Search results for: biological molecular networks

Authors: Xiang Jianhong, Wang Cong, Wang Linyu

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With the rise of medical IoT technologies, Wireless body area networks (WBANs) can collect fetal electrocardiogram (FECG) signals to support telemedicine analysis. The compressed sensing (CS)-based WBANs system can avoid the sampling of a large amount of redundant information and reduce the complexity and computing time of data processing, but the existing algorithms have poor signal compression and reconstruction performance. In this paper, a Joint block multi-orthogonal least squares (JBMOLS) algorithm is proposed. We apply the FECG signal to the Joint block sparse model (JBSM), and a comparative study of sparse transformation and measurement matrices is carried out. A FECG signal compression transmission mode based on Rbio5.5 wavelet, Bernoulli measurement matrix, and JBMOLS algorithm is proposed to improve the compression and reconstruction performance of FECG signal by CS-based WBANs. Experimental results show that the compression ratio (CR) required for accurate reconstruction of this transmission mode is increased by nearly 10%, and the runtime is saved by about 30%.

Keywords: telemedicine, fetal ECG, compressed sensing, joint sparse reconstruction, block sparse signal

Procedia PDF Downloads 113

Authors: Anukriti Kumar, Tanmay Singh, Dinesh Kumar Vishwakarma

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Traffic control has been one of the most common and irritating problems since the time automobiles have hit the roads. Problems like traffic congestion have led to a significant time burden around the world and one significant solution to these problems can be the proper implementation of the Intelligent Transport System (ITS). It involves the integration of various tools like smart sensors, artificial intelligence, position technologies and mobile data services to manage traffic flow, reduce congestion and enhance driver's ability to avoid accidents during adverse weather. Road and traffic signs’ recognition is an emerging field of research in ITS. Classification problem of traffic signs needs to be solved as it is a major step in our journey towards building semi-autonomous/autonomous driving systems. The purpose of this work focuses on implementing an approach to solve the problem of traffic sign classification by developing a Convolutional Neural Network (CNN) classifier using the GTSRB (German Traffic Sign Recognition Benchmark) dataset. Rather than using hand-crafted features, our model addresses the concern of exploding huge parameters and data method augmentations. Our model achieved an accuracy of around 97.6% which is comparable to various state-of-the-art architectures.

Keywords: multiclass classification, convolution neural network, OpenCV

Procedia PDF Downloads 159

Authors: Valeria Bondarenko

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We proposed algorithms for: estimation of parameters fBm (volatility and Hurst exponent) and for the approximation of random time series by functional of fBm. We proved the consistency of the estimators, which constitute the above algorithms, and proved the optimal forecast of approximated time series. The adequacy of estimation algorithms, approximation, and forecasting is proved by numerical experiment. During the process of creating software, the system has been created, which is displayed by the hierarchical structure. The comparative analysis of proposed algorithms with the other methods gives evidence of the advantage of approximation method. The results can be used to develop methods for the analysis and modeling of time series describing the economic, physical, biological and other processes.

Keywords: mathematical model, random process, Wiener process, fractional Brownian motion

Procedia PDF Downloads 342

Authors: L. Spitaleri, C. M. A. Gangemi, R. Purrello, G. Nicotra, G. Trusso Sfrazzetto, G. Casella, M. Casarin, A. Gulino

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Hybrid molecular–nanoparticle materials, obtained with a bottom-up approach, are suitable for the fabrication of functional nanostructures showing structural control and well-defined properties, i.e., optical, electronic or catalytic properties, in the perspective of applications in different fields of nanotechnology. Gold nanoparticles (Au NPs) exhibit important chemical, electronic and optical properties due to their size, shape and electronic structures. In fact, Au NPs containing no more than 30-40 atoms are only luminescent because they can be considered as large molecules with discrete energy levels, while nano-sized Au NPs only show the surface plasmon resonance. Hence, it appears that gold nanoparticles can alternatively be luminescent or plasmonic, and this represents a severe constraint for their use as an optical material. The aim of this work was the fabrication of nanoscale assembly of Au NPs covalently anchored to each other by means of novel bi-functional porphyrin molecules that work as bridges between different gold nanoparticles. This functional architecture shows a strong surface plasmon due to the Au nanoparticles and a strong luminescence signal coming from porphyrin molecules, thus, behaving like an artificial organized plasmonic and fluorescent network. The self-assembly geometry of this porphyrin on the Au NPs was studied by investigation of the conformational properties of the porphyrin derivative at the DFT level. The morphology, electronic structure and optical properties of the conjugated Au NPs – porphyrin system were investigated by TEM, XPS, UV–vis and Luminescence. The present nanostructures can be used for plasmon-enhanced fluorescence, photocatalysis, nonlinear optics, etc., under atmospheric conditions since our system is not reactive to air nor water and does not need to be stored in a vacuum or inert gas.

Keywords: gold nanoparticle, porphyrin, surface plasmon resonance, luminescence, nanostructures

Procedia PDF Downloads 139

Authors: Raana Babadi Fathipour

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Hydrogels are three-dimensional structures formed by the interweaving of polymeric materials, possessing the remarkable ability to imbibe copious amounts of water. Numerous methodologies have been devised for examining and understanding the properties of these synthesized gels. Amongst them, spectroscopic techniques such as ultraviolet/visible (UV/Vis) and Fourier-transform infrared (FTIR) spectroscopy offer a glimpse into molecular and atomic aspects. Additionally, diffraction methods like X-ray diffraction (XRD) enable one to measure crystallinity within the gel's structure, while microscopy tools encompassing scanning electron microscopy (SEM) and transmission electron microscopy (TEM) provide insights into surface texture and morphology. Furthermore, rheology serves as an invaluable tool for unraveling the viscoelastic behavior inherent in hydrogels—a parameter crucial not only to numerous industries, including pharmaceuticals, cosmetics, food processing, agriculture and water treatment, but also pivotal to related fields of research. Likewise, the ultimate configuration of the product is contingent upon its characterization at a microscopic scale in order to comprehend the intricacies of the hydrogel network's structure and interaction dynamics in response to external forces. Within this present scrutiny, our attention has been devoted to unraveling the intricate rheological tendencies exhibited by materials founded on synthetic, natural, and semi-synthetic hydrogels. We also explore their practical utilization within various facets of everyday life from an industrial perspective.

Keywords: rheology, hydrogels characterization, viscoelastic behavior, application

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Authors: Fei Gao, Xinyang Dong, Yisu Ge, Shufang Lu, Libo Weng

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Vehicle color recognition plays an important role in traffic accident investigation. However, due to the influence of illumination, weather, and noise, vehicle color recognition still faces challenges. In this paper, a vehicle color classification network based on spatial cluster loss and channel attention mechanism (SCNet) is proposed for vehicle color recognition. A channel attention module is applied to extract the features of vehicle color representative regions and reduce the weight of nonrepresentative color regions in the channel. The proposed loss function, called spatial clustering loss (SC-loss), consists of two channel-specific components, such as a concentration component and a diversity component. The concentration component forces all feature channels belonging to the same class to be concentrated through the channel cluster. The diversity components impose additional constraints on the channels through the mean distance coefficient, making them mutually exclusive in spatial dimensions. In the comparison experiments, the proposed method can achieve state-of-the-art performance on the public datasets, VCD, and VeRi, which are 96.1% and 96.2%, respectively. In addition, the ablation experiment further proves that SC-loss can effectively improve the accuracy of vehicle color recognition.

Keywords: feature extraction, convolutional neural networks, intelligent transportation, vehicle color recognition

Procedia PDF Downloads 160

Authors: Karthika Durairaj, Buvaneswari G., Gowdham M., Gilles M., Velmurugan G.

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Background: Hyperglycaemia is the primary cause of metabolic illness. Recently, researchers focused on the possibility that chemical exposure could promote metabolic disease. Hyperglycaemia causes a variety of metabolic diseases dependent on its etiologic conditions. According to animal and population-based research, individual chemical exposure causes health problems through alteration of endocrine function with the influence of microbial influence. We were intrigued by the function of gut microbiota variation in high fat and chemically induced hyperglycaemia. Methodology: C57/Bl6 mice were subjected to two different treatments to generate the etiologic-based diabetes model: I – a high-fat diet with a 45 kcal diet, and II - endocrine disrupting chemicals (EDCs) cocktail. The mice were monitored periodically for changes in body weight and fasting glucose. After 120 days of the experiment, blood anthropometry, faecal metagenomics and metabolomics were performed and analyzed through statistical analysis using one-way ANOVA and student’s t-test. Results: After 120 days of exposure, we found hyperglycaemic changes in both experimental models. The treatment groups also differed in terms of plasma lipid levels, creatinine, and hepatic markers. To determine the influence on glucose metabolism, microbial profiling and metabolite levels were significantly different between groups. The gene expression studies associated with glucose metabolism vary between hosts and their treatments. Conclusion: This research will result in the identification of biomarkers and molecular targets for better diabetes control and treatment.

Keywords: hyperglycaemia, endocrine-disrupting chemicals, gut microbiota, host metabolism

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Authors: Bowei Yuan, Shi Li, Liuyang Song, Huaqing Wang, Lingli Cui

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Convolutional neural networks (CNN) have received extensive attention in the field of fault diagnosis. Many fault diagnosis methods use CNN for fault type identification. However, when the amount of raw data collected by sensors is massive, the neural network needs to perform a time-consuming classification task. In this paper, a mechanical fault diagnosis method based on vibration signal down-sampling and the improved one-dimensional convolutional neural network is proposed. Through the robust principal component analysis, the low-rank feature matrix of a large amount of raw data can be separated, and then down-sampling is realized to reduce the subsequent calculation amount. In the improved one-dimensional CNN, a smaller convolution kernel is used to reduce the number of parameters and computational complexity, and regularization is introduced before the fully connected layer to prevent overfitting. In addition, the multi-connected layers can better generalize classification results without cumbersome parameter adjustments. The effectiveness of the method is verified by monitoring the signal of the centrifugal pump test bench, and the average test accuracy is above 98%. When compared with the traditional deep belief network (DBN) and support vector machine (SVM) methods, this method has better performance.

Keywords: fault diagnosis, vibration signal down-sampling, 1D-CNN

Procedia PDF Downloads 118

Authors: Pui Shan Wong, Kosuke Tashiro, Satoru Kuhara, Sachiyo Aburatani

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Functional genomics and gene regulation inference has readily expanded our knowledge and understanding of gene interactions with regards to expression regulation. With the advancement of transcriptome sequencing in time-series comes the ability to study the sequential changes of the transcriptome. This method presented here works to augment existing regulation networks accumulated in literature with transcriptome data gathered from time-series experiments to construct a sequential representation of transcription factor activity. This method is applied on a time-series RNA-Seq data set from Escherichia coli as it transitions from growth to stationary phase over five hours. Investigations are conducted on the various metabolic activities in gene regulation processes by taking advantage of the correlation between regulatory gene pairs to examine their activity on a dynamic network. Especially, the changes in metabolic activity during phase transition are analyzed with focus on the pagP gene as well as other associated transcription factors. The visualization of the sequential transcriptional activity is used to describe the change in metabolic pathway activity originating from the pagP transcription factor, phoP. The results show a shift from amino acid and nucleic acid metabolism, to energy metabolism during the transition to stationary phase in E. coli.

Keywords: Escherichia coli, gene regulation, network, time-series

Procedia PDF Downloads 358

Authors: Nieto Bernal Wilson, Carmona Suarez Edgar

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The organizations have structured and unstructured information in different formats, sources, and systems. Part of these come from ERP under OLTP processing that support the information system, however these organizations in OLAP processing level, presented some deficiencies, part of this problematic lies in that does not exist interesting into extract knowledge from their data sources, as also the absence of operational capabilities to tackle with these kind of projects.  Data Warehouse and its applications are considered as non-proprietary tools, which are of great interest to business intelligence, since they are repositories basis for creating models or patterns (behavior of customers, suppliers, products, social networks and genomics) and facilitate corporate decision making and research. The following paper present a structured methodology, simple, inspired from the agile development models as Scrum, XP and AUP. Also the models object relational, spatial data models, and the base line of data modeling under UML and Big data, from this way sought to deliver an agile methodology for the developing of data warehouses, simple and of easy application. The methodology naturally take into account the application of process for the respectively information analysis, visualization and data mining, particularly for patterns generation and derived models from the objects facts structured.

Keywords: data warehouse, model data, big data, object fact, object relational fact, process developed data warehouse

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Authors: Nandhana Vivek, Bhaskar Gogoi, Ayyavu Mahesh

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Breast cancer metastasis plays a key role in cancer progression and fatality. The present study examines the potential causes of metastasis in breast cancer by investigating the novel interactions between genes and their pathways. The gene expression profile of GSE99394, GSE1246464, and GSE103865 was downloaded from the GEO data repository to analyze the differentially expressed genes (DEGs). Protein-protein interactions, target factor interactions, pathways and gene relationships, and functional enrichment networks were investigated. The proliferation pathway was shown to be highly expressed in breast cancer progression and metastasis in all three datasets. Gene Ontology analysis revealed 11 DEGs as gene targets to control breast cancer metastasis: LYN, DLGAP5, CXCR4, CDC6, NANOG, IFI30, TXP2, AGTR1, MKI67, and FTH1. Upon studying the function, genomic and proteomic data, and pathway involvement of the target genes, DLGAP5 proved to be a promising candidate due to it being highly differentially expressed in all datasets. The study takes a unique perspective on the avenues through which DLGAP5 promotes metastasis. The current investigation helps pave the way in understanding the role DLGAP5 plays in metastasis, which leads to an increased incidence of death among breast cancer patients.

Keywords: genomics, metastasis, microarray, cancer

Procedia PDF Downloads 85

Authors: Eiman Kattan

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This study aims to evaluate the impact of classifying labelled remote sensing images conventional neural network (CNN) architecture, i.e., AlexNet on different land cover scenarios based on two remotely sensed datasets from different point of views such as the computational time and performance. Thus, a set of experiments were conducted to specify the effectiveness of the selected convolutional neural network using two implementing approaches, named fully trained and fine-tuned. For validation purposes, two remote sensing datasets, AID, and RSSCN7 which are publicly available and have different land covers features were used in the experiments. These datasets have a wide diversity of input data, number of classes, amount of labelled data, and texture patterns. A specifically designed interactive deep learning GPU training platform for image classification (Nvidia Digit) was employed in the experiments. It has shown efficiency in training, validation, and testing. As a result, the fully trained approach has achieved a trivial result for both of the two data sets, AID and RSSCN7 by 73.346% and 71.857% within 24 min, 1 sec and 8 min, 3 sec respectively. However, dramatic improvement of the classification performance using the fine-tuning approach has been recorded by 92.5% and 91% respectively within 24min, 44 secs and 8 min 41 sec respectively. The represented conclusion opens the opportunities for a better classification performance in various applications such as agriculture and crops remote sensing.

Keywords: conventional neural network, remote sensing, land cover, land use

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Authors: Ali Afaghi, Sehraneh Ghaemi

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The coordination of the multi-agent systems has been one of the interesting topic in recent years, because of its potential applications in many branches of science and engineering such as sensor networks, flocking, underwater vehicles and etc. In the most of the related studies, it is assumed that the dynamics of the multi-agent systems are integer-order and linear and the multi-agent systems with the fractional-order nonlinear dynamics are rarely considered. However many phenomena in nature cannot be described within integer-order and linear characteristics. This paper investigates the leader-following consensus problem for a class of nonlinear fractional-order multi-agent systems based on observer-based cooperative control. In the system, the dynamics of each follower and leader are nonlinear. For a multi-agent system with fixed directed topology firstly, an observer-based consensus protocol is proposed based on the relative observer states of neighboring agents. Secondly, based on the property of the stability theory of fractional-order system, some sufficient conditions are presented for the asymptotical stability of the observer-based fractional-order control systems. The proposed method is applied on a five-agent system with the fractional-order nonlinear dynamics and unavailable states. The simulation example shows that the proposed scenario results in the good performance and can be used in many practical applications.

Keywords: fractional-order multi-agent systems, leader-following consensus, nonlinear dynamics, directed graphs

Procedia PDF Downloads 380

Authors: Jin Boo Jeong

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In this study, we elucidated anti-cancer activity and potential molecular mechanism of DKC against human colorectal cancer cells. DKC-E70 suppressed the proliferation of human colorectal cancer cell lines such as HCT116, SW480, LoVo and HT-29. Although DKC-E70 decreased cyclin D1 expression in protein and mRNA level, decreased level of cyclin D1 protein by DKC-E70 occurred at the earlier time than that of cyclin D1 mRNA, which indicates that DKC-E70-mediated downregulation of cyclin D1 protein may be a consequence of the induction of degradation and transcriptional inhibition of cyclin D1. In cyclin D1 degradation, we found that cyclin D1 downregulation by DKC-E70 was attenuated in presence of MG132. In addition, DKC-E70 phosphorylated threonine-286 (T286) of cyclin D1 and T286A abolished cyclin D1 downregulation by DKC-E70. We also observed that DKC-E70-mediated T286 phosphorylation and subsequent cyclin D1 degradation was blocked in presence of the inhibitors of ERK1/2, p38 or GSK3β. In cyclin D1 transcriptional inhibition, DKC-E70 inhibited the expression of β-catenin and TCF4, and β–catenin/TCF-dependent luciferase activity. Our results suggest that DKC-E70 may downregulate cyclin D1 as one of the potential anti-cancer targets through cyclin D1 degradation by T286 phosphorylation dependent on ERK1/2, p38 or GSK3β, and cyclin D1 transcriptional inhibition through Wnt signaling. From these findings, DKC-E70 has potential to be a candidate for the development of chemoprevention or therapeutic agents for human colorectal cancer. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A3B03931713).

Keywords: anticancer, calyx of persimmon, cyclin D1, Diospyros kaki Thunb., human colorectal cancer

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Authors: Jawid Ahmad Baktash, Tadashi Shiroma, Tomokazu Nagata, Yuji Taniguchi, Morikazu Nakamura

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The goal of this paper is to explore the use of formal methods for Dynamic Routing, The purpose of network communication with dynamic routing is sending a massage from one node to others by using pacific protocols. In dynamic routing connections are possible based on protocols of Distance vector (Routing Information Protocol, Border Gateway protocol), Link State (Open Shortest Path First, Intermediate system Intermediate System), Hybrid (Enhanced Interior Gateway Routing Protocol). The responsibility for proper verification becomes crucial with Dynamic Routing. Formal methods can play an essential role in the Routing, development of Networks and testing of distributed systems. Event-B is a formal technique consists of describing rigorously the problem; introduce solutions or details in the refinement steps to obtain more concrete specification, and verifying that proposed solutions are correct. The system is modeled in terms of an abstract state space using variables with set theoretic types and the events that modify state variables. Event-B is a variant of B, was designed for developing distributed systems. In Event-B, the events consist of guarded actions occurring spontaneously rather than being invoked. The invariant state properties must be satisfied by the variables and maintained by the activation of the events.

Keywords: dynamic rout RIP, formal method, event-B, pro-B

Procedia PDF Downloads 390

Authors: Swapnil A. Dharaskar, Kailas L. Wasewar, Mahesh N. Varma, Diwakar Z. Shende

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Owing to the stringent environmental regulations in many countries for production of ultra low sulfur petroleum fractions intending to reduce sulfur emissions results in enormous interest in this area among the scientific community. The requirement of zero sulfur emissions enhances the prominence for more advanced techniques in desulfurization. Desulfurization by extraction is a promising approach having several advantages over conventional hydrodesulphurization. Present work is dealt with various new approaches for desulfurization of ultra clean gasoline, diesel and other liquid fuels by extraction with ionic liquids. In present paper experimental data on extractive desulfurization of liquid fuel using trihexyl tetradecyl phosphonium chloride has been presented. The FTIR, 1H-NMR, and 13C-NMR have been discussed for the molecular confirmation of synthesized ionic liquid. Further, conductivity, solubility, and viscosity analysis of ionic liquids were carried out. The effects of reaction time, reaction temperature, sulfur compounds, ultrasonication, and recycling of ionic liquid without regeneration on removal of dibenzothiphene from liquid fuel were also investigated. In extractive desulfurization process, the removal of dibenzothiophene in n-dodecane was 84.5% for mass ratio of 1:1 in 30 min at 30OC under the mild reaction conditions. Phosphonium ionic liquids could be reused five times without a significant decrease in activity. Also, the desulfurization of real fuels, multistage extraction was examined. The data and results provided in present paper explore the significant insights of phosphonium based ionic liquids as novel extractant for extractive desulfurization of liquid fuels.

Keywords: ionic liquid, PPIL, desulfurization, liquid fuel, extraction

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Authors: Iván González, Pablo Andrés Maya, Sebastián Jaén

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Eco-industrial parks (EIPs) work as networks for the exchange of by-products, such as materials, water, or energy. This research identifies the relevant factors in the formation of EIPs in different industrial environments around the world. Then an aggregation of these factors is carried out to reduce them from 50 to 17 and classify them according to 5 fundamental axes. Subsequently, the Vester Sensitivity Model (VSM) systemic methodology is used to determine the influence of the 17 factors on an EIP system and the interrelationship between them. The results show that the sequence of effects between factors: Trust and Cooperation → Business Association → Flows → Additional Income represents the “backbone” of the system, being the most significant chain of influences. In addition, the Organizational Culture represents the turning point of the Industrial Symbiosis on which it must act correctly to avoid falling into unsustainable economic development. Finally, the flow of Information should not be lost since it is what feeds trust between the parties, and the latter strengthens the system in the face of individual or global imbalances. This systemic understanding will enable the formulation of pertinent policies by the actors that interact in the formation and permanence of the EIP. In this way, it seeks to promote large-scale sustainable industrial development, integrating various community actors, which in turn will give greater awareness and appropriation of the current importance of sustainability in industrial production.

Keywords: critical factors, eco-industrial park, industrial symbiosis, system methodology

Procedia PDF Downloads 103

Authors: H. Lahlali F. El Haouzi, A.M.Al-Baradi, I. El Aboudi, M. El Azhari, A. Mdarhri

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Polytetrafluoroethylene (PTFE) is a high performance thermoplastic polymer with exceptional physical and chemical properties, such as a high melting temperature, high thermal stability, and very good chemical resistance. Nevertheless, manufacturing PTFE is problematic due to its high melt viscosity (10 12 Pa.s). In practice, it is by now well established that this property presents a serious problem when the classical methods are used to synthesized the dense PTFE materials in particularly hot pressing, high temperature extrusion. In this framework, we use here a new process namely spark plasma sintering (SPS) to elaborate PTFE samples from the micro metric particles powder. It consists in applying simultaneous electric current and pressure directly on the sample powder. By controlling the processing parameters of this technique, a series of PTFE samples are easy obtained and associated to remarkably short time as is reported in an early work. Our central goal in the present study is to understand how the non conventional SPS affects the mechanical properties at room temperature. For this end, a second commercially series of PTFE synthesized by using the extrusion method is investigated. The first data according to the tensile mechanical properties are found to be superior for the first set samples (SPS). However, this trend is not observed for the results obtained from the compression testing. The observed macro-behaviors are correlated to some physical properties of the two series of samples such as their crystallinity or density. Upon a close examination of these properties, we believe the SPS technique can be seen as a promising way to elaborate the polymer having high molecular mass without compromising their mechanical properties.

Keywords: PTFE, extrusion, Spark Plasma Sintering, physical properties, mechanical behavior

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Authors: Pedro Esteban

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Today’s electrical networks, including microgrids, are evolving into smart grids. The smart grid concept brings the idea that the power comes from various sources (continuous or intermittent), in various forms (AC or DC, high, medium or low voltage, etc.), and it must be integrated into the electric power system in a smart way to guarantee a continuous and reliable supply that complies with power quality and energy efficiency standards and grid code requirements. This idea brings questions for the different players like how the required power will be generated, what kind of power will be more suitable, how to store exceeding levels for short or long-term usage, and how to combine and distribute all the different generation power sources in an efficient way. To address these issues, there has been lots of development in recent years on the field of on-grid and off-grid hybrid power systems (HPS). These systems usually combine one or more modes of electricity generation together with energy storage to ensure optimal supply reliability and high level of energy security. Hybrid power systems combine power generation and energy storage technologies together with real-time energy management and innovative power quality and energy efficiency improvement functionalities. These systems help customers achieve targets for clean energy generation, they add flexibility to the electrical grid, and they optimize the installation by improving its power quality and energy efficiency.

Keywords: microgrids, hybrid power systems, energy storage, power quality improvement

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Authors: Mun Yee Chan, Sin Ming Goh, Lisa Gaik Ai Ong

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Approximately 10,000 different types of dyes and pigments are being used in various industrial applications yearly, which include the textile and printing industries. However, these dyes are difficult to degrade naturally once they enter the aquatic system. Their high persistency in natural environment poses a potential health hazard to all form of life. Hence, there is a need for alternative dye removal strategy in the environment via bioremediation. In this study, fungi laccase is investigated via commercial agar dyes plates and submerged fermentation to explore the application of fungi laccase in textile dye wastewater treatment. Two locally isolated basidiomycetes were screened for laccase activity using media added with commercial dyes such as 2, 2-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid (ABTS), guaiacol and Remazol Brillant Blue R (RBBR). Isolate TBB3 (1.70±0.06) and EL2 (1.78±0.08) gave the highest results for ABTS plates with the appearance of greenish halo on around the isolates. Submerged fermentation performed on Isolate TBB3 with the productivity 3.9067 U/ml/day, whereas the laccase activity for Isolate EL2 was much lower (0.2097 U/ml/day). As isolate TBB3 showed higher laccase production, it was subjected to molecular characterization by DNA isolation, PCR amplification and sequencing of ITS region of nuclear ribosomal DNA. After being compared with other sequences in National Center for Biotechnology Information (NCBI database), isolate TBB3 is probably from species Trametes hirsutei. Further research work can be performed on this isolate by upscale the production of laccase in order to meet the demands of the requirement for higher enzyme titer for the bioremediation of textile dyes.

Keywords: bioremediation, dyes, fermentation, laccase

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Authors: Xiaolin Zeng, Lei Cheng, Zhirong Li, Xueping Liu

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Aiming at the problem of low accuracy and susceptibility to cultural relic diseases in the generation of high-resolution archaeology drawings by current image generation algorithms, an archaeology drawings generation algorithm based on a conditional generative adversarial network is proposed. An attention mechanism is added into the high-resolution image generation network as the backbone network, which enhances the line feature extraction capability and improves the accuracy of line drawing generation. A dual-branch parallel architecture consisting of two backbone networks is implemented, where the semantic translation branch extracts semantic features from orthophotographs of cultural relics, and the gradient screening branch extracts effective gradient features. Finally, the fusion fine-tuning module combines these two types of features to achieve the generation of high-quality and high-resolution archaeology drawings. Experimental results on the self-constructed archaeology drawings dataset of grotto temple statues show that the proposed algorithm outperforms current mainstream image generation algorithms in terms of pixel accuracy (PA), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR) and can be used to assist in drawing archaeology drawings.

Keywords: archaeology drawings, digital heritage, image generation, deep learning

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Authors: S. Arabnejad, D. W. C. Cheong, H. Chaobin, V. P. W. Shim

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Debonding is the one of the fundamental damage mechanisms in particle field composites. This phenomenon gains more importance in nano composites because of the extensive interfacial region present in these materials. Understanding the debonding mechanism accurately, can help in understanding and predicting the response of nano composites as the interface deteriorates. The small length scale of the phenomenon makes the experimental characterization complicated and the results of it, far from real physical behavior. In this study the damage process in nylon-6/silica interface is examined through Molecular Dynamics (MD) modeling and simulations. The silica has been modeled with three forms of surfaces – without any surface treatment, with the surface treatment of 3-aminopropyltriethoxysilane (APTES) and with Hexamethyldisilazane (HMDZ) surface treatment. The APTES surface modification used to create functional groups on the silica surface, reacts and form covalent bonds with nylon 6 chains while the HMDZ surface treatment only interacts with both particle and polymer by non-bond interaction. The MD model in this study uses a PCFF force field. The atomic model is generated in a periodic box with a layer of vacuum on top of the polymer layer. This layer of vacuum is large enough that assures us from not having any interaction between particle and substrate after debonding. Results show that each of these three models show a different traction separation behavior. However, all of them show an almost bilinear traction separation behavior. The study also reveals a strong correlation between the length of APTES surface treatment and the cohesive strength of the interface.

Keywords: debonding, surface treatment, cohesive response, separation behaviour

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Authors: Claudius Peter Van Wyk

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Given the increasingly manifest phenomena, as described in complexity theory, of volatility, uncertainty, complexity and ambiguity (VUCA), Taleb's notion of 'antifragility, has a significant contribution to make to positive psychology applied to wellbeing. Antifragility is argued to be fundamentally different from the concepts of resiliency; as the ability to recover from failure, and robustness; as the ability to resist failure. Rather it describes the capacity to reorganise in the face of stress in such a way as to cope more effectively with systemic challenges. The concept, which has been applied in disciplines ranging from physics, molecular biology, planning, engineering, and computer science, can now be considered for its application in individual human and social wellbeing. There are strong correlations to Antonovsky's model of 'salutogenesis' in which an attitude and competencies are developed of transforming burdening factors into greater resourcefulness. We demonstrate, from the perspective of neuroscience, how technology measuring nervous system coherence can be coupled to acquired psychodynamic approaches to not only identify contextual stressors, utilise biofeedback instruments for facilitating greater coherence, but apply these insights to specific life stressors that compromise well-being. Employing an on-going case study with BMW South Africa, the neurological mapping is demonstrated together with 'reframing' and emotional anchoring techniques from neurolinguistic programming. The argument is contextualised in the discipline of psychoneuroimmunology which describes the stress pathways from the CNS and endocrine systems and their impact on immune function and the capacity to restore homeostasis.

Keywords: antifragility, complexity, neuroscience, psychoneuroimmunology, salutogenesis, volatility

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Authors: Xinge Liao, Francesc Xavier Roige Ventura, Dolores Sanchez Aguilera

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With the development of the Internet, the study of tourism behavior has rapidly expanded from the traditional physical market to the online market. Data on the Internet is characterized by dynamic changes, and new data appear all the time. In recent years the generation of a large volume of data was characterized, such as forums, blogs, and other sources, which have expanded over time and space, together they constitute large-scale Internet data, known as Big Data. This data of technological origin that derives from the use of devices and the activity of multiple users is becoming a source of great importance for the study of geography and the behavior of tourists. The study will focus on cultural heritage tourist practices in the context of Big Data. The research will focus on exploring the characteristics and behavior of Chinese tourists in relation to the cultural heritage of Catalonia. Geographical information, target image, perceptions in user-generated content will be studied through data analysis from Weibo -the largest social networks of blogs in China. Through the analysis of the behavior of heritage tourists in the Big Data environment, this study will understand the practices (activities, motivations, perceptions) of cultural tourists and then understand the needs and preferences of tourists in order to better guide the sustainable development of tourism in heritage sites.

Keywords: Barcelona, Big Data, Catalonia, cultural heritage, Chinese tourism market, tourists’ behavior

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Authors: Jialin Ma, Jia Meng

Abstract:

Genomic features, the genome-based coordinates, are commonly used for the representation of biological features such as genes, RNA transcripts and transcription factor binding sites. For the analysis of RNA-related genomic features, such as RNA modification sites, a common task is to correlate these features with transcript components (5'UTR, CDS, 3'UTR) to explore their distribution characteristics in terms of transcriptomic coordinates, e.g., to examine whether a specific type of biological feature is enriched near transcription start sites. Existing approaches for performing these tasks involve the manipulation of a gene database, conversion from genome-based coordinate to transcript-based coordinate, and visualization methods that are capable of showing RNA transcript components and distribution of the features. These steps are complicated and time consuming, and this is especially true for researchers who are not familiar with relevant tools. To overcome this obstacle, we develop a dedicated web app TARF, which represents web toolkit for annotating RNA-related genomic features. TARF web tool intends to provide a web-based way to easily annotate and visualize RNA-related genomic features. Once a user has uploaded the features with BED format and specified a built-in transcript database or uploaded a customized gene database with GTF format, the tool could fulfill its three main functions. First, it adds annotation on gene and RNA transcript components. For every features provided by the user, the overlapping with RNA transcript components are identified, and the information is combined in one table which is available for copy and download. Summary statistics about ambiguous belongings are also carried out. Second, the tool provides a convenient visualization method of the features on single gene/transcript level. For the selected gene, the tool shows the features with gene model on genome-based view, and also maps the features to transcript-based coordinate and show the distribution against one single spliced RNA transcript. Third, a global transcriptomic view of the genomic features is generated utilizing the Guitar R/Bioconductor package. The distribution of features on RNA transcripts are normalized with respect to RNA transcript landmarks and the enrichment of the features on different RNA transcript components is demonstrated. We tested the newly developed TARF toolkit with 3 different types of genomics features related to chromatin H3K4me3, RNA N6-methyladenosine (m6A) and RNA 5-methylcytosine (m5C), which are obtained from ChIP-Seq, MeRIP-Seq and RNA BS-Seq data, respectively. TARF successfully revealed their respective distribution characteristics, i.e. H3K4me3, m6A and m5C are enriched near transcription starting sites, stop codons and 5’UTRs, respectively. Overall, TARF is a useful web toolkit for annotation and visualization of RNA-related genomic features, and should help simplify the analysis of various RNA-related genomic features, especially those related RNA modifications.

Keywords: RNA-related genomic features, annotation, visualization, web server

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Authors: Maryam Pirdehghan

Abstract:

The myth of Leviathan, its ontology and appearance is often one of the problems of Judeo-Christian religious commentators so that some of them have tried to interpret and explain formation or symbolic implications of this myth in different contexts their specific methods and proofs. However, the Bible has presented only vague references in this field and it is not clear why and how to develop such mentions to create a powerful myth with allegorical and symbolic capacity as Leviathan. Therefore, the paper aims to clarify the process of formation of Leviathan and explore the mythical and symbolic systems related to it, first by adopting the imagological approach and then using the Northrop Frye's Archetypal Criticism. Finally, it is concluded that The Leviathan is rooted in the stories of legendary battles of the beginning of creation and almost continues to live with the same nature into the Old Testament, but continuously, in an interactive process between the Greek and Egyptian mythological networks, it attracts more stories and implications about his existence while maintaining its satanic nature. After intense metamorphosis in Jewish interpretations, it appears in the book of Revelation and finally, becomes one of the princes of Hell in the tradition of Christian demonology. The myth, that has become the archetype and fluidized symbol of evil because of the ambiguity and lack of objectivity on its apparent characteristics, finds symbolical extensive capabilities in Judeo-Christian culture, especially in the mysticism, so that its presence or death has special implications and also fighting against it is taken into account as an external and more internal action.

Keywords: Leviathan, The Evil, Bible, myth, Northrop Frye

Procedia PDF Downloads 202

Authors: Surajit Bhattacharya, Daniel Veltri, Atit A. Patel, Daniel N. Cox

Abstract:

miRNAs have emerged as key post-transcriptional regulators of gene expression, however identification of biologically-relevant target genes for this epigenetic regulatory mechanism remains a significant challenge. To address this knowledge gap, we have developed a novel tool in R, Intra-miR-ExploreR, that facilitates integrated discovery of miRNA targets by incorporating target databases and novel target prediction algorithms, using statistical methods including Pearson and Distance Correlation on microarray data, to arrive at high confidence intragenic miRNA target predictions. We have explored the efficacy of this tool using Drosophila melanogaster as a model organism for bioinformatics analyses and functional validation. A number of putative targets were obtained which were also validated using qRT-PCR analysis. Additional features of the tool include downloadable text files containing GO analysis from DAVID and Pubmed links of literature related to gene sets. Moreover, we are constructing interaction maps of intragenic miRNAs, using both micro array and RNA-seq data, focusing on neural tissues to uncover regulatory codes via which these molecules regulate gene expression to direct cellular development.

Keywords: miRNA, miRNA:mRNA target prediction, statistical methods, miRNA:mRNA interaction network

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Authors: Asim Abbasi, Muhammad Sufyan, Iqra, Muhammad Ibrahim Shahid, Muhammad Ashfaq

Abstract:

The foundation of human life on earth relies on many ecosystem services provided by insects of which pollination owes a vital role. The pollination service offered by insects has annual worth of approximately €153 billion. The majority of the flowering plants depends on entomophiles pollination for their reproduction and formation of seeds and fruits. The quantity and quality of insect pollination have multiple implications for stable ecosystem, diverse species level, food security and climate change resilience. The rapidly mounting human population, depletion of natural resources and the global climate change forced us to enter an era of pollination crisis. Climate change not only alters the phenology, population abundance and geographic ranges of different pollinators but also hinders their pollination activities. The successful pollination process relies heavily on the synchronization of biological events of pollinators with the phenological stages of the flowering plants. However, there are possibilities that impending climatic changes may result in asynchrony between plant-pollinators interactions and also mitigate the extent of pollination. The trophic mismatch mostly occurs when pollinators and plants inhabiting the same environment use different environmental cues to regulate their biological events, as these cues are not equally affected by climate change. Synchrony has also been disrupted when one of the interacting species has migratory nature and depend on cues for migration. Moreover, irregular rainfalls and up-surging temperature also disrupts the foraging behaviour of pollinators resulting in reduced flowers visits by insect. Climate change has a direct impact on the behavior and physiology of honey bees, the best known pollinators owing to their extreme floral fidelity. Rising temperature not only alleviates the quantity and quality of floral environment but also alters the bee’s colony harvesting and development ability. Furthermore, a possible earlier decline of flowers is expected in a growing season due to this rising temperature. This may also lead to disrupt the efficiency bumblebee queen that require a constant and adequate nectar and pollen supply throughout the entire growing season for healthy colony production. Considering the role of insect pollination in our ecosystem, their associated risks regarding climate change should be addressed properly for devising a well-focused research needed for their conservation.

Keywords: climate change, phenological, pollination, synchronization

Procedia PDF Downloads 203

Authors: Weiming Hu, Xu Li, Xiaonan Li, Zhong Xu, Li Yuan, Xuan Dong

Abstract:

Maneuver decision-making plays a critical role in high-performance intelligent driving. This paper proposes a risk assessment-based decision-making network (RADMN) to address the problem of driving strategy for the commercial vehicle. RADMN integrates two networks, aiming at identifying the risk degree of collision and rollover and providing decisions to ensure the effectiveness and reliability of driving strategy. In the risk assessment module, risk degrees of the backward collision, forward collision and rollover are quantified for hazard recognition. In the decision module, a deep reinforcement learning based on multi-objective optimization (DRL-MOO) algorithm is designed, which comprehensively considers the risk degree and motion states of each traffic participant. To evaluate the performance of the proposed framework, Prescan/Simulink joint simulation was conducted in highway scenarios. Experimental results validate the effectiveness and reliability of the proposed RADMN. The output driving strategy can guarantee the safety and provide key technical support for the realization of autonomous driving of commercial vehicles.

Keywords: decision-making strategy, risk assessment, multi-objective optimization, commercial vehicle

Procedia PDF Downloads 122

Authors: Akbarova Gulnozakhon

Abstract:

Relevance. Physical exercises can prolong the function of the growth zones of long tubular bones, delay the fusion of the epiphyses and diaphyses of bones and, thus, increase the growth of the body. At the same time, intensive strength exercises can accelerate the process of ossification of bone growth zones and slow down their growth in length. The influence of physical exercises on the process of biological maturation is noted. Gymnastics, which requires intense speed and strength loads, delays puberty. On the other hand, it is indicated that the relatively slow puberty of gymnasts is associated with the selection of girls with a special somatotype in this sport. It was found that the later onset of menstruation in female athletes does not have a negative effect on the maturation process and fertility (the ability to procreate). Observations are made about the normalizing influence of sports on the puberty of girls. The purpose of the study. Our goal is to study physical activity of varying intensity on the formation of secondary sexual characteristics and hormonal status of girls in adolescence. Each biological process peculiar to a given organism is not in a stationary state, but fluctuates with a certain frequency. According to the duration, there are, for example, circadian cycles, and infradian cycles, a typical example of which is the menstrual cycle. Materials and methods, results. Violations of menstrual function in athletes were detected by applying a questionnaire survey that contains several paragraphs and sub-paragraphs where passport data, anthropometric indicators, taking into account anthropometric indices, information about the menstrual cycle are indicated. Of 135 female athletes aged 1-3 to 16 years engaged in various sports - gymnasts, menstrual function disorders were noted in 86.7% (primary or secondary amenorrhea, irregular MC), in swimming-in 57.1%. The general condition also changes during the menstrual cycle. In a large percentage of cases, athletes indicate an increase in irritability in the premenstrual (45%) and menstrual (36%) phases. During these phases, girls note an increase in fatigue of 46.5% and 58% (respectively). In girls, secondary sexual characteristics continue to form during puberty and the clearest indicator of the onset of puberty is the age of the onset of the first menstruation - menarche. Conclusions. 1. Physical exercise has a positive effect on all major systems of the body and thus promotes health.2. Along with a beneficial effect on human health, physical exercise, if the requirements of sports are not observed, can be harmful.

Keywords: girls health, anthropometric, physical development, reproductive health

Procedia PDF Downloads 92