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Influenza leads to many deaths every year and is a threat to human health. For effective prevention, traditional national-scale statistical surveillance systems have been developed, and numerous studies have been conducted to predict influenza outbreaks using web data. Most studies have captured the short-term signs of influenza outbreaks, such as one-week prediction using the characteristics of web data uploaded in real time; however, long-term predictions of more than 2-10 weeks are required to effectively cope with influenza outbreaks. In this study, we determined that web data uploaded in real time have a time-precedence relationship with influenza outbreaks. For example, a few weeks before an influenza pandemic, the word ``colds'' appears frequently in web data. The web data after the appearance of the word ``colds'' can be used as information for forecasting future influenza outbreaks, which can improve long-term influenza prediction accuracy. In this study, we propose a novel long-term influenza outbreak forecast model utilizing the time precedence between the emergence of web data and an influenza outbreak. Based on the proposed model, we conducted experiments on: 1) selecting suitable web data for long-term influenza prediction; 2) determining whether the proposed model is regionally dependent; and 3) evaluating the accuracy according to the prediction timeframe. The proposed model showed a correlation of 0.87 in the long-term prediction of ten weeks while significantly outperforming other state-of-the-art methods.

In this article, we present an extended data-fitting model which involves different and conflicting criteria, and we propose an algorithm based on a scalarization technique to solve it. Our model integrates in a unique framework three different criteria, namely, a data-fitting term, and the entropy and the sparsity of the set of unknown parameters. This model can be analyzed by means of multiple criteria decision-making techniques. We then validate the proposed modified algorithm using two computational experiments: We analyze the problem of handwritten digit recognition using a logistic regression model and a deep neural network model, respectively. In the final part of the article, we employ this methodology to forecasting instead. Given the importance of forecasting techniques to predict the future, which in turn can lead to positive impacts on firm performance, we propose two numerical experiments focusing on the forecast of the US GDP. In the first one, we proceed by means of a modified iterated function system with grayscale maps-type fractal operator, and, in the second one, we implement a modified neural network-based model.

We study long-run shareholder outcomes for over 64,000 global common stocks during the January 1990 to December 2020 period. We document that the majority, 55.2% of U.S. stocks and 57.4% of non-U.S. stocks, underperform one-month U.S. Treasury bills in terms of compound returns over the full sample. Focusing on aggregate shareholder outcomes, we find that the top-performing 2.4% of firms account for all of the $US 75.7 trillion in net global stock market wealth creation from 1990 to December 2020. Outside the US, 1.41% of firms account for the $US 30.7 trillion in net wealth creation.

This article suggests a collection of model-based and model-free output-feedback optimal solutions to a general ${H_{∞}}$ control design criterion of a continuous-time linear system. The goal is to obtain a static output-feedback controller while the design criterion is formulated with an exponential term, divergent or convergent, depending on the designer's choice. Two offline policy-iteration algorithms are presented first, which form the foundations for a family of online off-policy designs. These algorithms cover all different cases of partial or complete model knowledge and provide the designer with a collection of design alternatives. It is shown that such a design for partial model knowledge can reduce the number of unknown matrices to be solved online. In particular, if the disturbance input matrix of the model is given, off-policy learning can be done with no disturbance excitation. This alternative is useful in situations where a measurable disturbance is not available in the learning phase. The utility of these design procedures is demonstrated for the case of an optimal lane tracking controller of an automated car.

Empirical research aims to establish generalizable claims from data. Such claims involve concepts that often must be measured indirectly by using indicators. Construct validity is concerned with whether one can justifiably make claims at the conceptual level that are supported by results at the operational level. We report a quantitative analysis of the awareness of construct validity in the software engineering literature between 2000 and 2019 and a qualitative review of 83 articles about human-centric experiments published in five high-quality journals between 2015 and 2019. Over the two decades, the appearance in the literature of the term construct validity increased sevenfold. Some of the reviewed articles we reviewed employed various ways to ensure that the indicators span the concept in an unbiased manner. We also found articles that reuse formerly validated constructs. However, the articles disagree about how to define construct validity. Several interpret construct validity excessively by including threats to internal, external, or statistical conclusion validity. A few articles also include fundamental challenges of a study, such as cheating and misunderstandings of experiment material. The diversity of topics discussed makes us recommend a minimalist approach to construct validity. We propose seven guidelines to establish a common ground for addressing construct validity in software engineering.

This paper introduces an algorithmic approach to the analysis of bifurcation of limit cycles from the centers of nonlinear continuous differential systems via the averaging method. We develop three algorithms to implement the averaging method. The first algorithm allows one to transform the considered differential systems to the normal form of averaging. Here, we restricted the unperturbed term of the normal form of averaging to be identically zero. The second algorithm is used to derive the computational formulae of the averaged functions at any order. The third algorithm is based on the first two algorithms and determines the exact expressions of the averaged functions for the considered differential systems. The proposed approach is implemented in Maple and its effectiveness is shown by several examples. Moreover, we report some incorrect results in published papers on the averaging method.

This brief investigates the problem of state estimation of quaternion-valued neural networks (QVNNs) with time-varying delays. First, by extending the Jensen inequality to quaternion domain, an extended Jensen inequality with quaternion term is derived. Second, a class of proportional-integral state estimator (PISE) with exponential decay term is proposed. Then, by constructing a suitable Lyapunov-Krasovskii functional (LKF), some sufficient conditions are obtained to ensure the existence of the designed PISE and the gain matrices of the designed PISE can be directly computed. Simulations are given to illustrate the advantage of the proposed method.

The prevalence of smartphones has promoted the popularity of mobile apps in recent years. In this paper, we study how mobile app usage evolves over a long-term period. We first introduce an app usage collection platform named carat, from which we have gathered app usage records of 1,465 users from 2012 to 2017. We then conduct the first study on the long-term evolution processes on a macro-level, i.e., app-category, and micro-level, i.e., individual app. We discover that, on both levels, there is a growth stage enabled by the introduction of new technologies. Then there is a plateau stage caused by high correlations between app categories and a Pareto effect in individual app usage, respectively. Additionally, the evolution of individual app usage undergoes an elimination stage due to fierce intra-category competition. The inter-diversity of app-category and individual app usage exhibits opposing trends: app-category usage assimilates while individual app usage diversifies. Nevertheless, the intra-diversity of both app-category and app usage declines over time. Also, we demonstrate the country barriers of app category usage. We further investigate how different demographics affect the evolutionary processes of app usage. Our study provides useful implications for app developers, market intermediaries, and service providers

Limit distribution theory in the econometric literature for functional coefficient cointegrating regression is incorrect in important ways, influencing rates of convergence, distributional properties, and practical work. The correct limit theory reveals that components from both bias and variance terms contribute to variability in the asymptotics. The errors in the literature arise because random variability in the bias term has been neglected in earlier research. In stationary regression this random variability is of smaller order and can be ignored in asymptotic analysis but not without consequences for finite sample performance. Implications of the findings for rate efficient estimation are discussed. Simulations in the Online Supplement provide further evidence supporting the new limit theory in nonstationary functional coefficient regressions.

Aspect level sentiment classification aims to classify the sentiment polarity of a given aspect term or aspect category in a sentence. For sentiment classification towards a given aspect term, since a sentence may contain more than one aspect term, there may exist some opinions which are not the modifiers of the given aspect term. It is necessary to capture relevant opinion for a certain aspect term. Previous works use the relative distance between an aspect term and all other words in a sentence, in order to capture the nearest opinion of the aspect term. This can be infeasible when the sentence has a complex syntactic structure. In this paper, we detect the dependency relation between an aspect term and its related sentiment words in the dependency parse tree. Then, we integrate this relationship into CNN and Bi-LSTM respectively. Experiments show that the related sentiment features for an aspect term is helpful for models to discriminate its sentiment polarity, and our proposed models achieve state-of-the-art results among neural network models.

Stock price forecasting is a type of time series problem that forecasts the future price or status of a company on the basis of analysis of time respective values. As the price of stock or company varies with respect to time, its behavior can be analyzed by different machine learning approaches. In this work, methodology is proposed to evaluate the stock position with variation in time using deep learning approach such as recurrent neural network (RNN). This methodology used the technical parameters to evaluate the long term and short-term analysis of any stock or share. This approach also evaluates and gives suggestions to investors either to buy or sell any stock for long term and gives return at very low risk. In this paper work, hybridization of co-relation analysis and deep learning approach for stock price and long-term behavior analysis. The proposed work is termed as time lagged weight optimized RNN (TL-WO-RNN) that is adopted in this work and effectively predict the technical parameters and on the basis of that stock behavior is also predicted. The result analysis was performed on data from different sectors and such as Telecom, Powers, Manufacturing, Finance, Software sectors, etc. The result analysis shows the effectiveness of the TL-WO-RNN algorithm as compared to existing work.

Traffic congestion is a major problem in developing and developed countries vehicle traffic management systems. Traffic control system works based on the idea of removing instabilities and avoid accidents in order to minimize the traffic and maximize the vehicle flow. To control the congestion need to predict the upcoming traffic flow and it will be useful for Advanced Traffic Information Systems (ATIS), Advanced Traffic Management Systems (ATMS) and traffic analytics. Non–linear historical data and uncertain factors influence the vehicle congestion at peak hours which cannot be considered in existing algorithms. This study proposes hybrid neural network algorithms such as Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) network for short term traffic flow prediction based on multivariate analysis. Widely referred datasets Performance Measurement Systems (PEMS) and Mesowest have been used to evaluate this model. Experiment results shows that CNN-LSTM Hybrid prediction model achieves high accuracy compared with other models.

We use longitudinal data from Massachusetts that link high school course-taking records in career and technical education (CTE) to postsecondary student outcomes to provide the first empirical evidence linking characteristics of CTE teachers to later student outcomes. We find that CTE teachers who received better scores on subject performance tests required for licensure tend to have students with higher longer-term earnings than CTE teachers who received lower scores on these tests, controlling for other factors. Specifically, we estimate that a 1 standard deviation increase in teacher performance on these tests is associated with about a $1,000 increase in average expected earnings for the teacher's students five years after their expected graduation date, controlling for licensure test area and observable differences between students.

In this article, the event-triggered bipartite consensus problem for stochastic nonlinear multiagent systems (MASs) with unknown dead-zone input under the prescribed performance is studied. To surmount the influence of the dead-zone input, the dead-zone model is transformed into a linear term and a disturbance term. Meanwhile, the prescribed tracking performance is realized by developing a speed function, which means that all tracking errors of MASs can converge to a predefined set in a given finite time. Moreover, the unknown nonlinear dynamics are approximated by fuzzy-logic systems. By combining the dynamic surface approach and the Lyapunov stability theory, we design an adaptive event-triggered control algorithm, such that the bipartite consensus problem of stochastic nonlinear MASs can be achieved, and all signals are semiglobally uniformly ultimately bounded in probability of the closed-loop systems. Finally, simulation examples are proposed to verify the feasibility of the algorithm.

Early parent-child relationship and child negative emotionality have both been studied as contributors to attachment security, but few studies have examined whether negative emotionality can moderate effects of parent-child relationship on security and whether the process is comparable across mother- and father-child dyads and different security measures. In 102 community families, we observed parent-child shared positive affect and infants' anger proneness at 7 months, and attachment security at 15 months, using observer-rated Attachment Q-Set (AQS) and a continuous measure derived from Strange Situation Paradigm (SSP). For mother-child dyads, high shared positive affect and low anger proneness were associated with AQS security. Those effects were qualified by their interaction: Variations in shared positive affect were associated with security only for relatively more anger-prone children. That effect reflected the diathesis-stress model. For father-child dyads, shared positive affect was associated with security. There were no effects for SSP security with either parent.

In the autonomous ocean monitoring task, the sampling robot moves in the environment and accumulates data continuously. The widely adopted spatial modeling method - standard Gaussian process (GP) regression - becomes inadequate in processing the growing sensing data of a large size. To overcome the computational challenge, this paper presents an environmental modeling framework using a sparse variant of GP called streaming sparse GP (SSGP). The SSGP is able to handle streaming data in an online and incremental manner, and is therefore suitable for long-term autonomous environmental monitoring. The SSGP summarizes the collected data using a small set of pseudo data points that best represent the whole dataset, and updates the hyperparameters and pseudo point locations in a streaming fashion, leading to high-quality approximation of the underlying environmental model with significantly reduced computational cost and memory demand., Proceedings of OCEANS 2019, SEATTLE

BackgroundThe first month of life is the most critical in a child’s heath because it is associated with the highest risk of adverse health outcomes. In Tanzania the risk of adverse health outcomes in preterm infants is five times higher compared to term infants.The objective of this study was to assess common adverse health outcomes and compare the risk of such outcomes between preterm and term infants, in Tanzania, within the first 28 days of life.MethodsThis was a case-control study involving preterm (cases) and term (controls) infants delivered at the Muhimbili National Hospital between August and October 2019 . About 222 pairs of cases and controls were reviewed for their medical records. Logistic regression was used to compare the risk of neonatal outcomes between the study groups. Statistical significance was achieved at P-value < 0.05 and 95% confidence interval.ResultsPreterm infants have an increased risk of mortality (OR = 7.2, 95% CI: 3.4-15.1), apnea (OR = 4.7, 95% CI: 3.4-15.1), respiratory distress syndrome (OR = 4.8, 95% CI: 3.2-7.3), necrotizing enterocolitis (OR = 5.5, 95% CI: 1.2-25.3), anemia (OR = 4.3 , 95% CI: 2.8-6.6), pneumonia (OR = 2.7, 95% CI: 1.6-4.6) and sepsis (OR = 2.6, 95% CI: 1.7-3.9) in the first month of life compared to term infants. No differences in the risk of intraventricular hemorrhage, bronchopulmonary dysplasia, patent ductus arteriosus and jaundice were observed between preterm and term infants. ConclusionThe findings of this study informs the Tanzanian health sector about the most common and high risk neonatal outcomes in preterm infants. Additionaly, for promoting neonates' health, the health sector needs to consider preventing and treating the most common and high risk adverse neonatal outcomes in preterm infants.

The aim of this work is to study the rigidity problem for Steiner's inequality for the anisotropic perimeter, that is, the situation in which the only extremals of the inequality are vertical translations of the Steiner symmetral that we are considering. Our main contribution consists in giving conditions under which rigidity in the anisotropic setting is equivalent to rigidity in the Euclidean setting. Such conditions are given in term of a restriction to the possible values of the normal vectors to the boundary of the Steiner symmetral (see Corollary 1.17, and Corollary 1.18).

Service disruptions of rail transit systems have become more frequent in the past decade in urban cities, due to various reasons, such as power failures, signal errors, and so on. Smart transit cards provide detailed tapping records of commuters, which enable us to infer their trajectories under both normal and disruptive circumstances. In this article, we study and predict the impact of disruptions on commuters and further evaluate the vulnerability of the rail system. Specifically, we define two metrics, stay ratio and travel delay, to quantify the impact, and we derive the predictor of each metric based on the inferred alternative route choices of commuters under disruptive circumstances. We demonstrate that the alternative route choices contribute to more similar feature distribution among different disruptions, which is crucial to tackling the main challenge of abnormal data scarcity and is beneficial for obtaining more reliable predictors for future disruptions. We evaluate our approach with a real-world transit card dataset. The result demonstrates the effectiveness of our method. Based on the predictors, we further analyse the vulnerability of the rail system. An evaluation with cross validation from taxi GPS trajectory data indicates its efficacy in discovering vulnerable rail stations as well as Origin-Destination pairs.

Purpose: To evaluate the long-term effects of uncomplicated traumatic hyphema on endothelial morphology, anterior segment structure, and corneal and lenticular densitometryMethods: In this retrospective comparative study, eyes with a history of uncomplicated traumatic hyphema were compared with the healthy contralateral unaffected eyes. The corneal endothelial cell properties were captured using specular microscopy. Anterior segment analysis, corneal densitometry (12-mm corneal diameter), and lens densitometry measurements were performed using the Pentacam imaging system.Results: Measurements were obtained at a mean follow-up of 49.5 ± 15.8 months after injury. The average endothelial cell density was significantly lower in the study group than in the control group (2,506.6 ± 294.0 cells/mm² vs. 2,665.7 ± 195.0 cells/mm², p = 0.020). There was no difference between the groups in respect of polymegathism and pleomorphism (p = 0.061 and p = 0.558, respectively). All the investigated corneal tomographic and angle parameters were similar in both groups (all p > 0.05). The corneal densitometry values in all concentric zones and layers showed no statistically significant difference between the groups (p > 0.05 for all). The lens zone 1 densitometry value was significantly higher in the study group than in the control group (9.6% ± 1.1% vs. 8.9% ± 1.2%, p = 0.031). No difference was observed in zone 2 and 3 (p = 0.170 and p = 0.322, respectively). The degree of hyphema was not correlated with endothelial cell and lenticular clarity loss (p = 0.087 and p = 0.294, respectively).Conclusions: Even if traumatic hyphema is not complicated, long-term outcomes indicate endothelial cell loss and increased lenticular density.

Today, firms pursuing a pioneering strategy are often engaged in supply chain relationships to benefit from external resources and to improve their innovation. However, this effort can be impeded by power asymmetries in such relationships and especially by the execution of coercive power by their partner firm. Contracts could potentially reduce this risk of opportunistic behavior. Our survey study on 778 small to medium-sized enterprises in the European packaging and medical equipment industries examines how coercive power of the partner and the contractual arrangement between firms moderate the pioneering strategy's innovation outcomes in the short and long run. Our results confirm the negative effect of coercive power on innovation performance in both the short and long term. However, the compensating effect of rather complete contracts differs temporally. Whereas, contract completeness protects against higher dependence at the beginning of the collaboration, their effect diminishes over time. In contrast, rather incomplete contracts enhance the innovation performance in the long term, possibly complemented with trust.

South Korea established the National Construction Standards Center to efficiently manage the national construction standards and orchestrate various efforts such as the development and export of the unified code system, and the conduction of code reform research for securing safety and ever increasing disaster prevention of buildings and infrastructure. Significant national budgets must be put in place to develop, promote, and continually improve the unified construction code system. In order to confirm the appropriateness of this continuous investment, this article intends to estimate the socioeconomic ripple effect of the coordinated efforts over the next 30 years by developing a hybrid method that combines an analytical hierarchical process and system dynamics (SD). In this article, the interacting behavior of the developed SD model is illustrated parametrically. We found that the center's integrative codification efforts including the development and diffusion of the unified code system has the effect of offsetting the decreasing national construction budget. We also found that the Monte Carlo multivariate simulation-based uncertainty analysis on the developed SD model is well-suited for effectively quantifying and integrating various benefits to empower decision makers; in particular, providing information both on the mean and the most conservative socioeconomic ripple effects over the long term.

Colormapping is an effective and popular visualization technique for analyzing patterns in scalar fields. Scientists usually adjust a default colormap to show hidden patterns by shifting the colors in a trial-and-error process. To improve efficiency, efforts have been made to automate the colormap adjustment process based on data properties (e.g., statistical data value or histogram distribution). However, as the data properties have no direct correlation to the spatial variations, previous methods may be insufficient to reveal the dynamic range of spatial variations hidden in the data. To address the above issues, we conduct a pilot analysis with domain experts and summarize three requirements for the colormap adjustment process. Based on the requirements, we formulate colormap adjustment as an objective function, composed of a boundary term and a fidelity term, which is flexible enough to support interactive functionalities. We compare our approach with alternative methods under a quantitative measure and a qualitative user study (25 participants), based on a set of data with broad distribution diversity. We further evaluate our approach via three case studies with six domain experts. Our method is not necessarily more optimal than alternative methods of revealing patterns, but rather is an additional color adjustment option for exploring data with a dynamic range of spatial variations.

Stock price prediction is a significant index which helps to achieve maximum benefit with minimum risk by increasing the decision making capability of financial investigators and investors. However, the problem of short term stock price prediction is a complex task due to its uncertainty, discontinuity, and random nonlinear nature. In this paper, a prediction model is proposed to predict random nonlinear stock market price using Intrinsic Time-Scale Decomposition (ITD), Cluster based Modified Crow Search Algorithm (CMCSA), and Optimized Extreme Learning Machine (OELM). ITD is adopted to decompose the non-stationary stock price data into some Proper-Rotation-Components (PRCs) and a residual component. ITD is used to convert non-stationary stock price data to stationary data which are simpler and steady for analysis. The CMCSA is proposed by modifying Crow Search Algorithm (CSA) with better capability to select optimal weight and biases of ELM. Thereafter, the optimized ELM is used to predict the PRCs and residual component individually which are then incorporated to predict closing price of short term stocks. The effectiveness of proposed CMCSA is tested and validated by solving benchmark problems. The experimental study indicates that the proposed ITD-CMCSA-OELM model outperforms the CMCSA-OELM, CSA-OELM, DE-OELM (Differential Evolution optimized ELM) and ANN models.

This article investigates the stability problem for discrete-time neural networks with a time-varying delay by focusing on developing new Lyapunov-Krasovskii (L-K) functionals. A novel L-K functional is deliberately tailored from two aspects: 1) the quadratic term and 2) the single-summation term. When the variation of the discrete-time delay is further considered, the constant matrix involved in the quadratic term is extended to be a delay-dependent one. All these innovations make a contribution to a quadratic function with respect to the delay from the forward differences of L-K functionals. Consequently, tractable stability criteria are derived that are shown to be more relaxed than existing results via numerical examples.

In this paper, we analyze the global asymptotic stability and global exponential stability with respect to the Clifford-valued neutral-type neural network (NN) models with time delays. By considering the neutral term, a Clifford-valued NN model with time delays is formulated, which encompasses real-valued, complex-valued, and quaternion-valued NN models as special cases. In order to achieve our main results, the n -dimensional Clifford-valued NN model is decomposed into 2 m n -dimensional real-valued models. Moreover, a proper function is constructed to handle the neutral term and prove that the equilibrium point exists. Utilizing the homeomorphism theory, linear matrix inequality as well as Lyapunov functional methods, we derive the sufficient conditions corresponding to the existence, uniqueness, and global asymptotic stability with respect to the equilibrium point of the Clifford-valued neutral-type NN model. Numerical examples to demonstrate the effectiveness of the results are provided, and the simulations results are analyzed and discussed.

This study used Soil and Water Assessment Tool (SWAT) to investigate the impacts of groundwater use increase and forest growth on the watershed hydrology of Geum River basin (9,645.5 km2), South Korea. Groundwater use increase and forest growth data from 1976 to 2015 were prepared in 10-year interval and were reflected to SWAT corresponding to each decade. SWAT was calibrated in the aspect of evapotranspiration, soil moisture, and streamflow using the observation data. The model performance for streamflow was evaluated by coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), and percent bias (PBIAS). The calibration achieved the average R2 value of 0.73 ~ 0.82, NSE value of 0.75 ~ 0.81, RMSE value of 0.53 ~ 2.35 mm/day, and PBIAS value of -2.51 ~ + 11.74%, respectively. The model performance for evapotranspiration and soil moisture was evaluated by R2. The calibration result of evapotranspiration and soil moisture achieved average R2 value of 0.45 and 0.44, respectively. The calibrated model evaluated the impact of two factors on watershed hydrology. Decadal increase of groundwater use has decreased groundwater flow and increased groundwater recharge while decadal forest growth has mainly increased evapotranspiration that led to the decrease of other hydrological components. Resultingly, the change of two factors have imposed temporal decrease of total runoff on the watershed while the influence of two factors on annual streamflow loss was bigger in lower flow rate.

The ability to consider the future is critical to many human behaviors. Individuals who consider future outcomes of their actions are more likely to report using emotion regulation strategies that have enduring effects on feelings. However, there has been little examination of how variation in short- and long-term motives across events predicts emotion regulation strategy use. We examined the roles of both interindividual and intraindividual variation in short- and long-term motives in emotion regulation in daily life, while controlling for hedonic and instrumental motives. In a daily diary study (Study 1) and a mobile application study (Study 2), participants (N = 107 and N = 98) reported on their short- and long-term motives for regulation and their use of multiple emotion regulation strategies across multiple negative events. Across both studies, momentary long-term motives were predictive of several strategies, including problem-solving and reappraisal, both of which are associated with more positive mental health outcomes in the long-term. The results suggest that people's long-term motives vary across contexts and relate to the implementation of different regulatory strategies, and that these associations are at least partially independent of the role of hedonic and instrumental motives. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A dilemma faced by classification is that the data is not collected at the same frequency in some applications. We investigate the mixed frequency data in a new way and recognize them as a special style of multi-view data, in which each view data is collected at a different sampling frequency. This paper proposes a discriminative dictionary pair learning method constrained by ordinal locality for mixed frequency data classification (shorted by DPLOL-MF). This method integrates synthesis dictionary and analysis dictionary into a dictionary pair, which not only improves computational cost caused by the ${\ell_0}$ or ${\ell_1}$ -norm constraint, but also can deal with the sampling frequency inconsistency. The DPLOL-MF utilizes a synthesis dictionary to learn class-specified reconstruction information and employs an analysis dictionary to generate coding coefficients by analyzing samples. Particularly, the ordinal locality preserving term is leveraged to constrain the atoms of dictionaries pair to further facilitate the learned dictionary pair to be more discriminative. Besides, we design a specific classification scheme for the inconsistent sample size of mixed frequency data. This paper illustrates a novel idea to solve the classification task of mixed frequency data and the experimental results demonstrate the effectiveness of the proposed method.

Inspired by the mean calculation of RPCA_OM and inductiveness of IRPCA, we first propose an inductive robust principal component analysis method with removing the optimal mean automatically, which is shorted as IRPCA_OM. Furthermore, IRPCA_OM is extended to Schatten-p norm and a more general framework (i.e., EIRPCA_OM) is presented. The objective function of EIRPCA_OM includes two terms, the first term is a robust reconstruction error term constrained by an l2,1-norm and the second term is a regularization term constrained by a Schatten-p norm. The proposed EIRPCA_OM method is robust, inductive and accurate. However, on the high-dimensional data, it would spend a large computation cost in training stage. To this end, a fast version of EIRPCA_OM called as FEIRPCA_OM is proposed, and its basic idea is to eliminate the zero eigenvalues of data matrix. More importantly, an effective theoretical proof is presented to ensure that FEIRPCA_OM has faster processing speed than EIRPCA_OM when processing high-dimensional data, but without any performance loss. Based on it, we also can exchange the less performance loss for the higher computation efficiency by removing the small eigenvalues of data matrix. Experimental results on the public datasets demonstrate that FEIRPCA_OM works efficiently on the high-dimensional data.

Rationale, aims and objectives: Patient-centered care is considered a vital component of good quality care for breast cancer patients. Nevertheless, the implementation of this valuable concept in clinical practice appears to be difficult. The goal of this study is to bridge the gap between theoretical elaboration of “patient-centered care” and clinical practice. To that purpose, a scoping analysis was performed of the application of the term “patient-centered care in breast cancer treatment” in present-day literature. Method: For data-extraction, a literature search was performed extracting references that were published in 2018 and included the terms “patient-centered care” and “breast cancer”. The articles were systematically traced for answers to the following three questions: “What is patient-centered care?”, “Why perform patient-centered care?”, and “How to realize patient-centered care?”. For the content analysis, these answers were coded and assembled into meaningful clusters until separate themes arose which concur with various interpretations of the term “patient-centered care”. Results: A total of 60 publications were retained for analysis. Traced answers to the three questions “what”, “why”, and “how” varied considerably in recent literature concerning breast cancer treatment. Despite the inconsistent use of the term “patient-centered care,” we did not find any critical consideration about the nature of the concept, regardless of the applied interpretation. Interventions that are supposed to contribute to the heterogeneous concept of patient-centered care as such, seem to be judged desirable, virtually without empirical justification. Conclusions: We propose, contrary to previous efforts to define “patient-centered care” more accurately, to embrace the heterogeneity of the concept and apply “patient-centered care” as an umbrella-term for all healthcare that intends to contribute to the acknowledgement of the person in the patient. For the justification of measures to realize patient-centered care for breast cancer patients, instead of a mere contribution to the abstract concept, we insist on the demonstration of desirable real-world effects.

Background: Electricity is considered as the essential unit in today’s high-tech world. The electricity demand has been increased very rapidly due to increased urbanization,(smart buildings, and usage of smart devices to a large extent). Building a reliable and accurate electricity consumption prediction model becomes necessary with the increase in demand for energy. From recent studies, prediction models such as support vector regression (SVR), gradient boosting decision tree (GBDT), artificial neural network (ANN), random forest (RF), and extreme gradient boosting (XGBoost) have been compared for the prediction of electricity consumption and XGBoost is found to be the most efficient method that leads to the motivation for the research. Objective: The objective of this research is to propose a model that performs future electricity consumption prediction for different time horizons: short term prediction and long term prediction using the extreme gradient boosting method and reduce prediction errors. Also, based on the prediction of the electricity consumption, the best and worst predicted days are being recognized. Methods: The method used in this research is the extreme gradient boosting for future building electricity consumption prediction. The extreme gradient boosting method performs predictions for different time horizons(short term and long term) for different seasons(summer and winter). The model was designed for a house building located in Paris. Results: The model has been trained and tested on the dataset and its prediction is accurate with the low rate of errors compared to other machine learning techniques. The model predicts accurately with RMSE of 140.45 and MAE of 28, which is the least value for errors when compared to the baseline prediction models. Conclusion: A model that is robust to all the conditions should be built by enhancing the prediction mechanism such that the model should be dependent on a few factors to make electricity consumption prediction.

Language-independent spoken term detection (LI-STD) refers to the process of locating the occurrences of spoken queries from speech databases of any language. This paper alization of a multilingual broad phoneme classifier (BPC) and its application for the development of an LI-STD system. This work proposes a multi-stage architecture to address the task of LI-STD for low-resourced languages, where there is limited amount of labelled training data. The proposed LI-STD system contains three stages; one label sequence matching stage and two template matching stages. A deep neural network (DNN) based BPC trained using 16 handcrafted, signal-based features is the backbone of the proposed LI-STD system. In LI-STD system, stage 1 performs a broad phoneme sequence matching, while stage 2 and 3 perform template matching on posteriorgram and feature sequence, respectively. Concatenation of multiple stages results in search space reduction for the later computationally intensive template matching stages. In order to adapt to a new/unseen language, the BPC gets retrained using selected broad phoneme labelled data of the language generated by itself. The effectiveness of the proposed system is demonstrated on a set of low-resourced Indian languages.

This paper reveals the power and frequency oscillation mechanism of the islanded cascaded-type virtual synchronous generators (VSGs), and proposes a decentralized mutual damping suppression method. In the proposed scheme, the mutual damping term is constructed via the decentralized manner with transmission line current. It contributes to increasing damping and accelerating the frequency convergence of cascaded-type VSGs in the dynamic process. As a result, the power and frequency oscillations are restrained, and the system dynamic performance is improved. Further, the system stability based on the Lyapunov energy function method is proved. Finally, the effectiveness of the proposed decentralized mutual damping control is verified by simulations and experimental results.

Objective Previous literature documented the traumatic consequences of exposure to disasters on psychological functioning, but little attention has been paid to the intergenerational transmission of the memory of disasters. We explored long-term effects on the memory of the Vajont dam disaster in Northeast Italy that claimed 1,910 lives in 1963. Method We collected data from 52 two-generation families in which the first generations were born before the disaster and the second generations after. The families were divided into an experimental group whose first generation survived the disaster and a control group whose first generation had moved there afterward. The interviews included an open-ended narrative on the memory of the disaster. We coded free narratives focusing on the richness of the memories (i.e., length, causes, core, aftermath), analyzing negative emotions and salience of the natural and psychological domains. Results We applied generalized linear mixed models. The richness of the memories, including references to negative emotions, diminished with lower exposure and with intergenerational transmission. Moreover, the participants built a shared representation of the disaster that did not markedly differ across exposure or generation. The reported causes were attributed more to the natural rather than the human domain; the consequences more to the psychological compared to the material domain. Conclusions Our findings highlight the processes through which collective memories of historical traumatic events are built over the long term and the way a collective identity develops to bear the burden of highly dramatic events and to transmit intergenerational lessons from the past. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Despite having plethora of works, wind speed time-series forecasting capabilities are prone to errors due to its intermittent and non-stationary nature as well as the limited generalization capabilities of forecasting methods for non-Gaussian distributed data. In this paper, a hybrid method that consists of elastic variational mode decomposition (eVMD) and forecasting random convolution nodes (fRCN) is proposed to forecast the Gaussian heteroscedastic wind speed time-series. The proposed eVMD algorithm gauges the non-stationary characteristics (complexity) of the wind speed signal and thereafter decomposes the signal into its intrinsic components (ICs) accordingly. The fRCN method rely on local receptive fields (LRF) to extract features that contribute to the local variations and the global trend in each IC. These features are subsequently learned using extreme learning machines (ELM) theories. An ensemble unit is employed to learn appropriate weightages for each forecasted IC before yielding the final forecasting values. Suitability of the proposed hybrid method for wind speed forecasting is evaluated via an actual wind speed dataset and comparing against various existing hybrid methods.

A biased assimilation model of opinion dynamics is a nonlinear model, in which opinions exchanged in a social network are multiplied by a state-dependent term having the bias as exponent and expressing the bias of the agents toward their own opinions. The aim of this article is to extend the bias assimilation model to signed social networks. We show that while for structurally balanced networks, polarization to an extreme value of the opinion domain (the unit hypercube) always occurs regardless of the value of the bias, for structurally unbalanced networks, a stable state of indecision (corresponding to the centroid of the opinion domain) also appears, at least for small values of the bias. When the bias grows and passes a critical threshold, which depends on the amount of "disorder" encoded in the signed graph, then a bifurcation occurs and opinions become again polarized. Funding Agencies|National Natural Science Foundation of China [61733018]; Australian Research Council [DP190103615]; Swedish Research Council [2020-03701]

This paper investigates the event-triggered control problem for switched affine systems. The presence of affine terms brings many difficulties on the exclusion of triggering Zeno behavior when ensuring asymptotic stability. We propose an event-triggered control strategy dynamically updated with triggering to solve this problem, in which the triggering condition is associated with the affine terms and given with a time-varying term that is updated at triggering instants. Based on the triggering strategy, the controllers for subsystems and a switching rule are designed to achieve asymptotic stability of the resulting closed-loop system. Moreover, due to the special feature of event-triggered switched affine systems, a new method for proving the exclusion of Zeno behavior is given. Finally, the developed results are illustrated by an electric circuit example.

Multiple disturbances are the main constraints that hinder the high-performance velocity tracking of the gimbal servo system in CMG. This paper presents a non-cascade structured velocity-tracking controller (NCVTC) based on a refined disturbance observer (RDO) for handling multiple disturbances and improve the tracking performance. Specifically, a disturbance observer (DO) is designed to estimate the imbalance disturbance which can be represented by an exogenous system. The disturbances that cannot be modeled a priori are treated as a lumped term and estimated via extended state observer (ESO). By resorting to disturbance estimation, a novel sliding mode controller (SMC) based on the disturbance estimation values is developed to handle the mismatched problem and chattering issue. Finally, experimental tests are conducted to verify the effectiveness of the proposed anti-disturbance control scheme.

Diyetlerdeki karbonhidrat çeşitleri hakkında bilimsel tartışmalar devam etmektedir. Fruktoz, gıda ürünlerinde yaygın olarak kullanılmaktadır. Çalışmada, ratlarda düşük ve yüksek fruktoz solüsyonlarının lipogenik ve inflamatuar etkileri araştırılmıştır. Hayvanlar, 10 hafta süreyle fruktoz solüsyonları ile beslenmiştir. Gruplar: Con (Kontrol), F15 (Fruktoz %15), F30 (Fruktoz %30), F60 (Fruktoz %60) şeklinde olmuştur. F60 en hafif grupken, F30 en ağır grup olarak belirlenmiştir. Trigliserit seviyeleri tüm deneme gruplarında Con'dan önemli ölçüde daha yüksek olmuştur (P, The aim of this study was to investigate the lipogenic and inflammatory effects of low and high percentage fructose solutions in rats. Wistar albino rats were fed with fructose solutions for 10 weeks. The groups were as follows: Cont (Control), F15 (Fructose 15%), F30 (Fructose 30%), and F60 (Fructose 60%). Rats' body weights were measured weekly. Also, lipogenic and inflammatory gene expression levels, biochemical parameters, and histopathological changes in the liver were investigated. After 10 weeks, it was observed that the animals in the F60 were the heaviest, while the animals in the F30 were the lightest. In all experimental groups, triglycerides were significantly higher than those of controls (P

Robots are a combination of mechatronics, computer science, and artificial intelligence. Robotics is a branch of engineering that involves the conception, design, manufacture, and operation of actions. Whenever a robot has to interact with the human-society, it has to adopt a special skill called Human-Robot Interaction and thus the term Social-Robot comes into account. A social robot has to be able to express emotions, communicate with high-level dialogue, use natural cues, and learn to recognize models of other agents. An autonomous social robot cannot follow orders instead of doing something on its own. To make the robot more interactive and communicative, lots of sensors and modules have to be used along with its moving mechanism. Therefore, a social robot becomes complex and expensive. To overcome the issue of the complexity and costliness, in this paper, a design of social robot using a combination of embedded systems, the Internet of Robotic Things (IORT) and Android operating system has been introduced to be interactive and communicative to human, be intelligent enough to solve complex mathematics and be able to follow the operator’s command simultaneously. By using the Internet as the robot’s source of information and the Android phone as the robot’s sensory and control system partially, and adding them all to the robot’s embedded system wirelessly, we have not only become able to make the robot more advanced and intelligent, but also reduce the cost of construction by a significant amount.

We investigated the asymptotic dynamics of a nonlinear system modeling binary mixture of solids with delay term. Using the recent quasi-stability methods introduced by Chueshov and Lasiecka, we prove the existence, smoothness and finite dimensionality of a global attractor. We also prove the existence of exponential attractors. Moreover, we study the upper semicontinuity of global attractors with respect to small perturbations of the delay terms.

In intelligent transportation system (ITS), accurate and real-time prediction of short-term multi-lane traffic flow with existing traffic data is an important part of urban traffic planning, traffic management and control. The data generated in the process of vehicle driving has the cooperative characteristics of multi-source, space-time and dynamic. Combining the data with high-performance computing or cloud computing, a new space-time information framework is designed, which is of great significance for the analysis and prediction of traffic flow, as well as intelligent traffic management, service and decision-making. This paper analyzes the statistical characteristics of urban road traffic flow from the two dimensions of time and space through the spatial-temporal correlation between multi-lane short term traffic flow in single observation point and multi observation points. We constructed a data fusion powered bi-directional long short term memory (DFBD-LSTM) model for individual lane and aggregate traffic flow, then used this model to predict multi-lane short term traffic flow. By taking individual lane traffic flow and aggregate traffic flow as different variables, the model produces more accurate predictions, which can better guide people to travel, alleviate the congestion of urban road traffic network to a certain extent, and improve the utilization rate and transportation efficiency of traffic road.

This paper studies the impact of labor market conditions during the education-to-work transition on workers' long-term skill development. Using representative survey data on measures of work-relevant cognitive skills for adults from 19 countries, I document four main findings: i) cohorts of workers who faced higher unemployment rates at ages 18–25 have lower skills at ages 36–59; ii) unemployment rates faced at later ages (26–35) do not have such an effect; iii) the former findings hold even though, on average, people get more formal education as a response to higher unemployment in their late teens and early twenties; iv) skill inequality is affected: workers whose parents were less educated bear most of the negative effects. These findings can be rationalized by on-the-job learning during the early twenties being an important factor of skill-development, and such learning being negatively impacted by bad macroeconomic conditions. Using German panel data on skills, I show that young workers at large firms experience higher skill growth than those at small firms. This finding suggests firm heterogeneity in human capital provision to young workers as a potential mechanism since, in bad economic times, young workers disproportionately match with small firms.

We show that, on the Riemann hypothesis, $\limsup_{X\to\infty}I(X)/X^{2} \leq 0.8603$, where $I(X) = \int_X^{2X} (\psi(x)-x)^2\,dx.$ This proves (and improves on) a claim by Pintz from 1982. We also show unconditionally that $\frac{1}{5\,374}\leq I(X)/X^2 $ for sufficiently large $X$, and that the $I(X)/X^{2}$ has no limit as $X\rightarrow\infty$., Comment: 23 pages

With the increasing demand for intelligent transportation systems, short-term traffic flow prediction has become an important research direction. The memory unit of a Long Short-Term Memory (LSTM) neural network can store data characteristics over a certain period of time, hence the suitability of this network for time series processing. This paper uses an improved Gate Recurrent Unit (GRU) neural network to study the time series of traffic parameter flows. The LSTM short-term traffic flow prediction based on the flow series is first investigated, and then the GRU model is introduced. The GRU can be regarded as a simplified LSTM. After extracting the spatial and temporal characteristics of the flow matrix, an improved GRU with a bidirectional positive and negative feedback called the Bi-GRU prediction model is used to complete the short-term traffic flow prediction and study its characteristics. The Rectified Adaptive (RAdam) model is adopted to improve the shortcomings of the common optimizer. The cosine learning rate attenuation is also used for the model to avoid converging to the local optimal solution and for the appropriate convergence speed to be controlled. Furthermore, the scientific and reliable model learning rate is set together with the adaptive learning rate in RAdam. In this manner, the accuracy of network prediction can be further improved. Finally, an experiment of the Bi-GRU model is conducted. The comprehensive Bi-GRU prediction results demonstrate the effectiveness of the proposed method.