Search results for: edge intelligence

Authors: J. Martin, A. Nominé, T. Czerwiec, G. Henrion, T. Belmonte

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The plasma electrolytic oxidation (PEO) is a particular electrochemical process to produce protective oxide ceramic coatings on light-weight metals (Al, Mg, Ti). When applied to aluminum alloys, the resulting PEO coating exhibit improved wear and corrosion resistance because thick, hard, compact and adherent crystalline alumina layers can be achieved. Several investigations have been carried out to improve the efficiency of the PEO process and one particular way consists in tuning the suitable electrical regime. Despite the considerable interest in this process, there is still no clear understanding of the underlying discharge mechanisms that make possible metal oxidation up to hundreds of µm through the ceramic layer. A key parameter that governs the PEO process is the numerous short-lived micro-discharges (micro-plasma in liquid) that occur continuously over the processed surface when the high applied voltage exceeds the critical dielectric breakdown value of the growing ceramic layer. By using a bipolar pulsed current to supply the electrodes, we previously observed that micro-discharges are delayed with respect to the rising edge of the anodic current. Nevertheless, explanation of the origin of such phenomena is still not clear and needs more systematic investigations. The aim of the present communication is to identify the relationship that exists between this delay and the mechanisms responsible of the oxide growth. For this purpose, the delay of micro-discharges ignition is investigated as the function of various electrical parameters such as the current density (J), the current pulse frequency (F) and the anodic to cathodic charge quantity ratio (R = Qp/Qn) delivered to the electrodes. The PEO process was conducted on Al2214 aluminum alloy substrates in a solution containing potassium hydroxide [KOH] and sodium silicate diluted in deionized water. The light emitted from micro-discharges was detected by a photomultiplier and the micro-discharge parameters (number, size, life-time) were measured during the process by means of ultra-fast video imaging (125 kfr./s). SEM observations and roughness measurements were performed to characterize the morphology of the elaborated oxide coatings while XRD was carried out to evaluate the amount of corundum -Al203 phase. Results show that whatever the applied current waveform, the delay of micro-discharge appearance increases as the process goes on. Moreover, the delay is shorter when the current density J (A/dm2), the current pulse frequency F (Hz) and the ratio of charge quantity R are high. It also appears that shorter delays are associated to stronger micro-discharges (localized, long and large micro-discharges) which have a detrimental effect on the elaborated oxide layers (thin and porous). On the basis of the results, a model for the growth of the PEO oxide layers will be presented and discussed. Experimental results support that a mechanism of electrical charge accumulation at the oxide surface / electrolyte interface takes place until the dielectric breakdown occurs and thus until micro-discharges appear.

Keywords: aluminium, micro-discharges, oxidation mechanisms, plasma electrolytic oxidation

Procedia PDF Downloads 234

Authors: Ya-Mei Chen

Abstract:

The participatory culture enabled by Web 2.0 technologies has led to the emergence of online translation crowdsourcing, which mainly relies on the collective intelligence of volunteer translators. Due to the fact that many volunteer translators do not have formal translator training, concerns have been raised about the quality of crowdsourced translations. Some empirical research has been done to examine the translation quality of for-profit crowdsourcing initiatives. However, quality assurance of non-profit translation crowdsourcing has rarely been explored in detail. Using the TED Open Translation Project as a case study, this paper investigates how the translation-review-approval method adopted by TED can (1) direct the volunteer translators’ use of translation strategies as well as the reviewers’ adoption of revising strategies and (2) shape the final translation products. To well examine the actual effect of TED’s translation-review-approval method, this paper will focus on its two major quality assurance mechanisms, that is, TED’s style guidelines and quality review. Based on an anonymous questionnaire, this research will first explore whether the volunteer translators and reviewers are aware of the style guidelines and whether their use of translation strategies is similar to that advised in the guidelines. The questionnaire, which will be posted online, will consist of two parts: demographic information and translation strategies. The invitations to complete it will then be distributed through TED Translator Facebook groups. With an aim to investigate if the style guidelines have any substantial impacts on actual subtitling practices, a comparison will be made between the original English subtitles of 20 TED talks (each around 5 to 7 minutes) and their Chinese subtitle translations to identify regularly adopted strategies. Concerning the function of the reviewing stage, a comparative study will be conducted between the drafts of Chinese subtitles for 10 short English talks and the revised versions of these drafts so as to examine the actual revising strategies and their effect on translation quality. According to the results obtained from the questionnaire and textual comparisons, this paper will provide in-depth analysis of quality assurance of the TED Open Translation Project. It is hoped that this research, through a detailed investigation of non-profit translation crowdsourcing, can enable translation researchers and practitioners to have a better understanding of quality control in translation crowdsourcing in the digital age.

Keywords: quality assurance, TED, translation crowdsourcing, volunteer translators

Procedia PDF Downloads 209

Authors: Yasamin Rahimi, Ali Kamandi, Abbas Hoseini, Hesam Haddad

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Online/offline chat is a convenient approach in the electronic markets of second-hand products in which potential customers would like to have more information about the products to fill the information gap between buyers and sellers. Online peer in peer market is trying to create artificial intelligence-based systems that help customers ask more informative questions in an easier way. In this article, we introduce a method for the question/answer system that we have developed for the top-ranked electronic market in Iran called Divar. When it comes to secondhand products, incomplete product information in a purchase will result in loss to the buyer. One way to balance buyer and seller information of a product is to help the buyer ask more informative questions when purchasing. Also, the short time to start and achieve the desired result of the conversation was one of our main goals, which was achieved according to A/B tests results. In this paper, we propose and evaluate a method for suggesting questions and answers in the messaging platform of the e-commerce website Divar. Creating such systems is to help users gather knowledge about the product easier and faster, All from the Divar database. We collected a dataset of around 2 million messages in Persian colloquial language, and for each category of product, we gathered 500K messages, of which only 2K were Tagged, and semi-supervised methods were used. In order to publish the proposed model to production, it is required to be fast enough to process 10 million messages daily on CPU processors. In order to reach that speed, in many subtasks, faster and simplistic models are preferred over deep neural models. The proposed method, which requires only a small amount of labeled data, is currently used in Divar production on CPU processors, and 15% of buyers and seller’s messages in conversations is directly chosen from our model output, and more than 27% of buyers have used this model suggestions in at least one daily conversation.

Keywords: smart reply, spell checker, information retrieval, intent detection, question answering

Procedia PDF Downloads 163

Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

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Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should properly evaluate their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, Neural Networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable to offer an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

Procedia PDF Downloads 60

Authors: Wanderson Goncalves e Goncalves, José Alberto Silva de Sá

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Human actions are a threat to the balance and conservation of the Amazon forest. Therefore the environmental monitoring services play an important role as the preservation and maintenance of this environment. This study classified forest types using data from a forest inventory provided by the 'Florestal e da Biodiversidade do Estado do Pará' (IDEFLOR-BIO), located between the municipalities of Santarém, Juruti and Aveiro, in the state of Pará, Brazil, covering an area approximately of 600,000 hectares, Bands 3, 4 and 5 of the TM-Landsat satellite image, and Self - Organizing Maps. The information from the satellite images was extracted using QGIS software 2.8.1 Wien and was used as a database for training the neural network. The midpoints of each sample of forest inventory have been linked to images. Later the Digital Numbers of the pixels have been extracted, composing the database that fed the training process and testing of the classifier. The neural network was trained to classify two forest types: Rain Forest of Lowland Emerging Canopy (Dbe) and Rain Forest of Lowland Emerging Canopy plus Open with palm trees (Dbe + Abp) in the Mamuru Arapiuns glebes of Pará State, and the number of examples in the training data set was 400, 200 examples for each class (Dbe and Dbe + Abp), and the size of the test data set was 100, with 50 examples for each class (Dbe and Dbe + Abp). Therefore, total mass of data consisted of 500 examples. The classifier was compiled in Orange Data Mining 2.7 Software and was evaluated in terms of the confusion matrix indicators. The results of the classifier were considered satisfactory, and being obtained values of the global accuracy equal to 89% and Kappa coefficient equal to 78% and F1 score equal to 0,88. It evaluated also the efficiency of the classifier by the ROC plot (receiver operating characteristics), obtaining results close to ideal ratings, showing it to be a very good classifier, and demonstrating the potential of this methodology to provide ecosystem services, particularly in anthropogenic areas in the Amazon.

Keywords: artificial neural network, computational intelligence, pattern recognition, unsupervised learning

Procedia PDF Downloads 342

Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

Abstract:

Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should evaluate properly their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, neural networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable of offering an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

Procedia PDF Downloads 48

Authors: Joy Cao, Min Zhou

Abstract:

Purpose: Acute Type A aortic dissection is a well-known cause of extremely high mortality rate. A highly accurate and cost-effective non-invasive predictor is critically needed so that the patient can be treated at earlier stage. Although various CFD approaches have been tried to establish some prediction frameworks, they are sensitive to uncertainty in both image segmentation and boundary conditions. Tedious pre-processing and demanding calibration procedures requirement further compound the issue, thus hampering their clinical applicability. Using the latest physics informed deep learning methods to establish an accurate and cost-effective predictor framework are amongst the main goals for a better Type A aortic dissection treatment. Methods: Via training a novel physics-informed deep residual network, with non-invasive 4D MRI displacement vectors as inputs, the trained model can cost-effectively calculate all these biomarkers: aortic blood pressure, WSS, and OSI, which are used to predict potential type A aortic dissection to avoid the high mortality events down the road. Results: The proposed deep learning method has been successfully trained and tested with both synthetic 3D aneurysm dataset and a clinical dataset in the aortic dissection context using Google colab environment. In both cases, the model has generated aortic blood pressure, WSS, and OSI results matching the expected patient’s health status. Conclusion: The proposed novel physics-informed deep residual network shows great potential to create a cost-effective, non-invasive predictor framework. Additional physics-based de-noising algorithm will be added to make the model more robust to clinical data noises. Further studies will be conducted in collaboration with big institutions such as Cleveland Clinic with more clinical samples to further improve the model’s clinical applicability.

Keywords: type-a aortic dissection, deep residual networks, blood flow modeling, data-driven modeling, non-invasive diagnostics, deep learning, artificial intelligence.

Procedia PDF Downloads 64

Authors: Vishnu Asokan, Zaid M. Paloba

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Flow over cavities is an important area of research due to the significant change in flow physics caused by cavity aspect ratio, free stream Mach number and the nature of upstream boundary layer approaching the cavity leading edge. Cavity flow finds application in aircraft wheel well, weapons bay, combustion chamber of scramjet engines, etc. These flows are highly unsteady, compressible and turbulent and it involves mass entrainment coupled with acoustics phenomenon. Variation of flow dynamics in an angled cavity with a heated and cooled pylon upstream to the cavity with spatial combinations of heat flux addition and removal to the wall studied numerically. The goal of study is to investigate the effect of energy addition, removal to the cavity walls and pylon cavity flow dynamics. Preliminary steady state numerical simulations on inclined cavities with heat addition have shown that wall pressure profiles, as well as the recirculation, are influenced by heat transfer to the compressible fluid medium. Such a hybrid control of cavity flow dynamics in the form of heat transfer and pylon geometry can open out greater opportunities in enhancement of mixing and flame holding requirements of supersonic combustors. Addition of pylon upstream to the cavity reduces the acoustic oscillations emanating from the geometry. A numerical unsteady analysis of supersonic flow past cavities exposed to cavity wall heating and cooling with heated and cooled pylon helps to get a clear idea about the oscillation suppression in the cavity. A Cavity of L/D 4 and aft wall angle 22 degree with an upstream pylon of h/D=1.5 mm with a wall angle 29 degree exposed to supersonic flow of Mach number 2 and heat flux of 40 W/cm² and -40 W/cm² modeled for the above study. In the preliminary study, the domain is modeled and validated numerically with a turbulence model of SST k-ω using an HLLC implicit scheme. Both qualitative and quantitative flow data extracted and analyzed using advanced CFD tools. Flow visualization is done using numerical Schlieren method as the fluid medium gives the density variation. The heat flux addition to the wall increases the secondary vortex size of the cavity and removal of energy leads to the reduction in vortex size. The flow field turbulence seems to be increasing at higher heat flux. The shear layer thickness increases as heat flux increases. The steady state analysis of wall pressure shows that there is variation on wall pressure as heat flux increases. Shift in frequency of unsteady wall pressure analysis is an interesting observation for the above study. The time averaged skin friction seems to be reducing at higher heat flux due to the variation in viscosity of fluid inside the cavity.

Keywords: energy addition, frequency shift, Numerical Schlieren, shear layer, vortex evolution

Procedia PDF Downloads 120

Authors: Michael N. O'Sullivan, Con Sheahan

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Mass customization is the idea of offering custom products or services to satisfy the needs of each individual customer while maintaining the efficiency of mass production. Technologies like 3D printing and artificial intelligence have many start-ups hoping to capitalize on this dream of creating personalized products at an affordable price, and well established companies scrambling to innovate and maintain their market share. However, the majority of them are failing as they struggle to understand one key question – where does customization make sense? Customization and personalization only make sense where the value of the perceived benefit outweighs the cost to implement it. In other words, will people pay for it? Looking at the Kano Model makes it clear that it depends on the product. In products where customization is an inherent need, like prosthetics, mass customization technologies can be highly beneficial. However, for products that already sell as a standard, like headphones, offering customization is likely only an added bonus, and so the product development team must figure out if the customers’ perception of the added value of this feature will outweigh its premium price tag. This can be done through the use of a ‘serious game,’ whereby potential customers are given a limited budget to collaboratively buy and bid on potential features of the product before it is developed. If the group choose to buy customization over other features, then the product development team should implement it into their design. If not, the team should prioritize the features on which the customers have spent their budget. The level of customization purchased can also be translated to an appropriate production method, for example, the most expensive type of customization would likely be free-form design and could be achieved through digital fabrication, while a lower level could be achieved through short batch production. Twenty-five teams of final year students from design, engineering, construction and technology tested this methodology when bringing a product from concept through to production specification, and found that it allowed them to confidently decide what level of customization, if any, would be worth offering for their product, and what would be the best method of producing it. They also found that the discussion and negotiations between players during the game led to invaluable insights, and often decided to play a second game where they offered customers the option to buy the various customization ideas that had been discussed during the first game.

Keywords: Kano model, mass customization, new product development, serious game

Procedia PDF Downloads 114

Authors: Laura Butabayeva, Svetlana Ismagulova, Gulbarshin Nogaibayeva, Maiya Temirbayeva, Aidana Zhussip

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The article presents the main results of the study conducted within the grant project Organizational and methodological foundations for ensuring the inclusiveness of school students’ career guidance. The main aim of the Project is to study the issue of the absence of developed mechanisms, coordinating the activities of all stakeholders in preparing school students for conscious career choice, considering their individual opportunities and special educational needs. To achieve the aim of the Project, according to the implementation plan, the analysis of foreign and national literature on the studied problem, as well as the study of the state of school students’ career guidance and their socialization in the context of inclusive education were conducted, the international experience on this issue was explored. The analysis of the national literature conducted by the authors has shown the State’s annual increase in the number of students with special educational needs as well as the rapid demand of the labor market, influencing their professional self-determination in modern society. The participants from 5 State regions, including students, their parents, general secondary schools’ administration, and educators, as well as employers, took part in the study, considering the geographical location: south, north, west, center and the cities of republican significance. To ensure the validity of the study’s results, the triangulation method was utilized, including both qualitative and quantitative methods. The data were analyzed independently and compared with each other. Ethical principles were considered during all stages of the study. The characteristics of the system of career guidance in the modern school, the role, and the involvement of stakeholders in the system of career guidance, the opinions of educators on school students’ preparedness for career choice, and the factors impeding the effectiveness of career guidance in schools were examined. The problem of stakeholders’ disunity and inconsistency, causing the systemic labor market distortions, the growth of low-skilled labor and the unemployed, including people with special educational needs, were revealed. The other issue identified by the researchers was educators’ insufficient readiness for students’ career choice preparation in the context of inclusive education. To study cutting-edge experience in organizing a system of career guidance for young people and develop mechanisms coordinating the actions of all stakeholders in preparing students for career choice, the institutions of career guidance in France, Japan, and Germany were explored by the researchers. To achieve the aim of the Project, the systemic innovative Model of school students’ professional self-determination, considering their individual opportunities and special educational needs, has been developed based on the study results and international experience. The main principles of this Model are consistency, accessibility, inclusiveness, openness, coherence, and continuity. The perspectives of students’ career guidance development in the context of inclusive education have been suggested.

Keywords: career guidance, inclusive education, special educational needs, psychological and pedagogical support, model of school students’ professional self-determination

Procedia PDF Downloads 18

Authors: Aymen Arfaoui, Abdelkader Soumaya, Noureddine Ben Ayed

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The study region is located in the Tunisian Dorsal Backland (Central Mediterranean), which is the easternmost part of the Saharan Atlas mountain range, trending southwest-northeast. Based on our fieldwork, seismic tomography images, seismicity, and previous studies, we propose an interpretation of the relationship between the surface deformation and fault kinematics in the study area and the internal dynamic processes acting in the Central Mediterranean from the Cenozoic to the present. The subduction and dynamics of internal forces beneath the complicated Maghrebides mobile belt have an impact on the Tertiary and Quaternary tectonic regimes in the Pelagian and Atlassic foreland that is part of our study region. The left lateral reactivation of the major "Tunisian N-S Axis fault" and the development of a compressional relay between the Hammamet Korbous and Messella-Ressas faults are possibly a result of tectonic stresses due to the slab roll-back following the Africa/Eurasia convergence. After the slab segmentation and its eastward migration (5–4 Ma) and the formation of the Strait of Sicily "rift zone" further east, a transtensional tectonic regime has been installed in this area. According to seismic tomography images, the STEP fault of the "North-South Axis" at Hammamet-Korbous coincides with the western edge of the "Slab windows" of the Sicilian Channel and the eastern boundary of the positive anomalies attributed to the residual Slab of Tunisia. On the other hand, significant E-W Plio-Quaternary tectonic activity may be observed along the eastern portion of this STEP fault system in the Grombalia zone as a result of recent vertical lithospheric motion in response to the lateral slab migration eastward to Sicily Channel. According to SKS fast splitting directions, the upper mantle flow pattern beneath Tunisian Dorsal is parallel to the NE-SW to E-W orientation of the Shmin identified in the study area, similar to the Plio-Quaternary extensional orientation in the Central Mediterranean. Additionally, the removal of the lithosphere and the subsequent uplift of the sub-lithospheric mantle beneath the topographic highs of the Dorsal and its surroundings may be the cause of the dominant extensional to transtensional Quaternary regime. The occurrence of strike-slip and extensional seismic events in the Pelagian block reveals that the regional transtensional tectonic regime persists today. Finally, we believe that the geodynamic history of the study area since the Cenozoic is primarily influenced by the preexisting weak zones, the African slab detachment, and the upper mantle flow pattern in the central Mediterranean.

Keywords: Tunisia, lithospheric discontinuity (STEP fault), geodynamic evolution, Tunisian dorsal backland, strike-slip fault, seismic tomography, seismicity, central Mediterranean

Procedia PDF Downloads 46

Authors: Mina Shokry Hanna Saleh Tadros

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The centrality of the Petroleum Industry in the world energy is undoubted. The world economy almost runs and depends on petroleum. Petroleum industry is a multi-trillion industry; it turns otherwise poor and underdeveloped countries into wealthy nations and thrusts them at the center of international diplomacy. Although these developing nations lack the necessary technology to explore and exploit petroleum resources they are not without help as developed nations, represented by their multinational corporations are ready and willing to provide both the technical and managerial expertise necessary for the development of this natural resource. However, the exploration of these petroleum resources comes with, sometimes, grave, concomitant consequences. These consequences are especially pronounced with respect to the environment. From the British Petroleum Oil rig explosion and the resultant oil spillage and pollution in New Mexico, United States to the Mobil Oil spillage along Egyptian coast, the story and consequence is virtually the same. Egypt’s delta Region produces Nigeria’s petroleum which accounts for more than ninety-five percent of Nigeria’s foreign exchange earnings. Between 1999 and 2007, Egypt earned more than $400 billion from petroleum exports. Nevertheless, petroleum exploration and exploitation has devastated the Delta environment. From oil spillage which pollutes the rivers, farms and wetlands to gas flaring by the multi-national corporations; the consequences is similar-a region that has been devastated by petroleum exploitation. This paper thus seeks to examine the consequences and impact of petroleum pollution in the Egypt Delta with particular reference on the right of the people of Niger Delta to a healthy environment. The paper further seeks to examine the relevant international, regional instrument and Nigeria’s municipal laws that are meant to protect the result of the people of the Egypt Delta and their enforcement by the Nigerian State. It is quite worrisome that the Egypt Delta Region and its people have suffered and are still suffering grave violations of their right to a healthy environment as a result of petroleum exploitation in their region. The Egypt effort at best is half-hearted in its protection of the people’s right.

Keywords: crude oil, fire, floating roof tank, lightning protection systemenvironment, exploration, petroleum, pollutionDuvernay petroleum system, oil generation, oil-source correlation, Re-Os

Procedia PDF Downloads 49

Authors: Cristian Păuna

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In the first decades of the 21st century, in the electronic trading environment, algorithmic capital investments became the primary tool to make a profit by speculations in financial markets. A significant number of traders, private or institutional investors are participating in the capital markets every day using automated algorithms. The autonomous trading software is today a considerable part in the business intelligence system of any modern financial activity. The trading decisions and orders are made automatically by computers using different mathematical models. This paper will present one of these models called Price Prediction Line. A mathematical algorithm will be revealed to build a reliable trend line, which is the base for limit conditions and automated investment signals, the core for a computerized investment system. The paper will guide how to apply these tools to generate entry and exit investment signals, limit conditions to build a mathematical filter for the investment opportunities, and the methodology to integrate all of these in automated investment software. The paper will also present trading results obtained for the leading German financial market index with the presented methods to analyze and to compare different automated investment algorithms. It was found that a specific mathematical algorithm can be optimized and integrated into an automated trading system with good and sustained results for the leading German Market. Investment results will be compared in order to qualify the presented model. In conclusion, a 1:6.12 risk was obtained to reward ratio applying the trigonometric method to the DAX Deutscher Aktienindex on 24 months investment. These results are superior to those obtained with other similar models as this paper reveal. The general idea sustained by this paper is that the Price Prediction Line model presented is a reliable capital investment methodology that can be successfully applied to build an automated investment system with excellent results.

Keywords: algorithmic trading, automated trading systems, high-frequency trading, DAX Deutscher Aktienindex

Procedia PDF Downloads 106

Authors: Jessie Rapoza, Petr Moroshkin, Jimmy Xu

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Chirality is omnipresent, in nature, in life, and in the field of physics. One intriguing example is the homochirality that has remained a great secret of life. Another is the pairs of mirror-image molecules – enantiomers. They are identical in atomic composition and therefore indistinguishable in the scalar physical properties. Yet, they can be either therapeutic or toxic, depending on their chirality. Recent studies suggest a potential link between abnormal levels of certain D-amino acids and some serious health impairments, including schizophrenia, amyotrophic lateral sclerosis, and potentially cancer. Although indistinguishable in their scalar properties, the chirality of a molecule reveals itself in interaction with the surrounding of a certain chirality, or more generally, a broken mirror-symmetry. In this work, we report on a system for chiral molecule detection, in which the mirror-symmetry is doubly broken, first by asymmetric structuring a nanopatterned plasmonic surface than by the incidence of circularly polarized light (CPL). In this system, the incident circularly-polarized light induces a surface plasmon polariton (SPP) wave, propagating along the asymmetric plasmonic surface. This SPP field itself is chiral, evanescently bound to a near-field zone on the surface (~10nm thick), but with an amplitude greatly intensified (by up to 104) over that of the incident light. It hence probes just the molecules on the surface instead of those in the volume. In coupling to molecules along its path on the surface, the chiral SPP wave favors one chirality over the other, allowing for chirality detection via the change in an optical rectification current measured at the edges of the sample. The asymmetrically structured surface converts the high-frequency electron plasmonic-oscillations in the SPP wave into a net DC drift current that can be measured at the edge of the sample via the mechanism of optical rectification. The measured results validate these design concepts and principles. The observed optical rectification current exhibits a clear differentiation between a pair of enantiomers. Experiments were performed by focusing a 1064nm CW laser light at the sample - a gold grating microchip submerged in an approximately 1.82M solution of either L-arabinose or D-arabinose and water. A measurement of the current output was then recorded under both rights and left circularly polarized lights. Measurements were recorded at various angles of incidence to optimize the coupling between the spin-momentums of the incident light and that of the SPP, that is, spin-momentum locking. In order to suppress the background, the values of the photocurrent for the right CPL are subtracted from those for the left CPL. Comparison between the two arabinose enantiomers reveals a preferential signal response of one enantiomer to left CPL and the other enantiomer to right CPL. In sum, this work reports on the first experimental evidence of the feasibility of chiral molecule detection via optical rectification in a metal meta-grating. This nanoscale interfaced electrical detection technology is advantageous over other detection methods due to its size, cost, ease of use, and integration ability with read-out electronic circuits for data processing and interpretation.

Keywords: Chirality, detection, molecule, spin

Procedia PDF Downloads 75

Authors: Keroles Akram Saed Ghatas

Abstract:

The near future will bring significant changes in modern organizations and management due to the growing role of intangible assets and knowledge workers. The area of copyright, intellectual property, digital (intangible) assets and media redistribution appears to be one of the greatest challenges facing business and society in general and management sciences and organizations in particular. The proposed article examines the views and perceptions of fairness in digital media sharing among Harvard Law School's LL.M.s. Students, based on 50 qualitative interviews and 100 surveys. The researcher took an ethnographic approach to her research and entered the Harvard LL.M. in 2016. at, a Face book group that allows people to connect naturally and attend in-person and private events more easily. After listening to numerous students, the researcher conducted a quantitative survey among 100 respondents to assess respondents' perceptions of fairness in digital file sharing in various contexts (based on media price, its availability, regional licenses, copyright holder status, etc.). to understand better . .). Based on the survey results, the researcher conducted long-term, open-ended and loosely structured ethnographic interviews (50 interviews) to further deepen the understanding of the results. The most important finding of the study is that Harvard lawyers generally support digital piracy in certain contexts, despite having the best possible legal and professional knowledge. Interestingly, they are also more accepting of working for the government than the private sector. The results of this study provide a better understanding of how “fairness” is perceived by the younger generation of lawyers and pave the way for a more rational application of licensing laws.

Keywords: cognitive impairments, communication disorders, death penalty, executive function communication disorders, cognitive disorders, capital murder, executive function death penalty, egyptian law absence, justice, political cases piracy, digital sharing, perception of fairness, legal profession

Procedia PDF Downloads 38

Authors: Andrew N. Saylor, James R. Peters

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Scoliosis is a complex 3D deformity of the thoracic and lumbar spines, clinically diagnosed by measurement of a Cobb angle of 10 degrees or more on a coronal X-ray. The Cobb angle is the angle made by the lines drawn along the proximal and distal endplates of the respective proximal and distal vertebrae comprising the curve. Traditionally, Cobb angles are measured manually using either a marker, straight edge, and protractor or image measurement software. The task of measuring the Cobb angle can also be represented by a function taking the spine geometry rendered using X-ray imaging as input and returning the approximate angle. Although the form of such a function may be unknown, it can be approximated using artificial neural networks (ANNs). The performance of ANNs is affected by many factors, including the choice of activation function and network architecture; however, the effects of these parameters on the accuracy of scoliotic deformity measurements are poorly understood. Therefore, the objective of this study was to systematically investigate the effect of ANN architecture and activation function on Cobb angle measurement from the coronal X-rays of scoliotic subjects. The data set for this study consisted of 609 coronal chest X-rays of scoliotic subjects divided into 481 training images and 128 test images. These data, which included labeled Cobb angle measurements, were obtained from the SpineWeb online database. In order to normalize the input data, each image was resized using bi-linear interpolation to a size of 500 × 187 pixels, and the pixel intensities were scaled to be between 0 and 1. A fully connected (dense) ANN with a fixed cost function (mean squared error), batch size (10), and learning rate (0.01) was developed using Python Version 3.7.3 and TensorFlow 1.13.1. The activation functions (sigmoid, hyperbolic tangent [tanh], or rectified linear units [ReLU]), number of hidden layers (1, 3, 5, or 10), and number of neurons per layer (10, 100, or 1000) were varied systematically to generate a total of 36 network conditions. Stochastic gradient descent with early stopping was used to train each network. Three trials were run per condition, and the final mean squared errors and mean absolute errors were averaged to quantify the network response for each condition. The network that performed the best used ReLU neurons had three hidden layers, and 100 neurons per layer. The average mean squared error of this network was 222.28 ± 30 degrees2, and the average mean absolute error was 11.96 ± 0.64 degrees. It is also notable that while most of the networks performed similarly, the networks using ReLU neurons, 10 hidden layers, and 1000 neurons per layer, and those using Tanh neurons, one hidden layer, and 10 neurons per layer performed markedly worse with average mean squared errors greater than 400 degrees2 and average mean absolute errors greater than 16 degrees. From the results of this study, it can be seen that the choice of ANN architecture and activation function has a clear impact on Cobb angle inference from coronal X-rays of scoliotic subjects.

Keywords: scoliosis, artificial neural networks, cobb angle, medical imaging

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Authors: Jessica BrodeFrank, Beka Bryer, Lacey Wilson, Sierra Van Ryck deGroot

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Museums are enjoying quite the moment in pop culture. From discussions of labor in Bob’s Burger to introducing cultural repatriation in The Black Panther, discussions of various museum issues are making their way to popular media. “Talking Back to Hollywood” analyzes the impact museums have on movies and television. The paper will highlight a series of cultural cameos and discuss what each reveals about critical themes in museums: repatriation, labor, obfuscated histories, institutional legacies, artificial intelligence, and holograms. Using a mixed methods approach to include surveys, descriptive research, thematic analysis, and context analysis, the authors of this paper will explore how we, as the museum staff, might begin to cite museums and movies together as texts. Drawing from their experience working in museums and public history, this contingent of mid-career professionals will highlight the impact museums have had on movies and television and the didactic lessons these portrayals can provide back to cultural heritage professionals. From tackling critical themes in museums such as repatriation, labor conditions/inequities, obfuscated histories, curatorial choice and control, institutional legacies, and more, this paper is grounded in the cultural zeitgeist of the 2000s and the message these media portrayals send to the public and the cultural heritage sector. In particular, the paper will examine how portrayals of AI, holograms, and more technology can be used as entry points for necessary discussions with the public on mistrust, misinformation, and emerging technologies. This paper will not only expose the legacy and cultural understanding of the museum field within popular culture but also will discuss actionable ways that public historians can use these portrayals as an entry point for discussions with the public, citing literature reviews and quantitative and qualitative analysis of survey results. As Hollywood is talking about museums, museums can use that to better connect to the audiences who feel comfortable at the cinema but are excluded from the museum.

Keywords: museums, public memory, representation, popular culture

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Authors: John Lamb, Robert Epstein, Vasundhara L. Bhupathi, Sanjeev Kumar Marimekala

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As we enter the new era of Artificial Intelligence (AI) and Cloud Computing, we mostly rely on the Machine and Natural Language Processing capabilities of AI, and Energy Efficient Hardware and Software Devices in almost every industry sector. In these industry sectors, much emphasis is on developing new and innovative methods for producing and conserving energy and sustaining the depletion of natural resources. The core pillars of sustainability are economic, environmental, and social, which is also informally referred to as the 3 P's (People, Planet and Profits). The 3 P's play a vital role in creating a core Sustainability Model in the Enterprise. Natural resources are continually being depleted, so there is more focus and growing demand for renewable energy. With this growing demand, there is also a growing concern in many industries on how to reduce carbon emissions and conserve natural resources while adopting sustainability in corporate business models and policies. In our paper, we would like to discuss the driving forces such as Climate changes, Natural Disasters, Pandemic, Disruptive Technologies, Corporate Policies, Scaled Business Models and Emerging social media and AI platforms that influence the 3 main pillars of Sustainability (3P’s). Through this paper, we would like to bring an overall perspective on enterprise strategies and the primary focus on bringing cultural shifts in adapting energy-efficient operational models. Overall, many industries across the globe are incorporating core sustainability principles such as reducing energy costs, reducing greenhouse gas (GHG) emissions, reducing waste and increasing recycling, adopting advanced monitoring and metering infrastructure, reducing server footprint and compute resources (Shared IT services, Cloud computing, and Application Modernization) with the vision for a sustainable environment.

Keywords: climate change, pandemic, disruptive technology, government policies, business model, machine learning and natural language processing, AI, social media platform, cloud computing, advanced monitoring, metering infrastructure

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Authors: Benachour Karima

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Pollination is a key factor in crop production and the presence of insect pollinators, mainly wild bees, is essential for improving yields. In this work, visiting apoids of two vegetable crops, the turnip (Brassica rapa L.) and the radish (Raphanus sativus L.) (Brassicaceae) were recorded during flowering times of 2003 and 2004 in Constantine area (36°22’N 06°37’E, 660 m). The observations were conducted in a plot of approximately 308 m2 of the Institute of Nutrition, Food and Food Technology (University of Mentouri Brothers). To estimate the density of bees (per 100 flowers or m2), 07 plots (01m2 for each one) are defined from the edge of the culture and in the first two rows. From flowering and every two days, foraging insects are recorded from 09 am until 17 pm (Gmt+1).The purpose of visit (collecting nectar, pollen or both) and pollinating efficiency (estimated by the number of flowers visited per minute and the number of positive visits) were noted for the most abundant bees on flowers. The action of pollinating insects is measured by comparing seed yields of 07 plots covered with tulle with 07 other accessible to pollinators. 04 families of Apoidea: Apidae, Halictidae, Andrenidae and Megachilidae were observed on the two plants. On turnip, the honeybee is the most common visitor (on average 214visites/ m2), it is followed by the Halictidae Lasioglossum mediterraneum whose visits are less intense (20 individuals/m2). Visits by Andrenidae, represented by several species such as Andrena lagopus, A.flavipes, A.agilissima and A.rhypara were episodic. The honeybee collected mainly nectar, its visits were all potentially fertilizing (contact with stigma) and more frequent (on average 14 flowers/min. L.mediterraneum visited only 05 flrs/min, it collected mostly the two products together and all its visits were also positive. On radish, the wild bee Ceratina cucurbitina recorded the highest number of visits (on average 06 individuals/100flo wers), the Halictidae represented mainly by L.mediterraneum, and L.malachurum, L.pauxillum were less abundant. C.cucurbitina visited on average 10 flowers /min and all its visits are positive. Visits of Halictidae were less frequent (05-06 flowers/min) and not all fertilizing. Seed yield of Brassica rapa (average number of pods /plant, seeds/ pods and average weight of 1000 seeds) was significantly higher in the presence of pollinators. Similarly, the pods of caged plants gave a percentage of aborted seeds (10.3%) significantly higher than that obtained on free plants (4.12%), the pods of caged plants also gave a percentage of malformed seeds (1.9%) significantly higher than that of the free plants (0.9%). For radish, the seed yield in the presence and absence of insects are almost similar. Only the percentage of malformed seeds (3.8%) obtained from the pods of caged plants was significantly higher in comparison with pods of free plants (1.9%). Following these results, it is clear that pollinators especially bees are essential for the production and improvement of crop yields and therefore it is necessary to protect this fauna increasingly threatened.

Keywords: foraging behavior, honey bee, radish, seed yield, turnip, wild bee

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Authors: Athena Daniilidou, Maria Platsidou

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Recently developed scales addressed -specifically- teachers’ resilience. Although they profited from the field, they do not include some of the critical protective factors of teachers’ resilience identified in the literature. To address this limitation, we aimed at designing a more comprehensive scale for measuring teachers' resilience which encompasses various personal and environmental protective factors. To this end, two studies were carried out. In Study 1, 407 primary school teachers were tested with the new scale, the Teachers’ Protective Factors of Resilience Scale (TPFRS). Similar scales, such as the Multidimensional Teachers’ Resilience Scale and the Teachers’ Resilience Scale), were used to test the convergent validity, while the Maslach Burnout Inventory and the Teachers’ Sense of Efficacy Scale was used to assess the discriminant validity of the new scale. The factorial structure of the TPFRS was checked with confirmatory factor analysis and a good fit of the model to the data was found. Next, item response theory analysis using a two-parameter model (2PL) was applied to check the items within each factor. It revealed that 9 items did not fit the corresponding factors well and they were removed. The final version of the TPFRS includes 29 items, which assess six protective factors of teachers’ resilience: values and beliefs (5 items, α=.88), emotional and behavioral adequacy (6 items, α=.74), physical well-being (3 items, α=.68), relationships within the school environment, (6 items, α=.73) relationships outside the school environment (5 items, α=.84), and the legislative framework of education (4 items, α=.83). Results show that it presents a satisfactory convergent and discriminant validity. Study 2, in which 964 primary and secondary school teachers were tested, confirmed the factorial structure of the TPFRS as well as its discriminant validity, which was tested with the Schutte Emotional Intelligence Scale-Short Form. In conclusion, our results confirmed that the TPFRS is a valid instrument for assessing teachers' protective factors of resilience and it can be safely used in future research and interventions in the teaching profession. In conclusion, our results showed that the TPFRS is a new multi-dimensional instrument valid for assessing teachers' protective factors of resilience and it can be safely used in future research and interventions in the teaching profession.

Keywords: resilience, protective factors, teachers, item response theory

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Authors: Khadija Refouh

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Culture bound-expressions have been a bottleneck for Natural Language Processing (NLP) and comprehension, especially in the case of machine translation (MT). In the last decade, the field of machine translation has greatly advanced. Neural machine translation NMT has recently achieved considerable development in the quality of translation that outperformed previous traditional translation systems in many language pairs. Neural machine translation NMT is an Artificial Intelligence AI and deep neural networks applied to language processing. Despite this development, there remain some serious challenges that face neural machine translation NMT when translating culture bounded-expressions, especially for low resources language pairs such as Arabic-English and Arabic-French, which is not the case with well-established language pairs such as English-French. Machine translation of opaque idioms from English into French are likely to be more accurate than translating them from English into Arabic. For example, Google Translate Application translated the sentence “What a bad weather! It runs cats and dogs.” to “يا له من طقس سيء! تمطر القطط والكلاب” into the target language Arabic which is an inaccurate literal translation. The translation of the same sentence into the target language French was “Quel mauvais temps! Il pleut des cordes.” where Google Translate Application used the accurate French corresponding idioms. This paper aims to perform NMT experiments towards better translation of opaque idioms using high quality clean multilingual corpus. This Corpus will be collected analytically from human generated idiom translation. AutoML translation, a Google Neural Machine Translation Platform, is used as a custom translation model to improve the translation of opaque idioms. The automatic evaluation of the custom model will be compared to the Google NMT using Bilingual Evaluation Understudy Score BLEU. BLEU is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another. Human evaluation is integrated to test the reliability of the Blue Score. The researcher will examine syntactical, lexical, and semantic features using Halliday's functional theory.

Keywords: multilingual corpora, natural language processing (NLP), neural machine translation (NMT), opaque idioms

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Authors: Sukran Acikel, Mehmet Ekmekci, Otgonbayar Namkhai

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Lignite coal fields in western Turkey generally occurs in tensional Neogene basins bordered by major faults. Karacahisar coal field in Mugla province of western Turkey is a large Neogene basin filled with alternation of silisic and calcerous layers. The basement of the basin is composed of mainly karstified carbonate rocks of Mesozoic and schists of Paleozoic age. The basement rocks are exposed at highlands surrounding the basin. The basin fill deposits forms shallow, low yield and local aquifers whereas karstic carbonate rock masses forms the major aquifer in the region. The karstic aquifer discharges through a spring zone issuing at intersection of two major faults. Municipal water demand in Bodrum city, a touristic attraction area is almost totally supplied by boreholes tapping the karstic aquifer. A well field has been constructed on the eastern edge of the coal basin, which forms a ridge separating two Neogene basins. A major concern was raised about the plausible impact of mining activities on groundwater system in general and on water supply well field in particular. The hydrogeological studies carried out in the area revealed that the coal seam is located below the groundwater level. Mining operations will be affected by groundwater inflow to the pits, which will require dewatering measures. Dewatering activities in mine sites have two-sided effects: a) lowers the groundwater level at and around the pit for a safe and effective mining operation, b) continuous dewatering causes expansion of cone of depression to reach a spring, stream and/or well being utilized by local people, capturing their water. Plausible effect of mining operations on the flow of the spring zone was another issue of concern. Therefore, a detailed representative hydrogeological conceptual model of the site was developed on the basis of available data and field work. According to the hydrogeological conceptual model, dewatering of Neogene layers will not hydraulically affect the water supply wells, however, the ultimate perimeter of the open pit will expand to intersect the well field. According to the conceptual model, the coal seam is separated from the bottom by a thick impervious clay layer sitting on the carbonate basement. Therefore, the hydrostratigraphy does not allow a hydraulic interaction between the mine pit and the karstic carbonate rock aquifer. However, the structural setting in the basin suggests that deep faults intersecting the basement and the Neogene sequence will most probably carry the deep groundwater up to a level above the bottom of the pit. This will require taking necessary measure to lower the piezometric level of the carbonate rock aquifer along the faults. Dewatering the carbonate rock aquifer will reduce the flow to the spring zone. All findings were put together to recommend a strategy for safe and effective mining operation.

Keywords: conceptual model, dewatering, groundwater, mining operation

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Authors: S. F. Afzali, W. L. Wong

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The epizootic ulcerative syndrome (EUS) causes by the oomycete fungus, Aphanomyces invadans; known to be one of the infectious fish diseases for farmed and wild fishes in fresh and brackish-water from the Asia-pacific region, America and Africa. Although, EUS had been documented by the Office International des Epizooties (OIE) since 1995, hitherto, there is neither standard chemical agents that can be used for successful treatment of this destructive infection in the time of outbreak; nor available vaccine for prevention. Plant-based remedies in controlling fish diseases are gaining much attention recently as an alternative to chemical treatments, which possess negative effects to the environment and human. In present study, Sonneratia alba, a mangrove plant belongs to the Sonneratiaceae family, was screened in vitro and in vivo for its antifungal activity against A. invadans mycelium growth and its effects on fish innate immune system and disease resistant. The in vitro tests was performed using the disc diffusion methods with measurements of minimum inhibitory concentration (MIC) and inhibition zone. For in vivo study, the S. alba extract supplemented diets were administrated at 0.0, 1.0%, 3.0%, and 5.0% on healthy goldfish, Carassius auratus, which challenged with A. invadans zoospores (100 spores/ml). To compare the significant differences in the hematological and immunological parameters obtained from the experiments, the data were analysed using the SPSS. The methanol extract of S. alba effectively inhibited the mycelial growth of A. invadans at a minimum concentration of 1000 ppm for agar and filter paper diffusion experiments. In the agar diffusion test, 500 ppm of the extract inhibited the fungus mycelial growth up to 96 hours after exposure. The mycelial growth from the edge of the pre-inoculated A. invadans agar discs treated with S. alba extracts at concentrations of 100, 500 and 1000 ppm were 15, 8 and 0 mm respectively. The results of the filter paper disc test showed that the S. alba extract at its minimal inhibitory concentration (1000 ppm) has similar qualitative inhibitory effect as malachite green at 1 ppm and formalin at 250 ppm. According to the in vivo tests findings, in the infected fish fed with 3.0% and 5.0% supplementation diet, the numbers of white blood cell and myeloperoxidase activity significantly increased after the second week of treatment. Whilst the numbers of red blood cell significantly decreased in the infected fish fed with 0.0 and 1.0% supplementation diet. After the third week of feeding, significant increases in the total protein, albumin level, lysozyme activity were recorded in the infected fish fed with 3.0% and 5.0% supplementation diet. Also, the enriched diets increased the survival rate as compared to the untreated group that suffered from 90% mortality. The present study indicated that S. alba extract may inhibit the mycelial growth of A. invadans effectively, suggesting an alternative to other chemotherapeutic agents, which brought much environmental and health concerns to the public, for EUS treatment.

Keywords: fungal pathogen, goldfish, organic extract, treatment

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Authors: Shehu Isyaku

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The general objective of the study was to investigate ‘the relationship between the external business environment and the sustainability of micro, small and medium enterprises (MSMEs) in Jigawa state’, Nigeria. Specifically, the study was to examine the relationship between 1) the economic environment, 2) the social environment, 3) the technological environment, and 4) the political environment and the sustainability of MSMEs in Jigawa state, Nigeria. The study was drawn on Resource-Based View (RBV) Theory and Knowledge-Based View (KBV). The study employed a descriptive cross-sectional survey design. A researcher-made questionnaire was used to collect data from the 350 managers/owners who were selected using stratified, purposive and simple random sampling techniques. Data analysis was done using means and standard deviations, factor analysis, Correlation Coefficient, and Pearson Linear Regression analysis. The findings of the study revealed that the sustainability potentials of the managers/owners were rated as high potential (economic, environmental, and social sustainability using 5 5-point Likert scale. Mean ratings of effectiveness of the external business environment were; as highly effective. The results from the Pearson Linear Regression Analysis rejected the hypothesized non-significant effect of the external business environment on the sustainability of MSMEs. Specifically, there is a positive significant relationship between 1) economic environment and sustainability; 2) social environment and sustainability; 3) technological environment and sustainability and political environment and sustainability. The researcher concluded that MSME managers/owners have a high potential for economic, social and environmental sustainability and that all the constructs of the external business environment (economic environment, social environment, technological environment and political environment) have a positive significant relationship with the sustainability of MSMEs. Finally, the researcher recommended that 1) MSME managers/owners need to develop marketing strategies and intelligence systems to accumulate information about the competitors and customers' demands, 2) managers/owners should utilize the customers’ cultural and religious beliefs as an opportunity that should be utilized while formulating business strategies.

Keywords: business environment, sustainability, small and medium enterprises, external business environment

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Authors: Maria A. Castillo H., Andrés R. Leandro C.

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During the last decades, agricultural productivity in Latin America has increased with precision agriculture and more efficient agricultural technologies. The use of automated systems, satellite images, geographic information systems, and tools for data analysis, and artificial intelligence have contributed to making more effective strategic decisions. Twenty years ago, the state of Mérida, located in the Venezuelan Andes, reported the largest area covered by greenhouses in the country, where certified seeds of potatoes, vegetables, ornamentals, and flowers were produced for export and consumption in the central region of the country. In recent years, it is estimated that production under greenhouses has changed, and the area covered has decreased due to different factors, but there are few historical statistical data in sufficient quantity and quality to support this estimate or to be used for analysis and decision making. The objective of this study is to compare data collected about geoposition, use, and covered areas of the greenhouses in 2007 to data available in 2021, as support for the analysis of the current situation of horticultural production in the main municipalities of the state of Mérida. The document presents the development of the work in the diagnosis and integration of geographic coordinates in GIS and data analysis phases. As a result, an evaluation of the process is made, a dashboard is presented with the most relevant data along with the geographical coordinates integrated into GIS, and an analysis of the obtained information is made. Finally, some recommendations for actions are added, and works that expand the information obtained and its geographical traceability over time are proposed. This study contributes to granting greater certainty in the supporting data for the evaluation of social, environmental, and economic sustainability indicators and to make better decisions according to the sustainable development goals in the area under review. At the same time, the methodology provides improvements to the agricultural data collection process that can be extended to other study areas and crops.

Keywords: greenhouses, geographic information system, protected agriculture, data analysis, Venezuela

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Authors: Fatih Frank Alparslan

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The aviation industry is at a crucial turning point due to modern technologies, environmental concerns, and changing ways of transporting people and goods globally. The paper examines these challenges and opportunities comprehensively. It emphasizes the role of innovative management and advanced technology in shaping the future of air travel. This study begins with an overview of the current state of the aviation industry, identifying key areas where innovation and technology could be highly beneficial. It explores the latest advancements in airplane design, propulsion, and materials. These technological advancements are shown to enhance aircraft performance and environmental sustainability. The paper also discusses the use of artificial intelligence and machine learning in improving air traffic control, enhancing safety, and making flight operations more efficient. The management of these technologies is critically important. Therefore, the research delves into necessary changes in organization, culture, and operations to support innovation. It proposes a management approach that aligns with these modern technologies, underlining the importance of forward-thinking leaders who collaborate across disciplines and embrace innovative ideas. The paper addresses challenges in adopting these innovations, such as regulatory barriers, the need for industry-wide standards, and the impact of technological changes on jobs and society. It recommends that governments, aviation businesses, and educational institutions collaborate to address these challenges effectively, paving the way for a more innovative and eco-friendly aviation industry. In conclusion, the paper argues that the future of aviation relies on integrating new management practices with innovative technologies. It urges a collective effort to push beyond current capabilities, envisioning an aviation industry that is safer, more efficient, and environmentally responsible. By adopting a broad approach, this research contributes to the ongoing discussion about resolving the complex issues facing today's aviation sector, offering insights and guidance to prepare for future advancements.

Keywords: aviation innovation, technology integration, environmental sustainability, management strategies, multidisciplinary approach

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Authors: Gaurav Kumar Sinha

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In an era defined by the proliferation of data and the utilization of cloud computing environments, the efficient transfer and processing of big data workloads across multi-cloud platforms have emerged as critical challenges. This research paper embarks on a comprehensive exploration of the complexities associated with managing and optimizing big data in a multi-cloud ecosystem.The foundation of this study is rooted in the recognition that modern enterprises increasingly rely on multiple cloud providers to meet diverse business needs, enhance redundancy, and reduce vendor lock-in. As a consequence, managing data across these heterogeneous cloud environments has become intricate, necessitating innovative approaches to ensure data integrity, security, and performance.The primary objective of this research is to investigate strategies and techniques for enhancing the efficiency of data transfer and processing in multi-cloud scenarios. It recognizes that big data workloads are characterized by their sheer volume, variety, velocity, and complexity, making traditional data management solutions insufficient for harnessing the full potential of multi-cloud architectures.The study commences by elucidating the challenges posed by multi-cloud environments in the context of big data. These challenges encompass data fragmentation, latency, security concerns, and cost optimization. To address these challenges, the research explores a range of methodologies and solutions. One of the key areas of focus is data transfer optimization. The paper delves into techniques for minimizing data movement latency, optimizing bandwidth utilization, and ensuring secure data transmission between different cloud providers. It evaluates the applicability of dedicated data transfer protocols, intelligent data routing algorithms, and edge computing approaches in reducing transfer times.Furthermore, the study examines strategies for efficient data processing across multi-cloud environments. It acknowledges that big data processing requires distributed and parallel computing capabilities that span across cloud boundaries. The research investigates containerization and orchestration technologies, serverless computing models, and interoperability standards that facilitate seamless data processing workflows.Security and data governance are paramount concerns in multi-cloud environments. The paper explores methods for ensuring data security, access control, and compliance with regulatory frameworks. It considers encryption techniques, identity and access management, and auditing mechanisms as essential components of a robust multi-cloud data security strategy.The research also evaluates cost optimization strategies, recognizing that the dynamic nature of multi-cloud pricing models can impact the overall cost of data transfer and processing. It examines approaches for workload placement, resource allocation, and predictive cost modeling to minimize operational expenses while maximizing performance.Moreover, this study provides insights into real-world case studies and best practices adopted by organizations that have successfully navigated the challenges of multi-cloud big data management. It presents a comparative analysis of various multi-cloud management platforms and tools available in the market.

Keywords: multi-cloud environments, big data workloads, data transfer optimization, data processing strategies

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Authors: Håkon Riiber

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This paper explores the extent to which Russian ‘Active Measures’ play a role in contemporary Russian foreign policy and in what way the legacy of the Soviet Union is still apparent in these practices. The analysis draws on a set of case studies from the 21st century to examine these aspects, showing which ‘Active Measures’ features are old and which are new in the post-Cold War era. The paper highlights that the topic has gained significant academic and political interest in recent years, largely due to the aggressive posture of the Russian Federation on the world stage, exemplified through interventions in Estonia, Georgia, and Ukraine and interference in several democratic elections in the West. However, the paper argues that the long-term impact of these measures may have unintended implications for Russia. While Russia is unlikely to stop using Active Measures, increased awareness of the exploitation of weaknesses, institutions, or other targets may lead to greater security measures and an ability to identify and defend against these activities. The paper contends that Soviet-style ‘Active Measures’ from the Cold War era have been modernized and are now utilized to create an advantageous atmosphere for further exploitation to support contemporary Russian foreign policy. It offers three key points to support this argument: the reenergized legacy of the Cold War era, the use of ‘Active Measures’ in a number of cases in the 21st century, and the applicability of AM to the Russian approach to foreign policy. The analysis reveals that while this is not a new Russian phenomenon, it is still oversimplified and inaccurately understood by the West, which may result in a decreased ability to defend against these activities and limit the unwarranted escalation of the ongoing security situation between the West and Russia. The paper concludes that the legacy of Soviet-era Active Measures continues to influence Russian foreign policy, and modern technological advances have only made them more applicable to the current political climate. Overall, this paper sheds light on the important issue of Russian ‘Active Measures’ and the role they play in contemporary Russian foreign policy. It emphasizes the need for increased awareness, understanding, and security measures to defend against these activities and prevent further escalation of the security situation between the West and Russia.

Keywords: Russian espionage, active measures, disinformation, Russian intelligence

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Authors: Sabina-Adriana Floria, Marius Gavrilescu, Florin Leon, Silvia Curteanu, Costel Anton

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Neuroevolution is a subfield of artificial intelligence used to solve various problems in different application areas. Specifically, neuroevolution is a technique that applies biologically inspired methods to generate neural network architectures and optimize their parameters automatically. In this paper, we use different biologically inspired optimization algorithms in an ensemble strategy with the aim of training multilayer perceptron neural networks, resulting in regression models used to simulate the industrial chemical process of obtaining bricks from silicone-based materials. Installations in the raw ceramics industry, i.e., bricks, are characterized by significant energy consumption and large quantities of emissions. In addition, the initial conditions that were taken into account during the design and commissioning of the installation can change over time, which leads to the need to add new mixes to adjust the operating conditions for the desired purpose, e.g., material properties and energy saving. The present approach follows the study by simulation of a process of obtaining bricks from silicone-based materials, i.e., the modeling and optimization of the process. Optimization aims to determine the working conditions that minimize the emissions represented by nitrogen monoxide. We first use a search procedure to find the best values for the parameters of various biologically inspired optimization algorithms. Then, we propose an adaptive ensemble strategy that uses only a subset of the best algorithms identified in the search stage. The adaptive ensemble strategy combines the results of selected algorithms and automatically assigns more processing capacity to the more efficient algorithms. Their efficiency may also vary at different stages of the optimization process. In a given ensemble iteration, the most efficient algorithms aim to maintain good convergence, while the less efficient algorithms can improve population diversity. The proposed adaptive ensemble strategy outperforms the individual optimizers and the non-adaptive ensemble strategy in convergence speed, and the obtained results provide lower error values.

Keywords: optimization, biologically inspired algorithm, neuroevolution, ensembles, bricks, emission minimization

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Authors: Zahid Ullah, Atlas Khan

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This abstract focuses on the advancements in mathematical modeling and optimization techniques that play a crucial role in enhancing the efficiency, reliability, and performance of these systems. In this era of rapidly evolving technology, mathematical modeling and optimization offer powerful tools to tackle the complex challenges faced by control, signal processing, and energy systems. This abstract presents the latest research and developments in mathematical methodologies, encompassing areas such as control theory, system identification, signal processing algorithms, and energy optimization. The abstract highlights the interdisciplinary nature of mathematical modeling and optimization, showcasing their applications in a wide range of domains, including power systems, communication networks, industrial automation, and renewable energy. It explores key mathematical techniques, such as linear and nonlinear programming, convex optimization, stochastic modeling, and numerical algorithms, that enable the design, analysis, and optimization of complex control and signal processing systems. Furthermore, the abstract emphasizes the importance of addressing real-world challenges in control, signal processing, and energy systems through innovative mathematical approaches. It discusses the integration of mathematical models with data-driven approaches, machine learning, and artificial intelligence to enhance system performance, adaptability, and decision-making capabilities. The abstract also underscores the significance of bridging the gap between theoretical advancements and practical applications. It recognizes the need for practical implementation of mathematical models and optimization algorithms in real-world systems, considering factors such as scalability, computational efficiency, and robustness. In summary, this abstract showcases the advancements in mathematical modeling and optimization techniques for control, signal processing, and energy systems. It highlights the interdisciplinary nature of these techniques, their applications across various domains, and their potential to address real-world challenges. The abstract emphasizes the importance of practical implementation and integration with emerging technologies to drive innovation and improve the performance of control, signal processing, and energy.

Keywords: mathematical modeling, optimization, control systems, signal processing, energy systems, interdisciplinary applications, system identification, numerical algorithms

Procedia PDF Downloads 88