Search results for: heterogeneous massive data

Authors: Naveed Ghani, Samreen Javed

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In today’s heterogeneous network environment, there is a growing demand for distrust clients to jointly execute secure network to prevent from malicious attacks as the defining task of propagating malicious code is to locate new targets to attack. Residual risk is always there no matter what solutions are implemented or whet so ever security methodology or standards being adapted. Security is the first and crucial phase in the field of Computer Science. The main aim of the Computer Security is gathering of information with secure network. No one need wonder what all that malware is trying to do: It's trying to steal money through data theft, bank transfers, stolen passwords, or swiped identities. From there, with the help of our survey we learn about the importance of white listing, antimalware programs, security patches, log files, honey pots, and more used in banks for financial data protection but there’s also a need of implementing the IPV6 tunneling with Crypto data transformation according to the requirements of new technology to prevent the organization from new Malware attacks and crafting of its own messages and sending them to the target. In this paper the writer has given the idea of implementing IPV6 Tunneling Secessions on private data transmission from financial organizations whose secrecy needed to be safeguarded.

Keywords: network worms, malware infection propagating malicious code, virus, security, VPN

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Authors: Saad M. Darwish, Adel A. El-Zoghabi, Moustafa F. Ashry

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The popularity of the Internet and the availability of powerful computers and high-speed networks as low-cost commodity components are changing the way we use computers today. These technical opportunities have led to the possibility of using geographically distributed and multi-owner resources to solve large-scale problems in science, engineering, and commerce. Recent research on these topics has led to the emergence of a new paradigm known as Grid computing. To achieve the promising potentials of tremendous distributed resources, effective and efficient load balancing algorithms are fundamentally important. Unfortunately, load balancing algorithms in traditional parallel and distributed systems, which usually run on homogeneous and dedicated resources, cannot work well in the new circumstances. In this paper, the concept of a fast fractal transform in heterogeneous grid computing based on R-tree and the domain-range entropy is proposed to improve fault tolerance and load balancing algorithm by improve connectivity, communication delay, network bandwidth, resource availability, and resource unpredictability. A novel two-dimension figure of merit is suggested to describe the network effects on load balance and fault tolerance estimation. Fault tolerance is enhanced by adaptively decrease replication time and message cost while load balance is enhanced by adaptively decrease mean job response time. Experimental results show that the proposed method yields superior performance over other methods.

Keywords: Grid computing, load balancing, fault tolerance, R-tree, heterogeneous systems

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Authors: Shilpa Balan

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Radio Frequency Identification, also popularly known as RFID is used to automatically identify and track tags attached to items. This study focuses on the application of RFID in healthcare. The adoption of RFID in healthcare is a crucial technology to patient safety and inventory management. Data from RFID tags are used to identify the locations of patients and inventory in real time. Medical errors are thought to be a prominent cause of loss of life and injury. The major advantage of RFID application in healthcare industry is the reduction of medical errors. The healthcare industry has generated huge amounts of data. By discovering patterns and trends within the data, big data analytics can help improve patient care and lower healthcare costs. The number of increasing research publications leading to innovations in RFID applications shows the importance of this technology. This study explores the current state of research of RFID in healthcare using a text mining approach. No study has been performed yet on examining the current state of RFID research in healthcare using a data mining approach. In this study, related articles were collected on RFID from healthcare journal and news articles. Articles collected were from the year 2000 to 2015. Significant keywords on the topic of focus are identified and analyzed using open source data analytics software such as Rapid Miner. These analytical tools help extract pertinent information from massive volumes of data. It is seen that the main benefits of adopting RFID technology in healthcare include tracking medicines and equipment, upholding patient safety, and security improvement. The real-time tracking features of RFID allows for enhanced supply chain management. By productively using big data, healthcare organizations can gain significant benefits. Big data analytics in healthcare enables improved decisions by extracting insights from large volumes of data.

Keywords: RFID, data mining, data analysis, healthcare

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Authors: Mohd Ridzwan Yaakub, Muhammad Iqbal Abu Latiffi

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Online business has become popular recently due to the massive amount of information and medium available on the Internet. This has resulted in the huge number of reviews where the consumers share their opinion, criticisms, and satisfaction on the products they have purchased on the websites or the social media such as Facebook and Twitter. However, to analyze customer’s behavior has become very important for organizations to find new market trends and insights. The reviews from the websites or the social media are in structured and unstructured data that need a sentiment analysis approach in analyzing customer’s review. In this article, techniques used in will be defined. Definition of the ontology and description of its possible usage in sentiment analysis will be defined. It will lead to empirical research that related to mobile phones used in research and the ontology used in the experiment. The researcher also will explore the role of preprocessing data and feature selection methodology. As the result, ontology-based approach in sentiment analysis can help in achieving high accuracy for the classification task.

Keywords: feature selection, ontology, opinion, preprocessing data, sentiment analysis

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Authors: Hanan Begali, Shahi Dost, Annett Ziegler, Markus Cornberg, Maria-Esther Vidal, Anke R. M. Kraft

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Chronic hepatitis B virus (HBV) infection can be treated with nucleot(s)ide analog (NA), for example, which inhibits HBV replication. However, they have hardly any influence on the functional cure of HBV, which is defined by hepatitis B surface antigen (HBsAg) loss. NA needs to be taken life-long, which is not available for all patients worldwide. Additionally, NA-treated patients are still at risk of developing cirrhosis, liver failure, or hepatocellular carcinoma (HCC). Although each patient has the same components of the immune system, immune responses vary between patients. Therefore, a deeper understanding of the immune response against HBV in different patients is necessary to understand the parameters leading to HBV cure and to use this knowledge to optimize HBV therapies. This requires seamless integration of an enormous amount of diverse and fine-grained data from viral markers, e.g., hepatitis B core-related antigen (HBcrAg) and hepatitis B surface antigen (HBsAg). The data integration system relies on the assumption that profiling human immune systems requires the analysis of various variables (e.g., demographic data, treatments, pre-existing conditions, immune cell response, or HLA-typing) rather than only one. However, the values of these variables are collected independently. They are presented in a myriad of formats, e.g., excel files, textual descriptions, lab book notes, and images of flow cytometry dot plots. Additionally, patients can be identified differently in these analyses. This heterogeneity complicates the integration of variables, as data management techniques are needed to create a unified view in which individual formats and identifiers are transparent when profiling the human immune systems. The proposed study (HBsRE) aims at integrating heterogeneous data sets of 87 chronically HBV-infected patients, e.g., clinical data, immune cell response, and HLA-typing, with knowledge encoded in biomedical ontologies and open-source databases into a knowledge-driven framework. This new technique enables us to harmonize and standardize heterogeneous datasets in the defined modeling of the data integration system, which will be evaluated in the knowledge graph (KG). KGs are data structures that represent the knowledge and data as factual statements using a graph data model. Finally, the analytic data model will be applied on top of KG in order to develop a deeper understanding of the immune profiles among various patients and to evaluate factors playing a role in a holistic profile of patients with HBsAg level loss. Additionally, our objective is to utilize this unified approach to stratify patients for new effective treatments. This study is developed in the context of the project “Transforming big data into knowledge: for deep immune profiling in vaccination, infectious diseases, and transplantation (ImProVIT)”, which is a multidisciplinary team composed of computer scientists, infection biologists, and immunologists.

Keywords: chronic hepatitis B infection, immune response, knowledge graphs, ontology

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Authors: Thanh Noi Phan, Martin Kappas, Jan Degener

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The currently launched Sentinel 2 (S2) satellite (June, 2015) bring a great potential and opportunities for land use/cover map applications, due to its fine spatial resolution multispectral as well as high temporal resolutions. So far, there are handful studies using S2 real data for land cover classification. Especially in northern Vietnam, to our best knowledge, there exist no studies using S2 data for land cover map application. The aim of this study is to provide the preliminary result of land cover classification using Sentinel -2 data with a rising state – of – art classifier, Random Forest. A case study with heterogeneous land use/cover in the eastern of Hanoi Capital – Vietnam was chosen for this study. All 10 spectral bands of 10 and 20 m pixel size of S2 images were used, the 10 m bands were resampled to 20 m. Among several classified algorithms, supervised Random Forest classifier (RF) was applied because it was reported as one of the most accuracy methods of satellite image classification. The results showed that the red-edge and shortwave infrared (SWIR) bands play an important role in land cover classified results. A very high overall accuracy above 90% of classification results was achieved.

Keywords: classify algorithm, classification, land cover, random forest, sentinel 2, Vietnam

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Authors: Vo Thi Hai Quan

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Nam Con Son sedimentary basin is one of the very important oil and gas basins in offshore Vietnam. Hai Thach field of block 05-2 contains mostly gas accumulations in fine-grained, sand/mud-rich turbidite system, which was deposited in a turbidite channel and fan environment. Major Upper Miocene reservoir of HT-3X lies above a well-developed unconformity. The main objectives of this study are to reconstruct depositional environment and to assess the reservoir quality using data from 14 meters of core samples and digital wireline data of the well HT-3X. The wireline log and core data showed that the vertical sequences of representative facies of the well mainly range from Tb to Te divisions of Bouma sequences with predominance of Tb and Tc compared to Td and Te divisions. Sediments in this well were deposited in a submarine fan association with very fine to fine-grained, homogeneous sandstones that have high porosity and permeability, high- density turbidity currents with longer transport route from the sediment source to the basin, indicating good quality of reservoir. Sediments are comprised mainly of the following sedimentary structures: massive, laminated sandstones, convoluted bedding, laminated ripples, cross-laminated ripples, deformed sandstones, contorted bedding.

Keywords: Hai Thach field, Miocene sand, turbidite, wireline data

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Authors: Rui Mao, Heming Ji, Xiaoyu Wang

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Keystroke dynamics are widely used in identity recognition. It has the advantage that the individual typing rhythm is difficult to imitate. It also supports continuous authentication through the keyboard without extra devices. The existing keystroke dynamics authentication methods based on machine learning have a drawback in supporting relatively complex scenarios with massive data. There are drawbacks to both feature extraction and model optimization in these methods. To overcome the above weakness, an authentication model of keystroke dynamics based on deep learning is proposed. The model uses feature vectors formed by keystroke content and keystroke time. It ensures efficient continuous authentication by cooperating attention mechanisms with the combination of CNN and Bi-LSTM. The model has been tested with Open Data Buffalo dataset, and the result shows that the FRR is 3.09%, FAR is 3.03%, and EER is 4.23%. This proves that the model is efficient and accurate on continuous authentication.

Keywords: keystroke dynamics, identity authentication, deep learning, CNN, LSTM

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Authors: Thilina Ranaweera, Enes Makalic, John L. Hopper, Adrian Bickerstaffe

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Data are the primary asset of biomedical researchers, and the engine for both discovery and research translation. As the volume and complexity of research datasets increase, especially with new technologies such as large single nucleotide polymorphism (SNP) chips, so too does the requirement for software to manage, process and analyze the data. Researchers often need to execute complicated queries and conduct complex analyzes of large-scale datasets. Existing tools to analyze such data, and other types of high-dimensional data, unfortunately suffer from one or more major problems. They typically require a high level of computing expertise, are too simplistic (i.e., do not fit realistic models that allow for complex interactions), are limited by computing power, do not exploit the computing power of large-scale parallel architectures (e.g. supercomputers, GPU clusters etc.), or are limited in the types of analysis available, compounded by the fact that integrating new analysis methods is not straightforward. Solutions to these problems, such as those developed and implemented on parallel architectures, are currently available to only a relatively small portion of medical researchers with access and know-how. The past decade has seen a rapid expansion of data management systems for the medical domain. Much attention has been given to systems that manage phenotype datasets generated by medical studies. The introduction of heterogeneous genomic data for research subjects that reside in these systems has highlighted the need for substantial improvements in software architecture. To address this problem, we have developed SPARK, an enabling and translational system for medical research, leveraging existing high performance computing resources, and analysis techniques currently available or being developed. It builds these into The Ark, an open-source web-based system designed to manage medical data. SPARK provides a next-generation biomedical data management solution that is based upon a novel Micro-Service architecture and Big Data technologies. The system serves to demonstrate the applicability of Micro-Service architectures for the development of high performance computing applications. When applied to high-dimensional medical datasets such as genomic data, relational data management approaches with normalized data structures suffer from unfeasibly high execution times for basic operations such as insert (i.e. importing a GWAS dataset) and the queries that are typical of the genomics research domain. SPARK resolves these problems by incorporating non-relational NoSQL databases that have been driven by the emergence of Big Data. SPARK provides researchers across the world with user-friendly access to state-of-the-art data management and analysis tools while eliminating the need for high-level informatics and programming skills. The system will benefit health and medical research by eliminating the burden of large-scale data management, querying, cleaning, and analysis. SPARK represents a major advancement in genome research technologies, vastly reducing the burden of working with genomic datasets, and enabling cutting edge analysis approaches that have previously been out of reach for many medical researchers.

Keywords: biomedical research, genomics, information systems, software

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Authors: Gaetano Zazzaro, Angelo Martone, Roberto V. Montaquila, Luigi Pavone

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Seizure is the main factor that affects the quality of life of epileptic patients. The diagnosis of epilepsy, and hence the identification of epileptogenic zone, is commonly made by using continuous Electroencephalogram (EEG) signal monitoring. Seizure identification on EEG signals is made manually by epileptologists and this process is usually very long and error prone. The aim of this paper is to describe an automated method able to detect seizures in EEG signals, using knowledge discovery in database process and data mining methods and algorithms, which can support physicians during the seizure detection process. Our detection method is based on Artificial Neural Network classifier, trained by applying the multilayer perceptron algorithm, and by using a software application, called Training Builder that has been developed for the massive extraction of features from EEG signals. This tool is able to cover all the data preparation steps ranging from signal processing to data analysis techniques, including the sliding window paradigm, the dimensionality reduction algorithms, information theory, and feature selection measures. The final model shows excellent performances, reaching an accuracy of over 99% during tests on data of a single patient retrieved from a publicly available EEG dataset.

Keywords: artificial neural network, data mining, electroencephalogram, epilepsy, feature extraction, seizure detection, signal processing

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Authors: Nai-Chia Chao, Meng-Chi Shih

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Under today’s fast technology and massive data era, the working method start to change. In this study, based under literature meaning of “Co-working” we had implemented the new “Co-working innovation model”. Research concluded that co-working innovation model shall not be limited in co-working space but use under different field when applying multiple art integration stragies. Research show co-working should not be limited in special field or group, should be use or adapt whenever different though or ideas where found, it should be use under different field and plans.

Keywords: arts integration, co-working, children's musical

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Authors: Jie Yin, Jun Zhang

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Measuring the growth performance and critical supersaturation of particle group have a high reference value for constructing a supersaturated water vapor environment that can improve the removal efficiency of the high-concentration particle group. The critical supersaturation and the variation of the growth performance with supersaturation for high-concentration particles were measured by a flow cloud chamber. Findings suggest that the influence of particle concentration on the growth performance will reduce with the increase of supersaturation. Reducing residence time and increasing particle concentration have similar effects on the growth performance of the high-concentration particle group. Increasing particle concentration and shortening residence time will increase the critical supersaturation of the particle group. The critical supersaturation required to activate a high-concentration particle group is lower than that of the single-particle when the minimum particle size in the particle group is the same as that of a single particle.

Keywords: sub-micron particles, heterogeneous condensation, critical supersaturation, nucleation

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Authors: H. Mahdavinezhad, M. Labbaf, H. R. Tavakoli

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In recent decades, the study and control of the destructive effects of explosive vibration in construction projects has received more attention, and several experimental equations in the field of vibration prediction as well as allowable vibration limit for various structures are presented. Researchers have developed a number of experimental equations to estimate the peak particle velocity (PPV), in which the experimental constants must be obtained at the site of the explosion by fitting the data from experimental explosions. In this study, the most important of these equations was evaluated for strong massive conglomerates around Dez Dam by collecting data on explosions, including 30 particle velocities, 27 displacements, 27 vibration frequencies and 27 acceleration of earth vibration at different distances; they were recorded in the form of two types of detonation systems, NUNEL and electric. Analysis showed that the data from the explosion had the best correlation with the cube root of the explosive, R2=0.8636, but overall the correlation coefficients are not much different. To estimate the vibration in this project, data regression was performed in the other formats, which resulted in the presentation of new equation with R2=0.904 correlation coefficient. Finally according to the importance of the studied structures in order to ensure maximum non damage to adjacent structures for each diagram, a range of application was defined so that for distances 0 to 70 meters from blast site, exponent n=0.33 and for distances more than 70 m, n =0.66 was suggested.

Keywords: blasting, blast-induced vibration, empirical equations, PPV, tunnel

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Authors: Pankaj Kumar Kalita

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Ineffectiveness of formal institutional mechanisms such as official audit to improve public service delivery has been a serious concern to scholars working on governance reforms in developing countries. Scholars argue that public service delivery in local self-governing institutions can be improved through application of informal mechanisms such as social accountability. Social accountability has been reinforced with the engagement of citizens and civic organizations in the process of service delivery to reduce the governance gap in developing countries. However, there are challenges that may impede the scope of establishing social accountability initiatives in the performance of local self-governing institutions. This study makes an attempt to investigate the factors that may impede the scope of establishing social accountability, particularly in culturally heterogeneous societies like India. While analyzing the implementation of two rural development schemes by Panchayats, the local self-governing institutions functioning in rural Assam in India, this study argues that the scope of establishing social accountability in the performance of local self-governing institutions, particularly in culturally heterogeneous societies in developing countries will be impeded by the absence of inter-caste and inter-religion networks. Data has been collected from five selected districts of Assam using in-depth interview method and survey method. The study further contributes to the debates on 'good governance' and citizen-centric approaches in developing countries.

Keywords: citizen engagement, local self-governing institutions, networks, social accountability

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Authors: Kaixuan Feng, Ruixiang Lin, Yuyan Hu, Yuheng Feng, Dezhen Chen, Tongcheng Cao

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In this study, we propose a reaction system for the low-temperature reduction of NO by H₂ on carbon-based materials decorated with 5%wt. Ni. This cost-effective catalyst system efficiently utilizes pyrolysis carbon-based materials and waste hydrogen. Additionally, it yields environmentally friendly products without requiring extra heat sources in practical SCR devices. Density functional theory elucidates the mechanism of NO heterogeneous reduction by H₂ on Ni-decorated char surfaces. Two distinct reaction paths were identified, one involving the intermediate product N₂O and the other not. These pathways exhibit different rate-determination steps and activation energies. Kinetic analysis indicates that the N₂O byproduct pathway has a lower activation energy. Experimental results corroborate the theoretical findings. Thus, this research enhances our mechanistic understanding of the NO-H₂ reaction over char and offers insights for optimizing catalyst design in low-temperature NO reduction.

Keywords: char-based catalysis, NO reduction, DFT study, heterogeneous reaction, low-temperature H₂-reduction

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Authors: Heeyeul Kwon, Yongtae Park

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Despite the highly touted benefits, emerging technologies have unleashed pervasive concerns regarding unintended and unforeseen social impacts. Thus, those wishing to create safe and socially acceptable products need to identify such side effects and mitigate them prior to the market proliferation. Various methodologies in the field of technology assessment (TA), namely Delphi, impact assessment, and scenario planning, have been widely incorporated in such a circumstance. However, literatures face a major limitation in terms of sole reliance on participatory workshop activities. They unfortunately missed out the availability of a massive untapped data source of futuristic information flooding through the Internet. This research thus seeks to gain insights into utilization of futuristic data, future-oriented documents from the Internet, as a supplementary method to generate social impact scenarios whilst capturing perspectives of experts from a wide variety of disciplines. To this end, network analysis is conducted based on the social keywords extracted from the futuristic documents by text mining, which is then used as a guide to produce a comprehensive set of detailed scenarios. Our proposed approach facilitates harmonized depictions of possible hazardous consequences of emerging technologies and thereby makes decision makers more aware of, and responsive to, broad qualitative uncertainties.

Keywords: emerging technologies, futuristic data, scenario, text mining

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Authors: G. Ravi Kiran, G. Radhakrishnamacharya

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This paper investigates the effects of slip boundary condition and wall properties on the dispersion of a solute matter in peristaltic flow of an incompressible micropolar fluid through a porous medium. Long wavelength approximation, Taylor's limiting condition and dynamic boundary conditions at the flexible walls are used to obtain the average effective dispersion coefficient in the presence of combined homogeneous and heterogeneous chemical reactions. The effects of various pertinent parameters on the effective dispersion coefficient are discussed. It is observed that peristalsis enhances dispersion. It also increases with micropolar parameter, cross viscosity coefficient, Darcy number, slip parameter and wall parameters. Further, dispersion decreases with homogenous chemical reaction rate and heterogeneous chemical reaction rate.

Keywords: chemical reaction, dispersion, peristalsis, slip condition, wall properties

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Authors: Edward W. Staley, Corban G. Rivera, Ashley J. Llorens

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Advances in reinforcement learning (RL) have resulted in recent breakthroughs in the application of artificial intelligence (AI) across many different domains. An emerging landscape of development environments is making powerful RL techniques more accessible for a growing community of researchers. However, most existing frameworks do not directly address the problem of learning in complex operating environments, such as dense urban settings or defense-related scenarios, that incorporate distributed, heterogeneous teams of agents. To help enable AI research for this important class of applications, we introduce the AI Arena: a scalable framework with flexible abstractions for distributed multi-agent reinforcement learning. The AI Arena extends the OpenAI Gym interface to allow greater flexibility in learning control policies across multiple agents with heterogeneous learning strategies and localized views of the environment. To illustrate the utility of our framework, we present experimental results that demonstrate performance gains due to a distributed multi-agent learning approach over commonly-used RL techniques in several different learning environments.

Keywords: reinforcement learning, multi-agent, deep learning, artificial intelligence

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Authors: Hanefi Cinar, Musa Cibuk, Ismail Erturk, Fikri Aggun, Munip Geylani

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Mobile Wireless Sensor Networks (MWSNs) carry heterogeneous data traffic with different urgency and quality of service (QoS) requirements. There are a lot of studies made on energy efficiency, bandwidth, and communication methods in literature. But delay, high throughput, utility parameters are not well considered. Increasing demand for real-time data transfer makes these parameters more important. In this paper we design new MAC protocol which is delay constrained and targets for improving delay, utility, and throughput performance of the network and finding solutions on collision and interference problems. Protocol improving QoS requirements by using TDMA, FDM, and OFDMA hybrid communication methods with multi-channel communication.

Keywords: MWSN, delay, hybrid MAC, TDMA, FDM, OFDMA

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Authors: Sheikh Shams Azam, Manoj Raju, Venkatesh Pagidimarri, Vamsi Kasivajjala

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Over the past decade, there has been a steep rise in the data-driven analysis in major areas of medicine, such as clinical decision support system, survival analysis, patient similarity analysis, image analytics etc. Most of the data in the field are well-structured and available in numerical or categorical formats which can be used for experiments directly. But on the opposite end of the spectrum, there exists a wide expanse of data that is intractable for direct analysis owing to its unstructured nature which can be found in the form of discharge summaries, clinical notes, procedural notes which are in human written narrative format and neither have any relational model nor any standard grammatical structure. An important step in the utilization of these texts for such studies is to transform and process the data to retrieve structured information from the haystack of irrelevant data using information retrieval and data mining techniques. To address this problem, the authors present Q-Map in this paper, which is a simple yet robust system that can sift through massive datasets with unregulated formats to retrieve structured information aggressively and efficiently. It is backed by an effective mining technique which is based on a string matching algorithm that is indexed on curated knowledge sources, that is both fast and configurable. The authors also briefly examine its comparative performance with MetaMap, one of the most reputed tools for medical concepts retrieval and present the advantages the former displays over the latter.

Keywords: information retrieval, unified medical language system, syntax based analysis, natural language processing, medical informatics

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Authors: Daehyoung Kim, Pervez Khan, Hoon Kim

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Efficient utilization of spectrum resources is a fundamental issue of wireless communications due to its scarcity. To improve the efficiency of spectrum utilization, the spectrum sharing for unlicensed bands is being regarded as one of key technologies in the next generation wireless networks. A number of schemes such as Listen-Before-Talk(LBT) and carrier sensor adaptive transmission (CSAT) have been suggested from this aspect, but more efficient sharing schemes are required for improving spectrum utilization efficiency. This work considers an opportunistic transmission approach and a dynamic Contention Window (CW) adjustment scheme for LTE-U users sharing the unlicensed spectrum with Wi-Fi, in order to enhance the overall system throughput. The decision criteria for the dynamic adjustment of CW are based on the collision evaluation, derived from the collision probability of the system. The overall performance can be improved due to the adaptive adjustment of the CW. Simulation results show that our proposed scheme outperforms the Distributed Coordination Function (DCF) mechanism of IEEE 802.11 MAC.

Keywords: spectrum sharing, adaptive opportunistic transmission, unlicensed bands, heterogeneous networks

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Authors: Ahmet Ozcan, Onder Alparslan, Anil Sezgin, Omer Cetin

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The use of unmanned systems for different purposes has become very popular over the past decade. Expectations from these systems have also shown an incredible increase in this parallel. But meeting the demands of the tasks are often not possible with the usage of a single unmanned vehicle in a mission, so it is necessary to use multiple autonomous vehicles with different abilities together in coordination. Therefore the usage of the same type of vehicles together as a swarm is helped especially to satisfy the time constraints of the missions effectively. In other words, it allows sharing the workload by the various numbers of homogenous platforms together. Besides, it is possible to say there are many kinds of problems that require the usage of the different capabilities of the heterogeneous platforms together cooperatively to achieve successful results. In this case, cooperative working brings additional problems beyond the homogeneous clusters. In the scenario presented as an example problem, it is expected that an autonomous ground vehicle, which is lack of its position information, manage to perform point-to-point navigation without losing its way in a previously unknown labyrinth. Furthermore, the ground vehicle is equipped with very limited sensors such as ultrasonic sensors that can detect obstacles. It is very hard to plan or complete the mission for the ground vehicle by self without lost its way in the unknown labyrinth. Thus, in order to assist the ground vehicle, the autonomous air drone is also used to solve the problem cooperatively. The autonomous drone also has limited sensors like downward looking camera and IMU, and it also lacks computing its global position. In this context, it is aimed to solve the problem effectively without taking additional support or input from the outside, just benefiting capabilities of two autonomous vehicles. To manage the point-to-point navigation in a previously unknown labyrinth, the platforms have to work together coordinated. In this paper, cooperative work of heterogeneous unmanned systems is handled in an applied sample scenario, and it is mentioned that how to work together with an autonomous ground vehicle and the autonomous flying platform together in a harmony to take advantage of different platform-specific capabilities. The difficulties of using heterogeneous multiple autonomous platforms in a mission are put forward, and the successful solutions are defined and implemented against the problems like spatially distributed tasks planning, simultaneous coordinated motion, effective communication, and sensor fusion.

Keywords: unmanned systems, heterogeneous autonomous vehicles, coordination, task planning

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Authors: Caiwei Chen, Jianhao Li, Jiasong Zhu

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The reform and opening-up stimulated economic and technological take-off in China while resulting in massive urbanization and motorization. Wujiaochang area was set as a secondary business district in Shanghai to meet the growing demand, with the reconstruction of Wujiaochang Plaza in 2005 being a milestone of this intended urban renewal. Wujiaochang is now an economically dynamic area providing much larger traffic and transit capacity transportation-wise. However, this rebuilding has completely changed the face of the district. It is, therefore, appropriate to evaluate its impact on neighborhoods and communities while assessing the overall sustainability of such an operation. In this study, via an online questionnaire survey among local residents and daily visitors, we assess the perceptions and the estimated impact of Wujiaochang Plaza's reconstruction. We then confront these results to the 62 answers from local residents to a questionnaire collected on paper. The analysis of our data, along with observation and other forms of information -such as maps analysis or online applications (Dianping)- demonstrate major improvement in economic sustainability but also significant losses in environmental sustainability, especially in terms of active transportation. As for the social viewpoint, local residents' opinions tend to be rather positive, especially regarding traffic safety and access to consumption, despite the lack of connectivity and radical changes induced by Wujiaochang massive transformations. In general, our investigation exposes the overall positive outcomes of Wujiaochang Plaza reconstruction but also unveils major drawbacks, especially in terms of soft mobility and traffic fluidity. We gather that our approach could be of tremendous help for future major urban interventions, as such approaches in municipal regeneration are widely implemented in Chinese cities and yet still need to be thoroughly assessed in terms of sustainability.

Keywords: China's reform and opening-up, economical revitalization, neighborhood identity, sustainability assessment, urban renewal

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Authors: Jang kyun Cho, Jeong-dong Lee

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The advent of the Internet, mobile communications, and social network services has stimulated social interactions among consumers, allowing people to affect one another’s innovation adoptions by exchanging information more frequently and more quickly. Previous diffusion models, such as the Bass model, however, face limitations in reflecting such recent phenomena in society. These models are weak in their ability to model interactions between agents; they model aggregated-level behaviors only. The agent based model, which is an alternative to the aggregate model, is good for individual modeling, but it is still not based on an economic perspective of social interactions so far. This study assumes the presence of social utility from other consumers in the adoption of innovation and investigates the effect of individual interactions on innovation diffusion by developing a new model called the interaction-based diffusion model. By comparing this model with previous diffusion models, the study also examines how the proposed model explains innovation diffusion from the perspective of economics. In addition, the study recommends the use of a small-world network topology instead of cellular automata to describe innovation diffusion. This study develops a model based on individual preference and heterogeneous social interactions using utility specification, which is expandable and, thus, able to encompass various issues in diffusion research, such as reservation price. Furthermore, the study proposes a new framework to forecast aggregated-level market demand from individual level modeling. The model also exhibits a good fit to real market data. It is expected that the study will contribute to our understanding of the innovation diffusion process through its microeconomic theoretical approach.

Keywords: innovation diffusion, agent based model, small-world network, demand forecasting

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Authors: Nabil Jallouli, Luisa M. Pastrana-Martínez, Ana R. Ribeiro, Nuno F. F. Moreira, Joaquim L. Faria, Olfa Hentati, Adrián M. T. Silva, Mohamed Ksibi

Abstract:

Degradation and mineralization of ibuprofen (IBU) were investigated using Ultraviolet (UV) Light Emitting Diodes (LEDs) in TiO2 photocatalysis. Samples of ultrapure water (UP) and a secondary treated effluent of a municipal wastewater treatment plant (WWTP), both spiked with IBU, as well as a highly concentrated IBU (230 mgL-1) pharmaceutical industry wastewater (PIWW), were tested in the TiO2/UV-LED system. Three operating parameters, namely, pH, catalyst load and number of LEDs were optimized. The process efficiency was evaluated in terms of IBU removal using high performance liquid chromatography (HPLC) and ultra-high performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS). Additionally, the mineralization was investigated by determining the dissolved organic carbon (DOC) content. The chemical structures of transformation products were proposed based on the data obtained using liquid chromatography with a high resolution mass spectrometer ion trap/time-of-flight (LC-MS-IT-TOF). A possible pathway of IBU degradation was accordingly proposed. Bioassays were performed using the marine bacterium Vibrio fischeri to evaluate the potential acute toxicity of original and treated wastewaters. TiO2 heterogeneous photocatalysis was efficient to remove IBU from UP and from PIWW, and less efficient in treating the wastewater from the municipal WWTP. The acute toxicity decreased by ca. 40% after treatment, regardless of the studied matrix.

Keywords: acute toxicity, Ibuprofen, UV-LEDs, wastewaters

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Authors: Qiuyue Peng, Vladimir Zachar

Abstract:

The application of adipose-derived stromal/stem cells (ASCs) in regenerative medicine is gaining more awareness due to their advanced translational potential and abundant source preparations. However, ASC-based translation has been confounded by high subpopulation heterogeneity, causing ambiguity about its precise therapeutic value. Some phenotypes defined by a unique combination of positive and negative surface markers have been found beneficial to the required roles. Therefore, the immunophenotypic repertoires of cultured ASCs and temporal changes of distinct subsets were investigated in this study. ASCs from three donors undergoing cosmetic liposuction were cultured in standard culturing methods, and the co-expression patterns based on the combination of selected markers at passages 1, 4, and 8 were analyzed by multi-chromatic flow cytometry. The results showed that the level of heterogeneity of subpopulations of ASCs became lower by in vitro expansion. After a few passages, most of the CD166⁺/CD274⁺/CD271⁺ based subpopulations converged to CD166 single positive cells. Meanwhile, these CD29⁺CD201⁺ double-positive cells, in combination with CD36/Stro-1 expression or without, feathered only the major epitopes and maintained prevailing throughout the whole process. This study suggested that, upon in vitro expansion, the phenotype repertoire of ASCs redistributed and stabilized in a way that cells co-expressing exclusively the strong markers remained dominant. These preliminary findings provide a general overview of the distribution of heterogeneous subsets residents within human ASCs during expansion in vitro. It is a critical step to fully characterize ASCs before clinical application, although the biological effects of heterogeneous subpopulations still need to be clarified.

Keywords: adipose-derived stromal/stem cells, heterogeneity, immunophenotype, subpopulations

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Authors: Maria Jędrzejczak, Patryk Pieniążek

Abstract:

The European legal reality is on the eve of significant change. In European Union law, there is talk of a “fourth industrial revolution”, which is driven by massive data resources linked to powerful algorithms and powerful computing capacity. The above is closely linked to technological developments in the area of artificial intelligence, which has prompted an analysis covering both the legal environment as well as the economic and social impact, also from an ethical perspective. The discussion on the regulation of artificial intelligence is one of the most serious yet widely held at both European Union and Member State level. The literature expects legal solutions to guarantee security for fundamental rights, including privacy, in artificial intelligence systems. There is no doubt that personal data have been increasingly processed in recent years. It would be impossible for artificial intelligence to function without processing large amounts of data (both personal and non-personal). The main driving force behind the current development of artificial intelligence is advances in computing, but also the increasing availability of data. High-quality data are crucial to the effectiveness of many artificial intelligence systems, particularly when using techniques involving model training. The use of computers and artificial intelligence technology allows for an increase in the speed and efficiency of the actions taken, but also creates security risks for the data processed of an unprecedented magnitude. The proposed regulation in the field of artificial intelligence requires analysis in terms of its impact on the regulation on personal data protection. It is necessary to determine what the mutual relationship between these regulations is and what areas are particularly important in the personal data protection regulation for processing personal data in artificial intelligence systems. The adopted axis of considerations is a preliminary assessment of two issues: 1) what principles of data protection should be applied in particular during processing personal data in artificial intelligence systems, 2) what regulation on liability for personal data breaches is in such systems. The need to change the regulations regarding the rights and obligations of data subjects and entities processing personal data cannot be excluded. It is possible that changes will be required in the provisions regarding the assignment of liability for a breach of personal data protection processed in artificial intelligence systems. The research process in this case concerns the identification of areas in the field of personal data protection that are particularly important (and may require re-regulation) due to the introduction of the proposed legal regulation regarding artificial intelligence. The main question that the authors want to answer is how the European Union regulation against data protection breaches in artificial intelligence systems is shaping up. The answer to this question will include examples to illustrate the practical implications of these legal regulations.

Keywords: data protection law, personal data, AI law, personal data breach

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Authors: N. Marzban, J. K. Heydarzadeh M. Pourmohammadbagher, M. H. Hatami, A. Samia

Abstract:

A nano-alumina-zirconia composite catalyst was synthesized by a simple aqueous sol-gel method using AlCl₃.6H₂O and ZrCl₄ as precursors. Thermal decomposition of the precursor and subsequent formation of γ-Al₂O₃ and t-Zr were investigated by thermal analysis. XRD analysis showed that γ-Al₂O₃ and t-ZrO₂ phases were formed at 700 °C. FT-IR analysis also indicated that the phase transition to γ-Al₂O₃ occurred in corroboration with X-ray studies. TEM analysis of the calcined powder revealed that spherical particles were in the range of 8-12 nm. The nano-alumina-zirconia composite particles were mesoporous and uniformly distributed in their crystalline phase. In order to measure the catalytic activity, esterification reaction was carried out. Biodiesel, as a renewable fuel, was formed in a continuous packed column reactor. Free fatty acid (FFA) was esterified with ethanol in a heterogeneous catalytic reactor. It was found that the synthesized γ-Al₂O₃/ZrO₂ composite had the potential to be used as a heterogeneous base catalyst for biodiesel production processes.

Keywords: nano alumina-zirconia, composite catalyst, thin film, biodiesel

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Authors: Ngabilas Salama

Abstract:

Background: Indonesia has the 2nd highest number of incidents of tuberculosis (TB). It accounts for 1.020.000 new cases per year, only 30% of which has been reported. To find the lost 70%, a massive active case discovery was conducted through two programs: Ketuk Pintu Layani Dengan Hati (KPLDH) and Kader Juru Pemantau Batuk (Jumantuk cadres), who also plays a role in child TB screening. Methods: Data was collected and analyzed through Tuberculosis Integrated Online System from 2014 to 2017 involving 129 DOTS facility with 86 primary health centers in East Jakarta. Results: East Jakarta consists of 2.900.722 people. KPLDH program started in February 2016 consisting of 84 teams (310 people). Jumantuk cadres was formed 4 months later (218 orang). The number of new TB cases in East Jakarta (primary health center) from 2014 to June 2017 respectively is as follows: 6.499 (2.637), 7.438 (2.651), 8.948 (3.211), 5.701 (1.830). Meanwhile, the percentage of child TB case discovery in primary health center was 8,5%, 9,8%, 12,1% from 2014 to 2016 respectively. In 2017, child TB case discovery was 13,1% for the first 3 months and 16,5% for the next 3 months. Discussion: Increased TB incidence rate from 2014 to 2017 was 14,4%, 20,3%, and 27,4% respectively in East Jakarta, and 0,5%, 21,1%, and 14% in primary health center. This reveals the positive role of KPLDH and Jumantuk in TB detection and reporting. Likewise, these programs were responsible for the increase in child TB case discovery, especially in the first 3 months of 2017 (Ketuk Pintu TB Day program) and the next 3 months (active TB screening). Conclusion: KPLDH dan Jumantuk are actively involved in increasing TB case discovery in both adults and children.

Keywords: tuberculosis, case discovery program, primary health center, cadre

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Authors: Zhiguo Yang, Xiangan Yue, Minglu Shao, Yue Yang, Rongjie Yan

Abstract:

It is difficult to realize deep profile control because of the small pore-throats and easy water channeling in low-permeability heterogeneous reservoir, and the traditional polymer microspheres have the contradiction between injection and plugging. In order to solve this contradiction, the controllable self-aggregating colloidal particles (CSA) containing amide groups on the surface of microspheres was prepared based on emulsion polymerization of styrene and acrylamide. The dispersed solution of CSA colloidal particles, whose particle size is much smaller than the diameter of pore-throats, was injected into the reservoir. When the microspheres migrated to the deep part of reservoir, , these CSA colloidal particles could automatically self-aggregate into large particle clusters under the action of the shielding agent and the control agent, so as to realize the plugging of the water channels. In this paper, the morphology, temperature resistance and self-aggregation properties of CSA microspheres were studied by transmission electron microscopy (TEM) and bottle test. The results showed that CSA microspheres exhibited heterogeneous core-shell structure, good dispersion, and outstanding thermal stability. The microspheres remain regular and uniform spheres at 100℃ after aging for 35 days. With the increase of the concentration of the cations, the self-aggregation time of CSA was gradually shortened, and the influence of bivalent cations was greater than that of monovalent cations. Core flooding experiments showed that CSA polymer microspheres have good injection properties, CSA particle clusters can effective plug the water channels and migrate to the deep part of the reservoir for profile control.

Keywords: heterogeneous reservoir, deep profile control, emulsion polymerization, colloidal particles, plugging characteristic

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