Food waste in time of COVID-19: The heterogeneous effects on consumer groups in Italy and the Netherlands.

Since COVID-19 outbreak, States adopted different combinations of measures to restrain its spread that affected individual behaviors and the already fragile local and global food systems. The aim of this research is to contribute to the scientific debate around food systems sustainability through the analysis of behavioral shifts in household food waste drivers, specifically occurring during the recent global pandemic. A survey was developed based on an extended version of the Motivation-Opportunity-Ability (MOA) approach. A representative sample of 3000 respondents in Italy and in the Netherlands (1500 per country) completed this survey in May 2020, while lockdown to mitigate the first wave of COVID-19 outbreak was active in both countries. A cluster analysis based on individual food-waste- related behaviors identified four homogenous groups of consumers in the Italian sample and five in the Dutch sample. The comparative analysis of these groups led to the identification of several communalities in behavioral patterns, both within and between the two countries. Results suggest that in both countries, self-reported quantities of household food waste actually decreased, with a stronger reduction reported by Italian consumers. The MOA approach allowed to explain this perceived reduction as largely depending on the increase of opportunity to dedicate more time - to food-related activities as compared to the pre-COVID-19 period, with positive consequences on food management ability. These findings assist in drafting recommendations for tailored interventions to reduce the amount of domestic food waste and preserve positive behaviors emerged during lockdown, that could be continued in the absence of crisis.

Does the COVID-19 external shock matter on household food waste? The impact of social distancing measures during the lockdown.

The COVID-19 outbreak forced national governments to the adoption of social distancing and movement limitation measures aimed to reduce the diffusion of the virus and to mitigate its highly disruptive impact on the healthcare systems. Reduced income, job insecurity, distribution system interruptions, product shortages, localized price hikes, and time availability resulted in changes in food-related behaviors of households, including food waste generation. Although the significant progress achieved in the understanding of the multidimensional determinants of food losses and waste, no study has been considering the role of uncertainty generated by exogenous generalized shocks on consumer behavior. Building on an original and nationally representative survey, this work aims to investigate the impact of the measures introduced to contain the outbreak of COVID-19 on the main behavioral factors underpinning household food waste generation. The study develops a theoretical model introducing uncertainty validated through a Structural Equations Modelling approach. Results showed that during the quarantine period declared household food waste decreased, with more than half of the respondents reporting to waste less. The model suggested that the amount of material and non-material resources that consumers can dedicate to food-related activities represents the most influential factor for the generation of household food waste and that uncertainty is significantly affecting the drivers and indirectly influencing the self-declared values of food waste. Results suggest several potential policy implications, of which the most relevant being related to the importance of stimulating improvements in food management opportunities at home.

Computer-aided detection of lung nodules via 3D fast radial transform, scale space representation, and Zernike MIP classification.

PURPOSE: The authors presented a novel system for automated nodule detection in lung CT exams. METHODS: The approach is based on (1) a lung tissue segmentation preprocessing step, composed of histogram thresholding, seeded region growing, and mathematical morphology; (2) a filtering step, whose aim is the preliminary detection of candidate nodules (via 3D fast radial filtering) and estimation of their geometrical features (via scale space analysis); and (3) a false positive reduction (FPR) step, comprising a heuristic FPR, which applies thresholds based on geometrical features, and a supervised FPR, which is based on support vector machines classification, which in turn, is enhanced by a feature extraction algorithm based on maximum intensity projection processing and Zernike moments. RESULTS: The system was validated on 154 chest axial CT exams provided by the lung image database consortium public database. The authors obtained correct detection of 71% of nodules marked by all radiologists, with a false positive rate of 6.5 false positives per patient (FP/patient). A higher specificity of 2.5 FP/patient was reached with a sensitivity of 60%. An independent test on the ANODE09 competition database obtained an overall score of 0.310. CONCLUSIONS: The system shows a novel approach to the problem of lung nodule detection in CT scans: It relies on filtering techniques, image transforms, and descriptors rather than region growing and nodule segmentation, and the results are comparable to those of other recent systems in literature and show little dependency on the different types of nodules, which is a good sign of robustness.

Computer-aided mass detection in mammography: false positive reduction via gray-scale invariant ranklet texture features.

In this work, gray-scale invariant ranklet texture features are proposed for false positive reduction (FPR) in computer-aided detection (CAD) of breast masses. Two main considerations are at the basis of this proposal. First, false positive (FP) marks surviving our previous CAD system seem to be characterized by specific texture properties that can be used to discriminate them from masses. Second, our previous CAD system achieves invariance to linear/nonlinear monotonic gray-scale transformations by encoding regions of interest into ranklet images through the ranklet transform, an image transformation similar to the wavelet transform, yet dealing with pixels' ranks rather than with their gray-scale values. Therefore, the new FPR approach proposed herein defines a set of texture features which are calculated directly from the ranklet images corresponding to the regions of interest surviving our previous CAD system, hence, ranklet texture features; then, a support vector machine (SVM) classifier is used for discrimination. As a result of this approach, texture-based information is used to discriminate FP marks surviving our previous CAD system; at the same time, invariance to linear/nonlinear monotonic gray-scale transformations of the new CAD system is guaranteed, as ranklet texture features are calculated from ranklet images that have this property themselves by construction. To emphasize the gray-scale invariance of both the previous and new CAD systems, training and testing are carried out without any in-between parameters' adjustment on mammograms having different gray-scale dynamics; in particular, training is carried out on analog digitized mammograms taken from a publicly available digital database, whereas testing is performed on full-field digital mammograms taken from an in-house database. Free-response receiver operating characteristic (FROC) curve analysis of the two CAD systems demonstrates that the new approach achieves a higher reduction of FP marks when compared to the previous one. Specifically, at 60%, 65%, and 70% per-mammogram sensitivity, the new CAD system achieves 0.50, 0.68, and 0.92 FP marks per mammogram, whereas at 70%, 75%, and 80% per-case sensitivity it achieves 0.37, 0.48, and 0.71 FP marks per mammogram, respectively. Conversely, at the same sensitivities, the previous CAD system reached 0.71, 0.87, and 1.15 FP marks per mammogram, and 0.57, 0.73, and 0.92 FPs per mammogram. Also, statistical significance of the difference between the two per-mammogram and per-case FROC curves is demonstrated by the p-value < 0.001 returned by jackknife FROC analysis performed on the two CAD systems.

A ranklet-based image representation for mass classification in digital mammograms.

Regions of interest (ROIs) found on breast radiographic images are classified as either tumoral mass or normal tissue by means of a support vector machine classifier. Classification features are the coefficients resulting from the specific image representation used to encode each ROI. Pixel and wavelet image representations have already been discussed in one of our previous works. To investigate the possibility of improving classification performances, a novel nonparametric, orientation-selective, and multiresolution image representation is developed and evaluated, namely a ranklet image representation. A dataset consisting of 1000 ROIs representing biopsy-proven tumoral masses (either benign or malignant) and 5000 ROIs representing normal breast tissue is used. ROIs are extracted from the digital database for screening mammography collected by the University of South Florida. Classification performances are evaluated using the area Az under the receiver operating characteristic curve. By achieving Az values of 0.978 +/- 0.003 and 90% sensitivity with a false positive fraction value of 4.5%, experiments demonstrate classification results higher than those reached by the previous image representations. In particular, the improvement on the Az value over that achieved by the wavelet image representation is statistically relevant with the two-tailed p value <0.0001. Besides, owing to the tolerance that the ranklet image representation reveals to variations in the ROIs' gray-level intensity histogram, this approach discloses to be robust also when tested on radiographic images having gray-level intensity histogram remarkably different from that used for training.

A novel featureless approach to mass detection in digital mammograms based on support vector machines.

In this work, we present a novel approach to mass detection in digital mammograms. The great variability of the appearance of masses is the main obstacle to building a mass detection method. It is indeed demanding to characterize all the varieties of masses with a reduced set of features. Hence, in our approach we have chosen not to extract any feature, for the detection of the region of interest; in contrast, we exploit all the information available on the image. A multiresolution overcomplete wavelet representation is performed, in order to codify the image with redundancy of information. The vectors of the very-large space obtained are then provided to a first support vector machine (SVM) classifier. The detection task is considered here as a two-class pattern recognition problem: crops are classified as suspect or not, by using this SVM classifier. False candidates are eliminated with a second cascaded SVM. To further reduce the number of false positives, an ensemble of experts is applied: the final suspect regions are achieved by using a voting strategy. The sensitivity of the presented system is nearly 80% with a false-positive rate of 1.1 marks per image, estimated on images coming from the USF DDSM database.