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PURPOSE. Digital technology has enabled improvements in the fitting accuracy of denture bases via milling techniques. The aim of this study was to evaluate the trueness and precision of digital and analog techniques for manufacturing complete dentures (CDs). MATERIALS AND METHODS. Sixty identical CDs were manufactured using different production protocols. Digital and analog technologies were compared using the reference geometric approach, and the Delta-error values of eight areas of interest (AOI) were calculated. For each AOI, a precise number of measurement points was selected according to sensitivity analyses to compare the Delta-error of trueness and precision between the original model and manufactured prosthesis. Three types of statistical analysis were performed: to calculate the intergroup cumulative difference among the three protocols, the intergroup among the AOIs, and the intragroup difference among AOIs. RESULTS. There was a statistically significant difference between the dentures made using the oversize process and injection molding process (P < .001), but no significant difference between the other two manufacturing methods (P = .1227). There was also a statistically significant difference between the dentures made using the monolithic process and the other two processes for all AOIs (P = .0061), but there was no significant difference between the other two processes (P = 1). Within each group, significant differences among the AOIs were observed. CONCLUSION. The monolithic process yielded better results, in terms of accuracy (trueness and precision), than the other groups, although all three processes led to dentures with Delta-error values well within the clinical tolerance limit. [J Adv Prosthodont 2023;15:22-32]

We propose a new estimator of the regression coefficients for a high-dimensional linear regression model, which is de rived by replacing the sample predictor covariance matrix in the OLS estimator with a different predictor covariance matrix estimate obtained by a nuclear norm plus l1 norm penalization. We call the estimator ALCE-reg. We make a direct theoretical comparison of the expected mean square error of ALCE-reg with OLS and RIDGE. We show in a sim ulation study that ALCE-reg is particularly effective when both the dimension and the sample size are large, due to its ability to find a good compromise between the large bias of shrinkage estimators (like RIDGE and LASSO) and the large variance of estimators conditioned by the sample predictor covariance matrix (like OLS and POET).

PURPOSE. The aim of this study is to evaluate the accuracy of removable partial denture (RPD) frameworks produced using different digital protocols. MATERIALS AND METHODS. 80 frameworks for RPDs were produced using CAD-CAM technology and divided into four groups of twenty (n = 20): Group 1, Titanium frameworks manufactured by digital metal laser sintering (DMLS); Group 2, Co-Cr frameworks manufactured by DMLS; Group 3, Polyamide PA12 castable resin manufactured by multi-jet fusion (MJF); and Group 4, Metal (Co-Cr) casting by using lost-wax technique. After the digital acquisition, eight specific areas were selected in order to measure the Δ-error value at the intaglio surface of RPD. The minimum value required for point sampling density (0.4 mm) was derived from the sensitivity analysis. The obtained Δ-error mean value was used for comparisons: 1. between different manufacturing processes; 2. between different manufacturing techniques in the same area of interest (AOI); and 3. between different AOI of the same group. RESULTS. The Δ-error mean value of each group ranged between -0.002 (Ti) and 0.041 (Co-Cr) mm. The Pearson’s Chi-squared test revealed significant differences considering all groups paired two by two, except for group 3 and 4. The multiple comparison test documented a significant difference for each AOI among group 1, 3, and 4. The multiple comparison test showed significant differences among almost all different AOIs of each group. CONCLUSION. All Δ-mean error values of all digital protocols for manufacturing RPD frameworks optimally fit within the clinical tolerance limit of trueness and precision.

The presence of imbalanced classes is more and more common in practical applications and it is known to heavily compromise the learning process. In this paper we propose a new method aimed at addressing this issue in binary supervised classification. Re-balancing the class sizes has turned out to be a fruitful strategy to overcome this problem. Our proposal performs re-balancing through matrix sketching. Matrix sketching is a recently developed data compression technique that is characterized by the property of preserving most of the linear information that is present in the data. Such property is guaranteed by the Johnson-Lindenstrauss’ Lemma (1984) and allows to embed an n-dimensional space into a reduced one without distorting, within an $$\epsilon $$ ϵ -size interval, the distances between any pair of points. We propose to use matrix sketching as an alternative to the standard re-balancing strategies that are based on random under-sampling the majority class or random over-sampling the minority one. We assess the properties of our method when combined with linear discriminant analysis (LDA), classification trees (C4.5) and Support Vector Machines (SVM) on simulated and real data. Results show that sketching can represent a sound alternative to the most widely used rebalancing methods.

We propose a bootstrap test for unconditional and conditional Granger-causality spectra in the frequency domain. Our test aims to detect if the causality at a particular frequency is systematically different from zero. In particular, we consider a stochastic process derived applying independently the stationary bootstrap to the original series. At each frequency, we test the sample causality against the distribution of the median causality across frequencies estimated for that process. Via our procedure, we infer about the relationship between money stock and GDP in the Euro Area during the period 1999–2017. We point out that the money stock aggregate M1 had a significant impact on economic output at all frequencies, while the opposite relationship is significant only at low frequencies.

This work addresses the unsupervised classification issue for high-dimensional data by exploiting the general idea of Random Projection Ensemble. Specifically, we propose to generate a set of low-dimensional independent random projections and to perform model-based clustering on each of them. The top B∗ projections, i.e., the projections which show the best grouping structure, are then retained. The final partition is obtained by aggregating the clusters found in the projections via consensus. The performances of the method are assessed on both real and simulated datasets. The obtained results suggest that the proposal represents a promising tool for high-dimensional clustering.

Random projections (RPs) have shown to provide promising results in the context of high-dimensional supervised classification. In this work, we address the unsupervised classification issue by exploiting the general idea of RP ensemble. Specifically, we generate a set of low dimensional independent random projections and we perform a model-based clustering on each of them. The top B* projections, i.e. the projections which show the best grouping structure, are then retained. The final partition is obtained by aggregating the chosen classifiers via consensus. The performances of the method are assessed on a set of both real and simulated data.

Recently, the interplay of disciplines involved in data science, most notably statistics and computer science has intensified. Impressive advances in statistical, deep, and machine learning (both supervised and unsupervised) have been achieved by developing and applying more and more complex methods for data, data stream, text, or image processing. They are now further developed and used in many fields of applications like, e.g., engineering, finance, genomics, industrial automation, industry 4.0, marketing, personalised medicine or health care, systems biology.

Pea, lentil, faba bean, chickpea and bean proteins are potentially renewable raw materials for bioplastic production that can be obtained from agricultural waste. Plastics are usually processed under heating, and thus thermal stability is a mandatory requirement for the application. In this study, the thermal behavior of several legume protein isolates at different purity degrees was investigated.; Results: The thermal stability of proteins extracted from legumes was maximum for chickpeas and minimum for beans and decreased with decreasing protein purity in the range 30-88%. A similar dependence on purity was observed for the glass transition temperature. On the contrary, the denaturation temperature was found not to depend on sample purity and origin and was lower than the degradation temperature only in the case of protein samples with purity higher than 60%.; Conclusion: Proteins from legumes are suitable to produce thermoplastic biopolymeric materials if isolated at purity higher than 60%. In fact, under this circumstance, they can be denaturized without degrading and thus are suitable for extrusion processing. © 2018 Society of Chemical Industry.; © 2018 Society of Chemical Industry.

Starting from Spearman’s 1904 pioneering work on factor analysis, latent variable models have witnessed an ever increasing, even though sometimes controversial, diffusion in the statistical literature. They have been extended to deal with different kinds of data structures, and thereby helped to analyse more and more complex situations. Finally, they turned out to be both a powerful instrument for a better understanding of reality and a necessary tool to perform dimension reduction. With the development of refined latent variable models new computational algorithms have been designed that rendered the corresponding parameter estimation fast and reliable. New research lines have incorporated latent variables as a necessary building block.

In this paper we propose a new non parametric estimator of the spectral matrix of a multivariate stationary stochastic process, with the main goal to locally improve the deficiencies of the smoothed periodogram in terms of mean square error of the estimates. Our estimator is based on a convex linear combination of the frequency averaged periodogram and an estimate of the true mean spectral matrix across frequencies. In a wide simulation study we show that our estimator turns out to be able to markedly improve the frequency averaged periodogram especially at central frequencies.

Random Projections (RP) ensemble classifiers allow to improve classification accuracy while extending to the high-dimensional context methods originally developed for low dimensional data. However, reducing {em redundancy} and understanding the properties of the variable ranking induced by the RP ensemble classifier are still open issues. In fact, despite such classifiers highly improve the classification accuracy, they do not allow the identification of the variables with the highest discriminative power and their performance could still be enhanced by a suitable selection of a good subset of them. With the aim to identify both the most accurate subset of classifiers and the most discriminant input features, in this work we investigated two different directions. On one hand, combining the original idea of using the Multiplicative Binomial Distribution (MBD) as the reference model to describe and predict the ensemble accuracy and an important result on such distribution, we devised a simple forward-selection technique called Ensemble Selection Algorithm (ESA). On the other, inspired by the Random Forest (RF) process for feature selection, we adjusted the RP ensemble classifier so as to keep the information on variable importance. Specifically, we measured the relative importance of each input feature through a specific coefficient, called Variable Importance in Projection (VIP), and then we removed the variables that present the smallest values of such coefficient. Results of applying both the ESA and the VIP criterion in simulated and real data demonstrate that our proposal successfully controls the misclassification rate by using a very small number of individual classifiers and by ranking the features in terms of their discriminative power.

Random projections (RPs) have shown to provide promising results for high-dimensional classification. In this work, we address the issue of high-dimensional clustering by exploiting the general idea of RP ensemble to perform unsupervised classification. Specifically, we generate a set of low dimensional independent random projections and we perform a model-based clustering on each of them. The top B1 projections, i.e. the ones showing the best grouping structure according to different cluster quality measures, are then selected. The final partition is obtained by aggregating, via consensus, the chosen classifiers. The performances of the method are assessed on a set of both real and simulated data.

Metal containers are used for packaging foodstuffs; more specifically, the cans used for preserving fruit have a plain body with side seam protected by a lacquer film. The traditionally used side stripe is adopted “out of abundance of caution”, as the tin present on the seam is removed by the welding process. Not using a side stripe would have practical/functional benefits for the entire production chain, with consequent reduction in the cost of the containers and in their environmental impact. Packaging of medium-acidity products in tinplate cans is based on the principle of cathodic protection of steel by tin. To ensure that this condition persists throughout the product's shelf life, it is necessary for the anode area to be larger than the cathode area. When the seam is protected with lacquer, this condition is met. In cans where no side stripe is applied on the inner seam, the cathode area increases; this could lead to an increase in corrosion rate and consequent reduction of shelf life. However, the use of tinplate with high tin coating weight (D 11.2 g/m2) can limit this effect, under suitable packaging conditions (absence of oxygen). The aim of this study is to analyze the possibility of using cans without lacquer stripe on the electric side seam for the packaging of fruit and, more generally, of medium-acidity products. Electrochemical measurements of model-systems, and pack tests were used in the study.

This work examines the chemical and morphological properties of the different types of aluminum foil lids used for packaging pureed fruit and vegetables (thickness, porosity and type of lacquer applied), their resistance to corrosion when in contact with simulant solutions and influence of the presence of residual air inside food packages. Resistance to corrosion was studied using electrochemical impedance spectroscopy (EIS). In an initial approach focused on quality, this technique was used to determine the protective power of the different types of lacquers and to observe changes in the electrode system over time (storage at 37 °C for three days) with respect to permeability to electrolytes, occurrence of porosity and onset of a corrosive peeling process in the metal substrate. The results of the first accelerated tests carried out in electrochemical cells in air with saline solution, provided a quick, complete feedback on the corrosive behavior of peel-off lids and made it possible to determine the effectiveness of the different lacquering systems considered and the corrosion mechanisms at play. Electrochemical impedance measurements were also carried out in electrochemical cells, which enable the system to be kept in an oxygen-less environment for some time, in order to reproduce the physicochemical conditions that are observed in food packages and in a citric simulant solution with pH 4.0. The cells were stored at a temperature of 37 °C and observed over time for up to 14 days of contact. On the one hand, the results obtained confirmed the different corrodibility of the lid groups examined; on the other hand, they provided information on the commercial life of the packages made with materials that have a good degree of protection. The electrochemical impedance measurements performed with both electrochemical cells show how the value for resistance Rp and capacity Cc registered in the initial phases of the test are fundamental for predicting the corrosion resistance of materials. In conclusion, from the results obtained it was possible to define key physicochemical and electrochemical parameters, such as nature and porosity of the lacquer or presence of additives, to be taken into account in the production of peel-off lids for which aluminum protection is of fundamental importance. The electrochemical tests have provided information on the impedance values needed to ensure the required shelf life for this type of products, recently introduced on the market.

Summary The analysis of broken glass is forensically important to reconstruct the events of a criminal act. In particular, the comparison between the glass fragments found on a suspect (recovered cases) and those collected at the crime scene (control cases) may help the police to identify the offender(s) correctly. The forensic issue can be framed as a one-class classification problem. One-class classification is a recently emerging and special classification task, where only one class is fully known (the so-called target class), whereas information on the others is completely missing. We propose to consider Gini's classical transvariation probability as a measure of typicality, i.e. a measure of resemblance between an observation and a set of well-known objects (the control cases). The aim of the proposed transvariation-based one-class classifier is to identify the best boundary around the target class, i.e. to recognize as many target objects as possible while rejecting all those deviating from this class.

In this paper, we retrace the recent history of statistics by analyzing all the papers published in five prestigious statistical journals since 1970, namely: The Annals of Statistics, Biometrika, Journal of the American Statistical Association, Journal of the Royal Statistical Society, Series B and Statistical Science. The aim is to construct a kind of “taxonomy” of the statistical papers by organizing and clustering them in main themes. In this sense being identified in a cluster means being important enough to be uncluttered in the vast and interconnected world of the statistical research. Since the main statistical research topics naturally born, evolve or die during time, we will also develop a dynamic clustering strategy, where a group in a time period is allowed to migrate or to merge into different groups in the following one. Results show that statistics is a very dynamic and evolving science, stimulated by the rise of new research questions and types of data.

The problem of clustering probability density functions is emerging in different scientific domains. The methods proposed for clustering probability density functions are mainly focused on univariate settings and are based on heuristic clustering solutions. New aspects of the problem associated with the multivariate setting and a model-based perspective are investigated. The novel approach relies on a hierarchical mixture modeling of the data. The method is introduced in the univariate context and then extended to multivariate densities by means of a factorial model performing dimension reduction. Model fitting is carried out using an EM-algorithm. The proposed method is illustrated through simulated experiments and applied to two real data sets in order to compare its performance with alternative clustering strategies.

Pea, lentil, faba bean, chickpea and bean proteins are potentially renewable raw materials for bioplastic production that can be obtained from agricultural waste. Plastics are usually processed under heating, and thus thermal stability is a mandatory requirement for the application. In this study, the thermal behavior of several legume protein isolates at different purity degrees was investigated.The thermal stability of proteins extracted from legumes was maximum for chickpeas and minimum for beans and decreased with decreasing protein purity in the range 30-88%. A similar dependence on purity was observed for the glass transition temperature. On the contrary, the denaturation temperature was found not to depend on sample purity and origin and was lower than the degradation temperature only in the case of protein samples with purity higher than 60%.Proteins from legumes are suitable to produce thermoplastic biopolymeric materials if isolated at purity higher than 60%. In fact, under this circumstance, they can be denaturized without degrading and thus are suitable for extrusion processing. © 2018 Society of Chemical Industry.

The qualitative examination of canned foods can be performed with many possible options, depending on the desired result. The microbiological evaluation of canned foods requires generally microbial examination testing methods. In addition, the chemical risk has to be evaluated in general with relation to the possible detection of undeclared allergens, genetically modified organisms, mycotoxins, pesticide residues, etc. Finally, the evaluation of food and beverage products can be carried out by means of sensorial testing methods. In this ambit, simple colorimetric tests may be created and implemented for industrial quality control purpose, and some of these procedures are direct expression of ‘digital image analysis and processing’ systems. Consequently, the possible alteration of certain tints can be analysed and critically discussed on condition that a reliable relationship has been established between the above-mentioned chromatic modification and the cause. This chapter is dedicated to ‘digital image analysis and processing’ practical applications for the evaluation of thermally treated canned foods.

The modern industry of canned foods is correlated with a selected portion of commercial products: fruits and vegetable foods, meat and meat products, shellfish and fish. Two of these products, tomato sauces and baked beans, may be described in detail with the aim of highlighting the importance of several processing steps: the packaging (and the role of metal cans) with quality control procedures, and general defects of canned tomato sauces and baked beans, with a general description of traceability needs. Good or excellent microbiological quality has to be assured when speaking of vegetables, water for washing operations and production equipment. Basic controls on metal cans should consider and evaluate the minimum positive features expected for similar rigid containers. Finally, the hygienic production of canned foods has to be performed by means of adequate good manufacturing practices. In this ambit, the importance of traceability has to be considered when speaking of foods and packaging materials.

The history of food industry is strictly correlated with a peculiar category of long-durability edible products: canned foods. Differently from other packaged foods, canned foods show several unique properties, including risks and failures, depending on the composition of edible contents, the production of metal packages and preservation techniques. Thermal processes have the basic aim of destroying microorganisms (bacteria and spore-forming life forms) in foods. The inhibition of microbial growth and the inactivation of microbial toxins are also needed. Other factors—pH, presence of fatty molecules, calcium, etc—are important. As a result, the choice of the ‘right’ thermal treatment (pasteurisation, sterilisation) and related process parameters (time, temperature) have to be considered in different ambits, including canned foods (Chen in J Food: Microbiol Saf Hyg 02(1), 2017; Chen et al. in J Sci Food Agric 93(5):981–986, 2013; Gupta and Balasubramaniam in Novel thermal and non-thermal technologies for fluid foods. Academic Press, London, Waltham, and San Diego, pp. 109–133, 2012; IciEr in Novel thermal and non-thermal technologies for fluid foods. Academic Press, London, Waltham, and San Diego, pp. 305–367, 2012; Jongyingcharoen and Ahmad in Functional foods and dietary supplements: processing effects and health benefits. Wiley, Chichester, UK, 2014; Manas and Pagan in J Appl Microbiol 98(6):1387–1399, 2005; Rastogi in Novel thermal and non-thermal technologies for fluid foods. Academic Press, London, Waltham, and San Diego, pp. 411–432, 2012; Sahin and Sumnu in Physical properties of foods, pp. 107–155, 2006; Tiwari and Mason in Novel Thermal and non-thermal technologies for fluid foods. Academic Press, London, Waltham, and San Diego, pp. 135–165, 2012; Vasseur et al. in J Appl Microbiol 86(3):469–476, 1999). Final results are the construction of logarithmic ‘survival curves’, the definition of factors which can reduce thermal destruction of microorganisms (water activity and pH). In addition, some reflection should be made when speaking of ‘commercial sterility’ and the correlated concept of long durability for canned foods (storage at room temperature).

The description of thermal treatments for canned foods is only the first step towards the comprehension of these products because of the implicit possibility of commercial and safety failures. This chapter discusses several of the most known defects on canned foods with relation to the entire product (food/packaging system), the food or the container only. The concept of ‘failure’ or ‘defect’ is the opposite concept of ‘excellent’ or ‘good performance’ that should be expected by canners. The ‘right’ approach should be the discussion of failures with reference to the global food/packaging system, the food only, or the container only. Failures of the whole system can be considered as the synergic effect of food- and packaging-related defects; there is abundant literature for these problems. On the contrary, literature concerning defects of the metal container—and related consequences—is not abundant, and this chapter aims to give more specific information to Readers.

Second special issue on “Advances in latent variables: methods, models and applications”

Classical factor analysis relies on the assumption of normally distributed factors that guarantees the model to be estimated via the maximum likelihood method. Even when the assumption of Gaussian factors is not explicitly formulated and estimation is performed via the iterated principal factors' method, the interest is actually mainly focussed on the linear structure of the data, since only moments up to the second ones are involved. In many real situations, the factors could not be adequately described by the first two moments only. For example, skewness characterizing most latent variables in social analysis can be properly measured by the third moment: the factors are not normally distributed and covariance is no longer a sufficient statistic. In this work we propose a factor model characterized by skew-normally distributed factors. Skew-normal refers to a parametric class of probability distributions, that extends the normal distribution by an additional shape parameter regulating the skewness. The model estimation can be solved by the generalized EM algorithm, in which the iterative Newthon-Raphson procedure is needed in the M-step to estimate the factor shape parameter. The proposed skew-normal factor analysis is applied to the study of student satisfaction towards university courses, in order to identify the factors representing different aspects of the latent overall satisfaction.

This paper concerns large covariance matrix estimation via composite minimization under the assumption of low rank plus sparse structure. In this approach, the low rank plus sparse decomposition of the covariance matrix is recovered by least squares minimization under nuclear norm plus l 1 norm penalization. The objective is minimized via a singular value thresholding plus soft thresholding algorithm. This paper proposes a new estimator based on an additional least-squares re-optimization step aimed at un-shrinking the eigenvalues of the low rank component estimated in the first step. We prove that such un-shrinkage causes the final estimate to approach the target as closely as possible in spectral and Frobenius norm, while recovering exactly the underlying low rank and sparsity pattern. The error bounds are derived imposing that the latent eigenvalues scale to p α and the maximum number of non-zeros per row in the sparse component scales to p δ , where p is the dimension, α ∈ [ 0 , 1 ] , δ ∈ [ 0 , 0 . 5 ] , and δ α . The sample size n is imposed to scale at least to p 1 . 5 δ . The resulting estimator is called UNALCE (UNshrunk ALgebraic Covariance Estimator), and it is shown to outperform state-of-the-art estimators, especially for what concerns fitting properties and sparsity pattern detection. The effectiveness of UNALCE is highlighted by a real example regarding ECB (European Central Bank) banking supervisory data.

The vast and ever-growing amount of agricultural and food wastes has become a major concern throughout the whole world. Therefore, strategies for their processing and value-added reuse are needed to enable a sustainable utilization of feedstocks and reduce the environmental burden. By-products of potato, tomato, cereals and olive arise in significant amounts in European countries and are consequently of high relevance. Due to their composition with various beneficial ingredients, the waste products can be valorized by different techniques leading to economic and environmental advantages. This paper focuses on the waste generation during industrial processing of potato, tomato, cereals and olives within the European Union and reviews state-of-the-art technologies for their valorization. Furthermore, current applications, future perspectives and challenges are discussed. This work was carried out in the framework of the AgriMax project (Valorization of agricultural residues and side streams from the agro-food industry), which is supported by the European Commission and has received funding from the Bio Based Industries Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under the grant agreement No. 720719.