Search results for: biological data mining

Authors: Anusha Bhardwaj, Shekhar Shinde

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Nitric oxide (NO•) has been documented in research papers as one of the most versatile player in the therapeutics. It is identified as a biological multifunctional messenger molecule which is synthesized by the action of nitric oxide synthase (NOS) enzyme from L-arginine. The protective and the toxic effect in conjunction form the complete picture of the biological function of nitric oxide in humans. The dual nature is because of various factors such as concentration of NO, the isoform of NOS involved, type of cells in which it is synthesized, reaction partners like proteins, reactive oxygen intermediates, prosthetic groups, thiols etc., availability of the substrate L-arginine, intracellular environment in which NO is produced and generation of guanosine 3, 5’- cyclic monophosphate (cGMP). Activation of NOS through infection or trauma leads to one or more systemic effects including enhanced immune activity against invading pathogens, vaso/bronchodilatation in the cardiovascular and respiratory systems and altered neurotransmission which can be protective or toxic. Hence, NO affects the balance between healthy signaling and neurodegeneration in the brain. In lungs, it has beneficial effects on the function of airways as a bronchodilator and acts as the neurotransmitter of bronchodilator nerves. Whereas, on the other hand, NO may have deleterious effects by amplifying the asthmatic inflammatory response and also act as a vasodilator in the airways by increasing plasma exudation. But NOS Inhibitors and NO donors hamper the signalling pathway and hence a therapeutic application of NO is compromised.

Keywords: nitric oxide, multifunctional, dual nature, therapeutic applications

Procedia PDF Downloads 498

Authors: Jeferson Gomes Dos Santos, Mirelle Cristina De Abreu Quintela

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Considering that after the 1988 Constitution, in Brazil, municipalities have acquired new roles in the face of a financial reality that jeopardizes more substantial actions, the Public Budget is essential for the establishment of guidelines for action, within each budgetary reality. Within this, the present work sought to understand the budget profile of the mining municipality of Ataleia, with a view to identifying its budget composition, in relation to the main sources of revenue and expenditure. To achieve the purposes of the study, information was collected on the municipality's finances, from the years 2000 to 2016, visualizing the progress of its revenues in terms of funding and origin, and expenses in terms of nature and purpose. It was evidenced that the municipality, having its budget revenue in the period, still shows great dependence on intergovernmental transfers, as the own collection was relatively low. The budget expenditure of the period was mainly influenced by social expenditures, but it must be said that the municipality complied with the limits of spending, minimum and maximum, established by law.

Keywords: expenses, municipal budget, planning, revenue

Procedia PDF Downloads 224

Authors: Ifigenia Stylianou

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As more than three-quarters of students going online daily via computers, tablets, and smartphones, the phenomenon of cyberbullying is growing rapidly. Knowing that victims of online bullying are often also victims of traditional bullying and that traditional bullying is considered as an extension of cyberbullying. In this study, we aim to identify (1) whether cyberbullying lead to more intense forms of school bullying, and (2) whether some biological and environmental factors mediate between this relation, and act protectively to bullying and inappropriate behaviour in school. To answer this questions, a sample of X students, aged X, were asked to complete eight questionnaires (Personal Experiences Checklist, Inventory of Peers Attachment, Questionnaire on Teacher Interaction, School Climate Survey for Bullying, Strengths and Difficulties Questionnaire, Youth Psychopathic Traits Inventory-Short Form, Barratt Impulsiveness Scale-11) in X time periods. Results can provide us important information to improve understanding the factors that are related to bullying. In addition, the results can assist in developing intervention programs to tangle the issue of bullying at schools. All data have been collected and are currently being processed for statistical analyses.

Keywords: cyberbullying, bullying, school climate, psychopathy traits, attachment, mediation factors

Procedia PDF Downloads 234

Authors: Ying Ying Liu, Austin Cooke, Parimala Thulasiraman

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Internet of Things (IoT) has attracted a recent spark in research on Internet of Vehicles (IoV). In this paper, we focus on one research area in IoV: preserving location privacy of vehicle data. We discuss existing location privacy preserving techniques and provide a scheme for evaluating these techniques under IoV traffic condition. We propose a different strategy in applying Differential Privacy using k-d tree data structure to preserve location privacy and experiment on real world Gowalla data set. We show that our strategy produces differentially private data, good preservation of utility by achieving similar regression accuracy to the original dataset on an LSTM (Long Term Short Term Memory) neural network traffic predictor.

Keywords: differential privacy, internet of things, internet of vehicles, location privacy, privacy preservation scheme

Procedia PDF Downloads 180

Authors: Huang Hao, Li Weiwen

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Global biodiversity continues to decline as a result of global climate change and various human activities, such as habitat destruction, pollution, introduction of alien species and overfishing. Although there are connections between global marine organisms more or less, it is better to have clear geographical boundaries in order to facilitate the assessment and management of different biogeographical zones. And so area based management tools (ABMT) are considered as the most effective means for the conservation and sustainable use of marine biodiversity. On a large scale, the geographical gap (or barrier) is the main factor to influence the connectivity, diffusion, ecological and evolutionary process of marine organisms, which results in different distribution patterns. On a small scale, these factors include geographical location, geology, and geomorphology, water depth, current, temperature, salinity, etc. Therefore, the analysis on geographic and environmental factors is of great significance in the study of biodiversity characteristics. This paper summarizes the marine spatial classification and ABMTs used in coastal area, open oceans and deep sea. And analysis principles and methods of marine spatial classification based on biogeographic related factors, and take China Near Sea (CNS) area as case study, and select key biogeographic related factors, carry out marine spatial classification at biological region scale, ecological regionals scale and biogeographical scale. The research shows that CNS is divided into 5 biological regions by climate and geographical differences, the Yellow Sea, the Bohai Sea, the East China Sea, the Taiwan Straits, and the South China Sea. And the bioregions are then divided into 12 ecological regions according to the typical ecological and administrative factors, and finally the eco-regions are divided into 98 biogeographical units according to the benthic substrate types, depth, coastal types, water temperature, and salinity, given the integrity of biological and ecological process, the area of the biogeographical units is not less than 1,000 km². This research is of great use to the coastal management and biodiversity conservation for local and central government, and provide important scientific support for future spatial planning and management of coastal waters and sustainable use of marine biodiversity.

Keywords: spatial classification, marine biodiversity, bio-geographical, conservation

Procedia PDF Downloads 152

Authors: Zahid Ullah, Atlas Khan

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The rapid advancements in data collection, storage, and processing capabilities have led to an explosion of data in various domains. In this era of big data, mathematical sciences play a crucial role in uncovering valuable insights and driving informed decision-making through data analytics. The purpose of this abstract is to present the latest advances in mathematical sciences and their application in harnessing the power of data analytics. This abstract highlights the interdisciplinary nature of data analytics, showcasing how mathematics intersects with statistics, computer science, and other related fields to develop cutting-edge methodologies. It explores key mathematical techniques such as optimization, mathematical modeling, network analysis, and computational algorithms that underpin effective data analysis and interpretation. The abstract emphasizes the role of mathematical sciences in addressing real-world challenges across different sectors, including finance, healthcare, engineering, social sciences, and beyond. It showcases how mathematical models and statistical methods extract meaningful insights from complex datasets, facilitating evidence-based decision-making and driving innovation. Furthermore, the abstract emphasizes the importance of collaboration and knowledge exchange among researchers, practitioners, and industry professionals. It recognizes the value of interdisciplinary collaborations and the need to bridge the gap between academia and industry to ensure the practical application of mathematical advancements in data analytics. The abstract highlights the significance of ongoing research in mathematical sciences and its impact on data analytics. It emphasizes the need for continued exploration and innovation in mathematical methodologies to tackle emerging challenges in the era of big data and digital transformation. In summary, this abstract sheds light on the advances in mathematical sciences and their pivotal role in unveiling the power of data analytics. It calls for interdisciplinary collaboration, knowledge exchange, and ongoing research to further unlock the potential of mathematical methodologies in addressing complex problems and driving data-driven decision-making in various domains.

Keywords: mathematical sciences, data analytics, advances, unveiling

Procedia PDF Downloads 93

Authors: Douglas A. Menezes, Isabel D. Nunes, Ulrich Schiel

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Most Virtual Learning Environments do not provide support mechanisms for the integrated planning, construction and follow-up of Instructional Design supported by Learning Analytic results. The present work aims to present an authoring tool that will be responsible for constructing the structure of an Instructional Design (ID), without the data being altered during the execution of the course. The visual interface aims to present the critical situations present in this ID, serving as a support tool for the course follow-up and possible improvements, which can be made during its execution or in the planning of a new edition of this course. The model for the ID is based on High-Level Petri Nets and the visualization forms are determined by the specific kind of the data generated by an e-course, a population of students generating sequentially dependent data.

Keywords: educational data visualization, high-level petri nets, instructional design, learning analytics

Procedia PDF Downloads 243

Authors: Yas Barzegaar, Atrin Barzegar

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The aim of this research is to develop an intelligent system to analyze the risk level of wheat flour product manufacturing system. The model consists of five Fuzzy Inference Systems in two different layers to analyse the risk of a wheat flour product manufacturing system. The first layer of the model consists of four Fuzzy Inference Systems with three criteria. The output of each one of the Physical, Chemical, Biological and Environmental Failures will be the input of the final manufacturing systems. The proposed model based on Mamdani Fuzzy Inference Systems gives a performance ranking of wheat flour products manufacturing systems. The first step is obtaining data to identify the failure modes from expert’s opinions. The second step is the fuzzification process to convert crisp input to a fuzzy set., then the IF-then fuzzy rule applied through inference engine, and in the final step, the defuzzification process is applied to convert the fuzzy output into real numbers.

Keywords: failure modes, fuzzy rules, fuzzy inference system, risk assessment

Procedia PDF Downloads 102

Authors: Adel M. Qatawneh

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This paper aimed to determine the importance of knowledge innovation for external audit on anti-corruption in the entire Jordanian bank companies are listed in Amman Stock Exchange (ASE). The study importance arises from the need to recognize the Knowledge innovation for external audit and anti-corruption as the development in the world of business, the variables that will be affected by external audit innovation are: reliability of financial data, relevantly of financial data, consistency of the financial data, Full disclosure of financial data and protecting the rights of investors to achieve the objectives of the study a questionnaire was designed and distributed to the society of the Jordanian bank are listed in Amman Stock Exchange. The data analysis found out that the banks in Jordan have a positive importance of Knowledge innovation for external audit on anti-corruption. They agree on the benefit of Knowledge innovation for external audit on anti-corruption. The statistical analysis showed that Knowledge innovation for external audit had a positive impact on the anti-corruption and that external audit has a significantly statistical relationship with anti-corruption, reliability of financial data, consistency of the financial data, a full disclosure of financial data and protecting the rights of investors.

Keywords: knowledge innovation, external audit, anti-corruption, Amman Stock Exchange

Procedia PDF Downloads 465

Authors: Ashish Kumar, Munesh Raghuraj Varma, Nisarg Joshi, Gujjula Vishwa Teja, Srikanth Sambi, Arpit Awasthi

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Autonomous driving vehicles are revolutionizing the transportation system of the 21st century. This has been possible due to intensive research put into making a robust, reliable, and intelligent program that can perceive and understand its environment and make decisions based on the understanding. It is a very data-intensive task with data coming from multiple sensors and the amount of data directly reflects on the performance of the system. Researchers have to design the preprocessing pipeline for different datasets with different sensor orientations and alignments before the dataset can be fed to the model. This paper proposes a solution that provides a method to unify all the data from different sources into a uniform format using the intrinsic and extrinsic parameters of the sensor used to capture the data allowing the same pipeline to use data from multiple sources at a time. This also means easy adoption of new datasets or In-house generated datasets. The solution also automates the complete deep learning pipeline from preprocessing to post-processing for various tasks allowing researchers to design multiple custom end-to-end pipelines. Thus, the solution takes care of the input and output data handling, saving the time and effort spent on it and allowing more time for model improvement.

Keywords: augmentation, autonomous driving, camera, custom end-to-end pipeline, data unification, lidar, post-processing, preprocessing

Procedia PDF Downloads 123

Authors: Elena Maria Scalisi

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The increasing production and the use of TiO₂ nanoparticles (NPs) have inevitably led to their release into the environment, thereby posing a threat to organisms and also for human. Human exposure to TiO₂-NPs may occur during both manufacturing and use. TiO₂-NPs are common in consumer products for dermal application, toothpaste, food colorants, and nutritional supplements, then oral exposure may occur during use of such products. Into the body, TiO₂-NPs thanks to their small size (<100 nm), can, through testicular blood barrier inducing effect on testis and then on male reproductive health. The nanoscale size of TiO₂ increase the surface-to-volume ratio making them more reactive in a cell, then TiO₂ NPs increase their ability to produce reactive oxygen species (ROS). In male germ cells, ROS may have important implications in maintaining the normal functions of mature spermatozoa at physiological levels, moreover, in spermatozoa they are important signaling molecules for their hyperactivation and acrosome reaction. Nevertheless, an excess of ROS by external inputs such as NPs can increased the oxidative stress (OS), which results in damage DNA and apoptosis. The aim of our study has been investigate the impact of TiO₂ NPs on human spermatozoa, evaluating DNA damage and the expression of proteins involved in cell stress. According WHO guidelines 2021, we have exposed human spermatozoa in vitro to TiO₂ NP at concentrations 50 ppm, 100 ppm, 250 ppm, and 500 ppm for 1 hour (at 37°C and CO₂ at 5%). DNA damage was evaluated by Sperm Chromatin Dispersion Test (SCD) and TUNEL assay; moreover, we have evaluated the expression of biomarkers of oxidative stress like Heat Shock Protein 70 (HSP70) and Metallothioneins (MTs). Also, sperm parameters as motility viability have been evaluated. Our results not report a significant reduction in motility of spermatozoa at the end of the exposure. On the contrary, the progressive motility was increased at the highest concentration (500 ppm) and was statistically significant compared to control (p <0.05). Also, viability was not changed by exposure to TiO₂-NPs (p <0.05). However, increased DNA damage was observed at all concentrations, and the TUNEL assay highlighted the presence of single strand breaks in the DNA. The spermatozoa responded to the presence of TiO₂-NPs with the expression of Hsp70, which have a protective function because they allow the maintenance of cellular homeostasis in stressful/ lethal conditions. A positivity for MTs was observed mainly for the concentration of 4 mg/L. Although the biological and physiological function of the metallothionein (MTs) in the male genital organs is unclear, our results highlighted that the MTs expressed by spermatozoa maintain their biological role of detoxification from metals. Our results can give additional information to the data in the literature on the toxicity of TiO₂-NPs and reproduction.

Keywords: human spermatozoa, DNA damage, TiO₂-NPs, biomarkers

Procedia PDF Downloads 144

Authors: Mohamed Hamed

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Population studies, essentially deals with the size, growth, and distribution of the population in a given area. Size, growth, and distribution are determined by three major factors, which are reproduction, mortality, and migration. Of these factors, reproduction is a potent socio-demographic force in vital process of population growth. It is a major component of population growth, and has crucial role in population dynamic, because it measures the rate at which a population increased. In fact the most striking feature of human reproduction is its variation. Its levels are vary widely among nations, countries, geographic regions, ethnic. The variations of reproductive behaviour among married women have been empirically documented in a large numbers of countries. For instance, many researchers in developing and developed countries investigated the differential of reproductive behaviour among married women. Most of these studies found that reproductive behaviour is strongly influenced by the socioeconomic and biological factors.Such as education, income, employment of women, marriage pattern, age at marriage, contraceptive use, education, and employment. However, the above socioeconomic and biological factors are determined by cultural factors surrounded by married women. So, this study is going to find out the effect of culture on reproductive behaviour among married women in Sudan, a case of Shendi town.

Keywords: fertilty pattern, sudan, shendi town, factors affecting reproductive behaviour, married women

Procedia PDF Downloads 301

Authors: Ulas Vural, M. Ergun Okay, E. Mesut Yildiz

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Telecommunication service providers demand accurate and precise prediction of customer churn probabilities to increase the effectiveness of their customer relation services. The large amount of customer data owned by the service providers is suitable for analysis by machine learning methods. In this study, expenditure data of customers are analyzed by using an artificial neural network (ANN). The ANN model is applied to the data of customers with different billing duration. The proposed model successfully predicts the churn probabilities at 83% accuracy for only three months expenditure data and the prediction accuracy increases up to 89% when the nine month data is used. The experiments also show that the accuracy of ANN model increases on an extended feature set with information of the changes on the bill amounts.

Keywords: customer relationship management, churn prediction, telecom industry, deep learning, artificial neural networks

Procedia PDF Downloads 147

Authors: Bekkem Chakradhar Reddy, Y. Soni Priya, Mathivanan G., L. K. Joshila Grace, N. Srinivasan, Asha P.

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Currently, there are a lot of problems related to marking attendance in schools, offices, or other places. Organizations tasked with collecting daily attendance data have numerous concerns. There are different ways to mark attendance. The most commonly used method is collecting data manually by calling each student. It is a longer process and problematic. Now, there are a lot of new technologies that help to mark attendance automatically. It reduces work and records the data. We have proposed to implement attendance marking using the latest technologies. We have implemented a system based on face identification and analyzing faces. The project is developed by gathering faces and analyzing data, using deep learning algorithms to recognize faces effectively. The data is recorded and forwarded to the host through mail. The project was implemented in Python and Python libraries used are CV2, Face Recognition, and Smtplib.

Keywords: python, deep learning, face recognition, CV2, smtplib, Dlib.

Procedia PDF Downloads 58

Authors: Didem Güven, Oytun Hanhan, Elif Ceren Aksoy, Emine Ubay Çokgör

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This paper presents a biological treatability study ofpaintshopwastewaterof a bus factory by an anoxic/aerobic sequencing batch reactor.A lab scale 14L SBR system was implementedto investigate carbon and nitrogen removal performance frompaint shop waste streams combined with domestic and process wastewater of a bus production factory in Istanbul (Turkey).The wastewater collected from decanters of the paint boots and pre-treatmentplant was usedforthefeeding of SBR. The reactor was operated with a total hydraulic retention time of 24 hrs, and a total sludge age of 18.7 days. Initially the efficiency and stability of the reactor were studied when fed with main wastewater stream to simulate the current wastewater treatment plant. Removal efficiency of 57% nitrogen and 90% COD were obtained. Once the paint shop wastewater was introduced to mainstream feeding with a ratio of 1:5, nitrification completely, carbon removal were partially inhibited. SBR system was successful to handle even at very high COD concentrations of paint shop wastewater after feeding of 2 months, with an average effluent COD of 100 mg/L. For the determination of kinetic parameters, respirometric analysis was also conducted with/without paint shop wastewater addition. Model simulation indicated lower maximum specific growth and hydrolysis rates when paint shop wastewater was mixed with the mainstream wastewater of the factory.

Keywords: biological treatability, nitrogen removal, paint shop wastewater, sequencing batch reactor

Procedia PDF Downloads 296

Authors: Nizar Karim Uribe-Orihuela, Fernando Brambila-Paz, Ivette Caldelas, Rodrigo Montufar-Chaveznava

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In this paper, we present the advances corresponding to the implementation of a strategy to visualize geographical data using a Software Development Kit (SDK) for video games. We use multispectral images from Landsat 7 platform and Laser Imaging Detection and Ranging (LIDAR) data from The National Institute of Geography and Statistics of Mexican (INEGI). We select a place of interest to visualize from Landsat platform and make some processing to the image (rotations, atmospheric correction and enhancement). The resulting image will be our gray scale color-map to fusion with the LIDAR data, which was selected using the same coordinates than in Landsat. The LIDAR data is translated to 8-bit raw data. Both images are fused in a software developed using Unity (an SDK employed for video games). The resulting image is then displayed and can be explored moving around. The idea is the software could be used for students of geology and geophysics at the Engineering School of the National University of Mexico. They will download the software and images corresponding to a geological place of interest to a smartphone and could virtually visit and explore the site with a virtual reality visor such as Google cardboard.

Keywords: virtual reality, interactive technologies, geographical data visualization, video games technologies, educational material

Procedia PDF Downloads 246

Authors: Debajit Kalita, Macmillan Nongkhlaw, S. R. Joshi

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Mercury (Hg) is a highly toxic heavy metal released both from naturally occurring volcanoes and anthropogenic activities like alkali and mining industries as well as biomedical wastes. Exposure to mercury is known to affect the nervous, gastrointestinal and renal systems. In the present study, a bacterial isolate identified using 16S rRNA marker as Paenibacillus jamilae PKR1 isolated from India’s largest sandstone-type uranium deposits, containing an average of 0.1% U3O8, was found to be resistance to Hg contamination under culture conditions. It showed strong hydrophobicity as revealed by SAT, MATH, PAT, SAA adherence assays. The Fourier Transform Infrared (FTIR) spectra showed the presence of hydroxyl, amino and carboxylic functional groups on the cell surface EPS which are known to contribute in the binding of metals. It is proposed that the characterized isolate tolerating up to 4.0mM of mercury provides scope for its application in bioremediation of mercury from contaminated sites.

Keywords: mercury, Domiasiat, uranium, paenibacillus jamilae, hydrophobicity, FTIR

Procedia PDF Downloads 409

Authors: Marco Magasic

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This article examines how a local newspaper’s closure impacts the way everyday people in a rural Australian town are informed about and engage with political affairs. It draws on a two-month focused ethnographic study in the outback town of Lighting Ridge, New South Wales and explores people’s media-related practices following the closure of the towns’ only newspaper, The Ridge News, in 2015. While social media is considered to have partly filled the news void, there is an increasingly fragmented and less vibrant local public sphere that has led to growing complacency among individuals about political affairs. Local residents highlight a dearth of reliable, credible information and lament the loss of the newspaper and its role in community advocacy and fostering people’s engagement with political institutions, especially local government.

Keywords: public sphere, political participation, local news, democratic deficit

Procedia PDF Downloads 155

Authors: Chad Brown

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This paper establishes general conditions for the convergence rates of nonparametric sieve estimators with dependent data. We present two key results: one for nonstationary data and another for stationary mixing data. Previous theoretical results often lack practical applicability to deep neural networks (DNNs). Using these conditions, we derive convergence rates for DNN sieve estimators in nonparametric regression settings with both nonstationary and stationary mixing data. The DNN architectures considered adhere to current industry standards, featuring fully connected feedforward networks with rectified linear unit activation functions, unbounded weights, and a width and depth that grows with sample size.

Keywords: sieve extremum estimates, nonparametric estimation, deep learning, neural networks, rectified linear unit, nonstationary processes

Procedia PDF Downloads 41

Authors: Y. K. Park, B. K. Kim, J. W. Lee, S. J. Lee

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To assess the risk of the underground structures and surrounding ground, we collect basic data by the engineering method of measurement, exploration and surveys and, derive the risk through proper analysis and each assessment for urban railroad underground structures and surrounding ground including station inflow. Basic data are obtained by the fiber-optic sensors, MEMS sensors, water quantity/quality sensors, tunnel scanner, ground penetrating radar, light weight deflectometer, and are evaluated if they are more than the proper value or not. Based on these data, we analyze the risk level of urban railroad underground structures and surrounding ground. And we develop the risk management system to manage efficiently these data and to support a convenient interface environment at input/output of data.

Keywords: urban railroad, underground structures, ground subsidence, station inflow, risk

Procedia PDF Downloads 336

Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.

Keywords: big data, machine learning, smart city, social cost, transportation network

Procedia PDF Downloads 260

Authors: N. S. B. Ambarini, Tito Sofyan, Edra Satmaidi

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Coastal and marine are two potential natural resource one of the pillars of the national economy. The Indonesian archipelago has marine and coastal which is quite spacious. Various important natural resources such as fisheries, mining and so on are in coastal areas and the sea, so that this region is a unique area with a variety of interests to exploit it. Therefore, to preserve a sustainable manner need good management and comprehensive. To the national and local level legal regulations have been published relating to the management of coastal and marine areas. However, in practice it has not been able to function optimally. Substantially has not touched the problems of the region, especially concerning the interests of local communities (local). This study is a legal non-doctrinal approach to socio-legal studies. Based on the results of research in some coastal and marine areas in Bengkulu province - Indonesia, there is a fact that the system of customary law and local wisdom began to weaken implementation. Therefore harmonization needs to be done in implementing laws and regulations that apply to the values of indigenous and local knowledge that exists in the community.

Keywords: coastal and marine, harmonization, law, local

Procedia PDF Downloads 347

Authors: Amani A. Saad, Ahmed A. El-Farag, El-Sayed A. Helali

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Cloud computing is an important and promising field in the recent decade. Cloud computing allows sharing resources, services and information among the people of the whole world. Although the advantages of using clouds are great, but there are many risks in a cloud. The data security is the most important and critical problem of cloud computing. In this research a new security model for cloud computing is proposed for ensuring secure communication system, hiding information from other users and saving the user's times. In this proposed model Blowfish encryption algorithm is used for exchanging information or data, and SHA-2 cryptographic hash algorithm is used for data integrity. For user authentication process a user-name and password is used, the password uses SHA-2 for one way encryption. The proposed system shows an improvement of the processing time of uploading and downloading files on the cloud in secure form.

Keywords: cloud Ccomputing, data security, SAAS, PAAS, IAAS, Blowfish

Procedia PDF Downloads 477

Authors: Farooq Anwar, Gokhan Zengin, Khalid M. Alkharfy

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Wild rice (Zizania sp.) is an annual cross-pollinated, emergent, aquatic grass that mainly grows naturally in the Great Lakes region of the North America. The nutritional quality attributes of wild rice are superior to the conventional brown rice (Oryza sativa L.) in terms of higher contents of important minerals (especially phosphorous, potassium, magnesium and calcium), B-complex vitamins, vitamin E and amino acids. In some parts of the world, wild rice is valued as a primary food source. The lipids content of wild rice is reported to be low in the range of 0.7 and 1.1%, however, the lipids are recognized as a rich source of polyunsaturated fatty acids (including linoleic and α-linolenic acid) and phytosterols in addition to containing reasonably good amount of tocols. Besides, wild rice is reported to contain an appreciable amount of high-value compounds such as phenolics with antioxidant properties. Presence of such nutritional bioactives contributes towards medicinal benefits and multiple biological activities of this specialty rice. The present lecture is mainly designed to focus on the detailed nutritional attributes, profile of high-value bioactive components and pharmaceutical/biological activities of wild rice leading to exploring functional food and nutraceutical potential of this food commodity.

Keywords: alpha-linolenic acid, phenolics, phytosterols, tocols, wild rice lipids

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Authors: Mandeep Kaur, K. C. Sharma, P. L. Sharma, R. S. Chandel

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Cauliflower is an important vegetable crop grown throughout the world and is attacked by a large number of insect pests at various stages of the crop growth. Amongst them, the cabbage aphid, Brevicoryne brassicae (Linnaeus) (Hemiptera: Aphididae) is an important insect pest. Continued feeding by both nymphs and adults of this aphid causes yellowing, wilting and stunting of plants. Amongst various management practices, the use of resistant cultivars is important and can be an effective method of reducing the population of this aphid. So it is imperative to know the complete record on various biological parameters and life table on specific cultivars. The biology and life fertility of the cabbage aphid were studied on five cauliflower cultivars viz. Megha, Shweta, K-1, PSB-1 and PSBK-25 under controlled temperature conditions of 20 ± 2°C, 70 ± 5% relative humidity and 16:8 h (Light: Dark) photoperiods. For studying biology; apterous viviparous adults were picked up from the laboratory culture of all five cauliflower cultivars after rearing them at least for two generations and placed individually on the desired plants of cauliflower cultivars grown in pots with ten replicates of each. Daily record on the duration of nymphal period, adult longevity, mortality in each stage and the total number of progeny produced per female was made. This biological data were further used to construct life fertility table on each cultivar. Statistical analysis showed that there was a significant difference ( P  < 0.05) between the different growth stages and the mean number of laid nymphs. The maximum and minimum growth periods were observed on Shweta and Megha (at par with K-1) cultivars, respectively. The maximum number of nymphs were laid on Shweta cultivar (26.40 nymphs per female) and minimum on Megha (at par with K-1) cultivar (15.20 nymphs per female). The true intrinsic rate of increase (rm) was found to be maximum on Shweta (0.233 nymphs/female/day) followed by PSB K-25 (0.207 nymphs/female/day), PSB-1 (0.203 nymphs/female/day), Megha (0.166 nymphs/female/day) and K-1 (0.153 nymphs/female/day). The finite rate of natural increase (λ) was also found to be in the order: K-1 < Megha < PSB-1 < PSBK-25 < Shweta whereas the doubling time (DT) was in the order of K-1 >Megha> PSB-1 >PSBk-25> Shweta. The aphids reared on the K-1 cultivar had the lowest values of rm & λ and the highest value of DT whereas on Shweta cultivar the values of rm & λ were the highest and the lowest value of DT. So on the basis of these studies, K-1 cultivar was found to be the least suitable and the Shweta cultivar was the most suitable for the cabbage aphid population growth. Although the cauliflower cultivars used in different parts of the world may be different yet the results of the present studies indicated that the application of cultivars affecting multiplication rate and reproductive parameters could be a good solution for the management of the cabbage aphid.

Keywords: biology, cauliflower, cultivars, fertility

Procedia PDF Downloads 184

Authors: Wang Yichen, Haruka Yamashita

Abstract:

In recent years, in the field of sports, decision making such as member in the game and strategy of the game based on then analysis of the accumulated sports data are widely attempted. In fact, in the NBA basketball league where the world's highest level players gather, to win the games, teams analyze the data using various statistical techniques. However, it is difficult to analyze the game data for each play such as the ball tracking or motion of the players in the game, because the situation of the game changes rapidly, and the structure of the data should be complicated. Therefore, it is considered that the analysis method for real time game play data is proposed. In this research, we propose an analytical model for "determining the optimal lineup composition" using the real time play data, which is considered to be difficult for all coaches. In this study, because replacing the entire lineup is too complicated, and the actual question for the replacement of players is "whether or not the lineup should be changed", and “whether or not Small Ball lineup is adopted”. Therefore, we propose an analytical model for the optimal player selection problem based on Small Ball lineups. In basketball, we can accumulate scoring data for each play, which indicates a player's contribution to the game, and the scoring data can be considered as a time series data. In order to compare the importance of players in different situations and lineups, we combine RNN (Recurrent Neural Network) model, which can analyze time series data, and NN (Neural Network) model, which can analyze the situation on the field, to build the prediction model of score. This model is capable to identify the current optimal lineup for different situations. In this research, we collected all the data of accumulated data of NBA from 2019-2020. Then we apply the method to the actual basketball play data to verify the reliability of the proposed model.

Keywords: recurrent neural network, players lineup, basketball data, decision making model

Procedia PDF Downloads 133

Authors: A. Baloglu, A. Çınar

Abstract:

Different tools and technologies were implemented for Crisis Response and Management (CRM) which is generally using available network infrastructure for information exchange. Depending on type of disaster or crisis, network infrastructure could be affected and it could not be able to provide reliable connectivity. Thus any tool or technology that depends on the connectivity could not be able to fulfill its functionalities. As a solution, a new message exchange framework has been developed. Framework provides offline/online information exchange platform for CRM Information Systems (CRMIS) and it uses XML compression and packet prioritization algorithms and is based on open source web technologies. By introducing offline capabilities to the web technologies, framework will be able to perform message exchange on unreliable networks. The experiments done on the simulation environment provide promising results on low bandwidth networks (56kbps and 28.8 kbps) with up to 50% packet loss and the solution is to successfully transfer all the information on these low quality networks where the traditional 2 and 3 tier applications failed.

Keywords: crisis response and management, XML messaging, web services, XML compression, mining

Procedia PDF Downloads 339

Authors: Rajeev Kumar Bedi, Jaswinder Singh, Sunil Kumar Gupta

Abstract:

The demand for cloud storage is increasing because users want continuous access their data. Cloud Storage revolutionized the way how users access their data. A lot of cloud storage service providers are available as DropBox, G Drive, and providing limited free storage and for extra storage; users have to pay money, which will act as a burden on users. To avoid the issue of limited free storage, the concept of Multi Cloud Storage introduced. In this paper, we will discuss the limitations of existing Multi Cloud Storage systems for mobile devices.

Keywords: cloud storage, data privacy, data security, multi cloud storage, mobile devices

Procedia PDF Downloads 699

Authors: Wuttigrai Ngamsirijit

Abstract:

Talent management in today’s modern organizations has become data-driven due to a demand for objective human resource decision making and development of analytics technologies. HR managers have been faced with some obstacles in exploiting data and information to obtain their effective talent management decisions. These include process-based data and records; insufficient human capital-related measures and metrics; lack of capabilities in data modeling in strategic manners; and, time consuming to add up numbers and make decisions. This paper proposes a framework of talent management through integration of talent value chain and human capital analytics approaches. It encompasses key data, measures, and metrics regarding strategic talent management decisions along the organizational and talent value chain. Moreover, specific predictive and prescriptive models incorporating these data and information are recommended to help managers in understanding the state of talent, gaps in managing talent and the organization, and the ways to develop optimized talent strategies.    

Keywords: decision making, human capital analytics, talent management, talent value chain

Procedia PDF Downloads 187

Authors: Marziyeh Bahrami, Hamed Pahlevan Hsseini, Behnam Ghamami, Arman Alvanpour, Hamed Ezzati, Amir Salar Sadeghi

Abstract:

One of the ways to increase the efficiency of services in the system of agents and, of course, in the world of cloud computing, is to use virtualization techniques. The aim of this research is to create changes in cloud computing services that will reduce as much as possible the energy consumption related to the migration of virtual machines and, in some way, the energy related to the allocation of resources and reduce the amount of pollution. So far, several methods have been proposed to increase the efficiency of cloud computing services in order to save energy in the cloud environment. The method presented in this article tries to prevent energy consumption by data centers and the subsequent production of carbon and biological pollutants as much as possible by increasing the efficiency of cloud computing services. The results show that the proposed algorithm, using the improvement in virtualization techniques and with the help of a genetic algorithm, improves the efficiency of cloud services in the matter of migrating virtual machines and finally saves consumption. becomes energy.

Keywords: consumption reduction, cloud computing, genetic algorithm, live migration, virtual Machine

Procedia PDF Downloads 60