DA Approach Research Articles

Improving CONUS Convective‐Scale Forecasting With Simultaneous Multiscale Data Assimilation

AbstractAccurate initialization of CONUS convective‐scale forecasting requires a proper estimate of all resolved scales. This study further develops and examines a simultaneous multiscale data assimilation (MDA) approach in EnVar with modulated cross‐scale and cross‐variable covariances. The method is examined using 10 retrospective cases with the assimilation of both in situ and radar reflectivity observations (hereafter, SimMDA). The necessity of the modulated and therefore weakened cross‐covariances in simultaneous MDA for CONUS convective‐scale forecasting is first demonstrated. The relative benefits of increasing the decomposed‐scale number with increased computational cost in SimMDA are also discussed. The impact of the further developed simultaneous MDA method is revealed by comparing it with a commonly adopted DA approach (Baseline), which separately assimilates in situ and reflectivity observations using individual single‐scale localization. During DA cycling, SimMDA improves analysis accuracy for temperature and reflectivity and reduces biases in all variables compared to Baseline. SimMDA yields significantly better forecasts than Baseline for most lead times. Additional experiments are conducted to attribute such improvements in a case study. Specifically, an experiment the same as Baseline except using simultaneous MDA for reflectivity assimilation enhances cold pools and inflows and thus improves storms by making larger‐scale increments. An experiment the same as Baseline except using simultaneous MDA for in situ assimilation more properly constrains small‐scale covariances, leading to more reasonable correlations along the front and more accurate moisture near the dryline and consequently improved analyses and forecasts. Both effects together largely contribute to the overall improvements of SimMDA compared to Baseline.

Natural Language Processing Approaches to Text Data Augmentation: A Computational Linguistic Analysis

In the context of Natural Language Processing (NLP) tasks, problems such as insufficient or skewed data are frequently encountered. One practical solution to this problem is to generate additional textual data. Text Data Augmentation (TDA) refers to small changes made to accessible text at the character, word, or sentence level to generate synthetic data that is subsequently inserted into data loaders to train the model. By producing synthetic data, models can learn from a larger range of instances and, hence, enhance their resilience and generalization skills. Despite the fact that the entire NLP community has extensively studied many NLP DA approaches, recent research on the subject suggests that the relationship between the several DA techniques now in use is not entirely known in practice. Therefore, this study applies and extends the advances of TDA to encounter and cover varied tools on multiple settings or contexts. To carry out a thorough practical implementation of NLP DA approaches, comparing the way they perform and highlighting some of the significant similarities and differences in these various scenarios, this work depends on different tools of easy data augmentation and neural-based augmentation. This study suggests that some typical DA techniques might not be suitable in some circumstances or text environments. Specifically, according to the initial results, the context and word count of a text may have a significant impact on the quality of the synthetic data.

Variational data assimilation to improve subsurface drainage model parameters

• Identification of the most influential model parameters using the global adjoint SA. • Variational data assimilation significantly reduces the water volume discrepancy. • The gradient-based stochastic procedure performs without the need for the prior. • A new hybrid BV method offers an automatic tool to select a robust parameter set. Variational data assimilation (VDA) has been implemented to enhance the estimation of the unknown input parameters of a new agricultural subsurface drainage model (SIDRA-RU) through assimilating drainage discharge observations. The adjoint model of SIDRA-RU has been successfully generated through the generic automatic differentiation tool (TAPENADE). First, the adjoint model is used to explore the local and global adjoint sensitivities of the valuable function defined over the drainage discharge simulations with respect to model input parameters. Next, the most influential parameters are estimated by applying the Variational DA approach. The performed sensitivity analysis shows that the most influential parameters on drainage discharge are those controlling the dynamics of the water table; the second most influential parameters manage the drainflow start of each drainage season. Compared to an alternative gradient-free calibration performance, the estimation of these governing parameters by the variational method improves the overall quality of the drainage discharge prediction, in particular in terms of the cumulative water volume. Improved parameters generate less than 5 mm (1%) of the discrepancy between simulated and observed water volumes, based on the five years of daily discharge observations on the Chantemerle agricultural parcels (36 ha). Preliminary numerical tests have shown the potential presence of multiple local minima, thus pointing out the equifinality issues. The latter can be highlighted by the self-compensation of both the physical soil parameters and the main conceptual parameters. For improving the robustness of the parameter estimates, a novel hybrid “Bayesian Variational” method is suggested. This method is based on the Bayesian averaging of an ensemble of optimal estimates.

Self-Labeling Framework for Novel Category Discovery over Domains

Unsupervised domain adaptation (UDA) has been highly successful in transferring knowledge acquired from a label-rich source domain to a label-scarce target domain. Open-set domain adaptation (open-set DA) and universal domain adaptation (UniDA) have been proposed as solutions to the problem concerning the presence of additional novel categories in the target domain. Existing open-set DA and UniDA approaches treat all novel categories as one unified unknown class and attempt to detect this unknown class during the training process. However, the features of the novel categories learned by these methods are not discriminative. This limits the applicability of UDA in the further classification of these novel categories into their original categories, rather than assigning them to a single unified class. In this paper, we propose a self-labeling framework to cluster all target samples, including those in the ''unknown'' categories. We train the network to learn the representations of target samples via self-supervised learning (SSL) and to identify the seen and unseen (novel) target-sample categories simultaneously by maximizing the mutual information between labels and input data. We evaluated our approach under different DA settings and concluded that our method generally outperformed existing ones by a wide margin.

A hybrid approach for prediction of machining performances of glass fiber reinforced plastic (Epoxy) composites

In recent years, Glass Fiber Reinforced Plastic (GFRP) composites are more potential materials for usage in various structural applications in aerospace and automobile industries, owing to its favorable properties, such as light weight, specific strength, high elastic modulus and excellent corrosion resistance. This paper presents a new hybrid approach for multi-objective optimization of machining parameters on GFRP composite using Taguchi coupled grey relational (GR) and desirability function (DF) approach. The end milling of GFRP composite was performed as per Taguchi’s L16 orthogonal array by considering three input parameters: viz cutting speed (V), feed rate (F) and depth of cut (D), each at four levels. The material removal rate (MRR), surface roughness (SR) and tool wear (TW) were chosen as output responses. The main effect plot was used to determine the optimum level of machining parameters on the multiple responses. An experimental result revealed that, the optimum level of parameters obtained by GR analysis is V2F3D4. Similarly, the optimum level of parameters obtained by DF approach is V2F4D4. Moreover, analysis of variance (ANOVA) was applied to examine the significance of the parameters. Based on the ANOVA results, it has been proved that the depth of cut was the most significant parameter, trailed by feed rate and cutting speed, respectively. It was also confirmed that the P-values of depth of cut for both analyses are 0.017 and 0.039, which is less than 0.05. Finally, the confirmation tests exhibited that the DA approach was employed better in terms of determining the optimum level of machining parameters as compared to GR analysis.

Parameter optimization in sea ice models with elastic–viscoplastic rheology

Abstract. The modern sea ice models include multiple parameters which strongly affect model solution. As an example, in the CICE6 community model, rheology and landfast grounding/arching effects are simulated by functions of the sea ice thickness and concentration with a set of fixed parameters empirically adjusted to optimize the model performance. In this study, we consider the extension of a two-dimensional elastic–viscoplastic (EVP) sea ice model using a spatially variable representation of these parameters. The feasibility of optimization of the landfast sea ice parameters and rheological parameters is assessed via idealized variational data assimilation experiments with synthetic observations of ice concentration, thickness and velocity. The experiments are configured for a 3 d data assimilation window in a rectangular basin with variable wind forcing. The tangent linear and adjoint models featuring EVP rheology are found to be unstable but can be stabilized by adding a Newtonian damping term into the adjoint equations. A set of observation system simulation experiments shows that landfast parameter distributions can be reconstructed after 5–10 iterations of the minimization procedure. Optimization of sea ice initial conditions and spatially varying parameters in the stress tensor equation requires more computation but provides a better hindcast of the sea ice state and the internal stress tensor. Analysis of inaccuracy in the wind forcing and errors in sea ice thickness observations show reasonable robustness of the variational DA approach and the feasibility of its application to available and incoming observations.

Assimilation of future SWOT-based river elevations, surface extent observations and discharge estimations into uncertain global hydrological models

Global estimates of river dynamics are needed in order to manage water resources, mainly in developing countries where in-situ observation is limited. Remote sensors such as nadir altimeters can complement ground data. Current altimeters miss however a large number of continental surface water bodies. This issue will be largely resolved by the future Surface Water and Ocean Topography (SWOT) mission, thanks to its wide swath altimeter. SWOT will provide almost globally two-dimensional water elevation maps for rivers over 100 m wide and water bodies over 250 m × 250 m. During this research, we investigated the potential of SWOT to correct hydrological models on a global/continental scale, through data assimilation. For this purpose, an Observing System Simulation Experiment (OSSE), also known as “twin experiment”, has been implemented. Model forcings and parameters were perturbed to jointly achieve global hydrological models (GHMs) uncertainties, which is the expected scenario in which the SWOT community will mainly evaluate the future SWOT data. SWOT-like observations of water surface elevation (WSE), flooded water extent (FWE), and/or SWOT derived discharge (Q) were used to correct modelled Q, WSE and FWE from a large-scale hydrological and hydrodynamic model (MGB – portuguese acronym of “Modelo de Grandes Bacias”), using a Ensemble Kalman filter (EnKF). The results indicate that SWOT products could largely improve hydrological simulations on a global and continental scale. SWOT-like discharge can reduce ~40% of model errors in daily discharge. Furthermore, when anomalies of the WSE DA approach were implemented, the error reduction was even greater for all state variables compared to the absolute WSE DA, achieving average error reduction values of about ~30% compared to ~24%. Finally, the simultaneous DA of all the SWOT-like variables together reduces errors from ~14% to ~22% compared to the average of assimilating only one variable.

Direct anterior versus posterolateral approach for bipolar hip hemiarthroplasty in femoral neck fractures: a prospective randomised study.

Femoral neck fractures are a major problem in orthopaedic practice, having a huge impact on society, and involving a large number of elderly patients for whom early recovery is paramount. Thus, the aim of this study was to compare 2 surgical approaches, direct anterior (DA group) versus posterolateral (PL group), used for bipolar hip hemiarthroplasty (BHA) with femoral neck fractures in order to assess pain recovery after surgery. Our hypothesis was that early pain recovery would be faster in the DA group. 100 patients were randomised to surgery using either a DA group or PL group approach, and were then followed up for 6 months. Surgical time, intra- and postoperative complications were recorded for each patient. The main outcome, pain, was recorded using an NRS scale at 3 days, 1, 3 and 6 months after surgery. At the same time, patient status was evaluated using Activities of Daily Living and Cumulated Ambulation Score scales. Surgical time was longer in the DA group (p < 0.0001). Pain perception at 3 days and at 1 month after surgery was significantly lower in DA group patients (p < 0.0001). The results of the other scales were comparable in the 2 groups (at 3 days, 1 and 3 months after surgery). The DA approach offers a significant advantage in terms of pain perception up to 1 month after the operation. Early recovery in terms of pain perception is an important finding. Future studies should explore whether this early pain reduction could translate into a faster rehabilitation programme for an earlier recovery of full function.Clinical trial registration: Protocol 423/CE; Study n. CE 41/15.

The use of dynamic assessment by educational psychologists in the early years foundation stage

ABSTRACTDynamic assessment (DA) is an interactive assessment that encompasses planned mediational teaching and assessment of the effects of that teaching on subsequent performance. DA has been motivated by the inadequacy of conventional static tests to provide accurate information about the individual’s learning ability, deficient functions, change processes, and mediational strategies. This paper explores the nature and potential of DA when used in the Early Years Foundation Stage (EYFS) by educational psychologists (EPs). Two EPs were interviewed using semi-structured interview schedules and observed delivering DA. Interviews were analysed using TA and a content analysis was used to analyse the videoed observations. The DA approach was useful in assessing “learning how to learn” skills. Findings highlight promising evidence regarding the utility of EPs’ play-based DA approach in the EYFS to elicit positive information about children’s skills, learning potential and intervention strategies.

A Study on Real-World Complex Queries In DHT-Based for Secure Cloud Storage

Dispersed hash tables (DHT) really are a key structure hinder for current P2P content-dissemination framework, as an examplein actualizing the appropriated tracker of BitTorrent Mainline DHT. DHTs, for their completely appropriated nature, are known to be helpless against particular kinds of assaults and various kinds of resistances have been proposed against these assaults. We have distinguished an oversight in past approachs used to quantify the measure of the device and our procedure redresses this. The proposed DA engineering for 1-D DHT has extremely less calculations when contrasted with existing 1-D DCT. The proposed DHT engineering executed in FPGA shows a huge equipment investment funds when contrasted with FPGA assets utilized in a proficient memory based DA approach. The extra preferred standpoint of SDHT is that its converse change is identical to forward change with a regular division. Our technique is dependent upon demonstrating slithering errors as a Bernoulli process. It ensures an incredibly precise estimation and can provide the gauge in 5 seconds. the individuals, is closeness mindful, adjusts to arrange conditions, and recuperates rapidly and smoothly from system parcels and resulting fixes.

Synthetic studies on the bioactive tetramic acid JBIR-22 using a late stage Diels-Alder reaction.

A late stage Diels-Alder reaction is used to prepare a mixture of JBIR-22, a natural product from the Equisetin family of tetramic acids, and one of its diastereomers. This is achieved in just 8 steps from pyruvate. The success of the late stage DA approach is discussed in the context of the biosynthesis of JBIR-22 (and perhaps related natural products).

A New RNS based DA Approach for Inner Product Computation

This paper presents a novel method to perform inner product computation based on the distributed arithmetic principles. The input data are represented in the residue domain and are encoded using the thermometer code format while the output data are encoded in the one-hot code format. Compared to the conventional distributed arithmetic based system using binary coded format to represent the residues, the proposed system using the thermometer code encoded residues provides a simple means to perform the modular inner products computation due to the absence of the 2 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$^{n}$</tex> </formula> modulo operation encountered in conventional binary code encoded system. In addition, the modulo adder used in the proposed system can be implemented using simple shifter based circuit utilizing one-hot code format. As there is no carry propagation involved in the addition using one-hot code, while the modulo operation can be performed automatically during the addition process, the operating speed of the one-hot code based modulo adder is much superior compared to the conventional binary code based modulo adder. As inner product is used extensively in FIR filter design, SPICE simulation results for an FIR filter implemented using the proposed system is also presented to demonstrate the validity of the proposed scheme.

Analyzing the Advertising Discourse- A Journey from Sight to Mind

This research paper sets out to project an in-depth study of the advertising discourse by applying methodological approaches of Discourse Analysis and Critical Discourse Analysis to the chosen “advertisements”. It aims at examining the ‘power relations and struggles’ among product-producers and product-consumers, to gauge the level of psychological dominance exercised through the advertising discourse. The study, fundamentally, presents a counter-analysis of ‘advertising strategies’, as to how far they influence the consumers’ attitudes and to what extent the consumers direct their ‘discourse’. In other words, this research attempts to answer the question: ‘who turns out to be more powerful by controlling the other’s mind, in advertiser-consumer relationship?’, whether it is the consumer who shapes the discourse of advertisements through their demands, or the advertiser who affects the consumers’ beliefs through their ‘discourse’! Thus, the current paper presents a comprehensive review of the relevant literature leading towards a theoretical framework of preferred DA and CDA approaches to be further applied on the discourse of advertisements. And towards the end, it states the final remarks concluding the entire discussion and reflecting upon the effectiveness of Critical Discourse Analysis in its application on the advertising phenomena.

The use of CO2 flux time series for parameter and carbon stock estimation in carbon cycle research

AbstractData assimilation (DA) is increasingly being employed to estimate the parameters and states of terrestrial ecosystem models from eddy covariance measurements of net carbon (C) fluxes. The length of the observation time series used varies for each study. The impact of these differences has not been quantified explicitly. Therefore, in this study, we investigate the importance of the time series length relative to observation noise and data gaps. Different length synthetic time series are used to determine the parameter and C stocks of a simple ecosystem C model. Two commonly used DA schemes are tested: the sequential Ensemble Kalman Filter (EnKF) and a batch Metropolis Markov chain Monte Carlo algorithm. Longer time series improve both the parameter and C pool estimates of the EnKF, while adversely affecting those of the Metropolis algorithm. For both DA approaches, the length of the time series has more influence on the parameter and pool estimates than the level of random noise or amount of data. In this study, the EnKF provides more robust parameter and C pool estimates than the Metropolis algorithm. Optimized parameters and states are often used as the basis for forecasting future responses. Despite having better parameter and C pool estimates, EnKF forecasts estimates have much larger uncertainties than the Metropolis algorithm forecast estimates. Finally, we suggest that the structure of simple box models, as used in this study, introduces a large degree of equifinality into DA. Neither DA scheme correctly accounts for the equifinality, but our results suggest that it is particularly problematic for the batch Metropolis algorithm.

Dynamic Assessment in Perspective: Demarcating Dynamic and Non-dynamic Boundaries

Ever since the concept of dynamic assessment (also DA) was introduced into the literature on language pedagogy, it has received various interpretations in terms of its scope, meaning, purpose, and even its practical realization. This difference in interpretation, the raison d'etre of DA, however, has resulted in a burgeoning variation in the number and types of procedures and practices employed in L2 classrooms under DA programs, a fact which seems to have gone unnoticed by both the critics and advocates of DA approaches to L2. The present article, therefore, tries to define DA within the very conceptual framework it was originally introduced and to sketch the theoretical and practical scopes which serve to realize that framework. To this end, this article provides an account of the basic tenets and principles underlying DA and also examines the degree to which each of the major approaches to DA have realized those basic principles in practice.

Modern map methods for charged particle optics

The differential algebraic methods, a natural tool for the description and determination of the solution of differential equations, have proven useful for the computation of aberrations of any desired order in any particle optical system. Besides conventional symplectic systems based on strongly or weakly focusing elements including fringe fields, they also readily allow the treatment of spin dynamics as well as classical synchrotron radiation. In recent years, a variety of codes have been written based on these techniques, including COSY INFINITY, which is currently used by approximately 150 registered users. Several examples of recent applications employing high order methods will be given. While the method makes the problem of computation of Taylor maps straightforward and their manipulation and analysis convenient, for many applications it is important to have exact bounds of the truncation error. Recently it was shown how such information can be determined conveniently and with rather limited effort. Furthermore, it is often important to know the domain and the speed of convergence of the Taylor expansion. We will show that similar to the conventional DA approach, such information can be obtained by carrying the three elementary operations of addition, multiplication, and differentiation on the space of infinitely often differentiable functions to a suitable smaller space that can be described on a computer.

Long timestep dynamics of peptides by the dynamics driver approach.

Previous experience with the Langevin/implicit-Euler scheme for dynamics ("LI") on model systems (butane, water) has shown that LI is numerically stable for timesteps in the 5-20 fs range but quenches high-frequency modes. To explore applications to polypeptides, we apply LI to model systems (several dipeptides, a tetrapeptide, and a 13-residue oligoalanine) and also develop a new dynamics driver approach ("DA"). The DA scheme, based on LI, addresses the important issue of proper sampling, which is unlikely to be solved by small-timestep integration methods or implicit methods with intrinsic damping at room temperature, such as LI. Equilibrium averages, time-dependent molecular properties, and sampling trends at room temperature are reported for both LI and DA dynamics simulations, which are then compared to those generated by a standard explicit discretization of the Langevin equation with a 1 fs timestep. We find that LI's quenching effects are severe on both the fast and slow (due to vibrational coupling) frequency modes of all-atom polypeptides and lead to more restricted dynamics at moderate timesteps (40 fs). The DA approach empirically counteracts these damping effects by adding random atomic perturbations to the coordinates at each step (before the minimization of a dynamics function). By restricting the energetic fluctuations and controlling the kinetic energy, we are able with a 60 fs timestep to generate continuous trajectories that sample more of the relevant conformational space and also reproduce reasonably Boltzmann statistics. Although the timescale for transition may be accelerated by the DA approach, the transitional information obtained for the alanine dipeptide and the tetrapeptide is consistent with that obtained by several other theoretical approaches that focus specifically on the determination of pathways. While the trajectory for oligoalanine by the explicit scheme over the nanosecond timeframe remains in the vicinity of the full alpha R-helix starting structure, and a high-temperature (600 degrees K) MD trajectory departs slowly from the alpha helical structure, the DA-generated trajectory for the same CPU time exhibits unfolding and refolding and reveals a range of conformations with an intermediate helix content. Significantly, this range of states is more consistent with spectroscopic experiments on small peptides, as well as the cooperative two-state model for helix-coil transition. The good, near-Boltzmann statistics reported for the smaller systems above, in combination with the interesting oligoalanine results, suggest that DA is a promising tool for efficiently exploring conformational spaces of biomolecules and exploring folding/unfolding processes of polypeptides.

Behavioral and neurochemical consequences associated with stressors.

A series of neurochemical changes occur in response to stressors that may permit the organism to contend with environmental demands. When the organism is exposed to a stressor the utilization and synthesis of brain NE and DA increases. Under conditions where utilization exceeds synthesis, owing either to the nature of the stressor (uncontrollability), experiential factors (e.g., prior exposure to acute stressors), or organismic variables (e.g., strain, age), reductions of the amine may be incurred. It is suggested that the reduced amine concentrations leave the organism less well prepared to deal with the demands placed upon it, and ultimately increase vulnerability to psychological disturbances. It follows that the more persistent the amine reduction, the greater the probability of pathology being engendered. In effect, in our analyses of stressor effects it is not sufficient merely to determine whether amine reductions occur, but also to assess the ability of the system to re-establish adequate levels and turnover. Additionally, since stressors may result in the conditioning or sensitization of neurochemical processes, it is essential not only to assess the immediate impact of the stressor, but also the neurochemical variations that occur upon re-exposure to stressors or cues associated with the stressor. In considering the consequences of stressors and the potential implications for human pathology, it is important to consider the impact of chronic stressors. After all, many stressors encountered by humans are chronic in nature, particularly if one considers ruminations associated with the aversive event. It seems that with repeated stressor application a further series of adaptive neurochemical changes occur. The activity of tyrosine hydroxylase is increased, and concentrations of NE and DA approach those of nonstressed animals. Indeed, it appears that after stressor termination the increased amine synthesis may persist for some time leading to a further increase of amine concentrations, which may enable the organism to deal with environmental demands. In addition, receptor variations may occur, including down-regulation of beta-NE receptors, and possibly alterations of alpha-1 and alpha-2 receptors as well. It is believed that the receptor variations may be the essential element in maintaining the integrity of the organism. It is our contention that where such adaptive changes do not occur or are slow in occurring, pharmacological intervention may be necessary to engender such neuronal variations.

An Empirical Evaluation of Dialectial Inquiry, Devil's Advocate, and Consensus Approaches to Strategic Decision Making.

A laboratory study was conducted comparing the effectiveness of the Dialectical Inquiry, Devil's Advocate, and Consensus approaches to group strategic decision making. Both DI and DA approaches res...