Position Of Elements Research Articles

Code Recommendation for Schema Evolution of Mimic Storage Systems

Schema evolution of mimic storage systems is a time-consuming and error-prone task due to the redundant development of heterogeneous executors. The ORM-based proxy requires an entire class to represent the structure of a data table. There lacks domain-specific code recommendation techniques to boost storage development. To address this issue, we design a novel type of code context, i.e. schema context, that combines features of code text, syntax and structure. Regarding the requirements of class-level granularity, we focus on behavior and attribute in code syntax, and use element position and structural metrics to mine the hidden relationships. Based on schema context and an existing inference mode, we propose SchemaRec to recommend ORM-related class for the database executors once one of them has been changed. We conduct experiments with 110 open-source projects, and the results show that SchemaRec obtains more accurate results than Lucene, DeepCS, QobCS and SEA in terms of Top-1, Top-10 and MRR accuracy due to the better ability of context representation. We also find that code syntax is the most important information because it involves behavior and attribute information of ORM-related classes.

Identification of position, size and dynamic heat power of chips embedded in a real electronic board

Temperature is a critical factor in the lifespan of electronic systems. Therefore, it is essential to control all thermal parameters. In certain applications, such as commercial off-the-shelf (COTS) components, manufacturers may not have precise knowledge of the composition of electronic boards. As a result, it is important for them to define the positions of heat-dissipating elements, and the power dissipated to better predict the final thermal behavior of the system. Some electronic boards today even have components integrated into their volume. Since components can now be located not only on the surface but also within the volume of electronic boards, manufacturers need to define volume-based methods for detecting these heat sources. In this article, we address the problem of detecting sources within a volume using surface temperature data. To achieve this goal, we develop a representative numerical model of the system that can be used for an inverse source identification procedure. In a first time, we present a numerical approach that demonstrates the feasibility of the identification procedure on a board containing several electronic chips. Secondly, the volume source identification method is applied to a real case, using surfaces temperatures measured by infrared cameras. The experimental results obtained are consistent and show that this technique is suitable for detecting volumetric sources within an electronic board.

Additively Manufactured Structures for Precise and Robust Mounting of Optical Elements

The design freedom of Additive Manufacturing offers new opportunities for the design of mounting structures of optical systems, which are not feasible for conventional manufacturing approaches, thus opening up new areas of application for optical systems. We use this to develop a fully monolithic mounting structure for precise positioning of the optical elements, while simultaneously increasing their robustness against harsh environmental conditions. We additively manufacture such a mounting structure for an imaging lens and evaluate its optical performance with interferometric measurement of its wavefront, before and after applying a mechanical shock to the entire system. The results indicate a centering accuracy better than 6~µm, both in the initial positioning as well as the subsequent repositioning after a mechanical shock of 15G.

Computational Models of Dynamic Load Sources for Modeling of Construction Structures Operation Used in Monitoring of Technical Condition of Buildings and Structures

This research aims to develop principles for assessing the impact of mega-cyclic vibrodynamic loads on the reliability of construction structures. The relevance of the reasons for the development and improvement of algorithms for numerical modeling of multicycle dynamic loads on building structures is due to the steadily increasing intensity of such loads on buildings and structures in megacities, as well as the acute practical problem of significant differences in the dynamic characteristics of buildings and structures obtained as a result of mathematical modeling and determined by experimental methods. The article presents research materials on computational equivalent models of dynamic load sources for numerical modeling of the behavior of construction structures under their influence, using the method of vibroacoustic analogies. The article examines models of sources of dynamic impact on construction sites. Algorithms and final formulas for computational modeling of the simplest sources of dynamic load are developed using the method of vibroacoustic analogies. The dynamic properties of the simplest dynamic load sources were analyzed. A significant difference between the computational models of real and ideal dynamic load sources. The article presents research and development results intended for calculating the distribution of dynamic loads on elements of construction structures in industrial and civil engineering projects located in areas with high levels of transport vibrodynamic impacts. An important property of the proposed computational equivalent models of sources of dynamic impact on building structures is the possibility of computational verification of critical elements, points, or nodes of load-bearing structures of buildings and structures under dynamic overloads. The position of these critical elements, points, and nodes of load-bearing structures under dynamic loads can differ significantly from their position determined using static and quasistatic computational modeling methods.

Interpretable prediction of remanence in sintered NdFeB through machine learning strategy

The effects of composition on magnetic properties of sintered NdFeB are generally revealed by trial-and-error method, which is actually time-consuming and resource-intensive. It has been demonstrated that data-driven machine learning (ML) strategy has a great potential in accelerating processes of material research. In this study, accurate prediction of remanence in sintered NdFeB magnets has been achieved by a high-quality and few-shot dataset. We used a set of physical and chemical properties of element as inputs to build ML models based on element classification knowledge. Specifically, according to the position of elements, we divided them into three categories and then calculated the average properties of three positions separately. The strong generalization ability of best-performing ML model was further verified in nineteen unknown samples with only 0.724 % error. In addition, the symbolic regressor (SR) and SHapley Additive exPlanation (SHAP) algorithm were employed to explain the model results from physical perspective. In short, a high-efficiency and feasible ML strategy was developed for predicting remanence of sintered NdFeB magnets. We believe that this work will show great prospects in the composition design and performance screening of sintered NdFeB magnets.

ENGINEERING AND GEODESIC MONITORING OF DEFORMATIONS OF BUILDING STRUCTURES DAMAGED AS A RESULT OF MILITARY AGGRESSION

The problem of engineering and geodetic monitoring of damaged buildings as a result of Russian aggression is extremely relevant in today's environment. The importance of this issue for the construction industry is particularly high. The right approach to the engineering and geodetic monitoring of damaged buildings and structures will allow timely detection of deformation development and development of a stabilisation programme to help prevent future damage.There are many different methods for effective deformation monitoring, each with its own unique characteristics. Among them are standard methods such as geometric levelling and linear-angle measurements. Geometric levelling involves measuring the height differences between different points on an object, which allows you to accurately determine the change in the position of structural elements. Linear-angle measurements include measuring the lengths and angles between points, which helps to identify any changes in the geometry of the building.In addition to standard methods, automated observation methods are also actively used. These methods include the use of modern instruments and technologies that allow for continuous monitoring of deformations in real time. For example, automated total station surveys, GPS systems, laser scanners and other high-precision instruments allow data to be collected with high accuracy and speed.Automated monitoring significantly improves the efficiency and accuracy of observations of building deformations. The use of such systems allows for real-time data to be obtained, which is extremely important for timely response to any changes in the condition of facilities. This is particularly relevant in the context of post-war reconstruction, when the speed and accuracy of work are critical to ensuring the safety and stability of buildings.This article presents the results of engineering and geodetic monitoring of a residential complex on Zvirynetska Street. At this facility, geodetic works were carried out to measure the settlements and horizontal displacements of two sections.

Synthesis of maximally sparse conformal circular arc array with a required beam pattern by unitary matrix pencil method

This paper extends the unitary matrix pencil (UMP) method to synthesize maximally sparse conformal circular-arc array with a required beam pattern. Due to the nonlinearity between the circular-arc array pattern and its element pattern, Fourier transform preprocessing for the required beam pattern is introduced to achieve a mathematical expression, i.e., sum of a series of undamped complex exponentials, which is related to array element positions and their excitations. Then, the UMP method is used to determine the reduced number of elements and their position distributions. Moreover, the complex excitations of array elements are reconstructed by obtaining the least-square solution of an over-determined equation. A set of examples for synthesizing sparse conformal circular-arc arrays with different desired patterns and E-type patch element including the mutual coupling are conducted. Results show that the proposed UMP method can achieve a considerably lower pattern reconstruction error with a reduced number of elements than results in the literature, which demonstrates its effectiveness and robustness.

Position estimation of acoustic elements based on improved delay estimation algorithm

Array signal processing is extensively utilized in the field of underwater acoustics (UWA). The majority of existing array signal processing algorithms require precise array position information to optimize their functionality. However, the intricate nature of the UWA environment introduces challenges such as the influence of water flow, which may result in deviations from the predetermined array positions. Consequently, this can amplify errors in array processing algorithms. Therefore, a high-precision array positioning method is needed to estimate the actual position of the array. The effectiveness of current array localization algorithms employing delay differential matching depends significantly on the accuracy of delay estimation and the formulation of the ambiguity function. In response to these crucial factors, this paper presents an algorithm for estimating array element positions that leverages an improved approach to delay estimation. Firstly, we propose the ρ-PHAT algorithm enhanced by the artificial fish swarm algorithm (AFSA-PHAT), significantly improving delay estimation accuracy, particularly in low signal-to-noise ratio (SNR) conditions. Compared to the traditional ρ-PHAT algorithm, this approach achieves a 3 dB increase in precision and a reduction in the root-mean-square error (RMSE). Additionally, a novel method is introduced for constructing the ambiguity function, which focuses on minimizing the acoustic complexity to encompass only direct and surface-reflected sounds. This improvement makes it particularly suitable for hydrophone arrays deployed near the sea surface. Computer simulations and experimental results validate that the algorithm, incorporating the aforementioned improvements, achieves enhanced accuracy in position estimation, reduced RMSE, and increased robustness.

A 2D Ultrasonic Phased Array Optimization Framework Enabled by Reconfigurable Laser Induced Phased Arrays

Abstract Three-dimensional (3D) ultrasonic imaging enables the viewing of internal features in a more accurate way than cross-sectional imaging. 3D imaging requires two-dimensional (2D) ultrasonic phased arrays to resolve all three spatial dimensions through beamforming along azimuthal and elevation angles. 2D phased arrays pose a manufacturing and control challenge due to the requirement of large number of elements to satisfy the Nyquist sampling limit. This problem can be alleviated through the use of sparse ultrasonic array designs. The optimization process is critical, as this dictates the efficiency of suppression of grating lobes. The aim of this work is to establish a framework for design of optimized sparse 2D phased arrays. This is enabled by exploiting the reconfigurability of Laser Induced Phased Arrays (LIPAs). LIPAs are synthetic arrays produced by scanning one laser for ultrasound generation and another for ultrasound detection, independently of each other. The reconfigurability and decoupled ultrasonic generation and detection of this systems enables easy and fast implementation of any arbitrary array layout. The framework consists of an analytical model for parametric evaluation of designs. The model performs a parametric sweep on the generation and detection element positions for each array design in order to identify the optimal parameters, based on mean and maximum side-lobe level. In the presented framework, four array designs are utilized for the optimization process: matrix, random, Vernier and a novel array design the Rotated Array (RA).

The creative economy for sustainable tourism in the post-mining era in Pangkalpinang city

Micro, Small, and Medium Enterprises (MSMEs) in Pangkalpinang are crucial contributors to the local economy, especially in the post-tin mining era, by driving economic diversification and regional development. This research delves into the role of the creative economy in fostering sustainable tourism during this transitional period. Through a qualitative approach, the study employs the SWOT analysis framework to assess the development of MSMEs and their connection to sustainable tourism in Pangkalpinang. The strengths of these MSMEs are evident in their distinctive product offerings, robust stakeholder networks, and the recognition and certifications they have attained. These elements position them well within the market. However, they face notable internal challenges, including limited resources, technological constraints, and financial difficulties, which hinder their growth potential. Externally, MSMEs in Pangkalpinang contend with high economic inflation, stiff competition from larger retail entities, and shifting consumer preferences driven by economic downturns. These factors create a challenging environment for MSMEs to thrive. Despite these challenges, there are significant opportunities for growth. MSMEs can capitalize on product diversification, which allows them to cater to a broader range of consumer needs. Additionally, there is substantial support from the government and various institutions, which can provide the necessary backing for these enterprises. Aligning their business models with sustainable tourism trends also presents a promising avenue for long-term success. By leveraging these opportunities, MSMEs in Pangkalpinang can enhance their resilience and contribute meaningfully to sustainable regional development in the post-mining era.

Representing and assessing distributed situation awareness in multi-agency disaster response: A hypergraph-based methodology

This paper introduces a novel hypergraph-based methodology for representing and assessing distributed situation awareness (DSA) in multi-agency disaster response. The fundamental ideas and motivations of our methodology stem from the following widely acknowledged understandings and phenomenons: (a) DSA’s representation should be approached from social, information and task dimensions; (b) DSA is one of the collective behaviors that emerge from the interactions; (c) the interactions in the real world are not pairwise. Our methodology delineates the collaboration, co-activation, and co-existence interactions among social, information, and task elements as higher-order interactions. We then construct these interaction systems using hypergraph-structured data derived from disaster response scenarios. Subsequently, these systems are encoded into hypergraphs, which are validated against our dataset and proven to be practical tools for depicting higher-order interaction patterns. Analytical techniques tailored to hypergraphs are applied, yielding insights intrinsic to hypergraphs regarding DSA in emergency response. Moreover, we integrate these interaction systems into a comprehensive framework that allows for the visualization and quantitative analyses of DSA evolution dynamics. We propose several indicators of evolution, discussing their trends and implications throughout the development of the emergency response. We locate the system deficiencies by revealing a mismatch between the positions of specific elements in the network and their functions. We also identify the saturation phase in the DSA evolution process.

Multi-layered medium ultrasonic phased array sparse TFM imaging based on self-adaptive differential evolution algorithm

Abstract Multilayer Composite material structures have been widely used in modern engineering fields. However, defects within these materials can adversely affect mechanical properties. Ultrasonic phased array total focusing method (TFM) imaging has advantages of high precision and dynamic focusing over the entire range, achieving significant progress in homogeneous medium detection. However, heavy computational burdens of multilayer structures lead to inefficient imaging. To address this issue, a sparse-TFM imaging algorithm using ultrasonic phased arrays suitable for multilayer media is proposed in this paper. This method constructs a fitness function with constraints such as main lobe width and sidelobe peak. Its objective is to obtain the distribution of sparse array element positions using an self-adaptive differential evolution algorithm. Subsequently, the delay time of each array element in multilayer media sparse TFM is calculated using the root mean square (RMS) principle and combined with amplitude weighting, the method corrects the imaging results. Compared with the Ray-based full-matrix capture and TFM method (Ray-based FMC/TFM), the RMS-based full-matrix capture and TFM (RMS-based FMC/TFM), and the phase shift method, the experimental and simulation results demonstrate that the proposed method significantly reduces the imaging data volume, improves computational efficiency, and maintains quantitative errors within 0.2 mm.

Uniaxial tensile behaviors and Hall-Petch relationship of polycrystalline 316LN stainless steel via molecular dynamics simulation

A molecular dynamics (MD) simulation method was used to investigate the effects of element position, tensile strain rate (ε̇) and average grain diameter (d) on the uniaxial tensile properties of 316LN stainless steel at room temperature. It was found that the mechanical properties (elastic modulus, yield strength and tensile strength) of 316LN stainless steel were independent of the random position of the elements used in the model. Since the time of atoms relaxing to the equilibrium position was determined by the tensile strain rate, the mechanical properties deeply depended on the tensile strain rate. The fluctuations of simulated mechanical properties decreased with the increase number of randomly oriented grains in the molecular dynamics model, and the minimum number of grains should not be less than 500. Intragranular dislocation movement and grain boundary sliding were the main factors leading to plastic deformation, and their competition led to the normal to inverse Hall-Petch (HP) transformation of yield strength at the critical average grain diameter of 17 nm. When d was bigger than 17 nm, the relationship between yield strength and d was a normal HP relationship, σy(MPa) = 162 + 465.5d −0.5; while d was smaller than 17 nm, the forgoing relationship transformed into an inverse HP relationship, σy(MPa) = 3930-25d−0.5.

Enhancing the Ilizarov Apparatus: Mechanical Stiffness

The Ilizarov system is a form of external fixation device utilized by medical professionals to aid patients who have sustained injuries from accidents, bone shortening, or nonunion of the bone. The device is fixed onto the long bone of the patient and is adjusted according to the nature of the injury. Ilizarov's techniques are minimal invasiveness, not aggressive, spare tissues and involve little blood loss. It consists of wires that are secured to a modular circular frame and then tightened. The Ilizarov fixator is a valuable tool for treating acute fractures, especially in cases where there is bone loss and compromised soft tissue. Several studies have aimed to improve the effectiveness of Ilizarov fixation through modifications to its frame components, such as ring diameter, transosseous element diameter, ring separation, transosseous element count in each ring, and number of rings, as well as the type of transosseous element employed, including wires, full-pins, or half-pins. Furthermore, positioning of transosseous elements at the correct crossing angle without damaging the nerves and vessels while considering the intricacy of bone deformities. Recent advancements in Ilizarov fixation will be thoroughly reviewed in this manuscript, with a particular focus on improving the stiffness of the entire frame. The main objective of this review is to pinpoint the optimal configurations, with a particular focus on stiffness, in order to foster stability and ensure a successful recuperation.

The design of neighbourhood open spaces to improve mental health: a critical review of restorative spatial characteristics and their applicability in design practice

ABSTRACT As urbanisation continues to increase, a rising prevalence of individuals dealing with stress-related mental health issues can be observed. Psychological restoration research can teach designers which spatial characteristics should be implemented in neighbourhood open spaces to enhance psychological restoration and improve citizens’ mental health. Unfortunately, this information is scattered across different fields, and it is unclear if the research results can be applied in design practice. Therefore, this study aims to identify restorative spatial characteristics and their applicability in neighbourhood open space design to improve citizens’ mental health, and critically reflect upon the current literature to guide future research and design practice. A scoping literature review (N = 62) resulted in 32 restorative spatial characteristics that can be applied in design practice. According to the literature, designers should focus on a variety of vegetation, consider the position of design elements, and optimise the design of adjacent buildings to enhance restorative potential. Although more research is needed into the applicability of the currently available information in real-life settings and for a variety of participant groups, this study is a first step in bringing together research of different fields on the design of neighbourhood open spaces that improve mental health of all citizens.

Exploring the Trends and Patterns in Periodicity of Elements: from Mendeleev to Modern Periodic Table

Dmitri Mendeleev created the periodic table in 1869, and it has undergone significant modifications to become an essential tool in chemical science today. This abstract provides an informative summary of the evolution of periodic trends and patterns, from Mendeleev's work to the latest complexities in the current periodic table. The first step towards comprehending the periodicity of elements was Mendeleev's periodic table, which he offered as a means of classifying elements according to their atomic weights and chemical behaviours. The periodic table underwent additions and modifications during the ensuing decades as atomic theory and experimental methods advanced. Instead of focusing on atomic weights, the current periodic law is concerned with the precise atomic number. Henry Moseley's discovery of the atomic number altered the elemental positions. This modification allowed for a deeper examination of periodic trends between elements by clarifying elemental relationships. Periodic trend analysis takes into account several characteristics, such as atomic radius, ionisation energy, electron affinity, and electronegativity. This is evident from the fact that each group of elements, ranging from noble gases to alkali metals, has distinct tendencies in both physical and chemical properties. We gathered the necessary materials for this review from a variety of reliable sources, including physical chemistry by K. K. Sharma, inorganic chemistry by J. D. Lee, and other published works. In conclusion, Mendeleev's table's evolution to its current state demonstrates that scientists have been addressing the issue for many generations. The periodic table is still a vital tool for classifying and evaluating the wide range of elements and their characteristics, although there are still many uncharted territories in chemistry. Keywords: electronic configuration, group analysis, periodic trend, periodicity, atomic number, and quantum physics.

RDCSAE-RKRVFLN: A unified deep learning framework for robust and accurate DOA estimation

This paper introduces an innovative unified deep learning (DL) model, “reduced deep convolutional stack autoencoder (RDCSAE)-robust kernel-based random vector functional link network (RKRVFLN)” (RDCSAE-RKRVFLN), for addressing practical challenges like phase and gain uncertainty, mutual coupling effects, and element position errors in direction of arrival (DOA) estimation. The RDCSAE utilizes deep convolutional neural network (DCNN) and stack autoencoder (SAE) for robust feature extraction. The RKRVFLN integrates a concise SAE output with kernel-based learning. Thus, the integration of RDCSAE (suitable for unsupervised feature extraction) and RKRVFLN (for supervised learning) in the developed model results in efficient use of limited training data. Performance metrics of this method include among others the accuracy (separation between sources), root mean square error (RMSE), signal-to-noise ratio (SNR), etc. The method performs better than existing data driven methods in terms of computational efficiency, faster learning speed, enhanced model generalization, higher accuracy, and shorter event regression time and empirical methods. In the presence of above mentioned four perturbations, this method successfully resolves multiple sources with a resolution of 0.3° with a RMSE of 0.00376. For real time application, the paper implements RDCSAE-RKRVFLN on a high-speed reconfigurable Xilinx Virtex-5 field-programmable gate array (FPGA) realizing a digital DOA estimator for one source. The hardware estimates the DOA with a maximum error of 0.01953125 which follows closely the simulation results indicating the efficiency and feasibility of the method.

DOA estimation technology based on array signal processing nested array

DOA estimation technology based on array signal processing nested array

Wide Linearity Range 3D Magnetic Sensor and Angular Position Detector Based on a Single FePt Spin-Orbit Torque Device.

Three-dimensional (3D) vector magnetic sensors play a significant role in a variety of industries, especially in the automotive industry, which enables the control of precise position, angle, and rotation of motion elements. Traditional 3D magnetic sensors integrate multiple sensors with their sensing orientations along the three coordinate axes, leading to a large size and inevitable nonorthogonal misalignment. Here, we demonstrate a wide linearity range 3D magnetic sensor utilizing a single L10-FePt Hall-bar device, whose sensitivity is 291 VA-1 T-1 in the z-axis and 27 VA-1 T-1 in the in-plane axis. Based on the spin-orbit torque-dominated magnetization reversal, the linear response of anomalous Hall resistance within a large linear range (±200 Oe) for the x, y, and z components of magnetic fields has been obtained, respectively. Typically, it exhibits a relatively lower magnetic noise level of 7.9 nV at 1 Hz than previous results, improving measurement resolution at the low frequency. Furthermore, we provide a straightforward approach for noncontact angular position detection based on a single Hall-bar device, which shows great potential for application in rotational motion control.

Hybridization of almost difference sets and DFT technique for concentric circular antenna array thinning

This paper introduces a hybrid methodology for minimizing the peak sidelobe level (PSLL) in concentric circular antenna arrays (CCAAs). Traditionally, the almost difference set (ADS) approach has been utilized for determining active element positions, but its effectiveness in PSLL reduction is limited. To address this limitation, the proposed methodology integrates the ADS approach with the discrete Fourier transform (DFT) technique. This integration optimizes excitation amplitudes for activated elements within the ADS-based thinned array, resulting in significant PSLL reduction. Simulation results across various case studies demonstrate that the hybrid ADS-DFT method achieves lower PSLL values than existing thinning techniques while maintaining high computational efficiency. Additionally, electromagnetic (EM) simulations validate the efficiency of the proposed method, considering mutual coupling effects.