RF-RFE) was used to determine an optimal feature subset from existing spectral, texture and topographic features. Then, a two-layer stacking generalization ensemble model, incorporating Random Forest, XGBoost and AdaBoost, was trained. The results reveal that the stacking algorithm outperformed the other single classifiers, with overall accuracy higher than 85% (87.89% for the optimal feature subset and 85.38% for the original spectral band subset). In addition, the user’s and producer’s accuracies for wheat, rape and maize field all exceeded 90%. Elevation was the variable with the highest importance score, illustrating its importance in crop classification of high altitude regions. Overall, the framework, combining RF-RFE and a stacking algorithm, can improve the accuracy of the crop classification in high altitude regions.

Prediction of IDO1 Inhibitors by a Fingerprint-Based Stacking Ensemble Model Named IDO1Stack.

Indoleamine 2,3-dioxygenase 1 (IDO1) is viewed as an extremely promising target for cancer immunotherapy. Here, we proposed a two-layer stacking ensemble model, IDO1Stack, that can efficiently predict IDO1 inhibitors. First, we constructed a series of classification models based on five machine learning algorithms and eight molecular characterization methods. Then, a stacking ensemble model was built using the top five models as the base classifier and logistic regression as the meta-classifier. The areas under the receiver operating characteristic curve (AUC) of IDO1Stack on the test set and external validation set were 0.952 and 0.918, respectively. Furthermore, we computed the applicability domain and privileged substructures of the model and interpreted the model using SHapley Additive exPlanations (SHAP). It is expected that IDO1Stack can well study the interaction between target and ligand, providing practitioners with a reliable tool for rapid screening and discovery of IDO1 inhibitors.

Stacked lightweight metasurfaces for wider stopband in X-K band

Frequency selective surfaces printed on optically transparent, large-area, flexible plastic sheets are studied for their passband and stopband characteristics when present as single-layer, two-layer and three-layer stacks. The unit cell design, its size, spacing and thickness on the substrate enable the single layer metasurface to yield a stopband width of 4.46 GHz (corresponding to 34.80%), when the center frequency is at 12.83 GHz in simulation and 12.86 GHz in measurement. On stacking identical surfaces as two-layer and three-layer configurations with suitable displacements both within the surface plane and perpendicular to the surface plane, the structural geometry of the unit cell gets varied leading to an ultra-wide stopband of 8.96 GHz in the range from 12.16 GHz to 21.11 GHz which covers X to K band. This leads to a wider stopband width of 43.60% for two-layer stack and 48.10% for three-layer stack with no in-plane shift. This is further enhanced to 65.50% with suitable shift within the plane of the stacked design. The three-layer stacked structure is ultra-thin with a thickness of 0.012 λ0. The single layer as well as all the three stacked structures have stopband characteristics that are invariant to incidence angle variation up to 750.