A REVIEW OF CHALLENGES AND POSSIBILITIES OF BILAYER TABLET TECHNOLOGY

Bilayer tablet making process involves certain challenges as well as advantages. Bilayer tablets are the prescriptions which comprise of two same or various medications consolidated in a solitary portion for viable treatment of the illness. Persistent consistence and cost measure are two significant boundaries in treatments. Bilayer tablets manage these focuses adequately. To deliver a decent quality bi-layer tablet, the apparatus should be built according to GMP. Different hardware are accessible to beat normal bi-layer issues, for example, layer detachment, lacking hardness, weight control, cross defilement between the layers and so forth. Bilayer tablets give one of the significant plan approaches where inconsistent medications, with an alternate sign, and same medication with various delivery rate can be combined in a solid unit. Bilayer tablet is reasonable for consecutive arrival of two medications in blend, and for supported delivery tablets in which one layer is promptly delivered as introductory portion and the subsequent layer is a controlled portion. Controlled delivery dose structures have been broadly used to improve treatment with a few significant medications. Utilization of bilayer tablet is an altogether different viewpoint for calming and pain relieving drugs. This review article clarifies what are the outcomes to be looked and how to be faced during bilayer tablet production.

Can Potential Defects in LPBF Be Healed from the Laser Exposure of Subsequent Layers? A Quantitative Study

Additive manufacturing (AM) of metals and in particular laser powder bed fusion (LPBF) enables a degree of freedom in design unparalleled by conventional subtractive methods. To ensure that the designed precision is matched by the produced LPBF parts, a full understanding of the interaction between the laser and the feedstock powder is needed. It has been shown that the laser also melts subjacent layers of material underneath. This effect plays a key role when designing small cavities or overhanging structures, because, in these cases, the material underneath is feed-stock powder. In this study, we quantify the extension of the melt pool during laser illumination of powder layers and the defect spatial distribution in a cylindrical specimen. During the LPBF process, several layers were intentionally not exposed to the laser beam at various locations, while the build process was monitored by thermography and optical tomography. The cylinder was finally scanned by X-ray computed tomography (XCT). To correlate the positions of the unmolten layers in the part, a staircase was manufactured around the cylinder for easier registration. The results show that healing among layers occurs if a scan strategy is applied, where the orientation of the hatches is changed for each subsequent layer. They also show that small pores and surface roughness of solidified material below a thick layer of unmolten material (>200 µm) serve as seeding points for larger voids. The orientation of the first two layers fully exposed after a thick layer of unmolten powder shapes the orientation of these voids, created by a lack of fusion.

Hamming neural network application with FPGA device

The Hamming neural network is a kind of counterfeit neural system that substance of two kinds of layers (feed forward layers and repetitive layer). In this study, two pattern entries are utilization in the binary number. In the first layer, two nerves were utilization as the pure line work. In the subsequent layer, three nerves and a positive line work were utilization. The Hamming Neural system calculation was also implemented in three reproduction strategies (logical gate technique, programming program encryption strategy and momentary square chart technique). In this study in programming of VHDL and FPGA machine was utilization.

Fused Filament Fabrication Printer Modified to Dispense Cement Paste for Concrete Additive Manufacturing Studies

Additive manufacturing (AM) with cement-based materials is an emerging technology that has the potential to revolutionize concrete construction. The placement process is quite complex, requiring sufficient flow properties as the material leaves the nozzle, and sufficient stiffening properties before the subsequent layer is placed. Precise control of material proportions and in-line monitoring of the time-dependent rheology are required to ensure the successful adoption of AM in the concrete construction community. To facilitate the study of the rheological properties of cementitious materials, as they pertain to AM, a commercial bench-top fused filament fabrication three-dimensional (3-D) printer was modified to dispense cement paste mixtures. Modifications included the design and assembly of a pumping system and software modifications to the 3-D printer's firmware that were necessary to accommodate the new hardware. After assembly, a series of tests were conducted to verify machine movements and to calibrate the number of step pulses required per unit volume of extruded paste. The resulting software modifications and configuration files are publicly available.

Research on the Effect of Technical Attributes on the Tensile Strength of FDM Products

Nowadays, Fused Deposition Modelling (FDM) method has been growing rapidly, which can be used to fabricate complex parts within a reasonable time. The fabrication principle of FDM method is “layer by layer” so that the previous layer and subsequent layer don’t deposit each other to create the interface between two adjacent layers. Thus, the tensile strength of FDM product along building direction depends on various process parameters. In this study, five important process parameters such as layer thickness, build orientation, build style, infill density, and print temperature are considered. The effect on tensile strength is evaluated based on the tensile test of Polylactic Acid (PLA) part. The Design of experiment (DOE) based on the Central Composite Design (CCD) to consider the relationship between the process parameters and their response through the experimental data are gathered. The suitability of model is validated by Analysis of Variance (ANOVA) and t-test. Moreover, Artificial Neural Network (ANN) is also applied to predict the response for experimental model and compared with regression equation obtained from Response surface analysis (FCCCD). The results show that the predict value of ANN model is approximate to experiment value (R2 = 0.964), and the mean absolute error (MAE) of ANN model is smaller than those of FCCCD model. It is proved that ANN model is applicable to predict accurately the relationship between the process parameters and their response.

Bilateral Sensitivity Analysis for Understandable Neural Networks and its application to Reservoir Engineering

<div>In this paper, a model-independent sensitivity analysis</div><div>for (deep) neural network, Bilateral Sensitivity Analysis (BiSA), is proposed to measure the relationship between neurons and layers. Both the BiSA between pair of layers and the BiSA between any pair neurons in different layers are defined for (deep) neural networks. This sensitivity can measure the influence or contribution from any layer to another layer behind this layer in the (deep) neural networks. It provides a helpful tool to interpret the learned model. The BiSA can also measure the influence or contribution from any neuron to another neuron in a subsequent layer and is critical to analyze the relationship between neurons in different layers. Then the BiSA from any input to any output of a network is easily defined to assess the connections between the inputs and outputs. The proposed BiSA of (deep) neural networks is then applied to characterize the well connectivity in reservoir engineering. Given a network trained by Water Injection Rates (WIRs) and Liquid Production Rates (LPRs) data, the well connectivity can be efficiently described through BiSA. The empirical results verify the effectiveness of</div><div>the proposed method. The comparisons with the exiting methods demonstrate the robustness and the superior performance of the proposed method.</div>

Bilateral Sensitivity Analysis for Understandable Neural Networks and its application to Reservoir Engineering

<div>In this paper, a model-independent sensitivity analysis</div><div>for (deep) neural network, Bilateral Sensitivity Analysis (BiSA), is proposed to measure the relationship between neurons and layers. Both the BiSA between pair of layers and the BiSA between any pair neurons in different layers are defined for (deep) neural networks. This sensitivity can measure the influence or contribution from any layer to another layer behind this layer in the (deep) neural networks. It provides a helpful tool to interpret the learned model. The BiSA can also measure the influence or contribution from any neuron to another neuron in a subsequent layer and is critical to analyze the relationship between neurons in different layers. Then the BiSA from any input to any output of a network is easily defined to assess the connections between the inputs and outputs. The proposed BiSA of (deep) neural networks is then applied to characterize the well connectivity in reservoir engineering. Given a network trained by Water Injection Rates (WIRs) and Liquid Production Rates (LPRs) data, the well connectivity can be efficiently described through BiSA. The empirical results verify the effectiveness of</div><div>the proposed method. The comparisons with the exiting methods demonstrate the robustness and the superior performance of the proposed method.</div>

Numerical Simulation of Projectile Penetration into Laminated Transparent Armour

The paper shows that the main reasons behind a laminated glass panel failing while penetrated by a high-velocity projectile are the high stresses in the region affected by the projectile and tensile stresses at the interface that are caused by the glass layers bending. For all the glass layers but the frontal one, intense fracturing does not start at the interface with the previous layer but at the interface with the subsequent layer, in the region of the tensile stresses generated by the current layer bending. The fracturing propagates towards the impact. A low-strength adhesive layer between glass layers inhibits and even stops the fracture-inducing wave propagating from the previous layer into the subsequent one. Analysis of the projectile deceleration plots in laminated glass panels of the same total thickness showed that the projectile undergoes more dramatic deceleration in a single-layer barrier and in barriers consisting of fewer layers.

Multi-Information Flow CNN and Attribute-Aided Reranking for Person Reidentification

This paper presents a multi-information flow convolutional neural network (MiF-CNN) model for person reidentification (re-id). It contains several specific multilayer convolutional structures, where the input and output of a convolutional layer are concatenated together on channel dimension. With this idea, layers of model can go deeper and feature maps can be reused by each subsequent layer. Inspired by an image caption, a person attribute recognition network is proposed based on long-short-term memory network and attention mechanism. By fusing identification results of MiF-CNN and attribute recognition, this paper introduces the attribute-aided reranking algorithm to improve the accuracy of person re-id further. Experiments on VIPeR, CUHK01, and Market1501 datasets verify the proposed MiF-CNN can be trained sufficiently with small-scale datasets and obtain outstanding accuracy of person re-id. Contrast experiments also confirm the availability of the attribute-assisted reranking algorithm.

Synergistic electrodeposition of bilayer films and analysis by Raman spectroscopy

A novel methodology towards fabrication of multilayer organic devices, employing electrochemical polymer growth to form PEDOT and PEDTT layers, is successfully demonstrated. Moreover, careful control of the electrochemical conditions allows the degree of doping to be effectively altered for one of the polymer layers. Raman spectroscopy confirmed the formation and doped states of the PEDOT/PEDTT bilayer. The electrochemical deposition of a bilayer containing a de-doped PEDTT layer on top of doped PEDOT is analogous to a solution-processed organic semiconductor layer deposited on top of a PEDOT:PSS layer without the acidic PSS polymer. However, the poor solubility of electrochemically deposited PEDTT (or other electropolymerised potential candidates) raises the possibility of depositing a subsequent layer via solution-processing.