Correlation-based Model Research Articles

Performance of Machine Learning, Artificial Neural Network (ANN), and stacked ensemble models in predicting Water Quality Index (WQI) from surface water quality parameters, climatic and land use data

Assessing water quality is essential for managing freshwater resources, safeguarding ecosystems, and guaranteeing public health. Traditional water quality assessment methods suffer from seasonal sampling, multi-parameter requirements, and labor-intensive sampling processes, which are major constraints for the frequent monitoring of vast river basins. To overcome this issue, the study modeled the remote sensing-based climatic and land use parameters with Principal Component Analysis (PCA) to leverage Artificial Neural Networks (ANN) and machine learning (ML) algorithms to predict the Water Quality Index (WQI). The Weighted Arithmetic Water Quality Index (WAWQI) method was used to calculate the WQI of the Godavari River Basin for the available 19 stream water quality parameters (SWQPs). Further, PCA was applied to reduce the dimensionality of the parameters from 19 to 6. These results led to the development of two modeling methods to predict the WQI. In the first method, the correlation-based model was developed to predict WQI by evaluating six SWQPs. The second method, the causal-effect model, uses land use and meteorological factors to determine WQI using causality. Using advanced AutoML techniques, the initial pool of 40 ML models was meticulously evaluated and refined, culminating in the selection of the top three exemplary models such as Extreme Gradient Boosting (XGB), Extra Trees (ET), and Random Forest (RF). In both methods, XGB models show better prediction, with the coefficient of determination (R2) value of 0.95 during training and 0.83 during testing in method one. Whereas in the second method, R2 of 0.93 in training and 0.80 in testing are obtained. Further, XGB, ET, and ANN outputs were stacked with each model to enhance these results in both methods. Among these three stacked models, the stacked ANN_ML model performed better compared to stacked XGB_ML and stacked ET_ML. In the first method, the stacked ANN_ML model predicts R2 values of 0.95 and 0.91 for training and testing. In the second method, 0.95 and 0.90 for training and testing are obtained using stacked ANN_ML model. These findings emphasize the stacked model prediction ability to capture nonlinear relationships in the parameters and the novel approach of land use and climate parameters based WQI prediction, which replace the laborious, time-consuming SWQP measurements.

Development of temperature-controlled batch and 3-column counter-current protein A system for improved therapeutic purification

Maximizing product quality attributes by optimizing process parameters and performance attributes is a crucial aspect of bioprocess chromatography process design. Process parameters include but are not limited to bed height, eluate cut points, and elution pH. An under-characterized chromatography process parameter for protein A chromatography is process temperature. Here, we present a mechanistic understanding of the effects of temperature on the protein A purification of a monoclonal antibody (mAb) using a commercial chromatography resin for batch and continuous counter-current systems. A self-designed 3D-printed heating jacket controlled the 1 mL chromatography process temperature during the loading, wash, elution, and cleaning-in-place (CIP) steps. Batch loading experiments at 10, 20, and 30 °C demonstrated increased dynamic binding capacity (DBC) with temperature. The experimental data were fit to mechanistic and correlation-based models that predicted the optimal operating conditions over a range of temperatures. These model-based predictions optimized the development of a 3-column temperature-controlled periodic counter-current chromatography (TCPCC) and were validated experimentally. Operating a 3-column TCPCC at 30 °C led to a 47% increase in DBC relative to 20 °C batch chromatography. The DBC increase resulted in a two-fold increase in productivity relative to 20 °C batch. Increasing the number of columns to the TCPCC to optimize for increasing feed concentration resulted in further improvements to productivity. The feed-optimized TCPCC showed a respective two, three, and four-fold increase in productivity at feed concentrations of 1, 5, and 15 mg/mL mAb, respectively. The derived and experimentally validated temperature-dependent models offer a valuable tool for optimizing both batch and continuous chromatography systems under various operating conditions.

Unravelling uncertainty in trajectory prediction using a non-parametric approach

Predicting the trajectories of road agents is fundamental for self-driving cars. Trajectory prediction contains many sources of uncertainty in data and modelling. A thorough understanding of this uncertainty is crucial in a safety-critical task like auto-piloting a vehicle. In practice, it is necessary to distinguish between the uncertainty caused by partial observability of all factors that may affect a driver’s near-future decisions, the so-called aleatoric uncertainty, and the uncertainty of deploying a model in new scenarios that are possibly not present in the training set, the so-called epistemic uncertainty. They reflect the trade-off between data collection and model improvement In this paper, we propose a new framework to systematically quantify both sources of uncertainty. Specifically, to approximate the spatial distribution of an agent’s future position, we propose a 2D histogram-based deep learning model combined with deep ensemble techniques for measuring aleatoric and epistemic uncertainty by entropy-based quantities. The proposed Uncertainty Quantification Network (UQnet) employs a causal part to enhance its generalizability so rare driving behaviours can be effectively identified. Experiments on the INTERACTION dataset show that UQnet is able to give more robust predictions in generalizability tests compared to the correlation-based models. Further analysis presents that high aleatoric uncertainty cases are mainly caused by heterogeneous driving behaviours and unknown intended directions. Based on this aleatoric uncertainty component, we estimate the lower bounds of mean-square-error and final-displacement-error as indicators for the predictability of trajectories. Furthermore, the analysis of epistemic uncertainty illustrates that domain knowledge of speed-dependent driving behaviour is essential for adapting a model from low-speed to high-speed situations. Our paper contributes to motion forecasting with a new framework, that recasts the problem of accuracy improvement in a way that focuses on differentiating between unpredictable components and rare cases for which more and different data should be collected.

Rethinking the Development of Large Language Models from the Causal Perspective: A Legal Text Prediction Case Study

While large language models (LLMs) exhibit impressive performance on a wide range of NLP tasks, most of them fail to learn the causality from correlation, which disables them from learning rationales for predicting. Rethinking the whole developing process of LLMs is of great urgency as they are adopted in various critical tasks that need rationales, including legal text prediction (e.g., legal judgment prediction). In this paper, we first explain the underlying theoretical mechanism of their failure and argue that both the data imbalance and the omission of causality in model design and selection render the current training-testing paradigm failed to select the unique causality-based model from correlation-based models. Second, we take the legal text prediction task as the testbed and reconstruct the developing process of LLMs by simultaneously infusing causality into model architectures and organizing causality-based adversarial attacks for evaluation. Specifically, we base our reconstruction on our theoretical analysis and propose a causality-aware self-attention mechanism (CASAM), which prevents LLMs from entangling causal and non-causal information by restricting the interaction between causal and non-causal words. Meanwhile, we propose eight kinds of legal-specific attacks to form causality-based model selection. Our extensive experimental results demonstrate that our proposed CASAM achieves state-of-the-art (SOTA) performances and the strongest robustness on three commonly used legal text prediction benchmarks. We make our code publicly available at https://github.com/Carrot-Red/Rethink-LLM-development.

Pectoral angle: a glance at a traditional phenotypic trait in chickens from a new perspective

AbstractIn meat-type poultry breeding, pectoral angle (PA) is a conventional anatomical indicator for changes in body conformation and meat traits; its correlation to egg performance is however deemed controversial. In this context, we revisited, assessed and put forward evidence for the usefulness of this classic phenotypic variable and its specific integrative index of pectoral angle-to-body weight ratio (PA/BW). Specifically, we identified respective correlations and used them for distinguishing the major categories (production types) of diverse chicken breeds under the traditional classification model (TCM) and genotypic clustering models of the global chicken gene pool subdivision. Also, the usefulness of the supplementary integrative egg mass yield index (EMY) for this objective was demonstrated. Because of estimating the total mass of eggs laid (i.e. egg number times egg weight), EMY can serve as an indicator of egg production. Direct approximation of EMY values by PA and BW values did not lead to significant correlation dependences between these indicators in each of the four breed utility types according to TCM. However, using the ratio of PA to BW, instead of PA and BW alone, resulted in significant correlation of EMY with PA/BW, allowing for distinction between egg-type and non-productive breeds. The validity of the proposed correlation-based models was supported by PCA and Neighbor Joining clustering analyses. Collectively, we suggested that PA can be a potentially correlated trait for selecting hens and roosters in breeding flocks to boost egg yield. These results can also be applied to chicken breeding as well as conservation- and phenome-related research.

ConnSearch: A framework for functional connectivity analysis designed for interpretability and effectiveness at limited sample sizes

Functional connectivity studies increasingly turn to machine learning methods, which typically involve fitting a connectome-wide classifier, then conducting post hoc interpretation analyses to identify the neural correlates that best predict a dependent variable. However, this traditional analytic paradigm suffers from two main limitations. First, even if classifiers are perfectly accurate, interpretation analyses may not identify all the patterns expressed by a dependent variable. Second, even if classifiers are generalizable, the patterns implicated via interpretation analyses may not replicate. In other words, this traditional approach can yield effective classifiers while falling short of most neuroscientists’ goals: pinpointing the neural correlates of dependent variables. We propose a new framework for multivariate analysis, ConnSearch, which involves dividing the connectome into components (e.g., groups of highly connected regions) and fitting an independent model for each component (e.g., a support vector machine or a correlation-based model). Conclusions about the link between a dependent variable and the brain are based on which components yield predictive models rather than on interpretation analysis. We used working memory data from the Human Connectome Project (N = 50–250) to compare ConnSearch with four existing connectome-wide classification/interpretation methods. For each approach, the models attempted to classify examples as being from the high-load or low-load conditions (binary labels). Relative to traditional methods, ConnSearch identified neural correlates that were more comprehensive, had greater consistency with the WM literature, and better replicated across datasets. Hence, ConnSearch is well-positioned to be an effective tool for functional connectivity research.

Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learning

This study focuses on predicting the dynamic viscosity of nanofluids, specifically Polyalpha-Olefin-hexagonal boron nitride (PAO-hBN) using machine learning models. The primary goal of this research is to assess and contrast the effectiveness of three distinct machine learning models: Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The main objective is the identification of a model that demonstrates the highest level of accuracy in predicting a nanofluid’s viscosity namely, PAO-hBN nanofluids. The models were trained and validated using 540 experimental data points, where the mean square error (MSE) and the coefficient of determination R2 were utilized for performance evaluation. The results demonstrated that all three models could predict the viscosity of PAO-hBN nanofluids accurately, but the ANFIS and ANN models outperformed the SVR model. The ANFIS and ANN models had similar performance, but the ANN model was preferred due to its faster training and computation time. The optimized ANN model had an R2 of 0.99994, which indicates a high level of accuracy in predicting the viscosity of PAO-hBN nanofluids. The elimination of the shear rate parameter from the input layer improved the accuracy of the ANN model to an absolute relative error of less than 1.89% over the full temperature range (−19.7 °C–70 °C) compared to 11% in the traditional correlation-based model. These results suggest that the use of machine learning models can significantly improve the accuracy of predicting the viscosity of PAO-hBN nanofluids. Overall, this study demonstrated that the use of machine learning models, specifically ANN, can be effective in predicting PAO-hBN nanofluids’ dynamic viscosity. The findings provide a new perspective on how to predict the thermodynamic properties of nanofluids with high accuracy, which could have important applications in various industries.

A hybrid model to predict the pressure gradient for the liquid-liquid flow in both horizontal and inclined pipes for unknown flow patterns

Accurate prediction of the pressure gradient (PG) for the oil-water flow requires identification of the flow pattern (FP), which is usually achieved by using either an expensive measurement system or time-consuming manual observations. This study proposes a hybrid scheme where two machine-learning (ML) models are coupled in a series to predict the PG value without any conclusive FP information. The first model (M1) determines the oil-water FP, whereas the second model (M2) predicts the oil-water PG. 1637 experimental data points for the oil-water flow in both horizontal and inclined pipes are used to develop the models. The important feature subset is identified using the modified Binary Grey Wolf Optimization Particle Swarm Optimization (BGWOPSO) algorithm. The MLs' performance is evaluated using metrics including accuracy, sensitivity, specificity, and F1-score for the M1, and coefficient of variation of root mean squared error, mean absolute percentage error (MAPE), and median absolute percentage error for the M2. The evaluation metrics are cross-validated using a repeated train-test split strategy. The results showed that the overall FP classification accuracy is greater than 91%, with 90.61% sensitivity and 98.53% specificity using the weighted majority voting for M1. With the Gaussian Process regression for M2, the evaluation metrics for the PG prediction were found to be 10.65%, 86.26 Pa/m, and 0.96 for MAPE, root mean square error, and adjusted coefficient of determination, respectively. Statistical analysis showed that the selected features for liquids' and pipe's properties using the BGWOPSO algorithm were adequate to attain superior performance for both models. The achieved MAPE using the proposed hybrid model is superior to existing mechanistic or correlation-based models reported in the literature (between 26 and 69%). The proposed hybrid scheme can significantly reduce the costs associated with identifying the oil-water flow profile and be critical in designing energy-efficient transportation of liquid-liquid flow.

Mapping Impacts of Climate Change on the Distributions of Two Endemic Tree Species under Socioeconomic Pathway Scenarios (SSP)

Pistacia eurycarpa Yalt and Pistacia khinjuk Stocks are two important endemic tree species inhabiting mountainous regions in Iraq. Their cultural, medical, and ecological benefits have captured the interest of this study. Numerous researchers have revealed how and to what extent global climate change alters species’ habitats and distribution. This approach aims to quantify the current and future distribution of these tree species in the region and to provide baseline data on how Pistacia respond to the changing environment. Three socioeconomic pathway scenarios (SSP 126, 245, and 585) in two general circulating models (GCMs), MIROC-ES2L and BCC-CSM2-MR, have been utilized to examine the probable future geographical shift of these species during different time periods (2041–2060, 2061–2080, and 2081–2100). This study used the MaxEnt model and geospatial techniques for: (i) anticipating the present level of distributions and assessing the impact of climate change on these species’ possible future distributions; (ii) estimating the areas of species overlap; and (iii) finding the most significant environmental variables shaping their distributions, according to 11 environmental variables and 161 known localities. The findings revealed that 30 out of 36 modeling results showed range expansion in both the MIROC-ES2L and BCC-CSM2-MR models with 16/18 for P. eurycarpa and 14/18 for P. khinjuk. The overall species range expansions and increase in habitat suitability (mainly in the north and northeast) were related to precipitation during the wettest months, topography, and soil type structure (i.e., Chromic Vertisols, Lithosols, and Calcic Xerosols). These recent discoveries provide priceless new information for forestry management efforts and the conservation plan in Iraq, particularly in the overlapping areas in the mountainous regions and highlands. Geospatial approaches and correlation-based modeling are effective tools for predicting the spatial pattern of tree species in the mountain environment.

BZINB Model-Based Pathway Analysis and Module Identification Facilitates Integration of Microbiome and Metabolome Data.

Integration of multi-omics data is a challenging but necessary step to advance our understanding of the biology underlying human health and disease processes. To date, investigations seeking to integrate multi-omics (e.g., microbiome and metabolome) employ simple correlation-based network analyses; however, these methods are not always well-suited for microbiome analyses because they do not accommodate the excess zeros typically present in these data. In this paper, we introduce a bivariate zero-inflated negative binomial (BZINB) model-based network and module analysis method that addresses this limitation and improves microbiome-metabolome correlation-based model fitting by accommodating excess zeros. We use real and simulated data based on a multi-omics study of childhood oral health (ZOE 2.0; investigating early childhood dental caries, ECC) and find that the accuracy of the BZINB model-based correlation method is superior compared to Spearman's rank and Pearson correlations in terms of approximating the underlying relationships between microbial taxa and metabolites. The new method, BZINB-iMMPath, facilitates the construction of metabolite-species and species-species correlation networks using BZINB and identifies modules of (i.e., correlated) species by combining BZINB and similarity-based clustering. Perturbations in correlation networks and modules can be efficiently tested between groups (i.e., healthy and diseased study participants). Upon application of the new method in the ZOE 2.0 study microbiome-metabolome data, we identify that several biologically-relevant correlations of ECC-associated microbial taxa with carbohydrate metabolites differ between healthy and dental caries-affected participants. In sum, we find that the BZINB model is a useful alternative to Spearman or Pearson correlations for estimating the underlying correlation of zero-inflated bivariate count data and thus is suitable for integrative analyses of multi-omics data such as those encountered in microbiome and metabolome studies.

Correlation-Based Mutual Information Model for Analysis of Lung Cancer CT Image.

Most of the people all over the world pass away from complications related to lung cancer every single day. It is a deadly form of the disease. To improve a person's chances of survival, an early diagnosis is a necessary prerequisite. In this regard, the existing methods of tumour detection, such as CT scans, are most commonly used to recognize infected regions. Despite this, there are certain obstacles presented by CT imaging, so this paper proposes a novel model which is a correlation-based model designed for analysis of lung cancer. When registering pictures of thoracic and abdominal organs with slider motion, the total variation regularization term may correct the border discontinuous displacement field, but it cannot maintain the local characteristics of the image and loses the registration accuracy. The thin-plate spline energy operator and the total variation operator are spatially weighted via the spatial position weight of the pixel points to construct an adaptive thin-plate spline total variation regular term for lung image CT single-mode registration and CT/PET dual-mode registration. The regular term is then combined with the CRMI similarity measure and the L-BFGS optimization approach to create a nonrigid registration procedure. The proposed method assures the smoothness of interior of the picture while ensuring the discontinuous motion of the border and has greater registration accuracy, according to the experimental findings on the DIR-Lab 4D-CT public dataset and the CT/PET clinical dataset.

The Application of Grey Relational Analysis in the Evaluation of Financial Auditing Effect and Improvement

Financial institutions are confronting more complicated risks as a result of the current financial crisis. Financial auditing is an essential aspect of government auditing since it serves to protect the security and stability of the national financial system by evaluating financial systemic vulnerabilities. Therefore, this paper combines the grey relational analysis to carry out research work on the financial auditing effect and improvement evaluation. Firstly, we conducted a preliminary selection of relevant financial audit impact indicators and identified the design of the influencing factor system as well as appropriate data for the influencing factors, resulting in the first evaluation system of the evaluation indicators. Secondly, we created a factor set for evaluating the financial audit improvement effect. The completed task proportion component and the task completion quality factor are the two kinds of improvement impact evaluation factors. On this foundation, this research develops a grey correlation-based assessment model for audit effect enhancement. The residual graph fitting findings indicate that the scheme has a good implementation impact and may be utilised to assess the financial auditing improvement effect.

Choice set robustness and internal consistency in correlation-based logit stochastic user equilibrium models

The Stochastic User Equilibrium (SUE) traffic assignment model is a well-known approach for investigating the behaviours of travellers on congested road networks. SUE compensates for driver/modelling uncertainty of the route travel costs by supposing the costs include stochastic terms. Two key challenges for SUE modelling, however, are capturing route correlations and dealing with unrealistic routes. Numerous correlation-based SUE models have been proposed, but issues remain over both internal consistency and choice set robustness. This paper formulates (and proves solution existence for) new internally consistent SUE formulations of GEV structure and correction term logit route choice models, where the functional forms in the correlation components are based upon generalised, flow-dependent congested costs, rather than e.g. length / free-flow travel time as done typically. Numerical experiments are then conducted on the Sioux Falls and Winnipeg networks, where computational feasibility for obtaining internally consistent solutions, choice set robustness, and internal consistency are assessed/compared.

Servant leadership and job satisfaction in higher education: the mediating roles of organizational justice and organizational trust

ABSTRACT Drawing upon social exchange and self-determination theories, we propose a model to examine the mediating role of organizational justice and organizational trust in the relationship between servant leadership and job satisfaction. Data from 258 Palestinian academics were collected and analyzed using correlation-based structural equation modeling (partial least squares). Our findings show that organizational justice and organizational trust partly mediate the relationship between servant leadership and job satisfaction. In addition, the results show that there are significant differences in gender and experience pertaining to job satisfaction levels. This research advances the knowledge on servant leadership literature and adds insight into its relationship with job satisfaction in the field of higher education through examining the mediating role of organizational justice and organizational trust in the aforesaid relationship. Our findings encourage academic institutions to employ and nurture servant behaviors among academic leaders and managers. Academic institutions are also encouraged to embrace clear measures toward building a satisfied workforce through instilling a climate of justice and mutual trust.

Modeling the effect of dual-core energy recovery ventilator unit on the energy use of houses in northern Canada

The use of single-core energy recovery ventilator (ERV) reduces the heating energy use in northern houses, but has the disadvantage of reducing the required outdoor air supplied to the house during the defrost operation by air recirculation, which might affect the indoor air quality. A dual-core ERV also reduces the heating energy use, but in addition supplies a continuous outdoor air flow rate in compliance with standards. This paper evaluates the advantage of using the dual-core ERV in northern houses. First, this paper presents new correlation-based models of supply air temperature and humidity after the ERV unit, based on laboratory-controlled experimental data. Second, the energy use for ventilation and heating, and ventilation rates are simulated with TRNSYS program for northern houses at three arctic locations. They are compared with Montreal conditions as reference. The single-core ERV unit significantly reduces heating energy use in arctic locations by about 27%, compared with the case without heat recovery, however the outdoor airflow rate during the defrost is smaller than minimum standard requirements by about 13% in Kuujjuaq (below Arctic Circle) and 24% in Resolute (above Arctic Circle). The dual-core ERV unit removes the frost while continuously supplying the minimum required outdoor air to the indoors, however at the cost of minor increase of the heating and fan energy use compared with the single-core ERV unit.

High efficiency 3-D printed microchannel polymer heat exchangers for air conditioning applications

The design, fabrication, and experimental characterization of a counterflow Micro-pin array Polymer Heat eXchanger (MPHX) for HVAC applications is described. The design consists of water flow through microscale pin array plates, and airflow paths in a counterflow direction through adjacent rectangular channels. When compared to a Finned Tube Heat eXchanger (FTHX), the water plates take the place of fins and the crossflow tube bank is eliminated. The MPHX is fabricated by 3 D printing using a digital light synthesis method. A correlation-based model is developed and used to explore the parametric space to arrive at a baseline MPHX core geometry. Mechanical and fluidic simulations are performed on the baseline geometry to further develop the design. A sub-scale MPHX with a nominal cross duct area of 10.1 cm × 20.3 cm is designed and fabricated. Its thermal performance over a range of air and water flow rates corresponding to heat capacity rate ratios in the range of 0.25–1 is investigated. Results show that the effectiveness varies from 0.65 to 0.95 corresponding to a of 1 to 0.25. The experimental data are used to validate a correlation-based thermo-fluidic model of the MPHX. The validated model is further used to compare the performance advantages of the MPHX design over a FTHX. For a given heat exchanger volume and thermo-fluidic conditions, the MPHX transfers 55% more thermal energy when compared to a FTHX at comparable air-side pressure drops.

Prediction of pressure gradient for oil-water flow: A comprehensive analysis on the performance of machine learning algorithms

Pressure gradient (PG) in liquid-liquid flow is one of the key components to design an energy-efficient transportation system for wellbores. This study aims to develop five robust machine learning (ML) algorithms and their fusions for a wide range of flow patterns (FP) regimes. The MLs include Support Vector Machine (SVM), Gaussian Process (GP), Random Forest (RF), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), and fusions of these five MLs. A total of eleven hundred experimental data points for nine FPs (two stratified and seven dispersed patterns) in horizontal wellbores are used to develop the MLs. The MLs' performance is evaluated using the metrics including mean absolute percentage error (MAPE), median absolute percentage error (MdAPE), coefficient of variation of root mean squared error (CV-RMSE), and adjusted coefficient of determination. The evaluation metrics are cross-validated using a repeated train-test split strategy. Seven important predictor variables are identified using a supervised feature selection approach: oil and water velocities, FP, input diameter, oil and water density, and oil viscosity. The results show that the high PG prediction accuracy can be achieved using GP compared to other MLs except for the ML-fusions (p < 0.05). A Friedman's test and Wilcoxon Sign-Rank post hoc analysis with Bonferroni correction show that PG prediction errors using GP are significantly lower than using the ANN model (p < 0.05). The values are 18.44 % and 23.9 % for CV-RMSE, 11.6 % and 10.06 % for MAPE, and 7.5 % and 6.75 % for MdAPE, using ANN and GP, respectively. While the previous studies mostly used ANN to demonstrate the capability of MLs to predict PG over the mechanistic or correlation-based models, the present research has shown that GP is even better than ANN using a wide range of FPs and a large data set. • Machine-learning algorithms are developed to predict the pressure gradient. • The Gaussian Process model produces significantly lower prediction error. • The median absolute percentage error using Gaussian Process is 6.75 %. • More than 95 % variation of pressure gradient can be explained using seven variables.

Non-line-of-sight imaging under white-light illumination: a two-step deep learning approach.

Non-line-of-sight (NLOS) imaging has received considerable attentions for its ability to recover occluded objects from an indirect view. Various NLOS imaging techniques have been demonstrated recently. Here, we propose a white-light NLOS imaging method that is equipped only with an ordinary camera, and not necessary to operate under active coherent illumination as in other existing NLOS systems. The central idea is to incorporate speckle correlation-based model into a deep neural network (DNN), and form a two-step DNN strategy that endeavors to learn the optimization of the scattered pattern autocorrelation and object image reconstruction, respectively. Optical experiments are carried out to demonstrate the proposed method.

Canonical correlation-based model selection for the multilevel factors

We develop a novel approach based on the canonical correlation analysis to identify the number of the global factors in the multilevel factor model. We propose the two consistent selection criteria, the canonical correlations difference (CCD) and the modified canonical correlations (MCC). Via Monte Carlo simulations, we show that CCD and MCC select the number of global factors correctly even in small samples, and they are robust to the presence of serially correlated and weakly cross-sectionally correlated idiosyncratic errors as well as the correlated local factors. Finally, we demonstrate the utility of our approach with an application to the multilevel asset pricing model for the stock return data in 12 industries in the U.S.

An empirical correlation-based model to predict solid-fluid phase equilibria and phase separation of the ternary system CH4-CO2-H2S

To cover the expected increased demand for natural gas, energy industry has to exploit sour gas reserves located around the world. However, acid gases have to be removed before the natural gas produced from these fields could be used. One of the novel concepts in this field is the utilization of solid phase formation of carbon dioxide and/or hydrogen sulfide. The main aim of this study is to develop an empirical correlation model based on Peng-Robinson equation of state (PR EoS), with fugacity expressions, that is able for the first time to describe the solid-fluid phase equilibria for the ternary system of CH4-CO2-H2S at pressures from 5 to 30 bar and over a wide range of temperature (130–200 K). The model was first tested on the binary systems of CH4-CO2, CO2-H2S and CH4-H2S with optimized interaction parameters. When proven to be successful, it was then expanded in a predictive manner to describe the ternary system of CH4-CO2-H2S. The model predictions for the solidification points of 5 different mixtures were within the acceptable error when compared to the experimental data available in the literature. A model based on equilibrium stage separation unit was used to study the separation of three different feed compositions of this ternary system. Overall, it was found that separation of CO2 in solid phase improves when increasing the operating pressure up to 20 bar, and decreases at higher temperatures. Similarly, the separation of H2S in either liquid or solid phase improves at higher pressures and lower temperatures. The recovery of CH4 was high over the entire ranges of operating conditions, expect at high pressure (30 Bar) at temperatures below 190 K. This work provides scientists and engineers with an accurate tool that may be used with confidence for predicting solid-fluid phase equilibria. Consequently, this model eliminates difficulties associated with the need for experiments on ternary system solid-fluid phase equilibria.