A comparative study between PVO-based framework and multi-predictor mechanism in reversible data hiding

Sorting-based reversible data hiding (RDH) methods like pixel-value-ordering (PVO) can predict pixel values accurately and achieve an extremely low distortion on the embedded image. However, the excellent performance of these methods was not well explained in previous works, and there are unexploited common points among them. In this paper, we propose a general multi-predictor (GMP) framework to summarize PVO-based RDH methods and explain their high prediction accuracy. Moreover, by utilizing the proposed GMP framework, a more efficient sorting-based RDH method is given as an example to show the generality and applicability of our framework. Comparing with other PVO-based methods, the proposed example method can achieve significant improvement in embedding performance. It is hopeful that more efficient sorting-based RDH algorithms can be designed according to our proposed framework by designing better predictors and their combination methods.     

Study on Thermal Deformation Measurement of Optical Remote Sensing Satellite Platform Based on Machine Vision

To solve the special requirements of the high orbit optical remote sensing-satellite for the thermal deformation of the platform, the thermal deformation test and measurement scheme of the satellite platform is designed. Through the comparative analysis of test results, the rationality of the thermal deformation design of the platform structure is verified. The results of thermal deformation measurement show that the maximum deformation of A camera mounting surface is 57.5~〃, and the maximum deformation of B camera mounting surface points to 79.3~〃, which can be used as the basis for thermal deformation prediction of satellite during the orbit operation.     

Mixture ratio modeling of dynamic systems

Any knowledge extraction relies (possibly implicitly) on a hypothesis about the modelled-data dependence. The extracted knowledge ultimately serves to a decision-making (DM). DM always faces uncertainty and this makes probabilistic modelling adequate. The inspected black-box modeling deals with “universal” approximators of the relevant probabilistic model. Finite mixtures with components in the exponential family are often exploited. Their attractiveness stems from their flexibility, the cluster interpretability of components and the existence of algorithms for processing high-dimensional data streams. They are even used in dynamic cases with mutually dependent data records while regression and auto-regression mixture components serve to the dependence modeling. These dynamic models, however, mostly assume data-independent component weights, that is, memoryless transitions between dynamic mixture components. Such mixtures are not universal approximators of dynamic probabilistic models. Formally, this follows from the fact that the set of finite probabilistic mixtures is not closed with respect to the conditioning, which is the key estimation and predictive operation. The paper overcomes this drawback by using ratios of finite mixtures as universally approximating dynamic parametric models. The paper motivates them, elaborates their approximate Bayesian recursive estimation and reveals their application potential.     

Surfactant-free electrochemical synthesis of fluoridated hydroxyapatite nanorods for biomedical applications

Fluoridated hydroxyapatite (FHA) [Ca₁₀(PO₄)₆F_x(OH)_2−x, x = 0–2] is believed to be a promising calcium phosphate (CaP) to replace pure hydroxyapatite (HA) for next-generation implants, owing to its better biocompatibility, higher antibacterial activity, and lower solubility. Notably, the shape and size of the CaP crystals play key roles in their performance and can influence their applications. One-dimensional (1D) FHA nanorods are important CaP materials which have been widely used in regenerative medicine applications such as restorative dentistry. Unfortunately, the traditional synthesis methods for FHA nanorods either employ surfactants or take a relatively long time. In this study, we aimed to propose a facile synthesis route to fabricate FHA nanorods without any surfactants using an electrochemical deposition method for the first time. This study focused on preparing FHA nanorods without the assistance of any surfactant, unlike the traditional synthesis methods, to avoid chemical impurities. FHA nanorods with lengths of 124–2606 nm, diameters of 28–211 nm, and aspect ratios of 4.4–21.8 were synthesized using the electrochemical method, followed by a heat treatment. For the as-synthesized FHA nanorods, the Ca/P ratio was 1.60 and the atomic concentration of F was 2.06 at.%. An ultrastructure examination revealed that each FHA nanorod possessed long-range order, good crystallinity, and a defect-free lattice with a certain crystallographic plane orientation along the whole rod. In short, we propose a novel, surfactant-free, cost-saving, and more efficient route to synthesize FHA nanorods which can be widely applied in multiple biomedical applications, including drug delivery, bone repair, and restorative dentistry.     

A machine learning‐based operational control framework for reducing energy consumption of an amine‐based gas sweetening process

This study proposes a data‐driven operational control framework using machine learning‐based predictive modeling with the aim of decreasing the energy consumption of a natural gas sweetening process. This multi‐stage framework is composed of the following steps: (a) a clustering algorithm based on Density‐Based Spatial Clustering of Applications with Noise methodology is implemented to characterize the sampling space of all possible states of the operation and to determine the operational modes of the gas sweetening unit, (b) the lowest steam consumption of each operational mode is selected as a reference for operational control of the gas sweetening process, and (c) a number of high‐accuracy regression models are developed using the Gradient Boosting Machines algorithm for predicting the controlled parameters and output variables. This framework presents an operational control strategy that provides actionable insights about the energy performance of the current operations of the unit and also suggests the potential of energy saving for gas treating plant operators. The ultimate goal is to leverage this data‐driven strategy in order to identify the achievable energy conservation opportunity in such plants. The dataset for this research study consists of 29 817 records that were sampled over the course of 3 years from a gas train in the South Pars Gas Complex. Furthermore, our offline analysis demonstrates that there is a potential of 8% energy saving, equivalent to 5 760 000 Nm³ of natural gas consumption reduction, which can be achieved by mapping the steam consumption states of the unit to the best energy performances predicted by the proposed framework.     

Analysis of the water-soluble compounds as a tool for discriminating traditional and industrial high moisture mozzarella made with citric acid

High moisture mozzarella from cow milk is a pasta filata cheese that can be manufactured by different protocols. Fermentation by autochthonous starter is used for the traditional product, whereas direct acidification with citric acid is widely used at industrial level. Both types are stored immersed in a liquid for preserving freshness, but this packaging method is known to favour the changes of the quality characteristics. The present study aimed to assess the evolution of the soluble compounds and to evaluate their use as chemical indices for discriminating the two types of mozzarella. The contents of lactose, organic acids, water-soluble nitrogen and free amino acids differently changed over time. The whole patterns of these soluble compounds allowed discrimination of the two products during the whole storage period. The simple analyses of lactic acid and lactose could be a rapid tool to protect traditional mozzarella from industrial imitations obtained with citric acid.     

Integration of machine learning and first principles models

Model building and parameter estimation are traditional concepts widely used in chemical, biological, metallurgical, and manufacturing industries. Early modeling methodologies focused on mathematically capturing the process knowledge and domain expertise of the modeler. The models thus developed are termed first principles models (or white-box models). Over time, computational power became cheaper, and massive amounts of data became available for modeling. This led to the development of cutting edge machine learning models (black-box models) and artificial intelligence (AI) techniques. Hybrid models (gray-box models) are a combination of first principles and machine learning models. The development of hybrid models has captured the attention of researchers as this combines the best of both modeling paradigms. Recent attention to this field stems from the interest in explainable AI (XAI), a critical requirement as AI systems become more pervasive. This work aims at identifying and categorizing various hybrid models available in the literature that integrate machine-learning models with different forms of domain knowledge. Benefits such as enhanced predictive power, extrapolation capabilities, and other advantages of combining the two approaches are summarized. The goal of this article is to consolidate the published corpus in the area of hybrid modeling and develop a comprehensive framework to understand the various techniques presented. This framework can further be used as the foundation to explore rational associations between several models.     

Determining robust reaction kinetics from limited data

Accurate chemical kinetics are essential for reactor design and operation. However, despite recent advances in “big data” approaches, availability of kinetic data is often limited in industrial practice. Herein, we present a comparative proof-of-concept study for kinetic parameter estimation from limited data. Cross-validation (CV) is implemented to nonlinear least-squares (LS) fitting and evaluated against Markov chain Monte Carlo (MCMC) and genetic algorithm (GA) routines using synthetic data generated from a simple model reaction. As expected, conventional LS is fastest but least accurate in predicting true kinetics. MCMC and GA are effective for larger data sets but tend to overfit to noise for limited data. LS-CV strongly outperforms these methods at much reduced computational cost, especially for significant noise. Our findings suggest that implementation of CV with conventional regression provides an efficient approach to kinetic parameter estimation with high accuracy, robustness against noise, and only minimal increase in complexity.     

Prediction of risk factors of cyberbullying-related words in Korea: Application of data mining using social big data

The study examined a decision tree analysis using social big data to conduct the prediction model on types of risk factors related to cyberbullying in Korea. The study conducted an analysis of 103,212 buzzes that had noted causes of cyberbullying and data were collected from 227 online channels, such as news websites, blogs, online groups, social network services, and online bulletin boards. Using opinion-mining method and decision tree analysis, the types of cyberbullying were sorted using SPSS 25.0. The results indicated that the total rate of types of cyberbullying in Korea was 44%, which consisted of 32.3% victims, 6.4% perpetrators, and 5.3% bystanders. According to the results, the impulse factor was also the greatest influence on the prediction of the risk factors and the propensity for dominance factor was the second greatest factor predicting the types of risk factors. In particular, the impulse factor had the most significant effect on bystanders, and the propensity for dominance factor was also significant in influencing online perpetrators. It is necessary to develop a program to diminish the impulses that were initiated by bystanders as well as victims and perpetrators because many of those bystanders have tended to aggravate impulsive cyberbullying behaviors.     

A single-phase algorithm for mining high utility itemsets using compressed tree structures

Mining high utility itemsets (HUIs) from transaction databases considers such factors as the unit profit and quantity of purchased items. Two-phase tree-based algorithms transform a database into compressed tree structures and generate candidate patterns through a recursive pattern-growth procedure. This procedure requires a lot of memory and time to construct conditional pattern trees. To address this issue, this study employs two compressed tree structures, namely, Utility Count Tree and String Utility Tree, to enumerate valid patterns and thus promote fast utility computation. Furthermore, the study presents an algorithm called single-phase utility computation (SPUC) that leverages these two tree structures to mine HUIs in a single phase by incorporating novel pruning strategies. Experiments conducted on both real and synthetic datasets demonstrate the superior performance of SPUC compared with IHUP, UP-Growth, and UP-Growth+ algorithms.