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We propose a smooth hypothesis-testing type method for model fitting in regression and develop its theoretical properties in a moderately high-dimensional setting. We derive the asymptotic behavior of the L 2 prediction risk and the optimal choice of the threshold-determining parameter under the Gaussian regression framework with orthogonal covariates. When the covariates are not orthogonal, this method can be used in conjunction with some reasonable procedures for orthogonalizing the covariates, such as a stepwise regression coupled with the Gram–Schmidt procedure. Simulation results show that our proposed method often outperforms its competitors. Asymptotic approximation of the mean square error of the proposed estimator is obtained and it is shown that the optimal rate of convergence depends on the behavior of the smoothing function at zero. [ABSTRACT FROM AUTHOR]

Predictive least squares (PLS) using future data to be predicted by current data are defined in covariance structure analysis. The expected predictive least squares (EPLS) obtained by two-fold expectation of PLS are unknown fit indexes. Using the asymptotic biases of weighted least squares given by current data for estimation of EPLS in covariance structures, corrected least square criteria derived similarly to the Takeuchi information criterion are shown to be asymptotically unbiased under arbitrary distributions. Simulations for model selection in exploratory factor analysis show improvements over typical current fit indexes as RMSEA and AIC. [ABSTRACT FROM AUTHOR]

When observations are curves over some natural time interval, the field of functional data analysis comes into play. Functional linear processes account for temporal dependence in the data. The prediction problem for functional linear processes has been solved theoretically, but the focus for applications has been on functional autoregressive processes. We propose a new computationally tractable linear predictor for functional linear processes. It is based on an application of the Multivariate Innovations Algorithm to finite-dimensional subprocesses of increasing dimension of the infinite-dimensional functional linear process. We investigate the behavior of the predictor for increasing sample size. We show that, depending on the decay rate of the eigenvalues of the covariance and the spectral density operator, the resulting predictor converges with a certain rate to the theoretically best linear predictor. [ABSTRACT FROM AUTHOR]

An active object recognition system has the advantage of acting in the environment to capture images that are more suited for training and lead to better performance at test time. In this paper, we utilize deep convolutional neural networks for active object recognition by simultaneously predicting the object label and the next action to be performed on the object with the aim of improving recognition performance. We treat active object recognition as a reinforcement learning problem and derive the cost function to train the network for joint prediction of the object label and the action. A generative model of object similarities based on the Dirichlet distribution is proposed and embedded in the network for encoding the state of the system. The training is carried out by simultaneously minimizing the label and action prediction errors using gradient descent. We empirically show that the proposed network is able to predict both the object label and the actions on GERMS, a dataset for active object recognition. We compare the test label prediction accuracy of the proposed model with Dirichlet and Naive Bayes state encoding. The results of experiments suggest that the proposed model equipped with Dirichlet state encoding is superior in performance, and selects images that lead to better training and higher accuracy of label prediction at test time. [ABSTRACT FROM AUTHOR]

Disturbing the transport process is a crucial pathway for endocrine disrupting chemicals (EDCs) exerting disrupting endocrine function. However, this mechanism has not received enough attention compared with that of hormones receptors and synthetase. Recently, we have explored the interaction between EDCs and sex hormone-binding globulin of human (hSHBG). In this study, interactions between EDCs and sex hormone-binding globulin of eight fish species (fSHBG) were investigated by employing classification methods and quantitative structure-activity relationships (QSAR). In the modeling, the relative binding affinity ( RBA ) of a chemical with 17 β -estradiol binding to fSHBG was selected as the endpoint. Classification models were developed for two fish species, while QSAR models were established for the other six fish species. Statistical results indicated that the models had satisfactory goodness of fit, robustness and predictive ability, and that application domain covered a large number of endogenous and exogenous steroidal and non-steroidal chemicals. Additionally, by comparing the log RBA values, it was found that the same chemical may have different affinities for fSHBG from different fish species, thus species diversity should be taken into account. However, the affinity of fSHBG showed a high correlation for fishes within the same Order (i.e., Salmoniformes, Cypriniformes, Perciformes and Siluriformes), thus the fSHBG binding data for one fish species could be used to extrapolate other fish species in the same Order. [ABSTRACT FROM AUTHOR]

Accurate runoff prediction in data-poor catchments is important for water resource management, flood mitigation, environmental protection, and other tasks. One possible solution is to transfer a runoff prediction model constructed by using a machine learning model for gauged catchments to data-poor catchments. However, the transfer of runoff prediction model must consider the comprehensive spatiotemporal similarities between the catchments; otherwise, the transfer performance can be massively uncertain. Therefore, to improve the accuracy of runoff prediction and eliminate the uncertainty in identifying the differences between catchment environments, this paper proposes a novel measurement approach of comprehensive spatiotemporal similarity. This approach measures the similarities among catchments by fully considering which of the various catchments' spatiotemporal attributes can better represent the geographical similarity. Then, according to the similarities between the catchments, a runoff prediction model trained in gauged catchments is transformed for the most similar data-poor catchments to predict the runoff and the transfer performance is analyzed. To this end, a runoff prediction model is built using a gated recurrent unit (GRU) network based on the CAMELS catchments data set. A framework to extract the comprehensive spatiotemporal features of catchments is designed using three autoencoders. The catchments' similarities can be measured, further, and their spatiotemporal attributes determined once a measurement model of comprehensive spatiotemporal similarity is constructed. Finally, the transfer performance of the GRU runoff prediction model based on comprehensive spatiotemporal and other geographical similarities is evaluated and analyzed. The experimental results demonstrate that the proposed method outperforms comparable approaches. • The comprehensive spatiotemporal similarity wes obatained by combining three autoencoders and the genetic algorithm. • The proposed method can effectively capture the implicit information and local details of the catchment environments. • Experimental results demonstrate that the spatiotemporal similarity outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This study developed an emission inventory for 29 elements in PM 2.5 and PM 2.5-10 covering an area of approximately 300 by 420 km2 in the Athabasca Oil Sands Region in northern Alberta, Canada. Emission sources were aggregated into nine categories, of which the Oil Sands (OS) Sources emitted the most, followed by the Non-OS Dust sources for both fine and coarse elements over the study area. The top six fine particulate elements include Si, Ca, Al, Fe, S, and K (933, 442, 323, 269, 116, and 103 tonnes/year, respectively), the sum of which accounted for 20.5% of the total PM 2.5 emissions. The top five coarse elements include Si, Ca, Al, Fe, and K (3713, 1815, 1198, 1073, and 404 tonnes/year), and their sum accounted for 29% of the total PM 2.5-10 emissions. Using this emission inventory as input, the CALPUFF dispersion model simulated reasonable element concentrations in both PM 2.5 and PM 2.5-10 when compared to measurements collected at three sites during 2016–2017. Modeled PM 10 concentrations of all elements were very close to the measurements at an industrial site with the highest ambient concentration, overestimated by 65% at another industrial site with moderate ambient concentration, and underestimated by 27% at a remote site with very low ambient concentration. Model-measurement differences of annual average concentrations were within 20% for Si, Ca, Al, Fe, Ti, Mn, and Cu in PM 2.5 , and were 20–50% for K, S, and Zn in PM 2.5 at two sites located within the OS surface mineable area. Model-measurement differences were larger, but still within a factor of two for elements in PM 2.5-10 at these two sites and for elements in both PM 2.5 and PM 2.5-10 at a background site. • The study developed the first emission inventory for particulate elements in the Canadian oil sands region. • The top six fine particulate elements including Si, Ca, Al, Fe, S, and K accounted for 20.5% of the total PM 2.5 emissions. • The top five coarse elements including Si, Ca, Al, Fe, and K accounted for 29% of the total PM 2.5-10 emissions. • Modeled elements concentrations in both PM 2.5 and PM 2.5-10 were comparable with measurements at three sites. [ABSTRACT FROM AUTHOR]

[Display omitted] • VLE data are reported for binary systems involving xylenes and tetrapropylene glycol. • The VLE experimental data were correlated with the NRTL model. • Results from NRTL model are compared with data calculated by UNIFAC model. • NRTL model with our parameters shows different behavior than UNIFAC model. In this study are reported vapour-liquid equilibrium (VLE) data for three binary systems such as xylenes + tetrapropylene glycol (TePG). These data have been measured in the temperature range (310.15 to 538.15) K for the systems involving o -xylene, m -xylene, p -xylene and TePG. The VLE experimental data were determined by using static equilibrium apparatus. The p-T-x data were regressed using the NRTL model with three, five and eight parameters. The results obtained using NRTL with eight parameters presents the smallest relative deviations of the calculated values for vapour pressure and composition compared to experimental values. A comparison was made using the T - x-y diagrams between the data obtained with the NRTL resulted parameters based on experimental VLE data and the data calculated using the UNIFAC predictive model. [ABSTRACT FROM AUTHOR]

China's carbon reduction is of substantial significance in combating global climate change. In the context of the COVID-19 epidemic hit and economic and social development uncertainty, this study intends to discover whether China can attain the strategic destination of carbon peaking by 2030 and carbon neutrality by 2060 on schedule. Toward this aim, the grey relation analysis (GRA) is applied to filter the elements influencing carbon emissions to downgrade the dimensionality of indicators. A hybrid prediction is proposed integrated with Elman neural network (ENN) and sparrow search algorithm (SSA) to explore the potential for China to carbon neutrality from 2020 to 2060. The results reveal eight elements including GDP per capita, population, urbanization, total energy consumption and others are highly correlated with carbon emissions. China has a good chance of carbon peaking from 2028 to 2030, with a value of 11568.6–12330.5 Mt, while only one scenario can achieve carbon neutrality in 2060. In the neutral scenario, China should reach a proportion of renewable energy exceeding 80%, the urbanization rate reaching 85% and energy consumption controlling within 6.5 billion tons. A set of countermeasures for carbon abatement are presented to facilitate the implementation of carbon neutrality strategy. • Quantify the factors influencing carbon emissions by the GRA algorithm. • Propose a hybrid prediction model GRA-ENN-SSA. • Construct multiple scenarios based on different socio-economic conditions. • Apply the proposed model to discover China's pathway to carbon neutrality. [ABSTRACT FROM AUTHOR]

The apparent second-order rate constant with hexavalent ferrate (Fe(VI)) (k Fe(VI)) is a key indicator to evaluate the removal efficiency of a molecule by Fe(VI) oxidation. k Fe(VI) is often determined by experiment, but such measurements can hardly catch up with the rapid growth of organic compounds (OCs). To address this issue, in this study, a total of 437 experimental second-order k Fe(VI) rate constants at a range of conditions (pH and temperature) were used to train four machine learning (ML) algorithms (lasso regression (LR), ridge regression (RR), extreme gradient boosting (XGBoost), and the light gradient boosting machine (LightGBM)). Using the Morgan fingerprint (MF)) of a range of organic compounds (OCs) as the input, the performance of the four algorithms was comprehensively compared with respect to the coefficient of determination (R 2) and root-mean-square error (RMSE). It is shown that the RR, XGBoost, and LightGBM models displayed generally acceptable performance k Fe(VI) (R 2 test > 0.7). In addition, the shapely additive explanation (SHAP) and feature importance methods were employed to interpret the XGBoost/LightGBM and RR models, respectively. The results showed that the XGBoost/LightGBM and RR models suggestd pH as the most important predictor and the tree-based models elucidate how electron-donating and electron-withdrawing groups influence the reactivity of the Fe(VI) species. In addition, the RR model share eight common features, including pH, with the two tree-based models. This work provides a fast and acceptable method for predicting k Fe(VI) values and can help researchers better understand the degradation behavior of OCs by Fe(VI) oxidation from the perspective of molecular structure. [Display omitted] • Four ML algorithms were used for k Fe(VI) prediction models building. • Models were built with 437 k Fe(VI) values under various pHs and temperatures. • SHAP method was used to identify the pH governing the k Fe(VI) values. • Model interpretation showed the key groups interfering the reactivity of Fe(VI). [ABSTRACT FROM AUTHOR]

The 2-amino-3-methyl-imidazole [4,5-f] quinoline (IQ) is a compound generated during the Maillard reaction while heating protein-rich foods and has been classified as a class 2A possible human carcinogen that has attracted ongoing public concern. However, the formation mechanism of IQ remains unknown. Herein, we investigated the formation mechanism of IQ in the Maillard reaction model system under various reaction conditions (temperature, time, and pH). The results showed that various conditions significantly influenced the precursors, reactive carbonyl species, and IQ formation (P < 0.05). There was regularity between IQ and various conditions, and the R 2 in the fitting curves were between 0.90 and 0.99. To further explore the relationship between IQ and various conditions, a partial least squares (PLS) prediction model was constructed. The model indicates that temperature is a significant contributor to the formation of IQ during the Maillard reaction. Further analysis indicated that crotonaldehyde plays a vital role. Crotonaldehyde could promote IQ formation at low addition levels but inhibit its formation at high addition levels. Possible effects of crotonaldehyde on the formation of IQ were proposed. Overall, this research will help to identify more effective ways to reduce the formation of IQ in meat products, which can improve their safety for human consumption. [Display omitted] • The formation of IQ in the Maillard reaction model system was studied. • The regularity between the formation of IQ and various conditions was proposed. • The partial least squares (PLS) model was constructed for the prediction of IQ formation. • The effects of crotonaldehyde during the formation of IQ were illustrated. [ABSTRACT FROM AUTHOR]

Selenite (Se4+) is the most toxic of all the oxyanion forms of selenium. In this study, a feed forward back propagation (BP) based artificial neural network (ANN) model was developed for a fungal pelleted airlift bioreactor (ALR) system treating selenite-laden wastewater. The performance of the bioreactor, i.e., selenite removal efficiency (RE selenite) (%) was predicted through two input parameters, namely, the influent selenite concentration (IC selenite) (10 mg/L – 60 mg/L) and hydraulic retention time (HRT) (24 h – 72 h). After training and testing with 96 sets of data points using the Levenberg-Marquardt algorithm, a multi-layer perceptron model (2-10-1) was established. High values of the correlation coefficient (0.96 ≤ R ≤ 0.98), along with low root mean square error (1.72 ≤ RMSE ≤ 2.81) and mean absolute percentage error (1.67 ≤ MAPE ≤ 2.67), clearly demonstrate the accuracy of the ANN model (> 96%) when compared to the experimental data. To ensure an efficient and economically feasible operation of the ALR, the process parameters were optimized using the particle swarm optimization (PSO) algorithm coupled with the neural model. The RE selenite was maximized while minimizing the HRT for a preferably higher range of IC selenite. Thus, the most favourable optimum conditions were suggested as: IC selenite – 50.45 mg/L and HRT – 24 h, resulting in RE selenite of 69.4%. Overall, it can be inferred that ANN models can successfully substitute knowledge-based models to predict the RE selenite in an ALR, and the process parameters can be effectively optimized using PSO. [Display omitted] • Selenite removal from wastewater was modelled using artificial neural networks. • The mechanism of selenite biodegradation by fungi has been discussed. • Selenite removals were predicted using retention time and inlet concentration as the inputs. • Neural model was coupled to PSO to optimize selenite removal and retention time. [ABSTRACT FROM AUTHOR]

Wildfires have changed in recent decades. The catastrophic wildfires make it necessary to have accurate predictive models on a country scale to organize firefighting resources. In Mediterranean countries, the number of wildfires is quite high but they are mainly concentrated around summer months. Because of seasonality, there are territories where the number of fires is zero in some months and is overdispersed in others. Zero-inflated negative binomial mixed models are adapted to this type of data because they can describe patterns that explain both number of fires and their non-occurrence and also provide useful prediction tools. In addition to model-based predictions, a parametric bootstrap method is applied for estimating mean squared errors and constructing prediction intervals. The statistical methodology and developed software are applied to model and to predict number of wildfires in Spain between 2002 and 2015 by provinces and months. [Display omitted] • Negative binomial mixed models are suitable for predicting wildfires and its overdispersion. • Binomial logit mixed models capture the excess of zeros. • Random effects, varying with month and province, model temporal and spatial variability. • Forecasts under expected scenarios give valuable information for decision-making. [ABSTRACT FROM AUTHOR]

This study presents an advanced numerical prediction model based on nonlinear wake oscillators for the investigation of multidirectional vortex-induced vibration (VIV) responses of a long catenary riser depending on the structural curved configuration versus the incoming flow direction. By considering a uniform free-stream flow aligned with the initial curvature plane of the catenary riser in a convex or concave shape, the normal flow velocity component is nonlinearly sheared owing to the spanwise variation of inclination angles. This is different from the perpendicular flow case in which the normal flow velocity is spatially constant. To capture the influence of flow direction on the three-dimensionally coupled cross-flow and in-line VIV, distributed wake oscillators are introduced and applied to simulate the fluctuating hydrodynamic lift and drag forces that account for the important effects of cylinder inclination, variable vortex excitation frequencies, global–local​ displacement relationships, and relative flow-cylinder velocities. The amplified mean drag force and the axial force depending on the tangential flow velocity are also considered. These empirical hydrodynamic effects are nonlinearly coupled with the equations of riser three-dimensional motion, and the overall governing equations are numerically solved to assess the multimode, multi-frequency and multi-degree-of-freedom responses. Validation of the model is carried out for VIV of flexible straight and curved cylinders based on experimental results in the literature. Parametric investigations are performed by varying the incoming flow velocities in perpendicular, convex and concave configurations for a long catenary riser with a low mass-damping ratio. In-plane (horizontal and vertical) and out-of-plane VIV features are presented and discussed in terms of space–time varying amplitudes, drag-amplified shape reconfigurations, oscillation frequencies, lock-in occurrences, dual resonances, orbital motion trajectories, fluid–structure energy transfer, and multimodal contributions. Overall prediction results highlight the influence of the cylinder geometric configuration versus the incoming flow orientation on multidirectional VIV characteristics that should be recognised and incorporated into practical analysis tools. [ABSTRACT FROM AUTHOR]

Quality air to breathe is the basic necessity for an individual and in recent times, emission from various sources caused by human activities has resulted in substantial degradation in the air quality. This work focuses to study the inadvertent effect of COVID-19 lockdown on air pollution. Pollutants' concentration before- and during- COVID-19 lockdown is captured to understand the variation in air quality. Firstly, multi-pollutant profiling using hierarchical cluster analysis of pollutants' concentration is performed that highlights the differences in the cluster compositions between before- and during-lockdown time periods. Results show that the particulate matter (PM 10 and PM 2.5) in air that formed the primary cluster before lock-down, came down to close similarity with other clusters during lockdown. Secondly, predicting air quality index (AQI) based on the forecasts of pollutants' concentration is performed using neural networks, support vector machine, decision tree, random forest, and boosting algorithms. The best-fitted models representing AQI is identified separately for before- and during-lockdown time periods based on its predictive power. While deterministic method reactively evaluates present AQI when current pollutants' concentration at a particular time and place are known, this study uses the best fitted data-driven model to determine future AQIs based on the forecasts of pollutant's concentration accurately (overall RMSE<0.1 for before lockdown scenario and <0.3 for during lockdown scenario). The study contributes to visualize the variation in pollutants' concentrations between the two scenarios. The results show that the reduced economic activities during lockdown period had led to the drop in concentration of PM 10 and PM 2.5 by 27% and 50% on an average. The findings of this study have practical and societal implications and serve as a reference mechanism for policymakers and governing bodies to revise their actions plans for regulating individual air pollutants in the atmospheric air. • We assess the variation in pollutants' concentration and the subsequent effect on air quality due to COVID-19 lockdown at selected regions of Delhi, Hyderabad and Kolkata in India • We perform cluster analysis and present multi-pollutant clusters that are differently formed before and during COVID-19 lockdown • We employ machine learning methods to predict AQI at a particular region based on the forecasts of its determining pollutants' concentration for both before and during COVID-19 lockdown scenarios • We present the magnitude of reduced pollutants' concentration due to the reduced human outdoor activities at each region and the resultant practical and societal implications [ABSTRACT FROM AUTHOR]

Unsupervised person re-identification (ReID) aims at training a model through using fully unlabeled training images. Most successful approaches combine clustering-based pseudo-label prediction with ReID model learning and perform the two steps in an alternating fashion. However, there are some person images, which have common appearance but different identities or have same identity but large intra-variations, and they are prone to be assigned wrong pseudo-labels. Existing methods typically infer person image pseudo-labels in a single pseudo-label space, which is insufficient due to losing the accurate labels of these hard images. This limits model's power to cope with varying changes. To address this problem, we propose a Multi-granularity Pseudo-label Collaboration (MPC) method for unsupervised person ReID. Firstly, a multi-granularity pseudo-label prediction (MPP) method is proposed to predict the pseudo-labels of person images. MPP generates multiple pseudo-label spaces with each one inferring the person image pseudo-labels under a different granularity at cluster size. Secondly, the multiple pseudo-label spaces are applied to learn a mixture of experts (MoE) model. Each expert model is supervised by the pseudo-labels in a specific pseudo-label space, enabling MoE model to handle different variations. The output of these expert models are fused critically by a bilateral attention aggregation (BAA) to generate aggregation image features. Meanwhile, a label mix-up method is designed for refining pseudo-labels. It fuses labels across different pseudo-label spaces based on label co-occurrence to promote aggregation feature learning. We extensively evaluate the proposed MPC method under both image-based and video-based unsupervised person ReID settings. MPC significantly improves the current baseline method by a margin of 5.4% and 3.7% in mAP, and 3.4% and 3.9% in Rank1 accuracy on the image-based datasets Market-1501 and DukeMTMC-ReID, respectively. In particular, MPC method achieves state-of-the-art performance on video-based datasets with mAP of 71.4% and 87.3% on MARS and DukeMTMC-VideoReID, respectively. • A Multi-granularity Pseudo-label Collaboration (MPC) method is proposed for unsupervised person ReID. • A bilateral attention aggregation (BAA) module is developed to generate robust and diverse aggregation feature. • A label mix-up method with label co-occurrence is presented to produce refined labels to facilitate aggregation feature learning. • Extensive experiments verify the effectiveness of the proposed method on image-based and video-based two unsupervised person ReID settings. [ABSTRACT FROM AUTHOR]

When using predictive models to assess the risk posed by foodborne pathogens, certain strains exhibit resistance at low temperatures, which may affect the assessment result. To understand the influence of strain heterogeneity on growth dynamics, two Staphylococcus aureus strains, including comparatively low temperature tolerant BB-11 and sensitive BA-26, were inoculated into glutinous rice dough at temperatures ranging from 10 to 37 °C. The primary Gompertz model fit showed an obvious difference in the maximum specific growth (μ max) and duration of the lag phase (λ) values at 10 °C (0.071/h and 28 h for BB-11 and 0.049/h and 34 h for BA-26, respectively). When Huang and Ratkowsky models were compared as secondary models, a log-linear relationship was demonstrated between μ max and λ for both models. Ratkowsky's model fit was more accurate with a high R 2 of 0.95. The predicted minimum growth temperatures were 4.8 °C and 6.3 °C for BB-11 and BA-26, respectively. The time for achieving a 4-log increase (t 4.0) was 96 h for BB-11 at 10 °C, but only 2.4 log CFU/g increase was determined in BA-26 at 144 h. This study suggests that cold-tolerant strains should be considered when a predictive model is used for risk assessment of foodborne pathogens. • Cold tolerant and sensitive S. aureus exhibited different growth curves in glutinous rice dough. • The two strains showed an obvious difference in μ max and λ values at 10 °C. • Ratkowsky's model fit was more accurate when used as secondary model. • Cold-tolerant strains should be well considered when a predictive model is constructed. [ABSTRACT FROM AUTHOR]

The purpose of this study is to develop a growth predictive model for Leuconostoc in different salinities of Chinese cabbage juices for kimchi fermentation. Leuconostoc mesenteroides (approximately 6.3 log CFU/g) was inoculated in sterile Chinese cabbage juices to determine the growth of Leuconostoc mesenteroides at different salinities from 0 to 6%. The Baranyi and Roberts model was used to fit growth data, the modified Ratkowsky root square model was used to fit maximum specific growth rates, the polynomial model was used to fit maximum cell concentration and the modified inverse Ratkowsky model was used to fit lag phase with respect to salinities. The results showed that the primary models fitted the growth data well (all R 2 values > 0.900). The R 2 and root mean square error (RMSE) of the secondary models were 0.9136–0.9657 and 0.0142–0.2352, respectively, indicating a good fit of the model. The validation experiments proved that the changes of Leuconostoc mesenteroides with different salinities could be predicted accurately by the model. The predictive model will assist producers in understanding how microbes change during kimchi fermentation at different salinities. • The growth of fermentation microorganisms was significantly affected by salinity. • A prediction model of microorganisms change at different salinities established. • The change of microorganisms could be predicted accurately by the prediction model. [ABSTRACT FROM AUTHOR]

Chinese mitten crab (Eriocheir sinensis) is one of the most popular aquatic foods in China and Southeast Asian countries. However, the available forms of this commodity such as live individuals and processed products do not fulfil consumer demand because their shelf life is short or do not retain their shape. Here, biochemical and sensory qualities of E. sinensis during storage at −20, −30, and −40 °C for 12 weeks were investigated. Sensitive indices and a shelf-life prediction model were established to demonstrate the feasibility of commercial frozen E. sinensis. Storage at −40 °C reduced the movement of water, oxidation of proteins and lipids, and formation of thiobarbituric acid reactive substances (TBARS) in the edible tissues of E. sinensis. The eating safety of frozen E. sinensis was evaluated using indices such as histamine and total volatile base-nitrogen (TVB-N) content and total viable count (TVC) over 12 weeks. The safety indices and sensory evaluation predicted that the product shelf lives were 56, 59, and 78 d at −20, −30, and −40 °C, respectively. A shelf-life prediction model based on TVB-N content was developed by combining kinetic and Arrhenius equations and confirmed that frozen E. sinensis is feasible and has potential commercial value. • E. sinensis eating safety assessed by histamine & TVB-N content & TVC over 12 wks. • TVB-N is a sensitive indicator to determine the quality of frozen E. sinensis. • Sensory shelf life economically predicted combination kinetic & Arrhenius models. • Frozen E. sinensis shelf life was 56, 59, & 78 d at −20, −30, & −40 °C, respectively. • Frozen E. sinensis has potential commercial value. [ABSTRACT FROM AUTHOR]

A gas–liquid hybrid discharge system was applied to microcystin-LR (MC-LR) degradation. MC-LR degradation was completed after 1 min under a pulsed high voltage of 16 kV, gas–liquid interface gap of 10 mm and oxygen flow rate of 160 L/h. The Box–Behnken Design was proposed in Response Surface Methodology to evaluate the influence of pulsed high voltage, electrode distance and oxygen flow rate on MC-LR removal efficiency. Multiple regression analysis, focused on multivariable factors, was employed and a reduced cubic model was developed. The ANOVA analysis shows that the model is significant and the model prediction on MC-LR removal was also validated with experimental data. The optimum conditions for the process are obtained at pulsed voltage of 16 kV, gas-liquid interface gap of 10 mm and oxygen flow rate of 120 L/h with ta removal efficiency of MC-LR of 96.6%. The addition of catalysts (TiO 2 or Fe 2+ ) in the gas–liquid hybrid discharge system was found to enhance the removal of MC-LR. The intermediates of MC-LR degradation were analyzed by liquid chromatography/mass spectrometry. The degradation pathway proposed envisaged the oxidation of hydroxyl radicals and ozone, and attack of high-energy electrons on the unsaturated double bonds of Adda and Mdha, with MC-LR finally decomposing into small molecular products. [ABSTRACT FROM AUTHOR]

Experimental data and models for plant bioaccumulation of organic contaminants play a crucial role for assessing the potential human and ecological risks associated with chemical use. Plants are receptor organisms and direct or indirect vectors for chemical exposures to all other organisms. As new experimental data are generated they are used to improve our understanding of plant-chemical interactions that in turn allows for the development of better scientific knowledge and conceptual and predictive models. The interrelationship between experimental data and model development is an ongoing, never-ending process needed to advance our ability to provide reliable quality information that can be used in various contexts including regulatory risk assessment. However, relatively few standard experimental protocols for generating plant bioaccumulation data are currently available and because of inconsistent data collection and reporting requirements, the information generated is often less useful than it could be for direct applications in chemical assessments and for model development and refinement. We review existing testing guidelines, common data reporting practices, and provide recommendations for revising testing guidelines and reporting requirements to improve bioaccumulation knowledge and models. This analysis provides a list of experimental parameters that will help to develop high quality datasets and support modeling tools for assessing bioaccumulation of organic chemicals in plants and ultimately addressing uncertainty in ecological and human health risk assessments. [ABSTRACT FROM AUTHOR]

We present an integrated model of the direct consequences of climate change on land use, and the indirect effects of induced land use change upon the natural environment. The model predicts climate-driven shifts in the profitability of alternative uses of agricultural land. Both the direct impact of climate change and the induced shift in land use patterns will cause secondary effects on the water environment, for which agriculture is the major source of diffuse pollution. We model the impact of changes in such pollution on riverine ecosystems showing that these will be spatially heterogeneous. Moreover, we consider further knock-on effects upon the recreational benefits derived from water environments, which we assess using revealed preference methods. This analysis permits a multi-layered examination of the economic consequences of climate change, assessing the sequence of impacts from climate change through farm gross margins, land use, water quality and recreation, both at the individual and catchment scale. [ABSTRACT FROM AUTHOR]

This study presents a three-dimensional phenomenological model for the analysis and prediction of vortex-induced vibration (VIV) of a long flexible circular cylinder in uniform flows, by taking into consideration the two salient effects of cylinder axial dynamics and mean drag magnifications. A semi-empirical theoretical model consists of structural nonlinear equations of coupled cross-flow, in-line and axial motions excited by vortex-induced hydrodynamic lift and drag forces. These forces are governed by two distributed and coupled wake oscillators describing the variations of lift and drag coefficients. This reduced-order model contains cubic and quadratic nonlinearities associated with geometric displacement couplings and fluid-structure interactions. A numerical finite difference approach combined with a frequency domain and modal analysis is used for analyzing the highly nonlinear three-dimensional VIV responses. Several key features of flexible cylinder VIV are captured in the case of varying flow velocities. In particular, the dynamic responses exhibit a transition from standing to travelling wave characteristics, spatial dual 2:1 resonances with multi modal contributions, variable figure-of-eight trajectories, higher harmonic components, mean in-line/axial displacements and mean stresses as a result of VIV-amplified mean drag forces. Model validations are also performed through comparisons with published experimental results. Parametric studies highlight the individual and combined effects of geometric and hydrodynamic nonlinearities together with the axial dynamics which contribute to the space-time varying responses, frequencies, dominant modes and modal contributions, transverse bending and longitudinal stresses. Both quantitative and qualitative discrepancies are noticeable when the prediction model disregards these effects. [ABSTRACT FROM AUTHOR]

Herein, a minimal model for the common case of W/O solubilization of badly soluble compounds present in an excess phase by reverse micellar aggregates in chemical equilibrium with its single compounds is introduced. A simple model of such liquid-liquid extractions is crucial for obtaining predictive parameter for the modelling of nuclear waste management and hydrometallurgic recycling strategies. The standard Gibbs free energy of aggregation and the concentration of the corresponding aggregate is calculated within a multiple-equilibria approach for a set of aggregate compositions of solute and amphiphilic extractant molecules. This minimal model provides potential surfaces estimating the stability of different aggregate compositions with 6.2 kJ mol −1 as a generalized bending constant. The complete concentrations of free and aggregated extractant species as well as the favored aggregation numbers, the polydispersity, the activity of the organic solvent, and the critical concentrations are captured by this thermodynamic model. An increase of the apparent critical micelle concentration for an increasing solute content in the aqueous phase is detected by this method. [ABSTRACT FROM AUTHOR]

Existing energy saving schemes that have been developed for Energy Efficient Storage funnel I/O traffic on a few disks while allowing the rest idle. These schemes can cause long standing disks to overburden, resulting in a higher rate of disk failure and reliability degradation. In this paper, we develop a novel multiple criteria optimization scheme based on Fuzzy Decision Making theory, for the Temperature-constrained Energy Efficient Storage System called TEES. TEES aims to enforce a temperature constraint as well as performance requirements while also keeping energy consumption to a minimum. This is achieved by developing an online temperature prediction model and aggregating all the decision criteria, such as I/O performance, power consumption, estimated temperature and frequency of disk-status transition. The experimental results show that TEES is able to reduce disk temperature by 20–30% as compared with existing control methods, while obtaining comparable performance and power consumption. [ABSTRACT FROM AUTHOR]

Pharmaceutical products (PPs) are emerging water pollutants with adverse environmental and health-related impacts, owing to their toxic, persistent, and undetectable microscopic nature. Globally, increasing scientific knowledge and advanced technologies have allowed researchers to study PP-associated problems and their removal for water reuse. Experimental modeling methods require laborious, lengthy, expensive, and environmentally hazardous lab-work to optimize the process. On the other hand, predictive machine learning (ML) models can trace the complex input-output relationship of a process using available datasets. In this study, ensemble ML techniques, including decision tree (DT), random forest (RF), and Xtreme gradient boost (XGB), were used to explore PP (diclofenac, iopromide, propranolol, and trimethoprim) removal by a managed aquifer recharge (MAR) system. The model input parameters included characteristics of reclaimed water and soil used in the columns, pH, dissolved organic carbon, operating time, nitrogen dioxide, sulfate, nitrate, electrical conductivity, manganese, and iron. The selected PP removal was the model output. Datasets were collected through a one-year experimental study of continuous MAR system operation to predict the removal of PPs. DT, RF, and XGB models were then developed for one of the selected compounds and tested for the others to check the reliability of the ML model results. The developed models were assessed using statistical performance matrices. The experimental results showed >80% removal of propranolol and trimethoprim; however, removal of diclofenac and iopromide was only ≈50% by the MAR system. The proposed DT and RF models presented higher coefficients of determination (R2 ≥ 0.92) for diclofenac, propranolol, and trimethoprim than for iopromide (R2 ≤ 0.63). In contrast, the XGB model showed better results for diclofenac, iopromide, propranolol, and trimethoprim, with R2 values of 0.92, 0.72, 0.96, and 0.97, respectively. Therefore, XGB could be the best predictive model to provide insight into the adaptation of ML models to predict PP removal by the MAR system, thereby minimizing experimental work. [Display omitted] • Observed >80% removal of propranolol and trimethoprim and ≈50% of diclofenac and iopromide by the MAR system. • Studied predictive modeling of MAR system to remove PPs from reclaimed water. • Tested the feasibility of ensemble modeling techniques (DT, RF, and XGB). • Conducted a comparative analysis between DT, RF and XGB and previous study. • Proposed XGB model predicted reasonably better results for all PPs. [ABSTRACT FROM AUTHOR]

The prediction of air pollution plays an important role in reducing the emission of air pollutants and guiding people to carry out early warning and control, so it attracts many scholars to conduct modeling and research on it. However, most of the current researches fail to quantify the uncertainty in prediction and only use traditional fuzzy information granulation to process data, resulting in the loss of much detail information. Therefore, this paper proposes a hybrid model based on decomposition and granular fuzzy information to solve these problems. The trend item and the Granulation fluctuation item are respectively predicted and the results are combined to obtain the change trend and fluctuation range of the sequence. This paper selects PM 2.5 concentrations of 3 cities. The experimental results show that the evaluation index of the prediction model is significantly lower than other benchmark models, and a variety of statistical methods are used to further verify the effectiveness of the prediction model. • Quantify the uncertainty of data to obtain a more complete physical meaning. • Fuzzy theory strategies more scientifically measure data characteristics. • The combination of trend term and fluctuation term process data more rationally. • The modified optimization algorithm expands the application scope of the model. [ABSTRACT FROM AUTHOR]

The product of good design should render a tool invisible for a user who is executing a task. Unfortunately, web applications are often far from invisible to users, who struggle with poor design of websites and processes in them. We are particularly interested in web processes that involve form filling, so we have been studying how people interact with web forms. Besides cataloguing user interaction problems that are common in web forms, we have noticed that, in many cases, there is a single form element or widget to blame for a certain interaction problem, because such widget is not the most appropriate one for the required input in that particular context. This unfitness of the widget causes an extra burden to the user, which we call interaction effort. In this work we propose measuring the interaction effort of a widget with a unified score based on micro-measures automatically captured from interaction logs. We present the micro-measures that were found relevant to predict the interaction effort in 6 different types of web forms widgets. We describe a large data collection process and prediction models, showing that it is indeed possible to automatically predict a widget interaction effort score by learning from expert human ratings. We consequently believe that the interaction effort could be used as an effective metric to compare small variations in a design in terms of user experience. • A metric to compare user interaction effort in alternative web interface widgets for a specific task. • A set of micro-measures that capture the user's interaction behavior in 6 different types of widget. • An experiment to collect and rate user interactions with individual widgets in real websites. • A model that predicts the user interaction effort of a widget based on the corresponding micro-measures and the ratings of UX experts. [ABSTRACT FROM AUTHOR]

• Using multiple feature spaces in a joint encoding model improves prediction accuracy. • The variance explained by the joint model can be decomposed over feature spaces. • Banded ridge regression optimizes the regularization for each feature space. • Banded ridge regression contains an implicit feature-space selection mechanism. • Banded ridge regression can be solved with random search or gradient descent. Encoding models provide a powerful framework to identify the information represented in brain recordings. In this framework, a stimulus representation is expressed within a feature space and is used in a regularized linear regression to predict brain activity. To account for a potential complementarity of different feature spaces, a joint model is fit on multiple feature spaces simultaneously. To adapt regularization strength to each feature space, ridge regression is extended to banded ridge regression, which optimizes a different regularization hyperparameter per feature space. The present paper proposes a method to decompose over feature spaces the variance explained by a banded ridge regression model. It also describes how banded ridge regression performs a feature-space selection, effectively ignoring non-predictive and redundant feature spaces. This feature-space selection leads to better prediction accuracy and to better interpretability. Banded ridge regression is then mathematically linked to a number of other regression methods with similar feature-space selection mechanisms. Finally, several methods are proposed to address the computational challenge of fitting banded ridge regressions on large numbers of voxels and feature spaces. All implementations are released in an open-source Python package called Himalaya. [ABSTRACT FROM AUTHOR]

Emissions from aviation and airport-related activities degrade surface air quality but received limited attention relative to regular transportation sectors like road traffic and waterborne vessels. Statistically, assessing the impact of airport-related emissions remains a challenge due to the fact that its signal in the air quality time series data is largely dwarfed by meteorology and other emissions. Flight-ban policy has been implemented in a number of cities in response to the COVID-19 spread since early 2020, which provides an unprecedented opportunity to examine the changes in air quality attributable to airport closure. It would also be interesting to know whether such an intervention produces extra marginal air quality benefits, in addition to road traffic. Here we investigated the impact of airport-related emissions from a civil airport on nearby NO 2 air quality by applying machine learning predictive model to observational data collected from this unique quasi-natural experiment. The whole lockdown-attributable change in NO 2 was 16.7 μg/m3, equals to a drop of 73% in NO 2 with respect to the business-as-usual level. Meanwhile, the airport flight-ban aviation-attributable NO 2 was 3.1 μg/m3, accounting for a marginal reduction of 18.6% of the overall NO 2 change that driven by the whole lockdown effect. The airport-related emissions contributed up to 24% of the local ambient NO 2 under normal conditions. Additionally, the average impact of airport-related emissions on the nearby air quality was ∼0.01 ± 0.001 μg/m3 NO 2 per air-flight. Our results highlight that attention needs to be paid to such a considerable emission source in many places where regular air quality regulatory measures were insufficient to bring NO 2 concentration into compliance with the health-based limit. [Display omitted] • Flight-ban explained 19% of the overall reduction in NO 2 during the 2020 lockdown. • The airport emissions contributed up to 24% of the NO 2 under normal condition. • Each air-flight produces ∼0.01 μg/m3 NO 2 to the nearby air quality. • The XGB predictive model is robust in detecting and quantifying interventions. [ABSTRACT FROM AUTHOR]

An increasing number of studies have investigated the relationships between inter-individual variability in brain regions' connectivity and behavioral phenotypes, making use of large population neuroimaging datasets. However, the replicability of brain-behavior associations identified by these approaches remains an open question. In this study, we examined the cross-dataset replicability of brain-behavior association patterns for fluid cognition and openness predictions using a previously developed region-wise approach, as well as using a standard whole-brain approach. Overall, we found moderate similarity in patterns for fluid cognition predictions across cohorts, especially in the Human Connectome Project Young Adult, Human Connectome Project Aging, and Enhanced Nathan Kline Institute Rockland Sample cohorts, but low similarity in patterns for openness predictions. In addition, we assessed the generalizability of prediction models in cross-dataset predictions, by training the model in one dataset and testing in another. Making use of the region-wise prediction approach, we showed that first, a moderate extent of generalizability could be achieved with fluid cognition prediction, and that, second, a set of common brain regions related to fluid cognition across cohorts could be identified. Nevertheless, the moderate replicability and generalizability could only be achieved in specific contexts. Thus, we argue that replicability and generalizability in connectivity-based prediction remain limited and deserve greater attention in future studies. [ABSTRACT FROM AUTHOR]

• The mechanisms contributing to mismatch negativity generation include both adaptation and prediction error computations as two major candidates. • We used computational modeling and a novel experimental design to disentangle different mechanisms of mismatch processing depending on awareness and task relevance. • Results suggest that mismatch processing does not rely on either adaptation or prediction error computation alone, but the relative contributions of different mechanisms vary with task settings. Detection of regularities and their violations in sensory input is key to perception. Violations are indexed by an early EEG component called the mismatch negativity (MMN) – even if participants are distracted or unaware of the stimuli. On a mechanistic level, two dominant models have been suggested to contribute to the MMN: adaptation and prediction. Whether and how context conditions, such as awareness and task relevance, modulate the mechanisms of MMN generation is unknown. We conducted an EEG study disentangling influences of task relevance and awareness on the visual MMN. Then, we estimated different computational models for the generation of single-trial amplitudes in the MMN time window. Amplitudes were best explained by a prediction error model when stimuli were task-relevant but by an adaptation model when task-irrelevant and unaware. Thus, mismatch generation does not rely on one predominant mechanism but mechanisms vary with task relevance of stimuli. [ABSTRACT FROM AUTHOR]

Anthropogenic alterations have resulted in widespread degradation of stream conditions. To aid in stream restoration and management, baseline estimates of conditions and improved explanation of factors driving their degradation are needed. We used random forests to model biological conditions using a benthic macroinvertebrate index of biotic integrity for small, non-tidal streams (upstream area ≤200 km2) in the Chesapeake Bay watershed (CBW) of the mid-Atlantic coast of North America. We utilized several global and local model interpretation tools to improve average and site-specific model inferences, respectively. The model was used to predict condition for 95,867 individual catchments for eight periods (2001, 2004, 2006, 2008, 2011, 2013, 2016, 2019). Predicted conditions were classified as Poor, FairGood, or Uncertain to align with management needs and individual reach lengths and catchment areas were summed by condition class for the CBW for each period. Global permutation and local Shapley importance values indicated percent of forest, development, and agriculture in upstream catchments had strong impacts on predictions. Development and agriculture negatively influenced stream condition for model average (partial dependence [PD] and accumulated local effect [ALE] plots) and local (individual condition expectation and Shapley value plots) levels. Friedman's H-statistic indicated large overall interactions for these three land covers, and bivariate global plots (PD and ALE) supported interactions among agriculture and development. Total stream length and catchment area predicted in FairGood conditions decreased then increased over the 19-years (length/area: 66.6/65.4% in 2001, 66.3/65.2% in 2011, and 66.6/65.4% in 2019). Examination of individual catchment predictions between 2001 and 2019 showed those predicted to have the largest decreases in condition had large increases in development; whereas catchments predicted to exhibit the largest increases in condition showed moderate increases in forest cover. Use of global and local interpretative methods together with watershed-wide and individual catchment predictions support conservation practitioners that need to identify widespread and localized patterns, especially acknowledging that management actions typically take place at individual-reach scales. [Display omitted] • Restoration and management of streams need improved models and interpretability. • Random forests were used to predict stream condition for multiple time periods. • Multiple global and local methods were used to improve interpretability. • Global and local methods supported each other but gave different insight. • Extent matters: little predicted change watershed-wide, but large in some catchments. [ABSTRACT FROM AUTHOR]

For rapid nondestructive detection of peanut protein content, an experimental method combining hyperspectral imaging technology and spectrophotometry was proposed. For data redundancy and noise analysis, ten algorithms were selected for feature extraction, and revealed that the optimal characteristic band of protein content was between 400 and 550 nm. According to the results, the median filtering algorithm (MF) was used to preprocess original spectral data, the XGBoost algorithm was used to extract the top 30 feature bands, the Ridge algorithm was used to construct the protein content prediction model, and the protein content physicochemical data were measured by spectrophotometry. The optimal model was MF-XGBoost-Ridge, with hyperparameter α tuning by Optuna algorithm, with RMSE = 0.009, and a correlation R = 0.886 with a fitting time of only 0.02 s. Compared with the traditional machine learning algorithm models, the prediction accuracy of this study was high and the fitting time was short. [Display omitted] • Light scattering and light absorption effects of protein content were revealed. • For data redundancy and noise, ten algorithms are selected for feature extraction. • Design and build hot machine learning models of MF-XGBoost-Ridge. • Optimize ridge and XGBoost algorithms to achieve more ideal model results. • Rapid non-destructive protein prediction method was verified and satisfactory. [ABSTRACT FROM AUTHOR]

• Brain structural MRI features are used to predict cognitive deficits in neonates. • Ontology-guided Attribute Partitioning method is presented for feature clustering. • Brain ontology knowledge is used to facilitate cognitive deficit prediction. • Ensemble learning integrates maturation and geometric features for the prediction. Structural magnetic resonance imaging studies have shown that brain anatomical abnormalities are associated with cognitive deficits in preterm infants. Brain maturation and geometric features can be used with machine learning models for predicting later neurodevelopmental deficits. However, traditional machine learning models would suffer from a large feature-to-instance ratio (i.e., a large number of features but a small number of instances/samples). Ensemble learning is a paradigm that strategically generates and integrates a library of machine learning classifiers and has been successfully used on a wide variety of predictive modeling problems to boost model performance. Attribute (i.e., feature) bagging method is the most commonly used feature partitioning scheme, which randomly and repeatedly draws feature subsets from the entire feature set. Although attribute bagging method can effectively reduce feature dimensionality to handle the large feature-to-instance ratio, it lacks consideration of domain knowledge and latent relationship among features. In this study, we proposed a novel Ontology-guided Attribute Partitioning (OAP) method to better draw feature subsets by considering the domain-specific relationship among features. With the better-partitioned feature subsets, we developed an ensemble learning framework, which is referred to as OAP-Ensemble Learning (OAP-EL). We applied the OAP-EL to predict cognitive deficits at 2 years of age using quantitative brain maturation and geometric features obtained at term equivalent age in very preterm infants. We demonstrated that the proposed OAP-EL approach significantly outperformed the peer ensemble learning and traditional machine learning approaches. [ABSTRACT FROM AUTHOR]

[Display omitted] • The human health risk probability of pesticide sites prediction model was quantified. • The human health risk probability of un-surveyed pesticide sites was predicted. • The spatial distribution characteristics of human health risks from pesticide sites in China were revealed. [ABSTRACT FROM AUTHOR]

Determination of urea in milk is inevitable in the dairy industry for the quality control processes. A simple and cost-effective method was developed to determine the concentration of urea in milk by silver nanoparticles (uncapped and citrate capped) and ultraviolet–visible (UV–Vis) spectroscopy. The developed nanoparticles were characterized by UV–Vis, scanning electron microscope (SEM), X-ray diffraction (XRD), and Fourier transform infrared (FTIR) spectroscopy and a colour change was noted from yellow to blue upon nanoparticles interaction with urea. A predictive model was developed using the spectral data from the analyzed samples. The developed model was able to measure urea concentration (between 6 mg/dL to 90 mg/dL) in milk with the coefficient of determination (R2) more than0.90 and was validated through para -dimethylaminobenzaldehyde (DMAB) method suggested by the Food Safety and Standards Authority of India (FSSAI). The average accuracy and sample percent relative standard deviation (% RSD) obtained through the developed method + model ranged from 62.67 to 121.85% and 0.11–1.21%. The regression model for uncapped nanoparticles was found to be best-fitted (having R2 = 0.92, average accuracy 103.75 ± 8.69%), with higher closeness to the values obtained for urea by FSSAI-DMAB method. This proposed study proves to be useful for the qualitative and quantitative determination of urea in milk by small businesses in the dairy industry. • Detection of urea in milk through silver nanoparticles based UV-VIS spectroscopy. • Spectral data based development of predictive model for urea determination in milk. • The method is simple, cost-effective and beneficial for quality control of milk. [ABSTRACT FROM AUTHOR]

In murky water, the light interaction with the medium particles results in a complex image formation model that is hard to use effectively with a shape estimation framework like Photometric Stereo. All previous approaches have resorted to necessary model simplifications that were though used arbitrarily, without describing how their validity can be estimated in an unknown underwater situation. In this work, we evaluate the effectiveness of such simplified models and we show that this varies strongly with the imaging conditions. For this reason, we propose a novel framework that can predict the effectiveness of a photometric model when the scene is unknown. To achieve this we use a dynamic lighting framework where a robotic platform is able to probe the scene with varying light positions, and the respective change in estimated surface normals serves as a faithful proxy of the true reconstruction error. This creates important benefits over traditional Photometric Stereo frameworks, as our system can adapt some critical factors to an underwater scenario, such as the camera-scene distance and the light position or the photometric model, in order to minimize the reconstruction error. Our work is evaluated through both numerical simulations and real experiments for different distances, underwater visibilities and light source baselines. [ABSTRACT FROM AUTHOR]

The application of current Chinese agriculture soil quality standards fails to evaluate the land utilization functions appropriately due to the diversity of soil properties and plant species. Therefore, the standards should be amended. A greenhouse experiment was conducted to investigate arsenic (As) enrichment in various soils from 18 Chinese provinces in parallel with As transfer to 8 wheat varieties. The goal of the study was to build and calibrate soil–wheat threshold models to forecast the As threshold of wheat soils. In Shaanxi soils, Wanmai and Jimai were the most sensitive and insensitive wheat varieties, respectively; and in Jiangxi soils, Zhengmai and Xumai were the most sensitive and insensitive wheat varieties, respectively. Relationships between soil properties and the bioconcentration factor (BCF) were built based on stepwise multiple linear regressions. Soil pH was the best predictor of BCF, and after normalizing the regression equation (Log BCF=0.2054 pH- 3.2055, R 2 =0.8474, n=14, p <0.001), we obtained a calibrated model. Using the calibrated model, a continuous soil–wheat threshold equation (HC5=10 (−0.2054 pH+2.9935) +9.2) was obtained for the species-sensitive distribution curve, which was built on Chinese food safety standards. The threshold equation is a helpful tool that can be applied to estimate As uptake from soil to wheat. [ABSTRACT FROM AUTHOR]

Parameter estimation for and prediction of spatially or spatio-temporally correlated random processes are used in many areas and often require the solution of a large linear system based on the covariance matrix of the observations. In recent years, the dataset sizes to which these methods are applied have steadily increased such that straightforward statistical tools are computationally too expensive to be used. In the univariate context, tapering, i.e., creating sparse approximate linear systems, has been shown to be an efficient tool in both the estimation and prediction settings. The asymptotic properties are derived under an infill asymptotic setting. In this paper we use a domain increasing framework for estimation and prediction using multivariate tapering. Under this asymptotic regime we prove that tapering (one-tapered form) preserves the consistency of the untapered maximum likelihood estimator and show that tapering has asymptotically the same mean squared prediction error as using the corresponding untapered predictor. The theoretical results are illustrated with simulations. [ABSTRACT FROM AUTHOR]

To test predictor contribution in a model-free fashion, marginal coordinate tests based on sliced inverse regression (SIR) and sliced average variance estimation (SAVE) have been studied in Cook (2004), and Shao et al. (2007) respectively. Estimating the null distributions of the test statistics is a critical step for such tests. We propose a novel permutation test approach to facilitate the marginal coordinate tests, which applies to existing tests based on SIR and SAVE, and can be readily extended to a new marginal coordinate test based on directional regression (Li and Wang, 2007). [ABSTRACT FROM AUTHOR]

Experiencing stress during training is a way to prepare professionals for real-life crises. With the help of feedback tools, professionals can train to recognize and overcome negative effects of stress on task performances. This paper reports two studies that empirically examined the effect of such a feedback system. The system, based on the COgnitive Performance and Error (COPE) model, provides its users with physiological, predicted performance and predicted error-chance feedback. The first experiment focussed on creating stressful scenarios and establishing the parameters for the predictive models for the feedback system. Participants ( n =9) performed fire-extinguishing tasks on a virtual ship. By altering time pressure, information uncertainty and consequences of performance, stress was induced. COPE variables were measured and models were established that predicted performance and the chances on specific errors. In the second experiment a new group of participants ( n =29) carried out the same tasks while receiving eight different combinations of the three feedback types in a counterbalanced order. Performance scores improved when feedback was provided during the task. The number of errors made did not decrease. The usability score for the system with physiological feedback was significantly higher than a system without physiological feedback, unless combined with error feedback. This paper shows effects of feedback on performances and usability. To improve the effectiveness of the feedback system it is suggested to provide more in-depth tutorial sessions. Design changes are recommended that would make the feedback system more effective in improving performances. [ABSTRACT FROM AUTHOR]

In an approach aiming at high-dimensional situations, we first introduce a distribution-free approach to parameter estimation in the standard random factor model, that is shown to lead to the same estimating equations as maximum likelihood estimation under normality. The derivation is considerably simpler, and works equally well in the case of more variables than observations ( p > n ) . We next concentrate on the latter case and show results of type: • Albeit factor loadings and specific variances cannot be precisely estimated unless n is large, this is not needed for the factor scores to be precise, but only that p is large; • A classical fixed point iteration method can be expected to converge safely and rapidly, provided p is large. A microarray data set, with p = 2000 and n = 22 , is used to illustrate this theoretical result. [ABSTRACT FROM AUTHOR]

Fatty acid ethyl esters (FAEEs) are the main components in some biodiesels, which are important alternatives of fossil fuels. In this paper, we carry out the measurements of the speed of sound in two FAEEs at the temperatures from (293.15 to 473.15) K and at the pressures up to 10 MPa, including saturated liquid and compressed liquid. The relative expanded uncertainty in the speed of sound is estimated to be less than 1.0% with coverage factor of k to be 2. The data in saturated liquid and compressed liquid are fitted into polynomials. For saturated liquid, the D AAD is 0.16% for ethyl hexanoate and 0.09% for ethyl octanoate. For compressed liquid, the D AAD is 0.31% for ethyl hexanoate and 0.16% for ethyl octanoate. The data were also used to assess the predictive ability of Wada’s model. [ABSTRACT FROM AUTHOR]

We develop certified tracking procedures for Newton homotopies, which are homotopies for which only the constant terms are changed. For these homotopies, our certified procedures include using a constant predictor with Newton corrections, an Euler predictor with no corrections, and an Euler predictor with Newton corrections. In each case, the predictor is guaranteed to produce a point in the quadratic convergence basin of Newton's method. We analyze the complexity of a tracking procedure using a constant predictor with Newton corrections, with the number of steps bounded above by a constant multiple of the length of the path in the γ -metric. Examples are included to compare the behavior of these certified tracking methods. [ABSTRACT FROM AUTHOR]

A Rankine source method is developed adopting continuous free surface and seabed source panel distributions to solve numerically wave–body interaction problems involving various seabed conditions. The free surface and seabed surface profiles are represented by continuous panels rather than a discretization by isolated points. These panels are positioned exactly on the fluid boundary surfaces and the proposed method does not require a desingularization technique. For a two-dimensional forced oscillatory body problem, the influences of a flat, concave or convex/hump shaped seabed at different water depths are investigated. Both heave and sway motions are examined and the accuracy and efficiency of the numerical solutions are validated by comparison with published numerical predictions and, where possible, experimental data. Through these simulated findings, the influences of uneven seabeds on the hydrodynamic characteristics associated with wave–body interaction problems are discussed. [ABSTRACT FROM AUTHOR]

The revised Bathing Water Directive (2006/7/EC) requires EU member states to minimise the risk to public health from faecal pollution at bathing waters through improved monitoring and management approaches. While increasingly sophisticated measurement methods (such as microbial source tracking) assist in the management of bathing water resources, the use of deterministic predictive models for this purpose, while having the potential to provide decision making support, remains less common. This study explores an integrated, deterministic catchment-coastal hydro-environmental model as a decision-making tool for beach management which, based on advance predictions of bathing water quality, can inform beach managers on appropriate management actions (to prohibit bathing or advise the public not to bathe) in the event of a poor water quality forecast. The model provides a ‘moving window’ five-day forecast of Escherichia coli levels at a bathing water compliance point off the Irish coast and the accuracy of bathing water management decisions were investigated for model predictions under two scenarios over the period from the 11th August to the 5th September, 2012. Decisions for Scenario 1 were based on model predictions where rainfall forecasts from a meteorological source ( www.yr.no ) were used to drive the rainfall–runoff processes in the catchment component of the model, and for Scenario 2, were based on predictions that were improved by incorporating real-time rainfall data from a sensor network within the catchment into the forecasted meteorological input data. The accuracy of the model in the decision-making process was assessed using the contingency table and its metrics. The predictive model gave reasonable outputs to support appropriate decision making for public health protection. Scenario 1 provided real-time predictions that, on 77% of instances during the study period where both predicted and E. coli concentrations were available, would correctly inform a beach manager to either take action to mitigate for poor bathing water quality or take no action. However, Scenario 1 also provided data to support a decision to take action (when none was necessary – a type I error) in 4% of instances and to take no action (when action was required – a type II error) in 19% of the instances analysed. Type II errors are critical in terms of public health protection given that for this error, bathers can be exposed to risks from poor bathing water quality. Scenario 2, on the other hand, provided predictions that would support correct management actions for 79% of the instances but would result in type I and type II errors for 4% and 17% of the instances respectively. Comparison of Scenarios 1 and 2 for this study indicate that Scenario 2 gave a marginally better overall performance in terms of supporting correct management decisions, as it provided data that could result in a lower occurrence of the more critical type II errors. Given that the 28 member states of the European Union are required to engage with the public health provisions of the revised Bathing Water Directive, issues of compliance, pertaining particularly to the management of bathing water resources, remain topical. Decision supports for managing bathing waters in the context of the Directive are likely to become the focus of much attention and although, the current study has been validated in bathing waters off the east coast of Ireland, the approach of using a deterministic and integrated catchment-coastal model for such purposes is easily transferable to other bathing water jurisdictions. [ABSTRACT FROM AUTHOR]

Turbulence-induced vibration is typically considered as a type of vibration with one-way coupling between the fluid flow and the structural motion: the turbulence creates an incident force field on the structure, but the structural displacement does not influence the turbulence. It is however challenging to measure the turbulence forcing function experimentally. In this article, the forcing function in annular flow is computed by means of Large-eddy simulations. The pressure spectrum is applied to the inner cylinder and the resulting vibration is computed. It is shown that the commonly used multiplication hypothesis does not hold for the present results. The computed spectrum showed an upper limit to the coherence length. The results of these computations are compared to experimental results available in literature and to semi-empirical models. The predicted displacements compared well with experimental results. [ABSTRACT FROM AUTHOR]

Our investigation concerns the estimation of predictive densities and a study of efficiency as measured by the frequentist risk of such predictive densities with integrated squared error loss. Our findings relate to a d -variate spherically symmetric observable X ∼ p X ( ‖ x − μ ‖ 2 ) and the objective of estimating the density of Y ∼ q Y ( ‖ y − μ ‖ 2 ) based on X . We describe Bayes estimation, minimum risk equivariant estimation (MRE), and minimax estimation. We focus on the risk performance of the benchmark minimum risk equivariant estimator, plug-in estimators, and plug-in type estimators with expanded scale. For the multivariate normal case, we make use of a duality result with a point estimation problem bringing into play reflected normal loss. In three or more dimensions (i.e., d ≥ 3 ), we show that the MRE predictive density estimator is inadmissible and provide dominating estimators. This brings into play Stein-type results for estimating a multivariate normal mean with a loss which is a concave and increasing function of ‖ μ ˆ − μ ‖ 2 . We also study the phenomenon of improvement on the plug-in density estimator of the form q Y ( ‖ y − a X ‖ 2 ) , 0 < a ≤ 1 , by a subclass of scale expansions 1 c d q Y ( ‖ ( y − a X ) / c ‖ 2 ) with c > 1 , showing in some cases, inevitably for large enough d , that all choices c > 1 are dominating estimators. Extensions are obtained for scale mixture of normals including a general inadmissibility result of the MRE estimator for d ≥ 3 . [ABSTRACT FROM AUTHOR]

The specification of multivariate prediction regions, having coverage probability closed to the target nominal value, is a challenging problem both from the theoretical and the practical point of view. In this paper we define a well-calibrated multivariate predictive distribution giving suitable conditional prediction intervals with the desired overall coverage accuracy. This distribution is the extension in the multivariate setting of a calibrated predictive distribution defined for the univariate case and it is found on the idea of calibrating prediction regions for improving the coverage probability. This solution is asymptotically equivalent to that one based on asymptotic calculations and, whenever its explicit computation is not feasible, an approximation based on a simple bootstrap simulation procedure is readily available. Moreover, we state a simple, simulation-based, procedure for computing the associated improved conditional prediction limits. [ABSTRACT FROM AUTHOR]