Artificial Structure Research Articles

Planning, Design and Site Suitability for Artificial Recharge Structure using GIS at Sarayu River Uttar Pradesh

Abstract: Due to its widespread occurrence, ease of availability, and dependability, ground water has recently become a significant source of water in India for the household, industrial, and agricultural sectors. Due to the lack of recharging options, this trait had led to its uncontrolled abuse in some regions of the nation. This has caused significant ground water table loss in some locations, raising questions about long-term sustainability. Utilizing GIS is necessary for the development of the scarce ground water resources.

Artificial Post-Cycled Structure Modulation Towards Highly Stable Li-Rich Layered Cathode.

High-capacity Li-rich layered oxides (LLOs) suffer from severe structure degradation due to the utilization of hybrid anion- and cation-redox activity. The native post-cycled structure, composed of progressively densified defective spinel layer (DSL) and intrinsic cations mixing, is deemed as the hindrance of the rapid and reversible de/intercalation of Li+ . Herein, the artificial post-cycled structure consisting of artificial DSL and inner cations mixing is in situ constructed, which would act as a shield against the irreversible oxygen emission and undesirable transition metal migration by suppressing anion redox activity and modulating cation mixing. Eventually, the modified DSL-2% Li-rich cathode demonstrates remarkable electrochemical properties with a high discharge capacity of 187 mAh g-1 after 500 cycles at 2 C, and improved voltage stability. Even under harsh operating conditions of 50 °C, DSL-2% can provide a high discharge capacity of 168 mAh g-1 after 250 cycles at 2 C, which is much higher than that of pristine LLO (92 mAh g-1 ). Furthermore, the artificial post-cycled structure provides a novel perspective on the role of native post-cycled structure in sustaining the lattice structure of the lithium-depleted region and also provides an insightful universal design principle for highly stable intercalated materials with anionic redox activity.

Methods of controlled formation of instabilities during the electrical explosion of thin foils

The results of a study of the electrical explosion of aluminum foils with an artificial periodic surface structure created by laser engraving are presented. Experiments were carried out on pulsed high-current generators BIN (270 kA, 300 kV, 100 ns) and KING (200 kA, 40 kV, 200 ns) with Al foil of thicknesses 16 and 4 μm, respectively. Images of the exploded foils were recorded by point projection radiography in the radiation from hybrid X-pinches. It is found that the application of an artificial periodic structure to the foil leads to a much more uniform and well-defined periodic structure of the exploded foil. Images recorded in the UV range using a microchannel-plate-intensified detector show that the radiation from a surface-modified foil is more uniform along the entire length and width of the foil than that from a foil without modification.

Exploding foils with artificial structure as a source of ultraviolet radiation

The results of the study of radiation produced by the electrical explosion of Al foil with a thickness of 4 μm with an artificial periodic structure of periodic holes with 50–400 μm steps created by laser engraving are presented. The experiments were carried out on a KING pulse high-current generator (220 kA, 40 kV, 300 ns). Images of exploded foils were recorded using pinhole cameras with a 20 μm aperture on a four-frame microchannel plate camera with 5 ns temporal resolution. In experiments on the electrical explosion of thin foils, it was shown that the application of an artificial periodic structure to the foil leads to a much more uniform radiation in the UV spectrum range along the entire length and width of the foil. Uniform radiation was observed from 20 to 70 ns. It is shown that such radiation can be used for the imaging of test objects or plasma objects like exploded wires.

Considerations of an efficiency-intelligent geo-localised mobile application for personalised SME market predictions

The few applications used by Small and Medium Scale Enterprises (SMEs’) businesses lack efficiency and the appropriate intelligence to save them from price instability, inventory carrying costs, excess inventory, wrong decision making, inaccurate monitoring of stock levels, etc. through predictive analytics. The study explored various Artificial Intelligence Machine Learning (AI/ML) models and data structure array types that could be used with the day-to-day local weather conditions of low and high temperatures to predict market parameters and aid SMEs with predictive data to use for combating wrong decision-making, inaccurate business monitoring and excess inventory, etc. Among the ML models explored included sequential minimal optimisation, iterative reweighted least-squares, Fan-Chen-Lin support vector regression, linear regression newton method and multivariate linear regression Ordinary least squares for a multivariate linear regression and logistic regression. The models were compiled using visual C# and Accord.Net libraries. Multivariate linear regression Ordinary least squares models recorded the least predictive accuracy loss, for the test quantity prediction test samples, and varying acceptable square loss values, for usage in geo-localised mobile intelligent systems for SME predictions due to their favourable scores. The jagged array overall performed better than the multi-dimensional array on some time and space complexity tests. This work is contributing to the body of knowledge by evaluatively suggesting better data structures and ML models for building intelligent systems in Xamarin forms using C# and small data for the model training for applications in mobile phone systems that will aid SMEs’ in adjusting spending and sales targets.

Hybrid ECBO–ANN Algorithm for Shear Strength of Partially Grouted Masonry Walls

In recent years, artificial neural network (ANN) has become one of the popular and effective machine learning models, having a unique ability to handle very complex problems and the potential to predict accurate results without a defined algorithmic solution. However, the ANN structure and parameters are usually chosen by experience.The behavior of Partially Grouted (PG) masonry shear walls is complex due to the inherent anisotropic properties of the masonry materials and the nonlinear interactions between mortar, blocks, grouted cells, non-grouted cells, and reinforcing steel.In this study, the aim is to develop an artificial neural network model by combining the ECBO meta-heuristic algorithm with the artificial neural network structure to optimize the feed forward propagation network parameters for analyzing the shear strength of PG walls.A total of 255 test data on PG collected from the available literature were used to generate training and test data sets. Various validation criteria such as mean square error, root mean square error and correlation coefficient (R) are used to validate the models.In this study, the optimal number of neurons used in the hidden layer and also the optimal number of CBs required in the ECBO algorithm were obtained. The mathematical formulation of the optimized neural network model with the combination of meta-heuristic algorithm is also presented.

Regeneration of the ureter using a scaffold-free live-cell structure created with the bio-three-dimensional printing technique.

Ureteral strictures, which can be caused by ureteral injury, radiation therapy, ureterolithiasis, urinary tract infections, and ureteral endometriosis, typically require ureteral reconstruction. Although tissue engineering, autologous alternative tissue transplantation, and surgical techniques applying various flaps have been carried out for ureteral regeneration, all with some success, each method has its advantages and disadvantages. As an alternative, we created the first artificial ureter structures using only live cells and grafted them into healthy rat ureters. Spheroids were created using normal human dermal fibroblasts and human umbilical vein endothelial cells and subsequently laminated using a bio-three-dimensional printer. After molding the laminated spheroids into tubular structures, the artificial ureters were transplanted into live rats. After 2-12 weeks, the animals were sacrificed and their gross and pathological features were examined. In the artificial ureteral lumen of rats with Grade 0-1 hydronephrosis, regeneration of the ureteral epithelium was observed, the thickness of which increased over the course of the experiment. Regeneration of the muscular layer was also observed, extending from the normal ureteral side toward the artificial ureter structure over time. However, complete regeneration was not observed at the end of 12 weeks. Although ureteral peristalsis was noted in all cases, it was weaker than expected. Therefore, we achieved short-segment ureteral regeneration using a cell-only structure. This finding suggests that by applying alternative strategies to this method, such as changing the cell type and composition, regeneration over the entire length of the ureter may be possible in the future. STATEMENT OF SIGNIFICANCE: Until now, ureteral regeneration techniques have been dominated by the use of high-molecular-weight compounds and autologous tissues, and there have been no reports of regeneration using structures made entirely of cells. This is the first report of ureteral regeneration using a tubular structure made from stacked spheroids. Although this study only attained short-segment ureteral regeneration, regeneration of the ureter over a much longer proportion of its length can be achieved in the future by applying other strategies, such as changing the cell type. This study provides a foundation to achieve the future goal of complete regeneration.

Precedent of traditional water conservation: A case study of Ara and Keram villages, Ranchi, Jharkhand

Life on Earth is due to water. 71% of the earth is covered by water but 97% of water is stored in oceans as salt water, only 3% of water is fresh of which 2.4% is stored in the form of glaciers. 0.6% of fresh water is available for drinking and other purposes. Rainwater is the major source of freshwater, hence its conservation is extremely important. Today machines, concrete, artificial structure and technologies are being used for rainwater conservation. In the era of artificial intelligence, a few villages in the Ranchi district are conserving millions of litres of rainwater every year by traditional methods and using it for agriculture, animal husbandry, forestry, etc. People of two villages Ara and Keram have done extraordinary work by making thousands of Trench cum Bund (T.C.B.), Loose Bolder Structure (L.B.S.) and Dobha to conserve rainwater. Taking cognizance of this excellent performance of these villages, the Prime Minister of India has appreciated his program ‘Mann Ki Baat’. This paper is an attempt to present the traditional methods of rainwater conservation in these villages. In this research paper, mainly methods and benefits of water conservation including community participation and the motivation to do this work have been highlighted.

CIRCULAR POLARIZATION IN A $2\mathrm{D}$ PERIODIC ARTIFICIAL ANISOTROPIC DIELECTRIC MEDIUM

The phenomenon of polarization rotation in dielectric periodic structures is quite interesting. A similar structure was used in a sample with a 2D artificial periodic structure, where the plane wave propagation method was applied. Anisotropic dielectric constants were obtained, which were used for polarization rotation, and polarization rotation in an anisotropic medium was studied based on the dielectric permittivity of the effective medium. Those relationships, quantities, and parameters depending on the dielectric medium, dielectric permittivity, and 2D periodic structure have been studied and analyzed, which provide control of the degree of anisotropy, i.e. provide a distinction between fast and slow modes, which in turn provide the best polarization rotation per unit length.

A new combined approach of neural-metaheuristic algorithms for predicting and appraisal of landslide susceptibility mapping.

In this research, to predict landslide susceptibility mapping (LSM), we have studied and optimized an artificial neural network (ANN) by utilizing the backtracking search algorithm (BSA) as well as the Cuckoo optimization algorithm (COA). Multiple research studies have shown that ANN-based techniques can be used to figure out the LSM. Still, ANN computing models have big problems, like slow system learning and getting stuck in their local minimums. Optimization strategies may improve ANN performance results. Existing uses of the BSA and COA models in ANN training have not been used to map landslides, nor have the best ways to set up networks or other factors that affect this problem been examined. Consequently, the present research focuses on predicting landslide susceptibility for hazardous mapping using hybrid BSA and COA-based ANN algorithms (BSA-MLP and COA). A large data set was provided from an area in the province of Kurdistan, west of Iran, to provide training and testing datasets for the algorithms. All of the BSA and COA algorithms' parameters and weights, for instance, were fine-tuned to make the utmost accurate maps of landslide risk. The input dataset consists of elevation, slope angle, slope orientation, NDVI, fault tolerance, profile curvature, plan curvature, distance to the river, rainfall, far from the road, SPI, STI, TRI, TWI, land use, and geology; the output is landslide susceptibility value. In the testing phase, the AUC rose significantly from 0.701 to 0.864 for BSA-MLP and 0.738 to 0.822 for COA-MLP after using the abovementioned techniques. We have used the area under the curve (AUC) to evaluate how well the probabilistic models worked. In addition, the computed AUCs for the BSA-MLP available databases and the actual AUCs were 0.864, 0.857, 0.833, 0.778, 0.777, 0.769, 0.763, 0.758, 0.727, and 0.701 and 0.822, 0.808, 0.807, 0.805, 0.804, 0.777, and 0.769 for the COA-MLP combination. The integrated models can produce beneficial results for this area of research. The results suggest that the BSA-ANN model is better than the COA-ANN in optimizing an artificial neural network model's structure and computational parameters. The collected landslide susceptibility maps are significant for figuring out how dangerous landslides are in the studied area.

Continuous-phase-transformation elastic metasurface for flexural wave using notched structure

An elastic metasurface is an artificial structure composed of subwavelength structures that can manipulate elastic waves with an arbitrary phase gradient transformation. However, most traditional elastic metasurfaces are designed with discrete phase shift, due to the coupling effect and resonance mechanism. In this study, we present a pioneering approach to designing elastic metasurfaces with a continuous phase transformation by incorporating notched structures in solids. To achieve the continuous phase transformation, we establish the relationship between the notch depth and the effective phase velocity of flexural waves in notched plate-like structures by equivalently represent the structure as a thin plate. Two classical periodic elastic metasurface design concepts for anomalous refraction and wave focusing are demonstrated. The numerical and experimental results show that the continuous-phase-transformation elastic metasurface performs well in broadband. The proposed approach opens up new possibilities for realizing periodic curved elastic metasurfaces with a continuous phase gradient change. This innovation holds promise for a variety of applications in the field of elastic wave manipulation.

Artificial intelligect and human nature

The article presents the main transformational processes of scientific understanding of the role and interaction of «artificial» and «natural» intellect. The aim of the work is to analyze and summarize the most promising results of modern scientific research in the context of supporting the priority of solving «intellectual» problems by «natural intellect» and the role of «artificial intellect». Attention is drawn to the specifics of the artificial intellect functioning, which is significantly limited by the fundamental incompleteness of semantic information and the absence of axiological information. The article is based on the data of scientific developments in the field of natural and historical sciences. The natural intellect is presented as a social phenomenon that arose from the interaction of individual cerebral structures, the optimal characteristics of which were formed as a result of previous evolution, and therefore these characteristics allow to understand and make any dominant decisions. The material summarized in the article allowed to formulate some conclusions. In particular, that an artificial non-biological structure cannot be «smarter» than a person – regardless of how quickly and according to what programmes it processes the information, since it does not have its own goals. But the artificial intellect is a powerful tool for society in its interaction with the outside world. A tool – but in no case an agent that determines its goals and objectives. The artificial intellect as an auxiliary means is and will be created in future by people to achieve the same goal that «natural» intellect serves – better possible satisfaction of the needs of society as a whole and the individual in particular.

A hybrid approach for forecasting bitcoin series

Bitcoin price prediction is a substantial challenge for cryptocurrency investors. This study offers an innovative scheme to predict Bitcoin returns and volatilities using a hybrid model that incorporates the autoregressive fractionally integrated moving average (ARFIMA), empirical wavelet (EW) transform, and local linear wavelet neural network (LLWNN) approaches to produce an ARFIMA-EWLLWNN model. Our methodologies integrate the advantages of the long-memory model, EW decomposition technique, artificial neural network structure, and backpropagation and particle swarm optimization learning algorithms. The experimental results of the optimized hybrid approach outperform some classic models by providing more accurate out-of-sample forecasts over longer horizons. The model proves to be the most appropriate Bitcoin forecasting technique. Moreover, the implemented method produces smaller prediction errors than other computing techniques.

Achieving Photonic Spin Hall Effect, Spin-Selective Absorption, and Beam Deflection with a Vanadium Dioxide Metasurface.

Metasurface-based research with phase-change materials has been a prominent and rapidly developing research field that has drawn considerable attention in recent years. In this paper, we proposed a kind of tunable metasurface based on the simplest metal-insulator-metal structure, which can be realized by the mutual transformation of insulating and metallic states of vanadium dioxide (VO2) and can realize the functional switching of photonic spin Hall effect (PSHE), absorption and beam deflection at the same terahertz frequency. When VO2 is insulating, combined with the geometric phase, the metasurface can realize PSHE. A normal incident linear polarized wave will be split into two spin-polarized reflection beams traveling in two off-normal directions. When VO2 is in the metal state, the designed metasurface can be used as a wave absorber and a deflector, which will completely absorb LCP waves, while the reflected amplitude of RCP waves is 0.828 and deflects. Our design only consists of one layer of artificial structure with two materials and is easy to realize in the experiment compared with the metasurface of a multi-layer structure, which can provide new ideas for the research of tunable multifunctional metasurface.

ANALYSIS OF TEST RESULTS OF THE ENBANKMENT REINFORCEMENT WITH REINFORCED SOIL-CEMENT PILES

Purpose. The purpose of the work is to analyze the results of the test of the embankment reinforcement with reinforced soil-cement piles. Methodology. An analysis of the ground reinforcement situation was carried out with the definition of modern trends in the use of geosynthetic materials and soil-cement piles. An algorithm for strengthening the embankment with reinforced soil-cement piles using a special square bit has been developed and implemented in practice. The load-bearing capacity of reinforced soil-cement piles was determined by reasoned selection of the structure, soil base, material, depth of pile laying in accordance with engineering and geological conditions, the structural scheme of the building and the method of their arrangement. Findings. 6 piles with a length of 21 m to 23 m were tested. During the manufacture of piles Nos. 1, 4 and 6, the length of which was 22...23 m, during drilling it was found that massive monolithic granite lies at their base (the granite was sunk by 0.5 m), the presence of cracks in which was not fixed. Piles Nos. 2, 3, 5 had cracked granites as their base, they were also deepened by 0.5 m. For these reinforced soil-cement piles, the values of the vertical displacements of the pile during its loading and unloading in stages were obtained. The calculated pressing load on the tested pile ranges from 268.3 t·s to 275.0 t·s. Originality. It consists in obtaining dependences of settlements of reinforced soil-cement piles, created on the basis of jet mixing technology, during their testing in loading and unloading modes. Practical value. It was determined that by changing the stiffness of the embankment, since soil-cement reinforced elements can be manufactured with a deformation modulus of 300...600 MPa, the problem of its strengthening is solved. Practically, it is possible to reduce the difference in the stiffness of the artificial structure and the structure of the track superstructure, which will allow eliminating ballast recesses.

A circularly polarized broadband sequential array antenna with diamond-shaped artificial ground structure

A circularly polarized broadband sequential array antenna with diamond-shaped artificial ground structure

Nature's Needles: Adapting Bacterial “Syringes” into Programmable Protein Delivery Devices

Nature's Needles: Adapting Bacterial “Syringes” into Programmable Protein Delivery Devices

Axonemal structures reveal mechanoregulatory and disease mechanisms

Motile cilia and flagella beat rhythmically on the surface of cells to power the flow of fluid and to enable spermatozoa and unicellular eukaryotes to swim. In humans, defective ciliary motility can lead to male infertility and a congenital disorder called primary ciliary dyskinesia (PCD), in which impaired clearance of mucus by the cilia causes chronic respiratory infections1. Ciliary movement is generated by the axoneme, a molecular machine consisting of microtubules, ATP-powered dynein motors and regulatory complexes2. The size and complexity of the axoneme has so far prevented the development of an atomic model, hindering efforts to understand how it functions. Here we capitalize on recent developments in artificial intelligence-enabled structure prediction and cryo-electron microscopy (cryo-EM) to determine the structure of the 96-nm modular repeats of axonemes from the flagella of the alga Chlamydomonas reinhardtii and human respiratory cilia. Our atomic models provide insights into the conservation and specialization of axonemes, the interconnectivity between dyneins and their regulators, and the mechanisms that maintain axonemal periodicity. Correlated conformational changes in mechanoregulatory complexes with their associated axonemal dynein motors provide a mechanism for the long-hypothesized mechanotransduction pathway to regulate ciliary motility. Structures of respiratory-cilia doublet microtubules from four individuals with PCD reveal how the loss of individual docking factors can selectively eradicate periodically repeating structures.

Space-Time Thermal Binary Coding by a Spatiotemporally Modulated Metashell

The recently proposed space-time-coding electromagnetic metasurface introduces the temporal dimension into artificial structure design, greatly expanding its digital application in information processing. However, the counterpart in thermal digital metamaterial is still lacking but compelling because the absence of temporal dimension prohibits synergetic modulation of thermal signal in time and space, thus limiting the capacity of information storage and the efficiency of information transmission with heat. Here, we introduce the temporal modulation into existing spatially variable thermal coding structures and propose a space-time thermal binary coding scheme. We design a category of coding units with time-dependent metashells so that they can switch their functionalities between cloaking and concentrating in time sequences. Integrating these identical units on a two-dimensional spatial array, we numerically show that this single metasurface can output various digital signal sequences composed of 0 and 1 in time and space dimensions. We further experimentally realize the temporal modulation with a rotatable concentric multilayered structure driven by a time-stepping motor. These results demonstrate a practical space-time strategy for thermal binary coding, provide a feasible prototype for spatiotemporal regulation of thermal signal, and promisingly open an alternative way for realizing information processing in diffusion systems.

An investigation of artificial neural network structure and its effects on the estimation of the low‐cycle fatigue parameters of various steels

AbstractArtificial neural networks (ANNs) are a widely used machine learning approach for estimating low‐cycle fatigue parameters. ANN structure has its parameters such as hidden layers, hidden neurons, activation functions, training functions, and so forth, and these parameters have a significant influence over the results. Three hidden layer combinations, the hidden neurons ranging from 1 to 25, and different activation functions like hyperbolic tangent sigmoid (tansig), logistic sigmoid (logsig), and linear (purelin) were used, and their effects on the low‐cycle fatigue parameter estimation were investigated to determine optimal ANN structure. Based on the results, suggestions regarding ANN structure for the estimation of the low‐cycle fatigue parameters and transition fatigue life were presented. For the output layer and hidden layers, the most suitable activation function was tansig. The optimal hidden neuron range has been found between 4 and 9. The neural network structure with one hidden layer was determined to be most suitable in terms of less knowledge, structural complexity, and computational time and power.