Nature-based solutions for hydro-meteorological hazards: Revised concepts, classification schemes and databases.

Hydro-meteorological hazards (HMHs) have had a strong impact on human societies and ecosystems. Their impact is projected to be exacerbated by future climate scenarios. HMHs cataloguing is an effective tool to evaluate their associated risks and plan appropriate remediation strategies. However, factors linked to HMHs origin and triggers remain uncertain, which pose a challenge for their cataloguing. Focusing on key HMHs (floods, storm surges, landslides, droughts, and heatwaves), the goal of this review paper is to analyse and present a classification scheme, key features, and elements for designing nature-based solutions (NBS) and mitigating the adverse impacts of HMHs in Europe. For this purpose, we systematically examined the literature on NBS classification and assessed the gaps that hinder the widespread uptake of NBS. Furthermore, we critically evaluated the existing literature to give a better understanding of the HMHs drivers and their interrelationship (causing multi-hazards). Further conceptualisation of classification scheme and categories of NBS shows that relatively few studies have been carried out on utilising the broader concepts of NBS in tackling HMHs and that the classification and effectiveness of each NBS are dependent on the location, architecture, typology, green species and environmental conditions, as well as interrelated non-linear systems. NBS are often more cost-effective than hard engineering approaches used within the existing systems, especially when taking into consideration their potential co-benefits. We also evaluated the sources of available data for HMHs and NBS, highlighted gaps in data, and presented strategies to overcome the current shortcomings for the development of the NBS for HMHs. We highlighted specific gaps and barriers that need to be filled since the uptake and upscaling studies of NBS in HMHs reduction is rare. The fundamental concepts and the key technical features of past studies reviewed here could help practitioners to design and implement NBS in a real-world situation.

Long-term follow-up of web-based and group-based behavioural intervention in NAFLD in a real world clinical setting.

BACKGROUND: The long-term results of web-based behavioural intervention in non-alcoholic fatty liver disease (NAFLD) have not been described in patients followed in specialised centres. AIMS: To analyse the long-term effectiveness of web education compared with the results achieved by a group-based behavioural intervention in the same years 2012-2014. METHODS: We followed 679 patients with NAFLD (web-based, n = 290; group-based, n = 389) for 5 years. Weight loss ≥10% was the primary outcome; secondary outcomes were attrition, changes in liver enzymes and in biomarkers of steatosis (Fatty liver Index) and fibrosis (Fibrosis-4 index). RESULTS: The cohorts differed in age, education, working status and presence of diabetes. Attrition was higher in the web-based cohort (hazard ratio: 1.53; 95% CI: 1.24-1.88), but not different after adjustment for confounders. Among patients in active follow-up, 50% lost ≥5% of initial body weight and 19% lost ≥10%, without difference between cohorts. Alanine aminotransferase levels fell to within the normal range in 51% and 45% of web- and group-based cohorts, respectively. Fatty Liver Index declined progressively and, by year 5, it ruled out steatosis in 4.8%, whereas 24.9% were in the indeterminate range. Fibrosis-4 index increased in both cohorts, driven by age, but the prevalence of cases ruling-in advanced fibrosis remained very low (around 1%). Improvements in the class of both surrogate biomarkers were associated with ≥5% weight loss. CONCLUSIONS: Although burdened by attrition, web-based behavioural intervention is feasible and effective in NAFLD, expanding the cohort involved in behavioural programs and reducing the risk of progressive disease.

Normative data for COGITAB: An Italian tablet-based test battery conceived for the preclinical phase of Alzheimer's disease.

The number of people with dementia is increasing worldwide. Two main approaches have been adopted to identify subjects with Alzheimer's disease (AD): the neuropsychological evaluation and the identification of biomarkers of AD. The first method is less invasive and easier to perform. This study assesses the psychometric properties of COGITAB, a novel web application d esigned to be sensitive to the subtle cognitive changes distinctive of the early Mild Cognitive Impairment (MCI) and the preclinical phase of AD. We enrolled 518 healthy controls, classified according to several risk factors and the presence of a family history of dementia. The participants were given COGITAB after a neuropsychological screening. The COGITAB Total Score (TS) was significantly affected by age and years of education. Acquired risk factors and family history of dementia significantly impacted only the COGITAB total execution time (TET), not the TS. This study provides normative data for a newly developed web application. Control subjects with acquired risk factors performed slower, giving an important role to the TET recording. Further studies should examine the ability of this new technology to discriminate between healthy subjects and subjects with initial cognitive decline, even when not detected by standard neuropsychological assessments.

Are Social Networks Watermarking Us or Are We (Unawarely) Watermarking Ourself?

In the last decade, Social Networks (SNs) have deeply changed many aspects of society, and one of the most widespread behaviours is the sharing of pictures. However, malicious users often exploit shared pictures to create fake profiles, leading to the growth of cybercrime. Thus, keeping in mind this scenario, authorship attribution and verification through image watermarking techniques are becoming more and more important. In this paper, we firstly investigate how thirteen of the most popular SNs treat uploaded pictures in order to identify a possible implementation of image watermarking techniques by respective SNs. Second, we test the robustness of several image watermarking algorithms on these thirteen SNs. Finally, we verify whether a method based on the Photo-Response Non-Uniformity (PRNU) technique, which is usually used in digital forensic or image forgery detection activities, can be successfully used as a watermarking approach for authorship attribution and verification of pictures on SNs. The proposed method is sufficiently robust, in spite of the fact that pictures are often downgraded during the process of uploading to the SNs. Moreover, in comparison to conventional watermarking methods the proposed method can successfully pass through different SNs, solving related problems such as profile linking and fake profile detection. The results of our analysis on a real dataset of 8400 pictures show that the proposed method is more effective than other watermarking techniques and can help to address serious questions about privacy and security on SNs. Moreover, the proposed method paves the way for the definition of multi-factor online authentication mechanisms based on robust digital features.

No Matter What Images You Share, You Can Probably Be Fingerprinted Anyway.

The popularity of social networks (SNs), amplified by the ever-increasing use of smartphones, has intensified online cybercrimes. This trend has accelerated digital forensics through SNs. One of the areas that has received lots of attention is camera fingerprinting, through which each smartphone is uniquely characterized. Hence, in this paper, we compare classification-based methods to achieve smartphone identification (SI) and user profile linking (UPL) within the same or across different SNs, which can provide investigators with significant clues. We validate the proposed methods by two datasets, our dataset and the VISION dataset, both including original and shared images on the SN platforms such as Google Currents, Facebook, WhatsApp, and Telegram. The obtained results show that k-medoids achieves the best results compared with k-means, hierarchical approaches, and different models of convolutional neural network (CNN) in the classification of the images. The results show that k-medoids provides the values of F1-measure up to 0.91% for SI and UPL tasks. Moreover, the results prove the effectiveness of the methods which tackle the loss of image details through the compression process on the SNs, even for the images from the same model of smartphones. An important outcome of our work is presenting the inter-layer UPL task, which is more desirable in digital investigations as it can link user profiles on different SNs.