Seasonal River Discharge Forecasting Using Support Vector Regression: A Case Study in the Italian Alps

In this contribution we analyze the performance of a monthly river discharge forecasting model with a Support Vector Regression (SVR) technique in a European alpine area. We considered as predictors the discharges of the antecedent months, snow-covered area (SCA), and meteorological and climatic variables for 14 catchments in South Tyrol (Northern Italy), as well as the long-term average discharge of the month of prediction, also regarded as a benchmark. Forecasts at a six-month lead time tend to perform no better than the benchmark, with an average 33% relative root mean square error (RMSE%) on test samples. However, at one month lead time, RMSE% was 22%, a non-negligible improvement over the benchmark; moreover, the SVR model reduces the frequency of higher errors associated with anomalous months. Predictions with a lead time of three months show an intermediate performance between those at one and six months lead time. Among the considered predictors, SCA alone reduces RMSE% to 6% and 5% compared to using monthly discharges only, for a lead time equal to one and three months, respectively, whereas meteorological parameters bring only minor improvements. The model also outperformed a simpler linear autoregressive model, and yielded the lowest volume error in forecasting with one month lead time, while at longer lead times the differences compared to the benchmarks are negligible. Our results suggest that although an SVR model may deliver better forecasts than its simpler linear alternatives, long lead-time hydrological forecasting in Alpine catchments remains a challenge. Catchment state variables may play a bigger role than catchment input variables; hence a focus on characterizing seasonal catchment storage—Rather than seasonal weather forecasting—Could be key for improving our predictive capacity.JRC.H.1-Water Resource

Phase angle and Mediterranean diet in patients with acne: Two easy tools for assessing the clinical severity of disease

Acne is a chronic, inflammatory and debilitating skin disorder. Dietary factors and nutritional status are among the exacerbating factors of acne. Phase angle (PhA), a direct measure of Bioelectrical Impedance Analysis (BIA), represents an indicator of the chronic inflammatory state. The Mediterranean diet (MD) is a healthy dietary pattern that can exert anti-inflammatory effects in several inflammatory diseases. We aimed to investigate the difference in PhA and adherence to the MD and their associations with the severity of acne in a sample of naïve treatment patients with acne compared to control group

A Novel Data Fusion Technique for Snow Cover Retrieval

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a novel data fusion technique for improving the snow cover monitoring for a mesoscale Alpine region, in particular in those areas where two information sources disagree. The presented methodological innovation consists in the integration of remote-sensing data products and the numerical simulation results by means of a machine learning classifier (support vector machine), capable to extract information from their quality measures. This differs from the existing approaches where remote sensing is only used for model tuning or data assimilation. The technique has been tested to generate a time series of about 1300 snow maps for the period between October 2012 and July 2016. The results show an average agreement between the fused product and the reference ground data of 96%, compared to 90% of the moderate-resolution imaging spectroradiometer (MODIS) data product and 92% of the numerical model simulation. Moreover, one of the most important results is observed from the analysis of snow cover area (SCA) time series, where the fused product seems to overcome the well-known underestimation of snow in forest of the MODIS product, by accurately reproducing the SCA peaks of winter season

Factors Affecting Patient Compliance during Orthodontic Treatment with Aligners: Motivational Protocol and Psychological Well-Being

Objective:Compliance is critical for successful outcomes in orthodontics, and personality traits may play a role in determining patient adherence. This study aimed to monitor compliance during treatment with removable clear aligners (CA) [Align Technology Inc, San José, Calif ], and evaluate the influence of motivational techniques and the patient’s profiles assessed through the psychological wellbeing (PWB) questionnaire on clinical outcomes.Methods:Thirty-nine consecutive patients in permanent dentition seeking treatment with CA were recruited from two universities. Casts were obtained before treatment and after 3, 6, and 12 months and the corresponding digital Clincheck©.STL files were used to calculate the discrepancy index to check for differences between virtual and real treatment stages. Patients were divided into two groups: the Case group, which received motivational techniques at each appointment, and the control group which received instructions only at the beginning. Psychological profiles were evaluated before treatment (T0) and after 3 (T1), 6 (T2), and 12 (T3) months.Results:There were no differences between the Case and Control groups regarding the use of motivational reminders. The analysis of the PWB showed that almost all values increased, and there was a strong correlation between dental casts and correspondent. STL files at every time point. The PWB showed increased values from T0 to T3 in the sample.Conclusion:Motivational techniques did not affect patient compliance, and treatment outcomes were achieved as planned. The PWB of all patients improved throughout the treatment with CA

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Altitude-dependent influence of snow cover on alpine land surface phenology

Snow cover impacts alpine land surface phenology in various ways, but our knowledge about the effect of snow cover on alpine land surface phenology is still limited. We studied this relationship in the European Alps using satellite-derived metrics of snow cover phenology (SCP), namely, first snow fall, last snow day, and snow cover duration (SCD), in combination with land surface phenology (LSP), namely, start of season (SOS), end of season, and length of season (LOS) for the period of 2003–2014. We tested the dependency of interannual differences (Δ) of SCP and LSP metrics with altitude (up to 3000 m above sea level) for seven natural vegetation types, four main climatic subregions, and four terrain expositions. We found that 25.3% of all pixels showed significant (p < 0.05) correlation between ΔSCD and ΔSOS and 15.3% between ΔSCD and ΔLOS across the entire study area. Correlations between ΔSCD and ΔSOS as well as ΔSCD and ΔLOS are more pronounced in the northern subregions of the Alps, at high altitudes, and on north and west facing terrain—or more generally, in regions with longer SCD. We conclude that snow cover has a greater effect on alpine phenology at higher than at lower altitudes, which may be attributed to the coupled influence of snow cover with underground conditions and air temperature. Alpine ecosystems may therefore be particularly sensitive to future change of snow cover at high altitudes under climate warming scenarios

Relative influence of timing and accumulation of snow on alpine land surface phenology

Timing and accumulation of snow are among the most important phenomena influencing land surface phenology in mountainous ecosystems. However, our knowledge on their influence on alpine land surface phenology is still limited, and much remains unclear as to which snow metrics are most relevant for studying this interaction. In this study, we analyzed five snow and phenology metrics, namely, timing (snow cover duration (SCD) and last snow day), accumulation of snow (mean snow water equivalent, SWEm), and mountain land surface phenology (start of season and length of season) in the Swiss Alps during the period 2003–2014. We examined elevational and regional variations in the relationships between snow and alpine land surface phenology metrics using multiple linear regression and relative weight analyses and subsequently identified the snow metrics that showed strongest associations with variations in alpine land surface phenology of natural vegetation types.We found that the relationships between snow and phenology metrics were pronounced in high-elevational regions and alpine natural grassland and sparsely vegetated areas. Start of season was influenced primarily by SCD, secondarily by SWEm, while length of season was equally affected by SCD and SWEm across different elevational bands. We conclude that SCD plays the most significant role compared to other snow metrics. Future variations of snow cover and accumulation are likely to influence alpine ecosystems, for instance, their species composition due to changes in the potential growing season. Also, their spatial distribution may change as a response to the new environmental conditions if these prove persistent

Seasonal River Discharge Forecasting Using Support Vector Regression: A Case Study in the Italian Alps

In this contribution we analyze the performance of a monthly river discharge forecasting model with a Support Vector Regression (SVR) technique in a European alpine area. We considered as predictors the discharges of the antecedent months, snow-covered area (SCA), and meteorological and climatic variables for 14 catchments in South Tyrol (Northern Italy), as well as the long-term average discharge of the month of prediction, also regarded as a benchmark. Forecasts at a six-month lead time tend to perform no better than the benchmark, with an average 33% relative root mean square error (RMSE%) on test samples. However, at one month lead time, RMSE% was 22%, a non-negligible improvement over the benchmark; moreover, the SVR model reduces the frequency of higher errors associated with anomalous months. Predictions with a lead time of three months show an intermediate performance between those at one and six months lead time. Among the considered predictors, SCA alone reduces RMSE% to 6% and 5% compared to using monthly discharges only, for a lead time equal to one and three months, respectively, whereas meteorological parameters bring only minor improvements. The model also outperformed a simpler linear autoregressive model, and yielded the lowest volume error in forecasting with one month lead time, while at longer lead times the differences compared to the benchmarks are negligible. Our results suggest that although an SVR model may deliver better forecasts than its simpler linear alternatives, long lead-time hydrological forecasting in Alpine catchments remains a challenge. Catchment state variables may play a bigger role than catchment input variables; hence a focus on characterizing seasonal catchment storage—Rather than seasonal weather forecasting—Could be key for improving our predictive capacity