Review: the latest advances in biomedical applications of chitosan hydrogel as a powerful natural structure with eye-catching biological properties

Journal of Materials Science - Chitosan is one of the natural cationic polymers with unique properties such as non-toxicity, biodegradability, biocompatibility, environmentally friendly that has...     

A Full Connectable and High Scalable Key Pre-distribution Scheme Based on Combinatorial Designs for Resource-Constrained Devices in IoT Network

Considering the internet of things (IoT), end nodes such as wireless sensor network, RFID and embedded systems are used in many applications. These end nodes are known as resource-constrained devices in the IoT network. These devices have limitations such as computing and communication power, memory capacity and power. Key pre-distribution schemes (KPSs) have been introduced as a lightweight solution to key distribution in these devices. Key pre-distribution is a special type of key agreement that aims to select keys called session keys in order to establish secure communication between devices. One of these design types is the using of combinatorial designs in key pre-distribution, which is a deterministic scheme in key pre-distribution and has been considered in recent years. In this paper, by introducing a key pre-distribution scheme of this type, we stated that the model introduced in the two benchmarks of KPSs comparability had full connectivity and scalability among the designs introduced in recent years. Also, in recent years, among the combinatorial design-based key pre-distribution schemes, in order to increase resiliency as another criterion for comparing KPSs, attempts were made to include changes in combinatorial designs or they combine them with random key pre-distribution schemes and hybrid schemes were introduced that would significantly reduce the design connectivity. In this paper, using theoretical analysis and maintaining full connectivity, we showed that the strength of the proposed design was better than the similar designs while maintaining higher scalability.

     

Expression of Main Toll-Like Receptors in Patients with Different Types of Colorectal Polyps and Their Relationship with Gut Microbiota

Abnormal activation of Toll-like receptor (TLRs) signaling can result in colon cancer development. The aim of this study was to investigate the expression of important TLRs in different histological types of colorectal polyps and evaluate their relationship with intestinal microbiota. The expression levels of TLR2, 3, 4, and 5 were analyzed in intestinal biopsy specimens of 21 hyperplastic polyp (HP), 16 sessile serrated adenoma (SSA), 29 tubular adenoma (TA), 21 villous/tubulovillous (VP/TVP) cases, and 31 normal controls. In addition, selected gut bacteria including Streptococcus bovis, Enterococcus faecalis, Enterotoxigenic Bacteroides fragilis (ETBF), Fusobacterium nucleatum, Porphyromonas spp., Lactobacillus spp., Roseburia spp., and Bifidobacterium spp. were quantified in fecal samples using absolute qRT PCR, and, finally, the association between TLRs and these gut microbiota- was evaluated by Spearman’s correlation coefficient. Higher expression of TLR2 and TLR4 in VP/TVP and TA, and lower expression levels of TLR3 and TLR5 in all type of polyps were observed. The differences in TLR expression patterns was not only dependent on the histology, location, size, and dysplasia grade of polyps but also related to the intestinal microbiota patterns. TLR2 and TLR4 expression was directly associated with the F. nucleatum, E. faecalis, S. bovis, Porphyromonas, and inversely to Bifidobacterium, Lactobacillus, and Roseburia quantity. Furthermore, TLR3 and TLR5 expression was directly associated with Bifidobacterium, Roseburia, and Lactobacillus quantity. Our results suggest a possible critical role of TLRs during colorectal polyp progression. An abnormal regulation of TLRs in relation to gut microbial quantity may contribute to carcinogenesis.     

Evaluation of performance enhancing proxies in internet over satellite

Performance enhancing proxies (PEPs) are widely used to improve the performance of TCP over high delay‐bandwidth product links and links with high error probability. In this paper we analyse the performance of using TCP connection splitting in combination with web caching via traces obtained from a commercial satellite system. We examine the resulting performance gain under different scenarios, including the effect of caching, congestion, random loss and file sizes. We show, via analysing our measurements, that the performance gain from using splitting is highly sensitive to random losses and the number of simultaneous connections, and that such sensitivity is alleviated by caching. On the other hand, the use of a splitting proxy enhances the value of web caching in that cache hits result in much more significant performance improvement over cache misses when TCP splitting is used. We also compare the performance of using different versions of HTTP in such a system. Copyright © 2003 John Wiley & Sons, Ltd.     

Multi-objective mixture design and optimisation of steel fiber reinforced UHPC using machine learning algorithms and metaheuristics

Ultra-high-performance concrete (UHPC) is a recent class of concrete with improved durability, rheological and mechanical and durability properties compared to traditional concrete. The production cost of UHPC is considerably high due to a large amount of cement used, and also the high price of other required constituents such as quartz powder, silica fume, fibres and superplasticisers. To achieve specific requirements such as desired production cost, strength and flowability, the proportions of UHPC’s constituents must be well adjusted. The traditional mixture design of concrete requires cumbersome, costly and extensive experimental program. Therefore, mathematical optimisation, design of experiments (DOE) and statistical mixture design (SMD) methods have been used in recent years, particularly for meeting multiple objectives. In traditional methods, simple regression models such as multiple linear regression models are used as objective functions according to the requirements. Once the model is constructed, mathematical programming and simplex algorithms are usually used to find optimal solutions. However, a more flexible procedure enabling the use of high accuracy nonlinear models and defining different scenarios for multi-objective mixture design is required, particularly when it comes to data which are not well structured to fit simple regression models such as multiple linear regression. This paper aims to demonstrate a procedure integrating machine learning (ML) algorithms such as Artificial Neural Networks (ANNs) and Gaussian Process Regression (GPR) to develop high-accuracy models, and a metaheuristic optimisation algorithm called Particle Swarm Optimisation (PSO) algorithm for multi-objective mixture design and optimisation of UHPC reinforced with steel fibers. A reliable experimental dataset is used to develop the models and to justify the final results. The comparison of the obtained results with the experimental results validates the capability of the proposed procedure for multi-objective mixture design and optimisation of steel fiber reinforced UHPC. The proposed procedure not only reduces the efforts in the experimental design of UHPC but also leads to the optimal mixtures when the designer faces strength-flowability-cost paradoxes.

     

An integrated methodology to control the risk of cardiovascular disease in patients with hypertension and type 1 diabetes

Today, air pollution, smoking, use of fatty acids and ready‐made foods, and so on, have exacerbated heart disease. Therefore, controlling the risk of such diseases can prevent or reduce their incidence. The present study aimed at developing an integrated methodology including Markov decision processes (MDP) and genetic algorithm (GA) to control the risk of cardiovascular disease in patients with hypertension and type 1 diabetes. First, the efficiency of GA is evaluated against Grey Wolf optimization (GWO) algorithm, and then, the superiority of GA is revealed. Next, the MDP is employed to estimate the risk of cardiovascular disease. For this purpose, model inputs are first determined using a validated micro‐simulation model for screening cardiovascular disease developed at Tehran University of Medical Sciences, Iran by GA. The model input factors are then defined accordingly and using these inputs, three risk estimation models are identified. The results of these models support WHO guidelines that provide medicine with a high discount to patients with high expected LYs. To develop the MDP methodology, policies should be adopted that work well despite the difference between the risk model and the actual risk. Finally, a sensitivity analysis is conducted to study the behavior of the total medication cost against the changes of parameters.     

VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change

International Journal of Computer Vision - Visual place recognition (VPR) is the process of recognising a previously visited place using visual information, often under varying appearance...