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

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

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

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

Printability study by selective laser sintering of bio-based samples obtained by using PBS as polymeric matrix

The emerging request to reduce the environmental impact of plastics encourages scientists to use novel sustainable polymeric materials for many applications fields. The present paper aims to use for the first-time poly (butylene succinate) (PBS), a biodegradable and compostable polymer, for Selective Laser Sintering (SLS) applications. PBS is a flexible semicrystalline aliphatic polyester, which can represent a very good alternative to the traditional thermoplastic polymers obtained by fossil sources. The present work started from a lab-scale production of PBS powders by means of an emulsion solvent evaporation/precipitation method, with the purpose to increase the number of polymeric powders available for SLS. The obtained PBS powders were first characterized by morphological and thermal point of view, and then employed as innovative polymeric material in SLS to realized 3D printed parts with increasing geometrical complexity. To confirm PBS cytocompatibility, cell proliferation and cell viability assays (MTT and Live&Dead) were measured using a lung adenocarcinoma epithelial cell line (H1299). The in vitro cytotoxicity of the 3D printed material was also investigated, showing no harm on cells

Management of patients with a failed kidney transplant: what should we do?

Abstract The number of kidney transplant recipients returning to dialysis after graft failure is steadily increasing over time. Patients with a failed kidney transplant have been shown to have a significant increase in mortality compared with patients with a functioning graft or patients initiating dialysis for the first time. Moreover, the risk for infectious complications, cardiovascular disease and malignancy is greater than in the dialysis population due to the frequent maintenance of low-dose immunosuppression, which is required to reduce the risk of allosensitization, particularly in patients with the prospect of retransplantation from a living donor. The management of these patients present several controversial opinions and clinical guidelines are lacking. This article aims to review the leading evidence on the main issues in the management of patients with failed transplant, including the ideal timing and modality of dialysis reinitiation, the indications for an allograft nephrectomy or the correct management of immunosuppression during graft failure. In summary, retransplantation is a feasible option that should be considered in patients with graft failure and may help to minimize the morbidity and mortality risk associated with dialysis reinitiation