Search results for: data interpolating empirical orthogonal function

Authors: Seyed Sadegh Naseralavi, Sadegh Balaghi, Ehsan Khojastehfar

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Time history dynamic analysis of structures is considered as an exact method while being computationally intensive. Filtration of earthquake strong ground motions applying wavelet transform is an approach towards reduction of computational efforts, particularly in optimization of structures against seismic effects. Wavelet transforms are categorized into continuum and discrete transforms. Since earthquake strong ground motion is a discrete function, the discrete wavelet transform is applied in the present paper. Wavelet transform reduces analysis time by filtration of non-effective frequencies of strong ground motion. Filtration process may be repeated several times while the approximation induces more errors. In this paper, strong ground motion of earthquake has been filtered once applying each wavelet. Strong ground motion of Northridge earthquake is filtered applying various wavelets and dynamic analysis of sampled shear and moment frames is implemented. The error, regarding application of each wavelet, is computed based on comparison of dynamic response of sampled structures with exact responses. Exact responses are computed by dynamic analysis of structures applying non-filtered strong ground motion.

Keywords: wavelet transform, computational error, computational duration, strong ground motion data

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Authors: Abdullah Al Faruque, Ram Balachandar

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Series of laboratory tests were carried out to study the extent of scour caused by a three-dimensional wall jets exiting from a square cross-section nozzle and into a non-cohesive sand beds. Previous observations have indicated that the effect of the tailwater depth was significant for densimetric Froude number greater than ten. However, the present results indicate that the cut off value could be lower depending on the value of grain size-to-nozzle width ratio. Numbers of equations are drawn out for a better scaling of numerous scour parameters. Also suggested the empirical prediction of scour to predict the scour centre line profile and plan view of scour profile at any particular time.

Keywords: densimetric froude number, jets, nozzle, sand, scour, tailwater, time

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Authors: N. Boshkova, K. Kamburova, N. Tabakova, N. Boshkov, Ts. Radeva

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Coatings based on polyaniline (PANI) can improve the resistance of steel against corrosion. In this work, the preparation of stable suspensions of colloidal PANI-SiO₂ particles, suitable for obtaining of composite anticorrosive coating on steel, is described. Electrokinetic data as a function of pH are presented, showing that the zeta potentials of the PANI-SiO₂ particles are governed primarily by the charged groups at the silica oxide surface. Electrosteric stabilization of the PANI-SiO₂ particles’ suspension against aggregation is realized at pH>5.5 (EB form of PANI) by adsorption of positively charged polyelectrolyte molecules onto negatively charged PANI-SiO₂ particles. The PANI-SiO₂ particles are incorporated by electrodeposition into the metal matrix of zinc in order to obtain composite (hybrid) coatings. The latter are aimed to ensure sacrificial protection of steel mainly in aggressive media leading to local corrosion damages. The surface morphology of the composite zinc coatings is investigated with SEM. The influence of PANI-SiO₂ particles on the cathodic and anodic processes occurring in the starting electrolyte for obtaining of the coatings is followed with cyclic voltammetry. The electrochemical and corrosion behavior is evaluated with potentiodynamic polarization curves and polarization resistance measurements. The beneficial effect of the stabilized PANI-SiO₂ particles for the increased protective ability of the composites is commented and discussed.

Keywords: corrosion, polyaniline-silica particles, zinc, protective ability

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Authors: Misbah Khan, Jian Wen, Muhammad Asif Shakoori

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The Organic Rankine Cycle (ORC) plays an important role in converting low-temperature heat sources into electrical power by using refrigerants as working fluids. The thermophysical properties of working fluids are essential for designing ORC. HFO-1336mzz(Z) (cis-1,1,1,4,4,4-hexafluoro-2-butene) considered as working fluid and have almost 99% low GWP and relatively same thermophysical properties used as a replacement of HFC-245fa (1,1,1,3,3-pentafluoro-propane). The environmental, safety, healthy and thermophysical properties of HFO-1336mzz(Z) are needed to use it in a practical system. In this paper, Molecular dynamics simulations were used to investigate the Homogeneous condensation, thermophysical and structural properties of HFO-1336mzz(Z) and HFC-245fa. The effect of various temperatures and pressures on thermophysical properties and condensation was extensively investigated. The liquid densities and isobaric heat capacities of this refrigerant was simulated at 273.15K to 353.15K temperatures and pressure0.5-4.0MPa. The simulation outcomes were compared with experimental data to validate our simulation method. The mean square displacement for different temperatures was investigated for dynamical analysis. The variations in potential energies and condensation rate were simulated to get insight into the condensation process. The radial distribution function was simulated at the micro level for structural analysis and revealed that the phase transition of HFO-1336mzz(Z) did not affect the intramolecular structure.

Keywords: homogenous condensation, refrigerants, molecular dynamics simulations, organic rankine cycle

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Authors: Jiaqi Dong, Qing-Hua Qin, Yi Xiao

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Some of the most fundamental challenges of elastic metamaterials (EMMs) optimization can be attributed to the high consumption of computational power resulted from finite element analysis (FEA) simulations that render the optimization process inefficient. Furthermore, due to the inherent mesh dependence of FEA, minuscule geometry features, which often emerge during the later stages of optimization, induce very fine elements, resulting in enormously high time consumption, particularly when repetitive solutions are needed for computing the objective function. In this study, a surrogate modelling algorithm is developed to reduce computational time in structural optimization of EMMs. The surrogate model is constructed based on a multilayer feedforward artificial neural network (ANN) architecture, trained with prepopulated eigenfrequency data prepopulated from FEA simulation and optimized through regime selection with genetic algorithm (GA) to improve its accuracy in predicting the location and width of the primary elastic band gap. With the optimized ANN surrogate at the core, a Nelder-Mead (NM) algorithm is established and its performance inspected in comparison to the FEA solution. The ANNNM model shows remarkable accuracy in predicting the band gap width and a reduction of time consumption by 47%.

Keywords: artificial neural network, machine learning, mechanical metamaterials, Nelder-Mead optimization

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Authors: Mahmoud Ahmed Farrag, Ali Zain Elabdeen Heikal, Mohamed Shawky Ahmed, Ahmed Osama Amer

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Crowdsourced data refers to data that is collected and shared by a large number of individuals or organizations, often through the use of digital technologies such as mobile devices and social media. The shortage in crash data collection in developing countries makes it difficult to fully understand and address road safety issues in these regions. In developing countries, crowdsourced data can be a valuable tool for improving road safety, particularly in urban areas where the majority of road crashes occur. This study is the first to develop safety performance functions using crowdsourced data by adopting a negative binomial structure model and Full Bayes model to investigate traffic safety for urban road networks and provide insights into the impact of roadway characteristics. Furthermore, as a part of the safety management process, network screening has been undergone through applying two different methods to rank the most hazardous road segments: PCR method (adopted in the Highway Capacity Manual HCM) as well as a graphical method using GIS tools to compare and validate. Lastly, recommendations were suggested for policymakers to ensure safer roads.

Keywords: crowdsourced data, road crashes, safety performance functions, Full Bayes models, network screening

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Authors: Romeu Bonk Mesquita

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Argentina is the South American number 1 country of destiny to intraregional migration flows. This research aims to shed light on the main trends of the Argentine immigrant policy from 2016 on, when Mauricio Marci was elected President, taking the approval of the current and fairly protective of human rights Ley de Migraciones (2003) as an analytical starting point. Foreign Policy Analysis (FPA) serves as the theoretical background, highlighting decision-making processes and institutional designs that encourage or constraint political and social actors. The analysis goes through domestic and international levels, observing how immigration policy is formulated as a public policy and is simultaneously connected to Mercosur and other international organizations, such as the International Organization for Migration (IOM) and the United Nations High Commissioner for Refugees (UNHCR). Thus, the study revolves around the Direccion Nacional de Migraciones, which is the state agency in charge of executing the country’s immigrant policy, as to comprehend how its internal processes and the connections it has with both domestic and international institutions shape Argentina’s immigrant policy formulation and execution. Also, it aims to locate the migration agenda within the country’s contemporary social and political context. The methodology is qualitative, case-based and oriented by process-tracing techniques. Empirical evidence gathered includes official documents and data, media coverage and interviews to key-informants. Recent events, such as the Decreto de Necesidad y Urgencia 70/2017 issued by President Macri, and the return of discursive association between migration and criminality, indicate a trend of nationalization and securitization of the immigration policy in contemporary Argentina.

Keywords: Argentine foreign policy, human rights, immigrant policy, Mercosur

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Authors: Apurva Saxena, Sunita Gond

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Demand of computation, data storage in large amount are rapidly increases day by day. Cloud computing technology fulfill the demand of today’s computation but this will lead to high power consumption in cloud data centers. Initiative for Green IT try to reduce power consumption and its adverse environmental impacts. Paper also focus on various green computing techniques, proposed models and efficient way to make cloud greener.

Keywords: virtualization, cloud computing, green computing, data center

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Authors: S. Suman, G. N. Singh

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The present manuscript addresses the estimation procedure of population parameter using Poisson probability distribution when characteristic under study possesses a rare sensitive attribute. The generalized form of unrelated randomized response model is suggested in order to acquire the truthful responses from respondents. The resultant estimators have been proposed for two situations when the information on an unrelated rare non-sensitive characteristic is known as well as unknown. The properties of the proposed estimators are derived, and the measure of confidentiality of respondent is also suggested for respondents. Empirical studies are carried out in the support of discussed theory.

Keywords: Poisson distribution, randomized response model, rare sensitive attribute, non-sensitive attribute

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Authors: Xinyi Le

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In this paper, 3D printing orientations were determined through our perceptual model. Some FDM (Fused Deposition Modeling) 3D printers, which are widely used in universities and industries, often require support structures during the additive manufacturing. After removing the residual material, some surface artifacts remain at the contact points. These artifacts will damage the function and visual effect of the model. To prevent the impact of these artifacts, we present a fuzzy extreme learning machine approach to find printing directions that avoid placing supports in perceptually significant regions. The proposed approach is able to solve the evaluation problem by combing both the subjective knowledge and objective information. Our method combines the advantages of fuzzy theory, auto-encoders, and extreme learning machine. Fuzzy set theory is applied for dealing with subjective preference information, and auto-encoder step is used to extract good features without supervised labels before extreme learning machine. An extreme learning machine method is then developed successfully for training and learning perceptual models. The performance of this perceptual model will be demonstrated on both natural and man-made objects. It is a good human-computer interaction practice which draws from supporting knowledge on both the machine side and the human side.

Keywords: 3d printing, perceptual model, fuzzy evaluation, data-driven approach

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Authors: Vesna Karapetkovska Hristova, M. Ayaz Ahmad, Julijana Tomovska, Biljana Bogdanova Popov, Blagojce Najdovski

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The average total concentrations of heavy metals; (cadmium [Cd], copper [Cu], nickel [Ni], lead [Pb], and zinc [Zn]) were analyzed in soil and vegetables samples collected from the different region of Macedonia during the years 2010-2012. Basic soil properties such as pH, organic matter and clay content were also included in the study. The average concentrations of Cd, Cu, Ni, Pb, Zn in the A horizon (0-30 cm) of agricultural soils were as follows, respectively: 0.25, 5.3, 6.9, 15.2, 26.3 mg kg-1 of soil. We have found that neural networking model can be considered as a tool for prediction and spatial analysis of the processes controlling the metal transfer within the soil-and vegetables. The predictive ability of such models is well over 80% as compared to 20% for typical regression models. A radial basic function network reflects good predicting accuracy and correlation coefficients between soil properties and metal content in vegetables much better than the back-propagation method. Neural Networking / soft computing can support the decision-making processes at different levels, including agro ecology, to improve crop management based on monitoring data and risk assessment of metal transfer from soils to vegetables.

Keywords: soft computing approach, total concentrations, heavy metals, agricultural soils

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Authors: Lisete Barlach, Guilherme Ary Plonski

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Innovative ideas are not always synonymous with business opportunities. Any idea can be creative and not recognized as a potential project in which money and time will be invested, among other resources. Even in firms that promote and enhance innovation, there are two 'check-points', the first corresponding to the acknowledgment of the idea as creative and the second, its consideration as a business opportunity. Both the recognition of new business opportunities or new ideas involve cognitive and psychological frameworks which provide individuals with a basis for noticing connections between seemingly independent events or trends as if they were 'connecting the dots'. It also involves prototypes-representing the most typical member of a certain category–functioning as 'templates' for this recognition. There is a general assumption that these kinds of evaluation processes develop through experience, explaining why expertise plays a central role in this process: the more experienced a professional, the easier for him (her) to identify new opportunities in business. But, paradoxically, an increase in expertise can lead to the inflexibility of thought due to automation of procedures. And, besides this, other cognitive biases can also be present, because new ideas or business opportunities generally depend on heuristics, rather than on established algorithms. The paper presents a literature review about the Einstellung effect by tracking famous cases of rejected ideas, extracted from historical records. It also presents the results of empirical research, with data upon rejected ideas gathered from two different environments: projects rejected during first semester of 2017 at a large incubator center in Sao Paulo and ideas proposed by employees that were rejected by a well-known business company, at its Brazilian headquarter. There is an implicit assumption that Einstellung effect tends to be more and more present in contemporaneity, due to time pressure upon decision-making and idea generation process. The analysis discusses desirability, viability, and feasibility as elements that affect decision-making.

Keywords: cognitive biases, Einstellung effect, recognition of business opportunities, rejected ideas

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Authors: Dmitry Yu. Korulkin, Raissa A. Muzychkina, Evgenii N. Kojaev

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In review the generalized data about biological activity of anthraquinone-containing plants and specimens on their basis is presented. Data of traditional medicine, results of bioscreening and clinical researches of specimens are analyzed.

Keywords: anthraquinones, physiologically active substances, phytopreparation, Ramon

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Authors: Lars Laußat, Manfred Helmus, Kamil Szczesny, Markus König

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As one result of the project “Reactive Construction Project Scheduling using Real Time Construction Logistic Data and Simulation”, a procedure for using data about uncertain resource availability assumptions in reactive scheduling processes has been developed. Prediction data about resource availability is generated in a formalized way using real-time monitoring data e.g. from auto-ID systems on the construction site and in the supply chains. The paper focuses on the formalization of the procedure for monitoring construction logistic processes, for the detection of disturbance and for generating of new and uncertain scheduling assumptions for the reactive resource constrained simulation procedure that is and will be further described in other papers.

Keywords: auto-ID, construction logistic, fuzzy, monitoring, RFID, scheduling

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Authors: Agim Kukelii, Gevorg Sargsyan

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This study, focusing on the base regional area (county level), estimates the effect of natural disaster damages on aggregate personal income, aggregate wages, wages per worker, aggregate employment, and aggregate income transfer. The study further estimates the dynamics of personal income, employment, and wages under natural disaster shocks. Southeast Texas, located at the center of Golf Coast, is hit by meteorological and hydrological caused natural disasters yearly. On average, there are more than four natural disasters per year that cane an estimated damage average of 2.2% of real personal income. The study uses the panel data method to estimate the average effect of natural disasters on the area’s economy (personal income, wages, employment, and income transfer). It also uses Panel Vector Autoregressive (PVAR) model to study the dynamics of macroeconomic variables under natural disaster shocks. The study finds that the average effect of natural disasters is positive for personal income and income transfer and is negative for wages and employment. The PVAR and the impulse response function estimates reveal that natural disaster shocks cause a decrease in personal income, employment, and wages. However, the economy’s variables bounce back after three years. The novelty of this study rests on several aspects. First, this is the first study to investigate the effects of natural disasters on macroeconomic variables at a regional level. Second, the study uses direct measures of natural disaster damages. Third, the study estimates that the time that the local economy takes to absorb the natural disaster damages shocks is three years. This is a relatively good reaction to the local economy, therefore, adding to the “resiliency” hypothesis. The study has several implications for policymakers, businesses, and households. First, this study serves to increase the awareness of local stakeholders that natural disaster damages do worsen, macroeconomic variables, such as personal income, employment, and wages beyond the immediate damages to residential and commercial properties, physical infrastructure, and discomfort in daily lives. Second, the study estimates that these effects linger on the economy on average for three years, which would require policymakers to factor in the time area need to be on focus.

Keywords: natural disaster damages, macroeconomics effects, PVAR, panel data

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Authors: Ajish Sreedharan

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With the fast evolution of digital data exchange, security information becomes much important in data storage and transmission. Due to the increasing use of images in industrial process, it is essential to protect the confidential image data from unauthorized access. As encryption process is applied to the whole image in AES ,it is difficult to improve the efficiency. In this paper, wavelet decomposition is used to concentrate the main information of image to the low frequency part. Then, AES encryption is applied to the low frequency part. The high frequency parts are XORed with the encrypted low frequency part and a wavelet reconstruction is applied. Theoretical analysis and experimental results show that the proposed algorithm has high efficiency, and satisfied security suits for image data transmission.

Keywords: discrete wavelet transforms, AES, dynamic SBox

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Authors: Jae Bok Heo, Yun Mi Lee, Hee Rang Yun

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Several GTPases are required for ribosome biogenesis and assembly. We recently characterized rice (Oryza sativa) nuclear/nucleolar GTPase 2 (OsNug2), belonging to the YlqF/YawG family of GTPases, as playing a role in pre-60S ribosomal subunit maturation. To investigate the potential factors involved in regulating the function of OsNug2, yeast two-hybrid screens were carried out using OsNug2 as bait. Rice serine/threonine kinase 1 (OsSTK1) was identified as a potential interacting protein candidate. In vitro pull down and bimolecular fluorescence complementation assays confirmed the interaction between OsNug2 and OsSTK1, and like green fluorescent protein-tagged OsNug2, green fluorescent protein-tagged OsSTK1 was targeted to the nucleus of Arabidopsis protoplasts. OsSTK1 was not found to affect the GTP-binding activity of OsNug2; however, when recombinant OsSTK1 was included in OsNug2 assay reaction mixtures, OsSTK1 increased the GTPase activity of OsNug2. To test whether OsSTK1 phosphorylates OsNug2 in vitro, a kinase assay was performed. OsSTK1 was found to have weak autophosphorylation activity and strongly phosphorylated serine 209 of OsNug2. Yeast complementation testing resulted in a GAL::OsNug2(S209N) mutant-harboring yeast strain exhibiting a growth-defective phenotype on galactose medium at 39°C, divergent from that of a yeast strain harboring GAL::OsNug2. The intrinsic GTPase activity of mutant OsNug2(S209N) was found to be similar to that of OsNug2, was not fully enhanced upon weak binding of OsSTK1. Our findings reported here indicate that OsSTK1 functions as a positive regulator protein of OsNug2 by enhancing the GTPase activity of OsNug2, and that the phosphorylation of serine 209 of OsNug2 is essential for the complete function of OsNug2 in ribosome biogenesis.

Keywords: OsSTK1, OsNug2, GTPase activity, GTP binding activity, phosphorylation

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Authors: Mohammed Alassim

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In 2014, a UN survey stated that: "The greatest challenge to the adoption of whole-of government, which fundamentally rests on increased collaboration, is resistance to change among government actors". Change management has experienced both theoretically and practically many transformation over the years. When organizations have to implement radical changes, they have to encounter a plethora of issues which leads to ineffective or inefficient implementation of change in most cases. 70% of change projects fail because of human issues. It has been cited that” most studies still show a 60-70% failure rate for organizational change projects — a statistic that has stayed constant from the 1970’s to the present.”. E-government involves not just technical change but cultural, policy, social and organizational evolution. Managing change and overcoming resistance to change is seen as crucial in the success of E-government projects. Resistance can be from different levels in the organization (top management, middle management or employees at operational levels). There can be many reasons for resistance including fear of change and insecurity, lack of knowledge and absence of commitment from management to implement the change. The purpose of this study is to conduct in-depth research to understand the process of change and to identify the critical factors that have led to resistance from employees at different levels (top management, Middle management and operational employees) during e-government initiatives in the public sector in Saudi Arabia. The study is based on qualitative and empirical research methods conducted in the public sector in the Kingdom of Saudi Arabia. This research will use triangulation in data method (interview, group discussion and document review). This research will contribute significantly to knowledge in this field and will identify the measures that can be taken to reduce resistance to change, Upon analysis recommendations or model will be offered which can enable decision makers in public sector in Saudi Arabia how to plan, implement and evaluate change in e-government initiatives via change management strategy.

Keywords: change management, e-government, managing change, resistance to change

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Authors: John Hardy

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Over the last decade, the increasing utility of extremist narratives to the operational effectiveness of terrorist organizations has been evidenced by the proliferation of inspired or affiliated attacks across the world. Famous examples such as regional al-Qaeda affiliates and the self-styled “Islamic State” demonstrate the effectiveness of leveraging communication technologies to disseminate propaganda, recruit members, and orchestrate attacks. Terrorist organizations with the capacity to harness the communicative power offered by digital communication technologies and effective political narratives have held an advantage over their targets in recent years. Terrorists have leveraged the perceived legitimacy of grass-roots actors to appeal to a global audience of potential supporters and enemies alike, and have wielded a proficiency in profile-raising which remains unmatched by counter terrorism narratives around the world. In contrast, many attempts at propagating official counter-narratives have been received by target audiences as illegitimate, top-down and impersonally bureaucratic. However, the benefits provided by widespread communication and extremist narratives have come at an operational cost. Terrorist organizations now face a significant challenge in protecting their access to communications technologies and authority over the content they create and endorse. The dissemination of effective narratives has emerged as a core function of terrorist organizations with international reach via inspired or affiliated attacks. As such, it has become a critical function which can be targeted by intelligence and security forces. This study applies network targeting principles which have been used by coalition forces against a range of non-state actors in the Middle East and South Asia to the communicative function of terrorist organizations. This illustrates both a conceptual link between functional targeting and operational disruption in the abstract and a tangible impact on the operational effectiveness of terrorists by degrading communicative ability and legitimacy. Two case studies highlight the utility of applying functional targeting against terrorist organizations. The first case is the targeted killing of Anwar al-Awlaki, an al-Qaeda propagandist who crafted a permissive narrative and effective propaganda videos to attract recruits who committed inspired terrorist attacks in the US and overseas. The second is a series of operations against Islamic State propagandists in Syria, including the capture or deaths of a cadre of high profile Islamic State members, including Junaid Hussain, Abu Mohammad al-Adnani, Neil Prakash, and Rachid Kassim. The group of Islamic State propagandists were linked to a significant rise in affiliated and enabled terrorist attacks and were subsequently targeted by law enforcement and military agencies. In both cases, the disruption of communication between the terrorist organization and recruits degraded both communicative and operational functions. Effective functional targeting on member recruitment and operational tempo suggests that narratives are a critical function which can be leveraged against terrorist organizations. Further application of network targeting methods to terrorist narratives may enhance the efficacy of a range of counter terrorism techniques employed by security and intelligence agencies.

Keywords: countering violent extremism, counter terrorism, intelligence, terrorism, violent extremism

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Authors: Taras Agryzkov, Almudena Nolasco-Cirugeda, Jose L. Oliver, Leticia Serrano-Estrada, Leandro Tortosa, Jose F. Vicent

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This paper aims to represent the commercial activity of a city taking as source data the social network Foursquare. The city of Murcia is selected as case study, and the location-based social network Foursquare is the main source of information. After carrying out a reorganisation of the user-generated data extracted from Foursquare, it is possible to graphically display on a map the various city spaces and venues –especially those related to commercial, food and entertainment sector businesses. The obtained visualisation provides information about activity patterns in the city of Murcia according to the people`s interests and preferences and, moreover, interesting facts about certain characteristics of the town itself.

Keywords: social networks, spatial analysis, data visualization, geocomputation, Foursquare

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Authors: Kaznah Alshammari, Thomas Beach, Yacine Rezgui

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Technological advances in the construction sector are helping to make smart cities a reality by means of cyber-physical systems (CPS). CPS integrate information and the physical world through the use of information communication technologies (ICT). An increasingly common goal in the built environment is to integrate building information models (BIM) with the Internet of Things (IoT) and sensor technologies using CPS. Future advances could see the adoption of digital twins, creating new opportunities for CPS using monitoring, simulation, and optimisation technologies. However, researchers often fail to fully consider the security implications. To date, it is not widely possible to assimilate BIM data and cybersecurity concepts, and, therefore, security has thus far been overlooked. This paper reviews the empirical literature concerning IoT applications in the built environment and discusses real-world applications of the IoT intended to enhance construction practices, people’s lives and bolster cybersecurity. Specifically, this research addresses two research questions: (a) how suitable are the current IoT and CPS security stacks to address the cybersecurity threats facing digital twins in the context of smart buildings and districts? and (b) what are the current obstacles to tackling cybersecurity threats to the built environment CPS? To answer these questions, this paper reviews the current state-of-the-art research concerning digital twins in the built environment, the IoT, BIM, urban cities, and cybersecurity. The results of these findings of this study confirmed the importance of using digital twins in both IoT and BIM. Also, eight reference zones across Europe have gained special recognition for their contributions to the advancement of IoT science. Therefore, this paper evaluates the use of digital twins in CPS to arrive at recommendations for expanding BIM specifications to facilitate IoT compliance, bolster cybersecurity and integrate digital twin and city standards in the smart cities of the future.

Keywords: BIM, cybersecurity, digital twins, IoT, urban cities

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Authors: Pavani K. Sree, Ragahava Reddy Chandri

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Independent living and living with dignity have been the hallmarks of the movement of the persons with disabilities across the globe against the oppression perpetuated by society in the form of social and physical structural barriers. Advancements in assistive technologies have been providing a new lease of life to persons with disabilities. However, access to these technologies is marred by the issues of affordability and availability. Poor from the developing countries find it difficult to make independent living or live with dignity because of lack of access and inability to afford the advance technologies. Class and gender appear to be key factors influencing the access to modern assistive technologies. The present paper attempts to understand the dynamics of class and gender in accessing advanced technologies in the Indian context. Based on an empirical study in which data were collected from persons with locomotor disabilities and service providers, the paper finds that the advance technologies are expensive and inaccessible to all persons with disabilities. The paper also finds that men with disabilities are prioritized by the members of the family for the use of advance technologies while women with disabilities are forced to live with not so advanced technologies. The paper finds that the state institutions working in the field of prosthetics and assistive technologies fail to deliver to the requirements of the poor. It was found that because of lack of facilities at the state institutions the cost of prosthetics, in the case of orthopedically challenged, is expensive and unaffordable for the poor. It was found that while rich male access the private services the poor women depend on the state institutions. It may be said that the social, cultural stereotypes extend not only to the state organizations but also to the use of prosthetics. Thus the notions of independent living and living with dignity in third world countries context are still elusive.

Keywords: accessibility, assistive technology, class, gender, state

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Authors: Teklu Urgessa, Wookjae Maeng, Joong Seek Lee

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Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms.

Keywords: classification algorithms, data mining, knowledge discovery, tourism

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Authors: Ayşegül Çabuk, Mine Işıksal

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Mathematics is a sense-making activity so that it requires meaningful learning. Hence based on this idea, meaningful mathematical connections are necessary to learn mathematics. At that point, the major question has become that which educational methods can provide opportunities to provide mathematical connections and to understand mathematics. The amalgam of reasoning and proof can be the one of the methods that creates opportunities to learn mathematics in a meaningful way. However, even if reasoning and proof should be included from prekindergarten to grade 12, studies in literature generally include secondary school students and pre-service mathematics teachers. With the light of the idea that the amalgam of reasoning and proof has significant effect on middle school students' mathematical learning, this study aims to investigate middle grade students' tendencies while reasoning the correctness of statements related to numbers. The sample included 272 middle grade students, specifically 69 of them were sixth grade students (25.4%), 101 of them were seventh grade students (37.1%) and 102 of them were eighth grade students (37.5%). Data was gathered through an achievement test including 2 essay types of problems about algebra. The answers of two items were analyzed both quantitatively and qualitatively in terms of students' solutions strategies while reasoning the correctness of the statements. Similar on the findings in the literature, most of the students, in all grade levels, used numerical examples to judge the statements. Moreover the results also showed that the majority of these students appear to believe that providing one or more selected examples is sufficient to show the correctness of the statement. Hence based on the findings of the study, even students in earlier ages have proving and reasoning abilities their reasoning's generally based on the empirical evidences. Therefore, it is suggested that examples and example-based reasoning can be a fundamental role on to generate systematical reasoning and proof insight in earlier ages.

Keywords: reasoning, mathematics learning, middle grade students

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Authors: Anna M. Pretorius

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The emerging markets category for portfolio investment was introduced in 1986 in an attempt to promote capital market development in less developed countries. Investors traditionally diversified their portfolios by investing in different developed markets. However, high growth opportunities forced investors to consider emerging markets as well. Examples include the rapid growth of the “Asian Tigers” during the 1980s, growth in Latin America during the 1990s and the increased interest in emerging markets during the global financial crisis. As such, portfolio flows to emerging markets have increased substantially. In 2002 7% of all equity allocations from advanced economies went to emerging markets; this increased to 20% in 2012. The stronger links between advanced and emerging markets led to increased synchronization of asset price movements. This increased level of stock market integration for emerging markets is confirmed by various empirical studies. Against the background of increased interest in emerging market assets and the increasing level of integration of emerging markets, this paper focuses on the determinants of stock market integration of emerging market countries. Various studies have linked the level of financial market integration with specific economic variables. These variables include: economic growth, local inflation, trade openness, local investment, budget surplus/ deficit, market capitalization, domestic bank credit, domestic institutional and legal environment and world interest rates. The aim of this study is to empirically investigate to what extent trade-related determinants have an impact on stock market integration. The panel data sample include data of 16 emerging market countries: Brazil, Chile, China, Colombia, Czech Republic, Hungary, India, Malaysia, Pakistan, Peru, Philippines, Poland, Russian Federation, South Africa, Thailand and Turkey for the period 1998-2011. The integration variable for each emerging stock market is calculated as the explanatory power of a multi-factor model. These factors are extracted from a large panel of global stock market returns. Trade related explanatory variables include: exports as percentage of GDP, imports as percentage of GDP and total trade as percentage of GDP. Other macroeconomic indicators – such as market capitalisation, the size of the budget deficit and the effectiveness of the regulation of the securities exchange – are included in the regressions as control variables. An initial analysis on a sample of developed stock markets could not identify any significant determinants of stock market integration. Thus the macroeconomic variables identified in the literature are much more significant in explaining stock market integration of emerging markets than stock market integration of developed markets. The three trade variables are all statistically significant at a 5% level. The market capitalisation variable is also significant while the regulation variable is only marginally significant. The global financial crisis has highlighted the urgency to better understand the link between the financial and real sectors of the economy. This paper comes to the important finding that, apart from the level of market capitalisation (as financial indicator), trade (representative of the real economy) is a significant determinant of stock market integration of countries not yet classified as developed economies.

Keywords: emerging markets, financial market integration, panel data, trade

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Authors: Lucie Mrackova, Helena Kisvetrova

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Spirituality is an integral part of human life. In a secular environment, spiritual needs are often overlooked, especially in acute nursing care. Spiritual needs assessment for patients (SNAP), which also exists in the Czech version (SNAP-CZ), can be used for objective evaluation. The aim of this study was to measure the psychometric properties of SNAP-CZ and to find correlations between SNAP-CZ and sociodemographic and clinical variables. A cross-sectional study with tools assessing spiritual needs (SNAP-CZ), anxiety (Beck Anxiety Inventory; BAI), depression (Beck Depression Inventory; BDI), pain (Visual Analogue Scale; VAS), self-sufficiency (Barthel Index; BI); cognitive function (Montreal Cognitive Test; MoCa) and selected socio-demographic data was performed. The psychometric properties of SNAP-CZ were tested using factor analysis, reliability and validity tests, and correlations between the questionnaire and sociodemographic data and clinical variables. Internal consistency was established with Cronbach’s alfa for the overall score, respective domains, and individual items. Reliability was assessed by test-retest by Interclass correlation coefficient (ICC). Data for correlation analysis were processed according to Pearson's correlation coefficient. The study included 172 trauma patients (the mean age = 40.6 ± 12.1 years) who experienced polytrauma or severe monotrauma. There were a total of 106 (61.6%) male subjects, 140 (81.4%) respondents identified themselves as non-believers. The full-scale Cronbach's alpha was 0.907. The test-retest showed the reliability of the individual domains in the range of 0.924 to 0.960 ICC. Factor analysis resulted in a three-factor solution (psychosocial needs (alfa = 0.788), spiritual needs (alfa = 0.886) and religious needs (alfa = 0.841)). Correlation analysis using Pearson's correlation coefficient showed that the domain of psychosocial needs significantly correlated only with gender (r = 0.178, p = 0.020). Males had a statistically significant lower average value in this domain (mean = 12.5) compared to females (mean = 13.8). The domain of spiritual needs significantly correlated with gender (r = 0.199, p = 0.009), social status (r = 0.156, p = 0.043), faith (r = -0.250, p = 0.001), anxiety (r = 0.194, p = 0.011) and depression (r = 0.155, p = 0.044). The domain of religious needs significantly correlated with age (r = 0,208, p = 0,007), education (r = -0,161, p = 0,035), faith (r = -0,575, p < 0,0001) and depression (r = 0,179, p = 0,019). Overall, the whole SNAP scale significantly correlated with gender (r = 0.219, p = 0.004), social status (r = 0.175, p = 0.023), faith (r = -0.334, p <0.0001), anxiety (r = 0.177, p = 0.022) and depression (r = 0.173, p = 0.025). The results of this study corroborate the reliability of the SNAP-CZ and support its future use in the nursing care of trauma patients in a secular society. Acknowledgment: The study was supported by grant nr. IGA_FZV_2020_003.

Keywords: acute nursing care, assessment of spiritual needs, patient, psychometric validation, spirituality

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Authors: T. Thulare, M. Herselman, A. Botha

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Poor system use, including inappropriate design of health information systems, causes difficulties in communication with patients and increased time spent by healthcare professionals in recording the necessary health information for medical records. System features like pop-up reminders, complex menus, and poor user interfaces can make medical records far more time consuming than paper cards as well as affect decision-making processes. Although errors associated with health information and their real and likely effect on the quality of care and patient safety have been documented for many years, more research is needed to measure the occurrence of these errors and determine the causes to implement solutions. Therefore, the purpose of this paper is to identify data integrity challenges in hospital information systems through a scoping review and based on the results provide recommendations on how to manage these. Only 34 papers were found to be most suitable out of 297 publications initially identified in the field. The results indicated that human and computerized systems are the most common challenges associated with data integrity and factors such as policy, environment, health workforce, and lack of awareness attribute to these challenges but if measures are taken the data integrity challenges can be managed.

Keywords: data integrity, data integrity challenges, hospital information systems, South Africa

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Authors: Shoujia Fang, Guoqing Ding, Xin Chen

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The quality of press-fit assembly is closely related to reliability and safety of product. The paper proposed a keypoint detection method based on convolutional neural network to improve the accuracy of keypoint detection in press-fit curve. It would provide an auxiliary basis for judging quality of press-fit assembly. The press-fit curve is a curve of press-fit force and displacement. Both force data and distance data are time-series data. Therefore, one-dimensional convolutional neural network is used to process the press-fit curve. After the obtained press-fit data is filtered, the multi-layer one-dimensional convolutional neural network is used to perform the automatic learning of press-fit curve features, and then sent to the multi-layer perceptron to finally output keypoint of the curve. We used the data of press-fit assembly equipment in the actual production process to train CNN model, and we used different data from the same equipment to evaluate the performance of detection. Compared with the existing research result, the performance of detection was significantly improved. This method can provide a reliable basis for the judgment of press-fit quality.

Keywords: keypoint detection, curve feature, convolutional neural network, press-fit assembly

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Authors: Nouf Alourfi

Abstract:

This study examines the metabolism of amitriptyline (AMI) and verapamil (VER) using a KNIME-based method. KNIME improved workflow is an open-source data-analytics platform that integrates a number of open-source metabolomics tools such as CFMID and MetFrag to provide standard data visualisations, predict candidate metabolites, assess them against experimental data, and produce reports on identified metabolites. The use of this workflow is demonstrated by employing three types of liver microsomes (human, rat, and Guinea pig) to study the in vitro metabolism of the two drugs (AMI and VER). This workflow is used to create and treat UPLC-MS (Orbitrap) data. The formulas and structures of these drugs' metabolites can be assigned automatically. The key metabolic routes for amitriptyline are hydroxylation, N-dealkylation, N-oxidation, and conjugation, while N-demethylation, O-demethylation and N-dealkylation, and conjugation are the primary metabolic routes for verapamil. The identified metabolites are compatible to the published, clarifying the solidity of the workflow technique and the usage of computational tools like KNIME in supporting the integration and interoperability of emerging novel software packages in the metabolomics area.

Keywords: KNIME, CFMID, MetFrag, Data Analysis, Metabolomics

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Authors: Prasad Kalagura, B. Veeresh kumar

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We constructed an interface system that would allow a similarly paralyzed user to interact with a computer with almost full functional capability. A real-time tracking algorithm is implemented based on adaptive skin detection and motion analysis. The clicking of the mouse is activated by the user's eye blinking through a sensor. The keyboard function is implemented by voice recognition kit.

Keywords: embedded ARM7 processor, mouse pointer control, voice recognition

Procedia PDF Downloads 568