Hemorheological and Microcirculatory Factors in Liver Ischemia-Reperfusion Injury—An Update on Pathophysiology,Molecular Mechanisms and Protective Strategies

Hepatic ischemia-reperfusion injury (IRI) is a multifactorial phenomenon which has been associated with adverse clinical outcomes. IRI related tissue damage is characterized by various chronological events depending on the experimental model or clinical setting. Despite the fact that IRI research has been in the spotlight of scientific interest for over three decades with a significant and continuous increase in publication activity over the years and the large number of pharmacological and surgical therapeutic attempts introduced, not many of these strategies have made their way into everyday clinical practice. Furthermore, the pathomechanism of hepatic IRI has not been fully elucidated yet. In the complex process of the IRI, flow properties of blood are not neglectable. Hemorheological factors play an important role in determining tissue perfusion and orchestrating mechanical shear stress-dependent endothelial functions. Antioxidant and anti-inflammatory agents, ischemic conditioning protocols, dynamic organ preservation techniques may improve rheological properties of the post-reperfusion hepatic blood flow and target endothelial cells, exerting a potent protection against hepatic IRI. In this review paper we give a comprehensive overview of microcirculatory, rheological and molecular–pathophysiological aspects of hepatic circulation in the context of IRI and hepatoprotective approaches.     

VLCI approach for optimal capacitors allocation in distribution networks based on hybrid PSOGSA optimization algorithm

Neural Computing and Applications - The use of Shunt Capacitor Banks (SCB) as a convenient compensation source of reactive power in distribution networks has an efficient role in enhancement...     

Modelling and analysis of two-way composite slabs

The paper deals with the modeling and analysis of the ultimate behavior of two-way composite slabs. A software package of COSMOS/M 2.6 is used. Non-linear material properties are considered. The proposed finite element model is validated by making a comparison with full-scale tests published in literature for one and two-way composite slabs. Many parameters are studied, such as slab aspect ratio and slab slenderness ratio. The effect of embossments flattening is considered. The effect of cold steel straps which are fixed to the bottom steel deck flange perpendicular to corrugation direction is studied, including the variation of its thickness and distribution. Also the effect of shear studs is considered. The effect of all previous parameters on the ultimate capacity, distribution of reactions into both weak and strong directions, and slab deflection are investigated. The results are also recorded at the serviceability limit state. The analytical results of the two-way composite slabs are compared with the corresponding results of one-way composite slabs. A dramatic increase occurs on slab loads and distribution of reactions into the weak direction. A considerable decrease occurs on slab deflection as a result of using steel straps and studs.     

DoS Attack Detection Based on Deep Factorization Machine in SDN

Software-Defined Network (SDN) decouples the control plane of network devices from the data plane. While alleviating the problems presented in traditional network architectures, it also brings potential security risks, particularly network Denial-of-Service (DoS) attacks. While many research efforts have been devoted to identifying new features for DoS attack detection, detection methods are less accurate in detecting DoS attacks against client hosts due to the high stealth of such attacks. To solve this problem, a new method of DoS attack detection based on Deep Factorization Machine (DeepFM) is proposed in SDN. Firstly, we select the Growth Rate of Max Matched Packets (GRMMP) in SDN as detection feature. Then, the DeepFM algorithm is used to extract features from flow rules and classify them into dense and discrete features to detect DoS attacks. After training, the model can be used to infer whether SDN is under DoS attacks, and a DeepFM-based detection method for DoS attacks against client host is implemented. Simulation results show that our method can effectively detect DoS attacks in SDN. Compared with the K-Nearest Neighbor (K-NN), Artificial Neural Network (ANN) models, Support Vector Machine (SVM) and Random Forest models, our proposed method outperforms in accuracy, precision and F1 values.     

A new fuzzy C-means method for magnetic resonance image brain segmentation

In this paper, we introduce a new fuzzy c-means (FCM) method in order to improve the magnetic resonance images’ (MRIs) segmentation. The proposed method combines the FCM and possiblistic c-means (PCM) functions using a weighted Gaussian function. The weighted Gaussian function is given to indicate the spatial influence of the neighbouring pixels on the central pixel. The parameters of weighting coefficients are automatically determined in the implementation using the Gaussian function for every pixel in the image. The proposed method is realised by modifying the objective function of the PCM algorithm to produce memberships and possibilities simultaneously, along with the usual point prototypes or cluster centres for each cluster. The membership values can be interpreted as degrees of possibility of the points belonging to the classes, that is, the compatibilities of the points with the class prototypes to overcome the coincident clusters problem of PCM. The efficiency of the proposed algorithm is demonstrated by extensive segmentation experiments using MRIs and comparison with other state-of-the-art algorithms. In the proposed method, the effect of noise is controlled by incorporating the possibility (typicality) function in addition to the membership function. Consideration of these constraints can greatly control the noise in the image as shown in our experiments.     

A novel multiscale-multidirectional autocorrelation approach for defect detection in homogeneous flat surfaces

Defect detection in flat web surface products is a challenging task. Reliable vision-based systems for detection of defects require the suitable selection of a huge set of parameters which highly impact the performance of these systems such as image resolution/scale, size of the scanning window, feature extraction, direction of scanning, classifier type and parameters and system performance evaluation measures. This paper addresses these issues and introduces a novel multi-scale and multi-directional (MSMD) autocorrelation function (ACF)-based approach for reliable defect detection and localization in homogeneous web surfaces. The proposed approach has been experimentally tested on samples from the well-known TILDA textiles database and wallboards. Performance evaluation using the system Precision, Recall (Sensitivity), Specificity, Accuracy, Youden’s index, F-measure and Matthews correlation coefficient has shown that the MSMD ACF approach outperforms the state-of-the-art approaches like MSMD Log-Gabor filters. The MSMD ACFs approach results in better performance indicators for defect detection than the Log-Gabor based approach in addition to being about 2–6 times faster in defect detection.     

Data Secure Storage Mechanism of Sensor Networks Based on Blockchain

As the number of sensor network application scenarios continues to grow, the security problems inherent in this approach have become obstacles that hinder its wide application. However, it has attracted increasing attention from industry and academia. The blockchain is based on a distributed network and has the characteristics of nontampering and traceability of block data. It is thus naturally able to solve the security problems of the sensor networks. Accordingly, this paper first analyzes the security risks associated with data storage in the sensor networks, then proposes using blockchain technology to ensure that data storage in the sensor networks is secure. In the traditional blockchain, the data layer uses a Merkle hash tree to store data; however, the Merkle hash tree cannot provide non-member proof, which makes it unable to resist the attacks of malicious nodes in networks. To solve this problem, this paper utilizes a cryptographic accumulator rather than a Merkle hash tree to provide both member proof and nonmember proof. Moreover, the number of elements in the existing accumulator is limited and unable to meet the blockchain’s expansion requirements. This paper therefore proposes a new type of unbounded accumulator and provides its definition and security model. Finally, this paper constructs an unbounded accumulator scheme using bilinear pairs and analyzes its performance.     

Engineering molecular communications integrated with carbon nanotubes in neural sensor nanonetworks

There have been recent advances in the engineering of molecular communication (MC)‐based networks for nanomedical applications. However, the integration of MC with biomaterials such as carbon nanotubes (CNTs) presents various critical research challenges. In this study, the authors envisaged integrating MC‐based nanonetwork with CNTs to optimise nanonetwork performance. In neural networks, a chronic reduction in the concentration of the neurotransmitter acetylcholine (ACh) eventually leads to the development of neurodegenerative diseases; therefore, they used CNTs as a molecular switch to optimise ACh conductivity supported by artificial MC. Furthermore, MC enables communication between transmitter neurons and receiver neurons for fine‐tuning the ACh release rate according to the feedback concentration of ACh. Subsequently, they proposed a min/max feedback scheme to fine‐tune the expected throughput and ACh transmission efficiency. For demonstration purposes, they deduced analytical forms for the proposed schemes in terms of throughput, incurred traffic rates, and average packet delay.Inspec keywords: carbon nanotubes, cellular biophysics, diseases, feedback, nanomedicine, nanosensors, neural nets, neurophysiologyOther keywords: carbon nanotubes, neural sensor nanonetworks, nanomedical applications, biomaterials, molecular communication‐based nanonetwork, neural networks, neurotransmitter acetylcholine, neurodegenerative diseases, transmitter neurons, receiver neurons     

3D object recognition technique using multiple 2D views for Arabic sign language

Some objects in specific poses cannot be distinguished using a single view. A model is proposed and developed for 3D object recognition based on multiple-views; it was applied on hand postures recognition. A pulse-coupled neural network is used to generate features vector for single view. Two views with different view angles are used; each view generates its features’ vector. The two 2D-vectors are then linearly combined into one 3D vector. The hand postures are then combined to construct a dynamic gesture (word). The reconstruction is performed using best-match search algorithm. The experiment was conducted on 50 words and the result was 96% recognition accuracy confirming objects dataset offline extendibility.     

Ozone Monitoring Instrument aerosol products: a comparison study with ground-based airborne sun photometer measurements over Europe

Airborne sun photometer measurements are used to evaluate retrievals of extinction aerosol optical depth (AOD). These data are extracted from spatially coincident and temporally near-coincident measurements by the Ozone Monitoring Instrument (OMI) aboard the Aura satellite taken during 2005. OMI-measured top of atmosphere (TOA) reflectances are routinely inverted to yield aerosol products such as AOD using two different retrieval techniques: the Aura OMI Near-Ultraviolet Aerosol Data Product, OMAERUV, and the multi-wavelength Aura OMI Aerosol Data Product, OMAERO. In this work, we propose a study that specifically compares the instantaneous aerosol optical thicknesses retrieved from OMI at several locations containing sites and those of the Aerosol Robotic Network (AERONET). The result of the comparison shows that, just over Europe, OMI aerosol optical thicknesses are better retrieved in the multi-wavelength retrieval than in the near-ultraviolet. Correlations have been improved by applying a simple criterion to avoid scenes probably contaminated by thin clouds, and surface scattering. The ultraviolet irradiance positive bias in the OMI data is corrected using a procedure based on global climatological fields of aerosol absorption optical depth. The results generally show a bias significantly reduced by 5–20%, a lower variability and an unchanged, high correlation coefficient.