Search results for: heterogeneous massive data

Authors: Caspar Goeke, Holger Finger, Dorena Diekamp, Peter König

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Online-based research has recently gained increasing attention from various fields of research in the cognitive sciences. Technological advances in the form of online crowdsourcing (Amazon Mechanical Turk), open data repositories (Open Science Framework), and online analysis (Ipython notebook) offer rich possibilities to improve, validate, and speed up research. However, until today there is no cross-platform integration of these subsystems. Furthermore, implementation of online studies still suffers from the complex implementation (server infrastructure, database programming, security considerations etc.). Here we propose and test a new JavaScript framework that enables researchers to conduct any kind of behavioral research in the browser without the need to program a single line of code. In particular our framework offers the possibility to manipulate and combine the experimental stimuli via a graphical editor, directly in the browser. Moreover, we included an action-event system that can be used to handle user interactions, interactively change stimuli properties or store participants’ responses. Besides traditional recordings such as reaction time, mouse and keyboard presses, the tool offers webcam based eye and face-tracking. On top of these features our framework also takes care about the participant recruitment, via crowdsourcing platforms such as Amazon Mechanical Turk. Furthermore, the build in functionality of google translate will ensure automatic text translations of the experimental content. Thereby, thousands of participants from different cultures and nationalities can be recruited literally within hours. Finally, the recorded data can be visualized and cleaned online, and then exported into the desired formats (csv, xls, sav, mat) for statistical analysis. Alternatively, the data can also be analyzed online within our framework using the integrated Ipython notebook. The framework was designed such that studies can be used interchangeably between researchers. This will support not only the idea of open data repositories but also constitutes the possibility to share and reuse the experimental designs and analyses such that the validity of the paradigms will be improved. Particularly, sharing and integrating the experimental designs and analysis will lead to an increased consistency of experimental paradigms. To demonstrate the functionality of the framework we present the results of a pilot study in the field of spatial navigation that was conducted using the framework. Specifically, we recruited over 2000 subjects with various cultural backgrounds and consequently analyzed performance difference in dependence on the factors culture, gender and age. Overall, our results demonstrate a strong influence of cultural factors in spatial cognition. Such an influence has not yet been reported before and would not have been possible to show without the massive amount of data collected via our framework. In fact, these findings shed new lights on cultural differences in spatial navigation. As a consequence we conclude that our new framework constitutes a wide range of advantages for online research and a methodological innovation, by which new insights can be revealed on the basis of massive data collection.

Keywords: cultural differences, crowdsourcing, JavaScript framework, methodological innovation, online data collection, online study, spatial cognition

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Authors: Maria Laura Alzua, Maria Adelaida Lopera

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This paper measures the relative importance of conditional cooperation and unconditional cooperation in a large public goods experiment conducted in Mali. We use expectations about total public goods provision to estimate a structural choice model with heterogeneous preferences. While unconditional cooperation can be captured by common preferences shared by all participants, conditional cooperation is much more heterogeneous and depends on unobserved individual factors. This structural model, in combination with two experimental treatments, suggests that leadership and group communication incentivize public goods provision through different channels. First, We find that participation of local leaders effectively changes individual choices through unconditional cooperation. A simulation exercise predicts that even in the most pessimistic scenario in which all participants expect zero public good provision, 60% would still choose to cooperate. Second, allowing participants to communicate fosters conditional cooperation. The simulations suggest that expectations are responsible for around 24% of the observed public good provision and that group communication does not necessarily ameliorate public good provision. In fact, communication may even worsen the outcome when expectations are low.

Keywords: conditional cooperation, discrete choice model, expectations, public goods game, random coefficients model

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Authors: F. Lhafiane, A. Elbyed, M. Bouchoum

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Reverse Logistics (RL) Process is considered as complex and dynamic network that involves many stakeholders such as: suppliers, manufactures, warehouse, retails, and costumers, this complexity is inherent in such process due to lack of perfect knowledge or conflicting information. Ontologies, on the other hand, can be considered as an approach to overcome the problem of sharing knowledge and communication among the various reverse logistics partners. In this paper, we propose a semantic representation based on hybrid architecture for building the Ontologies in an ascendant way, this method facilitates the semantic reconciliation between the heterogeneous information systems (ICT) that support reverse logistics Processes and product data.

Keywords: Reverse Logistics, information management, heterogeneity, ontologies, semantic web

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Authors: Amirreza Fazely Hamedani, Muzzamil Aziz, Philipp Wieder, Ramin Yahyapour

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Software-defined networking in recent years came into the sight of so many network designers as a successor to the traditional networking. Unlike traditional networks where control and data planes engage together within a single device in the network infrastructure such as switches and routers, the two planes are kept separated in software-defined networks (SDNs). All critical decisions about packet routing are made on the network controller, and the data level devices forward the packets based on these decisions. This type of network is vulnerable to DDoS attacks, degrading the overall functioning and performance of the network by continuously injecting the fake flows into it. This increases substantial burden on the controller side, and the result ultimately leads to the inaccessibility of the controller and the lack of network service to the legitimate users. Thus, the protection of this novel network architecture against denial of service attacks is essential. In the world of cybersecurity, attacks and new threats emerge every day. It is essential to have tools capable of managing and analyzing all this new information to detect possible attacks in real-time. These tools should provide a comprehensive solution to automatically detect, predict and prevent abnormalities in the network. Big data encompasses a wide range of studies, but it mainly refers to the massive amounts of structured and unstructured data that organizations deal with on a regular basis. On the other hand, it regards not only the volume of the data; but also that how data-driven information can be used to enhance decision-making processes, security, and the overall efficiency of a business. This paper presents an intelligent big data framework as a solution to handle illegitimate traffic burden on the SDN network created by the numerous DDoS attacks. The framework entails an efficient defence and monitoring mechanism against DDoS attacks by employing the state of the art machine learning techniques.

Keywords: apache spark, apache kafka, big data, DDoS attack, machine learning, SDN network

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Authors: A. Kalizhanova, T. Shelestova

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Teacher professional development (TPD) in Kazakhstan has followed a fairly standard format for centuries, with teachers learning new information from a lecturer and being tested using multiple-choice questions. In the online world, self-access courses have become increasingly popular. Due to their extensive multimedia content, peer-reviewed assignments, adaptable class times, and instruction from top university faculty from across the world, massive open online courses (MOOCs) have found a home in Kazakhstan's system for lifelong learning. Recent studies indicate the limited use of connectivism-based tools such as discussion forums by Kazakhstani pre-service and in-service English teachers, whose professional interests are limited to obtaining certificates rather than enhancing their teaching abilities and exchanging knowledge with colleagues. This paper highlights the significance of connectivism-based tools and instruments, such as MOOCs, for the continuous professional development of pre- and in-service English teachers, facilitators' roles, and their strategies for enhancing trainees' conceptual knowledge within the MOOCs' curriculum and online learning skills. Reviewing the most pertinent papers on Connectivism Theory, facilitators' function in TPD, and connectivism-based tools, such as MOOCs, a code extraction method was utilized. Three experts, former active participants in a series of projects initiated across Kazakhstan to improve the efficacy of MOOCs, evaluated the excerpts and selected the most appropriate ones to propose the matrix of teacher professional competencies that can be acquired through MOOCs. In this paper, we'll look at some of the strategies employed by course instructors to boost their students' English skills and knowledge of course material, both inside and outside of the MOOC platform. Participants' interactive learning contributed to their language and subject conceptual knowledge and prepared them for peer-reviewed assignments in the MOOCs, and this approach of small group interaction was given to highlight the outcomes of participants' interactive learning. Both formal and informal continuing education institutions can use the findings of this study to support teachers in gaining experience with MOOCs and creating their own online courses.

Keywords: connectivism-based tools, teacher professional development, massive open online courses, facilitators, Kazakhstani context

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Authors: W. T. A. Mohammed, M. Eltaeb, R. Kashani

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The first bending modes of tall asymmetric structures in the two lateral X and Y-directions have two different natural frequencies. To add tuned damping to these bending modes, one needs to either a) use two pendulum-tuned mass dampers (PTMDs) with one tuning frequency, each PTMD targeting one of the bending modes, or b) use one PTMD with two tuning frequencies (one in each lateral directions). Option (a), being more massive, requiring more space, and being more expensive, is less attractive than option (b). Considering that the tuning frequency of a pendulum depends mainly on the pendulum length, one way of realizing option (b) is by constraining the swinging length of the pendulum in one direction but not in the other; such PTMD is dubbed passive Bi-PTMD. Alternatively, option (b) can be realized by actively setting the tuning frequencies of the PTMD in the two directions. In this work, accurate physical models of passive Bi-PTMD and active PTMD are developed and incorporated into the numerical model of a tall asymmetric structure. The model of PTMDs plus structure is used for a)synthesizing such PTMDs for particular applications and b)evaluating their damping effectiveness in mitigating the dynamic lateral responses of their target asymmetric structures, perturbed by wind load in X and Y-directions. Depending on how elaborate the control scheme is, the active PTMD can either be made to yield the same damping effectiveness as the passive Bi-PTMD of the same size or the passive Bi-TMD twice as massive as the active PTMD.

Keywords: active tuned mass damper, high-rise building, multi-frequency tuning, vibration control

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Authors: Taghreed Alghamdi, Wendy Hall, David Millard

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Massive Open Online Courses (MOOCs) have recently gained widespread interest in the academic world, starting a wide range of discussion of a number of issues. One of these issues, using MOOCs in teaching and learning in the higher education by integrating MOOCs’ contents with traditional face-to-face activities in blended learning format, is called blended MOOCs (bMOOCs) and is intended not to replace traditional learning but to enhance students learning. Most research on MOOCs has focused on students’ perception and institutional threats whereas there is a lack of published research on academics’ experiences and practices. Thus, the first aim of the study is to develop a classification of blended MOOCs models by conducting a systematic literature review, classifying 19 different case studies, and identifying the broad types of bMOOCs models namely: Supplementary Model and Integrated Model. Thus, the analyses phase will emphasize on these different types of bMOOCs models in terms of adopting MOOCs by lecturers. The second aim of the study is to improve the understanding of lecturers’ acceptance of bMOOCs by investigate the factors that influence academics’ acceptance of using MOOCs in traditional learning by distributing an online survey to lecturers who participate in MOOCs platforms. These factors can help institutions to encourage their lecturers to integrate MOOCs with their traditional courses in universities.

Keywords: acceptance, blended learning, blended MOOCs, higher education, lecturers, MOOCs, professors

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Authors: Anahita Mortazavi Manesh, Mojtaba Bagherzadeh

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Metalloporphyrins are well known to mimic the activity of monooxygenase enzymes. In this regard, metalloporphyrin complexes have been largely employed as valuable biomimetic catalysts, owing to the critical roles they play in oxygen transfer processes in catalytic oxidation reactions. Investigating in this area is based on different strategies to design selective, stable and high turnover catalytic systems. Immobilization of expensive metalloporphyrin catalysts onto supports appears to be a good way to improve their stability, selectivity and the catalytic performance because of the support environment and other advantages with respect to recovery, reuse. In other words, supporting metalloporphyrins provides a physical separation of active sites, thus minimizing catalyst self-destruction and dimerization of unhindered metalloporphyrins. Furthermore, heterogeneous catalytic oxidations have become an important target since their process are used in industry, helping to minimize the problems of industrial waste treatment. Hence, the immobilization of these biomimetic catalysts is much desired. An attractive approach is the preparation of the heterogeneous catalyst involves immobilization of complexes on silica coated magnetic nano-particles. Fe3O4@SiO2 magnetic nanoparticles have been studied extensively due to their superparamagnetism property, large surface area to volume ratio and easy functionalization. Using heterogenized homogeneous catalysts is an attractive option to facile separation of catalyst, simplified product work-up and continuity of catalytic system. Homogeneous catalysts immobilized on magnetic nanoparticles (MNPs) surface occupy a unique position due to combining the advantages of both homogeneous and heterogeneous catalysts. In addition, superparamagnetic nature of MNPs enable very simple separation of the immobilized catalysts from the reaction mixture using an external magnet. In the present work, an efficient heterogeneous catalyst was prepared by immobilizing manganese porphyrin on functionalized magnetic nanoparticles through the amino propyl linkage. The prepared catalyst was characterized by elemental analysis, FT-IR spectroscopy, X-ray powder diffraction, atomic absorption spectroscopy, UV-Vis spectroscopy, and scanning electron microscopy. Application of immobilized metalloporphyrin in the oxidation of various organic substrates was explored using Gas chromatographic (GC) analyses. The results showed that the supported Mn-porphyrin catalyst (Fe3O4@SiO2-NH2@MnPor) is an efficient and reusable catalyst in oxidation reactions. Our catalytic system exhibits high catalytic activity in terms of turnover number (TON) and reaction conditions. Leaching and recycling experiments revealed that nanocatalyst can be recovered several times without loss of activity and magnetic properties. The most important advantage of this heterogenized catalytic system is the simplicity of the catalyst separation in which the catalyst can be separated from the reaction mixture by applying a magnet. Furthermore, the separation and reuse of the magnetic Fe3O4 nanoparticles were very effective and economical.

Keywords: Fe3O4 nanoparticle, immobilized metalloporphyrin, magnetically separable nanocatalyst, oxidation reactions

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Authors: Manjusha C. Padole, Parag A. Deshpande

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Density functional theory calculations were carried out for identification of an active heterogeneous catalyst to carry out Heck coupling reaction which is of pharmaceutical importance. One of the carbonaceous nanomaterials, heterofullerene, was designed for the reaction. Stability and reactivity of the proposed heterofullerenes (C59M, M = Pd/Ni) were established with insights into the metal-carbon bond, electron affinity and chemical potential. Adsorbent potentials of both the heterofullerenes were examined from the adsorption study of four halobenzenes (C6H5F, C6H5Cl, C6H5Br and C6H5I). Oxidative addition activities of all four halobenzenes were investigated by developing free energy landscapes over both the heterofullerenes for rate determining step (oxidative addition). C6H5I showed a good catalytic activity for the rate determining step. Thus, C6H5I was proposed as a suitable halobenzene and complete free energy landscapes for Heck coupling reaction were developed over C59Pd and C59Ni. Smaller activation barriers observed over C59Pd in comparison with C59Ni put us in a position to propose C59Pd to be an efficient heterofullerene for carrying Heck coupling reaction.

Keywords: metal-substituted fullerene, density functional theory, electron affinity, oxidative addition, Heck coupling reaction

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Authors: Kamran Siddique, Yangwoo Kim, Zahid Akhtar

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In recent years, the technological advancements have led to a deluge of data from distinctive domains and the need for development of solutions based on parallel and distributed computing has still long way to go. That is why, the research and development of massive computing frameworks is continuously growing. At this particular stage, highlighting a potential research area along with key insights could be an asset for researchers in the field. Therefore, this paper explores one of the emerging distributed computing frameworks, Apache Hama. It is a Top Level Project under the Apache Software Foundation, based on Bulk Synchronous Processing (BSP). We present an unbiased and critical interrogation session about Apache Hama and conclude research directions in order to assist interested researchers.

Keywords: apache hama, bulk synchronous parallel, BSP, distributed computing

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Authors: Joy Marie Mora, Mark Daniel De Luna, Tsair-Wang Chung

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Transesterification reaction of soybean oil with methanol was carried out to produce fatty acid methyl esters (FAME) using calcined alkali metal (Na and Li) supported by pumice silica as the solid base catalyst. Pumice silica catalyst was activated by loading alkali metal ions to its surface via an ion-exchange method. Response surface methodology (RSM) in combination with Box-Behnken design (BBD) was used to optimize the operating parameters in biodiesel production, namely: reaction temperature, methanol to oil molar ratio, reaction time, and catalyst concentration. Using the optimized sets of parameters, FAME yields using sodium and lithium silicate catalysts were 98.80% and 98.77%, respectively. A pseudo-first order kinetic equation was applied to evaluate the kinetic parameters of the reaction. The prepared catalysts were characterized by several techniques such as X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), Brunauer-Emmett-Teller (BET) sorptometer, and scanning electron microscopy (SEM). In addition, the reusability of the catalysts was successfully tested in two subsequent cycles.

Keywords: alkali metal, biodiesel, Box-Behnken design, heterogeneous catalyst, kinetics, optimization, pumice, transesterification

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Authors: Mohan Li

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Nowadays, intelligent research technology is more and more important than traditional research methods in urban research work, and this proportion will greatly increase in the next few decades. Frequently such analyzing work cannot be carried without some software engineering knowledge. And here, domain models of urban research will be necessary when applying software engineering knowledge to urban work. In many urban plan practice projects, making rational models, feeding reliable data, and providing enough computation all make indispensable assistance in producing good urban planning. During the whole work process, domain models can optimize workflow design. At present, human beings have entered the era of big data. The amount of digital data generated by cities every day will increase at an exponential rate, and new data forms are constantly emerging. How to select a suitable data set from the massive amount of data, manage and process it has become an ability that more and more planners and urban researchers need to possess. This paper summarizes and makes predictions of the emergence of technologies and technological iterations that may affect urban research in the future, discover urban problems, and implement targeted sustainable urban strategies. They are summarized into seven major domain models. They are urban and rural regional domain model, urban ecological domain model, urban industry domain model, development dynamic domain model, urban social and cultural domain model, urban traffic domain model, and urban space domain model. These seven domain models can be used to guide the construction of systematic urban research topics and help researchers organize a series of intelligent analytical tools, such as Python, R, GIS, etc. These seven models make full use of quantitative spatial analysis, machine learning, and other technologies to achieve higher efficiency and accuracy in urban research, assisting people in making reasonable decisions.

Keywords: big data, domain model, urban planning, urban quantitative analysis, machine learning, workflow design

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Authors: Hossein Jafari, Xiangfang Li, Lijun Qian, Alexander Aved, Timothy Kroecker

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Traditionally in sensor networks and recently in the Internet of Things, numerous heterogeneous sensors are deployed in distributed manner to monitor a phenomenon that often can be model by an underlying stochastic process. The big time-series data collected by the sensors must be analyzed to detect change in the stochastic process as quickly as possible with tolerable false alarm rate. However, sensors may have different accuracy and sensitivity range, and they decay along time. As a result, the big time-series data collected by the sensors will contain uncertainties and sometimes they are conflicting. In this study, we present a framework to take advantage of Evidence Theory (a.k.a. Dempster-Shafer and Dezert-Smarandache Theories) capabilities of representing and managing uncertainty and conflict to fast change detection and effectively deal with complementary hypotheses. Specifically, Kullback-Leibler divergence is used as the similarity metric to calculate the distances between the estimated current distribution with the pre- and post-change distributions. Then mass functions are calculated and related combination rules are applied to combine the mass values among all sensors. Furthermore, we applied the method to estimate the minimum number of sensors needed to combine, so computational efficiency could be improved. Cumulative sum test is then applied on the ratio of pignistic probability to detect and declare the change for decision making purpose. Simulation results using both synthetic data and real data from experimental setup demonstrate the effectiveness of the presented schemes.

Keywords: CUSUM, evidence theory, kl divergence, quickest change detection, time series data

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Authors: Gul Afreen, Sreedevi Upadhyayula, Mahendra K. Sunkara

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One-dimensional (1D) nanostructures like nanowires, nanotubes, and nanorods find variety of practical application owing to their unique physico-chemical properties. In this work, TiO₂ nanowires were synthesized by direct oxidation of titanium particles in a unique microwave plasma jet reactor. The prepared TiO₂ nanowires manifested the flexible features, and were characterized by using X-ray diffraction, Brunauer-Emmett-Teller (BET) surface area analyzer, UV-Visible and FTIR spectrophotometers, Scanning electron microscope, and Transmission electron microscope. Further, the photodegradation efficiency of these nanowires were tested against toxic organic dye like methylene blue (MB) and the results were compared with the commercial TiO₂. It was found that TiO₂ nanowires exhibited superior photocatalytic performance (89%) as compared to commercial TiO₂ (75%) after 60 min of reaction. This is attributed to the lower recombination rate and increased interfacial charge transfer in TiO₂ nanowire. Pseudo-first order kinetic modelling performed with the experimental results revealed that the rate constant of photodegradation in case of TiO₂ nanowire was 1.3 times higher than that of commercial TiO₂. Superoxide radical (O₂˙⁻) was found to be the major contributor in the photodegradation mechanism. Based on the trapping experiments, a plausible mechanism of the photocatalytic reaction is discussed.

Keywords: heterogeneous catalysis, photodegradation, reactive oxygen species, TiO₂ nanowires

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Authors: Mansour Mohammadpour

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Data transformation refers to the modification of any point in a data set by a mathematical function. When applying transformations, the measurement scale of the data is modified. Data transformations are commonly employed to turn data into the appropriate form, which can serve various functions in the quantitative analysis of the data. This study addresses the investigation of the use of data transformations in Data Envelopment Analysis (DEA). Although data transformations are important options for analysis, they do fundamentally alter the nature of the variable, making the interpretation of the results somewhat more complex.

Keywords: data transformation, data envelopment analysis, undesirable data, negative data

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Authors: Thomas Prevenslik

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Weakly Interacting Massive Particle (WIMP) experiments suggesting dark matter does not exist are consistent with the argument that the long-standing galaxy rotation problem may be resolved without the need for dark matter if the redshift measurements giving the higher than expected galaxy velocities are corrected for the redshift in cosmic dust. Because of the ubiquity of cosmic dust, all velocity measurements in astronomy based on redshift are most likely overstated, e.g., an accelerating Universe expansion need not exist if data showing supernovae brighter than expected based on the redshift/distance relation is corrected for the redshift in dust. Extensions of redshift corrections for cosmic dust to other historical astronomical observations are briefly discussed.

Keywords: alternative theories, cosmic dust redshift, doppler effect, quantum mechanics, quantum electrodynamics

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Authors: Maria Ulfa, Nur Awaliyah Putri

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Madurese is known as Malayo-Sumbawan Austronesian language which is used by Madurese people in Madura Island, Indonesia. However, there was a massive migration of Madurese people due to Dutch colonization. The Madurese people were brought by force for cultivation system to the eastern salient north coast or called as Tapal Kuda that spread in region covers the regencies of Probolinggo, Lumajang, Jember, Situbondo, Bondowoso, and Banyuwangi, the eastern part of the Pasuruan Regency, as well as the city of Probolinggo. The city of Probolinggo has unique characteristic regarding the ethnic and language variation. Several ethnics can be found in this city, such as Madurese, Javanese, Tengger, Arabic, Mandhalungan, Osing, and Chinese. Hence, the hybrid culture happens in Probolinggo, they called the culture as Pendhalungan which is the combination of culture among Madurese and Javanese. Among those ethnics, Madurese is the strongest ethnic that still maintains their identity, such as their ethnic language. The massive growth of Madurese in Probolinggo city, East Java is interesting to be analyzed. The object of this study is to discover language ideology and literacy of Madurese to maintain their ethnic language in Probolinggo city, East Java. The researchers used the theory of language maintenance practice based on three types of practices social language, social literacy, and peripheral ritualized practices. The approach of this study was qualitative research with ethnography method. In order to collect the data, researchers used observation and interview techniques. The amount of informants were 20 families which consist of mother, father and children in 5 sub-districts in Probolinggo city and they were interviewed regarding language ideology and literacy of Madurese. In supporting the data, researchers employed the Madurese speakers outside family scope like in school, office, and market. The result of the study revealed that Madurese has been preserved heritably to young generations by ethnics of Madura in Probolinggo city. Primarily the language is being taught in the earlier age of their children as L1 and used as ethnic identity. The parents teach them with simple sentences that grammatically correct. This language literacy is applied to maintain ethnic language as their ethnicity marker since they inhabit in Javanese ethnic area. In fact, it is not the only ideology of Madurese ethnic but also the influence of economic situation like in trading communication. The usage of Madurese in the trading scope is very beneficial since people can bargain the goods cheaper and easier because most of the traders are from Madurese ethnic. In this situation, linguistic phenomena such as code mixing and code switching between Madurese and Javanese are emerged as the trading communication. From the result, it can be concluded that solidarity exists among Madurese people in many scopes.

Keywords: language literacy, language maintenance, Madurese, Probolinggo City

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Authors: Antar Bouhank, Youcef Beellal, Samir Adjel, Abdelmadjid Ababsa

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This paper investigates the impact of 3D printing parameters using the cold metal transfer (CMT) technique on the morphological, mechanical, and tribological properties of walls and massive parts made from aluminum alloy. The parameters studied include current intensity, torch movement speed, printing increment, and the flow rate of shielding gas. The manufactured parts, using the technique mentioned above, are walls and massive parts with different filling strategies, using grid and zigzag patterns and at different current intensities. The main goal of the article is to find out the welding parameters suitable for having parts with low defects and improved properties from the previously mentioned properties point of view. It has been observed from the results thus obtained that the high current intensity causes rapid solidification, resulting in high porosity and low hardness values. However, the high current intensity can cause very rapid solidification, which increases the melting point, and the part remains in the most stable shape. Furthermore, the results show that there is an evident relationship between hardness, coefficient of friction and wear test where the high intensity is, the low hardness is. The same note is for the coefficient of friction. The micrography of the walls shows a random granular structure with fine grain boundaries with a different grain size. Some interesting results are presented in this paper.

Keywords: aluminum alloy, porosity, microstructures, hardness

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Authors: Gichun Cha, Changgil Lee, Seunghee Park

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The objective of the study is to construct the shape management method contributing to the safety of the large structure. In Korea, the research of the shape management is lack because of the new attempted technology. Terrestrial Laser Scanning (TLS) is used for measurements of large structures. TLS provides an efficient way to actively acquire accurate the point clouds of object surfaces or environments. The point clouds provide a basis for rapid modeling in the industrial automation, architecture, construction or maintenance of the civil infrastructures. TLS produce a huge amount of point clouds. Registration, Extraction and Visualization of data require the processing of a massive amount of scan data. The octree can be applied to the shape management of the large structure because the scan data is reduced in the size but, the data attributes are maintained. The octree space partitioning generates the voxel of 3D space, and the voxel is recursively subdivided into eight sub-voxels. The point cloud of scan data was converted to voxel and sampled. The experimental site is located at Sungkyunkwan University. The scanned structure is the steel-frame bridge. The used TLS is Leica ScanStation C10/C5. The scan data was condensed 92%, and the octree model was constructed with 2 millimeter in resolution. This study presents octree space partitioning for handling the point clouds. The basis is created by shape management of the large structures such as double-deck tunnel, building and bridge. The research will be expected to improve the efficiency of structural health monitoring and maintenance. "This work is financially supported by 'U-City Master and Doctor Course Grant Program' and the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIP) (NRF- 2015R1D1A1A01059291)."

Keywords: 3D scan data, octree space partitioning, shape management, structural health monitoring, terrestrial laser scanning

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Authors: Zainab Atia, Hongwei He, Panagiotis Sarantopoulos

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Over the past few years, firms have witnessed a massive increase in sophisticated algorithmic deployment, which has become quite pervasive in today’s modern society. With the wide availability of data for retailers, the ability to track consumers using algorithmic pricing has become an integral option in online platforms. As more companies are transforming their businesses and relying more on massive technological advancement, pricing algorithmic systems have brought attention and given rise to its wide adoption, with many accompanying benefits and challenges to be found within its usage. With the overall aim of increasing profits by organizations, algorithmic pricing is becoming a sound option by enabling suppliers to cut costs, allowing better services, improving efficiency and product availability, and enhancing overall consumer experiences. The adoption of algorithms in retail has been pioneered and widely used in literature across varied fields, including marketing, computer science, engineering, economics, and public policy. However, what is more, alarming today is the comprehensive understanding and focus of this technology and its associated ethical influence on consumers’ perceptions and behaviours. Indeed, due to algorithmic ethical concerns, consumers are found to be reluctant in some instances to share their personal data with retailers, which reduces their retention and leads to negative consumer outcomes in some instances. This, in its turn, raises the question of whether firms can still manifest the acceptance of such technologies by consumers while minimizing the ethical transgressions accompanied by their deployment. As recent modest research within the area of marketing and consumer behavior, the current research advances the literature on algorithmic pricing, pricing ethics, consumers’ perceptions, and price fairness literature. With its empirical focus, this paper aims to contribute to the literature by applying the distinction of the two common types of algorithmic pricing, dynamic and personalized, while measuring their relative effect on consumers’ behavioural outcomes. From a managerial perspective, this research offers significant implications that pertain to providing a better human-machine interactive environment (whether online or offline) to improve both businesses’ overall performance and consumers’ wellbeing. Therefore, by allowing more transparent pricing systems, businesses can harness their generated ethical strategies, which fosters consumers’ loyalty and extend their post-purchase behaviour. Thus, by defining the correct balance of pricing and right measures, whether using dynamic or personalized (or both), managers can hence approach consumers more ethically while taking their expectations and responses at a critical stance.

Keywords: algorithmic pricing, dynamic pricing, personalized pricing, price ethicality

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Authors: Vishnu Nair, Rohan Kapoor

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Recent commercial aircraft crashes demand a paradigm shift in how the CVDRs are located and recovered, particularly if the aircraft crashes in the sea. CVDR (Cockpit Voice and Data Recorder) is most vital component out of the entire wreckage that can be obtained in order to investigate the sequence of events leading to the crash. It has been a taxing and exorbitantly expensive process locating and retrieving the same in the massive water bodies as it was seen in the air crashes in the recent past, taking the unfortunate Malaysia airlines MH-370 crash into account. The study aims to provide an aid to the persisting problem by improving the buoyant as-well-as the aerodynamic properties of the proposed CVDR encasing. Alongside this the placement of the deployable CVDR on the surface of the aircraft and floatability in case of water submersion are key factors which are taken into consideration for a better resolution to the problem. All of which results into the Deployable-CVDR emerging to the surface of the water-body. Also the whole system is designed such that it can be seamlessly integrated with the current crop of commercial aircraft. The work is supported by carrying out a computational study with the help Ansys-Fluent combination.

Keywords: encasing, buoyancy, deployable CVDR, floatability, water submersion

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Authors: Rawaz A. Ahmed, Katherine Huddersman

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High-lipid Fats, Oils and Grease (FOGs) from wastewater are underutilized despite their potential for conversion into valuable fuels; this work describes a surface-functionalized fibrous Polyacrylonitrile (PAN) mesh as a novel heterogeneous acid catalyst for the conversion of free fatty acids (FFAs), via a catalytic esterification process into biodiesel. The esterification of stearic acid (SA) with methanol was studied over an acidified PAN solid acid catalyst. Disappearance of the carboxylic acid (C=O) peak of the stearic acid at 1696 cm-1 in the FT-IR spectrum with the associated appearance of the ester (C=O) peak at 1739 cm-1 confirmed the production of the methyl stearate. This was further supported by 1H NMR spectra with the appearance of the ester (-CH₂OCOR) at 3.60-3.70 ppm. Quantitate analysis by GC-FID showed the catalyst has excellent activity with >95 % yield of methyl stearate (MS) at 90 ◦C after 3 h and a molar ratio of methanol to SA of 35:1. To date, to our best knowledge, there is no research in the literature on the esterification reaction for biodiesel production using a modified PAN mesh as a catalyst. It is noteworthy that this acidified PAN mesh catalyst showed comparable activity to conventional Brönsted acids, namely H₂SO₄ and p-TSA, as well as exhibiting higher activity than various other heterogeneous catalysts such as zeolites, ion-exchange resins and acid clay.

Keywords: fats oil and greases (FOGs), free fatty acid, esterification reaction, methyl ester, PAN

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Authors: Gholamreza Momenzadeh, Forough Nematolahi

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The Extended Enterprise Resource Planning (ERPII) system usually requires massive amounts of storage space, powerful servers, and large upfront and ongoing investments to purchase and manage the software and the related hardware which are not affordable for organizations. In recent decades, organizations prefer to adapt their business structures with new technologies for remaining competitive in the world economy. Therefore, cloud computing (which is one of the tools of information technology (IT)) is a modern system that reveals the next-generation application architecture. Also, cloud computing has had some advantages that reduce costs in many ways such as: lower upfront costs for all computing infrastructure and lower cost of maintaining and supporting. On the other hand, traditional ERPII is not responding for huge amounts of data and relations between the organizations. In this study, based on a literature study, ERPII is investigated in the context of cloud computing where the organizations operate more efficiently. Also, ERPII conditions have a response to needs of organizations in large amounts of data and relations between the organizations.

Keywords: extended enterprise resource planning, cloud computing, business process, enterprise information integration

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Authors: Samir Verdiyev, Fuad Huseynov, Islam Guliyev, Coşqun İsmayıl

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The Filizchay polymetallic deposit is located on the southern slope of the Greater Caucasus Mountain Range, northwest of Azerbaijan in the Balaken district. Filizchay is the largest polymetallic deposit in the region and the second-largest polymetallic deposit in Europe. The mineral deposits in the region are associated with two different geodynamic evolutions that began with the Mesozoic collision along the Eurasian continent and the formation of a magmatic arc after the collision and continued with subduction in the Cenozoic. The bedrocks associated with Filizchay mineralization are Early Jurassic aged. The stratigraphic sequence of the deposit is consisting of black metamorphic clay shales, sandstones, and ore layers. Shales, sandstones, and siltstones are encountered in the upper and middle sections of the ore body, while only shales are observed at the lowest ranges. The ore body is mainly layered by the geometric structure of the bedrock; folding can be observed in the ore layers along with the bedrock foliation, and just in few points indirect laying due to the metamorphism. This suggests that the Filizchay ore mineralization is syngenetic, which is proved by the mineralization by the bedrock. To determine the ore petrography properties of the Filizchay deposit, samples were collected from the region where the ore is concentrated, and a polished section was prepared. These collected samples were examined under the mineralogical microscope to reveal the paragenesis of the mineralization and to explain the relation of ore minerals to each other. In this study, macroscopically observed minerals and textures of these minerals were used in the cores revealed during drilling exploration made by AzerGold CJS company. As a result of all these studies, it has been determined that there are three main mineralization types in the Filizchay deposit: banded, massive, and veinlet ores. The mineralization is in the massive pyrite; furthermore, the basis of the ore-mass contains pyrite, chalcopyrite, sphalerite, and galena. The pyrite in some parts of the ore body transformed to pyrrhotite as a result of metamorphism. Pyrite-chalcopyrite, pyrite-sphalerite-galena, pyrite-pyrrhotite mineral assemblages were determined during microscopic studies of mineralization. The replacement texture is more developed in Filizchay ores. The banded polymetallic type mineralization and near bedrocks are cut by quartz-carbonate veins. The geotectonic position and lithological conditions of the Filizchay deposit, the texture, and interrelationship of the sulfide mineralization indicate that it is a sedimentary-exhalative type of Au-Cu-Ag-Zn-Pb polymetallic deposit that is genetically related to the massive sulfide deposits.

Keywords: Balaken, Filizchay, metamorphism, polymetallic mineralization

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Authors: Gabriele Iannotta

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This paper investigates credit cycles by means of an experiment based on a Kiyotaki & Moore (1997) model with heterogeneous expectations. The aim is to examine how a credit squeeze caused by high lender-level risk perceptions affects the real prices of a collateralised asset, with a special focus on the macroeconomic implications of rising price volatility in terms of total welfare and the number of bankruptcies that occur. To do that, a learning-to-forecast experiment (LtFE) has been run where participants are asked to predict the future price of land and then rewarded based on the accuracy of their forecasts. The setting includes one lender and five borrowers in each of the twelve sessions split between six control groups (G1) and six treatment groups (G2). The only difference is that while in G1 the lender always satisfies borrowers’ loan demand (bankruptcies permitting), in G2 he/she closes the entire credit market in case three or more bankruptcies occur in the previous round. Experimental results show that negative risk-driven supply shocks amplify the volatility of collateral prices. This uncertainty worsens the agents’ ability to predict the future value of land and, as a consequence, the number of defaults increases and the total welfare deteriorates.

Keywords: Behavioural Macroeconomics, Credit Cycle, Experimental Economics, Heterogeneous Expectations, Learning-to-Forecast Experiment

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Authors: Julian B. García, Virginie Q. R. Pinto, André P. Assis

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This paper presents numerical flow and slope stability simulations in three typical sections of earth dams built in tropical regions, two homogeneous with different slope inclinations, and the other one heterogeneous with impermeable core. The geotechnical material parameters used in this work were obtained from a lab testing of physical characterization, compaction, consolidation, variable load permeability and saturated triaxial type CD for compacted soil samples with standard proctor energy at optimum moisture content (23%), optimum moisture content + 2% and optimum moisture content +5%. The objective is to analyze the general behavior of earth dams built in rainy regions where optimum moisture is exceeded. The factor of safety is satisfactory for the three sections compacted in all moisture content during the stages of operation and end of construction. On The other hand, the rapid drawdown condition is the critical phase for homogeneus dams configuration, the factor of safety obtained were unsatisfactory. In general, the heterogeneous dam behavior is more efficient due to the fact that the slopes are made up of gravel, which favors the dissipation of pore pressures during the rapid drawdown. For the critical phase, the slopes should have lower inclinations of the upstream and downstream slopes to guarantee stability, although it increases the costs.

Keywords: earth dams, flow, moisture content, slope stability

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Authors: Zerroug Abdelhamid, Danielle Chassoux

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Various models are given to simulate homogeneous or heterogeneous cancerous tumors and extract in each case the boundary. The fractal dimension is then estimated by least squares method and compared to some previous methods.

Keywords: simulation, cancerous tumor, Markov fields, fractal dimension, extraction, recovering

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Authors: Paolo Fantozzi, Luigi Laura, Umberto Nanni

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The problem of Entity relation discovery in structured data, a well covered topic in literature, consists in searching within unstructured sources (typically, text) in order to find connections among entities. These can be a whole dictionary, or a specific collection of named items. In many cases machine learning and/or text mining techniques are used for this goal. These approaches might be unfeasible in computationally challenging problems, such as processing massive data streams. A faster approach consists in collecting the cooccurrences of any two words (entities) in order to create a graph of relations - a cooccurrence graph. Indeed each cooccurrence highlights some grade of semantic correlation between the words because it is more common to have related words close each other than having them in the opposite sides of the text. Some authors have used sliding windows for such problem: they count all the occurrences within a sliding windows running over the whole text. In this paper we generalise such technique, coming up to a Weighted-Distance Sliding Window, where each occurrence of two named items within the window is accounted with a weight depending on the distance between items: a closer distance implies a stronger evidence of a relationship. We develop an experiment in order to support this intuition, by applying this technique to a data set consisting in the text of the Bible, split into verses.

Keywords: cooccurrence graph, entity relation graph, unstructured text, weighted distance

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Authors: Mao Zhaofang, Xu Yida, Fang Kan, Fu Enyuan, Zhao Zhao

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Nowadays, last-mile distribution plays an increasingly important role in the whole industrial chain delivery link and accounts for a large proportion of the whole distribution process cost. Promoting the upgrading of logistics networks and improving the layout of final distribution points has become one of the trends in the development of modern logistics. Due to the discrete and heterogeneous needs and spatial distribution of customer demand, which will lead to a higher delivery failure rate and lower vehicle utilization, last-mile delivery has become a time-consuming and uncertain process. As a result, courier companies have introduced a range of innovative parcel storage facilities, including pick-up points and lockers. The introduction of pick-up points and lockers has not only improved the users’ experience but has also helped logistics and courier companies achieve large-scale economy. Against the backdrop of the COVID-19 of the previous period, contactless delivery has become a new hotspot, which has also created new opportunities for the development of collection services. Therefore, a key issue for logistics companies is how to design/redesign their last-mile distribution network systems to create integrated logistics and distribution networks that consider pick-up points and lockers. This paper focuses on the introduction of self-pickup facilities in new logistics and distribution scenarios and the heterogeneous demands of customers. In this paper, we consider two types of demand, including ordinary products and refrigerated products, as well as corresponding transportation vehicles. We consider the constraints associated with self-pickup points and lockers and then address the location-routing problem with self-pickup facilities and heterogeneous demands (LRP-PFHD). To solve this challenging problem, we propose a mixed integer linear programming (MILP) model that aims to minimize the total cost, which includes the facility opening cost, the variable transport cost, and the fixed transport cost. Due to the NP-hardness of the problem, we propose a hybrid adaptive large-neighbourhood search algorithm to solve LRP-PFHD. We evaluate the effectiveness and efficiency of the proposed algorithm by using instances generated based on benchmark instances. The results demonstrate that the hybrid adaptive large neighbourhood search algorithm is more efficient than MILP solvers such as Gurobi for LRP-PFHD, especially for large-scale instances. In addition, we made a comprehensive analysis of some important parameters (e.g., facility opening cost and transportation cost) to explore their impacts on the results and suggested helpful managerial insights for courier companies.

Keywords: city logistics, last-mile delivery, location-routing, adaptive large neighborhood search

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Authors: K. K. Soundra Pandian, Bhupendra Gupta

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In the modern era, as the application data’s are massive and complex, it needs to be secured from the adversary attack. In this context, a non-recursive key based integrated spritz stream cipher with the circulant hash function using delayed feedback with carry shift register (d-FCSR) is proposed in this paper. The novelty of this proposed stream cipher algorithm is to engender the improved keystream using d-FCSR. The proposed algorithm is coded using Verilog HDL to produce dynamic binary key stream and implemented on commercially available FPGA device Virtex 5 xc5vlx110t-2ff1136. The implementation of stream cipher using d-FCSR on the FPGA device operates at a maximum frequency of 60.62 MHz. It achieved the data throughput of 492 Mbps and improved in terms of efficiency (throughput/area) compared to existing techniques. This paper also briefs the cryptanalysis of proposed circulant hash value based spritz stream cipher using d-FCSR is against the adversary attack on a hardware platform for the hardware based cryptography applications.

Keywords: cryptography, circulant function, field programmable gated array, hash value, spritz stream cipher

Procedia PDF Downloads 250