Search results for: biological molecular networks

Authors: Dalal Alghawas, Jetty Lee, Kaisa Poutanen, Hani El-Nezami

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Oat β-glucan a water soluble non starch linear polysaccharide has been approved as a cholesterol lowering agent by various food safety administrations and is commonly used to reduce the risk of heart disease. The molecular weight of oat β-glucan can vary depending on the extraction and fractionation methods. It is not clear whether the molecular weight has a significant impact at reducing the acceleration of atherosclerosis. The aim of this study was to investigate three different oat β-glucan fractionations on the development of atherosclerosis in vivo. With special focus on plaque stability and the intestinal barrier function. To test this, ApoE-/- female mice were fed a high fat diet supplemented with oat bran, high molecular weight (HMW) oat β-glucan fractionate and low molecular weight (LMW) oat β-glucan fractionate for 16 weeks. Atherosclerosis risk markers were measured in the plasma, heart and aortic tree. Plaque size was measured in the aortic root and aortic tree. ICAM-1, VCAM-1, E-Selectin, P-Selectin, protein levels were assessed from the aortic tree to determine plaque stability at 16 weeks. The expression of p22phox at the aortic root was evaluated to study the NADPH oxidase complex involved in nitric oxide bioavailability and vascular elasticity. The tight junction proteins E-cadherin and beta-catenin from western blot analyses were analysed as an intestinal barrier function test. Plasma LPS, intestinal D-lactate levels and hepatic FMO gene expression were carried out to confirm whether the compromised intestinal barrier lead to endotoxemia. The oat bran and HMW oat β-glucan diet groups were more effective than the LMW β-glucan diet group at reducing the plaque size and showed marked improvements in plaque stability. The intestinal barrier was compromised for all the experimental groups however the endotoxemia levels were higher in the LMW β-glucan diet group. The oat bran and HMW oat β-glucan diet groups were more effective at attenuating the development of atherosclerosis. Reasons for this could be due to the LMW oat β-glucan diet group’s low viscosity in the gut and the inability to block the reabsorption of cholesterol. Furthermore the low viscosity may allow more bacterial endotoxin translocation through the impaired intestinal barrier. In future food technologists should carefully consider how to incorporate LMW oat β-glucan as a health promoting food.

Keywords: Atherosclerosis, beta glucan, endotoxemia, intestinal barrier function

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Authors: A. Mohammadpour, Ebrahim Najafi Kajabad, Ghazale Ipakchi

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Presently, computer networks’ security rise in importance and many studies have also been conducted in this field. By the penetration of the internet networks in different fields, many things need to be done to provide a secure industrial and non-industrial network. Fire walls, appropriate Intrusion Detection Systems (IDS), encryption protocols for information sending and receiving, and use of authentication certificated are among things, which should be considered for system security. The aim of the present study is to use the outcome of several algorithms, which cause decline in IDS errors, in the way that improves system security and prevents additional overload to the system. Finally, regarding the obtained result we can also detect the amount and percentage of more sub attacks. By running the proposed system, which is based on the use of multi-algorithmic outcome and comparing that by the proposed single algorithmic methods, we observed a 78.64% result in attack detection that is improved by 3.14% than the proposed algorithms.

Keywords: intrusion detection systems, clustering, k-means, k-medoids, SV clustering, ensemble

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Authors: Reem Alajmi, Hind Al Harbi, Tahany Ayaad, Zainab Al Musawi

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Hard ticks Hyalomma spp. (family: Ixodidae) are obligate ectoparasite in their all life stages on some domestic animals mainly camels and cattle. Ticks may lead to many economic and public health problems because of their blood feeding behavior. Also, they act as vectors for many bacterial, viral and protozoan agents which may cause serious diseases such as tick-born encephalitis, Rocky-mountain spotted fever, Q-fever and Lyme disease which can affect human and/or animals. In the present study, molecular identification of ticks that attack camels in Riyadh region, Saudi Arabia based on the partial sequence of mitochondrial 16s rRNA gene was applied. Also, the present study aims to detect natural infections of collected camel ticks with Rickessia spp. and Borelia spp. using PCR/hybridization of Citrate synthase encoding gene present in bacterial cells. Hard ticks infesting camels were collected from different camels located in a farm in Riyadh region, Saudi Arabia. Results of the present study showed that the collected specimens belong to two species: Hyalomma dromedari represent 99% of the identified specimens and Hyalomma marginatum which account for 1 % of identified ticks. The molecular identification was made through blasting the obtained sequence of this study with sequences already present and identified in GeneBank. All obtained sequences of H. dromedarii specimens showed 97-100% identity with the same gene sequence of the same species (Accession # L34306.1) which was used as a reference. Meanwhile, no intraspecific variations of H. marginatum mesured because only one specimen was collected. Results also had shown that the intraspecific variability between individuals of H. dromedarii obtained in 92 % of samples ranging from 0.2- 6.6%, while the remaining 7 % of the total samples of H. dromedarii showed about 10.3 % individual differences. However, the interspecific variability between H. dromedarii and H. marginatum was approximately 18.3 %. On the other hand, by using the technique of PCR/hybridization, we could detect natural infection of camel ticks with Rickettsia spp. and Borrelia spp. Results revealed the natural presence of both bacteria in collected ticks. Rickettsial spp. infection present in 29% of collected ticks, while 35% of collected specimen were infected with Borrelia spp. The valuable results obtained from the present study are a new record for the molecular identification of camel ticks in Riyadh, Saudi Arabia and their natural infection with both Rickettsia spp. and Borrelia spp. These results may help scientists to provide a good and direct control strategy of ticks in order to protect one of the most important economic animals which are camels. Also results of this project spotlight on the disease that might be transmitted by ticks to put out a direct protective plan to prevent spreading of these dangerous agents. Further molecular studies are needed to confirm the results of the present study by using other mitochondrial and nuclear genes for tick identification.

Keywords: Camel ticks, Rickessia spp. , Borelia spp. , mitochondrial 16s rRNA gene

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Authors: N. Harun, E. E. Masiren, W. H. W. Ibrahim, F. Adam

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Amine absorption process is an approach for mitigation of CO2 from flue gas that produces from power plant. This process is the most common system used in chemical and oil industries for gas purification to remove acid gases. On the challenges of this process is high energy requirement for solvent regeneration to release CO2. In the past few years, mixed alkanolamines have received increasing attention. In most cases, the mixtures contain N-methyldiethanolamine (MDEA) as the base amine with the addition of one or two more reactive amines such as PZ. The reason for the application of such blend amine is to take advantage of high reaction rate of CO2 with the activator combined with the advantages of the low heat of regeneration of MDEA. Several experimental and simulation studies have been undertaken to understand this process using blend MDEA/PZ solvent. Despite those studies, the mechanism of CO2 absorption into the aqueous MDEA is not well understood and available knowledge within the open literature is limited. The aim of this study is to investigate the intermolecular interaction of the blend MDEA/PZ using Molecular Dynamics (MD) simulation. MD simulation was run under condition 313K and 1 atm using NVE ensemble at 200ps and NVT ensemble at 1ns. The results were interpreted in term of Radial Distribution Function (RDF) analysis through two system of interest i.e binary and tertiary. The binary system will explain the interaction between amine and water molecule while tertiary system used to determine the interaction between the amine and CO2 molecule. For the binary system, it was observed that the –OH group of MDEA is more attracted to water molecule compared to –NH group of MDEA. The –OH group of MDEA can form the hydrogen bond with water that will assist the solubility of MDEA in water. The intermolecular interaction probability of –OH and –NH group of MDEA with CO2 in blended MDEA/PZ is higher than using single MDEA. This findings show that PZ molecule act as an activator to promote the intermolecular interaction between MDEA and CO2.Thus, blend of MDEA with PZ is expecting to increase the absorption rate of CO2 and reduce the heat regeneration requirement.

Keywords: amine absorption process, blend MDEA/PZ, CO2 capture, molecular dynamic simulation, radial distribution function

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Authors: Shweta Singh, Sudaman Katti

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The most well-known sequence models make use of complex recurrent neural networks in an encoder-decoder configuration. The model used in this research makes use of a transformer, which is based purely on a self-attention mechanism, without relying on recurrence at all. More specifically, encoders and decoders which make use of self-attention and operate based on a memory, are used. In this research work, results for various 3D visual and non-visual reinforcement learning tasks designed in Unity software were obtained. Convolutional neural networks, more specifically, nature CNN architecture, are used for input processing in visual tasks, and comparison with standard long short-term memory (LSTM) architecture is performed for both visual tasks based on CNNs and non-visual tasks based on coordinate inputs. This research work combines the transformer architecture with the proximal policy optimization technique used popularly in reinforcement learning for stability and better policy updates while training, especially for continuous action spaces, which are used in this research work. Certain tasks in this paper are long horizon tasks that carry on for a longer duration and require extensive use of memory-based functionalities like storage of experiences and choosing appropriate actions based on recall. The transformer, which makes use of memory and self-attention mechanism in an encoder-decoder configuration proved to have better performance when compared to LSTM in terms of exploration and rewards achieved. Such memory based architectures can be used extensively in the field of cognitive robotics and reinforcement learning.

Keywords: convolutional neural networks, reinforcement learning, self-attention, transformers, unity

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Authors: R. Boudjelthia

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The aim of this study is to determine the influence of mix composition of concrete as the content of water and cement, water–binder ratio, and the replacement of fly ash on the durability of self compacting concrete (SCC) by using artificial neural networks (ANNs). To achieve this, an ANNs model is developed to predict the durability of self compacting concrete which is expressed in terms of chloride ions permeability in accordance with ASTM C1202-97 or AASHTO T277. Database gathered from the literature for the training and testing the model. A sensitivity analysis was also conducted using the trained and tested ANN model to investigate the effect of fly ash on the durability of SCC. The results indicate that the developed model is reliable and accurate. the durability of SCC expressed in terms of total charge passed over a 6-h period can be significantly improved by using at least 25% fly ash as replacement of cement. This study show that artificial neural network have strong potentialas a feasible tool for predicting accurately the durability of SCC containing fly ash.

Keywords: artificial neural networks, durability, chloride ions permeability, self compacting concrete

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Authors: Hsiang-Ru Lin

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Cholesterol plays an essential role in the regulation of the progression of numerous important diseases including atherosclerosis and Alzheimer disease so the generation of suitable cholesterol-lowering reagents is urgent to develop. Liver X receptor (LXR) is a ligand-activated transcription factor whose natural ligands are cholesterols, oxysterols and glucose. Once being activated, LXR can transactivate the transcription action of various genes including CYP7A1, ABCA1, and SREBP1c, involved in the lipid metabolism, glucose metabolism and inflammatory pathway. Essentially, the upregulation of ABCA1 facilitates cholesterol efflux from the cells and attenuates the production of beta-amyloid (ABeta) 42 in brain so LXR is a promising target to develop the cholesterol-lowering reagents and preventative treatment of Alzheimer disease. Engelhardia roxburghiana is a deciduous tree growing in India, China, and Taiwan. However, its chemical composition is only reported to exhibit antitubercular and anti-inflammatory effects. In this study, four compounds, engelheptanoxides A, C, engelhardiol A, and B isolated from the root of Engelhardia roxburghiana were evaluated for their agonistic activity against LXR by the transient transfection reporter assays in the HepG2 cells. Furthermore, their interactive modes with LXR ligand binding pocket were generated by molecular modeling programs. By using the cell-based biological assays, engelheptanoxides A, C, engelhardiol A, and B showing no cytotoxic effect against the proliferation of HepG2 cells, exerted obvious LXR agonistic effects with similar activity as T0901317, a novel synthetic LXR agonist. Further modeling studies including docking and SAR (structure-activity relationship) showed that these compounds can locate in LXR ligand binding pocket in the similar manner as T0901317. Thus, LXR is one of nuclear receptors targeted by pharmaceutical industry for developing treatments of Alzheimer and atherosclerosis diseases. Importantly, the cell-based assays, together with molecular modeling studies suggesting a plausible binding mode, demonstrate that engelheptanoxides A, C, engelhardiol A, and B function as LXR agonists. This is the first report to demonstrate that the extract of Engelhardia roxburghiana contains LXR agonists. As such, these active components of Engelhardia roxburghiana or subsequent analogs may show important therapeutic effects through selective modulation of the LXR pathway.

Keywords: Liver X receptor (LXR), Engelhardia roxburghiana, CYP7A1, ABCA1, SREBP1c, HepG2 cells

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Authors: Abas Mohsenzadeh, Tariq Bashir, Waseen Tahir, Ulf Stigh, Mikael Skrifvars, Kim Bolton

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The area of artificial muscles has received significant attention from many research domains including soft robotics, biomechanics and smart textiles in recent years. Poly(vinylidene fluoride) (PVDF) has been used to form artificial muscles since it contracts upon heating when under load. In this study, PVDF fibers were produced by melt spinning technique at different solid state draw ratios and then actuation mechanism for PVDF tie molecules within the semicrystalline region of PVDF polymer has been investigated using molecular dynamics simulations. Tie molecules are polymer chains that link two (or more) crystalline regions in semicrystalline polymers. The changes in fiber length upon heating have been investigated using a novel simulation technique. The results show that conformational changes of the tie molecules from the longer all-trans conformation at low temperature (β structure) to the shorter conformation (α structure) at higher temperature accrue by increasing the temperature. These results may be applied to understand the actuation observed for PVDF upon heating.

Keywords: poly(vinylidene fluoride), molecular dynamics, simulation, actuators, tie molecules, semicrystalline

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Authors: Syed Mahbubuz Zaman, A. B. M. Abrar Haque, Mehedi Hassan Nayeem, Misbah Uddin Sagor

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Everyday e-mails are being used by millions of people as an effective form of communication over the Internet. Although e-mails allow high-speed communication, there is a constant threat known as spam. Spam e-mail is often called junk e-mails which are unsolicited and sent in bulk. These unsolicited emails cause security concerns among internet users because they are being exposed to inappropriate content. There is no guaranteed way to stop spammers who use static filters as they are bypassed very easily. In this paper, a smart system is proposed that will be using neural networks to approach spam in a different way, and meanwhile, this will also detect the most relevant features that will help to design the spam filter. Also, a comparison of different parameters for different neural network models has been shown to determine which model works best within suitable parameters.

Keywords: long short-term memory, bidirectional long short-term memory, gated recurrent unit, natural language processing, natural language processing

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Authors: Eugene Stepanov, Arkadi Ponossov

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Many mathematical models used in biological and other applications are ill-posed. The reason for that is the nature of differential equations, where the nonlinearities are assumed to be step functions, which is done to simplify the analysis. Prominent examples are switched systems arising from gene regulatory networks and neural field equations. This simplification leads, however, to theoretical and numerical complications. In the presentation, it is proposed to apply the theory of hybrid feedback controls to regularize the problem. Roughly speaking, one attaches a finite state control (‘automaton’), which follows the trajectories of the original system and governs its dynamics at the points of ill-posedness. The construction of the automaton is based on the classification of the attractors of the specially designed adjoint dynamical system. This ‘hybridization’ is shown to regularize the original switched system and gives rise to efficient hybrid numerical schemes. Several examples are provided in the presentation, which supports the suggested analysis. The method can be of interest in other applied fields, where differential equations contain step-like nonlinearities.

Keywords: hybrid feedback control, ill-posed problems, singular perturbation analysis, step-like nonlinearities

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Authors: Cesar Hernández, Diego Giral, Fernando Santa

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The spectral handoff is important in cognitive wireless networks to ensure an adequate quality of service and performance for secondary user communications. This work proposes a benchmarking of performance of the three spectrum handoff models: VIKOR, SAW and MEW. Four evaluation metrics are used. These metrics are, accumulative average of failed handoffs, accumulative average of handoffs performed, accumulative average of transmission bandwidth and, accumulative average of the transmission delay. As a difference with related work, the performance of the three spectrum handoff models was validated with captured data of spectral occupancy in experiments realized at the GSM frequency band (824 MHz-849 MHz). These data represent the actual behavior of the licensed users for this wireless frequency band. The results of the comparative show that VIKOR Algorithm provides 15.8% performance improvement compared to a SAW Algorithm and, 12.1% better than the MEW Algorithm.

Keywords: cognitive radio, decision making, MEW, SAW, spectrum handoff, VIKOR

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Authors: Eshta Ranyal, Kamal Jain, Vikrant Ranyal

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Potholes are a severe threat to road safety and a major contributing factor towards road distress. In the Indian context, they are a major road hazard. Timely detection of potholes and subsequent repair can prevent the roads from deteriorating. To facilitate the roadway authorities in the timely detection and repair of potholes, we propose a pothole detection methodology using convolutional neural networks. The YOLOv3 model is used as it is fast and accurate in comparison to other state-of-the-art models. You only look once v3 (YOLOv3) is a state-of-the-art, real-time object detection system that features multi-scale detection. A mean average precision(mAP) of 73% was obtained on a training dataset of 200 images. The dataset was then increased to 500 images, resulting in an increase in mAP. We further calculated the depth of the potholes using stereoscopic vision by reconstruction of 3D potholes. This enables calculating pothole volume, its extent, which can then be used to evaluate the pothole severity as low, moderate, high.

Keywords: CNN, pothole detection, pothole severity, YOLO, stereovision

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Authors: Mehrdad Shafiei Dizaji, Hoda Azari

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The research explores the pioneering integration of Physics-Informed Neural Networks (PINNs) into the domain of Ground-Penetrating Radar (GPR) data prediction, akin to advancements in medical imaging for tracking tumor progression in the human body. This research presents a detailed development framework for a specialized PINN model proficient at interpreting and forecasting GPR data, much like how medical imaging models predict tumor behavior. By harnessing the synergy between deep learning algorithms and the physical laws governing subsurface structures—or, in medical terms, human tissues—the model effectively embeds the physics of electromagnetic wave propagation into its architecture. This ensures that predictions not only align with fundamental physical principles but also mirror the precision needed in medical diagnostics for detecting and monitoring tumors. The suggested deep learning structure comprises three components: a CNN, a spatial feature channel attention (SFCA) mechanism, and ConvLSTM, along with temporal feature frame attention (TFFA) modules. The attention mechanism computes channel attention and temporal attention weights using self-adaptation, thereby fine-tuning the visual and temporal feature responses to extract the most pertinent and significant visual and temporal features. By integrating physics directly into the neural network, our model has shown enhanced accuracy in forecasting GPR data. This improvement is vital for conducting effective assessments of bridge deck conditions and other evaluations related to civil infrastructure. The use of Physics-Informed Neural Networks (PINNs) has demonstrated the potential to transform the field of Non-Destructive Evaluation (NDE) by enhancing the precision of infrastructure deterioration predictions. Moreover, it offers a deeper insight into the fundamental mechanisms of deterioration, viewed through the prism of physics-based models.

Keywords: physics-informed neural networks, deep learning, ground-penetrating radar (GPR), NDE, ConvLSTM, physics, data driven

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Authors: Somayeh Komeylian

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The antenna arrays for the entire 3D spherical coverage have been developed for their potential use in variety of applications such as radars and body-worn devices of the body area networks. In this study, we have rigorously revamped the hybrid antenna array using the optimum geometry of bowtie elements for achieving a significant improvement in the angular discrimination capability as well as in separating two adjacent targets. In this scenario, we have analogously investigated the effectiveness of increasing the virtual array length in fostering and enhancing the directivity and angular resolution in the 10 GHz frequency. The simulation results have extensively verified that the proposed antenna array represents a drastic enhancement in terms of size, directivity, side lobe level (SLL) and, especially resolution compared with the other available geometries. We have also verified that the maximum directivities of the proposed hybrid antenna array represent the robustness to the all  variations, which is accompanied by the uniform 3D scanning characteristic.

Keywords: bowtie antenna, hybrid antenna array, array signal processing, body area networks

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Authors: Drashti G. Daraji, Rosa E. Moo-Puc, Hitesh D. Patel

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Substituted 2-Thioimidazole analogues have been synthesized and confirmed by advanced spectroscopic techniques. Among them, ten compounds have been selected and evaluated for their in vitro anti-cancer activity at the National Cancer Institute (NCI) for testing against a panel of 60 different human tumor cell lines derived from nine neoplastic cancer types. Furthermore, synthesized compounds were tested for their in vitro antiprotozoal activity, and none of them exhibited significant potency against antiprotozoans. It was observed that the tested all compounds seem effective on the UACC-62 melanoma cancer cell line as compared to other cancer cell lines and also exhibited the least potent in the Non-Small Cell Lung Cancer cell line in one-dose screening. In silico studies of these derivatives were carried out by molecular docking techniques and Absorption, Distribution, Metabolism, and Excretion (ADME) using Schrödinger software to find potent B-Raf kinase inhibitor (PDB ID: 3OG7). All the compounds have been performed for docking study; Compound D4 has a good docking score for melanoma cancer as compared with other.

Keywords: anticancer activity, cancer cell line, 2-thio imidazole, one-dose assay, molecular docking

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Authors: Yasser F. Hassan

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Social networking involves use of an online platform or website that enables people to communicate, usually for a social purpose, through a variety of services, most of which are web-based and offer opportunities for people to interact over the internet, e.g. via e-mail and ‘instant messaging’, by analyzing the voting behavior and ratings of judges in a popular comments in social networks. While most of the party literature omits the electorate, this paper presents a model where elites and parties are emergent consequences of the behavior and preferences of voters. The research in artificial intelligence and psychology has provided powerful illustrations of the way in which the emergence of intelligent behavior depends on the development of representational structure. As opposed to the classical voting system (one person – one decision – one vote) a new voting system is designed where agents with opposed preferences are endowed with a given number of votes to freely distribute them among some issues. The paper uses ideas from machine learning, artificial intelligence and soft computing to provide a model of the development of voting system response in a simulated agent. The modeled development process involves (simulated) processes of evolution, learning and representation development. The main value of the model is that it provides an illustration of how simple learning processes may lead to the formation of structure. We employ agent-based computer simulation to demonstrate the formation and interaction of coalitions that arise from individual voter preferences. We are interested in coordinating the local behavior of individual agents to provide an appropriate system-level behavior.

Keywords: voting system, rough sets, multi-agent, social networks, emergence, power indices

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Authors: Ertuğ Yıldırım, Dile Kara, Salih Zeki Yıldız

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Metal containing polymers (MCPs) are macro molecules usually containing metal-ligand coordination units and are a multidisciplinary research field mainly based at the interface between coordination chemistry and polymer science. The progress of this area has also been reinforced by the growth of several other closely related disciplines including macro molecular engineering, crystal engineering, organic synthesis, supra molecular chemistry and colloidal and material science. Schiff base ligands are very effective in constructing supra molecular architectures such as coordination polymers, double helical and triple helical complexes. In addition, Schiff base derivatives incorporating a fluorescent moiety are appealing tools for optical sensing of metal ions. MCPs are well-known systems in which the combinations of local parameters are possible by means of fluoro metric techniques. Generally, without incorporation of the fluorescent groups with polymers is unspecific, and it is not useful to analyze their fluorescent properties. Therefore, it is necessary to prepare a new type epoxy polymers with fluorescent groups in terms of metal sensing prop and the other photo chemical applications. In the present study metal containing polymers were prepared via poly functional monomeric Schiff base metal chelate complexes in the presence of dis functional monomers such as diglycidyl ether Bisphenol A (DGEBA). The synthesized complexes and polymers were characterized by FTIR, UV-VIS and mass spectroscopies. The preparations of epoxy polymers have been carried out at 185 °C. The prepared composites having sharp and narrow excitation/emission properties are expected to be applicable in various systems such as heat-resistant polymers and photo voltaic devices. The prepared composite is also ideal for various applications, easily prepared, safe, and maintain good fluorescence properties.

Keywords: Schiff base ligands, crystal engineering, fluorescence properties, Metal Containing Polymers (MCPs)

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Authors: K. Żak, S. Przetocka, R. Kitel, K. Guzik, B. Musielak, S. Malicki, G. Dubin, T. A. Holak

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Studies on tumor genesis revealed a number of factors that may potentially serve as molecular targets for immunotherapies. One of such promising targets are PD1 and PDL1 proteins. PD1 (Programmed cell death protein 1) is expressed by activated T cells and plays a critical role in modulation of the host's immune response. One of the PD1 ligands -PDL1- is expressed by macrophages, monocytes and cancer cells which exploit it to avoid immune attack. The notion of the mechanisms used by cancer cells to block the immune system response was utilized in the development of therapies blocking PD1-PDL1 interaction. Up to date, human PD1-PDL1 complex has not been crystallized and structure of the mouse-human complex does not provide a complete view of the molecular basis of PD1-PDL1 interactions. The purpose of this study is to obtain crystal structure of the human PD1-PDL1 complex which shall allow rational design of small molecule inhibitors of the interaction. In addition, the study presents results of binding small-molecules to PD1 and fragment docking towards PD1 protein which will facilitate the design and development of small–molecule inhibitors of PD1-PDL1 interaction.

Keywords: PD1, PDL1, cancer, small molecule, drug discovery

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Authors: Mahdi Bazarganigilani

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Aortic enlargement, also known as an aortic aneurysm, can occur when the walls of the aorta become weak. This disease can become deadly if overlooked and undiagnosed. In this paper, a computer-aided diagnosis (CAD) system was introduced to accurately diagnose aortic enlargement from chest x-ray images. An enhanced convolutional neural network (CNN) was employed and then trained by transfer learning by using three different main areas from the original images. The areas included the left lung, heart, and right lung. The accuracy of the system was then evaluated on 1001 samples by using 4-fold cross-validation. A promising accuracy of 90% was achieved in terms of the F-measure indicator. The results showed using different areas from the original image in the training phase of CNN could increase the accuracy of predictions. This encouraged the author to evaluate this method on a larger dataset and even on different CAD systems for further enhancement of this methodology.

Keywords: computer-aided diagnosis systems, aortic enlargement, chest X-ray, image processing, convolutional neural networks

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Authors: Subodh Chandra Shakya, Badal Shrestha, Suni Thapa, Ashutosh Chauhan, Saugat Adhikari

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To tackle our problem of clothes identification, we used different architectures of Convolutional Neural Networks. Among different architectures, the outcome from Inception ResNet V2 and MobileNet V2 seemed promising. On comparison of the metrices, we observed that the Inception ResNet V2 slightly outperforms MobileNet V2 for this purpose. So this paper of ours proposes the cloth identifier using Inception ResNet V2 and also contains the comparison between the outcome of ResNet V2 and MobileNet V2. The document here contains the results and findings of the research that we performed on the DeepFashion Dataset. To improve the dataset, we used different image preprocessing techniques like image shearing, image rotation, and denoising. The whole experiment was conducted with the intention of testing the efficiency of convolutional neural networks on cloth identification so that we could develop a reliable system that is good enough in identifying the clothes worn by the users. The whole system can be integrated with some kind of recommendation system.

Keywords: inception ResNet, convolutional neural net, deep learning, confusion matrix, data augmentation, data preprocessing

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Authors: Johannes Günther

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Time Sensitive Networking (TSN), standardized in the IEEE 802.1 standard, has been lent increasing attention in the context of mission critical systems. Such mission critical systems, e.g., in the automotive domain, aviation, industrial, and smart factory domain, are responsible for coordinating complex functionalities in real time. In many of these contexts, a reliable data exchange fulfilling hard time constraints and quality of service (QoS) conditions is of critical importance. TSN standards are able to provide guarantees for deterministic communication behaviour, which is in contrast to common best-effort approaches. Therefore, the superior QoS guarantees of TSN may aid in the development of new technologies, which rely on low latencies and specific bandwidth demands being fulfilled. TSN extends existing Ethernet protocols with numerous standards, providing means for synchronization, management, and overall real-time focussed capabilities. These additional QoS guarantees, as well as management mechanisms, lead to an increased attack surface for potential malicious attackers. As TSN guarantees certain deadlines for priority traffic, an attacker may degrade the QoS by delaying a packet beyond its deadline or even execute a denial of service (DoS) attack if the delays lead to packets being dropped. However, thus far, security concerns have not played a major role in the design of such standards. Thus, while TSN does provide valuable additional characteristics to existing common Ethernet protocols, it leads to new attack vectors on networks and allows for a range of potential attacks. One answer to these security risks is to deploy defense mechanisms according to a moving target defense (MTD) strategy. The core idea relies on the reduction of the attackers' knowledge about the network. Typically, mission-critical systems suffer from an asymmetric disadvantage. DoS or QoS-degradation attacks may be preceded by long periods of reconnaissance, during which the attacker may learn about the network topology, its characteristics, traffic patterns, priorities, bandwidth demands, periodic characteristics on links and switches, and so on. Here, we implemented and tested several MTD-like defense strategies against different attacker models of varying capabilities and budgets, as well as collaborative attacks of multiple attackers within a network, all within the context of TSN networks. We modelled the networks and tested our defense strategies on an OMNET++ testbench, with networks of different sizes and topologies, ranging from a couple dozen hosts and switches to significantly larger set-ups.

Keywords: network security, time sensitive networking, moving target defense, cyber security

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Authors: Chia-Feng Lu, Yuh-Jen Wang, Yu-Te Wu, Sui-Hing Yan

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This study aimed at investigating whether the functional brain networks constructed using the initial EEG (obtained when patients first visited hospital) can be correlated with the progression of cognitive decline calculated as the changes of mini-mental state examination (MMSE) scores between the latest and initial examinations. We integrated the time–frequency cross mutual information (TFCMI) method to estimate the EEG functional connectivity between cortical regions, and the network analysis based on graph theory to investigate the organization of functional networks in aMCI. Our finding suggested that higher integrated functional network with sufficient connection strengths, dense connection between local regions, and high network efficiency in processing information at the initial stage may result in a better prognosis of the subsequent cognitive functions for aMCI. In conclusion, the functional connectivity can be a useful biomarker to assist in prediction of cognitive declines in aMCI.

Keywords: cognitive decline, functional connectivity, MCI, MMSE

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Authors: Wiseborn Manfe Danquah, D. Turgay Altilar

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Vehicular Ad-hoc Network (VANET) is an integral component of Intelligent Transport Systems (ITS) that has enjoyed a lot of attention from the research community and the automotive industry. This is mainly due to the opportunities and challenges it presents. Vehicular Ad-hoc Network being a class of Mobile Ad-hoc Networks (MANET) has all the security concerns existing in traditional MANET as well as new security and privacy concerns introduced by the unique vehicular communication environment. This paper provides a survey of the possible attacks in vehicular environment, as well as security and privacy concerns in VANET. It also provides an insight into the development of a comprehensive cloud framework to provide a more robust and secured communication among vehicular nodes and road side units. Our proposal, a Metropolitan Based Public Interconnected Vehicular Cloud (MIVC) infrastructure seeks to provide a more reliable and secured vehicular communication network.

Keywords: mobile Ad-hoc networks, vehicular ad hoc network, cloud, ITS, road side units (RSU), metropolitan interconnected vehicular cloud (MIVC)

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Authors: H. K. Ratre, M. Roy, S. Roy, M. S. Parmar, V. Bhagat

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Mastitis is a common problem of dairy industries. Reduction in milk production and an irreparable damage to the udder associated with the disease are common causes of culling of dairy cows. Milk from infected animals is not suitable for drinking and for making different milk products. So, it has a major economic importance in dairy cattle. The aims of this study were to investigate the bacteriological panorama in milk from udder quarters with subclinical mastitis and to carried out for the molecular characterization of the major isolated organisms, from subclinical mastitis-affected cows in and around Durg and Rajnandgaon district of Chhattisgarh. Isolation and identification of bacteria from the milk samples of subclinical mastitis-affected cows were done by standard and routine culture procedures. A total of 78 isolates were obtained from cows and among the various bacteria isolated, Staphylococcus spp. occupied prime position with occurrence rate of 51.282%. However, other bacteria isolated includeStreptococcus spp. (20.512%), Micrococcus spp. (14.102%), E. coli (8.974%), Klebsiela spp. (2.564%), Salmonella spp. (1.282%) and Proteus spp. (1.282%). Staphylococcus spp. was isolated as the major causative agent of subclinical mastitis in the studied area. Molecular characterization of Staphylococus aureusisolates was done for genetic expression of the virulence genes like ‘nuc’ encoding thermonucleaseexoenzyme, coa and spa by PCR amplification of the respective genes in 25 Staphylococcus isolates. In the present study, 15 isolates (77.27%) out of 20 coagulase positive isolates were found to be genotypically positive for ‘nuc’ where as 20 isolates (52.63%) out of 38 CNS expressed the presence of the same virulence gene. In the present study, three Staphylococcus isolates were found to be genotypically positive for coa gene. The Amplification of the coa gene yielded two different products of 627, 710 bp. The amplification of the gene segment encoding the IgG binding region of protein A (spa) revealed a size of 220 and 253bp in twostaphylococcus isolates. The X-region binding of the spa gene produced an amplicon of 315 bp in one Staphylococcal isolates. Staphylococcus aureus was found to be major isolate (51.28%) responsible for causing subclinical mastitis in cows which also showed expression of virulence genesnuc, coa and spa.

Keywords: mastitis, bacteria, characterization, expression, gene

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Authors: Jiahui Song

Abstract:

The use of electric fields with high intensity (~ 100kV/cm or higher) and ultra short pulse durations (nanosecond range) has been a recent development. Most of the studies of electroporation have ignored possible thermal effects because of the small duration of the applied voltage pulses. However, it has been predicted membrane temperature gradients ranging from 0.2×109 to 109 K/m. This research focuses on thermal effects that drive for electroporative enhancements, even though the actual temperature values might not have changed appreciably from their equilibrium levels. The dynamics of pore formation with the application of an externally applied electric field is studied on the basis of molecular dynamics (MD) simulations using the GROMACS package. MD simulations of a lipid layer with constant electric field strength of 0.5 V/nm at 25 °C and 47 °C are implemented to simulate the appropriate thermal effects. The GROMACS provides the force fields for the lipid membranes, which is taken to comprise of dipalmitoyl-phosphatidyl-choline (DPPC) molecules. The water model mimicks the aqueous environment surrounding the membrane. Velocities of water and membrane molecules are generated randomly at each simulation run according to a Maxwellian distribution. The high background electric field is typically used in MD simulations to probe electroporation. It serves as an accelerated test of the pore formation process since low electric fields would take inordinately long simulation time. MD simulation shows no pore is formed in a 1-ns snapshot for a DPPC membrane set at a temperature of 25°C after a 0.5 V/nm electric field is applied. A nano-sized pore is clearly seen in a 0.75-ns snapshot on the same geometry, but with the membrane surfaces kept at temperatures of 47°C. And the pore increases at 1 ns. The MD simulation results suggest the possibility that the increase in temperature can result in different degrees of electrically stimulated bio-effects. The results points to the role of thermal effects in facilitating and accelerating the electroporation process.

Keywords: high-intensity, ultra-short, electroporation, thermal effects, molecular dynamics

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Authors: Umesh Kumar Singh, Abhishek Raghuvanshi, Suyash Kumar Singh

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As IoT networks gain popularity, they are more susceptible to security breaches. As a result, it is crucial to analyze the IoT platform as a whole from the standpoint of core security concepts. The Internet of Things relies heavily on wireless networks, which are well-known for being susceptible to a wide variety of attacks. This article provides an analysis of many techniques that may be used to identify vulnerabilities in the software and hardware associated with the Internet of Things (IoT). In the current investigation, an experimental setup is built with the assistance of server computers, client PCs, Internet of Things development boards, sensors, and cloud subscriptions. Through the use of network host scanning methods and vulnerability scanning tools, raw data relating to IoT-based applications and devices may be collected. Shodan is a tool that is used for scanning, and it is also used for effective vulnerability discovery in IoT devices as well as penetration testing. This article presents an efficient mitigation plan for encountering vulnerabilities in the Internet of Things.

Keywords: internet of things, security, privacy, vulnerability identification, mitigation plan

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Authors: M. Heydari Vini

Abstract:

There is a complicated relation between effective input parameters of cold rolling and output rolling force and exit thickness of strips.in many mathematical models, the effect of some rolling parameters have been ignored and the outputs have not a desirable accuracy. In the other hand, there is a special relation among input thickness of strips,the width of the strips,rolling speeds,mandrill tensions and the required exit thickness of strips with rolling force and the real exit thickness of the rolled strip. First of all, in this paper the effective parameters of cold rolling process modeled using an artificial neural network according to the optimum network achieved by using a written program in MATLAB,it has been shown that the prediction of rolling stand parameters with different properties and new dimensions attained from prior rolled strips by an artificial neural network is applicable.

Keywords: cold rolling, artificial neural networks, rolling force, real rolled thickness of strips

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Authors: Yusuf Seedat

Abstract:

The necessity to understand global financial markets has increased following the unfortunate spread of the recent financial crisis around the world. Financial markets are considered to be complex systems consisting of highly volatile move-ments whose indexes fluctuate without any clear pattern. Analytic methods of stock prices have been proposed in which financial markets are modeled using common network analysis tools and methods. It has been found that two key components of social network analysis are relevant to modeling financial markets, allowing us to forecast accurate predictions of stock prices within the financial market. Financial markets have a number of interacting components, leading to complex behavioral patterns. This paper describes a social network approach to analyzing financial markets as a viable approach to studying the way complex stock markets function. We also look at how social network analysis techniques and metrics are used to gauge an understanding of the evolution of financial markets as well as how community detection can be used to qualify and quantify in-fluence within a network.

Keywords: network analysis, social networks, financial markets, stocks, nodes, edges, complex networks

Procedia PDF Downloads 178

Authors: Fakhrosadat Sajjadian, Ramin Ghasemi Shayan

Abstract:

The tumor microenvironment plays an fundamental part in tumor start, movement, metastasis, and treatment resistance. It varies from ordinary tissue in terms of its extracellular network, vascular and lymphatic arrange, as well as physiological conditions. The clinical application of atomic cancer imaging is regularly prevented by the tall commercialization costs of focused on imaging operators as well as the constrained clinical applications and little showcase measure of a few operators. . Since numerous cancer types share comparable characteristics of the tumor microenvironment, the capacity to target these biomarkers has the potential to supply clinically translatable atomic imaging advances for numerous types encompassing cancer and broad clinical applications. Noteworthy advance has been made in focusing on the tumor microenvironment for atomic cancer imaging. In this survey, we summarize the standards and methodologies of later progresses in atomic imaging of the tumor microenvironment, utilizing distinctive imaging modalities for early discovery and conclusion of cancer. To conclude, The tumor microenvironment (TME) encompassing tumor cells could be a profoundly energetic and heterogeneous composition of safe cells, fibroblasts, forerunner cells, endothelial cells, flagging atoms and extracellular network (ECM) components.

Keywords: molecular, imaging, TME, medicine

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Authors: Gizem Eser Erdek

Abstract:

This study is research on predicting the remaining life of industrial cutting tools used in the industrial production process with deep learning methods. When the life of cutting tools decreases, they cause destruction to the raw material they are processing. This study it is aimed to predict the remaining life of the cutting tool based on the damage caused by the cutting tools to the raw material. For this, hole photos were collected from the hole-drilling machine for 8 months. Photos were labeled in 5 classes according to hole quality. In this way, the problem was transformed into a classification problem. Using the prepared data set, a model was created with convolutional neural networks, which is a deep learning method. In addition, VGGNet and ResNet architectures, which have been successful in the literature, have been tested on the data set. A hybrid model using convolutional neural networks and support vector machines is also used for comparison. When all models are compared, it has been determined that the model in which convolutional neural networks are used gives successful results of a %74 accuracy rate. In the preliminary studies, the data set was arranged to include only the best and worst classes, and the study gave ~93% accuracy when the binary classification model was applied. The results of this study showed that the remaining life of the cutting tools could be predicted by deep learning methods based on the damage to the raw material. Experiments have proven that deep learning methods can be used as an alternative for cutting tool life estimation.

Keywords: classification, convolutional neural network, deep learning, remaining life of industrial cutting tools, ResNet, support vector machine, VggNet

Procedia PDF Downloads 58