Field Method Research Articles

Error fields: personalized robotic movement training that augments one's more likely mistakes.

Control of movement is learned and uses error feedback during practice to predict actions for the next movement. We previously showed that augmenting error can enhance learning, but while such findings are encouraging, the methods need to be refined to accommodate a person's individual reactions to error. The current study evaluates error fields (EF) method, where the interactive robot tempers its augmentation when the error is less likely. 22 healthy participants were asked to learn moving with a visual transformation, and we enhanced the training with error fields. We found that training with error fields led to greatest reduction in error. EF training reduced error 264% more than controls who practiced without error fields, but subjects learned more slowly than our previous error magnification technique. These robotic training enhancements should be further explored in combination to optimally leverage error statistics to teach people how to move better. This study reports results from a clinical trial registered on ClinicalTrials.gov with ID: NCT02720341.

Phase Field Simulation and Experimental Study of Carbide Precipitation Process in Submerged Arc Welding on Descaling Roll

The mechanical properties of surfacing layers are significantly affected by the precipitation and evolution of carbides in nickel-based alloys. At present, the study of carbide precipitation in a Ni-Cr-B-Si surfacing layer is described by using the phase field method. In this paper, the true Gibbs free energy of the M23C6 carbide phase in Ni-Cr-C ternary alloy was established by the CALPHAD method and thermodynamic database. The growth and coarsening process of M23C6 carbide was simulated based on phase field method. The microstructure of M23C6 carbide of Ni-Cr-C alloy at 1373 °C isothermal aging time was observed by scanning electron microscope (SEM). The results show that the growth and coarsening of the precipitated M23C6 carbide phase are undergone through multiple processes during isothermal aging. First, a single precipitate core is formed, and then the single precipitate continues to coarsen and grow, forming a lamellar structure. Two precipitates contact to form a single rod-like structure, and multiple precipitates form slender rod-like structures. Finally, the contacting elongated rod-like structures grow, forming a typical layered eutectic carbide. The precipitation behavior, growth, and coarsening process of M23C6-type carbides in Ni-Cr-B-Si series alloys are explored through phase field simulation and experimental research in this paper. A theoretical basis is provided for the rational control and distribution of carbides in surfacing layers. A reference is also offered for optimizing the nickel-based superalloy materials used for surfacing the surface of descaling rolls.

Research on seismic characteristics of supercharged boiler based on measured boundary

Supercharged boilers are susceptible to seismic impact loads, which can cause severe structural responses and exceed their ultimate bearing capacity. Compared with the traditional simplified structural model for earthquake resistance research in cold state, this paper establishes a real and complete three-dimensional physical model of the supercharged boiler base, and uses the reverse calculation method to obtain the boundary conditions of the boiler based on the temperature of the experimental measurement points. The temperature field and pressure field of the boiler are reconstructed, combined with the coupling method of thermal mechanical solid multiple physical fields. Using time-domain data of peak acceleration in various directions under typical earthquakes as external input loads, this study explores the seismic characteristics of boilers in the X, Y, and Z directions using time-domain analysis methods, and clarifies the vibration response mechanism in the stress concentration area of the tube sheet. The results show that the measurement and simulation error of the temperature of the inner and outer walls of the drum is 0.10 %; The individual effects of thermal stress and mechanical stress account for 107.56 % and 66.46 % of the total stress, respectively. Thermal stress plays a dominant role, while mechanical stress produces impedance effects; Under the action of an earthquake, the tube sheet undergoes reciprocating oscillation motion, and the impedance effect generated by the flexibility of the tube bundle gradually attenuates. The local stress in the X and Y directions increases by 7.70–8.27 MPa, while the local stress concentration phenomenon occurs in the Z direction due to low damping and stiffness.

A comparative assessment of grey water footprint estimation methods in paddy fields

A comparative assessment of grey water footprint estimation methods in paddy fields

Numerical simulation method for the laser powder bed fusion process by lattice Boltzmann and multi-phase field methods

Numerical simulation method for the laser powder bed fusion process by lattice Boltzmann and multi-phase field methods

A nonlocal macro-meso-scale damage model based modeling for crack propagation in ferroelectric materials

The accurate simulation of crack growth for ferroelectric materials plays a crucial role in the application of ferroelectric materials in electronic devices. In recent years, a nonlocal macro-meso-scale consistent damage (NMMD) model has been proposed for simulating the crack propagation in brittle materials, which offers the advantage of higher efficiency compared to the phase-field method. Different from the phase field method, this model does not need to solve the phase field equation at the same time. Therefore, the degree of freedom in solving can be reduced, thereby reducing the computational workload. In this paper, the authors proposed a new integration strategy for microscopic damage and devised a simple and efficient implementation of the NMMD model for the modelling of quasi-static fracture in the general purpose commercial software developer, COMSOL Multiphysics based on the finite element method (FEM) and studied the crack propagation for ferroelectric materials under the applied stress and electric fields. Different from the original NMMD model, only the tensile stress induced geometric damage is accounted for crack propagation by using the decomposition of elastic strain energy. The effects of different crack-face conditions, electrical boundary conditions and the electromechanical loading for crack growth of ferroelectric materials have been considered in our FEM simulation are discussed in this work.

A neural radiance fields method for 3D reconstruction of space target

A neural radiance fields method for 3D reconstruction of space target

Multi-modal denoised data-driven milling chatter detection using an optimized hybrid neural network architecture

Chatter, a type of self-excited vibration, deteriorates surface quality and reduces tool life and machining efficiency. Chatter detection serves as an effective approach to achieve stable cutting. To address the low accuracy in chatter detection caused by the limitations of both one-dimensional temporal and two-dimensional image modal information, this study proposes a multi-modal denoised data-driven milling chatter detection method using an optimized hybrid neural network architecture. A data denoising model combining Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Singular Value Decomposition (SVD) is established. The Ivy algorithm is employed to optimize the hyperparameters of CEEMD-SVD. Multi-modal data features of different machining states are then obtained using time–frequency domain methods and Markov transition field methods. Sensitivity analysis of time–frequency domain features is conducted using Pearson correlation coefficient analysis. A hybrid neural network model (DBMA) for chatter detection is constructed by integrating dual-scale parallel convolutional neural networks, bidirectional gated recurrent units, and multi-head attention mechanisms. The Ivy algorithm is utilized to optimize the hyperparameters of DBMA. The t-SNE algorithm is employed to visualize features extracted from different network layers of the chatter detection model. Results demonstrate that effective denoising of machining signals and the use of multi-modal data can significantly improve the accuracy of state detection. Compared with other methods, the proposed model exhibits superior stability and robustness.

Detection of Breath Nitric Oxide at Ppb Level Based on Multiperiodic Spectral Reconstruction Neural Network.

As breath nitric oxide (NO) is a biomarker of respiratory inflammation, reliable techniques for the online detection of ppb-level NO in exhaled breath are essential for the noninvasive diagnosis of respiratory inflammation. Here, we report a breath NO sensor based on the multiperiodic spectral reconstruction neural network. First, a spectral reconstruction method that transforms a spectrum from the wavelength domain to the intensity domain is proposed to remove noise and interference signals from the spectrum. Different from the traditional spectral processing method based on the wavelength domain, the method enhances the absorption characteristics of a target gas in the intensity domain, while discretizing noise and interference signals. This facilitates the extraction of the target gas spectrum. Then, a neural network is built to detect the concentration of breath NO. Laboratory-based results show that the sensor enables online detection of NO (1.63-846.68 ppb) with mean absolute error (MAE), mean absolute percentage error (MAPE), and detection accuracy of 0.31 ppb, 0.96% and 0.63%, respectively. Furthermore, an actual exhalation experiment proved that the sensor is capable of distinguishing breath NO of healthy people from that of simulated patients, which provides a reliable way to realize exhaled breath detection based on optical methods in the medical field.

Phases, metrics, and techniques of product discovery

This paper aims to explore, synthesize, and analyze gray literature on the product discovery process, focusing on its key metrics, phases, and techniques to offer valuable insights into effective product management practices. A comprehensive review and analysis of gray literature sources, including online articles, blog posts, reports, and books, was undertaken to construct a thorough perspective on the product discovery process. The review categorizes the main themes of product discovery into phases, metrics, and techniques, providing a holistic summary of the methods and best practices for successful product discovery. The review is based on gray literature, which lacks the academic rigor of peer-reviewed sources despite providing valuable industry insights. Hence, it is recommended that future research incorporate more peer-reviewed academic literature to supplement these findings for a more comprehensive understanding of the product discovery process. This review synthesizes the existing gray literature on product discovery, offering an informative overview of the field's current practices, methods, and strategies. It highlights key insights that could help organizations and practitioners better understand the product discovery landscape, potentially informing the enhancement of their product development practices to align with market opportunities and user needs. Improved understanding and application of product discovery could lead to more user-centric products, positively impacting user satisfaction and engagement. Additionally, it could influence the competitiveness and success of product companies in the market, influencing employment and economic growth. This review synthesizes the existing gray literature on product discovery, offering an informative overview of the field's current practices, methods, and strategies. It highlights key insights that could help organizations and practitioners better understand the product discovery landscape. Thus, it serves as a unique, timely resource for companies across industries, aiming to understand better and potentially optimize their product management strategies.

Social interactions in the digital environment: sociolinguistic approach

The article reveals the methodological application of sociolinguistic approach in the process of researching the relationship between lexical markers and social interactions in the digital environment. Social interaction in virtual space is investigated through the identification of mechanisms of social field construction through the symbolic nature of the text. As a method, a social network analysis is proposed, which relies on graph theory and visualisation of nodes and edges (‘nodes’) and links (‘links’) with certain types of relationships. The technology of building a corpus of key words and phrases is described, on the basis of which the structure is formed, clusters and the most significant lexical units reflecting the main discourses, themes and frames are singled out. The application of the network analysis method in the empirical field can reveal the mechanisms of interaction between different social groups.

Impact of nutrient management on physiological processes, biochemical properties, and

Aim. The purpose of the study was to determine the effect of different fertilizers and their aftereffects on the biometric and biochemical parameters of plants, the formation of yield, and the quality of beetroots. Methods. Field, statistical, calculation, analytical, and laboratory research methods were used in the study (dry matter (thermogravimetric method), total sugar (titration with red blood salt), ascorbic acid (according to Tillmans), nitrates (potentiometric method using nitrate-selective electrodes)). Results. The use of microbial preparations both separately and in the aftereffect of organic fertilizers led to an increase in the chlorophyll content in beetroot leaves (up to 7.36-9.14 mg/kg). The yield of table beetroots at the level of 52.6-57.6 t/ha is provided by mineral and organo-mineral fertilization systems, the aftereffect of 21 t/ha of manure, and a combination of organic fertilizers and microbial preparations. Using a complex of microbial preparations (Groundfix, Azotophyte, Organic Balance) to optimize the nutrition of beetroot plants increased the yield by 19 %. The values of most biochemical parameters in the products decreased with the increase in their yield. The use of high norms of mineral fertilizers or the localization of their administration conditioned the increase in the content of nitrates (1,490-2,349 mg/kg). With the increase in yield, a decrease in some biochemical parameters of beetroots (dry matter, total sugar, and ascorbic acid content) was observed. The use of high rates of mineral fertilizers or localization of their application led to an increase in the content of nitrates (1,490-2,349 mg/kg). Different fertilization systems for table beetroot provided the additional profit at the level of UAH 46.4-142.9 thousand/ha and the profitability of 42.6-69.6 % while reducing production costs to UAH 4.72-5.61 per one kg. Conclusions. The practical value of the research was determining the effect of different fertilization systems on a number of physiological processes and productivity of beetroot plants, the biochemical composition of root crops, and accumulation of nitrates in them, which allowed us to recommend the organo-mineral and biological fertilization systems with a complex of microbial preparations. In further research, it is important to establish the efficiency of alternative nutrition optimization systems with the combined use of humic and green manure fertilizers, and microbial preparations of various directions.

Developing a hierarchical analytical process to determine the best irrigation system for date palm trees in the NENA region: the case of Iran

The NENA region faces serious challenges in achieving sustainable development due to water scarcity problems, especially in the agricultural sector. Identifying the most effective irrigation method for agricultural fields can have a positive impact on water consumption and management in this region. The purpose of this study is to develop an analytical hierarchy process for determining the best irrigation system for date palm trees in the NENA region. Iran was selected as the case study, and the Expert Choice software was used. Using document studies, library and field data, and the opinions of 42 experts, the most important criteria and sub-criteria were identified. Based on the results of the evaluations, there were 39.1%, 8.9%, 21.0%, 19.9%, and 11.0% effects of technical, economic, social, operation and maintenance, and environmental criteria on selecting the suitable irrigation system, respectively. For palm fields in Khuzestan, a furrow irrigation system with low-pressure water distribution pipes was determined to be the most appropriate irrigation system. The bubbler irrigation system is the most suitable irrigation system for Bushehr. In Kerman, Sistan, and Baluchestan, bubbler and sprayer irrigation systems were introduced as the most effective irrigation systems. Using the model presented in this study, useful, comprehensive, and practical results can be obtained for evaluating and ranking irrigation systems in the NENA region and similar water-scarce arid regions. It is also suggested that a database for palm irrigation be developed in the NENA region in order to achieve the goals of sustainable development.

Characterizing the omics landscape based on 10,000+ datasets

The characteristics of data produced by omics technologies are pivotal, as they critically influence the feasibility and effectiveness of computational methods applied in downstream analyses, such as data harmonization and differential abundance analyses. Furthermore, variability in these data characteristics across datasets plays a crucial role, leading to diverging outcomes in benchmarking studies, which are essential for guiding the selection of appropriate analysis methods in all omics fields. Additionally, downstream analysis tools are often developed and applied within specific omics communities due to the presumed differences in data characteristics attributed to each omics technology. In this study, we investigate over ten thousand datasets to understand how proteomics, metabolomics, lipidomics, transcriptomics, and microbiome data vary in specific data characteristics. We were able to show patterns of data characteristics specific to the investigated omics types and provide a tool that enables researchers to assess how representative a given omics dataset is for its respective discipline. Moreover, we illustrate how data characteristics can impact analyses at the example of normalization in the presence of sample-dependent proportions of missing values. Given the variability of omics data characteristics, we encourage the systematic inspection of these characteristics in benchmark studies and for downstream analyses to prevent suboptimal method selection and unintended bias.

Additive-free 3D-printed nanostructured carboxymethyl cellulose aerogels.

3D printing of polysaccharide solutions is widely recognized as a highly promising method in the biomedical field for achieving complex customized shapes. One of the main challenges is in selecting conditions, in particular, the rheological properties of the system, to retain the printed shape. For the first time, the direct ink writing (DIW) is successfully applied to neat carboxymethyl cellulose (CMC) solutions without any additives or crosslinking, only by adjusting solutions' rheological properties. The influence of CMC molecular weight, degree of substitution and polymer concentration on solutions' viscoelastic properties is investigated. Extrusion velocity at various pressures and pressure calibration curves are determined to optimize printing parameters. Lightweight and nanostructured materials, aerogels, are then made from the printed structures through drying with supercritical CO2. 3D printed aerogels with high shape stability are of density (solid part) around 0.1 g/cm3 and specific surface area up to 140 m2/g, density being twice lower and surface area twice higher than those of the "bulk" (or moulded) counterparts. Customized aerogels with high specific surface area hold significant potential in biomedical applications, such as tissue engineering, wound dressings, drug delivery, etc.

The Legal Documents of Afghanistan's Higher Education and the Effects of the Country

Man is a social creature who has all kinds of needs in the society, and the fulfillment of such needs is impossible without legal documents. On the one hand, higher education develops systematically and seriously, and on the other hand, it leads to the development and development of the country, and on the other hand, obtaining higher education without legal documents and using it correctly is difficult and may be impossible. Because these legal documents are necessary for the development of higher education sectors, so that higher education sectors are developed appropriately, necessary and legally. It is important that a country's all-round growth, production, agriculture, livestock, technology, increase in exports and decrease in imports, employment, especially for the country and its citizens, higher education is necessary and necessary, so that higher education can develop appropriately and interestingly in the light of legal documents. Therefore, the research of the legal documents of higher education and their effects is of special and interesting importance. The most important problem is that the development of higher education is impossible without legal documents, because legal documents have many effects on higher education. Needs opportunities. This work eases this problem by obtaining higher education according to legal documents and makes it useful in less time with less possibilities. This type of research has been conducted in the library and in the field, and historical, descriptive, analytical and comparative methods have been used. As a result, I can say that by conducting this research, it will be proven that the citizens of the dear country of Afghanistan should have higher education for the development of the nation. And plays an important role. The life of the country and its citizens will move forward in a good way, and the country will develop and grow day by day.

A dynamic window model prediction artificial potential field method for reducing tracking errors of underactuated aerodynamic planing craft

ABSTRACT In order to improve the trajectory tracking accuracy of an underactuated aerodynamic planing craft to reach the point accurately, a DWA-MPAPF algorithm for the craft is developed in this study. The study first optimises the dynamics model of the craft. Then, the study designs an algorithm that specifically considers the characteristics of the craft, and conducts simulation tests under a variety of complex conditions. The simulation results show that the proposed algorithm can effectively control the craft to reach the target point accurately in the complex environment. Finally, the algorithm of this paper is analyzed and compared with some algorithms through the joint simulation platform of MATLAB and Unity3D, and the results show that the proposed algorithm is able to reduce the tracking error and the change of trajectory curvature is relatively smooth in path tracking control,thus confirming its effectiveness in the stability planning and control of aerodynamic planing craft.

Estimation of leaf area index in sagebrush steppe with low cost unoccupied aerial systems

ContextLeaf area index (LAI) strongly influences the carbon and water cycle in drylands, but accurate estimation of LAI relies on field methods that are expensive and time intensive. Very high-resolution imagery from unoccupied aerial systems (UAS) offers a potential solution for monitoring LAI, but estimation methods derived from cost effective red, green, and blue (RGB) sensors are untested in these semi-arid ecosystems.ObjectivesThe objective of our study was to test whether LAI could be estimated with very high resolution UAS collected RGB and canopy height data. Additionally, we sought to validate the model accuracy at the plot (1 m2) scale, test the accuracy at the macroplot (1 ha) scale, and assess the within plot impact of shadows.MethodsWe used a Random Forest machine learning model to estimate LAI in a Wyoming big sagebrush community in the Reynolds Creek Experimental Watershed using high resolution (< 1 cm2) UAS imagery collected in 2021 as predictors and plot scale point intercept (quadrat design) field data as the LAI reference.ResultsRandom Forest modeled estimates of LAI were accurate at the plot (r2 = 0.69, MAE = 0.08, RMSE = 0.10), and the macroplot scales (error of 0.065), and mean within plot shadow error was 0.06.ConclusionsThis research demonstrates high resolution UAS data can rapidly and accurately estimate LAI, with a limited number of field measurements, potentially allowing land managers to survey seasonally and spatially heterogeneous LAI 1 hectare at a time over the vast rangelands in the Great Basin and similar ecosystems worldwide.

A Density Functional Valence Bond Study on the Excited States

The accurate description of excited states is crucial for the development of electronic structure theory. In addition to determining excitation energies, strong state interactions arise when electronic states with the same symmetry are degenerate or nearly degenerate, often requiring a multi-state treatment. These strong correlation effects and state interactions can be effectively handled by the Hamiltonian matrix correction-based density functional valence bond (hc-DFVB) method, a multi-reference density functional theory capable of accurately describing electronic state interactions. In this paper, we explore the low-lying excited states of four isoelectronic systems (C2H, CN, CO⁺, BO) using valence bond methods, including the valence bond self-consistent field (VBSCF) and hc-DFVB methods. Our results show that the hc-DFVB method provides significantly better excitation energies compared to VBSCF. Furthermore, hc-DFVB can reliably predict the correct ordering of excited states, whereas VBSCF shows some ordering inconsistencies. By categorizing the VB structures into groups based on point group symmetry, we can extract the key structural contributions and bonding pictures of each state from the weight distribution of these groups. Additionally, we study the potential energy curves for lithium fluoride (LiF) and a mixed-valence spiro cation, demonstrating the superior performance of hc-DFVB when applied to the study of near-degenerate excited states in the avoided crossing region.

Modified field method for testing activated sludge condition in chambers with variable wastewater levels.

The paper presents a proposal to modify a field method of testing the condition of activated sludge using a 30-min volume of sludge (settling test). To verify the validity of the modified method of testing the condition of activated sludge, field tests were performed in two onsite wastewater treatment plants. In these plants, the reaction chambers were fed by gravity from the primary sedimentation tank throughout the day. For such treatment plants, the activated sludge condition test varies during a 24-h period. Testing the condition of activated sludge using the modified volume of sludge gives stable results over a 24-h period. To analyse the results, a statistical test was performed to compare the results obtained in the traditional volume of sludge and the modified one. The analysis showed that the results of the sludge volume test are incorrect if the wastewater sample is taken at a time other than the final filling of the bioreactor (just before the sedimentation phase) due to sludge thickening. The proposed modified sludge volume test allows the operator to perform the test at any time of day with a correct result.