Zk-SNARK unleashes the great potential of ZKP (zero-knowledge proof) in the blockchain, distributed storage, etc. However, the proof-generation of zk-SNARK is excessively time intensive, making it a challenge to deploy a high-performance zk-SNARK in most real applications. As a result, NTT (Number Theoretic Transform), one of the most time-consuming parts in proof-generation, needs to be accelerated significantly. To address this issue, we propose a novel and efficient “data reordering” technique to enable a highly pipelined architecture, on which an FPGA-based hardware accelerator is designed to support the large-bitwidth and large-scale NTT tasks in zk-SNARK. Our architecture achieves a two-level pipeline: 1) the top-level pipeline is achieved among smaller NTT sub-tasks, which are decomposed from a large-scale NTT task; 2) the bottom-level pipeline is achieved in each sub-task, among butterfly operations with different step sizes. This architecture can effectively reduce the data dependency and memory access requirements, meanwhile, can be flexibly scaled to different scales of FPGAs. To balance computing efficiency and flexibility, the OpenCL equipped with HLS is used to implement the heterogeneous acceleration system. We prototype the accelerator on the AMD-Xilinx Alveo U50 card (UltraScale+ XCU50 FPGA). The evaluation results show that 1) our accelerator shows high scalability for different scales of FPGAs with a stable performance improvement; 2) it performs 1.95× faster than the one in PipeZK; 3) and it achieves 27.98×, 1.74× speedup and 6.9×, 6× energy efficiency improvement than AMD Ryzen 9 5900X single core and 12 cores respectively when integrated into the well-known ZKP open-source project, Bellman.

A computational study of Non-Newtonian (Casson) free convective MHD unsteady fluid flow has been highlighted in this article with mass and heat transit property through a vertical infinite porous plate. A sinusoidal boundary conditions have been considered as well as chemical reaction and thermal radiation. Using a collection of non-dimensional variables, the flow related equations are also turned into non-dimensional form. The EFDM algorithm is employed in order to arrive at a numerical solution via Compaq Visual Fortran 6.6a. The reliability of the numerical solution has been confirmed using stability testing and convergence analysis. The whole system is convergent at the value of and . A visual depiction of the impact of the pertinent factors on dimensionless velocity, temperature, and concentration profiles is displayed along with thorough explanations and graphical representation as well as tabular representation. Key finding of this work is that when the magnetic component is regarded in sinusoidal form, it greatly affects the heat transfer factors of Casson fluid and the heat rises as the results of heat source parameter, radiation parameter and Eckert number. It is also found that the Sherwood number is increased as the impact of chemical reaction parameter and the Lewis number, also the skin friction is decreased as the influence of porosity term got accelerated. As a last step in verifying the earlier study, the present results are contrasted with the results that were previously published.

Metasurfaces tuning is performed using different ways for wide range of applications. This study presents the design of thermally-tuned all-dielectric reconfigurable metasurface. A microfluidic channel, filled with different concentrations of tellurium – selenium (Te-Se) alloy, is added on the top of the elliptical dielectric resonator (EDR) unit cell of the considered metasurface. The electrical properties of used semiconductor alloy are varied in the range of 400°C to 700°C (steps size of 100°C). The impact of thermal tuning on the reflection and transmission characteristics of the designed metasurface is analyzed in the frequency range 20-30 GHz using COMSOL Multiphysics. Obtained results demonstrated that the realized metasurface exhibits reconfigurable behavior in terms of variations in the reflection and transmission characteristics with a change in either temperature or concentrations of selenium and tellurium. The wider bands with high reflection and low transmission frequency bands are obtained with lower concentrations of selenium and tellurium for all operating temperatures.

With the quick development of flexible memory electronics, multifunctional organic materials have been the necessary for fabricating electronics. In this work, the highly transparent and flexible electrode was successfully prepared by coating the high-performance silver nanowires (AgNWs) onto the colorless polyimide (PI) substrate. The prepared flexible PI-AgNWs electrodes exhibited a low sheet resistance of 15 Ω/sq with the high transparency of 68 % at the wavelength of 400 nm. A novel kind of polyimide TPC6FPI was successfully synthesized and characterized with excellent thermal stabilities and high glass transition temperature (Tg) above 250 °C. Furthermore, a kind of flexible transparent PI-AgNWs/ TPC6FPI/Al resistive memory device was prepared and exhibited excellent SRAM switching behavior with the threshold voltage of around 2.1V and the ON/OFF current ratio of ~10-4, which indicated that multifunctional PI-based memory device showed the potential to the wearable devices.

To improve the thermal properties of thermite safely and stably, electrostatic spraying was used to prepare the Al/MoO3 thermite. The Al/MoO3 thermites were detected and characterized by scanning electron microscopy (SEM) and X-ray diffraction (XRD), and thermal decomposition experiments were carried out by differential scanning calorimetry (DSC). The heat release of the Al/MoO3 thermite prepared by the electrostatic spray (1044 J·g-1) is significantly higher than that of the thermite prepared by the ultrasonic (692 J·g-1), which is due to more uniform dispersion between Al and MoO3. The initial reaction temperature and activation energy (Ea) of the former keep it steady. Electrostatic spray ensures the safety and stability of the Al/MoO3 thermite. This study provides a new idea for safely and stably improving the thermal properties of thermite by enhancing surface homogenization, which is of great significance for practical applications.

In this paper, the leader-follower architecture is constructed by combining intermittent-influence leaders with a signed social network. Unlike a typical network with leaders where leaders are supposed to continuously influence followers, in this article, the leaders intermittently influence followers. Furthermore, the number of influences is limited. We focus on how intermittent-influence leaders impact the evolution of followers' opinions. The relationship between followers' opinions and the number of leader broadcasts is analyzed in detail. Then, the number of broadcasts is regarded as the cost, and the changing trend of the revenue per broadcast is obtained. The results show that as the number of broadcasts increases, the revenue per broadcast decreases gradually. Finally, the concept of assimilation is introduced to weigh the costs and benefits, and the minimum number of broadcasts required for the leader to assimilate the followers is derived. Two examples are given to demonstrate the validity of the main conclusions.

Full-color 3D printing technology has become more and more popular for industrial manufacturing applications. The voxelization of color 3D models and the expression of appearance colors become more and more important. To illustrate the complex appearance and internal characteristics of 3D models, we present a voxelization algorithm of the color 3D models for color 3D printing in this paper. Specifically, we use the 3D model of 3MF with color appearance features for surface voxelization first. Then, we propose an improved scan line filling algorithm to realize the voxelization of the interior of the model and obtain the color voxel model. Finally, to improve the high quality of color printing, we propose a surface color inward diffusion algorithm, which can make the surface color of the voxel model diffuse inward and further express the surface color of the voxel model. The experimental results show our proposed method can obtain an effective color voxel model and accurately. express the color of the voxel model surface, which can enhance the color effect of the color model and realize the control of voxels.

In Sub-Saharan Africa, Professionals visually analyse the plants by looking for disease markers on the leaves to diagnose cassava infections, however, this method is extremely subjective. Automating the identification and classification of crop diseases may improve the accuracy of professional disease diagnosis and enable farmers in remote areas to monitor their crops without the assistance of experts. Algorithms for machine learning have been used in the early detection and classification of crop diseases. Motivated by the current developments in the field of Gaussian Processes, this study proposes to integrate the transfer learning approach with a deep Gaussian convolutional neural network model (DGCNN) for the detection and classification of cassava diseases. During this study, we used MobileNet V2 and VGG16 pre-trained transfer learning models and a hybrid kernel. Experiments with MobileNet V2 and a hybrid kernel revealed an accuracy of 90.11%. Also, experiments with VGG16 and a hybrid kernel revealed an accuracy of 88.63%. The major limitation of this study was computing resources since we used an ordinary computer in all our experiments. In our future work, we will experiment with the three kernel functions used in this study with kernel algorithms such as support vector machines and compare the results with those obtained during this study.

Adapting the target dataset for a pre-trained model is still challenging. These adaptation problems result from a lack of adequate transfer of traits from the source dataset; this often leads to poor model performance resulting in trial and error in selecting the best performing pre-trained model. This paper introduces the conflation of source domain low-level textural features extracted using the first layer of the pretrained model. The extracted features are compared to the conflated low-level features of the target dataset to select a higher quality target dataset for improved pre-trained model performance and adaptation. From comparing the various probability distance metrics, Kullback-Leibler is adopted to compare the samples from both domains. We experiment on three publicly available datasets and two ImageNet pre-trained models used in past studies for results comparisons. This proposed approach method yields two categories of the target samples with those with lower Kullback-Leibler values giving better accuracy, precision and recall. The samples with the lower Kullback-Leibler values give a higher margin accuracy rate of 6.21% to 7.27%, thereby leading to better model adaptation for target transfer learning datasets and tasks

Fluoroelastomer has received increasing attention for energetic materials application due to its high fluorine contents. Different contents of poly(VDF-ter-HFP- ter- TFE) terpolymer are added into Al/MnO2 nanothermite. The peak exothermic temperature of thermite reaction for Al/MnO2 system is about 554 oC with 1070 Jg-1 heat release. After adding terpolymer, it mainly exists in the gap among Al nanoparticles and MnO2 nanorods, and can react with Al and MnO2 at the range of 350 oC to 540 oC before the occurrence of thermite reaction. 10wt% terpolymer has relatively little effect on the thermite reaction, and for the samples with higher terpolymer content, more nanothermite components reacts with terpolymer at early stage. Ignition and combustion performance show terpolymer can reduce ignition energy threshold by up to 9.82% and increase combustion duration time at least several times. The potential reasons for above results are analyzed. This work can shed light on application of fluoroelastomer in energetic-materials.

Application of deep learning (DL) for automatic condition assessment of bridge infrastructure has been on the rise in the last few years. From the published literature, it is evident that lot of research efforts has been put in identifying the surface defects such as cracks, potholes, spalling etc. using deep learning. However, a concrete bridge deck health is jeopardized by the presence of subsurface defects substantially, however, the task of defect detection using deep learning has not received the proper attention. The goal of this survey paper is to provide a critical review of existing technical knowledge for DL application on NDE data for bridge deck evaluation. The authors reviewed prominent NDE techniques for subsurface defect detection of bridge decks and explored the various DL models proposed to identify these defects. First a brief overview of the working principle of NDE techniques and DL architectures is provided, and then the information about proposed DL models and their efficacy is highlighted. Based on the existing knowledge gaps, various challenges and future prospects associated with application of DL in bridge subsurface inspection are discussed.

The core of bioprinting related research aims to reduce the gap between ex vivo cell cultures and in vivo cellular tissue models to further its application within the biomedical field. While additive manufacturing is touted as disruptive technology, bioprinter equipment costs exceed limited resource budgets of many research laboratories restricting the scope for further development for biomedical research and potential medical application. In line with this, a relatively low-cost bioprinter (SidneV1) was successfully designed and manufactured using a low-cost, commercially available FDM Delta 3D printer as a prototype base with a successfully custom designed and manufactured micro-extrusion printhead. Printing accuracies assessed were 65% (for width measurements) and 64% (for height measurements). This study aimed to demonstrate a way to achieve low-cost bioprinting and hopefully pave the way for future system modifications and refinements such that this technology becomes more accessible to under-funded research groups around the world. Although these findings are preliminary, further optimization of printing parameters, bioink formulations and sterilization techniques will allow for the engineering of viable, physiologically relevant tissue models using low-cost bioprinting technology.

The usage of the gas sensor has been increasing very rapidly in the industry and in daily life for various potential applications. In recent years, metal oxide semiconductors (MOS) become the primary choice for designing highly sensitive, stable, and low-cost real-life applications-based gas sensors due to their inherent physical and chemical properties. Researchers have proposed numerous sensing mechanism to explain the functionality of MOS based gas sensors. In this review, we have comprehensively covered different sensing mechanisms used for MOS. We have also discussed different parameters affecting the sensitivity and selectivity of the gas sensors. Moreover, the different techniques used to enhance the gas sensing response of MOS based sensors are also extensively covered. And finally, we give our prospective on recent opportunities and challenges on future applications of MOS based gas sensors.

Pipeline flow visualization of cemented tailings backfill slurry (CTBS) improves the safety and stability of transportation. High turbidity and low resolution make it difficult for conventional methods to monitor the particle distribution state of CTBS in a short period of time. Particle tracking technology (PTT) is used to simulate and investigate the flow characteristics of CTBS pipeline, combine with theoretical analysis to construct a CTBS pipeline visualization model, elaborate the particle distribution state when CTBS flows in the pipeline, and explore the effects of pipe diameter (PD), flow velocity (FV) and tailings gradation (TG) on the particle distribution. The results show that particle tracking technology is better applied to investigate the particle transport distribution characteristics of CTBS tailings. Three concepts of particle accumulated gravity Ga, static friction angle θ and diameter dividing line are defined, and the transport pipe is divided into light wear zone, medium wear zone and heavy wear zone. The increase in pipe diameter increases the content of fine particles at the pipe wall and the thickness of the lubrication layer becomes larger, which improves the safety and stability of CTBS transport. The increased flow velocity reduces the settling phenomenon of large size particles and improves the transport efficiency, which increases the pipeline transport resistance. The wider the range of tailings gradation and the smaller the ratio of the number of large size tailings to small size tailings, the more suitable the tailings are for pipeline transportation as a backfill aggregate.

Large eddy simulation (LES) is used to simulate flame acceleration (FA) and deflagration to detonation transition (DDT) of methane–air mixtures in a small-scale 3D channel. The simulation results show that, in the early stages, the flame velocity increases exponentially because of the expansion of combustion products and the wrinkle of flame surface. In the next stage, the interaction between flame and pressure wave makes flame accelerate continuously, and the acceleration rate of the flame velocity decreases first and then increases. As the pressure of the leading shock increases, the boundary layer is heated by the preheating area in front of the flame surface which causes the ultrafast flame propagates in the boundary layer. The ultrafast flame generates oblique shock waves continuously moving to the center of the channel and colliding with each other, which promote the occurrence of local explosion and the coupling of flame surface and leading shock wave.

During the twentieth century, scientific and technological progress has led to a sharp increase in energy consumption. Since the beginning of the XXI century, hydrocarbon raw materials have become the basis of energy generation. Scientists around the world are currently working on the development and implementation of new alternative methods of energy generation. The share of ecological generation is growing every year, but such growth does not keep up with the increase in consumption. With this in mind, the article conducted a study proposing a new approach to electricity generation. The operation of the proposed devices is based on the conversion of electric current by an anisotropic electrically conductive medium characterized by different p- and n-types of conductivity in selected crystallographic directions under ohmic contact. It is shown that in the case of an external sinusoidal electric current flowing through a device based on an anisotropic rectangular plate, vortices of electric current occur in its volume. Such electric vortices with turbulent flow are an effective mechanism that pumps energy between the environment and in our case, the anisotropic plate.

Aircraft cabins have high-performance ventilation systems, yet typically hold large numbers of people in close proximity for long periods. The current study estimated airborne virus exposure and infection reductions for vacant middle seats and masking in aircraft. Tracer particle data reported by U.S. Transportation Command (TRANSCOM) and CFD simulations reported by Boeing were used, along with NIOSH data, to build nonlinear regression models with particle exposure and distance from particle source as variables. These models that estimate exposure at given distances from the viral source were applied to evaluate exposure reductions when middle seats are vacant compared to full occupancy. Reductions averaged 54% for the seat row where an infectious passenger is located and 36% for a 24-row cabin containing one infectious passenger, with middle seats vacant. Analysis of the TRANSCOM data showed that universal masking (surgical masks) reduced exposures by 62% and showed masking and physical distancing provide further reductions when practiced together. For a notional scenario involving 10 infectious passengers, compared with no intervention, masking, distancing, and both would prevent 6.2, 3.8 and 7.6 secondary infections, respectively, using the Wells-Riley equation. These results suggest distancing and masking reduce SARS CoV-2 exposure risk when an infectious passenger is present.

When it comes to tracking goods from manufacturers to consumers, barcodes are useful instruments for monitoring and certifying their legitimacy. Because of its capacity to self-validate all generated codes, the EAN-13 is the most extensively used barcode for processed consumable products. Further investigations into the construction of the EAN-13 barcode revealed that several additional components were required to make the EAN-13 more credible for consumer use. Country code, production code, product code, and check digit make up the present EAN-13 format. Meanwhile, the type of product and the number of times it has been manufactured are key components that must be included in the EAN-13 barcode structure for barcode analysts to interpret. The study presented the GHBS-13, an upgraded barcode structure that captured the two new components, namely the product type and production count. The paper proposed a universal method called Tabiri Check Digit (TCD) as a mathematical means of easily computing the check digit of the two barcodes. The formula was validated using EAN-13 and GHBS-13 barcodes, and the results were correct. The study also established a central point platform for customers to use to validate processed consumable products they buy in Ghana