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.

High-throughput and area-efficient designs of hash functions and corresponding mechanisms for Message Authentication Codes (MACs) are in high demand due to new security protocols that have arisen and call for security services in every transmitted data packet. For instance, IPv6 incorporates the IPSec protocol for secure data transmission. However, the IPSec's performance bottleneck is the HMAC mechanism which is responsible for authenticating the transmitted data. HMAC's performance bottleneck in its turn is the underlying hash function. In this article a high-throughput and small-size SHA-256 hash function FPGA design and the corresponding HMAC FPGA design is presented. Advanced optimization techniques have been deployed leading to a SHA-256 hashing core which performs more than 30% better, compared to the next better design. This improvement is achieved both in terms of throughput as well as in terms of throughput/area cost factor. It is the first reported SHA-256 hashing core that exceeds 11Gbps (after place and route in Xilinx Virtex 6 board).

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In this study, novel pipelined architectures, optimised in terms of throughput and throughput/area factors, for the SHA-512 cryptographic hash function, are proposed. To achieve this, algorithmic-and circuit-level optimisation techniques such as loop unrolling, re-timing, temporal pre-computation, resource re-ordering and pipeline are applied. All the techniques, except pipeline are applied in the function's transformation round. The pipeline was applied through the development of all the alternative pipelined architectures and implementation in several Xilinx FPGA families and they are evaluated in terms of frequency, area, throughput and throughput/area factors. Compared to the initial un-optimised implementation of SHA-512 function, the introduced five-stage pipelined architecture improves the both the throughput and throughput/area factors by 123 and 61.5%, respectively. Furthermore, the proposed five-stage pipelined architecture outperforms the existing ones both in throughput (3.4× up to 16.9×) and throughput/area (19.5% up to 6.9×) factors.

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.

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Mental healthcare domain highlights the significance of trustworthiness between patient and psychiatrist for treatment process. In this paper, the issue of assessing psychiatrist trustworthiness from patient perspective, within a Ubiquitous Healthcare (UH) environment, is addressed. To meet that challenge, a Trust Assessment mechanism mimicking human cognitive judgment, is proposed. The exploitation of innovative fuzzy-probabilistic transformation model, denoted as cloud, for mechanism deployment enables fuzziness as well as adhered randomness of cognitive perception and assessment to be captured. A set of simulations within MATLAB software environment verify the introduced mechanism efficiency.

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.