Bagging Based Cross-Media Retrieval Algorithm

It is very challenging to propose a strong learning algorithm with high prediction accuracy of cross-media retrieval, while finding a weak learning algorithm which is slightly higher than that of random prediction is very easy. Inspired by this idea, we propose an imaginative Bagging based cross-media retrieval algorithm (called BCMR) in this paper. First, we utilize bootstrap sampling to carry out random sampling of the original training set. The amount of the sample abstracted by bootstrap is set to be same as the original dataset. Second, 50 bootstrap replicates are used for training 50 weak classifiers independently. We take advantage of homogenous individual classifiers and integrate eight different baseline methods in our experiments. Finally, we generate the final strong classifier from the 50 weak classifiers by the integration strategy of sample voting. We use collective wisdom to eliminate bad decisions so that the generalization ability of the integrated model could be greatly enhanced. Extensive experiments performed on three datasets show that BCMR can effectively improve the accuracy of cross-media retrieval.

Technical Note: Flood frequency study using partial duration series coupled with entropy principle

Quality discharge measurements and frequency analysis are two major prerequisites for defining a design flood. Flood frequency analysis (FFA) utilizes a comprehensive understanding of the probabilistic behavior of extreme events but has certain limitations regarding the sampling method and choice of distribution models. Entropy as a modern-day tool has found several applications in FFA, mainly in the derivation of probability distributions and their parameter estimation as per the principle of maximum entropy (POME) theory. The present study explores a new dimension to this area of research, where POME theory is applied in the partial duration series (PDS) modeling of FFA to locate the optimum threshold and the respective distribution models. The proposed methodology is applied to the Waimakariri River at the Old Highway Bridge site in New Zealand, as it has one of the best quality discharge data. The catchment also has a history of significant flood events in the last few decades. The degree of fitness of models to the exceedances is compared with the standardized statistical approach followed in literature. Also, the threshold estimated from this study is matched with some previous findings. Various return period quantiles are calculated, and their predictive ability is tested by bootstrap sampling. An overall analysis of results shows that entropy can be also be used as an effective tool for threshold identification in PDS modeling of flood frequency studies.

A Standard Criterion for Measuring Turbulence Quantities Using the Four-Receiver Acoustic Doppler Velocimetry

Acoustic Doppler velocimetry (ADV) enables three-dimensional turbulent flow fields to be obtained with high spatial and temporal resolutions in the laboratory, rivers, and oceans. Although such advantages have led ADV to become a typical approach for analyzing various fluid dynamics mechanisms, the vagueness of ADV system operation methods has reduced its accuracy and efficiency. Accordingly, the present work suggests a proper measurement strategy for a four-receiver ADV system to obtain reliable turbulence quantities by performing laboratory experiments under two flow conditions. Firstly, in still water, the magnitude of noises was evaluated and a proper operation method was developed to obtain the Reynolds stress with lower noises. Secondly, in channel flows, an optimal sampling period was determined based on the integral time scale by applying the bootstrap sampling method and reverse arrangement test. The results reveal that the noises of the streamwise and transverse velocity components are an order of magnitude larger than those of the vertical velocity components. The orthogonally paired receivers enable the estimation of almost-error-free Reynolds stresses and the optimal sampling period is 150–200 times the integral time scale, regardless of the measurement conditions.

Looking for meanings at work: unraveling the implications of smart working on organizational meaningfulness

Purpose This study aims to shed light on the ambiguous effects of smart working (SW) on work meaningfulness. On the one hand, SW enables people to benefit from greater work flexibility, advancing individual control over organizational activities. On the other hand, it may impair interpersonal exchanges at work, disrupting job meaningfulness. Hence, the implications of SW on work meaningfulness are investigated through the mediating role of interpersonal exchanges at work. Design/methodology/approach The authors investigate both the direct and indirect effects of SW on employees’ perceived meaningfulness at work. Secondary data come from the sixth European Working Conditions Survey. The study encompasses a sample of 30,932 employees. A mediation model based on ordinary least square regressions and bootstrap sampling is designed to obtain evidence of SW’s implications on meaningfulness at work through the mediating role of interpersonal relationships (IR). Findings The research findings suggest that SW triggers a positive sense of the significance of work. However, it negatively affects IR with peers and supervisors, entailing professional and spatial isolation. Impaired IR twists the positive implications of SW on organizational meaningfulness (OM), curtailing the employees’ sense of significance at work. Practical implications SW is a double-edged sword. It contributes to the enrichment of OM, enhancing the individual self-determination to shape the spatial context of work. However, its side effects on interpersonal exchanges generate a drift toward organizational meaninglessness. Tailored management interventions intended to sustain IR at work are needed to fit the design of SW arrangements to the employees’ evolving social needs. Originality/value The paper pushes forward what is currently known about the implications of SW on OM, examining them through the mediating role of IR at work.

Optimization of Acute Kidney Injury (AKI) Time Definitions Using the Electronic Health Record: A First Step in Automating In-Hospital AKI Detection

Kidney Disease: Improving Global Outcomes (KDIGO) acute kidney injury (AKI) definitions were evaluated for cases detected and their respective outcomes using expanded time windows to 168 h. AKI incidence and outcomes with expanded time intervals were identified in the electronic health records (EHRs) from 126,367 unique adult hospital admissions (2012–2014) and evaluated using multivariable logistic regression with bootstrap sampling. The incidence of AKI detected was 7.4% (n = 9357) using a 24-h time window for both serum creatinine (SCr) criterion 1a (≥0.30 mg/dL) and 1b (≥50%) increases from index SCr, with additional cases of AKI identified: 6963 from 24–48 h.; 2509 for criterion 1b from 48 h to 7 days; 3004 cases (expansion of criterion 1a and 1b from 48 to 168 h). Compared to patients without AKI, adjusted hospital days increased if AKI (criterion 1a and 1b) was observed using a 24-h observation window (5.5 days), 48-h expansion (3.4 days), 48-h to 7-day expansion (6.5 days), and 168-h expansion (3.9 days); all are p < 0.001. Similarly, the adjusted risk of in-hospital death increased if AKI was detected using a 24-h observation window (odds ratio (OR) = 16.9), 48-h expansion (OR = 5.5), 48-h to 7-day expansion (OR = 4.2), and 168-h expansion (OR = 1.6); all are p ≤ 0.01. Expanding the time windows for both AKI SCr criteria 1a and 1b standardizes and facilitates EHR AKI detection, while identifying additional clinically relevant cases of in-hospital AKI.

Small-scale dissolution test screening tool to select potentially substandard and falsified (SF) medicines requiring full pharmacopoeial analysis

The purpose of this study was to design a convenient, small-scale dissolution test for extracting potential substandard and falsified (SF) medicines that require full pharmacopoeial analysis. The probability of metronidazole samples complying with the US Pharmacopoeia (USP) dissolution test for immediate-release tablet formulations was predicted from small-scale dissolution test results using the following criteria: (1) 95% confidence interval lower limit (95% CIlow) of the average dissolution rate of any n = 3 of n = 24 units of each sample, and (2) average and minimum dissolution rates for any n = 3 of n = 24 units. Criteria values were optimized via bootstrap sampling with Thinkeye data-mining software. Compliant metronidazole samples in the USP first-stage and second-stage dissolution test showed complying probabilities of 99.7% and 81.0%, respectively, if the average dissolution rate of n = 3 units is equal to or greater than the monograph-specified amount of dissolved drug (Q; 85% of labeled content for metronidazole). The complying probabilities were 100.0% and 79.0%, respectively, if the average dissolution rate of n = 3 units is 91% or higher and the minimum dissolution rate is 87% or higher. Suitable compliance criteria for the small-scale dissolution test are: average dissolution rate of n = 3 units is Q + 6% or more and minimum dissolution rate is Q + 2% or more.

Impact of Industry 4.0 on Corporate Financial Performance: A Moderated Mediation Model

Many studies advance the contemporary technologies of Industry 4.0. However, relatively little is known about how Industry 4.0 affects corporate financial performance. Using a survey, bootstrap sampling, and structural-equation modeling, this study evaluates the moderated mediation effects of Industry 4.0 maturity on financial performance. The results show that Industry 4.0 maturity significantly affects internal business process performance (IBPP), which influences customer performance through the mediating effect of supply chain performance (SCP), and IBPP and SCP affect financial performance fully through the mediating effect of customer performance. The results also show that Industry 4.0 maturity moderates the positive relationship between customer performance and financial performance. Customer performance and IBPP have the largest direct and total effects on financial performance in the context of Industry 4.0 implementation, respectively. The results indicate that Industry 4.0 magnifies the potential returns to companies mainly through IBPP, SCP, and customer performance. This study offers an enhanced understanding of the financial implications of Industry 4.0 implementation and provides insights into the factors through which Industry 4.0 maturity influences financial performance.

Athlete Health Prediction Using Machine Learning Methods

Due to an increase in sports activities, the prediction of athletes’ health (AH) has recently become an important research topic. However, it is a challenging task to predict AH because of the nature of the data and the limitations of predictive models. The main objective of this work is to develop appropriate models that can forecast AH using historical data. This work will enable sport organizations to monitor the well-being of their athletes. In this thesis, we explore the applicability of various machine learning (ML) methods for predicting AH. Traditional ML methods do not perform well for class-imbalanced data as these methods are biased towards the majority class. In this work, we propose to use ensemble-based methods which utilize downsampling, bootstrap sampling, and boosting techniques to improve the classification performance. Various metrics are used to evaluate and to compare the model performance. Our results show the superiority of ensemble-based methods over traditional approaches. The random forest and the RUSBoost classier models are in particular found to produce the best performance in handling imbalanced classes.

Athlete Health Prediction Using Machine Learning Methods

Due to an increase in sports activities, the prediction of athletes’ health (AH) has recently become an important research topic. However, it is a challenging task to predict AH because of the nature of the data and the limitations of predictive models. The main objective of this work is to develop appropriate models that can forecast AH using historical data. This work will enable sport organizations to monitor the well-being of their athletes. In this thesis, we explore the applicability of various machine learning (ML) methods for predicting AH. Traditional ML methods do not perform well for class-imbalanced data as these methods are biased towards the majority class. In this work, we propose to use ensemble-based methods which utilize downsampling, bootstrap sampling, and boosting techniques to improve the classification performance. Various metrics are used to evaluate and to compare the model performance. Our results show the superiority of ensemble-based methods over traditional approaches. The random forest and the RUSBoost classier models are in particular found to produce the best performance in handling imbalanced classes.