Search results for: radial distribution networks

Authors: Rania Alshikhe, Vinita Jindal

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Digital innovation has played a crucial role in managing smart transportation. For this, big trajectory data collected from traveling vehicles, such as taxis through installed global positioning system (GPS)-enabled devices can be utilized. It offers an unprecedented opportunity to trace the movements of vehicles in fine spatiotemporal granularity. This paper aims to explore big trajectory data to measure the travel efficiency of road networks using the proposed statistical travel efficiency measure (STEM) across an entire city. Further, it identifies the cause of low travel efficiency by proposed least square approximation network-based causality exploration (LANCE). Finally, the resulting data analysis reveals the causes of low travel efficiency, along with the road segments that need to be optimized to improve the traffic conditions and thus minimize the average travel time from given point A to point B in the road network. Obtained results show that our proposed approach outperforms the baseline algorithms for measuring the travel efficiency of the road network.

Keywords: GPS trajectory, road network, taxi trips, digital map, big data, STEM, LANCE

Procedia PDF Downloads 151

Authors: Ali Abdrhman M. Ukasha

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Data security needed in data transmission, storage, and communication to ensure the security. The single step parallel contour extraction (SSPCE) method is used to create the edge map as a key image from the different Gray level/Binary image. Performing the X-OR operation between the key image and each bit plane of the original image for image pixel values change purpose. The Arnold transform used to changes the locations of image pixels as image scrambling process. Experiments have demonstrated that proposed algorithm can fully encrypt 2D Gary level image and completely reconstructed without any distortion. Also shown that the analyzed algorithm have extremely large security against some attacks like salt & pepper and JPEG compression. Its proof that the Gray level image can be protected with a higher security level. The presented method has easy hardware implementation and suitable for multimedia protection in real time applications such as wireless networks and mobile phone services.

Keywords: SSPCE method, image compression-salt- peppers attacks, bitplanes decomposition, Arnold transform, lossless image encryption

Procedia PDF Downloads 427

Authors: Azene Zenebe

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Deep learning is a subset of machine learning which incorporates techniques for the construction of artificial neural networks and found to be useful for modeling complex problems with large dataset. Deep learning requires a very high power computational and longer time for training. By aggregating computing power, high performance computer (HPC) has emerged as an approach to resolving advanced problems and performing data-driven research activities. Cyber threat intelligence (CIT) is actionable information or insight an organization or individual uses to understand the threats that have, will, or are currently targeting the organization. Results of review of literature will be presented along with results of experimental study that compares the performance of tree-based and function-base machine learning including deep learning algorithms using secondary dataset collected from darknet.

Keywords: deep-learning, cyber security, cyber threat modeling, tree-based machine learning, function-based machine learning, data science

Procedia PDF Downloads 143

Authors: Chad Goldsworthy, B. Rajeswari Matam

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The use of deep learning for species identification in camera trap images has revolutionised our ability to study, conserve and monitor species in a highly efficient and unobtrusive manner, with state-of-the-art models achieving accuracies surpassing the accuracy of manual human classification. The high imbalance of camera trap datasets, however, results in poor accuracies for minority (rare or endangered) species due to their relative insignificance to the overall model accuracy. This paper investigates the use of Focal Loss, in comparison to the traditional Cross Entropy Loss function, to improve the identification of minority species in the “255 Bird Species” dataset from Kaggle. The results show that, although Focal Loss slightly decreased the accuracy of the majority species, it was able to increase the F1-score by 0.06 and improve the identification of the bottom two, five and ten (minority) species by 37.5%, 15.7% and 10.8%, respectively, as well as resulting in an improved overall accuracy of 2.96%.

Keywords: convolutional neural networks, data imbalance, deep learning, focal loss, species classification, wildlife conservation

Procedia PDF Downloads 176

Authors: Xiaohan Bookman, Xiaoyan Zhu

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Margin-Based Principle has been proposed for a long time, it has been proved that this principle could reduce the structural risk and improve the performance in both theoretical and practical aspects. Meanwhile, feed-forward neural network is a traditional classifier, which is very hot at present with a deeper architecture. However, the training algorithm of feed-forward neural network is developed and generated from Widrow-Hoff Principle that means to minimize the squared error. In this paper, we propose a new training algorithm for feed-forward neural networks based on Margin-Based Principle, which could effectively promote the accuracy and generalization ability of neural network classifiers with less labeled samples and flexible network. We have conducted experiments on four UCI open data sets and achieved good results as expected. In conclusion, our model could handle more sparse labeled and more high-dimension data set in a high accuracy while modification from old ANN method to our method is easy and almost free of work.

Keywords: Max-Margin Principle, Feed-Forward Neural Network, classifier, structural risk

Procedia PDF Downloads 333

Authors: Claudio Burguez, María Castelló, Mikaela Pisani, Marcos Umpiérrez

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BIOFEEDBACK exhibition offers a unique experience for each visitor, combining art, neuroscience, and technology in an interactive way. Using a headband that captures the bioelectric activity of the brain, the visitors are able to generate sound and images in a sequence loop, making them an integral part of the artwork. Through this interactive exhibit, visitors gain a deeper appreciation of the beauty and complexity of the brain. As a special takeaway, visitors will receive an NFT as a present, allowing them to continue their engagement with the exhibition beyond the physical space. We used the EEG Biofeedback technique following a closed-loop neuroscience approach, transforming EEG data captured by a Muse S headband in real-time into audiovisual stimulation. PureData is used for sound generation and Generative Adversarial Networks (GANs) for image generation. Thirty participants have experienced the exhibition. For some individuals, it was easier to focus than others. Participants who said they could focus during the exhibit stated that at one point, they felt that they could control the sound, while images were more abstract, and they did not feel that they were able to control them.

Keywords: art, audiovisual, biofeedback, EEG, NFT, neuroscience, technology

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Authors: A. Pourahmad, Sh. Gharipour

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The present work describes the preparation and characterization of nanosized SiO₂@PbS core-shell particles by using a simple wet chemical route. This method utilizes silica spheres formation followed by successive ionic layer adsorption and reaction method assisted lead sulphide shell layer formation. The final product was characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), UV–vis spectroscopic, infrared spectroscopy (IR) and transmission electron microscopy (TEM) experiments. The morphological studies revealed the uniformity in size distribution with core size of 250 nm and shell thickness of 18 nm. The electron microscopic images also indicate the irregular morphology of lead sulphide shell layer. The structural studies indicate the face-centered cubic system of PbS shell with no other trace for impurities in the crystal structure.

Keywords: core-shell, nanostructure, semiconductor, optical property, XRD

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Authors: Hemant Kumar Pathak

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In 1994, Matthews (S.G. Matthews, Partial metric topology, in: Proc. 8th Summer Conference on General Topology and Applications, in: Ann. New York Acad. Sci., vol. 728, 1994, pp. 183-197) introduced the concept of a partial metric as a part of the study of denotational semantics of data flow networks. He gave a modified version of the Banach contraction principle, more suitable in this context. In fact, (complete) partial metric spaces constitute a suitable framework to model several distinguished examples of the theory of computation and also to model metric spaces via domain theory. In this paper, we introduce the concept of almost partial Hausdorff metric. We prove a fixed point theorem for multi-valued mappings on partial metric space using the concept of almost partial Hausdorff metric and prove an analogous to the well-known Nadler’s fixed point theorem. In the sequel, we derive a homotopy result as an application of our main result.

Keywords: fixed point, partial metric space, homotopy, physical sciences

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Authors: Serhat Tüzün, Tufan Demirel

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Decision Support Systems (DSS) have been investigated by researchers and technologists for more than 35 years. This paper analyses the developments in the architecture and software of these systems, provides a systematic analysis for different Web-based DSS approaches and Intelligent Decision-making Technologies (IDT), with the suggestion for future studies. Decision Support Systems literature begins with building model-oriented DSS in the late 1960s, theory developments in the 1970s, and the implementation of financial planning systems and Group DSS in the early and mid-80s. Then it documents the origins of Executive Information Systems, online analytic processing (OLAP) and Business Intelligence. The implementation of Web-based DSS occurred in the mid-1990s. With the beginning of the new millennia, intelligence is the main focus on DSS studies. Web-based technologies are having a major impact on design, development and implementation processes for all types of DSS. Web technologies are being utilized for the development of DSS tools by leading developers of decision support technologies. Major companies are encouraging its customers to port their DSS applications, such as data mining, customer relationship management (CRM) and OLAP systems, to a web-based environment. Similarly, real-time data fed from manufacturing plants are now helping floor managers make decisions regarding production adjustment to ensure that high-quality products are produced and delivered. Web-based DSS are being employed by organizations as decision aids for employees as well as customers. A common usage of Web-based DSS has been to assist customers configure product and service according to their needs. These systems allow individual customers to design their own products by choosing from a menu of attributes, components, prices and delivery options. The Intelligent Decision-making Technologies (IDT) domain is a fast growing area of research that integrates various aspects of computer science and information systems. This includes intelligent systems, intelligent technology, intelligent agents, artificial intelligence, fuzzy logic, neural networks, machine learning, knowledge discovery, computational intelligence, data science, big data analytics, inference engines, recommender systems or engines, and a variety of related disciplines. Innovative applications that emerge using IDT often have a significant impact on decision-making processes in government, industry, business, and academia in general. This is particularly pronounced in finance, accounting, healthcare, computer networks, real-time safety monitoring and crisis response systems. Similarly, IDT is commonly used in military decision-making systems, security, marketing, stock market prediction, and robotics. Even though lots of research studies have been conducted on Decision Support Systems, a systematic analysis on the subject is still missing. Because of this necessity, this paper has been prepared to search recent articles about the DSS. The literature has been deeply reviewed and by classifying previous studies according to their preferences, taxonomy for DSS has been prepared. With the aid of the taxonomic review and the recent developments over the subject, this study aims to analyze the future trends in decision support systems.

Keywords: decision support systems, intelligent decision-making, systematic analysis, taxonomic review

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Authors: Ibrahim Djelloul Berkane

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A survey was conducted to assess the presence of the virus in Citrus tristeza one of the main citrus regions of Algeria, namely the Chlef region, using the technique of Direct Tissue Print Immunoprinting Assay (DTBIA) and the Double Sandwich ELISA antibodies. A nursery citrus, lumber yards, and commercial orchards, which are the main varieties cultivated citrus were subjected to samples collected samples for laboratory analysis. 0.91% of the plants tested orchards were infected with CTV, while no positive case was detected at the nursery the yard, however, it is reported that an alarming rate of 10,5% of orchards tested at the common Chettia were infected with tristeza virus. The investigation was launched to identify the vector species tristeza revealed the presence of a vector is important Aphis gossypii.

Keywords: aphis, chlef, citrus, DAS-ELISA, DTBIA, tristeza

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Authors: Birgit A. A. Solem, Kristin Bentsen

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Scholarly attention in entrepreneurship is currently directed towards understanding entrepreneurial venture creation as a process -the journey of new economic activities from nonexistence to existence often studied through flow- or network models. To complement existing research on entrepreneurial venture creation with more interactivity-based research of organized activities, this study examines two pop-up stores as anchor events involving on-site activities of fifteen participating entrepreneurs launching their new ventures. The pop-up stores were arranged in two middle-sized Norwegian cities and contained different brand stores that brought together actors of sub-networks and communities executing venture creation activities. The pop-up stores became on-site arenas for the entrepreneurs to create, maintain, and rejuvenate their networks, at the same time as becoming venues for temporal coordination of activities involving existing and potential customers in their venture creation. In this work, we apply a conceptual framework based on frequently addressed dilemmas within entrepreneurship theory (discovery/creation, causation/effectuation) to further shed light on the broad aspect of on-site anchor event activities and their venture creation outcomes. The dilemma-based concepts are applied as an analytic toolkit to pursue answers regarding the nature of anchor event activities typically found within entrepreneurial venture creation and how these anchor event activities affect entrepreneurial venture creation outcomes. Our study combines researcher participation with 200 hours of observation and twenty in-depth interviews. Data analysis followed established guidelines for hermeneutic analysis and was intimately intertwined with ongoing data collection. Data was coded and categorized in NVivo 12 software, and iterated several times as patterns were steadily developing. Our findings suggest that core anchor event activities typically found within entrepreneurial venture creation are; a concept- and product experimentation with visitors, arrangements to socialize (evening specials, auctions, and exhibitions), store-in-store concepts, arranged meeting places for peers and close connection with municipality and property owners. Further, this work points to four main entrepreneurial venture creation outcomes derived from the core anchor event activities; (1) venture attention, (2) venture idea-realization, (3) venture collaboration, and (4) venture extension. Our findings show that, depending on which anchor event activities are applied, the outcomes vary. Theoretically, this study offers two main implications. First, anchor event activities are both discovered and created, following the logic of causation, at the same time as being experimental, based on “learning by doing” principles of effectuation during the execution. Second, our research enriches prior studies on venture creation as a process. In this work, entrepreneurial venture creation activities and outcomes are understood through pop-up stores as on-site anchor event arenas, particularly suitable for interactivity-based research requested by the entrepreneurship field. This study also reveals important managerial implications, such as that entrepreneurs should allow themselves to find creative physical venture creation arenas (e.g., pop-up stores, showrooms), as well as collaborate with partners when discovering and creating concepts and activities based on new ideas. In this way, they allow themselves to both strategically plan for- and continually experiment with their venture.

Keywords: anchor event, interactivity-based research, pop-up store, entrepreneurial venture creation

Procedia PDF Downloads 81

Authors: Seyed Mohammadbagher Jafari, Elahe Mahdizadeh, Masomeh Ghahremani

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Nowadays almost all organizations depend on information systems to run their businesses. Investment on information systems and their maintenance to keep them always in best situation to support firm business is one of the main issues for every organization. The new concept of cloud computing was developed as a technical and economic model to address this issue. In cloud computing the computing resources, including networks, applications, hardwares and services are configured as needed and are available at the moment of request. However, migration to cloud is not an easy task and there are many issues that should be taken into account. This study tries to provide a comprehensive model to assess a firm readiness to implement cloud computing. By conducting a systematic literature review, four dimensions of readiness were extracted which include technological, human, organizational and environmental dimensions. Every dimension has various criteria that have been discussed in details. This model provides a framework for cloud computing readiness assessment. Organizations that intend to migrate to cloud can use this model as a tool to assess their firm readiness before making any decision on cloud implementation.

Keywords: cloud computing, human readiness, organizational readiness, readiness assessment model

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Authors: Leila Rashidian, Mina Rajabali

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Industrialization has made progress and comfort for human beings in many aspects. It is not only achievement for the global environment but also factor for destruction and disruption of the Earth's climate. In this study, we used LARS.WG model and down scaling of general circulation climate model HADCM-3 daily precipitation amounts, minimum and maximum temperature and daily sunshine hours. These data are provided by the meteorological organization for Caspian Sea coastal station such as Anzali, Manjil, Rasht, Lahijan and Astara since their establishment is from 1982 until 2010. According to the IPCC scenarios, including series A1b, A2, B1, we tried to simulate data from 2010 to 2040. The rainfall pattern has changed. So we have a rainfall distribution inappropriate in different months.

Keywords: climate change, Lars.WG, HADCM3, Gillan province, climatic parameters, A2 scenario

Procedia PDF Downloads 266

Authors: Sham Kidane, Fitsum Gaim, Ibrahim Abdella, Sirak Asmerom, Yoel Ghebrihiwot, Simon Mulugeta, Natnael Ambassager

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The lack of annotated datasets is a bottleneck to the progress of NLP in low-resourced languages. The work presented here consists of large-scale annotated datasets and models for the named entity recognition (NER) system for the Tigrinya language. Our manually constructed corpus comprises over 340K words tagged for NER, with over 118K of the tokens also having parts-of-speech (POS) tags, annotated with 12 distinct classes of entities, represented using several types of tagging schemes. We conducted extensive experiments covering convolutional neural networks and transformer models; the highest performance achieved is 88.8% weighted F1-score. These results are especially noteworthy given the unique challenges posed by Tigrinya’s distinct grammatical structure and complex word morphologies. The system can be an essential building block for the advancement of NLP systems in Tigrinya and other related low-resourced languages and serve as a bridge for cross-referencing against higher-resourced languages.

Keywords: Tigrinya NER corpus, TiBERT, TiRoBERTa, BiLSTM-CRF

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Authors: Mohaddeseh Ahmadnezhad, Shahrbano Oryan, Mahmoud Bahmani, Mohammadd Sayad Bourani

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Changes in abundance and size of skin ionocytes were investigated in two larval stage of Caspian sea zander, Sander lucioperca, before and after yolk sac absorption, at 96h after transfer from fresh water (FW; <0.5‰) to 7‰ (estuary) and 12‰ (Caspian sea water=CW) salinity. Survival rate in the stage of after yolk sac absorption were more than larval pre-absorbed yolk sac in condition of salinity (p<0.05). Ionocyte abundance increased significantly in 7 and 12‰ salinity (p<0.05), but not about ionocyte size. The results of this study suggest that development of skin Ionocyte osmoregulatory function and osmoregulation capability of Caspian Sea zander larvae increased with growth of the larvae.

Keywords: Caspian Sea, larvae, Sander lucioperca, salinity, skin ionocyte

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Authors: G. Rosaz, V. Semblanet, S. Calatroni, A. Sublet, M. Taborelli

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We report in this paper the design and qualification of a cylindrical unbalanced magnetron source. The dedicated magnetic assemblies were simulated using a finite element model. A hall-effect magnetic probe was then used to characterize those assemblies and compared to the theoretical magnetic profiles. These show a good agreement between the expected and actual values. The qualification of the different magnetic assemblies was then performed by measuring the ion flux density reaching the surface of the sample to be coated using a commercial retarding field energy analyzer. The strongest unbalanced configuration shows an increase from 0.016 A.cm^-2 to 0.074 A.cm^-2 of the ion flux density reaching the sample surface compared to the standard balanced configuration for a pressure 5.10^-3 mbar and a plasma source power of 300 W.

Keywords: ion energy distribution function, magnetron sputtering, niobium, unbalanced, SRF cavities, thin film

Procedia PDF Downloads 244

Authors: Mecheri Zeid Belmecheri, Maamar Ahfir, Izzet Kale

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Automatic cardiac auscultation is still a subject of research in order to establish an objective diagnosis. Recorded heart sounds as Phonocardiogram signals (PCG) can be used for automatic segmentation into components that have clinical meanings. These are the first sound, S1, the second sound, S2, and the systolic and diastolic components, respectively. In this paper, an automatic method is proposed for the robust segmentation of heart sounds. This method is based on calculating an intermediate sawtooth-shaped signal from the length variation of the recorded Phonocardiogram (PCG) signal in the time domain and, using its positive derivative function that is a binary signal in training a Recurrent Neural Network (RNN). Results obtained in the context of a large database of recorded PCGs with their simultaneously recorded ElectroCardioGrams (ECGs) from different patients in clinical settings, including normal and abnormal subjects, show a segmentation testing performance average of 76 % sensitivity and 94 % specificity.

Keywords: heart sounds, PCG segmentation, event detection, recurrent neural networks, PCG curve length

Procedia PDF Downloads 172

Authors: Zhifang Liao, Yanbing Li, Li Xu, Yan Zhang, Xiaoping Fan, Jinsong Wu

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Pull Request (PR) is the primary method for code contributions from the external contributors in Github. PR review is an essential part of open source software developments for maintaining the quality of software. Matching a new PR of an appropriate integrator will make the PR review more effective. However, PR and integrator matching are now organized manually in Github. To reduce this cost, we presented an AIPM model to predict highly relevant integrator of incoming PRs. AIPM uses topic model to extract topics from the PRs, and builds a one-to-one correspondence between topics and integrators. Then, AIPM finds the most suitable integrator according to the maximum entry of the topic-document distribution. On average, AIPM can reach a precision of 60%, and even in some projects, can reach a precision of 80%.

Keywords: pull Request, integrator matching, Github, open source project, topic model

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Authors: Niloufar Yadgari

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GANs are a potent form of deep learning models that have found success in various fields. They are part of the larger group of generative techniques, which aim to produce authentic data using a probabilistic model that learns distributions from actual samples. In clinical settings, GANs have demonstrated improved abilities in capturing spatially intricate, nonlinear, and possibly subtle disease impacts in contrast to conventional generative techniques. This review critically evaluates the current research on how GANs are being used in imaging studies of different neurological conditions like Alzheimer's disease, brain tumors, aging of the brain, and multiple sclerosis. We offer a clear explanation of different GAN techniques for each use case in neuroimaging and delve into the key hurdles, unanswered queries, and potential advancements in utilizing GANs in this field. Our goal is to connect advanced deep learning techniques with neurology studies, showcasing how GANs can assist in clinical decision-making and enhance our comprehension of the structural and functional aspects of brain disorders.

Keywords: GAN, pathology, generative adversarial network, neuro imaging

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Authors: Olubunmi Kolawole

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This study explored how certain organizational antecedents like work environment, and individual antecedents (e.g. job level and tenure) could affect employees empowerment in the hospitality industry. A total of 200 valid responses from a survey conducted in 10 hotels in Lagos Nigeria were received. Data were analyzed using frequency distribution and percentage analysis. Findings suggest that leadership, work environment, as well as tenure and level in the organization are reliable predictors of employees empowerment in Nigeria's hotel sector. Empowerment is a major factor which determines how employees feel about themselves and their jobs. The study concluded that organizations need to learn that an empowered employee will put in superior performance which would positively impact on the organization.

Keywords: employee empowerment, hospitality industry, individual-level antecedents, leadership, organizational antecedents

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Authors: Mohadese Oghbaee, Borna Firouzi

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The emergence of the Internet and more specifically the formation of social media has provided the ground for paying attention to new types of content dissemination. In recent years, Social media users share information, communicate with others, and exchange opinions on social events in this space. Many of the information published in this space are suspicious and produced with the intention of deceiving others. These contents are often called "fake news". Fake news, by disrupting the circulation of the concept and similar concepts such as fake news with correct information and misleading public opinion, has the ability to endanger the security of countries and deprive the audience of the basic right of free access to real information; Competing governments, opposition elements, profit-seeking individuals and even competing organizations, knowing about this capacity, act to distort and overturn the facts in the virtual space of the target countries and communities on a large scale and influence public opinion towards their goals. This process of extensive de-truthing of the information space of the societies has created a wave of harm and worries all over the world. The formation of these concerns has led to the opening of a new path of research for the timely containment and reduction of the destructive effects of fake news on public opinion. In addition, the expansion of this phenomenon has the potential to create serious and important problems for societies, and its impact on events such as the 2016 American elections, Brexit, 2017 French elections, 2019 Indian elections, etc., has caused concerns and led to the adoption of approaches It has been dealt with. In recent years, a simple look at the growth trend of research in "Scopus" shows an increasing increase in research with the keyword "false information", which reached its peak in 2020, namely 524 cases, reached, while in 2015, only 30 scientific-research contents were published in this field. Considering that one of the capabilities of social media is to create a context for the dissemination of news and information, both true and false, in this article, the classification of strategies for spreading fake news in social networks was investigated in social events. To achieve this goal, thematic analysis research method was chosen. In this way, an extensive library study was first conducted in global sources. Then, an in-depth interview was conducted with 18 well-known specialists and experts in the field of news and media in Iran. These experts were selected by purposeful sampling. Then by analyzing the data using the theme analysis method, strategies were obtained; The strategies achieved so far (research is in progress) include unrealistically strengthening/weakening the speed and content of the event, stimulating psycho-media movements, targeting emotional audiences such as women, teenagers and young people, strengthening public hatred, calling the reaction legitimate/illegitimate. events, incitement to physical conflict, simplification of violent protests and targeted publication of images and interviews were introduced.

Keywords: fake news, social network, social events, thematic analysis

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Authors: Iliyasu Musa Omoyine, Musa Sunday Abraham, Oladele Sunday Blessing, Iliya Ibrahim Abdullahi, Ibegbu Augustine Oseloka, Nuhu Nana-Hawau, Animoku Abdulrazaq Amoto, Yusuf Abdullateef Onoruoiza, Sambo Sohnap James, Akpulu Steven Peter, Ajayi Abayomi

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The neuroprotective role of inflammation from detrimental intrinsic and extrinsic factors has been reported. However, the overactivation of astrocytes and microglia due to lead toxicity produce excessive pro-inflammatory cytokines, mediating neurodegenerative diseases. The present study investigated the mitigatory effects of quercetin on neuroinflammation, correlating with memory function in lead-exposed rats. In this study, Wistar rats were administered orally with Quercetin (Q: 60 mg/kg) and Succimer as a standard drug (S: 10 mg/kg) for 21 days after lead exposure (Pb: 125 mg/kg) of 21 days or in combination with Pb, once daily for 42 days. Working and reference memory was assessed using an Eight-arm radial water maze (8-ARWM). The changes in brain lead level, the neuronal nitric oxide synthase (nNOS) activity, and the level of neuroinflammatory markers such as tumour necrosis factor-alpha (TNF-α) and Interleukin 1 Beta (IL-1β) were determined. Immunohistochemically, astrocyte expression was evaluated. The results showed that the brain level of lead was increased significantly in lead-exposed rats. The expression of astrocytes increased in the CA3 and CA1 regions of the hippocampus, and the levels of brain TNF-α and IL-1β increased in lead-exposed rats. Lead impaired reference and working memory by increasing reference memory errors and working memory incorrect errors in lead-exposed rats. However, quercetin treatment effectively improved memory and inhibited neuroinflammation by reducing astrocytes’ expression and the levels of TNF-α and IL-1β. The expression of astrocytes and the levels of TNF-α and IL-1β correlated with memory function. The possible explanation for quercetin’s anti-neuroinflammatory effect is that it modulates the activity of cellular proteins involved in the inflammatory response; inhibits the transcription factor of nuclear factor-kappa B (NF-κB), which regulates the expression of proinflammatory molecules; inhibits kinases required for the synthesis of Glial fibrillary acidic protein (GFAP) and modifies the phosphorylation of some proteins, which affect the structure and function of intermediate filament proteins; and, lastly, induces Cyclic-AMP Response Element Binding (CREB) activation and neurogenesis as a compensatory mechanism for memory deficits and neuronal cell death. In conclusion, the levels of neuroinflammatory markers negatively correlated with memory function. Thus, quercetin may be a promising therapy in neuroinflammation and memory dysfunction in populations prone to lead exposure.

Keywords: lead, quercetin, neuroinflammation, memory

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Authors: Dessy Arumsari, Ais Assana Athqiya, Mulyaminingrum

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Healthcare in remote areas is one of the major concerns in Indonesia. Building hospitals in a nation of 18.000 islands with a larger-than-life bureaucracy and problems with corruption, a critical shortage of qualified medical professionals and well-heeled patients resigned to traveling abroad for health care is a hard feat to accomplish. To assuring that all populations have access to appropriate and cost-effective care, a new solution to tackle this problem is with the presence of RV Car Clinic. This car has a concept such as a walking hospital that provides health facilities inside it. All of the health professionals who work in RV Car Clinic will do the rotation for a year in order to the equitable distribution of health workers. We need to advocate the policy makers to help realize RV Car Clinic in remote areas. Health services can be disseminated by the present of RV Car Clinic. Summarily, the local communities can get cost effectively because RV Car Clinic will come to their place and serve the health services.

Keywords: health policy, health professional, remote areas, RV Car Clinic

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Authors: Sairoel Amertet, Girma Gebresenbet

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Smart mechatronics systems in agriculture can be traced back to the mid-1980s, when research into automated fruit harvesting systems began in Japan, Europe, and the United States. Since then, impressive advances have been made in smart mechatronics systems. Furthermore, smart mechatronics systems are promising areas, and as a result, we were intrigued to learn more about them. Consequently, the purpose of this study was to examine the smart mechatronic systems that have been applied to agricultural areas so far, with inspiration from the smart mechatronic system in other sectors. To get an overview of the current state of the art, benefits and drawbacks of smart mechatronics systems, various approaches were investigated. Moreover, smart mechatronic modules and various networks applied in agriculture processing were examined. Finally, we explored how the data retrieved using the one-way analysis of variance related to each other. The result showed that there were strongly related keywords for different journals. With the virtually limited use of sophisticated mechatronics in the agricultural industry and, at the same time, the low production rate, the demand for food security has fallen dramatically. Therefore, the application of smart mechatronics systems in agricultural sectors would be taken into consideration in order to overcome these issues.

Keywords: mechatronics, robotic, robotic system, automation, agriculture mechanism

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Authors: Turan Cetinkaya, Abdurrahman Kırtepe

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The aim of this study is to determine the relation of the level attitudes toward teaching as a profession to various variables of the students in physical education and sports departments. 277 students who are studying at the departments of physical education and sports teaching, sports management and coaching in Ahi Evran University, College of Physical Education and Sports participated to the research. Personal information tool and teaching profession scale consisting 34 items were used as data collection tool in the research. Distribution, frequency, t test and anova test were used in comparison of the related data. As a result of statistical analysis, attitudes toward teaching as a profession levels do not differ according to gender, but significant differences were detected in the exercise regularly and department.

Keywords: teaching profession, attitude, physical education and sports students, university students

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Authors: DEVASHISH KHULBE, MANU PATHAK

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The rise in popularity of major social media platforms have enabled people to share photos and textual information about their daily life. One of the popular topics about which information is shared is food. Since a lot of media about food are attributed to particular locations and restaurants, information like spatio-temporal popularity of various cuisines can be analyzed. Tracking the popularity of food types and retail locations across space and time can also be useful for business owners and restaurant investors. In this work, we present an approach using off-the shelf machine learning techniques to identify trends and popularity of cuisine types in an area using geo-tagged data from social media, Google images and Yelp. After adjusting for time, we use the Kernel Density Estimation to get hot spots across the location and model the dependencies among food cuisines popularity using Bayesian Networks. We consider the Manhattan borough of New York City as the location for our analyses but the approach can be used for any area with social media data and information about retail businesses.

Keywords: Web Mining, Geographic Information Systems, Business popularity, Spatial Data Analyses

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Authors: Kholod Hajo

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In this study COMSOL Multiphysics was used to design lateral flow biosensors (LFBs) which provide advantages in low cost, simplicity, rapidity, stability and portability thus making LFBs popular in biomedical, agriculture, food and environmental sciences. This study was focused on simulation model of biosensor based on gold nanoparticles (GNPs) designed using software package (COMSOL Multiphysics), the magnitude of the laminar velocity field in the flow cell, concentration distribution in the analyte stream and surface coverage of adsorbed species and average fractional surface coverage of adsorbed analyte were discussed from the model and couples of suggestion was given in order to functionalize GNPs and to increase the accuracy of the biosensor design, all above were obtained acceptable results.

Keywords: model, gold nanoparticles, biosensor, COMSOL Multiphysics

Procedia PDF Downloads 245

Authors: K. Akhil, J. Jayakumar, S. Sudheer Khan

Abstract:

Zinc oxide nanoparticles (ZnO NPs) are extensively used in a wide variety of commercial products including sunscreen, textile and paints. The present study evaluated the effect of surface capping agents including polyethylene glycol (EG), gelatin, polyvinyl alcohol(PVA) and poly vinyl pyrrolidone(PVP) on photocatalytic activity of ZnO NPs. The particles were also tested for its antibacterial and antibiofilm activity against Staphylococcus aureus (MTCC 3160) and Pseudomonas aeruginosa (MTCC 1688). Preliminary characterization was done by UV-Visible spectroscopy. Electron microscopic analysis showed that the particles were hexagonal in shape. The hydrodynamic size distribution was analyzed by using dynamic light scattering method and crystalline nature was determined by X-Ray diffraction method.

Keywords: antibacterial, antibiofilm, capping agents, photodegradation, surface coating, zinc oxide nanoparticles

Procedia PDF Downloads 266

Authors: Z. Olczykowski

Abstract:

The paper presents the impact of work on the electric arc furnace. Arc equipment is one of the largest receivers powered by the power system. Electric arc disturbances arising during melting process occurring in these furnaces are the cause of an abrupt change of the passive power of furnaces. Currents drawn by these devices undergo an abrupt change, which in turn cause voltage fluctuations and light flicker. The quantitative evaluation of the voltage fluctuations is now the basic criterion of assessment of an influence of unquiet receiver on the supplying net. The paper presents the method of determination of range of voltage fluctuations and light flicker at parallel operation of arc devices. The results of measurements of voltage fluctuations and light flicker indicators recorded in power supply networks of steelworks were presented, with different number of parallel arc devices. Measurements of energy quality parameters were aimed at verifying the proposed method in practice. It was also analyzed changes in other parameters of electricity: the content of higher harmonics, asymmetry, voltage dips.

Keywords: power quality, arc furnaces, propagation of voltage fluctuations, disturbances

Procedia PDF Downloads 126

Authors: Ali A. Ukasha

Abstract:

Data security needed in data transmission, storage, and communication to ensure the security. The single step parallel contour extraction (SSPCE) method is used to create the edge map as a key image from the different Gray level/Binary image. Performing the X-OR operation between the key image and each bit plane of the original image for image pixel values change purpose. The Arnold transform used to changes the locations of image pixels as image scrambling process. Experiments have demonstrated that proposed algorithm can fully encrypt 2D Gary level image and completely reconstructed without any distortion. Also shown that the analyzed algorithm have extremely large security against some attacks like salt & pepper and JPEG compression. Its proof that the Gray level image can be protected with a higher security level. The presented method has easy hardware implementation and suitable for multimedia protection in real time applications such as wireless networks and mobile phone services.

Keywords: SSPCE method, image compression, salt and peppers attacks, bitplanes decomposition, Arnold transform, lossless image encryption

Procedia PDF Downloads 487