Order Selection Research Articles

The potential of using corpus-based technologies for incidental vocabulary learning of General Maritime English

Introduction. The article addresses the problem of expanding the potential vocabulary of cadets of maritime educational institutions in the process of listening comprehension determined by the need to develop strategies for independent vocabulary acquisition necessary for autonomous learning of maritime English. The purpose of this article is to justify the application of corpus technologies for the selection of vocabulary items, which will allow students to master the strategies required to expand vocabulary in the process of autonomous listening, and increase the efficiency of learning and teaching maritime English. Materials and Methods. The study follows the two-level Maritime English learning model incorporating General Maritime English and Specialized Maritime English. The first component focuses on the linguistic content embedded in the generalized maritime context and prioritizes the lexical and cognitive approaches highlighting the necessity of their integration and emphasizing corpus tools the application of which is helpful in topic-related vocabulary selection through instructional, linguistic and statistic principles on the basis of authentic texts. Results. The article describes the preliminary stage of potential vocabulary acquisition by junior cadets through listening comprehension and proposes the procedure of text selection for context-specific raw corpus design and thematic word list generation. Through the computer processing of the wordlist and the analysis of the concordance lines, the independent vocabulary learning strategies were identified and ranked. The conclusion was drawn about the correlations between the basic word learning potential and the number of independent word learning strategies applied. Conclusions. The findings of the research add to the understanding of the potential of corpus-based technologies for the topical vocabulary selection and analysis in order to develop cadets’ independent word learning strategies that are significant for potential vocabulary acquisition through listening comprehension.

Low-rank approximation of Hankel matrices in denoising applications for statistical damage diagnosis of wind turbine blades

Model order selection is a fundamental task in subspace identification for estimation of modal parameters, uncertainty propagation and damage diagnosis. However, the true model order and the related low-rank structure of the dynamic system are generally unknown. In this paper, a statistical methodology to actively select the dynamic signal subspace in covariance-driven subspace identification is developed on the basis of statistical analysis of the eigenvalue condition numbers of the output covariance Hankel matrix. It is shown that the condition numbers highly sensitive to random perturbations characterize the noise subspace. The signal subspace is separated from the noise subspace by analyzing two statistical parameters associated with the condition number sensitivity, whose thresholds are user-defined. A practical algorithm to retrieve the system dynamics is designed and demonstrated on a running example of a simulated wind turbine blade benchmark. The resultant framework is then applied in the context of damage detection on a medium-size wind turbine blade. It is demonstrated that the detectability of small damage is enhanced compared to the classic approaches and robustness of damage diagnosis is increased by reducing the number of false alarms.

Comprehensive commodity price forecasting framework using text mining methods

AbstractExploiting advanced and appropriate methods to construct high‐quality features from different types of data becomes crucial in agricultural futures price forecasting. Thus, this study develops a comprehensive commodity price forecasting framework using text mining methods. First, the modal features of the price series are extracted using the proposed Integrated‐EEMD‐VMD‐SE method, and dynamic topic sentiment features are constructed from Weibo texts using the proposed dynamic topic model joint sentiment analysis method. Second, combined with statistical variables, lag order selection and feature selection are performed on these comprehensive factors. Finally, 12 comparative prediction models are designed based on random forest (RF), long short‐term memory (LSTM), and multilayer perceptron (MLP), and empirical analysis is carried out on two cases of pork prices and soybean futures prices. The experimental results show that the proposed prediction framework has high prediction accuracy, and the mean absolute percentage error (MAPE) values are 1.00 and 0.92, respectively. The constructed time series modal features and dynamic topic sentiment features can significantly improve the performance of the prediction model.

Multi‐level habitat selection of boreal breeding mallards

AbstractCanada's western boreal forest is a vital breeding habitat for North American duck populations. This region has experienced considerable demand for its valuable natural resources (e.g., oil and gas, forestry) resulting in extensive industrial development (e.g., infrastructure), which is predicted to continue. The potential effects of industrial development on breeding ducks in the western boreal forest, however, remains largely unexplored. We used backpack harness global positioning system (GPS) transmitters to document habitat selection by breeding female mallards (Anas platyrhynchos) across a gradient of industrial development in the western boreal forest of Alberta, Canada. We modeled breeding home range (second order) selection and habitat selection within the home range (third order) using resource selection functions, and spatially predicted our models across the landscape to highlight important breeding habitat. Contrary to our predictions, breeding female mallards did not avoid all industrial development at the second and third orders. Females established home ranges (second order) with greater proportions of marsh, graminoid fen, and well pads, and lower proportions of forest. Within their home range (third order), females selected shrub swamp, graminoid fen, marsh, well pads, and borrow pits, and avoided open water, swamp, treed peatland, forest, forest harvest areas, and industrials (e.g., buildings). Females also selected habitat that was close to pipelines and roads. Our results suggest that the magnitude and direction of breeding season habitat selection by female mallards varies depending on the scale and landscape features, but current levels of industrial development within our study area still allowed for the establishment of breeding season home ranges. Our spatially predicted maps contribute to the increasing body of work surrounding boreal waterfowl ecology by highlighting important breeding habitat for mallards in Canada's western boreal forest.

The impact of influencer-sourced brand endorsement on online consumer brand engagement

ABSTRACT This study provides a model for brand communication from social media influencers (SMIs) that evaluates the effect of source and content characteristics in the formation of consumers’ cognitive, emotional, and behavioural engagement with the endorsed brand. Survey responses collected from 316 Instagram users using systematic random sampling are analysed using structural equation modelling. The results delineate that SMIs’ credibility and SMI – consumer similarities have a significant positive effect on all consumer brand engagement (CBE) dimensions. However, informativeness of SMI’s content positively impacts cognitive and behavioural aspects, and not emotions, whereas content entertainment only enhances emotional engagement. The study provides insights to marketing practitioners for SMI selection and content design in order to achieve specific or multiple CBE goal(s). The results also confirm the indispensable mediating role of CBE dimensions in influencing consumers’ purchase intentions. This extends prior studies, which have predominantly explained these effects via mediators including consumer attitude and trust.

Spatially segmented SVD clutter filtering in cardiac blood flow imaging with diverging waves

Ultrafast ultrasound imaging enables the visualization of rapidly changing blood flow dynamics in the chambers of the heart. Singular value decomposition (SVD) filters outperform conventional high pass clutter rejection filters for ultrafast blood flow imaging of small and shallow fields of view (e.g., functional imaging of brain activity). However, implementing SVD filters can be challenging in cardiac imaging due to the complex spatially and temporally varying tissue characteristics. To address this challenge, we describe a method that involves excluding the proximal portion of the image (near the chest wall) and divides the reduced field of view into overlapped segments, within which tissue signals are expected to be spatially and temporally coherent. SVD filtering with automatic selection of cut-off singular vector orders to remove tissue and noise signals is implemented for each segment. Auto-thresholding is based on the coherence of spatial singular vectors, delineating tissue, blood, and noise subspaces within a spatial similarity matrix calculated for each segment. Filtered blood flow signals from the segments are reconstructed and then combined and Doppler processing is used to form a set of blood flow images. Preliminary experimental results suggest that the spatially segmented approach improves the separation of the tissue and blood subsets in the spatial similarity matrix so that automatic thresholding is significantly improved, and tissue clutter can then be rejected more effectively in cardiac ultrafast imaging, compared to using the full field of view. In the case studied, spatially segmented SVD improved the rate of correct automatic selection of thresholds from 78% to 98.7% for the investigated cases and improved the post-filter power of blood signals by an average of more than 10 dB during a cardiac cycle.

An In-Phase Unit Slot-Opening Shift Method for Cogging Torque Reduction in Interior Permanent Magnet Machine

The cogging torque reduction methods in permanent magnet (PM) machines are more or less FEA trial and error-based and cause undesirable side effects, such as distorted back EMF, new harmonic components, and structure asymmetry. A slot-opening grouping method can address these issues. However, it needs to model all slots in FEA design validation during practical cogging torque optimization iterations, and also leads to back-EMF reduction. In this paper, a new grouping strategy, termed in-phase unit (IPU), is introduced, together with the slot-opening angle-shift method. The slot openings with the same cogging torque distribution pattern are grouped into an IPU, and the cogging torque peaks of the slot openings within an IPU can now be interleaved. Thereby, the major harmonic components of the cogging torque are cancelled out, instead of being summed up. The IPU grouping principle, the slot-opening shift angle computation, and the selection of the harmonic order to cancel are analyzed in detail. By comparison, the proposed method not only simplifies cogging torque optimization iterations by only modeling the slots in one IPU in FEA, but also compensates for the back-EMF constant reduction. The effectiveness of the proposed methods is validated by two typical interior PM machine design cases.

Process optimization of C4 olefins by ethanol coupling based on BP neural network and genetic algorithm

C4 olefins are widely used in chemical production, and the preparation of C4 olefins by ethanol coupling is one of the important methods to obtain this raw material, so it is important to study the process conditions for the preparation of C4 olefins. In this paper, we analyze and discuss the correlation between catalyst combination and temperature on ethanol conversion and C4 olefin selectivity based on the experimental data collected from laboratory preparation, and explore the reasonable catalyst combination scheme and temperature selection in order to achieve the optimization of preparation efficiency. Further investigation of the preparation process to obtain the optimum yield is also carried out based on the error of the validation results.

The importance of relevant criteria of selection in ensuring the success of the national selection systems in athletics: A review

Scientific selection represents one of the most important concerns of contemporary sports, as evidenced by the major world investments made in order to identify unique combinations of physical and mental potential and abilities that can be developed towards human biological limits. Some countries have developed complex systems and programs aimed at identifying and especially transforming sports potential into performance, emphasizing the need to develop potentially talented performers and not just to early identify them. The critical analysis of the Romanian selection system in athletics highlights the shortcomings and allows corrections to be outlined. Athletics in particular is a complex sports discipline that requires a multitude of psychomotor and coordinative skills such as rhythm, tempo, balance, segmental coordination, space orientation, and laterality, skills recommended to be detected from the initial phase of the selection in order to foresee the motor endowment of the child and to guide him accordingly. The purpose of this review was to examine the discrepancies between the Romanian selection system in athletics and other successful national systems in order to emphasize the necessity of new perspectives regarding the selection criteria, which need to be relevant in detecting skills in the area of psychomotricity and coordination.

Биохимическая и товарно-потребительская оценка качества сортов черешни, интродуцированных в условиях Дагестана

Abstract. The purpose. Identification of among the variety of cherries introduced in the natural and climatic conditions of the northern foothills of Dagestan, the most promising variety samples distinguished by high nutritional value and the best commodity and consumer quality indicators for further use in selection and horticulture in order to optimize the industrial variety of cherries. Methods. The studies were carried out using generally accepted chemical methods of analysis and standard methods of sweet cherry variety study. Results. It was determined that the largest amount of soluble solids was contained in the fruits of the varieties: Melanbayer (15.3 %), Polyanka (14.8 %), Lyaskovska Khrushchalka (14.6 %) and Merton Bigarro (14.5 %). The best in terms of the mass concentration of sugars (11.57–12.81 %) were the varieties Sofiyska Khrushchalka, Merton Bigarro, Polyanka and Melanbayer. The varieties Gudzon, Merton Bigarro, Polyanka, Melanbayer and Meteor distinguished themselves with a high content of titrated acids (0.80–1.14 %). Sweet cherry varieties Polyanka, Sofiyska Khrushchalka, Gudzon, Van, Melanbayer and Merton Bigarro were distinguished by the greatest ability to accumulate vitamin C (7.21–9.16 mg%). The fruits of the Polyanka and Sofiyska Khrushchalka varieties received the highest tasting rating (5 points). The best indicators of fruit weight were distinguished by the varieties Van (6.7 g), Gudzon (7.2 g), Lyaskovska Khrushchalka (7.2 g) and Polyanka (9.0 g). The results of the technical analysis of the fruits of the sweet cherry prototypes showed that the Merton Bigarro, Van, Lyaskovska Hrushalka and Polyanka varieties belong to the highest commercial grade. Scientific novelty. For the first time, a comprehensive quality assessment of 12 varieties of sweet cherries introduced in the conditions of foothill Dagestan was carried out and the most promising varieties were identified (Polyanka, Gudzon, Merton Bigarro, Van and Sofiyska Khrushchalka), which are distinguished by the best quality indicators that can be recommended for successful optimization of the industrial variety, as well as used as donors and gene sources in selection work in order to improve the biochemical composition, commodity-consumer and tasting characteristics of fruits.

On prediction of air pollutants with Takagi-Sugeno models based on a hierarchical clustering identification method

In recent years, air pollution has attracted considerable attention worldwide. As an effective air protection method, the accurate prediction of air pollutants can help provide an early warning against harmful air pollutants and plan air pollution prevention and control strategies. Due to its high prediction capability, a novel method using a Takagi-Sugeno fuzzy model is presented to predict air pollutants in this study. This work investigates fuzzy model identification based on a larger dataset, and a hierarchical clustering-based identification method with model structure selection is proposed. The novelty of this work is as follows: First, a novel hierarchical clustering method has been employed in air quality forecasting. This method improves the widely used BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) by introducing a refinement phase for handling clusters with arbitrary shapes. Second, a cluster validity measure automatically determines the number of clusters. Then, the estimation of the model order selection and the model parameters is formulated as a sparse optimization problem, which can be solved using global optimization. Finally, the proposed Takagi-Sugeno model can learn the local trend pattern and spatial-temporal dependencies of multivariate air quality-related time series data. The presented method is demonstrated with air quality measurements in Shanghai, and the results show that the proposed method can achieve acceptable prediction performance.

Query Join Order Optimization Method Based on Dynamic Double Deep Q-Network

A join order directly affects database query performance and computational overhead. Deep reinforcement learning (DRL) can explore efficient query plans while not exhausting the search space. However, the deep Q network (DQN) suffers from the overestimation of action values in query optimization, which can lead to limited query performance. In addition, ε-greedy exploration is not efficient enough and does not enable deep exploration. Accordingly, in this paper, we propose a dynamic double DQN (DDQN) order selection method(DDOS) for join order optimization. First, the method models the join query as a Markov decision process (MDP), then solves the DRL model by integrating the network model DQN and DDQN weighting into the DRL model’s estimation error problem in query joining, and finally improves the quality of developing query plans. And actions are selected using a dynamic progressive search strategy to improve the randomness and depth of exploration and accumulate a high information gain of exploration. The performance of the proposed method is compared with those of dynamic programming, heuristic algorithms, and DRL optimization methods based on the query set Join Order Benchmark (JOB). The experimental results show that the proposed method can effectively improve the query performance with a favorable generalization ability and robustness, and outperforms other baselines in multi-join query applications.

Dynamic ordering and buyer selection policies when service affects future demand

Make-to-stock suppliers selling to regular buyers must balance the cost of overstocking against the cost of buyers’ reactions when items become unavailable. In selecting which buyers to satisfy when shortages occur, they must weigh the current revenue from the satisfied buyers against the loss in future demand from the dissatisfied buyers. To provide insight and decision support on these trade-offs, we (i) develop a novel newsvendor model of a firm with heterogeneous buyers with service-dependent demand, (ii) provide properties of the optimal ordering and buyer selection decisions of the firm, and (iii) derive a novel Lagrangian Relaxation (LR)-based index policy for selecting buyers and compare it with two other LR-based index policies. The model concerns a firm that orders items for a group of repeat buyers who generate different revenues and visit the firm with different average rates that depend on whether they are satisfied or dissatisfied with their last visit. The firm selects which buyers to serve if the demand exceeds the order quantity (current capacity). For two buyers, we show that a fixed order quantity (FOQ) policy and an index buyer selection policy are optimal. For more buyers, the optimal policy involves overstocking (understocking) when the overall buyer satisfaction is high (low) and selecting buyers that maximize the current revenue (future demand) when the overall buyer satisfaction after the demand is high (low). To tackle the problem, we derive three LR-based index policies: a Lagrangian index policy that uses a uniform capacity price, a Whittle index policy that uses a discriminatory capacity price and is myopically optimal, and a novel “active-constraint” index policy that uses a discriminatory capacity price when the capacity constraint is active. Numerical results indicate that the latter policy is near-optimal and outperforms the other two and that combining it with the right FOQ policy can be very efficient.

Assessment of Alzheimer-related pathologies of dementia using machine learning feature selection

Although a variety of brain lesions may contribute to the pathological assessment of dementia, the relationship of these lesions to dementia, how they interact and how to quantify them remains uncertain. Systematically assessing neuropathological measures by their degree of association with dementia may lead to better diagnostic systems and treatment targets. This study aims to apply machine learning approaches to feature selection in order to identify critical features of Alzheimer-related pathologies associated with dementia. We applied machine learning techniques for feature ranking and classification to objectively compare neuropathological features and their relationship to dementia status during life using a cohort (n=186) from the Cognitive Function and Ageing Study (CFAS). We first tested Alzheimer’s Disease and tau markers and then other neuropathologies associated with dementia. Seven feature ranking methods using different information criteria consistently ranked 22 out of the 34 neuropathology features for importance to dementia classification. Although highly correlated, Braak neurofibrillary tangle stage, beta-amyloid and cerebral amyloid angiopathy features were ranked the highest. The best-performing dementia classifier using the top eight neuropathological features achieved 79% sensitivity, 69% specificity and 75% precision. However, when assessing all seven classifiers and the 22 ranked features, a substantial proportion (40.4%) of dementia cases was consistently misclassified. These results highlight the benefits of using machine learning to identify critical indices of plaque, tangle and cerebral amyloid angiopathy burdens that may be useful for classifying dementia.

Continuous dynamics in behavior reveal interactions between perceptual warping in categorization and speech-in-noise perception.

Spoken language comprehension requires listeners map continuous features of the speech signal to discrete category labels. Categories are however malleable to surrounding context and stimulus precedence; listeners' percept can dynamically shift depending on the sequencing of adjacent stimuli resulting in a warping of the heard phonetic category. Here, we investigated whether such perceptual warping-which amplify categorical hearing-might alter speech processing in noise-degraded listening scenarios. We measured continuous dynamics in perception and category judgments of an acoustic-phonetic vowel gradient via mouse tracking. Tokens were presented in serial vs. random orders to induce more/less perceptual warping while listeners categorized continua in clean and noise conditions. Listeners' responses were faster and their mouse trajectories closer to the ultimate behavioral selection (marked visually on the screen) in serial vs. random order, suggesting increased perceptual attraction to category exemplars. Interestingly, order effects emerged earlier and persisted later in the trial time course when categorizing speech in noise. These data describe interactions between perceptual warping in categorization and speech-in-noise perception: warping strengthens the behavioral attraction to relevant speech categories, making listeners more decisive (though not necessarily more accurate) in their decisions of both clean and noise-degraded speech.

LOGER: A Learned Optimizer Towards Generating Efficient and Robust Query Execution Plans

Query optimization based on deep reinforcement learning (DRL) has become a hot research topic recently. Despite the achieved promising progress, DRL optimizers still face great challenges of robustly producing efficient plans, due to the vast search space for both join order and operator selection and the highly varying execution latency taken as the feedback signal. In this paper, we propose LOGER, a learned optimizer towards generating efficient and robust plans, aiming at producing both efficient join orders and operators. LOGER first utilizes Graph Transformer to capture relationships between tables and predicates. Then, the search space is reorganized, in which LOGER learns to restrict specific operators instead of directly selecting one for each join, while utilizing DBMS built-in optimizer to select physical operators under the restrictions. Such a strategy exploits expert knowledge to improve the robustness of plan generation while offering sufficient plan search flexibility. Furthermore, LOGER introduces ε -beam search, which keeps multiple search paths that preserve promising plans while performing guided exploration. Finally, LOGER introduces a loss function with reward weighting to further enhance performance robustness by reducing the fluctuation caused by poor operators, and log transformation to compress the range of rewards. We conduct experiments on Join Order Benchmark (JOB), TPC-DS and Stack Overflow, and demonstrate that LOGER can achieve a performance better than existing learned query optimizers, with a 2.07x speedup on JOB compared with PostgreSQL.

Causal Vector Autoregression Enhanced with Covariance and Order Selection

A causal vector autoregressive (CVAR) model is introduced for weakly stationary multivariate processes, combining a recursive directed graphical model for the contemporaneous components and a vector autoregressive model longitudinally. Block Cholesky decomposition with varying block sizes is used to solve the model equations and estimate the path coefficients along a directed acyclic graph (DAG). If the DAG is decomposable, i.e., the zeros form a reducible zero pattern (RZP) in its adjacency matrix, then covariance selection is applied that assigns zeros to the corresponding path coefficients. Real-life applications are also considered, where for the optimal order p≥1 of the fitted CVAR(p) model, order selection is performed with various information criteria.

Identification and validation of periodic autoregressive model with additive noise: finite-variance case

In this paper, we address the problem of modeling data with periodic autoregressive (PAR) time series and additive noise. In most cases, the data are processed assuming a noise-free model (i.e., without additive noise), which is not a realistic assumption in real life. The first two steps in PAR model identification are order selection and period estimation, so the main focus is on these issues. Finally, the model should be validated, so a procedure for analyzing the residuals, which are considered here as multidimensional vectors, is proposed. Both order and period selection, as well as model validation, are addressed by using the characteristic function (CF) of the residual series. The CF is used to obtain the probability density function, which is utilized in the information criterion and for residuals distribution testing. To complete the PAR model analysis, the procedure for estimating the coefficients is necessary. However, this issue is only mentioned here as it is a separate task (under consideration in parallel). The presented methodology can be considered as the general framework for analyzing data with periodically non-stationary characteristics disturbed by finite-variance external noise. The original contribution is in the selection of the optimal model order and period identification, as well as the analysis of residuals. All these findings have been inspired by our previous work on machine condition monitoring that used PAR modeling.

Comparison of digital and visual tooth shade selection.

In dental restorations, color determination is very important for achieving esthetic results. The aim of this study was to compare visual shade selection using digital methods and to assess the repeatability of the utilized intraoral scanners. In 31 probands, tooth color was determined on teeth 11, 13, and 16. Shade selection was performed visually by a dentist and digitally using Trios 3 and Cerec Omnicam. Three measurements were performed to determine the repeatability of intraoral scanners. Fleiss' κ was used for statistical evaluation of the repeatability and Cohen's κ was used for comparison of methods. The visual method showed only slight agreement with Trios 3 (Cohen's κ: 0.198) and Cerec Omnicam (Cohen's κ: 0.115). Moderate agreement was found between Trios 3 and Cerec Omnicam (Cohen's κ: 0.452). In terms of repeatability, Trios 3 scored higher overall than Cerec Omnicam (Fleiss' κ: 0.612 vs. 0.474). Intraoral scanners can facilitate the workflow in clinical practice. They are a good supplement for color determination, but should additionally be confirmed by the visual method.Clinical significance:The use of digital instruments is increasingly being preferred over conventional treatments. Therefore, it is essential to continuously improve the accuracy of intraoral scanners for color selection in order to offer an alternative to visual methods.

Identification of MISO Hammerstein system using sparse multiple kernel-based hierarchical mixture prior and variational Bayesian inference

The Hammerstein model is a cascade composition of a static memoryless nonlinear function followed by a linear time-invariant dynamical subsystem, which is capable of modeling a wide range of nonlinear dynamical systems. Model structural parameter selection (including the model order and the nonlinearity order) and sparse representation of the static nonlinear function are two issues that receive increasing interests in Hammerstein system identification. In this paper, a novel Bayesian sparse multiple kernel-based identification method (BSMKM) for multiple-input single-output (MISO) Hammerstein system is proposed to handle those issues, where the basis-function model and the finite impulse response model are used to represent the nonlinear part and the linear part respectively. Firstly, in order to jointly realize the model parameter estimation, the sparse representation of static nonlinear function (the nonlinearity order selection can also be realized indirectly) and the model order selection of linear dynamical system, a hierarchical prior distribution is constructed based on Gaussian scale mixture model and sparse multiple kernel, which can characterize both inter-group sparsity and intra-group correlation structure. Then, a full Bayesian method based on variational Bayesian inference is proposed to estimate all unknown model parameters, including finite impulse response coefficients, hyperparameters and noise variance. Finally, the performance of the proposed BSMKM identification method is evaluated by numerical experiments using both simulation and real data.