Position Values Research Articles

Node Calibration in UWB-Based RTLSs Using Multiple Simultaneous Ranging.

Ultra-wideband (UWB) networks are gaining wide acceptance in short- to medium-range wireless sensing and positioning applications in indoor environments due to their capability of providing high-ranging accuracy. However, the performance is highly related to the accuracy of measured position and antenna delay of anchor nodes, which form a reference positioning system of fixed infrastructure nodes. Usually, the position and antenna delay of the anchor nodes are measured separately as a standard initial procedure. Such separate measurement procedures require relatively more time and manual interventions. This paper presents a system that simultaneously measures the position and antenna delay of the anchor nodes. It provides comprehensive mathematical modeling, design, and implementation of the proposed system. An experimental evaluation in a line-of-sight (LOS) environment shows the effectiveness of the anchor nodes, whose position and antenna delay values are measured by the proposed system, in localizing a mobile node.

Electron’s Position Expectation Values and Energy Spectrum of Lithium Ion (Li^(2+)) on Principal Quantum Number n≤3

The purpose of this study is to determine the electron’s position expectation values and energy spectrum on the Li2+ ion on the principal quantum number n≤3. This research using literature study methods on quantum mechanics. The expectation values of the electron position and the energy spectrum of the Li2+ ion uses numerical calculations using the Matlab 2019a program. The steps in this research method include: preparation; theory development; simulation; validation of the results of theory development; results of theory development; discussion and conclusion. The results obtained in this study are the electron’s position expectation values and energy of the Lithium ion. The electron’s position expectation values indicates the presence of electrons that often appear around the x-axis by relying on the interval used. The larger the interval, the more constant the electron’s position expectation values will be and towards an almost constant value. From the analysis results, the expectation value varies in positions from 0,0001a0 to 0,1637a0. The electron energy spectrum of the Li2+ ion is inversely proportional to the square of the principal quantum number (n),E1= -122,4 eV ; E2= -30,6 eV ; E3= -13,6 eV

Ultrasonographic assessment of preoperative gastric volume in patients with dyspepsia: a prospective observational study

BackgroundPatients undergoing gastroenteroscopy during sedation are prone to aspiration, and most patients with dyspepsia have delayed gastric emptying. This study aimed to investigate the feasibility of measuring the gastric antrum cross-sectional area (CSA) to supply a novel clinical diagnostic reference value in patients with dyspepsia.MethodsPatients with dyspepsia undergoing elective gastroscopy were included. The Perlas qualitative 0–2 grading scale score was determined before the operation. The anteroposterior diameter (D1) and craniocaudal diameter (D2) between gastric antrum serosal surfaces were measured perpendicular to each other in the supine and right lateral decubitus (RLD) positions. CSA values in the supine position and RLD position were determined. Gastric contents were endoscopically suctioned with the volumes measured and noted as actual gastric volume. Multiple regression analysis was used to fit a mathematical model for estimating the gastric volume. Receiver operating characteristic (ROC) curves were constructed to determine the accuracy of RLD CSA to detect gastric volumes of > 0.8 ml/kg.ResultsA total of 117 patients were enrolled and divided into a functional dyspepsia (FD) group and an organic dyspepsia group according to gastroscopy findings. For a gastric volume of > 0.8 ml/kg, cut-off values for FD and organic dyspepsia were 6.7 cm2 and 10.0 cm2, respectively. Two new modified mathematical models were derived to predict an estimated gastric volume for FD and organic dyspepsia: volume = 3.93 × RLD CSA - 0.47 × age; and volume = 6.15 × RLD CSA - 0.61 × age.ConclusionWe used the cut-off value of the antral area for the fast diagnosis of gastric volumes in patients with dyspepsia, which may assist clinicians in identifying patients at risk of aspiration.Trial registrationwww.chictr.org.cn (CHICTR-DDD-17010871); registered 15 March 2017.

Pro-agility unpacked: Variability, comparability and diagnostic value

Change of direction (COD) is an important component of athlete performance and measuring and comparing athletes is an integral aspect of strength and conditioning practice. This article aimed to determine pro-agility shuttle utility, by quantifying variability and normative values for different sports, skill-levels and positions. Limitations of the pro-agility shuttle are identified, as are future research directions. A total of 67 studies were included for review. Pro-agility shuttle reliability was reported in 10 studies across 6 sports; however, comprehensive reliability statistics were absent in most papers. Additionally, only reliability of total-time from stopwatch and timing lights were reported. Data of 32,891 subjects in 12 sports (American football, basketball, cricket, general athletes, hockey, lacrosse, recreational athletes, resistance-trained athletes, rugby, soccer, swimming, and tennis) were extracted and aggregated, establishing sport, skill-level (elite, sub-elite, and novice) and positional normative values, where practical. Elite athletes showed the fastest performance times, whereas sub-elite and novice athletes showed similar spreads in performance, suggesting similar athletic capabilities. In conclusion, the pro-agility shuttle currently has limited diagnostic value and the variability of smaller performance sub-components within pro-agility shuttle should be examined. Furthermore, the value of other technologies such as smart phone, inertial sensor or radar should be investigated.

Hip joint mobility in relation to measurement position, gender and limb dominance

Purpose: The purpose of this study was to determine whether three different measurement position yield divergent results in ROM using a goniometer, and how is it affected by anthropometrical factors. Methods: We measured the range of rotation in the hip joint in healthy participants aged 21.6 ± 1.88, seeking to determine how the distribution of internal vs. external rotation (RI) within the total range of mobility (TR) was influenced by the measurement position used, the gender of the participant, and the dominant lower limb. Results: We found that not only gender and limb dominance, but also the body position in which hip joint’s range of motion is measured significantly affects the values of TR and RI. We found that TR achieves the highest values in the prone position – PrP (males: 95.35 ± 12.44 and 93.15 ± 12.49; females: 103.75 ± 14.87 and 106.25 ± 15.56) and the lowest values in supine position – SuP (male: 62.65 ± 8.51 and 57.85 ± 9.60; female: 59.5 ± 12.27 and 55.85 ± 8.54). The analysis shows that CERD occurs 1.72 RI in women (StP), and >2.08 RI in men (PrP). Conclusions: Due to the similarities between asymmetry of internal/external rotation in the hip joint and asymmetry in the rotation of the shoulder found in Glenohumeral Internal Rotation Deficit (GIRD), we propose the concepts of Coxal Internal Rotation Deficit (CIRD) and Coxal External Rotation Deficit (CERD) as tools to indicate the possibility for injury to the hip joint, and propose threshold rotation index values serving as indicators of these deficits.

Improving GNSS Positioning Using Neural-Network-Based Corrections

<h3>Abstract</h3> Deep neural networks (DNNs) are a promising tool for global navigation satellite system (GNSS) positioning in the presence of multipath and non-line-of-sight errors, owing to their ability to model complex errors using data. However, developing a DNN for GNSS positioning presents various challenges, such as (a) poor numerical conditioning caused by large variations in measurements and position values across the globe, (b) varying number and order within the set of measurements due to changing satellite visibility, and (c) overfitting to available data. In this work, we address the aforementioned challenges and propose an approach for GNSS positioning by applying DNN-based corrections to an initial position guess. Our DNN learns to output the position correction using the set of pseudorange residuals and satellite line-of-sight vectors as inputs. The limited variation in these input and output values improves the numerical conditioning for our DNN. We design our DNN architecture to combine information from the available GNSS measurements, which vary both in number and order, by leveraging recent advancements in set-based deep learning methods. Furthermore, we present a data augmentation strategy to reduce overfitting in the DNN by randomizing the initial position guesses. We, first, perform simulations and show an improvement in the initial positioning error when our DNN-based corrections are applied. After this, we demonstrate that our approach outperforms a weighted least squares (WLS) baseline on real-world data. Our implementation is available at github.com/Stanford-NavLab/deep_gnss.

The influence of medium elasticity on the cavitation cloud formation by the backscattering of focused ultrasound from a bubble interface

High Intensity Focused Ultrasound (HIFU) is utilized for a minimal invasive method to ablate tumors by the temperature rise at the focal point of the focused ultrasound. In HIFU therapy, the treatment time tends to be long due to the narrow region of the ablation. Hence, the ways to enhance the efficiency of HIFU therapy have been studied, and the usage of a dense cavitation bubble cloud is expected to enhance energy dissipation from acoustic energy to heat. However, the cavitation cloud may cause the damage of surrounding tissue due to shockwaves when it collapses. Therefore, it is necessary to control cavitation clouds for effective usage. Ogasawara et al. (2018) investigated the cavitation cloud formation in water by using a laser-induced bubble as a reflector of HIFU. In the present study, we investigate the influence of medium elasticity on the cavitation cloud formation by the backscattering of HIFU from microbubbles created by a focused laser beam in the elastic medium. As a tissue mimicking phantom, agarose gel is chosen because of its high optical clarity for photography and its wide usage in tissue engineering. Medium elasticity varies from approximately 2.7 kPa to 200 kPa. By using residual microbubbles in the gel as reflectors of HIFU, the cavitation cloud generation point can be controlled. It is shown that as gel stiffness increases, the maximum value of the top interface position of cavitation clouds decreases and the collapse time of cavitation clouds becomes shorter.

Estimating chlorophyll and nitrogen contents in maize leaves (Zea mays L.) with spectroscopic analysis

The relationship between reflectance and chlorophyll (Chl) and nitrogen (N) contents in maize leaves was analyzed to identify useful spectral indices for diagnosing the nutritional status of crops in terms of N. An experiment was carried out in random blocks with five treatments of nitrogen fertilizer (0, 50, 100, 150, 200 kg ha-1) and four repetitions and the foliar spectral responses were measured with a FieldSpec 4 spectroradiometer in three phenological stages. Several spectral indices and values of red edge position (REP) were calculated using various methods. Red-edge position linear interpolation (REP-L), Red-edge position linear extrapolation (REP-LE), REP-Inverted Gaussian fitting technique (REP-IG), REP-Polynomial fitting technique (REP-P) and NDVI had the best relationship with chlorophyll and nitrogen contents. The first derivative of reflectance, between 560 and 760 nm, transformed by the normal state variable (SNV) also had highly significant correlation coefficients with the N, Chl, and yield. Additionally, the corn yield showed highly significant correlations with the N and Chl contents. From the point of view of the diagnosis of the nutritional status of corn, the spectral indices and REP values were suitable for establishing the nutritional status of corn in relation to N in the phenological stages V8 and R1.

种植模式和坡位对茶园土壤细菌群落结构及功能类群的影响

PDF HTML阅读 XML下载 导出引用 引用提醒 种植模式和坡位对茶园土壤细菌群落结构及功能类群的影响 DOI: 10.5846/stxb202110122879 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 福建省自然科学基金项目（2021J01489）；国家重点研发计划项目（2016YFD0200903）；福建省科技厅公益类科研专项（2021R1029002）；中央引导地方科技发展专项（2020L3030） Effects of planting patterns and slope positions on soil bacterial community structure and functional groups in tea gardens Author: Affiliation: Fund Project: the Natural Science Foundation of Fujian Province,China(2021J01489)；the National Key Research and Development Program of China (2016YFD0200903)；；Special Founds Projects for Basic Scientific Research of Public Welfare in Fujian Province(2021R1029002);Special Program for Science and Technology Development of Fujian Province from the Ministry of Science and Technology of China(2020L3030) 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:茶树（Camellia sinensis L.）种植是亚热带丘陵山区主要的土地利用类型，茶园种植模式是影响土壤细菌群落结构的主要人为因素。为揭示种植模式和坡位对土壤细菌群落结构和功能的影响，选取两种不同种植模式（常规和有机种植模式）和3个坡位（上、中、下坡位）表层土壤（0-20cm）为对象，采用野外调查、Illumina Miseq高通量测序和PICRUSt2功能预测相结合的研究方法，研究不同种植模式和坡位下土壤细菌群落结构和功能特征，阐明土壤理化性质对土壤细菌群落结构的影响，预测土壤细菌功能特征。研究结果表明：（1）与常规种植模式相比，有机种植模式茶园土壤细菌Alpha多样性有所降低，其中中坡位常规茶园土壤细菌Sobs和Simpson指数显著高于有机茶园（P<0.05）；从坡面尺度看，两种种植模式下土壤细菌Alpha多样性指标均以中坡位最高，其中常规茶园中坡位土壤细菌Ace和Simpson指数均显著高于下坡位（P<0.05）。（2）各样地茶园土壤细菌共获得29个门82个纲190个目316个科517个属929个种，主要细菌优势门为绿弯菌门（Chloroflexi）、放线菌门（Actinobacterita）、变形菌门（Proteobacteria）、酸杆菌门（Acidobacteria）。土壤细菌群落优势属以AD3、热酸菌属（Acidothermus）、norank_f__norank_o__Elsterales和norank_f__Xanthobacteraceae为主。（3）主坐标分析（PCoA）显示，不同种植模式的茶园土壤细菌群落结构存在明显差异，常规种植模式下不同坡位之间的土壤细菌群落结构有显著差异（P<0.05），有机种植下不同坡位之间的土壤细菌群落结构无显著差异。置换多元方差分析（PERMANOVA）结果表明不同种植模式的土壤细菌群落结构差异显著（P<0.05），而不同坡位土壤细菌群落结构无显著差异（P>0.05），说明种植模式对茶园土壤细菌群落结构的影响更大。组间群落差异分析（LEfSe）表明，57个差异物种对种植模式非常敏感，不同种植模式富集了不同的细菌类群。（4） PICRUSt2功能预测共获得6个一级功能层和46个二级功能层，表现出功能上的丰富性，土壤细菌群落在代谢、遗传信息处理和环境信息方面功能活跃。有机种植模式提高了土壤细菌碳水化合物代谢、氨基酸代谢、膜运输、信号转导、脂质代谢及外源生物降解与代谢功能。（5）相关分析和冗余分析结果表明，土壤碱解氮、速效磷、全磷、全钾和pH是影响土壤细菌群落丰度和多样性的主要影响因子。总体而言，有机种植模式改变了茶园土壤细菌群落结构和代谢功能，增加土壤有益细菌的数量，有利于保持茶园土壤可持续的生态环境。 Abstract:Tea (Camellia sinensis L.) is one of the most important and traditional economic crops widely cultivated in acid red soils in the subtropical zones in China, and the planting pattern constitutes the major artificial factor affecting the soil bacterial community structure. However, the effect of the planting patterns on soil bacterial community structures and functions in the tea garden remains unclear. To study the characteristics of soil bacterial community compositions and functional groups under different planting patterns and slope positions, soil samples derived from topsoil (0-20cm) were collected from the upper, middle, and lower slope positions of conventional (CT) and organic (OT) tea gardens. Illumina MiSeq sequencing and PICRUSt2 predictions were performed to investigate the bacterial community structures and functional groups, respectively. Further, the Spearman correlation analysis and redundancy analysis (RDA) were also used to examine the soil property factors that drived the community structure. The results showed that:(1) compared with the conventional planting pattern, the soil bacterial diversity under organic planting pattern decreased remarkably, and the Sobs and Simpson indices significantly decreased in the middle slope position (P<0.05). Regardless of the planting patterns, the soil bacterial diversities were the highest in the middle slope position. Ace and Simpson indices of the bacterial community in this slope position were significantly higher than the respective values of the lower slope position under the conventional planting pattern (P<0.05). (2) A total of 29 phyla, 82 classes, 190 orders, 316 families, 517 genera and 929 species were detected in soil bacteria of tea gardens. The dominant bacterial phyla were Chloroflexi, Actinobacterita, Proteobacteria and Acidobacteria. The dominant bacterial genera were AD3, Acidothermus, norank_f__norank_o__Elsterales, and norank_f__Xanthobacteraceae. (3) The results of principal coordinates analysis (PCoA) showed that the soil bacterial community structures could be distinctly separated in different planting patterns. The community structures of soil bacteria in different slope positions were significantly different under the conventional planting pattern (P<0.05), whereas they were similar under the organic planting pattern. The permutational multivariate analysis of variance (PERMANOVA) indicated that the bacterial community structures under different planting patterns showed significant differences (P<0.05), whereas those in different slope positions did not (P>0.05). The linear discriminant analysis effect size (LEfSe) analysis showed that 57 biomarkers were very sensitive to the planting patterns, with different planting patterns enriching different bacterial populations. (4) PICRUSts2 platform predicted 6 biological metabolic pathways and 46 subfunctions in the soil bacteria of tea gardens, suggesting of abundant soil bacterial functions. These pathways mainly involved metabolic processes, genetic information processing, and environmental information processing. The organic planting pattern increased carbohydrate metabolism, amino acid metabolism, membrane transport, signal transduction, and lipid metabolism of cofactors and vitamins functional genes. (5) The results of the Spearman correlation analysis and RDA showed that soil alkaline nitrogen, available phosphorus, total phosphorus, total potassium and pH were the main factors affecting the abundance and diversity of the soil bacterial community. In conclusion, the organic planting changes the community structure and the metabolic functions of soil bacteria, promotes soil bacteria carbon and nitrogen metabolism and enriches beneficial bacteria, and thus is beneficial to maintain the sustainable ecosystem in tea garden soil. 参考文献 相似文献 引证文献

Neural networks for classification of strokes in electrical impedance tomography on a 3D head model

&lt;abstract&gt;&lt;p&gt;We consider the problem of the detection of brain hemorrhages from three-dimensional (3D) electrical impedance tomography (EIT) measurements. This is a condition requiring urgent treatment for which EIT might provide a portable and quick diagnosis. We employ two neural network architectures - a fully connected and a convolutional one - for the classification of hemorrhagic and ischemic strokes. The networks are trained on a dataset with $ 40\, 000 $ samples of synthetic electrode measurements generated with the complete electrode model on realistic heads with a 3-layer structure. We consider changes in head anatomy and layers, electrode position, measurement noise and conductivity values. We then test the networks on several datasets of unseen EIT data, with more complex stroke modeling (different shapes and volumes), higher levels of noise and different amounts of electrode misplacement. On most test datasets we achieve $ \geq 90\% $ average accuracy with fully connected neural networks, while the convolutional ones display an average accuracy $ \geq 80\% $. Despite the use of simple neural network architectures, the results obtained are very promising and motivate the applications of EIT-based classification methods on real phantoms and ultimately on human patients.&lt;/p&gt;&lt;/abstract&gt;

Solar Transposition Modeling via Deep Neural Networks With Sky Images

This article presents a notable advance toward the development of a new method of increasing the single-axis tracking photovoltaic (PV) system power output by improving the determination and near-term prediction of the optimum module tilt angle. The tilt angle of the plane receiving the greatest total irradiance changes with Sun position and atmospheric conditions including cloud formation and movement, aerosols, and particulate loading, as well as varying albedo within a module's field of view. In this article, we present a multi-input convolutional neural network that can create a profile of plane-of-array irradiance versus surface tilt angle over a full 180 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> arc from horizon to horizon. As input, the neural network uses the calculated solar position and clear-sky irradiance values, along with sky images. The target irradiance values are provided by the multiplanar irradiance sensor (MPIS). In order to account for varying irradiance conditions, the MPIS signal is normalized by the theoretical clear-sky global horizontal irradiance. Using this information, the neural network outputs an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> -dimensional vector, where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> is the number of points to approximate the MPIS curve via Fourier resampling. The output vector of the model is smoothed with a Gaussian kernel to account for error in the downsamping and subsequent upsampling steps, as well as to smooth the unconstrained output of the model. These profiles may be used to perform near-term prediction of angular irradiance, which can then inform the movement of a PV tracker.

Prediction of Soil Penetration Resistance with Three Different Artificial Neural Networking Methods

Soil compaction is one of the major problems of the agricultural sector affecting negatively the soil structure and impedes plant root growth. Soil penetration resistance should be measured from many points of the production land to determine the effects of plant growth problems. Collection of soil penetration values from huge lands is time-consuming, tiring, and tedious for researchers. Also, the number of measured points to what extent will be sufficient to evaluation on whole production area is not clear. To eliminate this ambiguity, soil penetration values of the unmeasured points should be estimated to evaluate the whole land. Artificial Neural Networks (ANN) are one of the most popular mathematical computing and modeling method used to estimate unknown data with the help of known data. In this study, we collected 1603 samples of geographical position and soil penetration value from 40 cm depth within the 20 ha field. From the 1603 values, 24% records were selected for testing and the remaining 76% records were used for training. Soil penetration values of the unmeasured points were estimated using Generalized Regression Neural Network (GRNN), Multi-Layer Perceptron (MLP) and Radial Bias Function (RBF) methods in MATLAB. In addition to mean squared error (MSE), root mean square error (RMSE) and mean absolute error (MAE) has been also used for evaluation of prediction accuracy on these methods. RBF results showed very good agreement between the predicted and the measured real values of soil resistance (MSE: 0.1608; RMSE: 0.3717; MAE: 0.3682).

Trajectory Prediction of Workers to Improve AGV and AMR Operation based on the Manufacturing Schedule

In semi-automated manufacturing, an increasing amount of intelligent mobile robots operate in close proximity to human workers. Considering future positions of humans allows to further improve the efficiency in terms of throughput of Autonomous Mobile Robots (AMR) and Automated Guided Vehicles (AGV). The longer the prediction horizon, i.e. the more position values of humans can be predicted in the near and distant future, the more a robot can adjust its route accordingly and optimize the process. This paper discusses the challenges of human motion trajectory prediction in manufacturing and presents a schedule-based approach that uses real-time schedule data obtained from Manufacturing Execution Systems (MES). Schedule-awareness in human motion trajectory prediction extends semantic mapping approaches and effectively reduces the number of probable destinations by considering which process steps are next for the currently produced goods. With a reduced set of destinations, the performance of forward-planning trajectory prediction can be improved. For evaluation, a commercial MES is used together with an Ultra-wideband-based Real-Time Locating System (RTLS) for obtaining position data of humans. On this basis, a naive Bayes classifier utilizes MES-schedule and real-time position data to predict human motion intentions. Abstract activity modeling ensures that only a few training data sets are required for deployment, thus making this approach suitable for rapidly changing manufacturing environments such as in flexible manufacturing.

Bir Quadcopterin PID, Adaptif Ve LQR Kontrol Yöntemleri Kullanılarak Yapılan Dinamik Analizi

In this study, different control methods for controlling the position and angle values for the quadcopter (UAV) that take off and land vertically are simulated with MATLAB/Simulink. First of all, a mathematical model was created with the Newton-Euler method, taking into account the dynamics of the quadcopter system. The focus of the study is to investigate the appropriate control method for the position control of the quadcopter. For the linear model of the quadcopter system, PID, LQR and Adaptive Control methods, and for the nonlinear model of the quadcopter system. PD Control simulation has been done. The mentioned control methods have been applied to the system and the control of the movement of the system in each axis has been examined. The obtained results are compared with each other to see the performance of the controllers and the most appropriate control method for the quadcopter was determined with comparisons and tracking scenarios.

Accuracy in Determining Aircraft Position by Terrestrial Radio Navigation

The main objective of terrestrial radio navigation is position determination. In this study, the accuracy of the distance measurement, distance difference measurement, and integrated angle measurement/distance measurement terrestrial radio navigation methods is investigated. in order to calculate the position errors, simulations for the aircraft flight dynamics were carried out, and the obtained position values were compared with the actual values. The aircraft position determination methods were evaluated in the sense of accuracy. The position determination method with better accuracy was determined by comparing the absolute errors of the examined methods. Simulation and error analysis shows that the distance difference method is superior and gives more accurate position results. It was observed that the distance measurement method errors were smaller than the errors of the integrated angle measurement/distance measurement method.

Speaking of business ethics: Bourdieu and market morality as a discursive practice

AbstractWhile communication of business ethics has increased in importance, it is often understood as being more of immaterial than material value. However, recent studies have demonstrated that language and communication can have important social consequences, changing institutional logics and daily practices in economic fields. This conceptual paper explores how sociologist Pierre Bourdieu’s theory of practice can help us understand how market morality as a discursive practice for value formation is subject not only to linguistic exchange but also market positioning and economic value. The paper offers an inter-disciplinary view of business ethics in combination with socio-linguistics, contributing with propositions for how moral language serves as a currency for business ethics, providing implications for future studies of business ethics as a socio-economic practice.

THE DYNAMICS OF A PARTICLE IN A TIGHT-BINDING LATTICE UNDER A COULOMB POTENTIAL

We study the tight-binding dynamics of a charged particle by the hopping mechanism in a one - dimensional lattice under the action of a Coulomb potential due to another fixed particle (impurity). The quantum dynamics is studied using the pseudo-spectral method applied to a tight-binding Hamiltonian with nearest neighbors interactions. The resulting expected values for position and velocity are then compared with those deduced by the semi-classical method invoking the effective potential in the lattice. These results are notably similar when the particle is located far from the impurity position since the Coulomb field is approximately uniform, while when near to the impurity the results are different due to the quantum dispersion of the particle’s wavepacket under a highly inhomogeneous field. Interestingly, however, the quantum and semiclassical results coincide in the continuous regime (parabolic dispersion relation) due to the transmission of the particle’s wavepacket through the impurity.

The extreme solar and geomagnetic storms on 1940 March 20–25

ABSTRACT In late 1940 March, at least five significant solar flares were reported. They likely launched interplanetary coronal mass ejections (ICMEs), and were associated with one of the largest storm sudden commencements (SSCs) since 1868, resulting in space weather hazards that would have significant societal impacts should it occur today. The initial solar activity is associated with a solar proton event. Afterwards, another flare was reported in the eastern solar quadrant (N12 E37-38) at 11:30–12:30 ut on March 23, with significant magnetic crochets (up to ≈ |80| nT at Eskdalemuir) during 11:07–11:40 ut. On their basis, we conservatively estimate the required energy flux of the source solar flare as X35 ± 1 in soft X-ray class. The resultant ICMEs caused enormous SSCs (up to &amp;gt;425 nT recorded at Tucson) and allowed us to estimate an extremely inward magnetopause position (estimated magnetopause stand-off position ≈3.4 RE). The time series of the resultant geomagnetic storm is reconstructed using a Dst estimate, which peaked at 20 ut on March 24 at ≈ −389 nT. Around the storm main phase, the equatorward boundary of the auroral oval extended ≤46.3° in invariant latitudes. This sequence also caused a solar proton event and Forbush decrease (≈3 per cent). These sequences indicate pile-up of multiple ICMEs, which even achieved a record value of inward magnetopause position. Our analyses of this historical pioneer event bring more insights into possible serious space weather hazards and provide a quantitative basis for future analyses and predictions.

An experimental and numerical study on phase change material melting rate enhancement for a horizontal semi-circular shell and tube thermal energy storage system

In the present study, the melting characteristics of a lauric acid in a semi-circular and circular latent heat thermal energy storage unit have been studied. The low melting rate of phase change materials in the lower half of a circular heat storage unit can be enhanced by confining the phase change material in the upper region by configuring the outer shell of a conventional circular latent heat thermal energy storage unit into a semi-circular shape. The recent literature shows that no work has been carried out on the effect of the melting performance of phase change material in a semi-circular latent heat thermal energy storage unit hence an effort has been made to investigate the melting performance of phase change material experimentally and numerically for semi-circular latent heat thermal energy storage unit. The enhancement in the melting rate at three different inner tube position values from the bottom surface of outer semi-circular shell has been investigated and it is found that the enhancement is more when the inner tube is nearest to the bottom surface of the outer shell.The semi-circular heat storage unit is also compared with circular heat storage unit by considering the equal volume of phase change material in both the cases. The semi-circular latent heat thermal energy storage system shows enhancement in melting performance of phase change material as compared to circular latent heat thermal energy storage system. The semi-circular latent heat thermal energy storage system melts the phase change material completely in almost half time as compared to circular latent heat thermal energy storage system. It is found that the thermal energy stored in phase change material for semi-circular latent heat thermal energy storage system is 416.4 kJ which is 12.04 % more as compared to thermal energy stored of phase change material for circular latent heat thermal energy storage for the same time duration of 4800 sec. It is also found that the maximum thermal energy storage rate attained in semi-circular latent heat thermal energy storage system is 0.15 kW whereas in circular latent heat thermal energy storage system it is 0.13 kW which is 25 % higher as compared to that of circular latent heat thermal energy storage system.

Epistemic errors in students\u2019 four fundamental operations of whole numbers: a radical constructivist grounded theory (RCGT) approach

The purpose of this study was to explore four fourth-grade students’ errors in whole number operations and associated causes. Task-based interviews were conducted to explore students' errors and their causes in four operations—addition, subtraction, multiplication, and division of whole numbers. The qualitative data in the form of students’ tasks and interview transcripts were analyzed to examine the nature of errors committed by the students. The findings of the study revealed that students committed epistemic and non-epistemic errors in the operations of whole numbers. Most of the errors in addition, subtraction, multiplication, and division problems were epistemic in nature due to a lack of understanding of the regrouping of numbers with position values and the role of a zero in different operations, a lack of understanding of the minuend and subtrahend in subtraction problems, a lack of understanding the role of zero in position values, and a misalignment of positional places for each numeral in minuend and subtrahend. The students could not comprehend the role of the remainder in the context of division problems and could not relate divisor, dividend, quotient and remainder. The non-epistemic errors seemed to arise from students’ lack of attention and curiosity, forgetting, and de-learning of the concepts in higher order operations and interpretations.