Search results for: protein-protein interaction networks

Authors: Masrur Alam Khan

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The purpose of this study was to examine the dyadic interaction techniques that social media users utilize to reduce uncertainty in their day to day business engagements in the absence of their physical interaction. The study empirically tested assumptions of uncertainty reduction theory while addressing self-disclosure, seeking questions to develop consensus, and subsequently to achieve intimacy in very conducive environment. Moreover, this study examined the effect of dyadic interaction through social media among business community while identifying the strength of their reciprocity in relationships and compares it with those having no dyadic relations due to absence of social media. Using socio-metric survey, the study revealed a better understanding of their partners for upholding their professional relations more credible. A sample of unacquainted, both male and female, was randomly asked questions regarding their nature of dyadic interaction within their office while using social media (face-to-face, visual CMC (webcam) or text-only). Primary results explored that the social media users develop their better know-how about their professional obligations to reduce ambiguity and align with one to one interact.

Keywords: dyadic-interaction, social media, uncertainty reduction, socio-metric survey, self-disclosure, intimacy, reciprocity in relationship

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Authors: Danilo López, Johana Hernández, Edwin Rivas

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The use of the Multilayer Perceptron Neural Networks (MLPNN) technique is presented to estimate the future state of use of a licensed channel by primary users (PUs); this will be useful at the spectral decision stage in cognitive radio networks (CRN) to determine approximately in which time instants of future may secondary users (SUs) opportunistically use the spectral bandwidth to send data through the primary wireless network. To validate the results, sequences of occupancy data of channel were generated by simulation. The results show that the prediction percentage is greater than 60% in some of the tests carried out.

Keywords: cognitive radio, neural network, prediction, primary user

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Authors: Ratchada Sopakayang, Gerhard A. Holzapfel

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In this study, a new constitutive model is developed to describe the hyperelastic behavior of collagenous tissues with a parallel arrangement of collagen fibers such as ligaments and tendons. The model is formulated using a continuum approach incorporating the structural changes of the main tissue components: collagen fibers, proteoglycan-rich matrix and fiber-matrix interaction. The mechanical contribution of the interaction between the fibers and the matrix is simply expressed by a coupling term. The structural change of the collagen fibers is incorporated in the constitutive model to describe the activation of the fibers under tissue straining. Finally, the constitutive model can easily describe the stress-stretch nonlinearity which occurs when a ligament/tendon is axially stretched. This study shows that the interaction between the fibers and the matrix contributes to the mechanical tissue response. Therefore, the model may lead to a better understanding of the physiological mechanisms of ligaments and tendons under axial loading.

Keywords: constitutive model, fiber-matrix, hyperelasticity, interaction, ligament, tendon

Procedia PDF Downloads 299

Authors: Manuel Meranza-Castillon, Rolando Diaz-Castillo, Adrian Arellano-Delgado, Cesar Cruz-Hernandez, Rosa Martha Lopez-Gutierrez

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In this work, we deal with experimental network synchronization of chaotic nodes with different topologies. Our approach is based on complex system theory, and we use a master-slave configuration to couple the nodes in the networks. In particular, we design and implement electronically complex dynamical networks composed by nine coupled chaotic Chua’s circuits with topologies: in nearest-neighbor, small-world, open ring, star, and global. Also, network synchronization is evaluated according to a particular coupling strength for each topology. This study is important by the possible applications to private transmission of information in a chaotic communication network of multiple users.

Keywords: complex networks, Chua's circuit, experimental synchronization, multiple users

Procedia PDF Downloads 348

Authors: Elaheh Vaezpour

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Data sensing in wireless sensor networks is done by query deceleration the network by the users. In many applications of the wireless sensor networks, many users send queries to the network simultaneously. If the queries are processed separately, the network’s energy consumption will increase significantly. Therefore, it is very important to aggregate the queries before sending them to the network. In this paper, we propose a multiple query optimization framework based on sensors physical and temporal correlation. In the proposed method, queries are merged and sent to network by considering correlation among the sensors in order to reduce the communication cost between the sensors and the base station.

Keywords: wireless sensor networks, multiple query optimization, data correlation, reducing energy consumption

Procedia PDF Downloads 334

Authors: S. Adjiri-Bailiche, S. M. Boudia, H. Daaou, S. Hadouche, A. Benzaoui

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In this work the study of changes in the wind speed depending on the altitude is calculated and described by the model of the neural networks, the use of measured data, the speed and direction of wind, temperature and the humidity at 10 m are used as input data and as data targets at 50m above sea level. Comparing predict wind speeds and extrapolated at 50 m above sea level is performed. The results show that the prediction by the method of artificial neural networks is very accurate.

Keywords: MATLAB, neural network, power low, vertical extrapolation, wind energy, wind speed

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Authors: Engin Eyceyurt, Josko Zec

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The current and future cellular mobile communication networks generate enormous amounts of data. Networks have become extremely complex with extensive space of parameters, features and counters. These networks are unmanageable with legacy methods and an enhanced design and optimization approach is necessary that is increasingly reliant on machine learning. This paper proposes that machine learning as a viable approach for uplink throughput prediction. LTE radio metric, such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal to Noise Ratio (SNR) are used to train models to estimate expected uplink throughput. The prediction accuracy with high determination coefficient of 91.2% is obtained from measurements collected with a simple smartphone application.

Keywords: drive test, LTE, machine learning, uplink throughput prediction

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Authors: Rashini Maduka, C. R. Wijesinghe, A. R. Weerasinghe

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Drug-drug interactions (DDIs) can happen when two or more drugs are taken together. Today DDIs have become a serious health issue due to adverse drug effects. In vivo and in vitro methods for identifying DDIs are time-consuming and costly. Therefore, in-silico-based approaches are preferred in DDI identification. Most machine learning models for DDI prediction are used chemical and biological drug properties as features. However, some drug features are not available and costly to extract. Therefore, it is better to make automatic feature engineering. Furthermore, people who have diabetes already suffer from other diseases and take more than one medicine together. Then adverse drug effects may happen to diabetic patients and cause unpleasant reactions in the body. In this study, we present a model with a graph convolutional autoencoder and a graph decoder using a dataset from DrugBank version 5.1.3. The main objective of the model is to identify unknown interactions between antidiabetic drugs and the drugs taken by diabetic patients for other diseases. We considered automatic feature engineering and used Known DDIs only as the input for the model. Our model has achieved 0.86 in AUC and 0.86 in AP.

Keywords: drug-drug interaction prediction, graph embedding, graph convolutional networks, adverse drug effects

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Authors: Luciano Roberto Fernandez-Sola, Cesar Augusto Arredondo-Velez, Miguel Angel Jaimes-Tellez

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This paper explores the modifications on peak acceleration, velocity and displacement profiles over the structure due to dynamic soil-structure interaction (DSSI). A shear beam model is used for the structure. Soil-foundation flexibility (inertial interaction) is considered by a set of springs and dashpots at the structure base. Kinematic interaction is considered using transfer functions. Impedance functions are computed using simplified expressions for rigid foundations. The research studies the influence of the slenderness ratio on the value of the peak floor response. It is shown that the modifications of peak floor responses are not the same for acceleration, velocity and displacement. This is opposite to the hypothesis used by methods included in several building codes. Results show that modifications produced by DSSI on different response quantities are not equal.

Keywords: peak floor intensities, dynamic soil-structure interaction, buildings with flexible base, kinematic and inertial interaction

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Authors: Motahareh Fadaei

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Wireless sensor networks that are used to monitor a special environment, are formed from a large number of sensor nodes. The role of these sensors is to sense special parameters from ambient and to make connection. In these networks, the most important challenge is the management of energy usage. Clustering is one of the methods that are broadly used to face this challenge. In this paper, a distributed clustering protocol based on learning automata is proposed for underwater wireless sensor networks. The proposed algorithm that is called LA-Clustering forms clusters in the same energy level, based on the energy level of nodes and the connection radius regardless of size and the structure of sensor network. The proposed approach is simulated and is compared with some other protocols with considering some metrics such as network lifetime, number of alive nodes, and number of transmitted data. The simulation results demonstrate the efficiency of the proposed approach.

Keywords: clustering, energy consumption‎, learning automata, underwater sensor networks

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Authors: Al-Amin, Huanjun Jiang, Anayat Ali

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Reinforced concrete frames (RC), especially Ordinary RC frames, are prone to structural failures/collapse during seismic events, leading to a large proportion of debris from the structures, which obstructs adjacent areas, including streets. These blocked areas severely impede post-earthquake resilience. This study uses computational simulation (FEM) to investigate the amount of debris generated by the seismic collapse of an ordinary reinforced concrete moment frame building and its effects on the adjacent pedestrian and road network. A three-story ordinary reinforced concrete frame building, primarily designed for gravity load and earthquake resistance, was selected for analysis. Sixteen different ground motions were applied and scaled up until the total collapse of the tested building to evaluate the failure mode under various seismic events. Four types of collapse direction were identified through the analysis, namely aligned (positive and negative) and skewed (positive and negative), with aligned collapse being more predominant than skewed cases. The amount and distribution of debris around the collapsed building were assessed to investigate the interaction between collapsed buildings and adjacent street networks. An interaction was established between a building that collapsed in an aligned direction and the adjacent pedestrian walkway and narrow street located in an unplanned old city. The FEM model was validated against an existing shaking table test. The presented results can be utilized to simulate the interdependency between the debris generated from the collapse of seismic-prone buildings and the resilience of street networks. These findings provide insights for better disaster planning and resilient infrastructure development in earthquake-prone regions.

Keywords: building collapse, earthquake-induced debris, ORC moment resisting frame, street network

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Authors: Davide Forcellini

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The most recent development of earthquake engineering is based on concept of design consisting in prescribed performance rather than the more traditional prescriptive approaches. The paper aims to assess the effects of isolation devices and soil structure interaction on a benchmark bridge adopting a Performance-Based Earthquake Engineering methodology. Several isolated configurations of abutments and pier connections are compared performing the most representative isolation devices. Isolation systems suitability depends on many factors, mainly connected with ground effects. In this regard, the second purpose of this paper is to assess the effects of soil-structure interaction (SSI) on the studied bridge configurations. Contributions of isolation technique and soil structure interaction are assessed evaluating the resistance effects applied to Peak Ground Acceleration (PGA) levels in terms of cost and time repair quantities.

Keywords: base isolation, bridge, earthquake engineering, non linearity, PBEE methodology, seismic assessment, soil structure interaction

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Authors: Sara Alshehri, Bayan Alenzi, Atheer Alshehri, Samia Chelloug, Zainab Almry, Hussah Albugmai

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This paper concerns body area networks sensor that collect signals around a human body. The black hole attacks are the main security challenging problem because the data traffic can be dropped at any node. The focus of our proposed solution is to efficiently route data packets while detecting black hole nodes.

Keywords: body sensor networks, security, black hole, routing, broadcasting, OMNeT++

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Authors: C. N. Vanitha, M. Usha

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In a real world scenario, the viability of the sensor networks has been proved by standardizing the technologies. Wireless sensor networks are vulnerable to both electronic and physical security breaches because of their deployment in remote, distributed, and inaccessible locations. The compromised sensor nodes send malicious data to the base station, and thus, the total network effectiveness will possibly be compromised. To detect and seclude the Route modifiers, a neural network based Pattern Learning predictor (PLP) is presented. This algorithm senses data at any node on present and previous patterns obtained from the en-route nodes. The eminence of any node is upgraded by their predicted and reported patterns. This paper propounds a solution not only to detect the route modifiers, but also to seclude the malevolent nodes from the network. The simulation result proves the effective performance of the network by the presented methodology in terms of energy level, routing and various network conditions.

Keywords: neural networks, pattern learning, security, wireless sensor networks

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Authors: Chung-Yuan Huang, Yu-Hsiang Fu, Chuen-Tsai Sun

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Community detection represents an important data-mining tool for analyzing and understanding real-world complex network structures and functions. We believe that at least four criteria determine the appropriateness of a community detection algorithm: (a) it produces useable normalized mutual information (NMI) and modularity results for social networks, (b) it overcomes resolution limitation problems associated with synthetic networks, (c) it produces good NMI results and performance efficiency for Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks, and (d) it produces good modularity and performance efficiency for large-scale real-world complex networks. To our knowledge, no existing community detection algorithm meets all four criteria. In this paper, we describe a simple hierarchical arc-merging (HAM) algorithm that uses network topologies and rule-based arc-merging strategies to identify community structures that satisfy the criteria. We used five well-studied social network datasets and eight sets of LFR benchmark networks to validate the ground-truth community correctness of HAM, eight large-scale real-world complex networks to measure its performance efficiency, and two synthetic networks to determine its susceptibility to resolution limitation problems. Our results indicate that the proposed HAM algorithm is capable of providing satisfactory performance efficiency and that HAM-identified communities were close to ground-truth communities in social and LFR benchmark networks while overcoming resolution limitation problems.

Keywords: complex network, social network, community detection, network hierarchy

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Authors: Gabriel Chavira, Jorge Orozco, Salvador Nava, Eduardo Álvarez, Julio Rolón, Roberto Pichardo

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Ambient Intelligence promotes a shift in computing which involves fitting-out the environments with devices to support context-aware applications. One of main objectives is the reduction to a minimum of the user’s interactive effort, the diversity and quantity of devices with which people are surrounded with, in existing environments; increase the level of difficulty to achieve this goal. The mobile phones and their amazing global penetration, makes it an excellent device for delivering new services to the user, without requiring a learning effort. The environment will have to be able to perceive all of the interaction techniques. In this paper, we present the PICTAC model (Perceiving touch Interaction through TAgging Context), which similarly delivers service to members of a research group.

Keywords: ambient intelligence, tagging context, touch interaction, touching services

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Authors: Rahul Malhotra, Anurag Sharma

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Fiber based access networks can deliver performance that can support the increasing demands for high speed connections. One of the new technologies that have emerged in recent years is Passive Optical Networks. This paper is targeted to show the simultaneous delivery of triple play service (data, voice, and video). The comparative investigation and suitability of various data rates is presented. It is demonstrated that as we increase the data rate, number of users to be accommodated decreases due to increase in bit error rate.

Keywords: FTTH, quad play, play service, access networks, data rate

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Authors: Haozhe Xiang

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With the rise of the Internet of Things era, intelligent products are gradually integrating into people's lives. Pedestrian trajectory prediction has become a key issue, which is crucial for the motion path planning of intelligent agents such as autonomous vehicles, robots, and drones. In the current technological context, deep learning technology is becoming increasingly sophisticated and gradually replacing traditional models. The pedestrian trajectory prediction algorithm combining neural networks and attention mechanisms has significantly improved prediction accuracy. Based on in-depth research on deep learning and pedestrian trajectory prediction algorithms, this article focuses on physical environment modeling and learning of historical trajectory time dependence. At the same time, social interaction between pedestrians and scene interaction between pedestrians and the environment were handled. An improved pedestrian trajectory prediction algorithm is proposed by analyzing the existing model architecture. With the help of these improvements, acceptable predicted trajectories were successfully obtained. Experiments on public datasets have demonstrated the algorithm's effectiveness and achieved acceptable results.

Keywords: deep learning, graph convolutional network, attention mechanism, LSTM

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Authors: Si-Gwan Kim

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In Delay Tolerant Networks (DTN), there may not exist an end-to-end path between source and destination at the time of message transmission. Employing ‘Store Carry and Forward’ delivery mechanism for message transmission in such networks usually incurs long message delays. In this paper, we present the modified Binary Spray and Wait (BSW) routing protocol that enhances the performance of the original one. Our proposed algorithm adjusts the number of forward messages depending on the number of neighbor nodes. By using beacon messages periodically, the number of neighbor nodes can be managed. The simulation using ONE simulator results shows that our modified version gives higher delivery ratio and less latency as compared to BSW.

Keywords: delay tolerant networks, store carry and forward, one simulator, binary spray and wait

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Authors: Paulo Cesar Pereira Junior, Alexandre Monteiro, Rafael da Luz Ribeiro, Antonio Carlos Sobieranski, Aldo von Wangenheim

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In this paper, we present a comparison between convolutional neural networks and classical computer vision approaches, for the specific precision agriculture problem of weed mapping on sugarcane fields aerial images. A systematic literature review was conducted to find which computer vision methods are being used on this specific problem. The most cited methods were implemented, as well as four models of convolutional neural networks. All implemented approaches were tested using the same dataset, and their results were quantitatively and qualitatively analyzed. The obtained results were compared to a human expert made ground truth for validation. The results indicate that the convolutional neural networks present better precision and generalize better than the classical models.

Keywords: convolutional neural networks, deep learning, digital image processing, precision agriculture, semantic segmentation, unmanned aerial vehicles

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Authors: Daham Owaid Matrood, Naqaa Hussein Raheem

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Evolutionary algorithms and Artificial neural networks (ANN) are two relatively young research areas that were subject to a steadily growing interest during the past years. This paper examines the use of Particle Swarm Optimization (PSO) to train a multi-layer feed forward neural network for demand forecasting. We use in this paper weekly demand data for packed cement and towels, which have been outfitted by the Northern General Company for Cement and General Company of prepared clothes respectively. The results showed superiority of trained neural networks using particle swarm optimization on neural networks trained using error back propagation because their ability to escape from local optima.

Keywords: artificial neural network, demand forecasting, particle swarm optimization, weight optimization

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Authors: Maryam Savari, Mohamad Nizam Ayub, Ainuddin Wahid Abdul Wahab

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Game-based Learning (GBL) is an alternative that provides learners with an opportunity to experience a volatile environment in a safe and secure place. A volatile environment requires a different technique to facilitate learning and prevent injury and other hazards. Subjects involving elasticity are always considered hazardous and can cause injuries,for instance a bouncing ball. Elasticity is a topic that necessitates hands-on practicality for learners to experience the effects of elastic objects. In this paper the scope is to investigate the natural interaction between learners and elastic objects in a safe environment using GBL. During interaction, the potentials of natural contact in the process of learning were explored and gestures exhibited during the learning process were identified. GBL was developed using Kinect technology to teach elasticity to primary school children aged 7 to 12. The system detects body gestures and defines the meanings of motions exhibited during the learning process. The qualitative approach was deployed to constantly monitor the interaction between the student and the system. Based on the results, it was found that Natural Interaction GBL (Ni-GBL) is engaging for students to learn, making their learning experience more active and joyful.

Keywords: elasticity, Game-Based Learning (GBL), kinect technology, natural interaction

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Authors: Samira Laidaoui, Mohammed Djermane, Nazihe Terfaya

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The fluid-structure coupling is a natural phenomenon which reflects the effects of two continuums: fluid and structure of different types in the reciprocal action on each other, involving knowledge of elasticity and fluid mechanics. The solution for such problems is based on the relations of continuum mechanics and is mostly solved with numerical methods. It is a computational challenge to solve such problems because of the complex geometries, intricate physics of fluids, and complicated fluid-structure interactions. The way in which the interaction between fluid and solid is described gives the largest opportunity for reducing the computational effort. In this paper, a problem of fluid structure interaction is investigated with two-way coupling method. The formulation Arbitrary Lagrangian-Eulerian (ALE) was used, by considering a dynamic grid, where the solid is described by a Lagrangian formulation and the fluid by a Eulerian formulation. The simulation was made on the ANSYS software.

Keywords: ALE, coupling, FEM, fluid-structure, interaction, one-way method, two-way method

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Authors: Faisal Al Yahmadi, Muhammad R. Ahmed

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Electric power is a fundamental necessity in the 21^st century. Consequently, any break in electric power is probably going to affect the general activity. To make the power supply smooth and efficient, a smart grid network is introduced which uses communication technology. In any communication network, security is essential. It has been observed from several recent incidents that adversary causes an interruption to the operation of networks. In order to resolve the issues, it is vital to understand the threats and vulnerabilities associated with the smart grid networks. In this paper, we have investigated the threats and vulnerabilities in Smart Grid Networks (SGN) and the few solutions in the literature. Proposed solutions showed developments in electricity theft countermeasures, Denial of services attacks (DoS) and malicious injection attacks detection model, as well as malicious nodes detection using watchdog like techniques and other solutions.

Keywords: smart grid network, security, threats, vulnerabilities

Procedia PDF Downloads 139

Authors: Bing-Qing Lu, Hui-Tzu Min

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Recent research, supported by sociocultural theory, has shown that collaborative writing in the second language (L2) contexts afford students opportunities to interact with each other to co-construct knowledge during the co-composing process. To date, much research on pair interaction in L2 collaborative writing settings has centered on intermediate and advanced learners by using static categorization of pair interaction patterns. Little is known about the fluid nature of pair interaction during collaborative writing, especially among low-proficiency learners. This study, thus, is aimed to explore the interaction dynamics of low-proficiency L2 learners during collaborative writing via examining the interaction pattern, focus of interaction, and the language related episodes (LREs) of 5 low-proficiency L2 writers from Taiwan. Employing a micro-level functional analytical method to capture the changing nature of pair interaction dynamics, the researchers calculated the number of characters/words produced by each pair member during CW and then classified their utterances into four task related-aspects--content, organization, language use, and task management--to determine each pair member's relative contribution to different dimensions of the evolving text. The LREs were also identified and examined. The results show that, of the five pairs, three pairs changed their interaction patterns when discussing different aspects of writing. Regarding the focus of their interaction, all five pairs paid attention to content most, followed by language use, task management, and organization. They were able to successfully resolve the majority of language issues (75.2%) in LREs and use the correct forms in their writing. These findings lend support to the fluid nature of pairs’ interactions and the changing roles of L2 learners in collaborative writing and highlighted the necessity of examining learners’ interaction patterns from a micro-level perspective. These findings also support previous research that low-proficiency pairs are able to correctly revolve 2/3 of their produced LREs, suggesting that collaborative writing may also be suitable for L2 low-proficiency learners.

Keywords: collaborative writing, low-proficiency L2 learners, micro-level functional analysis, pair interaction pattern

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Authors: Taha Kadir Yesin, Hanyu Liu, Zhangfan Ding, Amit Singh, Qi Tian, Yuheng Zhang, Biswajyoti Borah, Junyu Chen, Anjali P. Kusumbe

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The skeletal structure and bone marrow endothelium collectively form a critical functional unit essential for bone development, health, and aging. At the core of osteogenesis and bone formation lies the dynamic process of angiogenesis. In this study, we reveal a potent endogenous anabolic NCAM1-14.3.3. ζδ-derived- Peptide interaction, which stimulates bone angiogenesis and osteogenesis during homeostasis, aging, and age-related bone diseases. Employing high-resolution imaging and inducible cell-specific mouse genetics, our results elucidate the pivotal role of the NCAM1-14.3.3.ζδ-derived-Peptide interaction in driving the expansion of Clec14a+ angiogenic endothelial cells. Notably, Clec14a+ endothelial cells express key osteogenic factors. The NCAM1-14.3.3.ζδ-derived-Peptide interaction in osteoblasts drives osteoblast differentiation, ultimately contributing to the genesis of bone. Moreover, the NCAM1-14.3.3.ζδ-derived-Peptide interaction leads to a reduction in bone resorption. In age-associated vascular and bone loss diseases, stimulating the NCAM1-14.3.3.ζδ-derived-Peptide interaction not only promotes angiogenesis but also reverses bone loss. Consequently, harnessing the endogenous anabolic potential of the NCAM1-14.3.3.ζδ-derived-Peptide interaction emerges as a promising therapeutic modality for managing age-related bone diseases.

Keywords: endothelial cell, NCAM1, Clec14a, 14.3.3.ζδ

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Authors: Ardavan Zamanpour, Somaieh Yassari

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ZigBee protocol network was developed in industries and MIT laboratory in 1997. ZigBee is a wireless networking technology by alliance ZigBee which is designed to low board and low data rate applications. It is a Protocol which connects between electrical devises with very low energy and cost. The first version of IEEE 802.15.4 which was formed ZigBee was based on 2.4GHZ MHZ 912MHZ 868 frequency band. The name of system is often reminded random directions that bees (BEES) traversing during pollination of products. Such as alloy of the ways in which information packets are traversed within the mesh network. This paper aims to study the performance and effectiveness of this protocol in wireless networks.

Keywords: ZigBee, protocol, wireless, networks

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Authors: W. Shin, Y. Kim

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This study focuses on disconfirming that wide-area networks can be made mobile, highly-available, and wireless. This methodological test shows that IPv7 and context-free grammar are mismatched. In the cases of robots, a similar tendency is also revealed. Further, we also prove that public-private key pairs could be built embedded, adaptive, and wireless. Finally, we disconfirm that although hash tables can be made distributed, interposable, and autonomous, XML and DNS can interfere to realize this purpose. Our experiments soon proved that exokernelizing our replicated Knesis keyboards was more significant than interrupting them. Our experiments exhibited degraded average sampling rate.

Keywords: collaborative communication, DNS, local-area networks, XML

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Authors: Konstans Ruseva, Kristina Ivanova, Katerina Todorova, Margarita Gabrashanska, Tzanko Tzanov, Elena Vassileva

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Zwitterionic polymers (ZP) are well-known with their ultralow biofouling. They are successfully competing with poly(ethylene glycols) (PEG), which are considered as the “golden standard” in this respect. These unique properties are attributed to their strong hydration capacity, defined by the dipole-dipole interactions, arising between the ZP pendant groups as well as to the dipoles interaction with water molecules. Beside, ZP are highly resistant to bacterial adhesion thus ensuring an excellent anti-biofilm formation ability. Moreover, ZP are able to respond upon external stimuli such as temperature, pH, salt concentration changes which in combination with their anti-biofouling effect render this type of polymers as materials with a high potential in biomedical applications. The present work is focused on the development of zwitterionic hydrogels for efficient treatment of highly exudating and hard-to-heal chronic wounds. To this purpose, two types of ZP networks with different crosslinking degree were synthesized - polysulfobetaine (PSB) and polycarboxybetaine (PCB) ones. They were characterized in terms of their physico-mechanical properties, e.g. microhardness, swelling ability, smart behaviour. Furthermore, the potential of ZP networks to resist biofilm formation towards Staphylococcus aureus and Escherichia coli was studied. Their ability to reduce the high levels of myeloperoxidase and metalloproteinase, two enzymes that are part of the chronic wounds enviroenment, was revealed. Moreover, the in vitro cytotoxic assessment of PSB and PCB networks along with their in vivo performance in rats was also studied to reveal their high biocompatibility.

Keywords: absorption properties, biocompatibility, enzymatic inhibition activity, wound healing, zwitterionic polymers

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Authors: Ying Su, Morgan C. Wang

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Long-term time series forecasting is an important research area for automated machine learning (AutoML). Currently, forecasting based on either machine learning or statistical learning is usually built by experts, and it requires significant manual effort, from model construction, feature engineering, and hyper-parameter tuning to the construction of the time series model. Automation is not possible since there are too many human interventions. To overcome these limitations, this article proposed to use recurrent neural networks (RNN) through the memory state of RNN to perform long-term time series prediction. We have shown that this proposed approach is better than the traditional Autoregressive Integrated Moving Average (ARIMA). In addition, we also found it is better than other network systems, including Fully Connected Neural Networks (FNN), Convolutional Neural Networks (CNN), and Nonpooling Convolutional Neural Networks (NPCNN).

Keywords: automated machines learning, autoregressive integrated moving average, neural networks, time series analysis

Procedia PDF Downloads 105