A deep learning integrated Lee-Carter model

In the field of mortality, the Lee–Carter based approach can be considered the milestone to forecast mortality rates among stochastic models. We could define a “Lee–Carter model family” that embraces all developments of this model, including its first formulation (1992) that remains the benchmark for comparing the performance of future models. In the Lee–Carter model, the kt parameter, describing the mortality trend over time, plays an important role about the future mortality behavior. The traditional ARIMA process usually used to model kt shows evident limitations to describe the future mortality shape. Concerning forecasting phase, academics should approach a more plausible way in order to think a nonlinear shape of the projected mortality rates. Therefore, we propose an alternative approach the ARIMA processes based on a deep learning technique. More precisely, in order to catch the pattern of kt series over time more accurately, we apply a Recurrent Neural Network with a Long Short-Term Memory architecture and integrate the Lee–Carter model to improve its predictive capacity. The proposed approach provides significant performance in terms of predictive accuracy and also allow for avoiding the time-chunks’ a priori selection. Indeed, it is a common practice among academics to delete the time in which the noise is overflowing or the data quality is insufficient. The strength of the Long Short-Term Memory network lies in its ability to treat this noise and adequately reproduce it into the forecasted trend, due to its own architecture enabling to take into account significant long-term patterns

Mediterranean diet and nonalcoholic fatty liver disease

Nonalcoholic fatty liver disease (NAFLD) is emerging as the most common chronic liver disease, and is characterized by a wide spectrum of fat-liver disorders that can result in severe liver disease and cirrhosis. Inflammation and oxidative stress are the major risk factors involved in the pathogenesis of NAFLD. Currently, there is no consensus concerning the pharmacological treatment of NAFLD. However, lifestyle interventions based on exercise and a balanced diet for quality and quantity, are considered the cornerstone of NAFLD management. Mediterranean diet (MD), rich in polyunsaturated fats, polyphenols, vitamins and carotenoids, with their anti-inflammatory and antioxidant effects, has been suggested to be effective in preventing cardiovascular risk factors. In adults, MD has also been demonstrated to be efficacious in reducing the risk of metabolic syndrome. However, few studies are available on the effects of the MD in both adult and pediatric subjects with NAFLD. Thus, the aims of the present narrative review are to analyze the current clinical evidence on the impact of MD in patients with NAFLD, and to summarize the main mechanisms of action of MD components on this condition

An integrated approach to the study of Ri de pomme, a painting by Julian Schnabel

The painting Ri de Pomme (1988) by American artist Julian Schnabel was recently subjected to an extensive and disputed restoration with polyvinyl acetate (PVAc) paints. To characterize and locate on the painting the materials used in the original and in the repainted areas, we employed several spectroscopic and chromatographic techniques. Fibre Optics Reflectance Spectroscopy (FORS), Micro-Raman, Pyrolysis-Gas Chromatography/Mass Spectrometry (Py- GC/MS) and Gas Chromatography/Mass Spectrometry (GC/MS) were used. The original and restoration paint layers were differentiated by a preliminary FORS survey. The pigments were studied with Micro-Raman and the oil binder was characterized by GC/MS. Moreover, the support of the painting, a weathered tarpaulin, was characterized by Py-GC/MS

Reliability and validity of the geriatric depression scale in Italian subjects with Parkinson's disease

Introduction. The Geriatric Depression Scale (GDS) is commonly used to assess depressive symptoms, but its psychometric properties have never been examined in Italian people with Parkinson's disease (PD). The aim of this study was to study the reliability and validity of the Italian version of the GDS in a sample of PD patients. Methods. The GDS was administered to 74 patients with PD in order to study its internal consistency, test-retest reliability, construct, and discriminant validity. Results. The internal consistency of GDS was excellent (α = 0.903), as well as the test-retest reliability (ICC = 0.941 [95% CI: 0.886-0.970]). GDS showed a strong correlation with instruments related to the depression (ρ = 0.880) in PD (ρ = 0.712) and a weak correlation with generic measurement instruments (-0.320 < ρ <-0.217). An area under the curve of 0.892 (95% CI 0.809-0.975) indicated a moderate capability to discriminate depressed patients to nondepressed patient, with a cutoff value between 15 and 16 points that predicts depression (sensitivity = 87%; specificity = 82%). Conclusion. The GDS is a reliable and valid tool in a sample of Italian PD subjects; this scale can be used in clinical and research contexts

Quality of life in Parkinson’s disease: Italian validation of the Parkinson’s Disease Questionnaire (PDQ-39-IT)

Translation and cross-cultural adaptation of the 39-item Parkinson’s Disease Questionnaire (PDQ-39) to the Italian culture was performed by Oxford University Innovation in 2008, but this version has never been validated. Therefore, we performed the process of validation of the Italian version of the PDQ-39 (PDQ-39-IT) following the “Consensus-Based Standards for the Selection of Health Status Measurement Instruments” checklist. The translated PDQ-39-IT was tested with 104 patients diagnosed with Parkinson’s disease (PD) who were recruited between June and October 2017. The mean age of the participants was 65.7 ± 10.2 years, and the mean duration of symptoms was 7.4 ± 5.3 years. The internal consistency of the PDQ-39-IT was assessed by Cronbach’s alpha and ranged from 0.69 to 0.92. In an assessment of test-retest reliability in 35 of the 104 patients, the infraclass correlation coefficient (ICC) ranged from 0.85 to 0.96 for the various subitems of the PDQ-39-IT (all p < 0.01). Spearman’s rank correlation coefficient for the validity of the PDQ-39-IT and the Italian version of the 36-Item Short Form (SF-36) was − 0.50 (p < 0.01). The results show that the PDQ-39-IT is a reliable and valid tool to assess the impact of PD on functioning and well-being. Thus, the PDQ-39-IT can be used in clinical and research practice to assess this construct and to evaluate the overall effect of different treatments in Italian PD patients

L1-regularization for multi-period portfolio selection

In this work we present a model for the solution of the multi-period portfolio selection problem. The model is based on a time consistent dynamic risk measure. We apply l1-regularization to stabilize the solution process and to obtain sparse solutions, which allow one to reduce holding costs. The core problem is a nonsmooth optimization one, with equality constraints. We present an iterative procedure based on a modified Bregman iteration, that adaptively sets the value of the regularization parameter in order to produce solutions with desired financial properties. We validate the approach showing results of tests performed on real data

Synthetic materials in art: a new comprehensive approach for the characterization of multi-material artworks by analytical pyrolysis

Abstract Modern art materials introduced since the end of XIX century include a large number of formulations of synthetic polymers and pigments, whose degradation processes and best preservation conditions are a major issue in heritage science. Analytical pyrolysis coupled with gas chromatography and mass spectrometry (Py-GC/MS) is widely used for the characterisation of polymeric materials and organic pigments, however the interpretation of the pyrograms obtained from samples containing different analytes is not straightforward. To improve our understanding on how these materials behave in complex matrices, we used evolved gas analysis coupled with mass spectrometry (EGA-MS) and multi shot Py-GC/MS to highlight and analyse the different fractions in a sample from a pop-art made of painted polyurethane (PU) foam. The study represents a proof of concept to evaluate EGA-MS potential in studying composite modern art materials in combination with multi-shot pyrolysis. The aim of the investigation was establishing the composition of the PU formulation, the paint binder and the pigments, thereby contributing to planning the stabilisation and conservation of the object. The polymers and the class of synthetic organic pigments present in the paint were assessed by determining their specific pyrolysis products and through comparisons with data in the literature. EGA-MS analysis provided both thermal and chemical information in one analytical run, so that we could select four temperatures for use in multi-shot Py-GC/MS analysis and thus to selectively study the different fractions evolved at different temperatures. Information on the various components of the mixture was obtained, including additives and organic pigments, separating them on the basis of their different thermal degradation temperatures. The multianalytical approach included also non-destructive ATR-FTIR and enabled us to characterize in detail different synthetic materials: polyether-based polyurethane produced by the polyaddition of 2,6-diisocyanate toluene, hexamethylene diisocyanate and polypropylene glycol, vinyl paint, and a mixture of β-naphthol and mono-azo as pigments. HPLC–DAD and HPLC–ESI–MS analyses confirmed the pigments, and provided a positive identification of two β-naphthols (PO5 and PR1) and two monoazo pigments (PY1 and PY3)

Deep Learning Forecasting for Supporting Terminal Operators in Port Business Development

Accurate forecasts of containerised freight volumes are unquestionably important for port terminal operators to organise port operations and develop business plans. They are also relevant for port authorities, regulators, and governmental agencies dealing with transportation. In a time when deep learning is in the limelight, owing to a consistent strip of success stories, it is natural to apply it to the tasks of forecasting container throughput. Given the number of options, practitioners can benefit from the lessons learned in applying deep learning models to the problem. Coherently, in this work, we devise a number of multivariate predictive models based on deep learning, analysing and assessing their performance to identify the architecture and set of hyperparameters that prove to be better suited to the task, also comparing the quality of the forecasts with seasonal autoregressive integrated moving average models. Furthermore, an innovative representation of seasonality is given by means of an embedding layer that produces a mapping in a latent space, with the parameters of such mapping being tuned using the quality of the predictions. Finally, we present some managerial implications, also putting into evidence the research limitations and future opportunities

Cardiometabolic risk factors in children with celiac disease on a gluten-free diet

Celiac disease (CD) is an immune-mediated systemic condition evoked by gluten and related prolamines in genetically predisposed subjects. It is characterised by a variable combination of gluten-dependent clinical symptoms, CD-specific antibodies, HLA-DQ2 and HLA-DQ8 haplotypes, and enteropathy. The only therapy of CD consists of a life-long gluten free diet (GFD). Strict GFD adherence results in full clinical, serological and histological remission, avoiding long-term complications in CD patients. However, this diet is not without problems. Gluten free products have high levels of lipids, sugar and salt to improve food palatability and consistency, and subjects with CD show an excessive consumption of hypercaloric and hyperlipidic foods to compensate dietetic restriction. GFD may therefore have a negative impact on cardiometabolic risk factors such as obesity, serum lipid levels, insulin resistance, metabolic syndrome, and atherosclerosis. In adults, some studies have suggested that GFD have a beneficial effect on cardiovascular profile, whereas others have shown an atherogenic effect of GFD. In children, very few studies are available on the issue. Thus, the aim of the present narrative review was to analyze the current clinical evidence on the impact of GFD on cardiometabolic risk factors in children with CD

Analytical Pyrolysis and Mass Spectrometry to Characterise Lignin in Archaeological Wood

This review describes the capability of analytical pyrolysis-based techniques to provide data on lignin composition and on the chemical alteration undergone by lignin in archaeological wooden objects. Applications of Direct Exposure Mass Spectrometry (DE-MS), Evolved Gas Analysis Mass Spectrometry (EGA-MS), and single and double-shot Pyrolysis-Gas Chromatography/Mass Spectrometry (Py-GC/MS) in archaeological lignin characterisation are described. With comparison to cellulose and hemicelluloses, lignin is generally less prone to most degradation processes affecting archaeological artefacts in burial environments, especially waterlogged ones, which are the most favourable for wood preservation. Nevertheless, lignin also undergoes significant chemical changes. As wood from waterlogged environments is mainly composed of lignin, knowledge of its chemical structure and degradation pathways is fundamental for choosing preventive conservation conditions and for optimising consolidation methods and materials, which directly interact with the residual lignin. Analytical pyrolysis coupled with mass spectrometry, used in several complementary operational modes, can gather information regarding the chemical modifications and the state of preservation of lignin, especially concerning oxidation and depolymerisation phenomena. Several applications to the analysis of wood from archaeological artefacts affected by different conservation problems are presented to showcase the potential of analytical pyrolysis in various scenarios that can be encountered when investigating archaeological waterlogged wood