Vandana Rawat
email: vandanarawat2405@gmail.com

A Classification System for Diabetic Patients with Machine Learning Techniques

Keywords: Bagging, Boosting techniques, Diabetes mellitus (DM), Machine learning techniques, Naive Bayes Classifier, RobustBoost techniques, Prediction

Diabetes mellitus (DM) is a group of metallic disorder characterized by steep levels of blood glucose prolonged over a time. It results the defection in insulin production or improper action of the cells to the insulin produced. It is one of the significant public health care challenge worldwide. Diabetes exists in a body when pancreas does not construct enough hormone insulin or the human body is not being able to use the insulin properly. The diagnosis of diabetes (diagnosis, etiopathophysiology, therapy etc.) need to generate and process the vast amount of data. Data mining techniques have proven its usefulness and effectiveness in order to evaluate the unknown relationships or patterns if exists with such vast data. In the present work, five techniques based on machine learning namely, AdaBoost, LogicBoost, RobustBoost, Naïve Bayes and Bagging have been proposed for the analysis and prediction of DM patients. The proposed techniques are employed on the data set of Pima Indians Diabetes patients. The results computed are found to be very accurate with classification accuracy of 81.77% and 79.69% by bagging and AdaBoost techniques, respectively. Hence, the proposed techniques employed here are highly adorable, effective and efficient in order to predict the DM.

Introduction

Diabetes Mellitus (DM) is a chronic disorder resulting due to the deficiency of insulin or abnormality in the use of insulin of carbohydrate, protein and fat metabolism. Insulin is an energy transport. It brings the blood sugar (glucose) into body's cells and therefore, makes it an important hormone. Excessive of thirst or urination, fatigue, weight loss or blurred vision are some symptom. It causes the risk of polyuria (frequent urination), polydipsia (increasingly thirsty) and polyphagia (hungry). DM is becoming a pre-eminent and foremost reason of death day by day, which is an ordinary non-communicable disease.

India is world second most populated country on the globe and DM has been severely approached epidemic proportions globally. The report of International Diabetes Federation (IDF) (2013), reveals that approximately 50% diabetes infected world populations resides in only three countries: China (98.4 million), India (65.1 million) and the USA (24.4 million) [START_REF] Varma | Prevalence of and risk factors for diabetic macular edema in the United States[END_REF]. Further, IDF reported that 387 million individuals has been severely affected by diabetes worldwide in 2014, and this number is expected to increase up to 300 million by 2025 and 592 million by the 2035 [START_REF] King | Global burden of diabetes, 1995-2025: prevalence, numerical estimates, and projections[END_REF].

DM has been prolonged over a time that causes high blood sugar levels and it may be caused at any age. There are different forms of diabetes which depends upon the blood glucose values of the body [START_REF] Alberti | Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus. Provisional report of a WHO consultation[END_REF]. So according to these values there are different categories of it like Type 1 diabetes mellitus (T1DM), Type 2 diabetes mellitus (T2DM), and Gestational diabetes, which can take place in any age of a person [START_REF] Al-Goblan | Mechanism linking diabetes mellitus and obesity[END_REF]. T1DM is popularly known as juvenile diabetes, usually diagnosed in childhood caused by a DNA abnormality however, it can also occur in adults. T1DM damage the cells that produce insulin because of the autoimmune of the infected body. The infected body is unable to produce sufficient insulin. T1DM has been initiated when immune system destroyed the pancreatic beta cells. This cell is responsible for the generation of insulin hormone that regulates blood glucose.

T2DM is also named as "non-insulin-dependent" or "adult onset diabetes" characterized by hyperglycemia with resistance to ketosis [START_REF] Xie | A Bayesian network model for predicting type 2 diabetes risk based on electronic health records[END_REF]. T2DM can occur in any stage of life. Most commonly, it takes place in overweight, older and middle-aged people. T2DM has been initiated with the improper consumption of energy in the liver cells, fat and muscles where the insulin resistance cells are incapable to pass on the glucose level inside the body cells. As the need of insulin increases, the ability to produce insulin has been lost by the pancreas gradually. Gestational diabetes occurred due to high blood sugar (glucose) usually found in pregnant females during pregnancy and get disappear after giving birth a baby. Hence, DM may affect the human body by heart disease, nerve damage, kidney diseases [START_REF] Sharma | A decision support system for classification of normal and medical renal disease using ultrasound images: A decision support system for medical renal diseases[END_REF] and stroke problem. Type-1 and Type-2 DM are long lasting and incurable whereas, Gestational diabetes occurs due to changes in hormonal during the pregnancy period. Furthermore, DM increases the glucose/ sugar level in blood, which causes the disability of the small vessels and produces many of the disease in the body such as heart problems, damaging of nerve cells, affecting the kidney, stroke problems etc. It also increases the probability of sexual dysfunction [START_REF] Cooper | Patients' perspectives on diabetes health care education[END_REF][START_REF] Hoshi | An analysis of thyroid function diagnosis using Bayesian-type and SOM-type neural networks[END_REF].

To avoid this situation, DM should be essentially recognized at earlier stage with the approach of some efficient and adorable techniques and methods for future outbreak. As per the reported cases of infected DM patients, only 5-6% has been suffered from T1DM and 94-95% of cases has been reported from T2DM. Reports reveals the common factors of T2DM are obesity, older age, family history and physical inactiveness [START_REF] Wang | Geneexpression profiles to predict distant metastasis of lymph-node-negative primary breast cancer[END_REF]. Hence, it is important to apply various techniques for predicting the Diabetes for future outbreak. So for predicting diabetes at initial level some of the techniques of machine learning and data mining have proven its usefulness, effectiveness with a high amount of contemplation in the scientific community [START_REF] Polat | An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease[END_REF].

Since, the data collected from the medical healthcare system in Healthcare Centers or all other sources usually the diabetic data are unstructured in nature and therefore, it is necessary to structure, emphasis its size into nominal value with possible solution and process such a vast data from databases. Hence, conventional manual data analysis has become inadequate for the analysis and prediction. The acquired data doesn't have any use except some useful information has not been derived from it. Therefore, it's necessary to evolve the predictive algorithms for high accuracy and ease to approach for analyzing the huge data and convert it into the meaningful information [START_REF] Acharjya | A comparative study of statistical and rough computing models in predictive data analysis[END_REF]. Hence, to accomplish this goal, various machine learning based predictive analysis has been employed on diabetic patient's dataset to increase the diagnostic test accuracy with low cost and few human resources. The various machine learning methods have been employed with hybridization of data mining techniques such as statistics, and game theory. It utilizes the present and past data, process these data using the statistical or other analytical tools, and finally evaluating or predicting or classify certain future events. Machine learning techniques are most suitable techniques for the classification and prediction with high accuracy. Hence, to achieve high accuracy in the prediction of diabetes patients, it becomes necessary to approach these techniques for an appropriate protection. Therefore, the prediction accuracy have been accomplished in the present work and compared with the reported results of previous literature. The purpose of this effort is only to achieve high accuracy based on the proposed Machine Learning Techniques for the diagnosis of diseases such as heart disease and diabetic disease. Hence, these intelligent classification techniques conclusively proved the foremost functional estimation and creation methods to handle such type of diseases. The determination to check the ability and working of different machine learning techniques has been employed for the categorization of heart disease [START_REF] Acharjya | A comparative study of statistical and rough computing models in predictive data analysis[END_REF]. In context to this study, six machine learning techniques have been employed including Artificial Neural Network (ANN), Support Vector Machine (SVM), logistic regression, k-Nearest Neighbor (k-NN), classification tree and Naive Bayes. For comparison between performances with previous studies some Receiver Operative Characteristic (ROC) and Calibration Graph are also used. Machine learning may be used in different context depending upon the area of application [START_REF] Kayaer | Medical diagnosis on Pima Indian diabetes using general regression neural networks[END_REF]Thongkam et al., 2008;[START_REF] Temurtas | A comparative study on diabetes disease diagnosis using neural networks[END_REF][START_REF] Parthiban | Applying machine learning methods in diagnosing heart disease for diabetic patients[END_REF]; Machine learning techniques has been employed to predict and classify many of the biomedical diseases such as for the prediction of heart disease, liver disease, breast cancer survival and diabetes etc. [START_REF] Tu | A comparative study of medical data classification methods based on decision tree and bagging algorithms[END_REF][START_REF] Zhang | A comparative study of ensemble learning approaches in the classification of breast cancer metastasis[END_REF][START_REF] Kalaiselvi | Prediction of heart diseases and cancer in diabetic patients using data mining techniques[END_REF]. There are various types of classification techniques, the most popular is neural network structure based general regression, multilayer neural network structure, decision tree C4.5 algorithm, bagging with decision tree C4.5 algorithm and bagging with Naïve Bayes algorithm, Support Vector Machines, Radial Basis Function (RBF), Multi-Layer Perceptron (MLP) and Multi Level Counter Propagation Network (MLCPN) for analysis and prediction of these diseases [START_REF] Kukar | An application of machine learning in the diagnosis of ischaemic heart disease[END_REF][START_REF] Hui | Machine learning for tongue diagnosis[END_REF][START_REF] Velu | Multi-Level counter propagation network for diabetes classification[END_REF].

Adaboost algorithm is the first real boosting machine learning technique developed for the binary classification. However, this algorithm has been first applied on the dataset in the area of health care for the classification and prediction of breast cancer survival patients to help the medical practitioner to take the decision (Thongkam et al., 2008). In the dataset of PIMA Indian Diabetic patients GRNN (general regression neural network structure) has been applied for better accuracy and for achieving this, ARTMAP-IC structured model is combined with this network structure [START_REF] Kayaer | Medical diagnosis on Pima Indian diabetes using general regression neural networks[END_REF]. The study [START_REF] Temurtas | A comparative study on diabetes disease diagnosis using neural networks[END_REF] presents the better accuracy on pima-diabetes disease dataset using multilayer neural network structure. In this structure Levenberg-Marquardt (LM) algorithm and a probabilistic neural network structure employed for computing the better accuracy, for diagnosing the Heart Disease for Diabetic Patients, Naïve Bayes and Support Vector Machine techniques have been employed. Since, Heart disease is the leading cause of death; therefore researchers have used several data mining techniques for diagnosing and predicting the heart disease in Diabetic Patients [START_REF] Parthiban | Applying machine learning methods in diagnosing heart disease for diabetic patients[END_REF]. Some classification techniques such as AdaBoost, LogitBoost and random forest have also been employed for the prediction of many diseases. However, these techniques have first applied to predict the breast cancer [START_REF] Zhang | A comparative study of ensemble learning approaches in the classification of breast cancer metastasis[END_REF]. Firstly, a small group of sub networks have been created as disease markers and then after they have used for the classification of metastasis. Heart disease prediction is tendered through the medium of decision tree C4.5 algorithm [START_REF] Farvaresh | A data mining framework for detecting subscription fraud in telecommunication[END_REF], bagging with decision tree C4.5 algorithm and bagging with Naïve Bayes algorithm [START_REF] Tu | A comparative study of medical data classification methods based on decision tree and bagging algorithms[END_REF][START_REF] Jiang | Deep feature weighting for naive Bayes and its application to text classification[END_REF][START_REF] De Sá | A customized classification algorithm for credit card fraud detection[END_REF]. These algorithms are spread upon "coronary artery disease" taken from UCI KDD Archive dataset and to build an assess model for the prediction of heart disease. Different metrics have been employed in machine learning and data mining. Some of the important performance parameters are precision, recall, Fmeasure and ROC space. These parameters have been used to evaluate the best accuracy. It is well known that diabetes and cancer both are the most chronic diseases which have a composite relationship as when the glucose level of human body increase up to the divergent level then it leads to diabetes [START_REF] Kalaiselvi | A new approach for diagnosis of diabetes and prediction of cancer using ANFIS[END_REF]. So for finding the classification accurately in this dataset, some of the recommender system has been proposed based on adaptive and personalized basal insulin on Kalman filter theory [START_REF] Torrent-Fontbona | Adaptive basal insulin recommender system based on Kalman filter for type 1 diabetes[END_REF]. The proposed algorithm may use with or without continuous glucose monitoring systems.

For the prediction of diabetes and any other disease different number of classification techniques have been implemented such as Radial Basis Function (RBF), Multi-Layer Perceptron (MLP), Multi-Level Counter Propagation Network (MLCPN), Rotation Forest Algorithm, Bagging, Back Propagation Network (BPN), Generalized Discriminate Analysis (GDA) and Least Square Support Vector Machine (LS-SVM) [START_REF] Polat | A cascade learning system for classification of diabetes disease: Generalized discriminant analysis and least square support vector machine[END_REF][START_REF] Karegowda | Application of genetic algorithm optimized neural network connection weights for medical diagnosis of pima Indians diabetes[END_REF][START_REF] Palivela | Survey on mining techniques for breast cancer related data[END_REF][START_REF] Velu | Multi-Level counter propagation network for diabetes classification[END_REF]. However, some different types of classification techniques which have been introduced but due to some improper balance between datasets, their performance become worst because these have been represented by majority of classes. Hence, some balancing classification technique like RHS-Boost can be applied on them [START_REF] Gong | RHSBoost: Improving classification performance in imbalance data[END_REF]. This technique is applied on misbalancing datasets for finding out the best accuracy and prediction.

In order to achieve the better accuracy, some of the methods such as "modified spline smooth support vector machine (MS-SSVM)" have been employed with proper selection measure. The concentration has been made on the classification of diabetic patients and clubbed 10-fold cross validation method to this vector machine technique with accuracy, confusion matrix, sensitivity and specificity (Thongkam et al., 2008). By working in the field of prediction, some of the techniques may be combined together, for example, "principal component analysis (PCA) and ANFIS, combined together for diagnosing the diabetic patients [START_REF] Dwivedi | Analysis of computational intelligence techniques for diabetes mellitus prediction[END_REF]. In first part, 8 attributes of dataset has been divided into 4 attributes using PCA. Hence, diagnosis has done by ANFIS classifier. There are other classification techniques also like MS-SSVM (modified spline smooth support vector machine) which is used for diagnosing diabetic disease [START_REF] Purnami | A new expert system for diabetes disease diagnosis using modified spline smooth support vector machine[END_REF] and used with the 10-fold cross validation with accuracy, confusion matrix, sensitivity and specificity.

Classification Analysis System Architecture 2.1 Diabetes Dataset Attributes

Since 1965, Pima Indians have resided at the bank of Gila River of Indian Community in southern Arizona, USA. This tribe has involved in a longitudinal study of diabetes and its complications. This Indian community has the highest reported prevalence of infected diabetes on the globe (50% at 35 years of age) [START_REF] Mercaldo | Diabetes mellitus affected patients classification and diagnosis through machine learning techniques[END_REF]. Since, "The Pima Indians Diabetes Dataset" includes highest recorded dataset worldwide for the diabetes patient. These datasets are officially maintained by NIDDK (National Institute of Diabetes and Digestive and Kidney Diseases) [START_REF] Rasooly | The national institute of diabetes and digestive and kidney diseases central repositories: a valuable resource for nephrology research[END_REF][START_REF] Whetzel | The NIDDK Information Network: a community portal for finding data, materials, and tools for researchers studying diabetes, digestive, and kidney diseases[END_REF]. A total number of 768 instances have used in dataset, every instance have 8 input attributes (from 1 to 8) and 1 output attribute (Y) as presented in Table 1.

Classification Methods

AdaBoost

AdaBoost is a type of boosting classification algorithm [START_REF] Freund | Experiments with a new boosting algorithm[END_REF][START_REF] Freund | A short introduction to boosting[END_REF]. This algorithm is used to intensify the predictive performance of multiple "weak classifiers" into a single "strong classifier" by focusing on data points. This technique works on "training" and "weighting" of classifiers. It assigns particular weight value to each instance given in the training dataset then iteration applied on the given algorithm. According to iteration the incorrect instances are increased and correct instances are decreased. This iteration is done to get the accuracy over weak classifiers. AdaBoost technique minimizes the exponential loss [START_REF] Baig | AdaBoost-based artificial neural network learning[END_REF] as follows:

The Adaboost Algorithms starts with the consideration of the training set form (a1,b1).....(an,bn), Where ai is the training set which belongs to the A (instance space) and bi belongs to B, where B = (-1,+1) [START_REF] Li | AdaBoost with SVM-based component classifiers[END_REF]. Adaboost always assigns a weak learning algorithm constantly in a series of different rounds such as m = 1........M. The weight of the training example 'i' on round, where't' is denoted as 𝑇 𝑛 (𝑖). The same weight has been set for the starting point (𝑇 1 (𝑖) = 1 𝑁 , 𝑖 = 1 … … … … 𝑁, and the weight for the misclassification will be increased as described in the following seven steps:

(i) Assign N examples as (𝑎 1 , 𝑏 1 ) … … … … … . (𝑎 𝑛 , 𝑏 𝑛 ) ∶ 𝑎 𝑖 Є 𝐴, 𝑏 𝑖 Є (-1, +1). (iv) Get weak hypothesis ℎ 𝑚 ∶ 𝐴 → 𝑅 with its error :

ℇ 𝑛 = ∑ 𝑇 𝑚 (𝑖) 𝑖=ℎ 𝑚 (𝑎 𝑖 )≠𝑏 𝑖 (1) (v) Choose ℇ 𝑘𝑚 = 𝑅 (2)
(vi) Update :

𝐷 𝑚+1 (i) = 𝐷 𝑚 (𝑖)exp (-𝛼 𝑚 𝑏 𝑚 𝑀 𝑚 (𝑎 𝑚 )) 𝑐 𝑚 (3)
where 𝑐 𝑚 is a normalization factor which is chosen so that 𝐷 𝑚+1 will be a distribution.

(vii) Output the final hypothesis will be like as follows:

𝐻(𝑎) = 𝑠𝑖𝑔𝑛(∑ 𝛼 𝑚 ℎ 𝑚 (𝑎) 𝑀 𝑚=1 (4)
On the basis of the above algorithm, different types of Adaboost algorithms have been developed such as ARTMAP-IC and GRNN [START_REF] Kayaer | Medical diagnosis on Pima Indian diabetes using general regression neural networks[END_REF]. Some other Adaboost algorithms may also helped in the decision making of medical practitioners to reduce the cost of medicines etc. Therefore, data pre-processing relief attributes selection and modest Adaboost algorithms may be used for the better outcomes [START_REF] Gong | RHSBoost: Improving classification performance in imbalance data[END_REF].

LogitBoost

LogitBoost is a type of binary classification technique. It is a pre-selection method used to perform the additive logistic regression for controlling the datasets with number of descriptive variables. It minimizes the logistic loss whereas the adaboost technique minimizes the exponential loss with training errors (Cusumano-Towner, 2012). It also minimizes the binomial deviance with the following formula:

∑ 𝑤 𝑛 log (1 + exp(-2𝑏 𝑛 𝑓(𝑎 𝑛 ))) (5) 𝑁 𝑛=1
Here 𝑏 𝑛 Є (-1, +1) and 𝑤 𝑛 are observation weights normalized up to 1.

𝑓(𝑎 𝑛 )Є (-∞, +∞), which is the classification score (assumed).

The Binomial deviance allocate less weight to the inadequate misclassified observations means the observations with large negative values of 𝑏 𝑛 𝑓(𝑎 𝑛 ). By applying LogitBoost, we can get better accuracy than AdaBoostM1.

RobustBoost

Robustboost boosting algorithm is a modified version of AdaBoost algorithm. It is used for the justification of robustness. As AdaBoost and LogitBoost algorithm increases the weight of incorrect instances by focusing on data points. Hence, weight can become very large due to this type of correlation. However, RobustBoost algorithm has been applied because it allocates the entire data weight to incorrect/misclassified instances [START_REF] Pouya | A new margin-based AdaBoost algorithm: Even more robust than RobustBoost to class-label noise[END_REF]. This type of boosting technique is employed to number of application on various classification techniques. Robustboost algorithm doesn't allocate the entire data weights to the poorly misclassified observations. This algorithm may produce better average classification accuracy comparison to others. This algorithm does not minimize a specific loss function as occurred in AdaBoostM1 LogitBoost.

Robustboost algorithm exaggerates the sample number with distribution margin above a defined threshold [START_REF] Venema | Non-Convex potential function boosting versus noise peeling:-a comparative study[END_REF].

The RobustBoost algorithm trains with time evolution. The algorithm initiate with t = 0 and after each iteration, it solves the formulated optimization problem in order to search in a direction of a positive step in interval of time Δt. A similar searching has been formulated to compute positive changes in the average margin for considered training data sample Δk. The algorithm stops and exits the training if any one of three logical conditions becomes positive. The algorithm failed to compute the optimization problem solution in direction of positive updates Δt and Δk, when time t reaches up to 1. The algorithm is going to breed as many learners as request occurred. The results obtained from this algorithm may set at any of the termination condition. Hence, to achieve better classification accuracy using RobustBoost algorithm, fitc ensemble: Robust_Error_Goal, Robust_Max_Margin, and Robust_Margin_Sigma may set and it starts with the variation of the values for Robust_Error_Goal from 0 to 1.

Naïve Bayes

A Naïve Bayes classifier is a type of probabilistic classifier. It is usually used for medical diagnosis. This classifier has one of the significant advantage over the other, it require only a small scale of training dataset for estimation. This technique work with the assumption of the existence or non-existence of a specific feature of a class that is unrelated to the existence or nonexistence of any other feature [START_REF] Farid | Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks[END_REF]. Naive Bayes Classifier focuses only on the textual content of the messages. The working filters consider the information such as the existence of doubtful headers, which add the additional attributes in the message representation. For example, let us consider an experiment in which all messages are denoted such as vector vx1 . . . . . . . .vxn where, x1 ……….. xn are the values of attributes. All the attributes gives the information of specific token of the messages. In the easiest case, every attributes are from Boolean:

𝑋 𝑖 = 1 , (if the message holds the token); 𝑋 𝑖 = 0 , (else).

From Bayes' theorem, the probability that a message with vector x ⃗ = 𝑥 1 … … … … . 𝑥 𝑚 which belongs in the following category c: p(𝑐|𝑥 ) = p(𝑐). p(𝑥 |𝑐) p(𝑥 ) (6)

Bagging

Bagging is one of an ensemble data mining techniques. It generates multiple classifier versions and aggregates the dataset predictions [START_REF] Perveen | Performance analysis of data mining classification techniques to predict diabetes[END_REF]. The numeric prediction works with the averaging the individual resultant prediction whereas, classifiers decide the prediction class by voting system of each predictors votes. However, for the better accuracy the requirement of bagging is instability of the underlying predictors. Instability of the predictor means significant changes in the predictor construction if any of perturbation of training set causes.

Bagging is also known as "bootstrap aggregation," because of the ensemble learning properties.

In this algorithm bagging of a weak learner on a dataset has been done by generation of many bootstrap replicas on this dataset and decision trees have been grown on these replicas. Each bootstrap replica has been obtained by selecting N observations randomly out of N with replacement, where N is the size of dataset. Finally, an average has been computed over predictions from individual trees to compute the predicted response of a trained ensemble. Equation ( 7) represents the formula to find the result for the assumed dataset.

𝑒 𝑎𝑑𝑑 𝑎𝑣𝑒 = 𝐸 𝑇 {𝐸 𝑡 𝑏 |𝑇= 𝑡 𝜖 2 (𝑥 𝑏 (𝑡 𝐵 ) + 1 𝑚 [𝑉 𝑡 𝐵 |𝑇=𝑡 𝜖(𝑥 𝑏 (𝑡 𝐵 ); )𝑡 𝐵 ])} (7)
In this equation e add ave is allocated by the same bias component of the prospect added error e add of a single bootstrap. Here

Measurement and Experimental Result 4.1 Gold Standards Test

Gold standards test refers to a diagnostic test that is the best available test under reasonable conditions. Following are some conditions with description, which are applied in gold standards which are tabulated in Table 2.

Sensitivity

In terms of Gold standard, sensitivity means the capacity to correctly choose the positive patients who are suffered with the disease. Basically, sensitivity has been used to determine any disease in classification technique and therefore, sensitivity of the test is equal to the proportion of number of true positive patients and total number of sick individuals in population. Mathematically, it may be represented as Equation ( 8):

𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 = 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑇𝑃 𝑇𝑃 + 𝐹𝑁 (8)
where TP represents True Positive and FN represents False Negative.

Sensitivity means the probability of positive test that patients have the disease.

Specificity

In terms of Gold standard, specificity relates to choose the patients correctly but without any condition. In classification technique, when we diagnose the disease by applying the classification technique then it is equal to the number of true negative patients divided by total number of individuals in dataset. It is formulated as in Equation ( 9):

𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦 = 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑇𝑁 𝑇𝑁 + 𝐹𝑃 (9)
where TN represents True Negative and FP represents False Positive.

A positive result in any classification test with high range of specificity is effective for taking any decision in any type of disease.

Positive Predictive Value

In terms of Gold Standard, the positive predictive value (PPV) is an event and it predict only the positive prediction. Positive prediction is done by false positive (FP) event. Mathematically, it is defined by Equation ( 10):

𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑣𝑒 𝑉𝑎𝑙𝑢𝑒 = 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑇𝑃 𝑇𝑃 + 𝐹𝑃 (10) where TP represents True Positive and FP represents False Positive.

Negative Predictive Value

Negative predictive value (NPV) predicts the true negatives among the complete dataset by choosing negative test. It represents the negative result for statistics and diagnostic test with the defined mathematical equation. It is equal to probability of patient not having any disease when test is negative. This can be done with the following formula:

𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑣𝑒 𝑉𝑎𝑙𝑢𝑒 = 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑇𝑁 𝐹𝑁 + 𝑇𝑁 (11)
where TN represents True Negative and FN represents False Negative.

Accuracy

Accuracy of a measurement system represents number of correctly diagnosed patients in dataset weather it is positive or negative. This can be calculated with the following mathematical formula as shown in Equation ( 12

Experimental Result 5.1 Results with Adaboost Technique

The Adaboost technique algorithm has been employed on the considered testing data sets and the overall accuracy has been found to 79.68%. However, this algorithm improves the accuracy marginally over the neural network techniques. The generated confusion matrix is illustrated by Table 3.

Results with Logitboost Technique

With the help of Logitboost technique the overall accuracy has been found is 78.64, shown in Table 4.

Results with Robustboost Technique

The Robustboost technique algorithm has been employed on the considered testing data sets and the overall accuracy has been found to 78.64%. However, this algorithm improves the accuracy marginally over the neural network techniques. The generated confusion matrix is illustrated by Table 5.

Results with Naïve Bayes Technique

Table 6 shows the confusion matrix for Naïve Bayes technique and with the help of this technique 76.04% accuracy found.

Results with Bagging Technique

Bagging technique is applied on test dataset and the resultant overall accuracy obtained was 81.77%. The confusion matrix generated for showing result using bagging, which is shown below in Table 7:

Result and Comparison with Performance Measure 6.1 Result Analysis

In this study, the prediction competences of six machine learning techniques have been employed for the classification of diabetes. Total number of 768 instances has been used in this dataset. Every instance has eight input attributes and one output attribute. The predicted results using the six proposed machine learning techniques with confusion matrix of Adaboost, Logitboost, Robustboost, Naive Bayes and Bagging have been presented in Tables 3 to 7 respectively. All the predicted results using the proposed techniques have been compared and plotted in Figure 2. The predicted results clearly reveal that bagging algorithm predicts highest accuracy as comparison to other technique.

Comparison with Previous Techniques

In this study, a comparative analysis has been made with proposed techniques and previous reported studies in the Table 8. The first comparison has been made with ARTMAP-IC network and GRNN structure [START_REF] Kayaer | Medical diagnosis on Pima Indian diabetes using general regression neural networks[END_REF] and is found the accuracy of 81% and 80.21% respectively. In second study, [START_REF] Temurtas | A comparative study on diabetes disease diagnosis using neural networks[END_REF] MLNN with LM method (10*FC) has been used and results with classification accuracy of 79.62. The next algorithm has been considered to compare the predicted results is Bayes Network [START_REF] Guo | Using Bayes network for prediction of type-2 diabetes[END_REF]. Bayes Network has been implemented for the classification and result yield with accuracy of 72.3%. The next study has been considered with the use of ANFIS with Adaptive kNN algorithm [START_REF] Kalaiselvi | A new approach for diagnosis of diabetes and prediction of cancer using ANFIS[END_REF] and employed on the considered dataset with accuracy of 80%. The next algorithm has been implemented very recently in 2018 [START_REF] Dwivedi | Comparative study of artificial neural network for classification of hot and cold recombination regions in Saccharomyces cerevisiae[END_REF]. These algorithms are Logistic regression, ANN and Naive Bayes and its results revels the accuracy of 78%, 77% and 75% respectively. Finally, the proposed algorithm, Bagging has been implemented on the same database and generating the accuracy of 81.77%. The result clearly indicates that Bagging algorithm yield highest accuracy than others compared to others. ANN [START_REF] Dwivedi | Comparative study of artificial neural network for classification of hot and cold recombination regions in Saccharomyces cerevisiae[END_REF]) 77%

Naive Bayes [START_REF] Dwivedi | Comparative study of artificial neural network for classification of hot and cold recombination regions in Saccharomyces cerevisiae[END_REF]) 75%

Bagging [START_REF] Dwivedi | Comparative study of artificial neural network for classification of hot and cold recombination regions in Saccharomyces cerevisiae[END_REF] 81.77

Conclusion

Prediction of diabetes in the primary stage is better to cure. Therefore, some of the important Machine Learning Techniques have been applied to present work for the classification of "Diabetes Prediction" based on eight attributes. The considered machine learning techniques are AdaBoost, LogitBoost, Robust Boost, Naïve Bayes and Bagging. The proposed algorithms have been employed on the database of PIMA Indian Diabetic patients for classification diabetic patients. Out of these techniques, Bagging performed the highest accuracy of 81.77% in comparison to other proposed techniques and previous reported studies. Hence, the proposed technique i.e. bagging implemented in the present work is highly adorable, effective and efficient in order to predict the diabetic patients. Additionally, this work can be considered for the functional diabetes prediction as it is generalizing the best accuracy.

  Now using distribution 𝑇 𝑛 , Train the weak learner.

1m

  is denoting the variance component. If the ensemble size m increases than the 1 m (bagging expected error) decrease and will be equals to single bias component of single bootstrap as E T {E t b |T=t 2 ɛ (x b (t B ); t B )}. This result holds a training set t, without taking the expectation over T.

Figure 1 .

 1 Figure 1. Comparison chart for GOLD standards

Figure 2 .

 2 Figure 2. Comparison Result with applied machine learning techniques

Table 1 .

 1 Dataset attributes of PIMA Indian Diabetic patients All types of input attributes include the numeric data values only with 768 female samples and the very first attribute indicate the counts of the pregnant patient. The next attribute referred to concentration level of glucose in the human body. The third attribute symbolize the measurement of blood pressure (diastolic) in mmHg. The fourth attribute illustrates the thickness of skin fold measured in mm. The fifth, sixth and seventh attributes describes the total quantity of insulin production, body mass index [Body Mass Index (BMI) = (Patient's weight in kg) / (Patient's height in meter)] of the infected patients and dependence of diabetes family hierarchy respectively. The last attribute cited to the current age of patients. The proposed classification techniques namely Bagging, Boosting and Naive Bayes Classifier have been employed on the above data set to evaluate the efficient, adorable and fine accuracy.

	Attribute No.	Attribute Description
	1	Counts of pregnant patient
	2	Concentration level of plasma glucose in an oral glucose tolerance test
	3	Blood pressure (Diastolic) in mmHg
	4	Thickness of Triceps skin fold in mm
	5	Quantity of insulin production
	6	Body mass index
	7	Diabetes pedigree function
	8	Age of patient in years

Table 2 .

 2 Conditions for GOLD standards test

	Sr. No	Condition	Description
	1	True positive	Sick people correctly diagnosed as sick
	2	False positive	Healthy people incorrectly identified as sick
	3	True negative	Healthy people correctly identified as healthy
	4	False negative	Sick people incorrectly identified as healthy

Table 3 .

 3 Confusion matrix with Adaboost technique

	Predicted ->	False	True
	False	109	13
	True	26

Table 4 .

 4 Confusion matrix with Logitboost technique

	Predicted ->	False	True
	False	106	16
	True	25	45

Table 5 .

 5 Confusion matrix with Robustboost technique

	Predicted ->	False	True
	False	106	16
	True	25	45

Table 6 .

 6 Confusion matrix with Naïve Bayes technique

	Predicted ->	False	True
	False	103	19
	True	27	43

Table 7 .

 7 Confusion matrix with bagging technique

	Predicted ->	False	True
	False	112	10
	True	25

Table 8 .

 8 Comparison with previous techniques

	Method

Acknowledgement

The authors would like to express their sincere thanks to the Graphic Era Deemed to be University for providing the resources and support to complete this paper.

Conflict of interest

The authors confirm that there is no conflict of interest to declare for this publication.


Martin Lemay

Fabien Ors
email: fabien.ors@mines-paristech.frflumy@geosciences.ensmp.fr

Jean-Louis Grimaud

Jacques Rivoirard

Isabelle Cojan

Xavier Freulon

Forward model applied to channelized turbidite systems: a case

come

Turbidite Model Overview

Cross-section view

Model Results

Objectives

Channelized turbidite systems are associated with extensive hydrocarbon reservoirs. Yet building realistic turbidite reservoir models is still a challenge. We take advantage of some similarities between fluvial and turbidite environments to transpose the Flumy model, initially dedicated to fluvial reservoirs (Lopez et al., 2008), to channelized turbidite systems by simulating the main processes at play in the submarine realm: channel lateral migration, avulsion, aggradation, overflowing, flow stripping, and sediment transport (Lemay et al., 2018). A flow compatible with the input channel geometry parameters is first built. This flow controls the channel evolution through time and thus the stratigraphic architecture of deposits, as well as their grain size. In this study, we present the application of Flumy to the case study of the Benin major valley. (1999)

Bibliography

  Deptuck, M. E., Sylvester, Z., Pirmez, C., & O'Byrne, C. (2007) Marine and Petroleum Geology, 24(6-9), 406-433. Imran, J., Parker, G., & Pirmez, C. (1999) Journal of Fluid Mechanics, 400, 295-331. Lemay, M. (2018). PhD, PSL Université Paris, 176p. Lopez et al. (2008) Analogue and Numerical Modelling of Sedimentary Systems: From Understanding to Prediction, 139-144. reproduces the morphology of the valley, most of observed geomorphic features, and the various styles of filling architectures. It also results in a complex grain size arrangement which controls reservoir connectivity. This study shows that the model reproduces realistic stratigraphic architecture and can be used to simulate channelized turbidite reservoirs.

Acknowledgments

This study has been conducted within the scope of the Flumy Research Program. The authors are grateful to ENGIE and ENI partners for support.


Martine Gadille
email: martine.gadille@univ-amu.frimpedovo

Maria Antonietta A Impedovo

Gadille Martine

Maria Antonietta
email: maria-antonietta.impedovo@univ-amu.fr

School Goes Online With Avatars: Extended Learning in a Secondary School

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

For long time, the walls of the classroom and the school were the limited space where students shaped their learning. This "broadcasted mode" gave a central role to teachers and scholastic books. Technology, the multicultural dimension and continuous social and economic change have disrupted and undermined the traditional system: learning environments have multiplied, going online [START_REF] Tapscott | Grown up Digital[END_REF] in clouds. It is acknowledged that learning technologies do not necessarily involve innovation, rather they are catalysts that, when used well to carry out a communal piece of work or task, allow high engagement and active learning between school and life [START_REF] Dawley | Situated learning in virtual worlds and immersive simulations[END_REF] and within virtual communities [START_REF] Preece | Sociability and usability in online communities: Determining and measuring success[END_REF].

One way to extend learning between the classroom and the virtual realm is the use of MUVEs. Made popular by Second Life, the MUVE is an immersive 3D virtual space where people, entering the space via their avatars, meet and interact with one another and with 3D objects in real time. The use of a virtual space in the classroom can introduce changes to how the teacher executes, facilitates and releases the learning through authentic tasks involving 3D object manipulation, creativity and corporal mobility via avatars, which bring change to how the learner acquires, applies and constructs knowledge [START_REF] Jung | A model for education: Extended teaching spaces and extended learning spaces[END_REF]. This paper focuses on the initial implications for students' extended activity between virtual and in-presence learning, from the general broad perspective of how technology-supported collaboration can facilitate the sharing and dissemination of knowledge and competencies among community members (Di Blass and Paolini, 2014). The study is part of an ongoing project entitled "e-PIm" taking place in a pilot secondary school in France (students from 11 to 15 years old). For this study, some data are analysed and initial research results are presented.

The Project: Incubator of Immersive Pedagogy for Virtual Reality

The project "e-PIm" takes place in a pilot school, in line with the Digital Plan for Education launched by the French government in May 2015 and related to the appropriation of tablets distributed by the academy as part of the "Connected Schools" project. The school set out on the path of digital experimentation in 2016 and is being financially supported by the Digital Education Directorate from 2018 to 2021. The three aims of the project are: 1) to propose a scientific study over a period of 4 years (since September 2018). It is about the technological and social conditions of appropriation of virtual reality, in a network of primary and secondary schools; 2) to support, on the territory of the academy via a collaborative network, the sharing of knowledge resulting from the joint work of researchers and teachers; 3) to create a training guide for trainers or teachers wishing to get involved in this field.

The general project considers Participatory Action Research [START_REF] Wadsworth | What is Participatory Action Research? Action Research International[END_REF] for an interdisciplinary approach, involving didactics, sociology, psychology and management science. Within this method, participatory and collaborative processes are developed with and the aim of achieving a critical change of practices, through continuous spirals of planning, action, observation, reflection and re-planning [START_REF] Mcintyre | Participatory action research[END_REF]. The research is built on four immersive pedagogy projects developed by the teachers at the school in collaboration with the ITC and research partners: 1) "Interdisciplinary" is an experiment in 3D scriptwriting of lessons (English, Italian, French, History, Mathematics, Technology), on two 5 th -year classes in which students are asked to build 3D objects in the virtual school and give meaning to them within the framework of a scenario dealing with the seven new wonders of the world; 2)"Language and International" focuses on remote linguistic exchanges between the French students and a class of correspondents, via their avatars who visit and explain the meaning of a virtual exhibition prepared beforehand and showing photos of their favourite places in their city;

3) "Homework" is about help with distance work that allows small groups of students to benefit from homework support provided by a school teacher in their home environment at the end of the afternoon; 4) "Work on SEGPA": The MUVE is used here for learning in two different trades as well as in general disciplines.

The global strategy of data-gathering, launched back in 2016 and still ongoing, is proposed in a longitudinal approach. A multilevel approach considers the territorial, organisational, collective and individual level. In this way, we view the appropriation of the 3D space from a different, complementary and interdisciplinary perspective, as represented in Figure 1: A Figure 1: Multilevel approach to data-gathering

Methodology

Ethnographic observations (with note-taking and photos), analysis of the virtual space (written text in chat, forum, recording of online activity and online discussion) and collection of practices inside the school (verbal exchanges, report minutes, individual interviews) have been made since January 2016. To organise the data, the NVivo software is used to structure the corpus and encode it with specific dimensions and shared encoding between the researchers' group following a grounded approach.

The aim of the study

The research questions are: What are the main first direct implications of the project for students' extended activity between virtual and in-presence learning? What indirect implications does the project have for teachers and the school in general?

Initial analysis of data

For an answer to the research questions of this study about the initial direct implications of the project for students' extended activity between virtual and in-presence learning and the indirect implications of the project for the teachers involved and the school in general, the corpuses of data selected are: five student interviews and direct online and in-classroom observation; nine teacher interviews on different subjects relating to students online and in-classroom activity; two video recordings of meetings between teachers and researchers. An analysis of content and discursive exchanges is applied to data [START_REF] Bezemer | Young people, facebook, and pedagogy: Recognizing contemporary forms of multimodal text making[END_REF]. Four dimensions emerged from the selected data: MUVE didactic affordances; MUVE student-teacher interaction; MUVE students' corporal and socio-cognitive behaviour. Regarding the 3D space's affordances for didactic activity, for the teachers, we observed a rise in mental load with an increased permeability between working time and home time for the scripting activity. This time seems counterbalanced by the benefit of being able to make children work with this use of technology. The teachers wonder about the opportunity presented by the introduction of the 3D space in relation to their pedagogical script, linked to the affordances of the technological tools. The understanding of the 3D environment's potential offers new opportunities, as this extract shows: "When the coordinator told me about avatars, I said to myself, why not? We can exchange objects, and on Skype, it does not always work very well". In maths also, the 3D space is interesting to some teachers as it abolishes the physical limitation: "the skills learned: moving on a map, I extended it to the virtual school, we had only two dimensions". For each discipline at the school, the use of the 3D space represents differentiated interests besides the fact that it improves the possibility of situated learning. Students, on their part, stress the evolution of the use of the virtual space, as a girl from 5 class put it: "We started before the Christmas holidays, but at the beginning we did not go as often as now, when you do not know what to do about it ...".

Findings

Regarding the effect of the 3D space on student-teacher interaction, linked with the ITC opportunity, teachers suggest that use of the 3D environment improves interaction with students, which can be different, focusing on a new modality of learning. For example: "In the classroom I move around to advise and help, and it's easier when you have an assistant. On the platform, it is all the more possible because we have the headphones and the microphone, in the classroom it is difficult, there is a lot of noise that can disrupt communication, but in the virtual school not yet". The use of 3D helps teachers to interact with students in a new way, with a "silent body", the avatar, which allows idealistic communication without disturbing interferences.

Regarding the effect of the 3D space on the behaviour of the students, the teacher interviews stress the physical implications for their classroom activity. Although the students can be "quieter" in the classroom because they are sitting ("the students are no longer disturbing the class when they are able to connect"), paradoxically, students are very active online ("Children in the space are much more dynamic, there they were very active behind their screen"). At the same time, the use of the 3D space can also help to support the students in a particular part of the day or of the year, when they are more tired ("at the end of the year we must also give new motivation to the good students, who begin to get tired, and the students with difficulties..."). Finally, for the children interviewed, the 3D space represents either a space of creativity, or a space of motivation and resilience used at school as well as at home. Its uses when children are at home, helps and support distant exchanges with their teacher in the aid device to homework's in particular. When used to achieve collective tasks in class, it enhances exchanges also, for profiles of very shy children who have difficulty participating.

Regarding the effect of the 3D space on institutional change, during the process and the exchange between teachers, researchers and students, different points are constantly being negotiated, like the right to take pictures of students' faces and the authorisation to destroy 3D objects or virtual documents ("if we leave the documents in the immersive space, they can be damaged, modified, or deleted, how do we protect them?"). As a consequence, the script with the MUVE is continually evolving in relation to technical and administrative aspects, linked to the prescriptions of the curriculum and teachers' preferences, like in this teacher's comment: "… unless the government comes back on those two hours a week, we work together with a maths teacher who guided us to the virtual school: with the avatar it is better to break the ice, offering a nice, warm welcome in a space for discussion".

Discussion

As shown by the initial data analysis, the implementation of the project, and in particular the MUVE technology, have had and still have implication for didactic, student-teacher interaction, students' corporal and cognitive behaviour and institutional change, becoming an ongoing transformative learning experience.

Regarding the 3D space affordances for didactic activity and student-teachers' interaction, we stress how the introduction of technology oblige the teacher and also the student to rethink the traditional model, as widely discussed in the technology. The process of appropriation also includes aspects concerning the mutual influence between the technology and the users [START_REF] Overdijk | Appropriation of a shared workspace: Organizing principles and their application[END_REF], with a simultaneous transformation process including the learner and the tool and where the learners are also teachers. The originality of our initial finding lies in the socio-professional and organisational conditions for teachers to develop individually and collectively creative scripts within their professional group Regarding MUVE students' corporal and cognitive behaviour, the introduction of new digital technologies such as MUVE puts a new non-linguistic, bodily and visually learning dimension at the centre of users' experience, removing language from the dominant vector of the experience. Moreover, teachers are becoming aware through their experience of the MUVE that extended learning between online and in-presence also implies an embodiment framework as a unifying perspective, considering that all psychological processes are influenced by body morphology, sensory systems, motor systems, and emotions [START_REF] Glenberg | Embodiment as a unifying perspective for psychology[END_REF][START_REF] Schubert | Embodiment as a Unifying Perspective for Psychology[END_REF].

Regarding technology in MUVE institutional change, an educational institution generates tensions between an old system of working and the new one, which needs to be accommodated and assimilated by all of the community involved [START_REF] Jones | Learning technologies: Affective and social issues[END_REF]. At the same time, the students, teachers and all the administrative staff of the school, supported by the ITC office and also researchers in geographic proximity, need to make sense of the new technology alone and as a group, finding a new opportunity for learning and action.

Conclusion

The observation and data collected show that this experimentation with a MUVE extends and strongly transforms the learning space by giving teachers, students, parents and the whole educational team the opportunity to exchange, strengthen and build new ways of experiential learning [START_REF] Jarmon | Virtual world teaching, experiential learning, and assessment: An interdisciplinary communication course in Second Life[END_REF] through projects, involving a wide range of experience (manipulation, social exchange, 3D object creation) from various disciplines. The originality of our work lies in the general application of the MUVE space in the school. We can consider how extended learning is enacted following a multilevel approach, with implications for organisational, collective, individual and territorial transformation. In order for expansive learning to take place, students, teachers and researchers need to be involved in collaborative actions, generating a rich exchange of practices enabling the transformation of the activity. The adoption of the Participatory Action Research, involving the students, the research team, teachers and ITC partners on a daily basis, enables this collective work oriented towards a critical change of practices, through continuous spirals of planning, action, observation, reflection and re-planning. These initial results will be further analysed in the longitudinal appropriation of the 3D MUVE by the school.


Manon Rossano

Nicolas Hucher

Céline Picard

Daniel Colletta

Frank Le Foll

Michel Grisel

Effects of aging on the structure and stability of TiO 2 nanoparticle-containing oil-in-water emulsions

Keywords: Titanium dioxide nanoparticle, sunscreen, particle size measurement, rheology, destabilization, coating

Formulations incorporating different cosmetic grade TiO 2 nanoparticles were developed according to a self-produced protocol on the basis of typical sunscreen cream oil-in-water emulsions. Role of nanoparticles and, more specifically, the impact of two different lipophilic surface treatments on microstructure and stability of the formulations were assessed. Aging of formulations was performed under classical conditions at room temperature or under accelerated conditions at 50°C, with or without TiO 2 , and was characterized by several tools such as rheology, microscopy, and particle size measurements. Changes in emulsion stability and aggregation state of nanoparticles were followed over time. Destabilization phenomena were identified: under accelerated aging, the formulation without nanoparticles underwent a coalescence occurrence whereas the formulation incorporating nanoparticles was subjected to aggregation of the colloidal particles. Besides, TiO 2 nanoparticles strongly affected the particle-droplets interactions and thus modified the emulsion microstructure with a coating-dependent effect: destabilization mechanisms occur more rapidly with alumina (and) triethoxycaprylylsilane (coating 1) TiO 2 nanoparticles as compared to alumina (and) isopropyl titanium triisostearate (and) triethoxycaprylylsilane crosspolymer (coating 2) nanoparticles. Chemical compounds studied in this article: Titanium Dioxide (PubChem CID 26042); Alumina (PubChem CID 14769); Triethoxycaprylylsilane (PubChem CID 76262); Isopropyl Titanium Triisostearate (PubChem CID 162941)

Introduction

A nanomaterial is defined by the European commission (Commission Recommendation of 18 October 2011 on the Definition of Nanomaterial," Official Journal of the European Union, 2011, sec. L 275/38) as "a natural material formed accidentally or manufactured, containing not bonded particles, in an aggregate or agglomerate form, and where at least 50% of the particles in number size distribution have one or several dimensions of between 1 nm and 100 nm". In March 2011, a nanotechnology consumer products inventory reported a list of more than one thousand goods, including 33 sunscreens ("Nanotechnology -Project on Emerging Nanotechnologies"). Nano-sized TiO 2 is the most used inorganic filter in the cosmetic industry, and is incorporated as UV mineral sunscreen because this mineral provides effective protection from solar radiation [START_REF] Serpone | Inorganic and Organic UV Filters: Their Role and Efficacy in Sunscreens and Suncare Products[END_REF], together with a moderate skin covering effect related to their nanoscale size [START_REF] Popov | TiO2 Nanoparticles as an Effective UV-B Radiation Skin-protective Compound in Sunscreens[END_REF]. Raw TiO 2 nanoparticles have hydrophilic surfaces consisting in polar hydroxyl groups. Thus, surface treatment is used in cosmetic industry to improve both the compatibility and the performance of the particles. The most common treatments for nanoscale titanium dioxide cosmetic grade are: -A grafting of aluminum oxide Al(OH) 3 or Al 2 O 3 which reduces the photocatalytic activity and production of derived reactive oxygen species observed when TiO 2 is exposed to UV [START_REF] Dunford | Chemical Oxidation and DNA Damage Catalysed by Inorganic Sunscreen Ingredients[END_REF]; -A lipophilic surface treatment in order to improve the dispersion of nanoparticles in the oily phase of the emulsion, such as dimethicone, stearic acid or silane components. Assumptions on the locations of coatings have been issued [START_REF] Lewicka | The Structure, Composition, and Dimensions of TiO2 and ZnO Nanomaterials in Commercial Sunscreens[END_REF]): the lipophilic layer would overlap that of Al(OH) 3 , which itself covers the nanoparticle. TiO 2 based sunscreen emulsions have been until now poorly investigated [START_REF] Botta | TiO2-based Nanoparticles Released in Water from Commercialized Sunscreens in a Life-cycle Perspective: Structures and Quantities[END_REF][START_REF] Labille | Aging of TiO2 Nanocomposites Used in Sunscreen. Dispersion and Fate of the Degradation Products in Aqueous Environment[END_REF][START_REF] Lewicka | The Structure, Composition, and Dimensions of TiO2 and ZnO Nanomaterials in Commercial Sunscreens[END_REF]. Recently, aging of commercial sunscreens has been studied in order to assess the alteration of the nanoparticles used in cosmetic formulations [START_REF] Botta | TiO2-based Nanoparticles Released in Water from Commercialized Sunscreens in a Life-cycle Perspective: Structures and Quantities[END_REF]. However, as all previous investigations were based on commercial creams, authors could not state exactly on the role of the nanoparticle species by themselves. The originality of the present work lies in the production of a non commercial formulation, but similar to one, containing cosmetic grade nanoparticles. As a consequence, for the first time, characterization of the system was performed by using an emulsion where each ingredient was identified, obtained using a fully controlled process, and as a consequence, it was possible to understand the contribution of nanoparticles themselves. From a general point of view, colloidal residues containing TiO 2 nanoparticles from personal care products may be released into aquatic environment [START_REF] Botta | TiO2-based Nanoparticles Released in Water from Commercialized Sunscreens in a Life-cycle Perspective: Structures and Quantities[END_REF]. Indeed, destabilization mechanisms of such systems are important to understand, in order to optimize formulation processes, to adapt the duration of use of products or to correlate physicochemical properties with potential toxicity. Thus a detailed characterization and identification of nanomaterials is an essential requirement for risk assessment during its entire life cycle [START_REF] Johnston | Identification of the Mechanisms That Drive the Toxicity of TiO2 Particulates: The Contribution of Physicochemical Characteristics[END_REF].

Herein, we have developed three specific formulations consisting in an oil-in-water emulsion without nanoparticles and "derived" emulsions containing TiO 2 nanoparticles with two different coatings. A characterization was then performed at both initial state and during aging. Rheology, microscopy and particle size measurements were used to point out distinct features of the emulsions. Dynamic viscoelastic measurements provided information on the sample structure and more deeply on the intermolecular and interparticle forces in the material. Microscopy and particle size determination gave information on size distribution of the colloidal particles in the emulsion. Finally, combining the different results led to a better understanding of the role of the nanoparticles in the destabilization mechanisms.

Experimental section

Chemicals and reference materials

Purified water, with a resistivity of 15 m𝜴.cm, was used for all experiments, emulsions preparation and particle size analysis. TiO 2 nanoparticles with two different coatings (see Table 1) have been used in the present study: alumina (and) triethoxycaprylylsilane, named coating 1, alumina (and) isopropyl titanium triisostearate (and) triethoxycaprylylsilane crosspolymer named coating 2. Samples were kindly given by Kobo Products, France. According to the manufacturer, the caprylylsilyl group is chemically bonded to the pigment and the primary particle size of the particle is given to be 10 nm [START_REF] Kobo | Triethoxycaprylylsilane Treatment[END_REF]. Ingredients were chosen to achieve an oil-in-water emulsion, close to a commercial sunscreen formulation. This formula was deliberately optimized in order to allow an easy nanoparticles incorporation procedure, and also with a view to study toxicity afterwards. The formulation without nanoparticles is called F-0, and those with nanoparticles are F-N1 for the coating 1 and F-N2 for the coating 2, respectively. All the ingredients used for the formulations and their content in both formulations are listed in Table 2.

Emulsion preparation

This protocol has been developed in order to obtain similar emulsions whether the nanoparticles are added or not.

Emulsion without nanoparticles

The emulsion was prepared according the following optimized procedure: The gelling agent was first sprinkled in water and left without stirring for 20 min. This mixture and the phase A were then heated separately at 80°C under mechanical stirring to get homogenization. When both phases were at 80°C, phase A was added to the aqueous phase under stirring at 11,000 rpm for 1min30 with a rotor-stator type homogenizer (Ultra-Turrax, stator diameter 25mm, rotor diameter 18mm, IKA). Then the mixture was left cooling down under mechanical stirring at 500 rpm with mixer (Turbotest, radial flow turbine of 55mm diameter, VMI Rayneri), and triethanolamine was immediately added (before the temperature attained 50°C) to reach a final pH of 6.6 corresponding to polymer neutralization. The mixer speed was then increased to 1000 rpm, for 15 min thus making the cream becoming more consistent. Once at 40°C, the mixture was cooled down using a cold water bath, until 30°C was reached while continuously stirring. The preservative was added below 30°C, and the mixture was stirred for 5 additional minutes. The cream was then put under vacuum in order to remove the air bubbles incorporated during process.

Nanoparticle dispersion

To improve the dispersion of nanoparticles in the emulsion, a paste of TiO 2 nanoparticles was prepared by mixing with spatula 40% (w/w) titanium dioxide with 60% (w/w) cetearylethylhexanoate. This paste was then grounded on a 3-cylinder mill until a fine dispersion was obtained, controlled by optical microscopy for homogeneity. Typically, the dispersion with coating 1 needed to be grounded once, and the one with coating 2 required grounding for three times. The resulting paste was used to prepare the emulsion with nanoparticles.

Emulsion with nanoparticles

The previous protocol described above for emulsion preparation remains roughly the same except when phase A was added to the aqueous phase under stirring at 11,000 rpm for 1min; then the nanoparticle dispersion was immediately incorporated under stirring during one more minute under vigorous stirring. In order to get a final percentage of 5% of nanoparticles in the emulsion, the effective amount of cetearylethylhexanoate necessary to get a right dispersion had to be previously calculated.

Monitoring stability and aging process

In order to test the stability of the three emulsions over time, two batches of each emulsion were stored for three months at:

-Room temperature 20°C ± 2°C, in plastic opaque flasks, -High temperature 50°C± 1°C, in closed glass flasks in a heating ventilated oven (Heraues Thermo Scientific).

Storage at 50°C enabled to accelerate the aging process and speeded up the mechanism of destabilization of the emulsion. At each storage temperature, samplings for analysis were made at different time after emulsification: t 0 +24 hours, t 0 +72 hours, t 0 +7 days, t 0 +14 days, t 0 +21 days, t 0 +28 days, t 0 +43 days and t 0 +65 days. The different analyses described below were performed on each sampling.

Particle size measurement

Particle size analysis was performed using a laser diffractometer Mastersizer 2000 (Malvern Instruments, UK). The complex refractive index of the material to be measured was required. The real part of the refractive index has been determined by refractometric measurements. Considering previous experiments which gave the more relevant results, optical properties of the sample were thus defined as follow:

-For F-0: refractive index 1.450 and absorption 0.1 -For F-N1 and F-N2: refractive index 1.458 and absorption 0.1; The calculation model used was the general purpose model, with spherical particle shape. Approximately 0.1 g of emulsion was diluted in 600 mL of distilled water. The mixture was dispersed using ultrasonic waves for 2 minutes, until an obscuration rate of 10-20% was obtained. Background and sample were measured for 10 s, each sample being measured in triplicate.

Rheology

Rheological properties were carried out on a controlled-stress rheometer (AR 2000, TA Instruments) using a cone-plate acrylic geometry (diameter: 40 mm, nominal cone angle: 0.59°, truncation: 27 µm). Because of the presence of solid nanoparticles aggregates in emulsions F-N1 and F-N2, preliminary tests were performed on both emulsions with different measuring systems (plate-plate and cone-plate) in order to determine the right experimental conditions. Strain sweeps were carried out at 25°C, as controlled by Peltier effect, with imposed strain ranging from 0.01 to 100% at a fixed frequency of 1 Hz. This measurement permitted to evidencing the linear viscoelastic plateau corresponding to constant viscoelastic parameters whatever the applied strain. Measurements were performed in triplicate for each batch.

The rheological parameters measured are listed below:

-tan(𝛿) where 𝛿 is the phase angle of the stress with respect to the strain; -G'', the loss (or viscous) modulus, G''=G* sin 𝛿; -G', the storage (or elastic) modulus, G'=G* cos 𝛿; - c , the critical strain, corresponds to the value of the strain percentage when G' reaches 90% of its initial value, as determined within the linear viscoelastic region.

Microscopic measurements

Microstructure visualization, droplet sizes of the emulsions and nanoparticle sizes of the dispersion and their distribution in the emulsions were qualitatively and, when possible, quantitatively determined at a magnification of 200, using an optical microscope (DMLP/DC300, Leica Microsystems) equipped with a camera and the software IM1000.

Results and discussion

Formulation process

In the present work, we developed lab scale formulations without or with nanoparticles, as detailed in material and methods section. The corresponding specific step-by-step process aiming at producing such formulations similar to commercial products is described in Figure 1. Three emulsions with identical formulation were obtained, including or not nanoparticles: a raw emulsion (F-0) and emulsions with alumina and triethoxycaprylylsilane-coated nanoparticles (F-N1) and with alumina, isopropyl titanium triisostearate and triethoxycaprylylsilane crosspolymer-coated nanoparticles (F-N2). Primary nanoparticle size given by the supplier was 10nm [10]. This value corresponds to the size of the crystals obtained after the manufacturing process. In powder form, the crystals are known to aggregate, inducing global sizes larger than one micron (Aldous and Kent). Therefore, the milling step (step n°2, Figure 1) of nanoparticles dispersion enabled an optimal dispersion of nanoparticles in the final formulation (Delrieu and Shao). As an example, results of particle size analysis for coating 2 dispersion, showed that 36% of the resulting particles are smaller than 1µm while 10% are larger than 10µm (details not shown). The way of adding the nanoparticles paste was also an important parameter; the dispersion quality was better when the paste was incorporated after the emulsifying step.

Initial state and microstructure of the emulsions

Emulsion without nanoparticles

Our first criterion for characterization of the emulsion quality was the droplet size distribution. Figure 2a presents this distribution for emulsion F-0 as measured 72h after preparation. It appears as a polydisperse, bimodal distribution centered on two main sizes, 0.2 and 2.5 µm. Approximately 58 % of the droplets were smaller than 2.5 µm. Only 0.4% of the droplets were larger than 10 µm. The mean diameter by surface area to volume, commonly used to represent the finesse of an emulsion [START_REF] Abismail | Emulsification by Ultrasound: Drop Size Distribution and Stability[END_REF], is 1.33 µm, providing additional evidence that this emulsion is mainly made up of small droplets. The corresponding optical micrograph presented on Figure 2b clearly confirms the major presence of fine droplets. However, care is required for such an interpretation of particle size results, as analysis demands large dilution of emulsion sample in water followed by ultrasonic treatment. Such a preparation procedure might be unrepresentative of the initial droplets size distribution in the bulk emulsion. Therefore, the above results would rather reflect the dispersion ability of the emulsion in a diluted medium. An emulsion is generally regarded as a viscoelastic material that has both solid and viscous liquid properties; both behaviors were measured through dynamic oscillatory tests within the linear viscoelastic plateau. Figure 3 shows the results of the strain sweep test for F-0. G', G'' and tan(𝛿), remain constant, as far as the maximum strain amplitude remains below a critical value  c corresponding to the limit of the linear viscoelastic region (strain below 3% for this example). In this region, the only measurement of G', G'' and  c were sufficiently discriminating to provide valid identification of the sample. For F-0, the elastic modulus G' is much greater than the viscous modulus G'' and tan(𝛿) remains below 1, thus illustrating a predominant elastic behavior (Tabilo-Munizaga and Barbosa-Cánovas 2005). Beyond this domain, moduli sharply change as the emulsion structure progressively disrupts. When submitted to an increasing strain, the emulsion structure resists against deformation up to the critical strain  c , where G' starts decreasing, and G'' increases, simultaneously. This behavior is interpreted as a weak strain overshoot [START_REF] Hyun | Large Amplitude Oscillatory Shear as a Way to Classify the Complex Fluids[END_REF] and clearly illustrates the emulsion's structure breaking down under strain. The gel structure is related to the aqueous phase gelling agent used in the present emulsion, namely C10-30 Alkyl Acrylate Crosspolymer. Above the crossing point (G'=G"), the material behaves more and more markedly as a liquid. In addition, surfactant molecules may partially adsorbed onto particle surface, thus modifying their wettability, and consequently affecting the emulsifiers efficiency [START_REF] Binks | Synergistic Stabilization of Emulsions by a Mixture of Surface-Active Nanoparticles and Surfactant[END_REF]. The work of [START_REF] Pichot | O/W Emulsions Stabilised by Both Low Molecular Weight Surfactants and Colloidal Particles: The Effect of Surfactant Type and Concentration[END_REF] shows that at high surfactant concentration, the particles are completely displaced from the interface to the hydrophilic continuous medium and that the stability is only achieved by the presence of a minimum amount of surfactants. In the present work, the surfactant-particles system together with the concentration ranges used are different from those utilized by Pichot et al. Although a high surfactant concentration was used, the introduction of particles was performed after the emulsification process. Moreover, TiO 2 particles used in this study were hydrophobic and dispersed in the non-continuous oil phase. As a consequence, the whole process enabled surfactants species to reach the water-oil interface before particles did. At the initial state, droplets of the emulsions with or without nanoparticles were probably of similar size. As greater droplets sizes (>2.5 µm) were observed for F-N1, an important competition between surfactants molecules and hydrophobic particles may be considered, inducing a partial coalescence phenomenon.

Figure 3 illustrates that F-N1 presents a predominant elastic behavior similar to F-0. However, the presence of nanoparticles significantly affected the viscoelastic properties of this emulsion with a decrease of G' and G". This is attributed to the increase in the oil droplets size resulting from coalescence, as classically described [START_REF] Tadros | Rheology of Dispersions -Principles and Applications[END_REF]. Another observation is that G'' of F-N1 shows weaker strains overshoot than F-0, thus indicating that the presence of nanoparticles weakened the network structure of the emulsion and prevented the droplets network structuring.

Emulsion with nanoparticles : coating 2

Emulsion incorporating nanoparticles with coating 2 shows the same initial colloidal particles size distribution than for the coating 1 (Figure 2a,c,d). This demonstrates that the protocol enabled to produce comparable emulsions, whatever was the coating used. Again, as for F-N1, nanoparticles with coating 2 affected in the same way the final structure of the emulsion by weakening the strain overshoot Interestingly, the decrease of rheological parameters described for F-N1 is no more observed for F-N2, as the moduli remain equal to F-0 within the linear region (Table 3). This suggests a major role of particle coating on the viscoelastic properties of emulsions. As the coating 1 was made of short alkyl chains (C8) grafted on an alumina surface, electrostatic interactions with the oil phase components were probably superior to steric effects. On the opposite, with an hybrid coating as coating 2 (See Table 1), steric effects occurred predominantly. The reduction in G' may result from a change in particle-particle interaction, that can be estimated by the computation of cohesive energy (E c = ½ G'  c ²) according to [START_REF] Tadros | Application of Rheology for Assessment and Prediction of the Long-term Physical Stability of Emulsions[END_REF]. High E c reflects stronger particle-particle interaction giving rise to a more flocculated state. Both emulsions showed very similar droplet size distribution so that quantitative comparison between both systems could be assessed. E c values are 2292 Pa and 4955 Pa for F-N1 and F-N2, respectively. At the initial state, those differences are coating-dependent rather than due to the presence of nanoparticles by themselves. However, such systems are known as thermodynamically unstable, evolving to the physical separation of the aqueous and oil phases. Such instability may occur more or less rapidly through different mechanisms of destabilization, which could be influenced by initial interactions between components.

Stability at room temperature

Aging experiments were conducted on the three emulsions (F-0, F-N1 and F-N2) at room temperature over 65 days.

Formulation without nanoparticles

Results reported in Table 4 indicate no variation for the main rheological parameters (G', G" and tan()) measured on the plateau. This may be explained by the small droplet size and the highly packed structure evidenced for this emulsion at initial state (Figure 2b). However, the slight increases of both  c (Table 4) and droplet sizes (Table 5) over time are characteristic of an evolution towards coalescence. This could be explained by greater interdroplet interaction forces occurring in such highly packed emulsion system (Tan Hsiao 2009).

Formulation with nanoparticles

F-N1 and F-N2 show greater variations of rheological parameters with a decrease of G' by -29% and -39% respectively. A slight increase of tan(𝛿) together with a drop of  c are also observed, thus indicating that the emulsions become less elastic (Table 4). As for F-0, formulations with nanoparticles display a time-dependent increase of the average particle size.

To summarize, on the basis of rheological properties evolution, one can consider that the formulation developed without nanoparticles is relatively stable during three months, while the emulsions containing nanoparticles markedly destabilize.

Stability under accelerated aging

For accelerated aging conditions, as far as the temperature remains reasonable, the temperature stress applied speeds up, without altering, the mechanisms of deterioration operating at room temperature [START_REF] Masmoudi | The Evaluation of Cosmetic and Pharmaceutical Emulsions Aging Process Using Classical Techniques and a New Method: FTIR[END_REF]. Aging experiments were conducted on F-0, F-N1 and F-N2 at 50°C over 65 days. Thus, observations performed on emulsions stored at 50°C evidenced destabilization processes, as significant changes for all rheological parameters were observed up to the creaming phenomena of the emulsion; for this reason F-N1 is followed over time for 21 days corresponding to the macroscopic creaming of the product.

Formulation without nanoparticles

F-0 becomes less elastic with time (Table 4); the elastic modulus G' decreases by 56% after 28 days, thus illustrating a marked destabilization of the emulsion. This is mainly the consequence of droplets coalescence [START_REF] Tadros | Application of Rheology for Assessment and Prediction of the Long-term Physical Stability of Emulsions[END_REF]). This result is consistent with the microscopic observation that clearly evidences the occurrence of larger droplets aggregates. The formation of larger droplets enhanced the creaming rate of the droplets, thus increasing the phase separation occurrence. At 28 days, the linear viscoelastic plateau is no more reached, even at the lowest strain amplitude. Thus, after this time, physico-chemical investigations were stopped.

Particle size measurements and optical micrographs (Table 6 and Figure 4) unambiguously confirm the hypothesis of coalescence occurrence for F-0 stored at 50°C since an increase of larger sizes of the droplets was observed.

Formulation with nanoparticles

For F-N1, the storage at 50°C first induced a significant decrease of G' and  c , interpreted as the consequence of droplets coalescence. Afterwards, a sharp increase of G' is observed and interpreted as a result of nanoparticles aggregation [START_REF] Tadros | Application of Rheology for Assessment and Prediction of the Long-term Physical Stability of Emulsions[END_REF]). Particle size measurements confirm an increase of the mean size and a shift to larger sizes (Table 6). In addition, an inhomogeneous distribution with different sizes of particles was observed on micrographs after 72h (Figure 5a). At 22 days, three different phases of the system were observed: solid particles and highly aggregated particles well separated from the emulsion consisting of polydisperse oil droplets in the aqueous continuous phase (Figure 5b). Obviously, the destabilization mechanisms were mainly controlled by the particles behavior in the emulsion. The aggregation might result from the strong attractive interactions between TiO 2 particles as repulsion forces did not dominate anymore. Moreover, when the solids were much larger than the oil droplets, the emulsion acted as a continuous phase towards the solids, and the interaction between the solids and the oil droplets became negligible [START_REF] Masmoudi | The Evaluation of Cosmetic and Pharmaceutical Emulsions Aging Process Using Classical Techniques and a New Method: FTIR[END_REF].

As for initial state and during room temperature aging, G' values for F-N2 are higher than for F-N1. Evolution of rheological parameters and particle sizes are similar but delayed for F-N2 (Table 4). The fact that F-N2 was less fluid when compared to F-N1 might explain that the diffusion of nanoparticles in the emulsion was slower and therefore the aggregation phenomenon arose later. These observations point out general coating-dependent effects.

Conclusion

The impact of two TiO 2 nanoparticles, differing from their surface coating, in a cosmetic oil-in-water emulsion has been investigated. To this end, O/W emulsions have been developed at lab-scale, and the physicochemical behavior of the emulsions, with and without nanoparticles, has been examined at initial state and during aging. The whole results demonstrate that the presence of nanoparticle in O/W systems does not only affect the characteristics of emulsions at initial state, but also markedly governs its evolution over time. Particle size measurements, rheological properties and microscopic observations allowed evidencing that the presence of nanoparticles affected the initial microstructure of the emulsion by modifying the oil droplet size distribution. Indeed, the nature of the particle's coating alumina (and) triethoxycaprylylsilane (coating 1), and alumina (and) isopropyl titanium triisostearate (and) triethoxycaprylylsilane crosspolymer (coating 2) respectively, strongly affected the particledroplets interactions and thus modified the emulsion microstructure in terms of particle aggregation and rheological properties. During accelerated aging, it is obvious that the destabilization phenomena of the emulsions were mainly related to the nanoparticles. Oil droplets coalescence was observed without nanoparticles whereas nanoparticles aggregation was evidenced in the presence of TiO 2 . In the presence of nanoparticles, the kinetic of destabilization appeared coating-dependant but the mechanism remained unchanged. As we did not work with commercial sunscreens, unlike studies that focused on similar destabilization mechanisms but in an environmental context [START_REF] Botta | TiO2-based Nanoparticles Released in Water from Commercialized Sunscreens in a Life-cycle Perspective: Structures and Quantities[END_REF], our knowledge of the emulsion process and our control of the ingredients enabled us to clarify the role of nanoparticles in such a formulation. In all cases, nanoparticles coating appears of primary importance as it governs the emulsion properties at both initial and aged times. Further investigations are in progress to characterize coated nano-sized TiO 2 /emulsion interactions and nanoparticle surface modifications during aging.

Figure 1 -

 1 Figure 1 -Step-by-step process of formulations containing nanoparticles. A paste of TiO2 nanoparticles is prepared with cetearylethylhexanoate (step 1). This paste is then ground on a 3-cylinder mill until a fine dispersion is obtained (step 2), and controlled by optical microscopy. When both phases of the emulsion are at 80°C, phase B is added to the aqueous phase under stirring (step 3) and the nanoparticle dispersion is immediately incorporated under stirring during (step 4). The emulsion with nanoparticles is obtained (step5).

Figure 2 -

 2 Figure 2 -Initial particle size volume distribution of F-0, F-N1 and F-N2. Optical micrograph of F-0 (a), F-N1 (b) and F-N2 (c) taken at x200, represents droplets and nanoparticles distribution of the emulsion at 72 hours after emulsion preparation at room temperature. Particle size volume distribution (d) is performed on a particle size analyzer, at 72 hours after emulsion preparation

Figure

  Figure 3 -Dynamic strain sweep test of F-0, F-N1, and F-N2 at a frequency of 1 Hz, at 25°C: G' and G'' as a function of applied strain from 0.1 to 100 % Emulsion with nanoparticles : coating 1Figure2aindicates that F-N1 displays broader polydisperse distribution as compared to F-0. In addition, a new population of particles with larger sizes is observed. 43% of the colloidal particles are smaller than 2.5 µm while 24% are larger than 10µm. The curve reflects the distribution of both the droplets of the emulsion and the nanoparticles per se. As mentioned in section 2.1., our results indicate that the larger sizes in the emulsion F-N1 are mainly due to nanoparticles aggregates. The optical micrographs Figure2c and dclearly confirm the presence of dark objects corresponding to aggregates larger than 10 µm and homogeneously dispersed in the presence of many smaller droplets and particles. During emulsification, presence of particles and surfactants might lead to a competitive adsorption onto the oil droplet surfaces. In addition, surfactant molecules may partially adsorbed onto particle surface, thus modifying their wettability, and consequently affecting the emulsifiers efficiency[START_REF] Binks | Synergistic Stabilization of Emulsions by a Mixture of Surface-Active Nanoparticles and Surfactant[END_REF]. The work of[START_REF] Pichot | O/W Emulsions Stabilised by Both Low Molecular Weight Surfactants and Colloidal Particles: The Effect of Surfactant Type and Concentration[END_REF] shows that at high surfactant concentration, the particles are completely displaced from the interface to the hydrophilic continuous medium and that the stability is only achieved by the presence of a minimum amount of surfactants. In the present work, the surfactant-particles system together with the concentration ranges used are different from those utilized by Pichot et al. Although a high surfactant concentration was used, the introduction of particles was performed after the emulsification process. Moreover, TiO 2 particles used in this study were hydrophobic and dispersed in the non-continuous oil phase. As a consequence, the whole process enabled surfactants species to reach the water-oil interface before particles did. At the initial state, droplets of the emulsions with or without nanoparticles were probably of similar size. As greater droplets sizes (>2.5 µm) were observed for F-N1, an important competition between surfactants molecules and hydrophobic particles may be considered, inducing a partial coalescence phenomenon.

Figure 4 -

 4 Figure 4 -Optical micrograph taken at x200 of F-0 aged at 50°C, 72 hours after emulsion preparation (a) and after 21 days (b). Particle size volume distribution (c) over time from 72 hours until 21 days of F-0 aged at 50°C

Figure 5 -

 5 Figure 5 -Optical micrograph taken at x200 of F-N1 aged at 50°C, 72 hours after emulsion preparation (a) and aged at 50°C after 22 days (b)