Search results for: multi-access edge computing

Authors: Amani Tahat, Serdal Kirmizialtin

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Trajectory fragment methods such as Markov State Models (MSM), Milestoning (MS) and Transition Path sampling are the prime choice of extending the timescale of all atom Molecular Dynamics simulations. In these approaches, a set of structures that covers the accessible phase space has to be chosen a priori using cluster analysis. Structural clustering serves to partition the conformational state into natural subgroups based on their similarity, an essential statistical methodology that is used for analyzing numerous sets of empirical data produced by Molecular Dynamics (MD) simulations. Local transition kernel among these clusters later used to connect the metastable states using a Markovian kinetic model in MSM and a non-Markovian model in MS. The choice of clustering approach in constructing such kernel is crucial since the high dimensionality of the biomolecular structures might easily confuse the identification of clusters when using the traditional hierarchical clustering methodology. Of particular interest, in the case of MS where the milestones are very close to each other, accurate determination of the milestone identity of the trajectory becomes a challenging issue. Throughout this work we present two cluster analysis methods applied to the cis–trans isomerism of dinucleotide AA. The choice of nucleic acids to commonly used proteins to study the cluster analysis is two fold: i) the energy landscape is rugged; hence transitions are more complex, enabling a more realistic model to study conformational transitions, ii) Nucleic acids conformational space is high dimensional. A diverse set of internal coordinates is necessary to describe the metastable states in nucleic acids, posing a challenge in studying the conformational transitions. Herein, we need improved clustering methods that accurately identify the AA structure in its metastable states in a robust way for a wide range of confused data conditions. The single linkage approach of the hierarchical clustering available in GROMACS MD-package is the first clustering methodology applied to our data. Self Organizing Map (SOM) neural network, that also known as a Kohonen network, is the second data clustering methodology. The performance comparison of the neural network as well as hierarchical clustering method is studied by means of computing the mean first passage times for the cis-trans conformational rates. Our hope is that this study provides insight into the complexities and need in determining the appropriate clustering algorithm for kinetic analysis. Our results can improve the effectiveness of decisions based on clustering confused empirical data in studying conformational transitions in biomolecules.

Keywords: milestoning, self organizing map, single linkage, structure clustering

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Authors: Eduardo Lanzagorta Garcia, Chaitra Venkatesh, Romina Pezzoli, Laura Gabriela Rodriguez Barroso, Declan Devine, Margaret E. Brennan Fournet

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New antimicrobial interventions are urgently required to combat rising global health and medical infection challenges. Here, an innovative antimicrobial technology, providing price competitive alternatives to antibiotics and readily integratable with currently technological systems is presented. Two cutting edge antimicrobial materials, antimicrobial peptides (AMPs) and uncompromised sustained Ag+ action from triangular silver nanoplates (TSNPs) reservoirs, are merged for versatile effective antimicrobial action where current approaches fail. Antimicrobial peptides (AMPs) exist widely in nature and have recently been demonstrated for broad spectrum of activity against bacteria, viruses, and fungi. TSNP’s are highly discrete, homogenous and readily functionisable Ag+ nanoreseviors that have a proven amenability for operation within in a wide range of bio-based settings. In a design for advanced antimicrobial sustainable plastics, antimicrobial TSNPs are formulated for processing within biodegradable biopolymers. Histone H5 AMP was selected for its reported strong antimicrobial action and functionalized with the TSNP (AMP-TSNP) in a similar fashion to previously reported TSNP biofunctionalisation methods. A synergy between the propensity of biopolymers for degradation and Ag+ release combined with AMP activity provides a novel mechanism for the sustained antimicrobial action of biopolymeric thin films. Nanoplates are transferred from aqueous phase to an organic solvent in order to facilitate integration within hydrophobic polymers. Extrusion is used in combination with calendering rolls to create thin polymerc film where the nanoplates are embedded onto the surface. The resultant antibacterial functional films are suitable to be adapted for food packing and biomedical applications. TSNP synthesis were synthesized by adapting a previously reported seed mediated approach. TSNP synthesis was scaled up for litre scale batch production and subsequently concentrated to 43 ppm using thermally controlled H2O removal. Nanoplates were transferred from aqueous phase to an organic solvent in order to facilitate integration within hydrophobic polymers. This was acomplised by functionalizing the TSNP with thiol terminated polyethylene glycol and using centrifugal force to transfer them to chloroform. Polycaprolactone (PCL) and Polylactic acid (PLA) were individually processed through extrusion, TSNP and AMP-TSNP solutions were sprayed onto the polymer immediately after exiting the dye. Calendering rolls were used to disperse and incorporate TSNP and TSNP-AMP onto the surface of the extruded films. Observation of the characteristic blue colour confirms the integrity of the TSNP within the films. Antimicrobial tests were performed by incubating Gram + and Gram – strains with treated and non-treated films, to evaluate if bacterial growth was reduced due to the presence of the TSNP. The resulting films successfully incorporated TSNP and AMP-TSNP. Reduced bacterial growth was observed for both Gram + and Gram – strains for both TSNP and AMP-TSNP compared with untreated films indicating antimicrobial action. The largest growth reduction was observed for AMP-TSNP treated films demonstrating the additional antimicrobial activity due to the presence of the AMPs. The potential of this technology to impede bacterial activity in food industry and medical surfaces will forge new confidence in the battle against antibiotic resistant bacteria, serving to greatly inhibit infections and facilitate patient recovery.

Keywords: antimicrobial, biodegradable, peptide, polymer, nanoparticle

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Authors: Stefan Papastefanou

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Artificial intelligence (AI) is an interdisciplinary field of computer science with the aim of creating intelligent machine behavior. Early approaches to AI have been configured to operate in very constrained environments where the behavior of the AI system was previously determined by formal rules. Knowledge was presented as a set of rules that allowed the AI system to determine the results for specific problems; as a structure of if-else rules that could be traversed to find a solution to a particular problem or question. However, such rule-based systems typically have not been able to generalize beyond the knowledge provided. All over the world and especially in IT-heavy industries such as the United States, the European Union, Singapore, and China, machine learning has developed to be an immense asset, and its applications are becoming more and more significant. It has to be examined how such products of machine learning models can and should be protected by IP law and for the purpose of this paper patent law specifically, since it is the IP law regime closest to technical inventions and computing methods in technical applications. Genetic breeding models are currently less popular than recursive neural network method and deep learning, but this approach can be more easily described by referring to the evolution of natural organisms, and with increasing computational power; the genetic breeding method as a subset of the evolutionary algorithms models is expected to be regaining popularity. The research method focuses on patentability (according to the world’s most significant patent law regimes such as China, Singapore, the European Union, and the United States) of AI inventions and machine learning. Questions of the technical nature of the problem to be solved, the inventive step as such, and the question of the state of the art and the associated obviousness of the solution arise in the current patenting processes. Most importantly, and the key focus of this paper is the problem of patenting inventions that themselves are developed through machine learning. The inventor of a patent application must be a natural person or a group of persons according to the current legal situation in most patent law regimes. In order to be considered an 'inventor', a person must actually have developed part of the inventive concept. The mere application of machine learning or an AI algorithm to a particular problem should not be construed as the algorithm that contributes to a part of the inventive concept. However, when machine learning or the AI algorithm has contributed to a part of the inventive concept, there is currently a lack of clarity regarding the ownership of artificially created inventions. Since not only all European patent law regimes but also the Chinese and Singaporean patent law approaches include identical terms, this paper ultimately offers a comparative analysis of the most relevant patent law regimes.

Keywords: algorithms, inventor, genetic breeding models, machine learning, patentability

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Authors: Alejandro Adorjan

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Universities are motivated to understand the factor contributing to low retention of engineering undergraduates. While precollege students for engineering increases, the number of engineering graduates continues to decrease and attrition rates for engineering undergraduates remains high. Calculus 1 (C1) is the entry point of most undergraduate Engineering Science and often a prerequisite for Computing Curricula courses. Mathematics continues to be a major hurdle for engineering students and many students who drop out from engineering cite specifically Calculus as one of the most influential factors in that decision. In this context, creating course activities that increase retention and motivate students to obtain better final results is a challenge. In order to develop several competencies in our students of Software Engineering courses, Calculus 1 at Universidad ORT Uruguay focuses on developing several competencies such as capacity of synthesis, abstraction, and problem solving (based on the ACM/AIS/IEEE). Every semester we try to reflect on our practice and try to answer the following research question: What kind of teaching approach in Calculus 1 can we design to retain students and obtain better results? Since 2010, Universidad ORT Uruguay offers a six-week summer noncompulsory bridge course of preparatory math (to bridge the math gap between high school and university). Last semester was the first time the Department of Mathematics offered the course while students were enrolled in C1. Traditional lectures in this bridge course lead to just transcribe notes from blackboard. Last semester we proposed a Hands On Lab course using Geogebra (interactive geometry and Computer Algebra System (CAS) software) as a Math Driven Development Tool. Students worked in a computer laboratory class and developed most of the tasks and topics in Geogebra. As a result of this approach, several pros and cons were found. It was an excessive amount of weekly hours of mathematics for students and, as the course was non-compulsory; the attendance decreased with time. Nevertheless, this activity succeeds in improving final test results and most students expressed the pleasure of working with this methodology. This teaching technology oriented approach strengthens student math competencies needed for Calculus 1 and improves student performance, engagement, and self-confidence. It is important as a teacher to reflect on our practice, including innovative proposals with the objective of engaging students, increasing retention and obtaining better results. The high degree of motivation and engagement of participants with this methodology exceeded our initial expectations, so we plan to experiment with more groups during the summer so as to validate preliminary results.

Keywords: calculus, engineering education, PreCalculus, Summer Program

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Authors: Ali R. Alshawawreh, Francisco Liébana-Cabanillas, Francisco J. Blanco-Encomienda

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Collaboration between marketing and business intelligence (BI) is crucial in today's ever-evolving business landscape. These two domains play pivotal roles in molding customers' experiential knowledge. Marketing insights offer valuable information regarding customer needs, preferences, and behaviors, thus refining marketing strategies and enhancing overall customer experiences. Conversely, BI facilitates data-driven decision-making, leading to heightened operational efficiency, product quality, and customer satisfaction. The analysis of customer data through BI unveils patterns and trends, informing product development, marketing campaigns, and customer service initiatives aimed at enriching experiences and knowledge. Customer experiential knowledge (CEK) encompasses customers' implicit comprehension of consumption experiences influenced by diverse factors, including social and cultural influences. This study primarily focuses on telecommunications companies in Jordan, scrutinizing how experiential customer knowledge mediates the relationship between marketing intelligence, business intelligence, and innovation in product and service offerings. Drawing on theoretical frameworks such as the resource-based view (RBV) and service-dominant logic (SDL), the research aims to comprehend how organizations utilize their resources, particularly knowledge, to foster innovation. Employing a quantitative research approach, the study collected and analyzed primary data to explore hypotheses. The chosen method was justified for its efficacy in handling large sample sizes. Structural equation modeling (SEM) facilitated by Smart PLS software evaluated the relationships between the constructs, followed by mediation analysis to assess the indirect associations in the model. The study findings offer insights into the intricate dynamics of organizational innovation, uncovering the interconnected relationships between business intelligence, customer experiential knowledge-based innovation (CEK-DI), marketing intelligence (MI), and product and service innovation (PSI), underscoring the pivotal role of advanced intelligence capabilities in developing innovative practices rooted in a profound understanding of customer experiences. Organizations equipped with cutting-edge BI tools are better positioned to devise strategies informed by precise insights into customer needs and behaviors. Furthermore, the positive impact of BI on PSI reaffirms the significance of data-driven decision-making in shaping the innovation landscape. Companies leveraging BI demonstrate adeptness in identifying market opportunities guiding the development of novel products and services. The substantial impact of CEK-DI on PSI highlights the crucial role of customer experiences in driving organizational innovation. Firms actively integrating customer insights into their innovation processes are more likely to create offerings aligned with customer expectations, fostering higher levels of product and service innovation. Additionally, the positive and significant effect of MI on CEK-DI underscores the critical role of market insights in shaping innovative strategies. While the relationship between MI and PSI is positive, a slightly weaker significance level indicates a nuanced association, suggesting that while MI contributes to innovation, other factors may also influence the innovation landscape, warranting further exploration. In conclusion, the study underscores the essential role of intelligence capabilities, particularly artificial intelligence, in driving innovation, emphasizing the necessity for organizations to leverage market and customer intelligence for effective and competitive innovation practices. Collaborative efforts between marketing and business intelligence serve as pivotal drivers of innovation, influencing experiential customer knowledge and shaping organizational strategies and practices, ultimately enhancing overall customer experiences and organizational performance.

Keywords: marketing intelligence, business intelligence, product, customer experiential knowledge-driven innovation

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Authors: Roven Tumaneng, Angelica Kristina Monzon, Ralph Sedricke Lapuz, Jose Don De Alban, Jennica Paula Masigan, Joanne Rae Pales, Laila Monera Pornel, Dennis Tablazon, Rizza Karen Veridiano, Jackie Lou Wenceslao, Edmund Leo Rico, Neil Aldrin Mallari

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Most protected area management plans in the Philippines, particularly the smaller and more remote islands suffer from insufficient baseline data, which should provide the bases for formulating measureable conservation targets and appropriate management interventions for these protected areas. Attempts to synthesize available data particularly on cultural and socio-economic characteristic of local peoples within and outside protected areas also suffer from the lack of comprehensive and detailed inventories, which should be considered in designing adaptive management interventions to be used for those protected areas. Mt Guiting-guiting Natural Park (MGGNP) located in Sibuyan Island is one of the poorly studied protected areas in the Philippines. In this study, we determined the highly biologically important areas of the protected area using Maximum Entropy approach (MaxEnt) from environmental predictors (i.e., topographic, bioclimatic,land cover, and soil image layers) derived from global remotely sensed data and point occurrence data of species of birds and trees recorded during field surveys on the island. A total of 23 trigger species of birds and trees was modeled and stacked to generate species richness maps for biological high conservation value areas (HCVAs). Forest habitat change was delineated using dual-polarised L-band ALOS-PALSAR mosaic data at 25 meter spatial resolution, taken at two acquisition years 2007 and 2009 to provide information on forest cover ad habitat change in the island between year 2007 and 2009. Determining the livelihood guilds were also conducted using the data gathered from171 household interviews, from which demographic and livelihood variables were extracted (i.e., age, gender, number of household members, educational attainment, years of residency, distance from forest edge, main occupation, alternative sources of food and resources during scarcity months, and sources of these alternative resources).Using Principal Component Analysis (PCA) and Kruskal-Wallis test, the diversity and patterns of forest resource use by people in the island were determined with particular focus on the economic activities that directly and indirectly affect the population of key species as well as to identify levels of forest resource use by people in different areas of the park.Results showed that there are gaps in the area occupied by the natural park, as evidenced by the mismatch of the proposed HCVAs and the existing perimeters of the park. We found out that subsistence forest gathering was the possible main driver for forest degradation out of the eight livelihood guilds that were identified in the park. Determining the high conservation areas and identifyingthe anthropogenic factors that influence the species richness and abundance of key species in the different management zone of MGGNP would provide guidance for the design of a protected area management plan and future monitoring programs. However, through intensive communication and consultation with government stakeholders and local communities our results led to setting conservation targets in local development plans and serve as a basis for the reposition of the boundaries and reconfiguration of the management zones of MGGNP.

Keywords: conservation gaps, livelihood guilds, MaxEnt, protected area

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Authors: Lauti Dwita Santy, Hermes Panggabean, Syahrir Andi Mangga

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The Bengkulu Basin is part of the Sunda Arc system, which is a classic convergent type margin that occur around the southern rim of the Eurasian continental (Sundaland) plate. The basin is located between deep sea trench (Mentawai Outer Arc high) and the volvanic/ magmatic Arc of the Barisan Mountains Range. To the northwest it is bounded by Padang High, to the northest by Barisan Mountains (Sumatra Fault Zone) to the southwest by Mentawai Fault Zone and to the southeast by Semangko High/ Sunda Strait. The stratigraphic succession and tectonic development can be broadly divided into four stage/ periods, i.e Late Jurassic- Early Cretaceous, Late Eocene-Early Oligocene, Late Oligocene-Early Miocene, Middle Miocene-Late Miocene and Pliocene-Plistocene, which are mainly controlled by the development of subduction activities. The Pre Tertiary Basement consist of sedimentary and shallow water limestone, calcareous mudstone, cherts and tholeiitic volcanic rocks, with Late Jurassic to Early Cretaceous in age. The sedimentation in this basin is depend on the relief of the Pre Tertiary Basement (Woyla Terrane) and occured into two stages, i.e. transgressive stage during the Latest Oligocene-Early Middle Miocene Seblat Formation, and the regressive stage during the Latest Middle Miocene-Pleistocene (Lemau, Simpangaur and Bintunan Formations). The Pre-Tertiary Faults were more intensive than the overlying cover, The Tertiary Rocks. There are two main fault trends can be distinguished, Northwest–Southwest Faults and Northeast-Southwest Faults. The NW-SE fault (Ketaun) are commonly laterally persistent, are interpreted to the part of Sumatran Fault Systems. They commonly form the boundaries to the Pre Tertiary basement highs and therefore are one of the faults elements controlling the geometry and development of the Tertiary sedimentary basins.The Northeast-Southwest faults was formed a conjugate set to the Northwest–Southeast Faults. In the earliest Tertiary and reactivated during the Plio-Pleistocene in a compressive mode with subsequent dextral displacement. The Block Faulting accross these two sets of faults related to approximate North–South compression in Paleogene time and produced a series of elongate basins separated by basement highs in the backarc and forearc region. The Bengkulu basin is interpreted having evolved from pull apart feature in the area southwest of the main Sumatra Fault System related to NW-SE trending in dextral shear.Based on Pyrolysis Yield (PY) vs Total Organic Carbon (TOC) diagram show that Seblat and Lemau Formation belongs to oil and Gas Prone with the quality of the source rocks includes into excellent and good (Lemau Formation), Fair and Poor (Seblat Formation). The fine-grained carbonaceous sediment of the Seblat dan Lemau Formations as source rocks, the coarse grained and carbonate sediments of the Seblat and Lemau Formations as reservoir rocks, claystone bed in Seblat and Lemau Formation as caprock. The source rocks maturation are late immature to early mature, with kerogen type II and III (Seblat Formation), and late immature to post mature with kerogen type I and III (Lemau Formation). The burial history show to 2500 m in depthh with paleo temperature reached 80oC. Trapping mechanism occur during Oligo–Miocene and Middle Miocene, mainly in block faulting system.

Keywords: fore arc, bengkulu, sumatra, sundaland, hydrocarbon, trapping mechanism

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Authors: Benjamin Blat Belmonte, Stephan Rinderknecht

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The transition to electric vehicles (EVs) stands at the forefront of innovative strategies designed to address environmental concerns and reduce fossil fuel dependency. As the number of EVs on the roads increases, so too does the potential for their integration into energy markets. This research dives deep into the transformative possibilities of using electric vehicle fleets, specifically electric bus fleets, not just as consumers but as active participants in the energy market. This paper investigates the feasibility and grid effects of electric vehicle fleet operators in the energy market. Our objective centers around a comprehensive exploration of the sector coupling domain, with an emphasis on the economic potential in both electricity and balancing markets. Methodologically, our approach combines data mining techniques with thorough pre-processing, pulling from a rich repository of electricity and balancing market data. Our findings are grounded in the actual operational realities of the bus fleet operator in Darmstadt, Germany. We employ a Mixed Integer Linear Programming (MILP) approach, with the bulk of the computations being processed on the High-Performance Computing (HPC) platform ‘Lichtenbergcluster’. Our findings underscore the compelling economic potential of EV fleets in the energy market. With electric buses becoming more prevalent, the considerable size of these fleets, paired with their substantial battery capacity, opens up new horizons for energy market participation. Notably, our research reveals that economic viability is not the sole advantage. Participating actively in the energy market also translates into pronounced positive effects on grid stabilization. Essentially, EV fleet operators can serve a dual purpose: facilitating transport while simultaneously playing an instrumental role in enhancing grid reliability and resilience. This research highlights the symbiotic relationship between the growth of EV fleets and the stabilization of the energy grid. Such systems could lead to both commercial and ecological advantages, reinforcing the value of electric bus fleets in the broader landscape of sustainable energy solutions. In conclusion, the electrification of transport offers more than just a means to reduce local greenhouse gas emissions. By positioning electric vehicle fleet operators as active participants in the energy market, there lies a powerful opportunity to drive forward the energy transition. This study serves as a testament to the synergistic potential of EV fleets in bolstering both economic viability and grid stabilization, signaling a promising trajectory for future sector coupling endeavors.

Keywords: electric vehicle fleet, sector coupling, optimization, electricity market, balancing market

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Authors: James Gallagher, Phillip Benachour

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As location aware mobile technologies become ever more omnipresent, the prospect of exploiting their context awareness to enforce learning approaches thrives. Utilizing the growing acceptance of ubiquitous computing, and the steady progress both in accuracy and battery usage of pervasive devices, we present a case study of remote location viewing, how the application can be utilized to support mobile learning in situ using an existing scenario. Through the case study we introduce a new innovative application: Mobipeek based around a request/response protocol for the viewing of a remote location and explore how this can apply both as part of a teacher lead activity and informal learning situations. The system developed allows a user to select a point on a map, and send a request. Users can attach messages alongside time and distance constraints. Users within the bounds of the request can respond with an image, and accompanying message, providing context to the response. This application can be used alongside a structured learning activity such as the use of mobile phone cameras outdoors as part of an interactive lesson. An example of a learning activity would be to collect photos in the wild about plants, vegetation, and foliage as part of a geography or environmental science lesson. Another example could be to take photos of architectural buildings and monuments as part of an architecture course. These images can be uploaded then displayed back in the classroom for students to share their experiences and compare their findings with their peers. This can help to fosters students’ active participation while helping students to understand lessons in a more interesting and effective way. Mobipeek could augment the student learning experience by providing further interaction with other peers in a remote location. The activity can be part of a wider study between schools in different areas of the country enabling the sharing and interaction between more participants. Remote location viewing can be used to access images in a specific location. The choice of location will depend on the activity and lesson. For example architectural buildings of a specific period can be shared between two or more cities. The augmentation of the learning experience can be manifested in the different contextual and cultural influences as well as the sharing of images from different locations. In addition to the implementation of Mobipeek, we strive to analyse this application, and a subset of other possible and further solutions targeted towards making learning more engaging. Consideration is given to the benefits of such a system, privacy concerns, and feasibility of widespread usage. We also propose elements of “gamification”, in an attempt to further the engagement derived from such a tool and encourage usage. We conclude by identifying limitations, both from a technical, and a mobile learning perspective.

Keywords: context aware, location aware, mobile learning, remote viewing

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Authors: Qiaosi Li, Frankie Kwan Kit Wong, Tung Fung

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Lightweight unmanned aerial vehicle (UAV) loading with novel sensors offers a low cost approach for data acquisition in complex environment. This study established a framework for applying UAV system in complex environment quick mapping and assessed the performance of UAV-based hyperspectral image and digital surface model (DSM) derived from photogrammetric point clouds for 13 species classification in wetland area Mai Po Inner Deep Bay Ramsar Site, Hong Kong. The study area was part of shallow bay with flat terrain and the major species including reedbed and four mangroves: Kandelia obovata, Aegiceras corniculatum, Acrostichum auerum and Acanthus ilicifolius. Other species involved in various graminaceous plants, tarbor, shrub and invasive species Mikania micrantha. In particular, invasive species climbed up to the mangrove canopy caused damage and morphology change which might increase species distinguishing difficulty. Hyperspectral images were acquired by Headwall Nano sensor with spectral range from 400nm to 1000nm and 0.06m spatial resolution image. A sequence of multi-view RGB images was captured with 0.02m spatial resolution and 75% overlap. Hyperspectral image was corrected for radiative and geometric distortion while high resolution RGB images were matched to generate maximum dense point clouds. Furtherly, a 5 cm grid digital surface model (DSM) was derived from dense point clouds. Multiple feature reduction methods were compared to identify the efficient method and to explore the significant spectral bands in distinguishing different species. Examined methods including stepwise discriminant analysis (DA), support vector machine (SVM) and minimum noise fraction (MNF) transformation. Subsequently, spectral subsets composed of the first 20 most importance bands extracted by SVM, DA and MNF, and multi-source subsets adding extra DSM to 20 spectrum bands were served as input in maximum likelihood classifier (MLC) and SVM classifier to compare the classification result. Classification results showed that feature reduction methods from best to worst are MNF transformation, DA and SVM. MNF transformation accuracy was even higher than all bands input result. Selected bands frequently laid along the green peak, red edge and near infrared. Additionally, DA found that chlorophyll absorption red band and yellow band were also important for species classification. In terms of 3D data, DSM enhanced the discriminant capacity among low plants, arbor and mangrove. Meanwhile, DSM largely reduced misclassification due to the shadow effect and morphological variation of inter-species. In respect to classifier, nonparametric SVM outperformed than MLC for high dimension and multi-source data in this study. SVM classifier tended to produce higher overall accuracy and reduce scattered patches although it costs more time than MLC. The best result was obtained by combining MNF components and DSM in SVM classifier. This study offered a precision species distribution survey solution for inaccessible wetland area with low cost of time and labour. In addition, findings relevant to the positive effect of DSM as well as spectral feature identification indicated that the utility of UAV-borne hyperspectral and photogrammetry deriving 3D data is promising in further research on wetland species such as bio-parameters modelling and biological invasion monitoring.

Keywords: digital surface model (DSM), feature reduction, hyperspectral, photogrammetric point cloud, species mapping, unmanned aerial vehicle (UAV)

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Authors: Mohammad H. Taheri, David J. Brown, Nasser Sherkat

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Engagement is one of the most important factors in determining successful outcomes and deep learning in students. Existing approaches to detect student engagement involve periodic human observations that are subject to inter-rater reliability. Our solution uses real-time multimodal multisensor data labeled by objective performance outcomes to infer the engagement of students. The study involves four students with a combined diagnosis of cerebral palsy and a learning disability who took part in a 3-month trial over 59 sessions. Multimodal multisensor data were collected while they participated in a continuous performance test. Eye gaze, electroencephalogram, body pose, and interaction data were used to create a model of student engagement through objective labeling from the continuous performance test outcomes. In order to achieve this, a type of continuous performance test is introduced, the Seek-X type. Nine features were extracted including high-level handpicked compound features. Using leave-one-out cross-validation, a series of different machine learning approaches were evaluated. Overall, the random forest classification approach achieved the best classification results. Using random forest, 93.3% classification for engagement and 42.9% accuracy for disengagement were achieved. We compared these results to outcomes from different models: AdaBoost, decision tree, k-Nearest Neighbor, naïve Bayes, neural network, and support vector machine. We showed that using a multisensor approach achieved higher accuracy than using features from any reduced set of sensors. We found that using high-level handpicked features can improve the classification accuracy in every sensor mode. Our approach is robust to both sensor fallout and occlusions. The single most important sensor feature to the classification of engagement and distraction was shown to be eye gaze. It has been shown that we can accurately predict the level of engagement of students with learning disabilities in a real-time approach that is not subject to inter-rater reliability, human observation or reliant on a single mode of sensor input. This will help teachers design interventions for a heterogeneous group of students, where teachers cannot possibly attend to each of their individual needs. Our approach can be used to identify those with the greatest learning challenges so that all students are supported to reach their full potential.

Keywords: affective computing in education, affect detection, continuous performance test, engagement, flow, HCI, interaction, learning disabilities, machine learning, multimodal, multisensor, physiological sensors, student engagement

Procedia PDF Downloads 66

Authors: Dimitrios Tzilopoulos, Panagiotis Symeonidis, Michael Loufakis, Dimosthenis Ioannidis, Dimitrios Tzovaras

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The domestic film industry in Greece has traditionally relied heavily on state support. While film productions are crucial for the country's economy, it has not fully capitalized on attracting and promoting foreign productions. The lack of motivation, organized state support for attraction and licensing, and the absence of location scouting have hindered its potential. Although recent legislative changes have addressed the first two of these issues, the development of a comprehensive location database and a search engine that would effectively support location scouting at the pre-production location scouting is still in its early stages. In addition to the expected benefits of the film, television, marketing, and multimedia industries, a location-scouting service platform has the potential to yield significant financial gains locally and nationally. By promoting featured places like cultural and archaeological sites, natural monuments, and attraction points for visitors, it plays a vital role in both cultural promotion and facilitating tourism development. This study introduces LOCATION3, an internet platform revolutionizing film production location management. It interconnects location providers, film crews, and multimedia stakeholders, offering a comprehensive environment for seamless collaboration. The platform's central geodatabase (PostgreSQL) stores each location’s attributes, while web technologies like HTML, JavaScript, CSS, React.js, and Redux power the user-friendly interface. Advanced functionalities, utilizing deep learning models, developed in Python, are integrated via Node.js. Visual data presentation is achieved using the JS Leaflet library, delivering an interactive map experience. LOCATION3 sets a new standard, offering a range of essential features to enhance the management of film production locations. Firstly, it empowers users to effortlessly upload audiovisual material enriched with geospatial and temporal data, such as location coordinates, photographs, videos, 360-degree panoramas, and 3D location models. With the help of cutting-edge deep learning algorithms, the application automatically tags these materials, while users can also manually tag them. Moreover, the application allows users to record locations directly through its user-friendly mobile application. Users can then embark on seamless location searches, employing spatial or descriptive criteria. This intelligent search functionality considers a combination of relevant tags, dominant colors, architectural characteristics, emotional associations, and unique location traits. One of the application's standout features is the ability to explore locations by their visual similarity to other materials, facilitated by a reverse image search. Also, the interactive map serves as both a dynamic display for locations and a versatile filter, adapting to the user's preferences and effortlessly enhancing location searches. To further streamline the process, the application facilitates the creation of location lightboxes, enabling users to efficiently organize and share their content via email. Going above and beyond location management, the platform also provides invaluable liaison, matchmaking, and online marketplace services. This powerful functionality bridges the gap between visual and three-dimensional geospatial material providers, local agencies, film companies, production companies, etc. so that those interested in a specific location can access additional material beyond what is stored on the platform, as well as access production services supporting the functioning and completion of productions in a location (equipment provision, transportation, catering, accommodation, etc.).

Keywords: deep learning models, film industry, geospatial data management, location scouting

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Authors: Andrew Fox, Qingping Yang, Yuanhong Zhao, Tao Zhang

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Furniture is a very significant subdivision of architecture and its inherent interior design activities. The furniture industry has developed from an artisan-driven craft industry, whose forerunners saw themselves manifested in their crafts and treasured a sense of pride in the creativity of their designs, these days largely reduced to an anonymous collective mass-produced output. Although a very conservative industry, there is great potential for the implementation of collaborative digital technologies allowing a reconfigured artisan experience to be reawakened in a new and exciting form. The furniture manufacturing industry, in general, has been slow to adopt new methodologies for a design using artificial and rule-based generative design. This tardiness has meant the loss of potential to enhance its capabilities in producing sustainable, flexible, and mass customizable ‘right first-time’ designs. This paper aims to demonstrate the concept methodology for the creation of alternative and inspiring aesthetic structures for robot-based additive manufacturing (RBAM). These technologies can enable the economic creation of previously unachievable structures, which traditionally would not have been commercially economic to manufacture. The integration of these technologies with the computing power of generative design provides the tools for practitioners to create concepts which are well beyond the insight of even the most accomplished traditional design teams. This paper aims to address the problem by introducing generative design methodologies employing the Autodesk Fusion 360 platform. Examination of the alternative methods for its use has the potential to significantly reduce the estimated 80% contribution to environmental impact at the initial design phase. Though predominantly a design methodology, generative design combined with RBAM has the potential to leverage many lean manufacturing and quality assurance benefits, enhancing the efficiency and agility of modern furniture manufacturing. Through a case study examination of a furniture artifact, the results will be compared to a traditionally designed and manufactured product employing the Ecochain Mobius product life cycle analysis (LCA) platform. This will highlight the benefits of both generative design and robot-based additive manufacturing from an environmental impact and manufacturing efficiency standpoint. These step changes in design methodology and environmental assessment have the potential to revolutionise the design to manufacturing workflow, giving momentum to the concept of conceiving a pre-industrial model of manufacturing, with the global demand for a circular economy and bespoke sustainable design at its heart.

Keywords: robot, manufacturing, generative design, sustainability, circular econonmy, product life cycle assessment, furniture

Procedia PDF Downloads 109

Authors: Dana R. Thomson, Catherine Linard, Sabine Vanhuysse, Jessica E. Steele, Michal Shimoni, Jose Siri, Waleska Caiaffa, Megumi Rosenberg, Eleonore Wolff, Tais Grippa, Stefanos Georganos, Helen Elsey

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The Sustainable Development Goals (SDGs) and Urban Health Equity Assessment and Response Tool (Urban HEART) identify dozens of key indicators to help local decision-makers prioritize and track inequalities in health outcomes. However, presentations and discussions at the International Conference on Urban Health (ICUH) 2017 suggested that additional indicators are needed to make decisions and policies. A local decision-maker may realize that malaria or road accidents are a top priority. However, s/he needs additional health determinant indicators, for example about standing water or traffic, to address the priority and reduce inequalities. Health determinants reflect the physical and social environments that influence health outcomes often at community- and societal-levels and include such indicators as access to quality health facilities, access to safe parks, traffic density, location of slum areas, air pollution, social exclusion, and social networks. Indicator identification and disaggregation are necessarily constrained by available datasets – typically collected about households and individuals in surveys, censuses, and administrative records. Continued advancements in earth observation, data storage, computing and mobile technologies mean that new sources of health determinants indicators derived from 'big data' are becoming available at fine geographic scale. Big data includes high-resolution satellite imagery and aggregated, anonymized mobile phone data. While big data are themselves not representative of the population (e.g., satellite images depict the physical environment), they can provide information about population density, wealth, mobility, and social environments with tremendous detail and accuracy when combined with population-representative survey, census, administrative and health system data. The aim of this paper is to (1) flag to data scientists important indicators needed by health decision-makers at the city and sub-city scale - ideally free and publicly available, and (2) summarize for local decision-makers new datasets that can be generated from big data, with layperson descriptions of difficulties in generating them. We include SDGs and Urban HEART indicators, as well as indicators mentioned by decision-makers attending ICUH 2017.

Keywords: health determinant, health outcome, mobile phone, remote sensing, satellite imagery, SDG, urban HEART

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Authors: Akhentoolove Corbin, Leta De Jonge, Charmaine De Jonge

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Introduction: Various writers have promoted the adoption of the 4th R in the education curricula (relationships, respect, reasoning, religion, computing, science, art, conflict management, music) and the 5th R (responsibility). They argue that the traditional 3 Rs are not adequate for the modern environment and the requirements for students to become functional citizens in society. In particular, the developing countries of the anglophone Caribbean (most of which are tiny islands) are susceptible to the dangers and complexities of climate change and global economic volatility. These proposed additions to the 3Rs do have some justification, but this research considers Resilience as even more important and relevant in a world that is faced with the negative prospects of climate change, poverty, discrimination, and economic volatility. It is argued that the foundation for resilient citizens, workers, and workplaces, must be built in the elementary and secondary/middle schools and then through the tertiary level, to achieve an outcome of more resilient students. Government, business, and society require widespread resilience to be capable of ‘bouncing back’ and be more adaptable, transformational, and sustainable. Methodology: The paper utilises a mixed-methods approach incorporating a questionnaire and interviews to determine participants’ opinions on the importance and relevance of resilience in the schools’ curricula and to government, business, and society. The target groups are as follows: educators at all levels, education administrators, members of the business sector, public sector, and 3rd sector. The research specifically targets the anglophone Caribbean developing countries (Barbados, Guyana, Jamaica, Trinidad, St. Lucia, and St Vincent, and the Grenadines). The research utilises SPSS for data analysis. Major Findings: The preliminary findings suggest that the majority of participants support the adoption of resilience as a 4th R in the curricula of the elementary, secondary/middle schools, and tertiary level in the anglophone Caribbean. The final results will allow the researchers to reveal more specific details on any variations among the islands in the sample andto engage in an in-depth discussion of the relevance and importance of resilience as the 4th R. Conclusion: Results seem to suggest that the education system should adopt the 4th R of resilience so that educators working in collaboration with the family and community/village can develop young citizens who are more resilient and capable of manifesting the behaviours and attitudes associated with ‘bouncing back,’ adaptability, transformation, and sustainability. These findings may be useful for education decision-makers and governments in these Caribbean islands, who have the authority and responsibility for the development of education policy, laws, and regulations.

Keywords: education, resilient students, adaptable, transformational, resilient citizens, workplaces, government

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Authors: Soumava Boral, Sanjay K. Chaturvedi, Ian Howard, Kristoffer McKee, V. N. A. Naikan

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Due to strict regulations from government to enhance the possibilities of sustainability practices in industries, and noting the advances in sustainable manufacturing practices, it is necessary that the associated processes are also sustainable. Maintenance of large scale and complex machines is a pivotal task to maintain the uninterrupted flow of manufacturing processes. Appropriate maintenance practices can prolong the lifetime of machines, and prevent associated breakdowns, which subsequently reduces different cost heads. Selection of the best maintenance strategies for such machines are considered as a burdensome task, as they require the consideration of multiple technical criteria, complex mathematical calculations, previous fault data, maintenance records, etc. In the era of the fourth industrial revolution, organizations are rapidly changing their way of business, and they are giving their utmost importance to sensor technologies, artificial intelligence, data analytics, automations, etc. In this work, the effectiveness of several maintenance strategies (e.g., preventive, failure-based, reliability centered, condition based, total productive maintenance, etc.) related to a large scale and complex gearbox, operating in a steel processing plant is evaluated in terms of economic, social, environmental and technical criteria. As it is not possible to obtain/describe some criteria by exact numerical values, these criteria are evaluated linguistically by cross-functional experts. Fuzzy sets are potential soft-computing technique, which has been useful to deal with linguistic data and to provide inferences in many complex situations. To prioritize different maintenance practices based on the identified sustainable criteria, multi-criteria decision making (MCDM) approaches can be considered as potential tools. Multi-Attributive Ideal Real Comparative Analysis (MAIRCA) is a recent addition in the MCDM family and has proven its superiority over some well-known MCDM approaches, like TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and ELECTRE (ELimination Et Choix Traduisant la REalité). It has a simple but robust mathematical approach, which is easy to comprehend. On the other side, due to some inherent drawbacks of Intuitionistic Fuzzy Sets (IFS) and Pythagorean Fuzzy Sets (PFS), recently, the use of Fermatean Fuzzy Sets (FFSs) has been proposed. In this work, we propose the novel concept of FF-MAIRCA. We obtain the weights of the criteria by experts’ evaluation and use them to prioritize the different maintenance practices according to their suitability by FF-MAIRCA approach. Finally, a sensitivity analysis is carried out to highlight the robustness of the approach.

Keywords: Fermatean fuzzy sets, Fermatean fuzzy MAIRCA, maintenance strategy selection, sustainable manufacturing, MCDM

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Authors: Kairat Kurakbayev, Dina Gungor, Adil Ashirbekov, Assel Kambatyrova

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Kazakhstan, a post-Soviet economy located in the Central Asia, is making great efforts to internationalize its national system of education. The country is very ambitious in making the national economy internationally competitive and education has become one of the main pillars of the nation’s strategic development plan for 2030. This paper discusses the role of professional teacher development in upgrading the secondary education curriculum with the introduction of English as a medium of instruction (EMI) in grades 10-11 grades. Having Kazakh as the state language and Russian as the official language, English bears a status of foreign language in the country. The development of trilingual education is very high on the agenda of the Ministry of Education and Science. It is planned that by 2019 STEM-related subjects – Biology, Chemistry, Computing and Physics – will be taught in EMI. Introducing English-medium education appears to be a very drastic reform and the teaching cadre is the key driver here. At the same time, after the collapse of the Soviet Union, the teaching profession is still struggling to become attractive in the eyes of the local youth. Moreover, the quality of Kazakhstan’s secondary education is put in question by OECD national review reports. The paper presents a case study of the nation-wide professional development programme arranged for 5 010 school teachers so that they could be able to teach their content subjects in English starting from 2019 onwards. The study is based on the mixed methods research involving the data derived from the surveys and semi-structured interviews held with the programme participants, i.e. school teachers. The findings of the study imply the significance of the school teachers’ attitudes towards the top-down reform of trilingual education. The qualitative research data reveal the teachers’ beliefs about advantages and disadvantages of having their content subjects (e.g. Biology or Chemistry) taught in EMI. The study highlights teachers’ concerns about their professional readiness to implement the top-down reform of English-medium education and discusses possible risks of academic underperforming on the part of students whose English language proficiency is not advanced. This paper argues that for the effective implementation of the English-medium education in secondary schools, the state should adopt a comprehensive approach to upgrading the national academic system where teachers’ attitudes and beliefs play the key role in making the trilingual education policy effective. The study presents lessons for other national academic systems considering to transfer its secondary education to English as a medium of instruction.

Keywords: teacher education, teachers' beliefs, trilingual education, case study

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Authors: Manaranjan Pradhan, Shailaja Grover, U. Dinesh Kumar

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Farming community in India, as well as other parts of the world, is one of the highly stressed communities due to reasons such as increasing input costs (cost of seeds, fertilizers, pesticide), droughts, reduced revenue leading to farmer suicides. Lack of integrated farm advisory system in India adds to the farmers problems. Farmers need right information during the early stages of crop’s lifecycle to prevent damage and loss in revenue. In this paper, we use deep learning techniques to develop an early warning system for detection of crop diseases using images taken by farmers using their smart phone. The research work leads to building a smart assistant using analytics and big data which could help the farmers with early diagnosis of the crop diseases and corrective actions. The classical approach for crop disease management has been to identify diseases at crop level. Recently, ImageNet Classification using the convolutional neural network (CNN) has been successfully used to identify diseases at individual plant level. Our model uses convolution filters, max pooling, dense layers and dropouts (to avoid overfitting). The models are built for binary classification (healthy or not healthy) and multi class classification (identifying which disease). Transfer learning is used to modify the weights of parameters learnt through ImageNet dataset and apply them on crop diseases, which reduces number of epochs to learn. One shot learning is used to learn from very few images, while data augmentation techniques are used to improve accuracy with images taken from farms by using techniques such as rotation, zoom, shift and blurred images. Models built using combination of these techniques are more robust for deploying in the real world. Our model is validated using tomato crop. In India, tomato is affected by 10 different diseases. Our model achieves an accuracy of more than 95% in correctly classifying the diseases. The main contribution of our research is to create a personal assistant for farmers for managing plant disease, although the model was validated using tomato crop, it can be easily extended to other crops. The advancement of technology in computing and availability of large data has made possible the success of deep learning applications in computer vision, natural language processing, image recognition, etc. With these robust models and huge smartphone penetration, feasibility of implementation of these models is high resulting in timely advise to the farmers and thus increasing the farmers' income and reducing the input costs.

Keywords: analytics in agriculture, CNN, crop disease detection, data augmentation, image recognition, one shot learning, transfer learning

Procedia PDF Downloads 95

Authors: Fani Sakellariadou

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The Blue Economy includes all marine-based and marine-related activities. They correspond to established, emerging as well as unborn ocean-based industries. Seabed mining is an emerging marine-based activity; its operations depend particularly on cutting-edge science and technology. The 21st century will face a crisis in resources as a consequence of the world’s population growth and the rising standard of living. The natural capital stored in the global ocean is decisive for it to provide a wide range of sustainable ecosystem services. Seabed mineral deposits were identified as having a high potential for critical elements and base metals. They have a crucial role in the fast evolution of green technologies. The major categories of marine mineral deposits are deep-sea deposits, including cobalt-rich ferromanganese crusts, polymetallic nodules, phosphorites, and deep-sea muds, as well as shallow-water deposits including marine placers. Seabed mining operations may take place within continental shelf areas of nation-states. In international waters, the International Seabed Authority (ISA) has entered into 15-year contracts for deep-seabed exploration with 21 contractors. These contracts are for polymetallic nodules (18 contracts), polymetallic sulfides (7 contracts), and cobalt-rich ferromanganese crusts (5 contracts). Exploration areas are located in the Clarion-Clipperton Zone, the Indian Ocean, the Mid Atlantic Ridge, the South Atlantic Ocean, and the Pacific Ocean. Potential environmental impacts of deep-sea mining include habitat alteration, sediment disturbance, plume discharge, toxic compounds release, light and noise generation, and air emissions. They could cause burial and smothering of benthic species, health problems for marine species, biodiversity loss, reduced photosynthetic mechanism, behavior change and masking acoustic communication for mammals and fish, heavy metals bioaccumulation up the food web, decrease of the content of dissolved oxygen, and climate change. An important concern related to deep-sea mining is our knowledge gap regarding deep-sea bio-communities. The ecological consequences that will be caused in the remote, unique, fragile, and little-understood deep-sea ecosystems and inhabitants are still largely unknown. The blue economy conceptualizes oceans as developing spaces supplying socio-economic benefits for current and future generations but also protecting, supporting, and restoring biodiversity and ecological productivity. In that sense, people should apply holistic management and make an assessment of marine mining impacts on ecosystem services, including the categories of provisioning, regulating, supporting, and cultural services. The variety in environmental parameters, the range in sea depth, the diversity in the characteristics of marine species, and the possible proximity to other existing maritime industries cause a span of marine mining impact the ability of ecosystems to support people and nature. In conclusion, the use of the untapped potential of the global ocean demands a liable and sustainable attitude. Moreover, there is a need to change our lifestyle and move beyond the philosophy of single-use. Living in a throw-away society based on a linear approach to resource consumption, humans are putting too much pressure on the natural environment. Applying modern, sustainable and eco-friendly approaches according to the principle of circular economy, a substantial amount of natural resource savings will be achieved. Acknowledgement: This work is part of the MAREE project, financially supported by the Division VI of IUPAC. This work has been partly supported by the University of Piraeus Research Center.

Keywords: blue economy, deep-sea mining, ecosystem services, environmental impacts

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Authors: C. A. Barros, Ana P. Barroso

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Any industrial company needs to determine the amount of variation that exists within its measurement process and guarantee the reliability of their data, studying the performance of their measurement system, in terms of linearity, bias, repeatability and reproducibility and stability. This issue is critical for automotive industry suppliers, who are required to be certified by the 16949:2016 standard (replaces the ISO/TS 16949) of International Automotive Task Force, defining the requirements of a quality management system for companies in the automotive industry. Measurement System Analysis (MSA) is one of the mandatory tools. Frequently, the measurement system in companies is not connected to the equipment and do not incorporate the methods proposed by the Automotive Industry Action Group (AIAG). To address these constraints, an R&D project is in progress, whose objective is to develop a web and cloud-based MSA tool. This MSA tool incorporates Industry 4.0 concepts, such as, Internet of Things (IoT) protocols to assure the connection with the measuring equipment, cloud computing, artificial intelligence, statistical tools, and advanced mathematical algorithms. This paper presents the preliminary findings of the project. The web and cloud-based MSA tool is innovative because it implements all statistical tests proposed in the MSA-4 reference manual from AIAG as well as other emerging methods and techniques. As it is integrated with the measuring devices, it reduces the manual input of data and therefore the errors. The tool ensures traceability of all performed tests and can be used in quality laboratories and in the production lines. Besides, it monitors MSAs over time, allowing both the analysis of deviations from the variation of the measurements performed and the management of measurement equipment and calibrations. To develop the MSA tool a ten-step approach was implemented. Firstly, it was performed a benchmarking analysis of the current competitors and commercial solutions linked to MSA, concerning Industry 4.0 paradigm. Next, an analysis of the size of the target market for the MSA tool was done. Afterwards, data flow and traceability requirements were analysed in order to implement an IoT data network that interconnects with the equipment, preferably via wireless. The MSA web solution was designed under UI/UX principles and an API in python language was developed to perform the algorithms and the statistical analysis. Continuous validation of the tool by companies is being performed to assure real time management of the ‘big data’. The main results of this R&D project are: MSA Tool, web and cloud-based; Python API; New Algorithms to the market; and Style Guide of UI/UX of the tool. The MSA tool proposed adds value to the state of the art as it ensures an effective response to the new challenges of measurement systems, which are increasingly critical in production processes. Although the automotive industry has triggered the development of this innovative MSA tool, other industries would also benefit from it. Currently, companies from molds and plastics, chemical and food industry are already validating it.

Keywords: automotive Industry, industry 4.0, Internet of Things, IATF 16949:2016, measurement system analysis

Procedia PDF Downloads 180

Authors: Ali Kazemi

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Those within the volatile and interconnected international economic markets, appropriately predicting market trends, hold substantial fees for traders and financial establishments. Traditional device mastering strategies have made full-size strides in forecasting marketplace movements; however, monetary data's complicated and networked nature calls for extra sophisticated processes. This observation offers a groundbreaking method for monetary marketplace prediction that leverages the synergistic capability of Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks. Our suggested algorithm is meticulously designed to forecast the traits of inventory market indices and cryptocurrency costs, utilizing a comprehensive dataset spanning from January 1, 2015, to December 31, 2023. This era, marked by sizable volatility and transformation in financial markets, affords a solid basis for schooling and checking out our predictive version. Our algorithm integrates diverse facts to construct a dynamic economic graph that correctly reflects market intricacies. We meticulously collect opening, closing, and high and low costs daily for key inventory marketplace indices (e.g., S&P 500, NASDAQ) and widespread cryptocurrencies (e.g., Bitcoin, Ethereum), ensuring a holistic view of marketplace traits. Daily trading volumes are also incorporated to seize marketplace pastime and liquidity, providing critical insights into the market's shopping for and selling dynamics. Furthermore, recognizing the profound influence of the monetary surroundings on financial markets, we integrate critical macroeconomic signs with hobby fees, inflation rates, GDP increase, and unemployment costs into our model. Our GCN algorithm is adept at learning the relational patterns amongst specific financial devices represented as nodes in a comprehensive market graph. Edges in this graph encapsulate the relationships based totally on co-movement styles and sentiment correlations, enabling our version to grasp the complicated community of influences governing marketplace moves. Complementing this, our LSTM algorithm is trained on sequences of the spatial-temporal illustration discovered through the GCN, enriched with historic fee and extent records. This lets the LSTM seize and expect temporal marketplace developments accurately. Inside the complete assessment of our GCN-LSTM algorithm across the inventory marketplace and cryptocurrency datasets, the version confirmed advanced predictive accuracy and profitability compared to conventional and opportunity machine learning to know benchmarks. Specifically, the model performed a Mean Absolute Error (MAE) of 0.85%, indicating high precision in predicting day-by-day charge movements. The RMSE was recorded at 1.2%, underscoring the model's effectiveness in minimizing tremendous prediction mistakes, which is vital in volatile markets. Furthermore, when assessing the model's predictive performance on directional market movements, it achieved an accuracy rate of 78%, significantly outperforming the benchmark models, averaging an accuracy of 65%. This high degree of accuracy is instrumental for techniques that predict the course of price moves. This study showcases the efficacy of mixing graph-based totally and sequential deep learning knowledge in economic marketplace prediction and highlights the fee of a comprehensive, records-pushed evaluation framework. Our findings promise to revolutionize investment techniques and hazard management practices, offering investors and economic analysts a powerful device to navigate the complexities of cutting-edge economic markets.

Keywords: financial market prediction, graph convolutional networks (GCNs), long short-term memory (LSTM), cryptocurrency forecasting

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Authors: Pangkaj Kumar Mahanta, Md. Rafizul Islam

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The rapid growth of the population causes a substantial amount of increase in household waste all over the world. Waste management is becoming one of the most challenging phenomena in the present day. The most environmentally friendly final disposal process of waste is sanitary landfilling, which is practiced in most developing countries. However, in Southeast Asia, most of the final disposal point is an open dump site. Due to the ignominy of proper management of waste and monitoring, the surrounding environment gets polluted more by the open dump site in comparison with a sanitary landfill. Khulna is 3rd largest metropolitan city in Bangladesh, having a population of around 1.5 million and producing approximately 450 tons per day of Municipal Solid Waste. The Municipal solid waste of Khulna city is disposed of in Rajbandh open dump site. The surrounding air is being polluted by the gas produced in the open dump site. Also, the open dump site produces leachate, which contains various heavy metals like Cadmium (Cd), Chromium (Cr), Lead (Pb), Manganese (Mn), Mercury (Hg), Strontium (Sr), etc. Leachate pollutes the soil as well as the groundwater of the open dump site and also the surrounding area through seepage. Moreover, during the rainy season, the surface water is polluted by leachate runoff. Also, the plastic waste flowing out from the open dump site through various drivers pollutes the nearby environment. The health risk assessment associated with heavy metals was carried out by computing the chronic daily intake (CDI), hazard quotient (HQ), and hazard index (HI) via different exposure pathways following the USEPA guidelines. For ecological risk, potential contamination index (Cp), Contamination factor (CF), contamination load index (PLI), numerical integrated contamination factor (NICF), enrichment factor (EF), ecological risk index (ER), and potential ecological risk index (PERI) were computed. The health risk and ecological risk assessment results reveal that some heavy metals possess strong health and ecological risk. In addition, the child faces higher harmful health risks from several heavy metals than the adult for all the exposure pathways and media. The conversion of an open dump site into a sanitary landfill and a proper management system can reduce the problems associated with an open dump site. In the sanitary landfill, the produced gas will be managed properly to save the surrounding atmosphere from being polluted. The seepage of leachate can be minimized by installing a compacted clay layer (CCL) as a baseline and leachate collection in a sanitary landfill to save the underlying soil layer and surrounding water bodies from leachate. Another important component of a sanitary landfill is the conversion of plastic waste to energy will minimize the plastic pollution in the landfill area and also the surrounding soil and water bodies. Also, in the sanitary landfill, the bio-waste can be used to make compost to reduce the volume of bio-waste and proper utilization of the landfill area.

Keywords: ecological risk, health risk, open dump site, sanitary landfill

Procedia PDF Downloads 164

Authors: Eyob Seife

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Immunization is a life-saving intervention which prevents needless suffering through sickness, disability, and death. Emphasis on data quality and use will become even stronger with the development of the immunization agenda 2030 (IA2030). Quality of data is a key factor in generating reliable health information that enables monitoring progress, financial planning, vaccine forecasting capacities, and making decisions for continuous improvement of the national immunization program. However, ensuring data of sufficient quality and promoting an information-use culture at the point of the collection remains critical and challenging, especially in hard-to-reach and pastoralist areas where Degehabur district is selected based on a hypothesis of ‘there is no difference in reported and recounted immunization data consistency. Data quality is dependent on different factors where organizational, behavioral, technical, and contextual factors are the mentioned ones. A cross-sectional quantitative study was conducted on September 2022 in the Degehabur district. The study used the world health organization (WHO) recommended data quality self-assessment (DQS) tools. Immunization tally sheets, registers, and reporting documents were reviewed at 5 health facilities (2 health centers and 3 health posts) of primary health care units for one fiscal year (12 months) to determine the accuracy ratio. The data was collected by trained DQS assessors to explore the quality of monitoring systems at health posts, health centers, and the district health office. A quality index (QI) was assessed, and the accuracy ratio formulated were: the first and third doses of pentavalent vaccines, fully immunized (FI), and the first dose of measles-containing vaccines (MCV). In this study, facility-level results showed both over-reporting and under-reporting were observed at health posts when computing the accuracy ratio of the tally sheet to health post reports found at health centers for almost all antigens verified where pentavalent 1 was 88.3%, 60.4%, and 125.6% for Health posts A, B, and C respectively. For first-dose measles-containing vaccines (MCV), similarly, the accuracy ratio was found to be 126.6%, 42.6%, and 140.9% for Health posts A, B, and C, respectively. The accuracy ratio for fully immunized children also showed 0% for health posts A and B and 100% for health post-C. A relatively better accuracy ratio was seen at health centers where the first pentavalent dose was 97.4% and 103.3% for health centers A and B, while a first dose of measles-containing vaccines (MCV) was 89.2% and 100.9% for health centers A and B, respectively. A quality index (QI) of all facilities also showed results between the maximum of 33.33% and a minimum of 0%. Most of the verified immunization data accuracy ratios were found to be relatively better at the health center level. However, the quality of the monitoring system is poor at all levels, besides poor data accuracy at all health posts. So attention should be given to improving the capacity of staff and quality of monitoring system components, namely recording, reporting, archiving, data analysis, and using information for decision at all levels, especially in pastoralist areas where such kinds of study findings need to be improved beside to improving the data quality at root and health posts level.

Keywords: accuracy ratio, Degehabur District, regular childhood immunization program, quality of monitoring system, Somali Region-Ethiopia

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Authors: Dian Zhang, Brendan Heery, Maria O’Neill, Ciprian Briciu-Burghina, Noel E. O’Connor, Fiona Regan

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Recent developments in sensors, wireless data communication and the cloud computing have brought the sensor web to a whole new generation. The introduction of the concept of ‘Internet of Thing (IoT)’ has brought the sensor research into a new level, which involves the developing of long lasting, low cost, environment friendly and smart sensors; new wireless data communication technologies; big data analytics algorithms and cloud based solutions that are tailored to large scale smart sensor network. The next generation of smart sensor network consists of several layers: physical layer, where all the smart sensors resident and data pre-processes occur, either on the sensor itself or field gateway; data transmission layer, where data and instructions exchanges happen; the data process layer, where meaningful information is extracted and organized from the pre-process data stream. There are many definitions of smart sensor, however, to summarize all these definitions, a smart sensor must be Intelligent and Adaptable. In future large scale sensor network, collected data are far too large for traditional applications to send, store or process. The sensor unit must be intelligent that pre-processes collected data locally on board (this process may occur on field gateway depends on the sensor network structure). In this case study, three smart sensing methods, corresponding to simple thresholding, statistical model and machine learning based MoPBAS method, are introduced and their strength and weakness are discussed as an introduction to the smart sensing concept. Data fusion, the integration of data and knowledge from multiple sources, are key components of the next generation smart sensor network. For example, in the water level monitoring system, weather forecast can be extracted from external sources and if a heavy rainfall is expected, the server can send instructions to the sensor notes to, for instance, increase the sampling rate or switch on the sleeping mode vice versa. In this paper, we describe the deployment of 11 affordable water level sensors in the Dublin catchment. The objective of this paper is to use the deployed river level sensor network at the Dodder catchment in Dublin, Ireland as a case study to give a vision of the next generation of a smart sensor network for flood monitoring to assist agencies in making decisions about deploying resources in the case of a severe flood event. Some of the deployed sensors are located alongside traditional water level sensors for validation purposes. Using the 11 deployed river level sensors in a network as a case study, a vision of the next generation of smart sensor network is proposed. Each key component of the smart sensor network is discussed, which hopefully inspires the researchers who are working in the sensor research domain.

Keywords: smart sensing, internet of things, water level sensor, flooding

Procedia PDF Downloads 350

Authors: Mauro Callejas-Cuervo, Andrea C. Alarcon-Aldana, Andres F. Ruiz-Olaya, Juan C. Alvarez

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Motion capture (MC) is the construction of a precise and accurate digital representation of a real motion. Systems have been used in the last years in a wide range of applications, from films special effects and animation, interactive entertainment, medicine, to high competitive sport where a maximum performance and low injury risk during training and competition is seeking. This paper presents an inertial and magnetic sensor based technological platform, intended for particular amplitude monitoring and telerehabilitation processes considering an efficient cost/technical considerations compromise. Our platform particularities offer high social impact possibilities by making telerehabilitation accessible to large population sectors in marginal socio-economic sector, especially in underdeveloped countries that in opposition to developed countries specialist are scarce, and high technology is not available or inexistent. This platform integrates high-resolution low-cost inertial and magnetic sensors with adequate user interfaces and communication protocols to perform a web or other communication networks available diagnosis service. The amplitude information is generated by sensors then transferred to a computing device with adequate interfaces to make it accessible to inexperienced personnel, providing a high social value. Amplitude measurements of the platform virtual system presented a good fit to its respective reference system. Analyzing the robotic arm results (estimation error RMSE 1=2.12° and estimation error RMSE 2=2.28°), it can be observed that during arm motion in any sense, the estimation error is negligible; in fact, error appears only during sense inversion what can easily be explained by the nature of inertial sensors and its relation to acceleration. Inertial sensors present a time constant delay which acts as a first order filter attenuating signals at large acceleration values as is the case for a change of sense in motion. It can be seen a damped response of platform virtual in other images where error analysis show that at maximum amplitude an underestimation of amplitude is present whereas at minimum amplitude estimations an overestimation of amplitude is observed. This work presents and describes the platform virtual as a motion capture system suitable for telerehabilitation with the cost - quality and precision - accessibility relations optimized. These particular characteristics achieved by efficiently using the state of the art of accessible generic technology in sensors and hardware, and adequate software for capture, transmission analysis and visualization, provides the capacity to offer good telerehabilitation services, reaching large more or less marginal populations where technologies and specialists are not available but accessible with basic communication networks.

Keywords: inertial sensors, joint amplitude measurement, MEMS, telerehabilitation

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Authors: Takoua Soltani, Imen Soltani, Taoufik Aguili

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The communications' and radar systems' demands give rise to new developments in the domain of active integrated antennas (AIA) and arrays. The main advantages of AIA arrays are the simplicity of fabrication, low cost of manufacturing, and the combination between free space power and the scanner without a phase shifter. The integrated active antenna modeling is the coupling between the electromagnetic model and the transport model that will be affected in the high frequencies. Global modeling of active circuits is important for simulating EM coupling, interaction between active devices and the EM waves, and the effects of EM radiation on active and passive components. The current review focuses on the modeling of the active element which is a MESFET transistor immersed in a rectangular waveguide. The proposed EM analysis is based on the Method of Moments combined with the Generalised Equivalent Circuit method (MOM-GEC). The Method of Moments which is the most common and powerful software as numerical techniques have been used in resolving the electromagnetic problems. In the class of numerical techniques, MOM is the dominant technique in solving of Maxwell and Transport’s integral equations for an active integrated antenna. In this situation, the equivalent circuit is introduced to the development of an integral method formulation based on the transposition of field problems in a Generalised equivalent circuit that is simpler to treat. The method of Generalised Equivalent Circuit (MGEC) was suggested in order to represent integral equations circuits that describe the unknown electromagnetic boundary conditions. The equivalent circuit presents a true electric image of the studied structures for describing the discontinuity and its environment. The aim of our developed method is to investigate the antenna parameters such as the input impedance and the current density distribution and the electric field distribution. In this work, we propose a global EM modeling of the MESFET AsGa transistor using an integral method. We will begin by describing the modeling structure that allows defining an equivalent EM scheme translating the electromagnetic equations considered. Secondly, the projection of these equations on common-type test functions leads to a linear matrix equation where the unknown variable represents the amplitudes of the current density. Solving this equation resulted in providing the input impedance, the distribution of the current density and the electric field distribution. From electromagnetic calculations, we were able to present the convergence of input impedance for different test function number as a function of the guide mode numbers. This paper presents a pilot study to find the answer to map out the variation of the existing current evaluated by the MOM-GEC. The essential improvement of our method is reducing computing time and memory requirements in order to provide a sufficient global model of the MESFET transistor.

Keywords: active integrated antenna, current density, input impedance, MESFET transistor, MOM-GEC method

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Authors: Tarek Saadawi, Rosario Gennaro, Jonathan Akeley

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There is rapidly growing demand for professionals to fill positions in Cybersecurity. This is recognized as a national priority both by government agencies and the private sector. Cybersecurity is a very wide technical area which encompasses all measures that can be taken in an electronic system to prevent criminal or unauthorized use of data and resources. This requires defending computers, servers, networks, and their users from any kind of malicious attacks. The need to address this challenge has been recognized globally but is particularly acute in the New York metropolitan area, home to some of the largest financial institutions in the world, which are prime targets of cyberattacks. In New York State alone, there are currently around 57,000 jobs in the Cybersecurity industry, with more than 23,000 unfilled positions. The Cybersecurity Program at City College is a collaboration between the Departments of Computer Science and Electrical Engineering. In Fall 2020, The City College of New York matriculated its first students in theCybersecurity Master of Science program. The program was designed to fill gaps in the previous offerings and evolved out ofan established partnership with Facebook on Cybersecurity Education. City College has designed a program where courses, curricula, syllabi, materials, labs, etc., are developed in cooperation and coordination with industry whenever possible, ensuring that students graduating from the program will have the necessary background to seamlessly segue into industry jobs. The Cybersecurity Program has created multiple pathways for prospective students to obtain the necessary prerequisites to apply in order to build a more diverse student population. The program can also be pursued on a part-time basis which makes it available to working professionals. Since City College’s Cybersecurity M.S. program was established to equip students with the advanced technical skills needed to thrive in a high-demand, rapidly-evolving field, it incorporates a range of pedagogical formats. From its outset, the Cybersecurity program has sought to provide both the theoretical foundations necessary for meaningful work in the field along with labs and applied learning projects aligned with skillsets required by industry. The efforts have involved collaboration with outside organizations and with visiting professors designing new courses on topics such as Adversarial AI, Data Privacy, Secure Cloud Computing, and blockchain. Although the program was initially designed with a single asynchronous course in the curriculum with the rest of the classes designed to be offered in-person, the advent of the COVID-19 pandemic necessitated a move to fullyonline learning. The shift to online learning has provided lessons for future development by providing examples of some inherent advantages to the medium in addition to its drawbacks. This talk will address the structure of the newly-implemented Cybersecurity Master’s Program and discuss the innovations, challenges, and possible future directions.

Keywords: cybersecurity, new york, city college, graduate degree, master of science

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Authors: Sardorbek Numonov, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Dongeun Choi, Byung-Woo Hong

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The detection of curvilinear structures often plays an important role in the analysis of images. In particular, it is considered as a crucial step for the diagnosis of chronic respiratory diseases to localize the fissures in chest CT imagery where the lung is divided into five lobes by the fissures that are characterized by linear features in appearance. However, the characteristic linear features for the fissures are often shown to be subtle due to the high intensity variability, pathological deformation or image noise involved in the imaging procedure, which leads to the uncertainty in the quantification of anatomical or functional properties of the lung. Thus, it is desired to enhance the linear features present in the chest CT images so that the distinctiveness in the delineation of the lobe is improved. We propose a recursive diffusion process that prefers coherent features based on the analysis of structure tensor in an anisotropic manner. The local image features associated with certain scales and directions can be characterized by the eigenanalysis of the structure tensor that is often regularized via isotropic diffusion filters. However, the isotropic diffusion filters involved in the computation of the structure tensor generally blur geometrically significant structure of the features leading to the degradation of the characteristic power in the feature space. Thus, it is required to take into consideration of local structure of the feature in scale and direction when computing the structure tensor. We apply an anisotropic diffusion in consideration of scale and direction of the features in the computation of the structure tensor that subsequently provides the geometrical structure of the features by its eigenanalysis that determines the shape of the anisotropic diffusion kernel. The recursive application of the anisotropic diffusion with the kernel the shape of which is derived from the structure tensor leading to the anisotropic scale-space where the geometrical features are preserved via the eigenanalysis of the structure tensor computed from the diffused image. The recursive interaction between the anisotropic diffusion based on the geometry-driven kernels and the computation of the structure tensor that determines the shape of the diffusion kernels yields a scale-space where geometrical properties of the image structure are effectively characterized. We apply our recursive anisotropic diffusion algorithm to the detection of curvilinear structure in the chest CT imagery where the fissures present curvilinear features and define the boundary of lobes. It is shown that our algorithm yields precise detection of the fissures while overcoming the subtlety in defining the characteristic linear features. The quantitative evaluation demonstrates the robustness and effectiveness of the proposed algorithm for the detection of fissures in the chest CT in terms of the false positive and the true positive measures. The receiver operating characteristic curves indicate the potential of our algorithm as a segmentation tool in the clinical environment. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: anisotropic diffusion, chest CT imagery, chronic respiratory disease, curvilinear structure, fissure detection, structure tensor

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Authors: Tatiana G. Smirnova, Stan G. Benjamin

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Rapid Update Cycle (RUC) land surface model (LSM) was a land-surface component in several generations of operational weather prediction models at the National Center for Environment Prediction (NCEP) at the National Oceanic and Atmospheric Administration (NOAA). It was designed for short-range weather predictions with an emphasis on severe weather and originally was intentionally simple to avoid uncertainties from poorly known parameters. Nevertheless, the RUC LSM, when coupled with the hourly-assimilating atmospheric model, can produce a realistic evolution of time-varying soil moisture and temperature, as well as the evolution of snow cover on the ground surface. This result is possible only if the soil/vegetation/snow component of the coupled weather prediction model has sufficient skill to avoid long-term drift. RUC LSM was first implemented in the operational NCEP Rapid Update Cycle (RUC) weather model in 1998 and later in the Weather Research Forecasting Model (WRF)-based Rapid Refresh (RAP) and High-resolution Rapid Refresh (HRRR). Being available to the international WRF community, it was implemented in operational weather models in Austria, New Zealand, and Switzerland. Based on the feedback from the US weather service offices and the international WRF community and also based on our own validation, RUC LSM has matured over the years. Also, a sea-ice module was added to RUC LSM for surface predictions over the Arctic sea-ice. Other modifications include refinements to the snow model and a more accurate specification of albedo, roughness length, and other surface properties. At present, RUC LSM is being tested in the regional application of the Unified Forecast System (UFS). The next generation UFS-based regional Rapid Refresh FV3 Standalone (RRFS) model will replace operational RAP and HRRR at NCEP. Over time, RUC LSM participated in several international model intercomparison projects to verify its skill using observed atmospheric forcing. The ESM-SnowMIP was the last of these experiments focused on the verification of snow models for open and forested regions. The simulations were performed for ten sites located in different climatic zones of the world forced with observed atmospheric conditions. While most of the 26 participating models have more sophisticated snow parameterizations than in RUC, RUC LSM got a high ranking in simulations of both snow water equivalent and surface temperature. However, ESM-SnowMIP experiment also revealed some issues in the RUC snow model, which will be addressed in this paper. One of them is the treatment of grid cells partially covered with snow. RUC snow module computes energy and moisture budgets of snow-covered and snow-free areas separately by aggregating the solutions at the end of each time step. Such treatment elevates the importance of computing in the model snow cover fraction. Improvements to the original simplistic threshold-based approach have been implemented and tested both offline and in the coupled weather model. The detailed description of changes to the snow cover fraction and other modifications to RUC soil and snow parameterizations will be described in this paper.

Keywords: land-surface models, weather prediction, hydrology, boundary-layer processes

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Authors: Amer Ait Sidhoum

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Interest in sustainability has gained ground among practitioners, academics and policy-makers due to growing stakeholders’ awareness of environmental and social concerns. This is particularly true for agriculture. However, relatively little research has been conducted on the quantification of social sustainability and the contribution of social issues to the agricultural production efficiency. This research's main objective is to propose a method for evaluating prices of social outputs, more precisely shadow prices, by allowing for the stochastic nature of agricultural production that is to say for production uncertainty. In this article, the assessment of social outputs’ shadow prices is conducted within the methodological framework of nonparametric Data Envelopment Analysis (DEA). An output-oriented directional distance function (DDF) is implemented to represent the technology of a sample of Catalan arable crop farms and derive the efficiency scores the overall production technology of our sample is assumed to be the intersection of two different sub-technologies. The first sub-technology models the production of random desirable agricultural outputs, while the second sub-technology reflects the social outcomes from agricultural activities. Once a nonparametric production technology has been represented, the DDF primal approach can be used for efficiency measurement, while shadow prices are drawn from the dual representation of the DDF. Computing shadow prices is a method to assign an economic value to non-marketed social outcomes. Our research uses cross sectional, farm-level data collected in 2015 from a sample of 180 Catalan arable crop farms specialized in the production of cereals, oilseeds and protein (COP) crops. Our results suggest that our sample farms show high performance scores, from 85% for the bad state of nature to 88% for the normal and ideal crop growing conditions. This suggests that farm performance is increasing with an improvement in crop growth conditions. Results also show that average shadow prices of desirable state-contingent output and social outcomes for efficient and inefficient farms are positive, suggesting that the production of desirable marketable outputs and of non-marketable outputs makes a positive contribution to the farm production efficiency. Results also indicate that social outputs’ shadow prices are contingent upon the growing conditions. The shadow prices follow an upward trend as crop-growing conditions improve. This finding suggests that these efficient farms prefer to allocate more resources in the production of desirable outputs than of social outcomes. To our knowledge, this study represents the first attempt to compute shadow prices of social outcomes while accounting for the stochastic nature of the production technology. Our findings suggest that the decision-making process of the efficient farms in dealing with social issues are stochastic and strongly dependent on the growth conditions. This implies that policy-makers should adjust their instruments according to the stochastic environmental conditions. An optimal redistribution of rural development support, by increasing the public payment with the improvement in crop growth conditions, would likely enhance the effectiveness of public policies.

Keywords: data envelopment analysis, shadow prices, social sustainability, sustainable farming

Procedia PDF Downloads 96