Nutritional Status and the Influence of the Vegan Diet on the Gut Microbiota and Human Health.

The human gut microbiota is considered a well-known complex ecosystem composed of distinct microbial populations, playing a significant role in most aspects of human health and wellness. Several factors such as infant transitions, dietary habits, age, consumption of probiotics and prebiotics, use of antibiotics, intestinal comorbidities, and even metabolic diseases may continously alter microbiota diversity and function. The study of vegan diet-microbiota interactions is a rapidly evolving field, since plenty of research has been focused on the potential effects of plant-based dietary patterns on the human gut microbiota. It has been reported that well-planned vegan diets and their associated components affect both the bacterial composition and metabolic pathways of gut microbiota. Certain benefits associated with medical disorders but also limitations (including nutritional deficiencies) have been documented. Although the vegan diet may be inadequate in calorific value, it is rich in dietary fiber, polyphenols, and antioxidant vitamins. The aim of the present study was to provide an update of the existing knowledge on nutritional status of vegan diets and the influence of their food components on the human gut microbiota and health.

A Trust Model for Ubiquitous Healthcare Environment on the Basis of Adaptable Fuzzy-Probabilistic Inference System.

Trust is considered to be a determinant on psychologist selection which can ensure patient satisfaction. Hence, trust concept is essential to be introduced into ubiquitous healthcare (UH) environment oriented on patients with anxiety disorders. This is accomplished by trust model estimating psychologists' trustworthiness, a priory to service delivery, with the use of patient's and his/her acquaintances testimonies, i.e., personal interaction experience and reputation (R). In this paper, a trust model is proposed to be materialized via an adaptable cloud inference system (ACIS) that performs trust value (TV) estimation. Taking advantage of a cloud theory, the introduced ACIS estimates TVs via fuzzy-probabilistic reasoning incorporating a cloud relation operator (soft AND) which is proposed to be tuned by trust information sources consistency and coherency. Theoretical analysis along with comparative study conducted within MATLAB environment and experimental investigation verify the effectiveness of the proposed ACIS materialization under different conditions. Especially, the innovative features of ACIS enable TV to be estimated with 45.5% and 62% on average higher accuracy to that providing state-of-the-art trust models, within clean environment and under the influence of large-scale collusive malicious attacks, respectively. The enhanced robustness permits the untrustworthy UH providers to be discriminated with true positive rate at the range of 0.9 although 40% of R testimonies are erroneous. Finally, experimental investigation validates that the adoption of the proposed trust model for psychologists trustworthiness estimation facilitates patient satisfaction to be achieved into UH environment.

A comprehensive Reputation mechanism for ubiquitous healthcare environment exploiting cloud model.

Absence of trust foundations may outweigh benefits of ubiquitous and personalized mental healthcare supervision provided within a Ubiquitous Healthcare environment (UH). Trust is composed by patient's Personal Interaction Experience (PIE) and social entourage accumulated PIE, i.e. Reputation (R). In this paper, a cloud-based Reputation mechanism is proposed. Since PIE is the elementary trust information source, also an Updating mechanism of PIE, is introduced, in this paper. Cloud materialization of combined mechanisms provides adaptability to UH Providers' dynamic behavior, facilitates detection of milking behaviors and complex malicious attacks while meets the challenge of limited accuracy in case of data sparseness. The effectiveness of the proposed mechanisms is verified via simulation in MATLAB.

A novel trust evaluation method for Ubiquitous Healthcare based on cloud computational theory.

The notion of trust is considered to be the cornerstone on patient-psychiatrist relationship. Thus, a trustfully background is fundamental requirement for provision of effective Ubiquitous Healthcare (UH) service. In this paper, the issue of Trust Evaluation of UH Providers when register UH environment is addressed. For that purpose a novel trust evaluation method is proposed, based on cloud theory, exploiting User Profile attributes. This theory mimics human thinking, regarding trust evaluation and captures fuzziness and randomness of this uncertain reasoning. Two case studies are investigated through simulation in MATLAB software, in order to verify the effectiveness of this novel method.

Stress-oriented driver assistance system for electric vehicles.

Stress is physiological and physical reaction that appears in highly demanding situations and affects human's perception and reaction capability. Occurrence of stress events within highly dynamic road environment could lead to life-threatening situation. With the perspective of safety and comfort driving provision to anxious drivers, in this paper a stress-oriented Driver Assistance System (DAS) is proposed. The DAS deployed on Electric Vehicle. This novel DAS customizes driving command signal in respect to road context, when stress is detected. The effectiveness of this novel DAS is verified by simulation in MATLAB/SIMULINK environment.