Different Number Of Layers Research Articles

Flexural performance of concrete beams internally reinforced with steel, geogrid and GFRP meshes

The focus of this study is to evaluate the internal reinforcement effect using different material meshes on the flexural capacity of concrete beams. For this purpose, eleven experimental reinforced concrete beams were tested under four-point bending. The beams were all made of normal concrete with a 150 × 200 mm2 rectangular cross-section and a length of 1200 mm. The conventional flexure and shear steel bar reinforcements were identical for the eleven beams. The reference beam was made without any internal mesh reinforcement, while the rest ten beams were reinforced with three different types of mesh materials, which were incorporated in different numbers of layers and different configurations. The used meshes were steel, geogrid and glass-fiber reinforced polymer (GFRP). One beam with U-shape mesh was made from each material, with the mesh wrapping the stirrups and extending vertically for 50 mm. In the experimental program, failure mode, load-deflection relationship, load capacity and ductility were the main characteristics that have been investigated. The test results showed that the percentage increase in the failure load capacity of the composite beams was approximately 3–25% compared to the reference beam. Furthermore, the beams with U-shape mesh configuration exhibited 17% increase in the load capacity comparing with their straight mesh counterparts. Similarly, the ductility index showed higher percentage improvement approximately 82–136% when layer number increased from 1 to 3 layers.

Recognition of Timestamps and Reconstruction of the Line of Organism Development

In this work, an artificial neural network is used to recognize timestamps of evolution. Timestamps are associated with outliers determined during the recognition of the genome attractors of organisms. The aim of this work is to present a new method of penetrating deep into evolution using the recognized timestamps. To achieve this aim, the neural networks of different number of layers were implemented in order to check the influence of the number of layers on the visibility of the timestamps. Moreover, the teaching process was repeated 10 times for each implemented neural network. The recognition of each organism evolution was also repeated 10 times for each taught neural network to increase the reliability of the results. It is presented, among other findings, that during the recognition of the timestamps of evolution not only the number of homologous comparisons and the lengths of compared sequences are important but also the distribution of similarities between sequences. It is also presented that the recognized timestamps allow for travel between genome attractors and reconstruct the line of organism development from the most advanced to the most primitive organisms. The results were validated by determining timestamps for exemplary sets of organisms and also in relation to semihomology approach and by phylogenetic tree generation.

A Numerical Study of Blast Resistance of Carbon Fiber Reinforced Aluminum Alloy Laminates

In this study, the dynamic responses under blast loading of carbon fiber reinforced aluminum alloy laminates with different curvature radii, different numbers of layers, and different layer directions of carbon fiber under blast loading were compared numerically. The finite element models were built with ABAQUS/Explicit. To calibrate the numerical models, experiments on curved carbon fiber and curved aluminum alloy were modeled, and the numerical results showed good agreement with the experimental data. The calibrated numerical models were used to simulate the dynamic response of cylindrical panels subject to external explosion loading. The stiffness degradation coefficient was introduced to more accurately simulate the failure mode of the composite structures. The deformation and energy absorption of carbon fiber reinforced aluminum alloy laminates under different structural parameters were obtained. These simulation findings can guide the theoretical study and optimal design of carbon fiber reinforced structures subject to external blast loading.

Optimization of Hydrophobic Coating Production by Pechini Method on Modified Aluminum Surface

To fabricate a hydrophobic coating using the sol-gel method (Pechini), an experimental design using the drying time and chemicals ratio as variables was developed. The coating was deposited on an aluminum substrate (AA6061-T6) treated by acid solution or anodization method. The surface wettability was measured using a goniometric instrument. Coatings were fabricated using different numbers of layers, heating rates, and drying temperatures, and their morphologies were observed using visual analysis and scanning electron microscopy. The use of anodization treatment with a furnace ramp of 1 °C min-1, a drying temperature of 220 °C ± 10 °C, and single-layer application, produced a coating free of visible flaws and apparent damage. The coating was obtained with a 15:1 glycerin:citric acid ratio and sample pre-polymerization in the drying oven for 1 hour at 100 °C resulted in the highest contact angle, 62° ± 2°, which meets the ASTM D-7334-08 standard for a hydrophobic surface.

Autophagy and LC3-associated phagocytosis: similarities and differences

Previously, autophagy was termed as a mechanism used by the cells with a lack of essential nutrients supporting homeostasis. Over the decade of studies, autophagy proved to be a more complex, ambiguous mechanism. Its activation depends on the nature of stimulus, type of immune cells and the final result. Both canonical and non-canonical autophagy, being similar in molecular events, but showing their own distinctive features, are key processes in protecting the body from penetration of intracellular pathogens, maintaining the required level of nutrients in the cell, and removing damaged organelles and cells. Canonical autophagy probably evolved as a homeostatic response to cellular stress and nutritional deficiencies, whereas non-canonical autophagy emerged as a response to suppression of inflammation. Non-canonical autophagy, hereinafter referred to as LC3-associated phagocytosis (LAP), combines the molecular mechanism of phagocytosis with an autophagy mechanism characterized by ingestion of exogenous pathogens, formation of phagosomes (laposomes) and enhanced fusion with lysosomes, followed by degradation of their contents.Significant differences were found between the processes of LAP- and canonical autophagy, which are similar in its mechanism of action. The presence of PI3K complexes in both processes, utilization and intracellular degradation of the “cargo” which is not required for the cells and organism proceeding in the lysosomes, and involvement of almost the same proteins provide similarity of their mechanisms. However, there are differences in the initiation of the processes, e.g., different types of PI3K complexes (in autophagy, PI3K III class 1 and 2 types; in LAP PI3K III, class 3 type), usage of reactive oxygen species in LAP, different types of regulatory proteins involved (ULK1, FIP200, ATG13 , Ambra1, WIPI2, ATG14 in autophagy; and Rubicon and NOX2 in LC3-associated phagocytosis), different number of layers in the membrane structure in which lysis occurs (double-membrane autophagolysosome and single-layer membrane in laposomes) clearly depict the variety of canonical and non-canonical autophagy. The two pathways are directed for different types of biological objects, i.e., intracellular pathogens, dysfunctional proteins and organelles in autophagy, and extracellular pathogens, apoptotic bodies, bacteria, utilized in LAP, thus making these mechanisms completely different in their significance.Collectively, the new data indicate that autophagy performed via both canonical and non-canonical pathways, has evolved into a host defense mechanism capable of resisting immunological and pathogenic stress and mediating immunological tolerance to both intra- and extracellular threats. The present review discusses fundamental molecular differences between these mechanisms, as well as their role in immunity, based on the latest literature data.

Structural and Optical Properties of Tungsten Disulfide Nanoscale Films Grown by Sulfurization from W and WO3.

Tungsten disulfide (WS2) was prepared from W metal and WO3 by ion beam sputtering and sulfurization in a different number of layers, including monolayer, bilayer, six-layer, and nine-layer. To obtain better crystallinity, the nine-layer of WS2 was also prepared from W metal and sulfurized in a furnace at different temperatures (800, 850, 900, and 950 °C). X-ray diffraction revealed that WS2 has a 2-H crystal structure and the crystallinity improved with increasing sulfurization temperature, while the crystallinity of WS2 sulfurized from WO3 (WS2-WO3) is better than that sulfurized from W-metal (WS2-W). Raman spectra show that the full-width at half maximum (FWHM) of WS2-WO3 is narrower than that of WS2-W. We demonstrate that high-quality monocrystalline WS2 thin films can be prepared at wafer scale by sulfurization of WO3. The photoluminescence of the WS2 monolayer is strongly enhanced and centered at 1.98 eV. The transmittance of the WS2 monolayer exceeds 80%, and the measured band gap is 1.9 eV, as shown by ultraviolet-visible-infrared spectroscopy.

Study on the effect of curved fiber laying angle on the vibration free attenuation of composite laminates

Firstly, three groups of fiber curve laminates with different numbers of layers and different fiber curve angles were designed and prepared. A cantilever plate free vibration test was used to analyze the influence of the fiber curve angle on the vibration free attenuation characteristics of the laminates. The three groups of contrast test results show that the fiber curve angle has a significant effect on the loss factor of the laminate structure. When the angle change of the fiber curve was ±<45|60>, the loss factor of the laminates reached the maximum value, and the vibration damping performance of the laminates was the best. Secondly, modal tests were carried out on the laminates, the modeling and simulation of the variable stiffness laminate were completed, and the influence of the fiber curve angle on the natural frequency of the laminate was explored. The results showed that when the number of laminate layers was the same, the natural frequencies of the laminates first increased and then decreased with an increase in the fiber curve angle. The first three natural frequencies of the laminates reached the maximum value when the fiber curve angle was ±<45|60 > or ±<60|75>. Finally, it is concluded that when the fiber curve angle of the laminates varies between ±<45|60>, the damping ratio and the first three natural frequencies of the laminates reach the maximum value, and the vibration damping performance is the best.

Multilayered Electrospun/Electrosprayed Polyvinylidene Fluoride+Zinc Oxide Nanofiber Mats with Enhanced Piezoelectricity

AbstractElectrospun polyvinylidene fluoride (PVDF) nanofibers have been widely used in the fabrication of flexible piezoelectric sensors and nanogenerators, due to their excellent mechanical properties. However, their relatively low piezoelectricity is still a critical issue. Herein, a new and effective route to enhance the piezoelectricity of PVDF nanofiber mats by electrospraying zinc oxide (ZnO) nanoparticles between layers of PVDF nanofibers is demonstrated. As compared to the conventional way of dispersing ZnO nanoparticles into PVDF solution for electrospinning nanofiber mats, this approach results in multilayered PVDF+ZnO nanofiber mats with significantly increased piezoelectricity. For example, 6.2 times higher output is achieved when 100% of ZnO (relative to PVDF quantity) is electrosprayed between PVDF nanofibers. Moreover, this new method enables higher loading of ZnO without having processing challenges and the maximum peak voltage of ≈3 V is achieved, when ZnO content increases up to 150%. Additionally, it is shown that the samples with equal amount of material but consisting of different number of layers have no significant difference. This work demonstrates that the proposed multilayer design provides an alternative strategy to enhance the piezoelectricity of PVDF nanofibers, which can be readily scaled up for mass production.

Microstructures and mechanical properties of MoNx isomeric multilayer films with different number of layers deposited by DC magnetron sputtering

In order to improve the mechanical properties of Mo2N films, MoNx isomeric multilayer films with Mo2N as the surface layer were prepared using DC magnetron sputtering by alternately stacking Mo2N deposited at 0.5 Pa and mixture of Mo3N2 and Mo5N6 deposited at 1.2 Pa. The microstructures and mechanical properties of films were investigated by X-ray diffraction, field emission scanning electron microscope, atomic force microscope and nanoindenter. With the increase of the number of layers, the surface particle size of multilayer films increases gradually, and the interface of multilayer films becomes much sharper. The average roughness, hardness and friction coefficient of films first decrease from 3.85 nm, 24 GPa, and 0.08 of Mo2N monolayer film to 2.83 nm, 18 GPa and 0.04 of six-layer film, and then increase to 3.45 nm, 19.8 GPa and 0.05 of ten-layer film, respectively. The Young's modulus of films decreases gradually from 289 GPa of Mo2N monolayer film to 222 GPa of ten-layer film. The critical load (Lc) of films decreases gradually from 24 mN of Mo2N monolayer film to 4 mN of ten-layer film. The changes in the performance of MoNx isomeric multilayer films are mainly attributed to the change in the microstructure of the films caused by the number of layers.

Low-cost depth/IMU intelligent sensor fusion for indoor robot navigation

AbstractThis paper presents a mobile robot platform, which performs both indoor and outdoor localization based on an intelligent low-cost depth–inertial fusion approach. The proposed sensor fusion approach uses depth-based localization data to enhance the accuracy obtained by the inertial measurement unit (IMU) pose data through a depth–inertial fusion. The fusion approach is based on feedforward cascade correlation networks (CCNs). The aim of this fusion approach is to correct the drift accompanied by the use of the IMU sensor, using a depth camera. This approach also has the advantage of maintaining the high frequency of the IMU sensor and the accuracy of the depth camera. The estimated mobile robot dynamic states through the proposed approach are deployed and examined through real-time autonomous navigation. It is shown that using both the planned path and the continuous localization approach, the robot successfully controls its movement toward the destination. Several tests were conducted with different numbers of layers and percentages of the training set. It is shown that the best performance is obtained with 12 layers and 80% of the pose data used as a training set for CCN. The proposed framework is then compared to the solution based on fusing the information given by the XSens IMU–GPS sensor and the Kobuki robot built-in odometry solution. As demonstrated in the results, an enhanced performance was achieved with an average Euclidean error of 0.091 m by the CCN, which is lower than the error achieved by the artificial neural network by 56%.

Iron Determination in Natural Waters Using a Synthesised 3-Hydroxy-4-Pyridione Ligand in a Newly Developed Microfluidic Paper-Based Device

This work describes the development of an iron sensor based on a microfluidic paper-based technique, to attain iron quantification in natural waters. A new water-soluble naphthalene-3-hydroxy-4-pyridione ligand was used as a colour reagent, as it formed an orange complex with iron. As a newly described ligand, several chemical and physical parameters, namely, the sample and reagent volumes and reagent concentrations, were studied related to the formation of the coloured complex. The microfluidic paper-based analytical device (μPAD) assembly, namely, the use of different types of filter paper and different numbers of layers, was developed to obtain the best performance. Under the optimal conditions, a linear correlation was obtained in the range of 0.25–2.00 mg/L of iron, with a minimum detectable value of 0.07 mg/L. The proposed μPAD method was validated by an analysis of the certified samples and by a comparison of the tested water samples with the inductively coupled plasma (ICP) results (RE &lt; 10%). Then, the μPAD device was successfully applied to the determination of iron in tap water, well water, river water, and seawater, with no need for any prior sample pre-treatment; recovery studies were also performed (average = 100.3% with RSD = 4.2%).

DeltaVs: A Method for Detecting Significant Layer Boundaries in Surface Wave Inversion Results

Surface wave testing is a powerful tool for noninvasive seismic site characterization. It includes a wide variety of active-source and passive-wavefield methods [e.g., spectral analysis of surface waves (SASW), multichannel analysis of surface waves (MASW), microtremor array measurements (MAM)] that can be applied to many different field conditions. The dispersion data collected with all these methods can be inverted to produce (one-dimensional) 1D shear wave velocity (VS) profiles of the subsurface. A critical part of this inversion procedure is the need to develop several trial model parameterizations with different numbers of layers as a means for investigating epistemic uncertainty and nonuniqueness when attempting to fit the experimental dispersion data. This is especially important when a priori information either is not available or does not extend to great enough depths to constrain the inversions. These trial parameterizations are used to vary the number of potential layers present in the subsurface models and to set the acceptable search ranges for depths to boundaries and stiffness in each layer. The inversion results are highly affected by the parameterizations. When performing high-quality inversions, it is important to use different parameterization options to fully explore potential conditions at the site and characterize the epistemic uncertainty of the inversion process. While this practice allows for a robust analysis of the experimental dispersion data and its uncertainty, it also generates many potential subsurface models for the site, which can represent a challenge for engineers when deciding which VS profiles best represent the true subsurface layering for use in subsequent analysis and design (e.g., seismic site response). This paper presents an approach, called the DeltaVs method, for systematically evaluating the depths of layer boundaries across all acceptable models determined from several different inversion layering parameterizations as a means to identify distinct clusters of boundaries. Using these clusters, the number and location of meaningful layer boundaries can be determined for many sites, thereby allowing for the elimination of some parameterization options containing more, or fewer, boundaries and a reduction in epistemic uncertainty. The distributions of layer boundaries within these clusters can then be used to determine statistics for the depths of layer boundaries that are consistent with the epistemic uncertainty of the models produced by the inversion procedure. The DeltaVs method is demonstrated on 12 synthetic ground models as well as one field data set.

All-in-one bioactive properties of photothermal nanofibers for accelerating diabetic wound healing

Diabetic wound healing has attracted widespread attention in biomedical engineering. However, the harsh hypoxic microenvironment (HME) comprising high glucose levels, local bleeding, and bacterial infection often leads to the formation of hyperplastic scars, increasing the clinical demand for wound dressings. Here, we report a comprehensive strategy using near-infrared NIR–assisted oxygen delivery combined with the bioactive nature of biopolymers for remodeling the HME. Black phosphorus (BP) nanosheets and hemoglobin (Hb) were self-assembled layerwise onto electrospun poly-l-lactide (PLLA) nanofibers using charged quaternized chitosan (QCS) and hyaluronic acid. BP converts NIR radiation into heat and stimulates Hb to release oxygen in situ. QCS is a hemostatic and broad-spectrum antibacterial material. Moderate BP-derived photothermal therapy can increase the sensitivity of bacteria to QCS. A series of composite wound dressings (coded as PQBH-n) with different numbers of layers were fabricated, and the in vivo diabetic wound healing potentials were tested. The molecular mechanism can be partly attributed to the cytokine-cytokine receptor interaction. Notably, this comprehensive strategy based on NIR-assisted oxygen delivery combined with the bioactive properties of biopolymers is not only applicable for fabricating multifunctional wound dressings but also has a great potential in expanding biomedical engineering fields.

ATICVis: A Visual Analytics System for Asymmetric Transformer Models Interpretation and Comparison

In recent years, natural language processing (NLP) technology has made great progress. Models based on transformers have performed well in various natural language processing problems. However, a natural language task can be carried out by multiple different models with slightly different architectures, such as different numbers of layers and attention heads. In addition to quantitative indicators such as the basis for selecting models, many users also consider the language understanding ability of the model and the computing resources it requires. However, comparing and deeply analyzing two transformer-based models with different numbers of layers and attention heads are not easy because it lacks the inherent one-to-one match between models, so comparing models with different architectures is a crucial and challenging task when users train, select, or improve models for their NLP tasks. In this paper, we develop a visual analysis system to help machine learning experts deeply interpret and compare the pros and cons of asymmetric transformer-based models when the models are applied to a user’s target NLP task. We propose metrics to evaluate the similarity between layers or attention heads to help users to identify valuable layers and attention head combinations to compare. Our visual tool provides an interactive overview-to-detail framework for users to explore when and why models behave differently. In the use cases, users use our visual tool to find out and explain why a large model does not significantly outperform a small model and understand the linguistic features captured by layers and attention heads. The use cases and user feedback show that our tool can help people gain insight and facilitate model comparison tasks.

Heat flux measurement using 3D-printed continuous wire polymer composite sensors

Fused filament fabrication (FFF) 3D printing was used to fabricate continuous wire polymer composite (CWPC) heat flux sensors; the integrated wires acted as resistive sensing elements. Sensor configurations consisting of polylactic acid (PLA) with copper wire (Cu), PLA with nickel (Ni) wire, and thermoplastic polyurethane (TPU) with Cu wire were investigated. For each composition, samples with different numbers of layers were 3D printed to investigate the effect of sensor thickness on performance. Element spacing, polymer conductivity, and wire temperature coefficient of resistances were quantified. Performance testing of the 3D-printed CWPC as a heat flux sensor showed promising results for all compositions and demonstrated their ability to be used as heat flux sensors for low temperature and low heat flux applications. Measurement errors were less than 17% for all configurations and less than 10% for 2-layer samples at the higher heat fluxes. A case study demonstrated the use of a 3D-printed flexible CWPC heat flux sensor to estimate heat loss from an insulated system with good accuracy. This sensor fabrication approach can potentially be employed in a wide range of applications because it allows for custom geometries and can use different types of polymers and sensing elements.

Верификация математической модели деформированного состояния цилиндрических CLT-панелей

In this paper, the verification of the mathematical model previously proposed by the author for studying the stress-strain state of multilayer cylindrical panels from CLT is carried out. For this purpose, the calculation results of the author's model of three shells of different geometry made of a different number of layers are compared with the calculation results of such structures obtained in PC «Lira-SAD».Structures fixed along the contour in a hinged and rigid manner were subjected to the action of a small evenly distributed transverse load, with the greatest possible panel deflections of 4 mm. The comparison of the deformed state was carried out by the biggest deflection and the deflection in the center of the shells, as well as by the shape of the deformed state of the structures. At the same time, there was studied the influence of the number of functions in the expansion in the Ritz method on the accuracy of the proposed mathematical model. It is shown that with 16 or more terms in the expansion, the deflection calculation error in comparison with the Lira-SAPR PC does not exceed 10%.

A New Efficient Method for Refining the Reinforcement Learning Algorithm to Train Deep Q Agents for Reaching a Consensus in P2P Networks

The usage of distributed Peer-to-Peer (P2P) networks has been growing steadily for a reasonable period. Various applications rely on the infrastructure of P2P networks, where nodes communicate to accomplish a task without the need for a central authority. One of the significant challenges in P2P networks is the ability of the network nodes to reach a consensus on a shared item; the challenge increases as time passes. Thus, this work proposes a new effective method for tweaking the Deep Reinforcement Learning (DRL) algorithm to train Deep Q Network (DQN) learning agents to reach a consensus among the P2P nodes. We propose various hierarchies of deep agents to address this crucial challenge in P2P networks. DRL is utilized to build and train agents; more precisely, DQN learning agents are constructed and trained. Two scenarios are proposed and evaluated. In the first scenario, one DQN agent is trained to find the consensus between the network nodes. In the second scenario, three hierarchies with different numbers of layers of agents are proposed and evaluated. In both scenarios, the P2P network used is a blockchain network. The best result was obtained using the third hierarchy of the second scenario; the overall accuracy of the model is 87.8%.

Merger identification through photometric bands, colours, and their errors

Aims. We present the application of a fully connected neural network (NN) for galaxy merger identification using exclusively photometric information. Our purpose is not only to test the method’s efficiency, but also to understand what merger properties the NN can learn and what their physical interpretation is. Methods. We created a class-balanced training dataset of 5860 galaxies split into mergers and non-mergers. The galaxy observations came from SDSS DR6 and were visually identified in Galaxy Zoo. The 2930 mergers were selected from known SDSS mergers and the respective non-mergers were the closest match in both redshift and r magnitude. The NN architecture was built by testing a different number of layers with different sizes and variations of the dropout rate. We compared input spaces constructed using: the five SDSS filters: u, g, r, i, and z; combinations of bands, colours, and their errors; six magnitude types; and variations of input normalization. Results. We find that the fibre magnitude errors contribute the most to the training accuracy. Studying the parameters from which they are calculated, we show that the input space built from the sky error background in the five SDSS bands alone leads to 92.64 ± 0.15% training accuracy. We also find that the input normalization, that is to say, how the data are presented to the NN, has a significant effect on the training performance. Conclusions. We conclude that, from all the SDSS photometric information, the sky error background is the most sensitive to merging processes. This finding is supported by an analysis of its five-band feature space by means of data visualization. Moreover, studying the plane of the g and r sky error bands shows that a decision boundary line is enough to achieve an accuracy of 91.59%.

Experimental study on evolution of pore structure of inclined layered cemented tailings backfill based on X-ray CT

In underground backfill mining, the stope usually needs to be filled several times, the cured backfill contains several inclined layered surfaces inside, which leads to the reduction of the overall stability of the backfill due to the existence of layered contact surfaces(LCS). In order to study the influence of inclined layered contact surface on the stability of backfill and its internal macroscopic and microscopic structure evolution law under loading conditions, the specimens of backfill with no layer, two layers, three layers and layered angle of 5° were prepared. Uniaxial compression tests of specimens with the different number of layers and CT real-time scanning tests of three-layered specimen were carried out to quantitatively analyze the porosity and density of layered cemented tailings backfill(LCTB) during compression by the gray value of CT scanning images and other parameters. The results showed that the load-bearing capacity of LCTB decreases with the appearance of the LCS; the higher the number of layers, the lower the uniaxial compressive strength, and all kind of LCTB specimens manifested as tensile failure. Before the compression reaches 70% of the peak stress, the pore and density changes mainly occur at the LCS and middle layer of LCTB, and show the same trend. That is, the porosity increases first and then decreases, and the density decreases and then increases.

On the Mechanical Buckling Analysis of FG-GRC Laminated Plates with Temperature-Dependent Material Properties Using Isogeometric Approach

In this paper, the isogeometric method is developed to study mechanical buckling behavior of nanocomposite plates reinforced by graphene sheets with temperature-dependent (TD) material properties in thermal environment. The plate is separately subjected to in-plane uniaxial, biaxial and shear loadings. It is assumed that the plate has different number of layers. By considering different volume fraction for each layer of graphene sheets, different functionally graded (FG) patterns of graphene sheets may be achieved. Furthermore, in some cases, it is considered that more than one FG patterns exist along the plate thickness. The energy statement of the plate is obtained using a logarithmic higher-order shear deformation theory (HSDT). Then, the isogeometric method is used to establish the desired eigenvalue problem. The comparison and convergence studies are presented for a wide range of numerical examples in all considered cases to show the correctness and ability of the solution. Afterwards, by presenting a set of numerical examples, the effects of plate significant parameters on the critical buckling load of the plate are examined. It is shown that the highest critical buckling loads occur when the plate has the minimum number of layers.