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.

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.

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.

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.

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.

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.

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.

Despite all the importance of dams, dam failure, always threatens downstream areas by both natural and man-made sources and caused great human and financial losses. Vulnerability analysis is one of the main assessments to achieve a risk map and help policymakers reduce the losses and consequences of the failure. Therefore, this study aims to develop a new integrated hybrid model based on the security vulnerability assessment (SVA) and hydraulic analysis of flood and Source-Pathway-Receptor–Consequences (SPRC) approach. For SVA forms, data were collected by questionnaires survey, and for flood mapping, the open-source software, HEC-RAS was used. Geographical Information System (GIS) was applied to a combination of layers based on the SPRC approach. Five different scenarios were modeled to obtain the flood map due to a dam break. Then, the output data from the HEC-RAS software were transferred to the GIS software to be merged with other data achieved from the survey based on the SVA approach. Finally, the risk map for a case study was developed by this new hybrid approach. The results of the modeling showed that the highest vulnerability was achieved by residential areas and the lowest vulnerability is achieved by agricultural land due to floods by the dam failure.

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.

Cancer diagnosis using gene expression data is significant research for facilitating early treatment and prevention of cancer. The classification of gene expression data is challenging due to its high dimensionality and smaller number of samples that renders classification a difficult task. Creation of well-defined class boundaries is the aim of every classification algorithm. The Fuzzy min-max (FMM) neural network classifier is known to create good decision boundaries using hyperboxes constructed for each class. In this paper, we explore the General Fuzzy min-max (GFMM) and Enhanced Fuzzy min-max (EFMM) neural network architectures for the classification of lung cancer subtypes from microarray gene expression data. Both GFMM and EFMM are advanced versions of Simpson’s FMM neural network classifier. The GFMM is extremely efficient because it involves very simple operations for hyperbox manipulation, and can handle both labeled and unlabeled data. On the other hand, EFMM proposes three heuristic rules related to hyperbox expansion, contraction and the overlap test, which enhances the learning algorithm. We perform the classification of gene expression data using these two algorithms, then we analyze the performance by visualizing the hyperboxes obtained after training, and compare the accuracies of these classifiers. LASSO is used for selecting the important genes from the high-dimensional gene expression data. After the analysis of the results, we observe that EFMM with LASSO gives the best performance as compared to GFMM, FMM and other machine learning algorithms.

The article discusses the interconnected fields of computing and machine learning, and their impact on various areas such as energy, economics, indoor positioning, and business. Computing provides the foundation for data processing and storage, while machine learning enables algorithms and models to learn from data and make predictions. These advancements have revolutionized how we approach complex problems and opened up new avenues for research and innovation. The article highlights the potential of computing and data science to solve complex problems and the importance of staying up-to-date with the latest developments.

Geopolymer concrete shares similar mechanical properties with ordinary Portland cement (OPC) concrete, and is even provided with better performances in high temperature and high corrosion circumstances. However, geopolymer binder is still subject to the disadvantages of large shrinkage and high brittleness, which greatly limit its application. Fiber reinforcement is widely used in various geopolymer systems to overcome the brittleness issue, but retains the high strength. Over the past 10 years, a significant advance has been made in the research of fiber reinforced geopolymers in terms of toughening efficiency and durability improvement. This paper, as a mini review, focuses on three types of fibers, i.e., inorganic fiber, natural fiber and synthetic fiber, in geopolymers, and their specific effects on compressive, flexural and tensile strengths, fractural toughness, shear strength and durability including shrinkage, chemical and freezing-thaw resistances. The recement understanding of bonding mechanism and fiber-geopolymer interface are also discussed, and knowledge gaps and future work challenges are correspondingly pointed out.

This paper presents a framework combining Monte Carlo Simulation (MCS) and the Newmark sliding block model with Representative slip surfaces (RSS) (model II) and Multiple response surfaces method (MRSM) to conduct seismic reliability analysis and risk assessment of soil slopes. An empirical threshold is introduced to define the limit state function to identify the failure samples in MCS and the sliding area and Newmark sliding displacement are multiplied to quantify the failure consequence. The proposed methodology is illustrated through a soil slope with multiple layers. The calculation results demonstrate that traditional Newmark sliding block model (model I) tend to underestimate the variations of yield acceleration. Both the failure probability and landslide risk exhibit decreasing trends with the increase of threshold. Significant discrepancy in failure probability and landslide risk between two models is found even for a small threshold. It is therefore, the proposed methodology is highly recommended in seismic reliability analysis and risk assessment. The contributions of RSSs to the failure probability and landslide risk are insensitive to the variation of displacement thresholds.

The failure mechanisms caused by electrostatic discharge (ESD) effects at ambient temperatures ranging from -75℃ to 125℃ are investigated by Silvaco TCAD simulator. The devices are NMOS transistors fabricated with 28nm fully depleted silicon-on-insulator (FDSOI) technology. Results indicate that with an increase in temperature, the first breakdown voltage of the device decreased by 27.32%, while the holding voltage decreased by approximately 8.49%. The total current density, lattice temperature, and potential etc. were extracted for a detailed insight into the failure process. These findings provide valuable references for the design and development of ESD protection devices applied at different temperature ranges.

Model ensemble is widely used in deep learning since it can balance the variance and bias of complex models. The mainstream model ensemble methods can be divided into “implicit” and “explicit”. The “implicit” method obtains different models by randomly inactivating the internal parameters in the complex structure of the deep learning model, and these models are integrated by sharing parameters. However, these methods lack flexibility because they can only ensemble homogeneous models with the similar structure. While the “explicit” ensemble method can fuse completely different heterogeneous model structures, which significantly enhances the flexibility of model selection and makes it possible to integrate more models with entirely different perspectives. However, the explicit ensemble will face the challenge of averaging the outputs, leading to a chaotic result. To this end, researchers further proposed using knowledge distillation and adversarial learning technologies to perform a nonlinear combination of multiple heterogeneous models to obtain better ensemble performance, however these require significant modifications to the training or testing procedure and are computationally expensive compared to simply averaging. In this paper, based on the linear combination assumption, we propose an interpretable ensemble method for averaging model results which is simple to implement, and conducting experiments on the representation learning tasks of Computer Vision(CV) and Natural Language Processing(NLP). The results show that our method is superior to direct averaging results while retaining the practicality of direct averaging.

Aiming at the problem of crosstalk between microstrip lines, a method of reducing crosstalk by using Cross-Shape Resonators (CSR) structures is proposed. On the premise of not changing the spacing of microstrip lines, this method adds CSR structures between the coupled microstrip lines to increase the capacitive coupling and thus to suppress the far-end crosstalk. Based on the analysis of the equivalent circuit of CSR structure, the parameters simulation and verification are carried out by ADS and HFSS software. Through HFSS simulation and physical test of the designed CSR structure, the results show that: the CSR structure can significantly reduce the far-end crosstalk by about 15 dB in the frequency of 0~10GHz, and the maximum can reach 43 dB. Compared with 3W crosstalk reduction method and RectangularShape Resonators (RSR) crosstalk reduction method, the crosstalk reduction effect is improved.

Engineering systems have been designed to facilitate society. These systems can be seen everywhere in our daily lives ranging from electrical systems to mechanical systems, and from bio-medical systems to industrial systems. With tight coupling with information and communication technology (ICT), these engineering systems can be even controlled and monitored remotely. These systems are supported massively with sensors through which they capture enormous data, which is then used to improve the performance of the systems. Moreover, complex processes are involved in the overall functioning of these engineering systems. The management of data and processes within these engineering systems has been done through traditional ways such as database management systems or spread sheets, however, involvement of multiple parties makes these engineering systems more complex to operate, track, and audit. Blockchain technology has the potential to replace traditional database systems and offers a level of trust in an untrusted environment. With features of immutability, traceability, transparency, availability, and decentralization, blockchain technology is a good match for engineering systems. Blockchain technology can help in supply chain in these engineering systems, but it can also be used to facilitate data, process, and parties. Considering enormous applications of blockchain technology in engineering systems, this Special Issue in Wiley Engineering Reports invited for the original scientific and technical contributions.