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A big challenge in terms or landslide risk mitigation is represented by increasing the resiliency of society exposed to the risk. Among the possible strategies with which to reach this goal, there is the implementation of early warning systems. This paper describes a procedure to improve early warning activities in areas affected by high landslide risk, such as those classified as critical infrastructures for their central role in society. This research is part of the project LEWIS (Landslides Early Warning Integrated System): An Integrated System for Landslide Monitoring, Early Warning and Risk Mitigation along Lifelines. LEWIS is composed of a susceptibility assessment methodology providing information for single points and areal monitoring systems, a data transmission network and a data collecting and processing center (DCPC), where readings from all monitoring systems and mathematical models converge and which sets the basis for warning and intervention activities. The aim of this paper is to show how logistic issues linked to advanced monitoring techniques, such as big data transfer and storing, can be dealt with compatibly with an early warning system. Therefore, we focus on the interaction between an areal monitoring tool (a ground-based interferometric radar) and the DCPC. By converting complex data into ASCII strings and through appropriate data cropping and average, and by implementing an algorithm for line-of-sight correction, we managed to reduce the data daily output without compromising the capability for performing.

For early warning of disasters induced by precipitation (such as floods and landslides), different kinds of rainfall thresholds are adopted, which vary from each other, on the basis on adopted hypotheses. In some cases, they represent the occurrence probability of an event (landslide or flood), in other cases the exceedance probability of a critical value for an assigned indicator I (a function of rainfall heights), and in further cases they only indicate the exceeding of a prefixed percentage a critical value for I, indicated as Icr. For each scheme, it is usual to define three different criticality levels (ordinary, moderate and severe), which are associated to warning levels, according to emergency plans. This work briefly discusses different schemes of rainfall thresholds, focusing attention on landslide prediction, with some applications to a real case study in Calabria region (southern Italy).

Rainfall-induced shallow landslides can lead to loss of life and significant damage to private and public properties, transportation systems, etc. Predicting locations that might be susceptible to shallow landslides is a complex task and involves many disciplines: hydrology, geotechnical science, geology, hydrogeology, geomorphology, and statistics. Two main approaches are commonly used: statistical or physically based models. Reliable model applications involve automatic parameter calibration, objective quantification of the quality of susceptibility maps, and model sensitivity analyses. This paper presents a methodology to systemically and objectively calibrate, verify, and compare different models and model performance indicators in order to identify and select the models whose behavior is the most reliable for particular case studies.The procedure was implemented in a package of models for landslide susceptibility analysis and integrated in the NewAge-JGrass hydrological model. The package includes three simplified physically based models for landslide susceptibility analysis (M1, M2, and M3) and a component for model verification. It computes eight goodness-of-fit indices by comparing pixel-by-pixel model results and measurement data. The integration of the package in NewAge-JGrass uses other components, such as geographic information system tools, to manage input–output processes, and automatic calibration algorithms to estimate model parameters. The system was applied for a case study in Calabria (Italy) along the Salerno–Reggio Calabria highway, between Cosenza and Altilia. The area is extensively subject to rainfall-induced shallow landslides mainly because of its complex geology and climatology. The analysis was carried out considering all the combinations of the eight optimized indices and the three models. Parameter calibration, verification, and model performance assessment were performed by a comparison with a detailed landslide inventory map for the area. The results showed that the index distance to perfect classification in the receiver operating characteristic plane (D2PC) coupled with the model M3 is the best modeling solution for our test case.

Rainfall is recognized as a major precursor of many types of slope movements. The technical literature reports both study cases and models of landslides induced by rainfall. Subsurface hydrology has a dominant role since changes in the soil water content significantly affect the soil shear strength. The analytical approaches used are very different, ranging from statistical models to distributed and complete models. The latter take several components into account, including specific site conditions, mechanical, hydraulic and physical soil properties, local seepage conditions, and the contribution of these to soil strength. This paper reports a study using a complete model, named SUSHI (Saturated Unsaturated Simulation for Hillslope Instability), to simulate the role of subsurface hydrology in rain-induced landslides, on a case of great interest both in terms of its complexity and its severity. The landslide-prone area in question is located in Campania (southern Italy), where disastrous mudflows occurred in May 1998. The region has long been affected by rainfall-induced slope instabilities, which often involve large areas and affect many people. The application allows a better understanding of the role of rainfall infiltration and suction changes in the triggering mechanism of the phenomena. These changes must be carefully considered when assessing hazard levels and planning mitigation interventions regarding slope stability.

The paper introduces a stochastic technique for forecasting rainfall in space-time domain: the PRAISEST Model (Prediction of Rainfall Amount Inside Storm Events: Space and Time). The model is based on the assumption that the rainfall height H accumulated on an interval Δt between the instants iΔt and (i+1)Δt and on a spatial cell of size ΔxΔy is correlated either with a variable Z, representing antecedent precipitation at the same point, either with a variable W, representing simultaneous rainfall at neighbour cells. The mathematical background is given by a joined probability density fH,W,Z (h,w,z) in which the variables have a mixed nature, that is a finite probability for null value and infinitesimal probabilities for the positive values. As study area, the Calabria region, in Southern Italy, has been selected. The region has been discretised by 10 km×10 km cell grid, according to the raingauge network density in this area. Storm events belonging to 1990–2004 period were analyzed to test performances of the PRAISEST model.

The paper introduces a stochastic model to forecast rainfall heights at site: the P.R.A.I.S.E. model (Prediction of Rainfall Amount Inside Storm Events). PRAISE is based on the assumption that the rainfall height Hi+1 accumulated on an interval Δt between the instants iΔt and (i+1Δt is correlated with a variable Zi(ν), representing antecedent precipitation. The mathematical background is given by a joined probability density fHi+1, Zi(ν)(hi+1 ,zi(ν)) in which the variables have a mixed nature, that is a finite probability in correspondence to the null value and infinitesimal probabilities in correspondence to the positive values. As study area, the Calabria region, in Southern Italy, was selected, to test performances of the PRAISE model.

BackgroundThere is preliminary evidence that the anticonvulsant topiramate increases the likelihood of both smoking and alcohol abstinence among smokers with alcohol use disorder (AUD), but its therapeutic mechanism has not been determined. We used event-related potentials (ERPs) to evaluate topiramate's effect on the salience of drug-related, emotional, and neutral pictorial cues to identify whether one of its potential therapeutic mechanisms involves reduction of the salience of motivationally relevant cues.MethodsParticipants enrolled in a multisite clinical trial treating smokers with AUD were randomly assigned to receive placebo, low-dose topiramate (up to 125 mg/day), or high-dose topiramate (up to 250 mg/day), along with brief behavioral compliance enhancement treatment. A subsample (n = 101) completed ERP assessments at baseline (1 week pre-medication) and week 5 (5 weeks on medication; 1 week pre-quit). We assessed the salience of pleasant, unpleasant, cigarette-related, alcohol-related, and neutral pictorial cues using the late positive potential (LPP) ERP component and measured self-reported substance use, reinforcement, craving, and withdrawal.ResultsFive weeks of high-dose topiramate treatment decreased LPP amplitudes in response to both emotional (pleasant and unpleasant) and drug-related cues (alcohol and cigarette), but not to neutral cues. However, results showed that the LPPs were not significant mediators of the relationship between topiramate dose and post-quit measures of substance use, reinforcement, craving, or withdrawal.ConclusionsThese findings suggest that high-dose topiramate (up to 250 mg/day) decreases the motivational salience of both drug-related and emotional cues among smokers with AUD. However, the nonsignificant mediation analyses preclude any firm conclusions about whether this effect represents one of topiramate's therapeutic mechanisms of action.

We examined genre-specific reading strategies for literary texts and hypothesized that text categorization (literary prose vs. poetry) modulates both how readers gather information from a text (eye movements) and how they realize its phonetic surface form (speech production). We recorded eye movements and speech while college students (N = 32) orally read identical texts that we categorized and formatted as either literary prose or poetry. We further varied the text position of critical regions (text-initial vs. text-medial) to compare how identical information is read and articulated with and without context; this allowed us to assess whether genre-specific reading strategies make differential use of identical context information. We observed genre-dependent differences in reading and speaking tempo that reflected several aspects of reading and articulation. Analyses of regions of interests revealed that word-skipping increased particularly while readers progressed through the texts in the prose condition; speech rhythm was more pronounced in the poetry condition irrespective of the text position. Our results characterize strategic poetry and prose reading, indicate that adjustments of reading behavior partly reflect differences in phonetic surface form, and shed light onto the dynamics of genre-specific literary reading. They generally support a theory of literary comprehension that assumes distinct literary processing modes and incorporates text categorization as an initial processing step.

While some individuals can defy the lure of temptation, many others find appetizing food irresistible. The goal of this study was to investigate the neuropsychological mechanisms that increase individuals' vulnerability to cue-induced eating. Using ERPs, a direct measure of brain activity, we showed that individuals with larger late positive potentials in response to food-related cues than to erotic images are more susceptible to cue-induced eating and, in the presence of a palatable food option, eat more than twice as much as individuals with the opposite brain reactivity profile. By highlighting the presence of individual brain reactivity profiles associated with susceptibility to cue-induced eating, these findings contribute to the understanding of the neurobiological basis of vulnerability to obesity.

Despite decades of research, the pathophysiology of bipolar disorder (BD) is still not well understood. Structural brain differences have been associated with BD, but results from neuroimaging studies have been inconsistent. To address this, we performed the largest study to date of cortical gray matter thickness and surface area measures from brain magnetic resonance imaging scans of 6503 individuals including 1837 unrelated adults with BD and 2582 unrelated healthy controls for group differences while also examining the effects of commonly prescribed medications, age of illness onset, history of psychosis, mood state, age and sex differences on cortical regions. In BD, cortical gray matter was thinner in frontal, temporal and parietal regions of both brain hemispheres. BD had the strongest effects on left pars opercularis (Cohen's d=-0.293; P=1.71 × 10-21), left fusiform gyrus (d=-0.288; P=8.25 × 10-21) and left rostral middle frontal cortex (d=-0.276; P=2.99 × 10-19). Longer duration of illness (after accounting for age at the time of scanning) was associated with reduced cortical thickness in frontal, medial parietal and occipital regions. We found that several commonly prescribed medications, including lithium, antiepileptic and antipsychotic treatment showed significant associations with cortical thickness and surface area, even after accounting for patients who received multiple medications. We found evidence of reduced cortical surface area associated with a history of psychosis but no associations with mood state at the time of scanning. Our analysis revealed previously undetected associations and provides an extensive analysis of potential confounding variables in neuroimaging studies of BD.