Application Protocols enabling Internet of Remote Things via Random Access Satellite Channels

Nowadays, Machine-to-Machine (M2M) and Internet of Things (IoT) traffic rate is increasing at a fast pace. The use of satellites is expected to play a large role in delivering such a traffic. In this work, we investigate the use of two of the most common M2M/IoT protocols stacks on a satellite Random Access (RA) channel, based on DVB-RCS2 standard. The metric under consideration is the completion time, in order to identify the protocol stack that can provide the best performance level

A Psychometric Properties Evaluation of the Italian Version of the Geriatric Depression Scale

Objective. The Geriatric Depression Scale (GDS) is an evaluation tool to diagnose older adult's depression. This questionnaire was defined by Yesavage and Brink in 1982; it was designed expressly for the older person and defines his/her degree of satisfaction, quality of life, and feelings. The objective of this study is to evaluate the psychometric properties of the Italian translation of the Geriatric Depression Scale (GDS-IT). Methods. The Italian version of the Geriatric Depression Scale was administered to 119 people (79 people with a depression diagnosis and 40 healthy ones). We examined the following psychometric characteristics: internal consistency reliability, test-retest reliability, concurrent validity, and construct validity (factor structure). Results. Cronbach's Alpha for the GDS-IT administered to the depressed sample was 0.84. Test-retest reliability was 0.91 and the concurrent validity was 0.83. The factorial analysis showed a structure of 5 factors, and the scale cut-off is between 10 and 11. Conclusion. The GDS-IT proved to be a reliable and valid questionnaire for the evaluation of depression in an Italian population. In the present study, the GDS-IT showed good psychometric properties. Health professionals now have an assessment tool for the evaluation of depression symptoms in the Italian population

Got to be real: An investigation into the co-fabrication of authenticity by fashion companies and digital influencers

This article investigates how fashion companies build their relationships with digital influencers (DIs), a new group of cultural intermediaries who are increasingly central to brand communication strategies. Scholars have mostly studied DIs’ role in influencing the market, but largely neglected the process through which they build their work. Through a qualitative inductive research directed at 21 Italian fashion companies, we describe the process through which companies fabricate the authenticity work, while collaborating with DIs. By taking the overlooked perspective of the company brand owner, we identify the underlying dynamics of achieving co-fabricated authenticity, unpacking the mechanisms through which companies select DIs, shape the connections and regulate the reciprocity with them. Our findings highlight how companies and DIs’ practices become intertwined, with the commodity of authenticity being constructed at the crossroads between the former’s commercial needs and the latter’s grassroots narratives and practices. ‘Co-fabricated authenticity’ ultimately emerges as the result of the work of those actors who are engaged in managing the authenticity or processes of authentication of marketable goods: the intangible and ephemeral value of authenticity is made tangible and co-produced through the collaboration between brands and cultural intermediaries such as DIs

Exploring the genetics of irritable bowel syndrome: A GWA study in the general population and replication in multinational case-control cohorts

OBJECTIVE: IBS shows genetic predisposition, but adequately powered gene-hunting efforts have been scarce so far. We sought to identify true IBS genetic risk factors by means of genome-wide association (GWA) and independent replication studies. DESIGN: We conducted a GWA study (GWAS) of IBS in a general population sample of 11\u2005326 Swedish twins. IBS cases (N=534) and asymptomatic controls (N=4932) were identified based on questionnaire data. Suggestive association signals were followed-up in 3511 individuals from six case-control cohorts. We sought genotype-gene expression correlations through single nucleotide polymorphism (SNP)-expression quantitative trait loci interactions testing, and performed in silico prediction of gene function. We compared candidate gene expression by real-time qPCR in rectal mucosal biopsies of patients with IBS and controls. RESULTS: One locus at 7p22.1, which includes the genes KDELR2 (KDEL endoplasmic reticulum protein retention receptor 2) and GRID2IP (glutamate receptor, ionotropic, delta 2 (Grid2) interacting protein), showed consistent IBS risk effects in the index GWAS and all replication cohorts and reached p=9.31 710(-6) in a meta-analysis of all datasets. Several SNPs in this region are associated with cis effects on KDELR2 expression, and a trend for increased mucosal KDLER2 mRNA expression was observed in IBS cases compared with controls. CONCLUSIONS: Our results demonstrate that general population-based studies combined with analyses of patient cohorts provide good opportunities for gene discovery in IBS. The 7p22.1 and other risk signals detected in this study constitute a good starting platform for hypothesis testing in future functional investigations. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions

Algorithms for Plant Monitoring Applications: A Comprehensive Review

Many sciences exploit algorithms in a large variety of applications. In agronomy, large amounts of agricultural data are handled by adopting procedures for optimization, clustering, or automatic learning. In this particular field, the number of scientific papers has significantly increased in recent years, triggered by scientists using artificial intelligence, comprising deep learning and machine learning methods or bots, to process field, crop, plant, or leaf images. Moreover, many other examples can be found, with different algorithms applied to plant diseases and phenology. This paper reviews the publications which have appeared in the past three years, analyzing the algorithms used and classifying the agronomic aims and the crops to which the methods are applied. Starting from a broad selection of 6060 papers, we subsequently refined the search, reducing the number to 358 research articles and 30 comprehensive reviews. By summarizing the advantages of applying algorithms to agronomic analyses, we propose a guide to farming practitioners, agronomists, researchers, and policymakers regarding best practices, challenges, and visions to counteract the effects of climate change, promoting a transition towards more sustainable, productive, and cost-effective farming and encouraging the introduction of smart technologies

Pathophysiology of Gastric Ulcer Development and Healing: Molecular Mechanisms and Novel Therapeutic Options

Peptic ulcer disease is one of the most common chronic infections in human population. Despite centuries of study, it still troubles a lot of people, especially in the third world countries, and it can lead to other more serious complications such as cancers or even to death sometimes. This book is a snapshot of the current view of peptic ulcer disease. It includes 5 sections and 25 chapters contributed by researchers from 15 countries spread out in Africa, Asia, Europe, North America and South America. It covers the causes of the disease, epidemiology, pathophysiology, molecular-cellular mechanisms, clinical care, and alternative medicine. Each chapter provides a unique view. The book is not only for professionals, but also suitable for regular readers at all levels

RobCaps: Evaluating the Robustness of Capsule Networks against Affine Transformations and Adversarial Attacks

Capsule Networks (CapsNets) are able to hierarchically preserve the pose relationships between multiple objects for image classification tasks. Other than achieving high accuracy, another relevant factor in deploying CapsNets in safety-critical applications is the robustness against input transformations and malicious adversarial attacks.In this paper, we systematically analyze and evaluate different factors affecting the robustness of CapsNets, compared to traditional Convolutional Neural Networks (CNNs). Towards a comprehensive comparison, we test two CapsNet models and two CNN models on the MNIST, GTSRB, and CIFAR10 datasets, as well as on the affine-transformed versions of such datasets. With a thorough analysis, we show which properties of these architectures better contribute to increasing the robustness and their limitations. Overall, CapsNets achieve better robustness against adversarial examples and affine transformations, compared to a traditional CNN with a similar number of parameters. Similar conclusions have been derived for deeper versions of CapsNets and CNNs. Moreover, our results unleash a key finding that the dynamic routing does not contribute much to improving the CapsNets' robustness. Indeed, the main generalization contribution is due to the hierarchical feature learning through capsules

RobCaps: Evaluating the Robustness of Capsule Networks against Affine Transformations and Adversarial Attacks

Capsule Networks (CapsNets) are able to hierarchically preserve the pose relationships between multiple objects for image classification tasks. Other than achieving high accuracy, another relevant factor in deploying CapsNets in safety-critical applications is the robustness against input transformations and malicious adversarial attacks. In this paper, we systematically analyze and evaluate different factors affecting the robustness of CapsNets, compared to traditional Convolutional Neural Networks (CNNs). Towards a comprehensive comparison, we test two CapsNet models and two CNN models on the MNIST, GTSRB, and CIFAR10 datasets, as well as on the affine-transformed versions of such datasets. With a thorough analysis, we show which properties of these architectures better contribute to increasing the robustness and their limitations. Overall, CapsNets achieve better robustness against adversarial examples and affine transformations, compared to a traditional CNN with a similar number of parameters. Similar conclusions have been derived for deeper versions of CapsNets and CNNs. Moreover, our results unleash a key finding that the dynamic routing does not contribute much to improving the CapsNets' robustness. Indeed, the main generalization contribution is due to the hierarchical feature learning through capsules.Comment: To appear at the 2023 International Joint Conference on Neural Networks (IJCNN), Queensland, Australia, June 202