Search results for: computational fluid dynamics.

Authors: Christina Eleftheria Tzeliou, Demeter Tzeli

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Introduction: Molecular Logic Gates (MLGs) are molecular machines that can perform complex work, such as solving logic operations. Molecular switches, which are molecules that can experience chemical changes are examples of successful types of MLGs. Recently, Quintana-Romero and Ariza-Castolo studied experimentally six stable pseudo-hemi-indigo-derived MLGs capable of solving complex logic operations. The MLG design relies on a molecular switch that experiences Z and E isomerism, thus the molecular switch's axis has to be a double bond. The hemi-indigo structure was preferred for the assembly of molecular switches due to its interaction with visible light. Z and E pseudo-hemi-indigo isomers can also be utilized for selective isomerization as they have distinct absorption spectra. Methodology: Here, the photophysical properties of pseudo-hemi-indigo derivatives are examined, i.e., derivatives of molecule 1 with anthracene, naphthalene, phenanthrene, pyrene, and pyrrole. In conjunction with some trials that were conducted, the level of theory mentioned subsequently was determined. The structures under study were optimized in both cis and trans conformations at the PBE0/6-31G(d,p) level of theory. The absorption spectra of the structures were calculated at PBE0/DEF2TZVP. In all cases, the absorption spectra of the studied systems were calculated including up to 50 singlet- and triplet-spin excited electronic states. Transition states (cis → cis, cis → trans, and trans → trans) were obtained in cases where it was possible, with PBE0/6-31G(d,p) for the optimization of the transition states and PBE0/DEF2TZVP for the respective absorption spectra. Emission spectra were obtained for the first singlet state of each molecule in cis both and trans conformations in PBE0/DEF2TZVP as well. All studies were performed in chloroform solvent that was added as a dielectric constant and the polarizable continuum model was also employed. Findings: Shifts of up to 25 nm are observed in the absorption spectra due to cis-trans isomerization, while the transition state is shifted up to about 150 nm. The electron density distribution is also examined, where charge transfer and electron transfer phenomena are observed regarding the three excitations of interest, i.e., H-1 → L, H → L and H → L+1. Emission spectra calculations were also carried out at PBE0/DEF2TZVP for the complete investigation of these molecules. Using protonation as input, selected molecules act as MLGs. Conclusion: Theoretical data so far indicate that both cis-trans isomerization, and cis-cis and trans-trans conformer isomerization affect the UV-visible absorption and emission spectra. Specifically, shifts of up to 30 nm are observed, while the transition state is shifted up to about 150 nm in cis-cis isomerization. The computational data obtained are in agreement with available experimental data, which have predicted that the pyrrole derivative is a MLG at 445 nm and 400 nm using protonation as input, while the anthracene derivative is a MLG that operates at 445 nm using protonation as input. Finally, it was found that selected molecules are candidates as MLG using protonation and light as inputs. These MLGs could be used as chemical sensors or as particular intracellular indicators, among several other applications. Acknowledgements: The author acknowledges the Hellenic Foundation for Research and Innovation for the financial support of this project (Fellowship Number: 21006).

Keywords: absorption spectra, DFT calculations, isomerization, molecular logic gates

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Authors: Ahmed Elaksher, Islam Omar

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Accurate geometric relation algorithms are imperative in Earth and planetary satellite and aerial image processing, particularly for high-resolution images that are used for topographic mapping. Most of these satellites carry push-broom sensors. These sensors are optical scanners equipped with linear arrays of CCDs. These sensors have been deployed on most EOSs. In addition, the LROC is equipped with two push NACs that provide 0.5 meter-scale panchromatic images over a 5 km swath of the Moon. The HiRISE carried by the MRO and the HRSC carried by MEX are examples of push-broom sensor that produces images of the surface of Mars. Sensor models developed in photogrammetry relate image space coordinates in two or more images with the 3D coordinates of ground features. Rigorous sensor models use the actual interior orientation parameters and exterior orientation parameters of the camera, unlike approximate models. In this research, we generate a generic push-broom sensor model to process imageries acquired through linear array cameras and investigate its performance, advantages, and disadvantages in generating topographic models for the Earth, Mars, and the Moon. We also compare and contrast the utilization, effectiveness, and applicability of available photogrammetric techniques and softcopies with the developed model. We start by defining an image reference coordinate system to unify image coordinates from all three arrays. The transformation from an image coordinate system to a reference coordinate system involves a translation and three rotations. For any image point within the linear array, its image reference coordinates, the coordinates of the exposure center of the array in the ground coordinate system at the imaging epoch (t), and the corresponding ground point coordinates are related through the collinearity condition that states that all these three points must be on the same line. The rotation angles for each CCD array at the epoch t are defined and included in the transformation model. The exterior orientation parameters of an image line, i.e., coordinates of exposure station and rotation angles, are computed by a polynomial interpolation function in time (t). The parameter (t) is the time at a certain epoch from a certain orbit position. Depending on the types of observations, coordinates, and parameters may be treated as knowns or unknowns differently in various situations. The unknown coefficients are determined in a bundle adjustment. The orientation process starts by extracting the sensor position and, orientation and raw images from the PDS. The parameters of each image line are then estimated and imported into the push-broom sensor model. We also define tie points between image pairs to aid the bundle adjustment model, determine the refined camera parameters, and generate highly accurate topographic maps. The model was tested on different satellite images such as IKONOS, QuickBird, and WorldView-2, HiRISE. It was found that the accuracy of our model is comparable to those of commercial and open-source software, the computational efficiency of the developed model is high, the model could be used in different environments with various sensors, and the implementation process is much more cost-and effort-consuming.

Keywords: photogrammetry, push-broom sensors, IKONOS, HiRISE, collinearity condition

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Authors: Jonathan Adeyemi

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Context: This study focuses on El Anatsui and his global acclaim in the art world despite his origins from the global artworld’s margins. It addresses the disparities in the treatment between Western and non-Western artists and questions whether Anatsui’s consecration is a result of exoticism or the growing consensus on decolonization. Research Aim: The aim of this study is to investigate how El Anatsui achieved global acclaim from the margins of the art world and determine if his consecration represents a mark of decolonization or the typical Western desire for exoticism. Methodology: The study utilizes a case study approach, literature analysis, and in-depth interviews. The artist, the organizers of the Venice Biennale, the relevant curators at Tate Modern London, and the October Gallery in London, and other galleries in Nigeria, which represent the artist were interviewed for data collection. Findings: The study seeks to determine the authenticity of the growing consensus on decolonization, inclusion, and diversity in the global artistic field. Preliminary findings show that domestic socio-economic and political factors debilitated the mechanisms for local validation in Nigeria, weakening the domestic foundation for international engagement. However, alternative systems of exhibition, especially in London and the USA contributed critically to providing the initial international visibility, which formed the foundation for his global acclaim. Out of the 21 winners of the Golden Lion for Lifetime Achievement since its inception at the 47th Venice Biennale in 1997, American artists have dominated with 10 recipients, 8 recipients from Europe, 2 recipients from Africa (2007 and 2015) and 1 from Asia. This aligns with Bourdieu’s concept of cultural and economic capital, which prevented Africa countries from participation until recently. Moreover, while the average age of recipients is 76 years, Anatsui received the award at the age of 71, while Malick Sidibé (Mali) was awarded at 72. Thus, the Venice Biennale award for El Anatsui incline more towards a commitment to decolonisation than exoticism. Theoretical Importance: This study contributes to the field by examining the dynamics of the art world's monopoly of legitimation and the role of national, ethnicity and cultural differences in the promotion of artists. It aims to challenge the Westernized hierarchy of valorization and consecration in the art world. The research supports Bourdieu’s artistic field theory, which emphasises the importance of cultural, economic and social capital in determining agents’ position and access to the field resources (symbolic capital). Bourdieu also established that dominated agents can change their position in the field’s hierarchy either by establishing or navigating alternative systems. Data Collection and Analysis Procedures: The opacity of art world’s operations places the required information within the purview of the insiders (agents). Thus, the study collects data through in-depth interviews with relevant and purposively selected individuals and organizations. The data was/will be analyzed using qualitative methods, such as thematic analysis and content analysis. The interpretive analytical approach adopted facilitated the construction of meanings that may not be apparent in the data or responses. Questions Addressed: The study addresses how El Anatsui achieved global acclaim despite being from the margins, whether his consecration represents decolonization or exoticism, and the extent to which the global artistic field embraces decolonization, inclusion, and diversity. Conclusion: The study will contribute to knowledge by providing insights into the extent of commitment to decolonization, inclusion, and diversity in the global artistic field. It also shed light on the mechanisms behind El Anatsui's rise to global acclaim and challenge Western-dominated artistic hierarchies.

Keywords: decolonisation, exorticism, artistic field, culture game

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Authors: Rubens P. Costa, Marcelo A. Leigui de Oliveira

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The comprehension of features on the energy spectra, the chemical compositions, and the origins of Ultra-High Energy Cosmic Rays (UHECRs) - mainly atomic nuclei with energies above ~1.0 EeV (exa-electron volts) - are intrinsically linked to the problem of determining the magnitude of their deflections in cosmic magnetic fields on cosmological scales. In addition, as they propagate from the source to the observer, modifications are expected in their original energy spectra, anisotropy, and the chemical compositions due to interactions with low energy photons and matter. This means that any consistent interpretation of the nature and origin of UHECRs has to include the detailed knowledge of their propagation in a three-dimensional environment, taking into account the magnetic deflections and energy losses. The parameter space range for the magnetic fields in the universe is very large because the field strength and especially their orientation have big uncertainties. Particularly, the strength and morphology of the Extragalactic Magnetic Fields (EGMFs) remain largely unknown, because of the intrinsic difficulty of observing them. Monte Carlo simulations of charged particles traveling through a simulated magnetized universe is the straightforward way to study the influence of extragalactic magnetic fields on UHECRs propagation. However, this brings two major difficulties: an accurate numerical modeling of charged particles diffusion in magnetic fields, and an accurate numerical modeling of the magnetized Universe. Since magnetic fields do not cause energy losses, it is important to impose that the particle tracking method conserve the particle’s total energy and that the energy changes are results of the interactions with background photons only. Hence, special attention should be paid to computational effects. Additionally, because of the number of particles necessary to obtain a relevant statistical sample, the particle tracking method must be computationally efficient. In this work, we present an analysis of the propagation of ultra-high energy charged particles in the intergalactic medium. The EGMFs are considered to be coherent within cells of 1 Mpc (mega parsec) diameter, wherein they have uniform intensities of 1 nG (nano Gauss). Moreover, each cell has its field orientation randomly chosen, and a border region is defined such that at distances beyond 95% of the cell radius from the cell center smooth transitions have been applied in order to avoid discontinuities. The smooth transitions are simulated by weighting the magnetic field orientation by the particle's distance to the two nearby cells. The energy losses have been treated in the continuous approximation parameterizing the mean energy loss per unit path length by the energy loss length. We have shown, for a particle with the typical energy of interest the integration method performance in the relative error of Larmor radius, without energy losses and the relative error of energy. Additionally, we plotted the distance amplification from rectilinear propagation as a function of the traveled distance, particle's magnetic rigidity, without energy losses, and particle's energy, with energy losses, to study the influence of particle's species on these calculations. The results clearly show when it is necessary to use a full three-dimensional simulation.

Keywords: cosmic rays propagation, extragalactic magnetic fields, magnetic deflections, ultra-high energy

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Authors: Rohit T. P., Sahil Athrij, Sasi Gopalan

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Currently, the content entertainment industry is dominated by mobile devices. As the trends slowly shift towards Augmented/Virtual Reality applications the computational demands on these devices are increasing exponentially and we are already reaching the limits of hardware optimizations. This paper proposes a software solution to this problem. By leveraging the capabilities of cloud computing we can offload the work from mobile devices to dedicated rendering servers that are way more powerful. But this introduces the problem of latency. This paper introduces a protocol that can achieve high-performance low latency Augmented/Virtual Reality experience. There are two parts to the protocol, 1) In-flight compression The main cause of latency in the system is the time required to transmit the camera frame from client to server. The round trip time is directly proportional to the amount of data transmitted. This can therefore be reduced by compressing the frames before sending. Using some standard compression algorithms like JPEG can result in minor size reduction only. Since the images to be compressed are consecutive camera frames there won't be a lot of changes between two consecutive images. So inter-frame compression is preferred. Inter-frame compression can be implemented efficiently using WebGL but the implementation of WebGL limits the precision of floating point numbers to 16bit in most devices. This can introduce noise to the image due to rounding errors, which will add up eventually. This can be solved using an improved interframe compression algorithm. The algorithm detects changes between frames and reuses unchanged pixels from the previous frame. This eliminates the need for floating point subtraction thereby cutting down on noise. The change detection is also improved drastically by taking the weighted average difference of pixels instead of the absolute difference. The kernel weights for this comparison can be fine-tuned to match the type of image to be compressed. 2) Dynamic Load distribution Conventional cloud computing architectures work by offloading as much work as possible to the servers, but this approach can cause a hit on bandwidth and server costs. The most optimal solution is obtained when the device utilizes 100% of its resources and the rest is done by the server. The protocol balances the load between the server and the client by doing a fraction of the computing on the device depending on the power of the device and network conditions. The protocol will be responsible for dynamically partitioning the tasks. Special flags will be used to communicate the workload fraction between the client and the server and will be updated in a constant interval of time ( or frames ). The whole of the protocol is designed so that it can be client agnostic. Flags are available to the client for resetting the frame, indicating latency, switching mode, etc. The server can react to client-side changes on the fly and adapt accordingly by switching to different pipelines. The server is designed to effectively spread the load and thereby scale horizontally. This is achieved by isolating client connections into different processes.

Keywords: 2D kernelling, augmented reality, cloud computing, dynamic load distribution, immersive experience, mobile computing, motion tracking, protocols, real-time systems, web-based augmented reality application

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Authors: Samuel McLennan, Tane Moleta, Andre Brown, Marc Aurel Schnabel

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The development of Ecclesiastical buildings within New Zealand has produced some unique design characteristics that take influence from both international styles and local building methods. What this research looks at is how procedural modelling can be used to define such common characteristics and understand how they are shared and developed within different examples of a similar architectural style. This will be achieved through the creation of procedural digital reconstructions of the various timber Gothic Churches built during the 19th century in the city of Wellington, New Zealand. ‘Procedural modelling’ is a digital modelling technique that has been growing in popularity, particularly within the game and film industry, as well as other fields such as industrial design and architecture. Such a design method entails the creation of a parametric ‘ruleset’ that can be easily adjusted to produce many variations of geometry, rather than a single geometry as is typically found in traditional CAD software. Key precedents within this area of digital heritage includes work by Haegler, Müller, and Gool, Nicholas Webb and Andre Brown, and most notably Mark Burry. What these precedents all share is how the forms of the reconstructed architecture have been generated using computational rules and an understanding of the architects’ geometric reasoning. This is also true within this research as Gothic architecture makes use of only a select range of forms (such as the pointed arch) that can be accurately replicated using the same standard geometric techniques originally used by the architect. The methodology of this research involves firstly establishing a sample group of similar buildings, documenting the existing samples, researching any lost samples to find evidence such as architectural plans, photos, and written descriptions, and then culminating all the findings into a single 3D procedural asset within the software ‘Houdini’. The end result will be an adjustable digital model that contains all the architectural components of the sample group, such as the various naves, buttresses, and windows. These components can then be selected and arranged to create visualisations of the sample group. Because timber gothic churches in New Zealand share many details between designs, the created collection of architectural components can also be used to approximate similar designs not included in the sample group, such as designs found beyond the Wellington Region. This creates an initial library of architectural components that can be further expanded on to encapsulate as wide of a sample size as desired. Such a methodology greatly improves upon the efficiency and adjustability of digital modelling compared to current practices found in digital heritage reconstruction. It also gives greater accuracy to speculative design, as a lack of evidence for lost structures can be approximated using components from still existing or better-documented examples. This research will also bring attention to the cultural significance these types of buildings have within the local area, addressing the public’s general unawareness of architectural history that is identified in the Wellington based research ‘Moving Images in Digital Heritage’ by Serdar Aydin et al.

Keywords: digital forensics, digital heritage, gothic architecture, Houdini, procedural modelling

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Authors: Pedro A. Marques, Francisco Monteiro, Alessandra Zumbo, Alessia Simonini, Miguel A. Mendez

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Cryogenic fuels such as Liquid Hydrogen (LH₂) must be transported and stored at extremely low temperatures. Without expensive active cooling solutions, preventing fuel boil-off over time is impossible. Hence, one must resort to venting systems at the cost of significant energy and fuel mass loss. These losses increase significantly in propellant tanks installed on vehicles, as the presence of external accelerations induces sloshing. Sloshing increases heat and mass transfer rates and leads to significant pressure oscillations, which might further trigger propellant venting. To make LH₂ economically viable, it is essential to minimize these factors by using advanced control techniques. However, these require accurate modelling and a full understanding of the tank's thermodynamics. The present research aims to implement a simple thermodynamic model capable of predicting the state of a cryogenic fuel tank under different operating conditions (i.e., filling, pressurization, fuel extraction, long-term storage, and sloshing). Since this model relies on a set of closure parameters to drive the system's transient response, it must be calibrated using experimental or numerical data. This work focuses on the former approach, wherein the model is calibrated through an experimental campaign carried out on a reduced-scale model of a cryogenic tank. The thermodynamic model of the system is composed of three control volumes: the ullage, the liquid, and the insulating walls. Under this lumped formulation, the governing equations are derived from energy and mass balances in each region, with mass-averaged properties assigned to each of them. The gas-liquid interface is treated as an infinitesimally thin region across which both phases can exchange mass and heat. This results in a coupled system of ordinary differential equations, which must be closed with heat and mass transfer coefficients between each control volume. These parameters are linked to the system evolution via empirical relations derived from different operating regimes of the tank. The derivation of these relations is carried out using an inverse method to find the optimal relations that allow the model to reproduce the available data. This approach extends classic system identification methods beyond linear dynamical systems via a nonlinear optimization step. Thanks to the data-driven assimilation of the closure problem, the resulting model accurately predicts the evolution of the tank's thermodynamics at a negligible computational cost. The lumped model can thus be easily integrated with other submodels to perform complete system simulations in real time. Moreover, by setting the model in a dimensionless form, a scaling analysis allowed us to relate the tested configurations to a representative full-size tank for naval applications. It was thus possible to compare the relative importance of different transport phenomena between the laboratory model and the full-size prototype among the different operating regimes.

Keywords: destratification, hydrogen, modeling, pressure-drop, pressurization, sloshing, thermodynamics

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Authors: Babak Forouraghi

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A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations of previous approaches, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with attention mechanism. In a previous work on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: transformers, generative ai, gene expression design, classification

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

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

Keywords: biomedical research, genomics, information systems, software

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Authors: Anitha Kandasamy, Thirumurugan Ramaiah

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Interestingly, organic materials exhibit large optical nonlinearity with quick responses and having the flexibility of molecular tailoring using computational modelling and favourable synthetic methodologies. Pyridine based organic compounds and carboxylic acid contained aromatic compounds play a crucial role in crystal engineering of NCS complexes that displays admirable optical nonlinearity with fast response and favourable physicochemical properties such as low dielectric constant, wide optical transparency and large laser damage threshold value requires for optoelectronics device applications. Based on these facts, it was projected to form an acentric molecule of π-conjugated system interaction with appropriately replaced electron donor and acceptor groups for achieving higher SHG activity in which quinoline-2-carboyxlic acid is chosen as an electron acceptor and capable of acting as an acid as well as a base molecule, while 2-amino-4-methylpyridine is used as an electron donor and previously employed in numerous proton transfer complexes for synthesis of NLO materials for optoelectronic applications. 2-amino-4-mehtylpyridinium quinoline-2-carboxylate molecular complex (2AQ) is having π-donor-acceptor groups in which 2-amino-4-methylpyridine donates one of its electron to quinoline -2-carboxylic acid thereby forming a protonated 2-amino-4-methyl pyridinium moiety and mono ionized quinoline-2-carboxylate moiety which are connected via N-H…O intermolecular interactions with non-centrosymmetric crystal packing arrangement at microscopic scale is accountable to the enhancement of macroscopic second order NLO activity. The 2AQ crystal was successfully grown by a slow evaporation solution growth technique and its structure was determined in orthorhombic crystal system with acentric, P212121, space group. Hirshfeld surface analysis reveals that O…H intermolecular interactions primarily contributed with 31.0 % to the structural stabilization of 2AQ. The molecular structure of title compound has been confirmed by 1H and 13C NMR spectral studies. The vibrational modes of functional groups present in 2AQ have been assigned by using FTIR and FT-Raman spectroscopy. The grown 2AQ crystal exhibits high optical transparency with lower cut-off wavelength (275 nm) within the region of 275-1500 nm. The laser study confirmed that 2AQ exhibits high SHG efficiency of 12.6 times greater than that of KDP. TGA-DTA analysis revealed that 2AQ crystal had a thermal stability of 223 °C. The low dielectric constant and low dielectric loss at higher frequencies confirmed good crystalline nature with fewer defects of grown 2AQ crystal. The grown crystal exhibits soft material and positive photoconduction behaviour. Mulliken atomic distribution and FMOs analysis suggested that the strong intermolecular hydrogen bonding which lead to the enhancement of NLO activity. These properties suggest that 2AQ crystal is a suitable material for optoelectronic and laser frequency conversion applications.

Keywords: crystal growth, NLO activity, proton transfer complex, quantum chemical investigation

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Authors: Stanislav Babaev, Vladimir Cuk, Sjef Cobben, Jan Desmet

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The procedures for performing harmonic studies for medium-voltage distribution feeders have become relatively mature topics since the early 1980s. The efforts of various electric power engineers and researchers were mainly focused on handling large harmonic non-linear loads connected scarcely at several buses of medium-voltage feeders. In order to assess the impact of these loads on the voltage quality of the distribution system, specific modeling and simulation strategies were proposed. These methodologies could deliver a reasonable estimation accuracy given the requirements of least computational efforts and reduced complexity. To uphold these requirements, certain analysis assumptions have been made, which became de facto standards for establishing guidelines for harmonic analysis. Among others, typical assumptions include balanced conditions of the study and the negligible impact of impedance frequency characteristics of various power system components. In latter, skin and proximity effects are usually omitted, and resistance and reactance values are modeled based on the theoretical equations. Further, the simplifications of the modelling routine have led to the commonly accepted practice of neglecting phase angle diversity effects. This is mainly associated with developed load models, which only in a handful of cases are representing the complete harmonic behavior of a certain device as well as accounting on the harmonic interaction between grid harmonic voltages and harmonic currents. While these modelling practices were proven to be reasonably effective for medium-voltage levels, similar approaches have been adopted for low-voltage distribution systems. Given modern conditions and massive increase in usage of residential electronic devices, recent and ongoing boom of electric vehicles, and large-scale installing of distributed solar power, the harmonics in current low-voltage grids are characterized by high degree of variability and demonstrate sufficient diversity leading to a certain level of cancellation effects. It is obvious, that new modelling algorithms overcoming previously made assumptions have to be accepted. In this work, a simulation approach aimed to deal with some of the typical assumptions is proposed. A practical low-voltage feeder is modeled in PowerFactory. In order to demonstrate the importance of diversity effect and harmonic interaction, previously developed measurement-based models of photovoltaic inverter and battery charger are used as loads. The Python-based script aiming to supply varying voltage background distortion profile and the associated current harmonic response of loads is used as the core of unbalanced simulation. Furthermore, the impact of uncertainty of feeder frequency-impedance characteristics on total harmonic distortion levels is shown along with scenarios involving linear resistive loads, which further alter the impedance of the system. The comparative analysis demonstrates sufficient differences with cases when all the assumptions are in place, and results indicate that new modelling and simulation procedures need to be adopted for low-voltage distribution systems with high penetration of non-linear loads and renewable generation.

Keywords: electric power system, harmonic distortion, power quality, public low-voltage network, harmonic modelling

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Authors: Nurkhairul Bariyah Baharun, Afzan Adam, Reena Rahayu Md Zin

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Tumor microenvironment (TME) in breast cancer is mainly composed of cancer cells, immune cells, and stromal cells. The interaction between cancer cells and their microenvironment plays an important role in tumor development, progression, and treatment response. The TME in breast cancer includes tumor-infiltrating lymphocytes (TILs) that are implicated in killing tumor cells. TILs can be found in tumor stroma (sTILs) and within the tumor (iTILs). TILs in triple negative breast cancer (TNBC) have been demonstrated to have prognostic and potentially predictive value. The international Immune-Oncology Biomarker Working Group (TIL-WG) had developed a guideline focus on the assessment of sTILs using hematoxylin and eosin (H&E)-stained slides. According to the guideline, the pathologists use “eye balling” method on the H&E stained- slide for sTILs assessment. This method has low precision, poor interobserver reproducibility, and is time-consuming for a comprehensive evaluation, besides only counted sTILs in their assessment. The TIL-WG has therefore recommended that any algorithm for computational assessment of TILs utilizing the guidelines provided to overcome the limitations of manual assessment, thus providing highly accurate and reliable TILs detection and classification for reproducible and quantitative measurement. This study is carried out to develop a TNBC digital whole slide image (WSI) dataset from H&E-stained slides and IHC (CD4+ and CD8+) stained slides. TNBC cases were retrieved from the database of the Department of Pathology, Hospital Canselor Tuanku Muhriz (HCTM). TNBC cases diagnosed between the year 2010 and 2021 with no history of other cancer and available block tissue were included in the study (n=58). Tissue blocks were sectioned approximately 4 µm for H&E and IHC stain. The H&E staining was performed according to a well-established protocol. Indirect IHC stain was also performed on the tissue sections using protocol from Diagnostic BioSystems PolyVue™ Plus Kit, USA. The slides were stained with rabbit monoclonal, CD8 antibody (SP16) and Rabbit monoclonal, CD4 antibody (EP204). The selected and quality-checked slides were then scanned using a high-resolution whole slide scanner (Pannoramic DESK II DW- slide scanner) to digitalize the tissue image with a pixel resolution of 20x magnification. A manual TILs (sTILs and iTILs) assessment was then carried out by the appointed pathologist (2 pathologists) for manual TILs scoring from the digital WSIs following the guideline developed by TIL-WG 2014, and the result displayed as the percentage of sTILs and iTILs per mm² stromal and tumour area on the tissue. Following this, we aimed to develop an automated digital image scoring framework that incorporates key elements of manual guidelines (including both sTILs and iTILs) using manually annotated data for robust and objective quantification of TILs in TNBC. From the study, we have developed a digital dataset of TNBC H&E and IHC (CD4+ and CD8+) stained slides. We hope that an automated based scoring method can provide quantitative and interpretable TILs scoring, which correlates with the manual pathologist-derived sTILs and iTILs scoring and thus has potential prognostic implications.

Keywords: automated quantification, digital pathology, triple negative breast cancer, tumour infiltrating lymphocytes

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Authors: Juergen Junk, Franz Ronellenfitsch, Michael Eickermann

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An appropriate weed management in autumn is a prerequisite for an economically successful harvest in the following year. In Luxembourg oilseed rape, wheat and barley is sown from August until October, accompanied by a chemical weed control with soil active herbicides, depending on the state of the weeds and the meteorological conditions. Based on regular ground and surface water-analysis, high levels of contamination by transformation products of respective herbicide compounds have been found in Luxembourg. The most ideal conditions for incorporating soil active herbicides are single rain events. Weed control may be reduced if application is made when weeds are under drought stress or if repeated light rain events followed by dry spells, because the herbicides tend to bind tightly to the soil particles. These effects have been frequently reported for Luxembourg throughout the last years. In the framework of a multisite long-term field experiment (EFFO) weed monitoring, plants observations and corresponding meteorological measurements were conducted. Long-term time series (1947-2016) from the SYNOP station Findel-Airport (WMO ID = 06590) showed a decrease in the number of days with precipitation. As the total precipitation amount has not significantly changed, this indicates a trend towards rain events with higher intensity. All analyses are based on decades (10-day periods) for September and October of each individual year. To assess the future meteorological conditions for Luxembourg, two different approaches were applied. First, multi-model ensembles from the CORDEX experiments (spatial resolution ~12.5 km; transient projections until 2100) were analysed for two different Representative Concentration Pathways (RCP8.5 and RCP4.5), covering the time span from 2005 until 2100. The multi-model ensemble approach allows for the quantification of the uncertainties and also to assess the differences between the two emission scenarios. Second, to assess smaller scale differences within the country a high resolution model projection using the COSMO-LM model was used (spatial resolution 1.3 km). To account for the higher computational demands, caused by the increased spatial resolution, only 10-year time slices have been simulated (reference period 1991-2000; near future 2041-2050 and far future 2091-2100). Statistically significant trends towards higher air temperatures, +1.6 K for September (+5.3 K far future) and +1.3 K for October (+4.3 K), were predicted for the near future compared to the reference period. Precipitation simultaneously decreased by 9.4 mm (September) and 5.0 mm (October) for the near future and -49 mm (September) and -10 mm (October) in the far future. Beside the monthly values also decades were analyzed for the two future time periods of the CLM model. For all decades of September and October the number of days with precipitation decreased for the projected near and far future. Changes in meteorological variables such as air temperature and precipitation did already induce transformations in weed societies (composition, late-emerging etc.) of arable ecosystems in Europe. Therefore, adaptations of agronomic practices as well as effective weed control strategies must be developed to maintain crop yield.

Keywords: CORDEX projections, dry spells, ensembles, weed management

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Authors: Maddalena Arigoni, Maria Luisa Ratto, Raffaele A. Calogero, Luca Alessandri

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The presence of tumor heterogeneity, where distinct cancer cells exhibit diverse morphological and phenotypic profiles, including gene expression, metabolism, and proliferation, poses challenges for molecular prognostic markers and patient classification for targeted therapies. Understanding the causes and progression of cancer requires research efforts aimed at characterizing heterogeneity, which can be facilitated by evolving single-cell sequencing technologies. However, analyzing single-cell data necessitates computational methods that often lack objective validation. Therefore, the establishment of benchmarking datasets is necessary to provide a controlled environment for validating bioinformatics tools in the field of single-cell oncology. Benchmarking bioinformatics tools for single-cell experiments can be costly due to the high expense involved. Therefore, datasets used for benchmarking are typically sourced from publicly available experiments, which often lack a comprehensive cell annotation. This limitation can affect the accuracy and effectiveness of such experiments as benchmarking tools. To address this issue, we introduce omics benchmark experiments designed to evaluate bioinformatics tools to depict the heterogeneity in single-cell tumor experiments. We conducted single-cell RNA sequencing on six lung cancer tumor cell lines that display resistant clones upon treatment of EGFR mutated tumors and are characterized by driver genes, namely ROS1, ALK, HER2, MET, KRAS, and BRAF. These driver genes are associated with downstream networks controlled by EGFR mutations, such as JAK-STAT, PI3K-AKT-mTOR, and MEK-ERK. The experiment also featured an EGFR-mutated cell line. Using 10XGenomics platform with cellplex technology, we analyzed the seven cell lines together with a pseudo-immunological microenvironment consisting of PBMC cells labeled with the Biolegend TotalSeq™-B Human Universal Cocktail (CITEseq). This technology allowed for independent labeling of each cell line and single-cell analysis of the pooled seven cell lines and the pseudo-microenvironment. The data generated from the aforementioned experiments are available as part of an online tool, which allows users to define cell heterogeneity and generates count tables as an output. The tool provides the cell line derivation for each cell and cell annotations for the pseudo-microenvironment based on CITEseq data by an experienced immunologist. Additionally, we created a range of pseudo-tumor tissues using different ratios of the aforementioned cells embedded in matrigel. These tissues were analyzed using 10XGenomics (FFPE samples) and Curio Bioscience (fresh frozen samples) platforms for spatial transcriptomics, further expanding the scope of our benchmark experiments. The benchmark experiments we conducted provide a unique opportunity to evaluate the performance of bioinformatics tools for detecting and characterizing tumor heterogeneity at the single-cell level. Overall, our experiments provide a controlled and standardized environment for assessing the accuracy and robustness of bioinformatics tools for studying tumor heterogeneity at the single-cell level, which can ultimately lead to more precise and effective cancer diagnosis and treatment.

Keywords: single cell omics, benchmark, spatial transcriptomics, CITEseq

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Authors: Loyd R. Hook, Maryam Moharek

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With the envisioned future growth of low altitude urban aircraft operations for airborne delivery service and advanced air mobility, strategies to coordinate and deconflict aircraft flight paths must be prioritized. Autonomous coordination and planning of flight trajectories is the preferred approach to the future vision in order to increase safety, density, and efficiency over manual methods employed today. Difficulties arise because any conflict resolution must be constrained by all other aircraft, all airspace restrictions, and all ground-based obstacles in the vicinity. These considerations make pair-wise tactical deconfliction difficult at best and unlikely to find a suitable solution for the entire system of vehicles. In addition, more traditional methods which rely on long time scales and large protected zones will artificially limit vehicle density and drastically decrease efficiency. Instead, strategic planning, which is able to respond to highly dynamic conditions and still account for high density operations, will be required to coordinate multiple vehicles in the highly constrained low altitude urban environment. This paper develops and evaluates such a planning algorithm which can be implemented autonomously across multiple aircraft and situations. Data from this evaluation provide promising results with simulations showing up to 10 aircraft deconflicted through a relatively narrow low-altitude urban canyon without any vehicle to vehicle or obstacle conflict. The algorithm achieves this level of coordination beginning with the assumption that each vehicle is controlled to follow an independently constructed flight path, which is itself free of obstacle conflict and restricted airspace. Then, by preferencing speed change deconfliction maneuvers constrained by the vehicles flight envelope, vehicles can remain as close to the original planned path and prevent cascading vehicle to vehicle conflicts. Performing the search for a set of commands which can simultaneously ensure separation for each pair-wise aircraft interaction and optimize the total velocities of all the aircraft is further complicated by the fact that each aircraft's flight plan could contain multiple segments. This means that relative velocities will change when any aircraft achieves a waypoint and changes course. Additionally, the timing of when that aircraft will achieve a waypoint (or, more directly, the order upon which all of the aircraft will achieve their respective waypoints) will change with the commanded speed. Put all together, the continuous relative velocity of each vehicle pair and the discretized change in relative velocity at waypoints resembles a hybrid reachability problem - a form of control reachability. This paper proposes two methods for finding solutions to these multi-body problems. First, an analytical formulation of the continuous problem is developed with an exhaustive search of the combined state space. However, because of computational complexity, this technique is only computable for pairwise interactions. For more complicated scenarios, including the proposed 10 vehicle example, a discretized search space is used, and a depth-first search with early stopping is employed to find the first solution that solves the constraints.

Keywords: strategic planning, autonomous, aircraft, deconfliction

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Authors: Priyanka Botny Srinath, Alessandro Suglia, Mel McKendrick

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Background: Organizational cultural studies integrate sociology and anthropology, portraying man as a creator of symbols, languages, beliefs, and ideologies -essentially, a creator and manager of meaning. In our research, we leverage analytical measures to discern whether an organization embodies a singular culture or a myriad of subcultures. Fast-forward to 2023, our research thesis focuses on digitally measuring culture, coining it as the "Work Culture Quotient." This entails conceptually mapping common experiential patterns to provide executives insights into the digital organization journey, aiding in understanding their current position and identifying future steps. Objectives: Finding the new age skills that help in defining the culture; understand the implications of post-COVID effects; derive a digital framework for measuring skillsets. Method: We conducted two comprehensive Delphi studies to distill essential insights. Delphi 1: Through a thematic analysis of interviews with 20 high-level leaders representing companies across diverse regions -India, Japan, the US, Canada, Morocco, and Uganda- we identified 20 key skills critical for cultivating a robust organizational culture. The skills are -influence, self-confidence, optimism, empathy, leadership, collaboration and cooperation, developing others, commitment, innovativeness, leveraging diversity, change management, team capabilities, self-control, digital communication, emotional awareness, team bonding, communication, problem solving, adaptability, and trustworthiness. Delphi 2: Subject matter experts were asked to complete a questionnaire derived from the thematic analysis in stage 1 to formalise themes and draw consensus amongst experts on the most important workplace skills. Results: The thematic analysis resulted in 20 workplace employee skills being identified. These skills were all included in the Delphi round 2 questionnaire. From the outputs, we analysed the data using R Studio for arriving at agreement and consensus, we also used sum of squares method to compare various agreements to extract various themes with a threshold of 80% agreements. This yielded three themes at over 80% agreement (leadership, collaboration and cooperation, communication) and three further themes at over 60% agreement (commitment, empathy, trustworthiness). From this, we selected five questionnaires to be included in the primary data collection phase, and these will be paired with the digital footprints to provide a workplace culture quotient. Implications: The findings from these studies bear profound implications for decision-makers, revolutionizing their comprehension of organizational culture. Tackling the challenge of mapping the digital organization journey involves innovative methodologies that probe not only external landscapes but also internal cultural dynamics. This holistic approach furnishes decision-makers with a nuanced understanding of their organizational culture and visualizes pivotal skills for employee growth. This clarity enables informed choices resonating with the organization's unique cultural fabric. Anticipated outcomes transcend mere individual cultural measurements, aligning with organizational goals to unveil a comprehensive view of culture, exposing artifacts and depth. Armed with this profound understanding, decision-makers gain tangible evidence for informed decision-making, strategically leveraging cultural strengths to cultivate an environment conducive to growth, innovation, and enduring success, ultimately leading to measurable outcomes.

Keywords: leadership, cooperation, collaboration, teamwork, work culture

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Authors: Anisa Suraya Ab Razak, Izza Hayat

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Introduction: The diagnosis and management of severe hyponatremia in neuropsychiatric patients present a significant challenge to physicians. Several factors contribute, including diagnostic shadowing and attributing abnormal behavior to intellectual disability or psychiatric conditions. Hyponatraemia is the commonest electrolyte abnormality in the inpatient population, ranging from mild/asymptomatic, moderate to severe levels with life-threatening symptoms such as seizures, coma and death. There are several documented fatal case reports in the literature of severe hyponatremia secondary to psychogenic polydipsia, often diagnosed only in autopsy. This paper presents a case study of acute severe hyponatremia in a neuropsychiatric patient with early diagnosis and admission to intensive care. Case study: A 21-year old Caucasian male with known epilepsy and learning disability was admitted from residential living with generalized tonic-clonic self-terminating seizures after refusing medications for several weeks. Evidence of superficial head injury was detected on physical examination. His laboratory data demonstrated mild hyponatremia (125 mmol/L). Computed tomography imaging of his brain demonstrated no acute bleed or space-occupying lesion. He exhibited abnormal behavior - restlessness, drinking water from bathroom taps, inability to engage, paranoia, and hypersexuality. No collateral history was available to establish his baseline behavior. He was loaded with intravenous sodium valproate and leveritircaetam. Three hours later, he developed vomiting and a generalized tonic-clonic seizure lasting forty seconds. He remained drowsy for several hours and regained minimal recovery of consciousness. A repeat set of blood tests demonstrated profound hyponatremia (117 mmol/L). Outcomes: He was referred to intensive care for peripheral intravenous infusion of 2.7% sodium chloride solution with two-hourly laboratory monitoring of sodium concentration. Laboratory monitoring identified dangerously rapid correction of serum sodium concentration, and hypertonic saline was switched to a 5% dextrose solution to reduce the risk of acute large-volume fluid shifts from the cerebral intracellular compartment to the extracellular compartment. He underwent urethral catheterization and produced 8 liters of urine over 24 hours. Serum sodium concentration remained stable after 24 hours of correction fluids. His GCS recovered to baseline after 48 hours with improvement in behavior -he engaged with healthcare professionals, understood the importance of taking medications, admitted to illicit drug use and drinking massive amounts of water. He was transferred from high-dependency care to ward level and was initiated on multiple trials of anti-epileptics before achieving seizure-free days two weeks after resolution of acute hyponatremia. Conclusion: Psychogenic polydipsia is often found in young patients with intellectual disability or psychiatric disorders. Patients drink large volumes of water daily ranging from ten to forty liters, resulting in acute severe hyponatremia with mortality rates as high as 20%. Poor outcomes are due to challenges faced by physicians in making an early diagnosis and treating acute hyponatremia safely. A low index of suspicion of water intoxication is required in this population, including patients with known epilepsy. Monitoring urine output proved to be clinically effective in aiding diagnosis. Early referral and admission to intensive care should be considered for safe correction of sodium concentration while minimizing risk of fatal complications e.g. central pontine myelinolysis.

Keywords: epilepsy, psychogenic polydipsia, seizure, severe hyponatremia

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Authors: Babak Forouraghi

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A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of the flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on the spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts, as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with an attention mechanism. In previous works on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work, with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on the presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: machine learning, classification and regression, gene circuit design, bidirectional transformers

Procedia PDF Downloads 60

Authors: Angshuman Raha, Abesh Kumar Sanyal, Uttaran Bandyopadhyay, Kaushik Mallick, Kamalika Bhattacharyya, Subrata Gayen, Gaurab Nandi Das, Mohd. Ali, Kailash Chandra

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Indian Himalayan Region (IHR), due to its sheer latitudinal and altitudinal expanse, acts as a mixing ground for different zoogeographic faunal elements. The innumerable unique and distributional restricted rare species of IHR are constantly being threatened with extinction by the ongoing climate change scenario. Many of which might have faced extinction without even being noticed or discovered. Monitoring the community dynamics of a suitable taxon is indispensable to assess the effect of this global perturbation at micro-habitat level. Lepidoptera, particularly moths are suitable for this purpose due to their huge diversity and strict herbivorous nature. The present study aimed to collate scattered historical records of moths from IHR and spatially disseminate the same in Geographic Information System (GIS) domain. The study also intended to identify moth species with significant altitudinal shifts which could be prioritised for monitoring programme to assess the effect of climate change on biodiversity. A robust database on moths recorded from IHR was prepared from voluminous secondary literature and museum collections. Historical sampling points were transformed into richness grids which were spatially overlaid on altitude, annual precipitation and vegetation layers separately to show moth richness patterns along major environmental gradients. Primary samplings were done by setting standard light traps at 11 Protected Areas representing five Indian Himalayan biogeographic provinces. To identify significant altitudinal shifts, past and present altitudinal records of the identified species from primary samplings were compared. A consolidated list of 4107 species belonging to 1726 genera of 62 families of moths was prepared from a total of 10,685 historical records from IHR. Family-wise assemblage revealed Erebidae to be the most speciose family with 913 species under 348 genera, followed by Geometridae with 879 species under 309 genera and Noctuidae with 525 species under 207 genera. Among biogeographic provinces, Central Himalaya represented maximum records with 2248 species, followed by Western and North-western Himalaya with 1799 and 877 species, respectively. Spatial analysis revealed species richness was more or less uniform (up to 150 species record per cell) across IHR. Throughout IHR, the middle elevation zones between 1000-2000m encompassed high species richness. Temperate coniferous forest associated with 1500-2000mm rainfall zone showed maximum species richness. Total 752 species of moths were identified representing 23 families from the present sampling. 13 genera were identified which were restricted to specialized habitats of alpine meadows over 3500m. Five historical localities with high richness of >150 species were selected which could be considered for repeat sampling to assess climate change influence on moth assemblage. Of the 7 species exhibiting significant altitudinal ascend of >2000m, Trachea auriplena, Diphtherocome fasciata (Noctuidae) and Actias winbrechlini (Saturniidae) showed maximum range shift of >2500m, indicating intensive monitoring of these species. Great Himalayan National Park harbours most diverse assemblage of high-altitude restricted species and should be a priority site for habitat conservation. Among the 13 range restricted genera, Arichanna, Opisthograptis, Photoscotosia (Geometridae), Phlogophora, Anaplectoides and Paraxestia (Noctuidae) were dominant and require rigorous monitoring, as they are most susceptible to climatic perturbations.

Keywords: altitudinal shifts, climate change, historical records, Indian Himalayan region, Lepidoptera

Procedia PDF Downloads 169

Authors: I. Stathopoulos, V. Syrgiamiotis, E. Karavasilis, A. Ploussi, I. Nikas, C. Hatzigiorgi, K. Platoni, E. P. Efstathopoulos

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Introduction: Brain and central nervous system (CNS) tumors form the second most common group of cancer in children, accounting for 30% of all childhood cancers. MRI is the key imaging technique used for the visualization and management of pediatric brain tumors. Initial characterization of tumors from MRI scans is usually performed via a radiologist’s visual assessment. However, different brain tumor types do not always demonstrate clear differences in visual appearance. Using only conventional MRI to provide a definite diagnosis could potentially lead to inaccurate results, and so histopathological examination of biopsy samples is currently considered to be the gold standard for obtaining definite diagnoses. Machine learning is defined as the study of computational algorithms that can use, complex or not, mathematical relationships and patterns from empirical and scientific data to make reliable decisions. Concerning the above, machine learning techniques could provide effective and accurate ways to automate and speed up the analysis and diagnosis for medical images. Machine learning applications in radiology are or could potentially be useful in practice for medical image segmentation and registration, computer-aided detection and diagnosis systems for CT, MR or radiography images and functional MR (fMRI) images for brain activity analysis and neurological disease diagnosis. Purpose: The objective of this study is to provide an automated tool, which may assist in the imaging evaluation and classification of brain neoplasms in pediatric patients by determining the glioma type, grade and differentiating between different brain tissue types. Moreover, a future purpose is to present an alternative way of quick and accurate diagnosis in order to save time and resources in the daily medical workflow. Materials and Methods: A cohort, of 80 pediatric patients with a diagnosis of posterior fossa tumor, was used: 20 ependymomas, 20 astrocytomas, 20 medulloblastomas and 20 healthy children. The MR sequences used, for every single patient, were the following: axial T1-weighted (T1), axial T2-weighted (T2), FluidAttenuated Inversion Recovery (FLAIR), axial diffusion weighted images (DWI), axial contrast-enhanced T1-weighted (T1ce). From every sequence only a principal slice was used that manually traced by two expert radiologists. Image acquisition was carried out on a GE HDxt 1.5-T scanner. The images were preprocessed following a number of steps including noise reduction, bias-field correction, thresholding, coregistration of all sequences (T1, T2, T1ce, FLAIR, DWI), skull stripping, and histogram matching. A large number of features for investigation were chosen, which included age, tumor shape characteristics, image intensity characteristics and texture features. After selecting the features for achieving the highest accuracy using the least number of variables, four machine learning classification algorithms were used: k-Nearest Neighbour, Support-Vector Machines, C4.5 Decision Tree and Convolutional Neural Network. The machine learning schemes and the image analysis are implemented in the WEKA platform and MatLab platform respectively. Results-Conclusions: The results and the accuracy of images classification for each type of glioma by the four different algorithms are still on process.

Keywords: image classification, machine learning algorithms, pediatric MRI, pediatric oncology

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Authors: Despoina Chochtoula, Aristidis Ilias, Yannis Stamatiou

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Modbus is a protocol that enables the communication among devices which are connected to the same network. This protocol is, often, deployed in connecting sensor and monitoring units to central supervisory servers in Supervisory Control and Data Acquisition, or SCADA, systems. These systems monitor critical infrastructures, such as factories, power generation stations, nuclear power reactors etc. in order to detect malfunctions and ignite alerts and corrective actions. However, due to their criticality, SCADA systems are vulnerable to attacks that range from simple eavesdropping on operation parameters, exchanged messages, and valuable infrastructure information to malicious modification of vital infrastructure data towards infliction of damage. Thus, the SCADA research community has been active over strengthening SCADA systems with suitable data protection mechanisms based, to a large extend, on cryptographic methods for data encryption, device authentication, and message integrity protection. However, due to the limited computation power of many SCADA sensor and embedded devices, the usual public key cryptographic methods are not appropriate due to their high computational requirements. As an alternative, Elliptic Curve Cryptography has been proposed, which requires smaller key sizes and, thus, less demanding cryptographic operations. Until now, however, no such implementation has been proposed in the SCADA literature, to the best of our knowledge. In order to fill this gap, our methodology was focused on integrating Modbus, a frequently used SCADA communication protocol, with Elliptic Curve based cryptography and develop a server/client application to demonstrate the proof of concept. For the implementation we deployed two C language libraries, which were suitably modify in order to be successfully integrated: libmodbus (https://github.com/stephane/libmodbus) and ecc-lib https://www.ceid.upatras.gr/webpages/faculty/zaro/software/ecc-lib/). The first library provides a C implementation of the Modbus/TCP protocol while the second one offers the functionality to develop cryptographic protocols based on Elliptic Curve Cryptography. These two libraries were combined, after suitable modifications and enhancements, in order to give a modified version of the Modbus/TCP protocol focusing on the security of the data exchanged among the devices and the supervisory servers. The mechanisms we implemented include key generation, key exchange/sharing, message authentication, data integrity check, and encryption/decryption of data. The key generation and key exchange protocols were implemented with the use of Elliptic Curve Cryptography primitives. The keys established by each device are saved in their local memory and are retained during the whole communication session and are used in encrypting and decrypting exchanged messages as well as certifying entities and the integrity of the messages. Finally, the modified library was compiled for the Android environment in order to run the server application as an Android app. The client program runs on a regular computer. The communication between these two entities is an example of the successful establishment of an Elliptic Curve Cryptography based, secure Modbus wireless communication session between a portable device acting as a supervisor station and a monitoring computer. Our first performance measurements are, also, very promising and demonstrate the feasibility of embedding Elliptic Curve Cryptography into SCADA systems, filling in a gap in the relevant scientific literature.

Keywords: elliptic curve cryptography, ICT security, modbus protocol, SCADA, TCP/IP protocol

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Authors: Qianwei Cheng, A. K. M. Mahbubur Rahman, Anis Sarker, Abu Bakar Siddik Nayem, Ovi Paul, Amin Ahsan Ali, M. Ashraful Amin, Ryosuke Shibasaki, Moinul Zaber

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Thomas Friedman, in his famous book, argued that the world in this 21st century is flat and will continue to be flatter. This is attributed to rapid globalization and the interdependence of humanity that engendered tremendous in-flow of human migration towards the urban spaces. In order to keep the urban environment sustainable, policy makers need to plan based on extensive analysis of the urban environment. With the advent of high definition satellite images, high resolution data, computational methods such as deep neural network analysis, and hardware capable of high-speed analysis; urban planning is seeing a paradigm shift. Legacy data on urban environments are now being complemented with high-volume, high-frequency data. However, the first step of understanding urban space lies in useful categorization of the space that is usable for data collection, analysis, and visualization. In this paper, we propose a pragmatic categorization method that is readily usable for machine analysis and show applicability of the methodology on a developing world setting. Categorization to plan sustainable urban spaces should encompass the buildings and their surroundings. However, the state-of-the-art is mostly dominated by classification of building structures, building types, etc. and largely represents the developed world. Hence, these methods and models are not sufficient for developing countries such as Bangladesh, where the surrounding environment is crucial for the categorization. Moreover, these categorizations propose small-scale classifications, which give limited information, have poor scalability and are slow to compute in real time. Our proposed method is divided into two steps-categorization and automation. We categorize the urban area in terms of informal and formal spaces and take the surrounding environment into account. 50 km × 50 km Google Earth image of Dhaka, Bangladesh was visually annotated and categorized by an expert and consequently a map was drawn. The categorization is based broadly on two dimensions-the state of urbanization and the architectural form of urban environment. Consequently, the urban space is divided into four categories: 1) highly informal area; 2) moderately informal area; 3) moderately formal area; and 4) highly formal area. In total, sixteen sub-categories were identified. For semantic segmentation and automatic categorization, Google’s DeeplabV3+ model was used. The model uses Atrous convolution operation to analyze different layers of texture and shape. This allows us to enlarge the field of view of the filters to incorporate larger context. Image encompassing 70% of the urban space was used to train the model, and the remaining 30% was used for testing and validation. The model is able to segment with 75% accuracy and 60% Mean Intersection over Union (mIoU). In this paper, we propose a pragmatic categorization method that is readily applicable for automatic use in both developing and developed world context. The method can be augmented for real-time socio-economic comparative analysis among cities. It can be an essential tool for the policy makers to plan future sustainable urban spaces.

Keywords: semantic segmentation, urban environment, deep learning, urban building, classification

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Authors: Diana González, Marta Gràcia, Ana Luisa Adam-Alcocer

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Incorporating digital tools into educational practices represents a valuable approach for enriching the quality of teachers' educational practices in oral competence and fostering improvements in student learning outcomes. This study aims to promote a collaborative and culturally sensitive approach to professional development between teachers and a speech therapist to enhance their self-awareness and reflection on high-quality educational practices that integrate school components to strengthen children’s oral communication and pragmatic skills. The study involved five bilingual teachers fluent in both English and Spanish, with three specializing in special education and two in general education. It focused on Spanish-English bilingual students, aged 3-6, who were experiencing speech delays or disorders in a New York City public school, with the collaboration of a speech therapist. Using EVALOE-DSS (Assessment Scale of Oral Language Teaching in the School Context - Decision Support System), teachers conducted self-assessments of their teaching practices, reflect and make-decisions throughout six classes from March to June, focusing on students' communicative competence across various activities. Concurrently, the speech therapist observed and evaluated six classes per teacher using EVALOE-DSS during the same period. Additionally, professional development meetings were held monthly between the speech therapist and teachers, centering on discussing classroom interactions, instructional strategies, and the progress of both teachers and students in their classes. Findings highlight the digital tool EVALOE-DSS's value in analyzing communication patterns and trends among bilingual children in inclusive settings. It helps in identifying improvement areas through teacher and speech therapist collaboration. After self-reflection meetings, teachers demonstrated increased awareness of student needs in oral language and pragmatic skills. They also exhibited enhanced utilization of strategies outlined in EVALOE-DSS, such as actively guiding and orienting students during oral language activities, promoting student-initiated communicative interactions, teaching students how to seek and provide information, and managing turn-taking to ensure inclusive participation. Teachers participating in the professional development program have shown positive progress in assessing their classes across all dimensions of the training tool, including instructional design, teacher conversation management, pupil conversation management, communicative functions, teacher strategies, and pupil communication functions. This includes aspects related to both teacher actions and child actions, particularly in child language development. This progress underscores the effectiveness of individual reflection (conducted weekly or biweekly using EVALOE-DSS) as well as collaborative reflection among teachers and the speech therapist during meetings. The EVALOE-SSD has proven effective in supporting teachers' self-reflection, decision-making, and classroom changes, leading to improved development of students' oral language and pragmatic skills. It has facilitated culturally sensitive evaluations of communication among bilingual children, cultivating collaboration between teachers and speech therapist to identify areas of growth. Participants in the professional development program demonstrated substantial progress across all dimensions assessed by EVALOE-DSS. This included improved management of pupil communication functions, implementation of effective teaching strategies, and better classroom dynamics. Regular reflection sessions using EVALOE-SSD supported continuous improvement in instructional practices, highlighting its role in fostering reflective teaching and enriching student learning experiences. Overall, EVALOE-DSS has proven invaluable for enhancing teaching effectiveness and promoting meaningful student interactions in diverse educational settings.

Keywords: bilingual students, collaboration, culturally sensitive, oral communication skills, self-reflection

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Authors: João Coutinho-Rodrigues, Nuno Sousa, Luís Alçada-Almeida

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Transportation infrastructure systems are fundamental in modern society and economy. However, they need modernizing, maintaining, and reinforcing interventions which require large investments. In many countries, accumulated intervention delays arise from aging and intense use, being magnified by financial constraints of the past. The decision problem of managing the renewal of large backlogs is common to several types of important transportation infrastructures (e.g., railways, roads). This problem requires considering financial aspects as well as operational constraints under a multidimensional framework. The present research introduces a linear programming multiobjective model for managing railway infrastructure asset renewal. The model aims at minimizing three objectives: (i) yearly investment peak, by evenly spreading investment throughout multiple years; (ii) total cost, which includes extra maintenance costs incurred from renewal backlogs; (iii) priority delays related to work start postponements on the higher priority railway sections. Operational constraints ensure that passenger and freight services are not excessively delayed from having railway line sections under intervention. Achieving a balanced annual investment plan, without compromising the total financial effort or excessively postponing the execution of the priority works, was the motivation for pursuing the research which is now presented. The methodology, inspired by a real case study and tested with real data, reflects aspects of the practice of an infrastructure management company and is generalizable to different types of infrastructure (e.g., railways, highways). It was conceived for treating renewal interventions in infrastructure assets, which is a railway network may be rails, ballasts, sleepers, etc.; while a section is under intervention, trains must run at reduced speed, causing delays in services. The model cannot, therefore, allow for an accumulation of works on the same line, which may cause excessively large delays. Similarly, the lines do not all have the same socio-economic importance or service intensity, making it is necessary to prioritize the sections to be renewed. The model takes these issues into account, and its output is an optimized works schedule for the renewal project translatable in Gantt charts The infrastructure management company provided all the data for the first test case study and validated the parameterization. This case consists of several sections to be renewed, over 5 years and belonging to 17 lines. A large instance was also generated, reflecting a problem of a size similar to the USA railway network (considered the largest one in the world), so it is not expected that considerably larger problems appear in real life; an average of 25 years backlog and ten years of project horizon was considered. Despite the very large increase in the number of decision variables (200 times as large), the computational time cost did not increase very significantly. It is thus expectable that just about any real-life problem can be treated in a modern computer, regardless of size. The trade-off analysis shows that if the decision maker allows some increase in max yearly investment (i.e., degradation of objective ii), solutions improve considerably in the remaining two objectives.

Keywords: transport infrastructure, asset renewal, railway maintenance, multiobjective modeling

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Authors: Christoph Opperer

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The rapid urbanization of modern societies and the need for sustainable urban development demand innovative solutions that meet both individual and collective needs while addressing environmental concerns. To address these challenges, this paper presents a study that explores the potential of spatial and energetic/ecological optimization to enhance the performance of urban settlements, focusing on both architectural and urban scales. The study focuses on the application of biological principles and self-organization processes in urban planning and design, aiming to achieve a balance between ecological performance, architectural quality, and individual living conditions. The research adopts a case study approach, focusing on a 10-hectare brownfield site in the south of Vienna. The site is surrounded by a small-scale built environment as an appropriate starting point for the research and design process. However, the selected urban form is not a prerequisite for the proposed design methodology, as the findings can be applied to various urban forms and densities. The methodology used in this research involves dividing the overall building mass and program into individual small housing units. A computational model has been developed to optimize the distribution of these units, considering factors such as solar exposure/radiation, views, privacy, proximity to sources of disturbance (such as noise), and minimal internal circulation areas. The model also ensures that existing vegetation and buildings on the site are preserved and incorporated into the optimization and design process. The model allows for simultaneous optimization at two scales, architectural and urban design, which have traditionally been addressed sequentially. This holistic design approach leads to individual and collective benefits, resulting in urban environments that foster a balance between ecology and architectural quality. The results of the optimization process demonstrate a seemingly random distribution of housing units that, in fact, is a densified hybrid between traditional garden settlements and allotment settlements. This urban typology is selected due to its compatibility with the surrounding urban context, although the presented methodology can be extended to other forms of urban development and density levels. The benefits of this approach are threefold. First, it allows for the determination of ideal housing distribution that optimizes solar radiation for each building density level, essentially extending the concept of sustainable building to the urban scale. Second, the method enhances living quality by considering the orientation and positioning of individual functions within each housing unit, achieving optimal views and privacy. Third, the algorithm's flexibility and robustness facilitate the efficient implementation of urban development with various stakeholders, architects, and construction companies without compromising its performance. The core of the research is the application of global and local optimization strategies to create efficient design solutions. By considering both, the performance of individual units and the collective performance of the urban aggregation, we ensure an optimal balance between private and communal benefits. By promoting a holistic understanding of urban ecology and integrating advanced optimization strategies, our methodology offers a sustainable and efficient solution to the challenges of modern urbanization.

Keywords: sustainable development, self-organization, ecological performance, solar radiation and exposure, daylight, visibility, accessibility, spatial distribution, local and global optimization

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Authors: Sofia N. Gonçalves, Duarte M. S. Albuquerque, José C. F. Pereira

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Microwave technology has been gaining importance in contributing to decarbonization processes in high energy demand industries. Despite the several numerical models presented in the literature, a proper Verification and Validation exercise is still lacking. This is important and required to evaluate the physical process model accuracy and adequacy. Another issue addresses impedance matching, which is an important mechanism used in microwave experiments to increase electromagnetic efficiency. Such mechanism is not available in current computational tools, thus requiring an external numerical procedure. A numerical model was implemented to study the continuous processing of limestone with microwave heating. This process requires the material to be heated until a certain temperature that will prompt a highly endothermic reaction. Both a 2D and 3D model were built in COMSOL Multiphysics to solve the two-way coupling between Maxwell and Energy equations, along with the coupling between both heat transfer phenomena and limestone endothermic reaction. The 2D model was used to study and evaluate the required numerical procedure, being also a benchmark test, allowing other authors to implement impedance matching procedures. To achieve this goal, a controller built in MATLAB was used to continuously matching the cavity impedance and predicting the required energy for the system, thus successfully avoiding energy inefficiencies. The 3D model reproduces realistic results and therefore supports the main conclusions of this work. Limestone was modeled as a continuous flow under the transport of concentrated species, whose material and kinetics properties were taken from literature. Verification and Validation of the coupled model was taken separately from the chemical kinetic model. The chemical kinetic model was found to correctly describe the chosen kinetic equation by comparing numerical results with experimental data. A solution verification was made for the electromagnetic interface, where second order and fourth order accurate schemes were found for linear and quadratic elements, respectively, with numerical uncertainty lower than 0.03%. Regarding the coupled model, it was demonstrated that the numerical error would diverge for the heat transfer interface with the mapped mesh. Results showed numerical stability for the triangular mesh, and the numerical uncertainty was less than 0.1%. This study evaluated limestone velocity, heat transfer, and load influence on thermal decomposition and overall process efficiency. The velocity and heat transfer coefficient were studied with the 2D model, while different loads of material were studied with the 3D model. Both models demonstrated to be highly unstable when solving non-linear temperature distributions. High velocity flows exhibited propensity to thermal runways, and the thermal efficiency showed the tendency to stabilize for the higher velocities and higher filling ratio. Microwave efficiency denoted an optimal velocity for each heat transfer coefficient, pointing out that electromagnetic efficiency is a consequence of energy distribution uniformity. The 3D results indicated the inefficient development of the electric field for low filling ratios. Thermal efficiencies higher than 90% were found for the higher loads and microwave efficiencies up to 75% were accomplished. The 80% fill ratio was demonstrated to be the optimal load with an associated global efficiency of 70%.

Keywords: multiphysics modeling, microwave heating, verification and validation, endothermic reactions modeling, impedance matching, limestone continuous processing

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Authors: Stefan K. Behfar

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The rapid growth of the Ethereum network has brought forth the urgent need for scalable analysis methods to handle the increasing volume of blockchain data. In this research, we propose efficient methodologies for making Ethereum network analysis scalable. Our approach leverages a combination of graph-based data representation, probabilistic sampling, and parallel processing techniques to achieve unprecedented scalability while preserving critical network insights. Data Representation: We develop a graph-based data representation that captures the underlying structure of the Ethereum network. Each block transaction is represented as a node in the graph, while the edges signify temporal relationships. This representation ensures efficient querying and traversal of the blockchain data. Probabilistic Sampling: To cope with the vastness of the Ethereum blockchain, we introduce a probabilistic sampling technique. This method strategically selects a representative subset of transactions and blocks, allowing for concise yet statistically significant analysis. The sampling approach maintains the integrity of the network properties while significantly reducing the computational burden. Graph Convolutional Networks (GCNs): We incorporate GCNs to process the graph-based data representation efficiently. The GCN architecture enables the extraction of complex spatial and temporal patterns from the sampled data. This combination of graph representation and GCNs facilitates parallel processing and scalable analysis. Distributed Computing: To further enhance scalability, we adopt distributed computing frameworks such as Apache Hadoop and Apache Spark. By distributing computation across multiple nodes, we achieve a significant reduction in processing time and enhanced memory utilization. Our methodology harnesses the power of parallelism, making it well-suited for large-scale Ethereum network analysis. Evaluation and Results: We extensively evaluate our methodology on real-world Ethereum datasets covering diverse time periods and transaction volumes. The results demonstrate its superior scalability, outperforming traditional analysis methods. Our approach successfully handles the ever-growing Ethereum data, empowering researchers and developers with actionable insights from the blockchain. Case Studies: We apply our methodology to real-world Ethereum use cases, including detecting transaction patterns, analyzing smart contract interactions, and predicting network congestion. The results showcase the accuracy and efficiency of our approach, emphasizing its practical applicability in real-world scenarios. Security and Robustness: To ensure the reliability of our methodology, we conduct thorough security and robustness evaluations. Our approach demonstrates high resilience against adversarial attacks and perturbations, reaffirming its suitability for security-critical blockchain applications. Conclusion: By integrating graph-based data representation, GCNs, probabilistic sampling, and distributed computing, we achieve network scalability without compromising analytical precision. This approach addresses the pressing challenges posed by the expanding Ethereum network, opening new avenues for research and enabling real-time insights into decentralized ecosystems. Our work contributes to the development of scalable blockchain analytics, laying the foundation for sustainable growth and advancement in the domain of blockchain research and application.

Keywords: Ethereum, scalable network, GCN, probabilistic sampling, distributed computing

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Authors: Utkarsh A. Mishra, Ankit Bansal

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At high temperature, radiative heat transfer is the dominant mode of heat transfer. It is governed by various phenomena such as photon emission, absorption, and scattering. The solution of the governing integrodifferential equation of radiative transfer is a complex process, more when the effect of participating medium and wavelength properties are taken into consideration. Although a generic formulation of such radiative transport problem can be modeled for a wide variety of problems with non-gray, non-diffusive surfaces, there is always a trade-off between simplicity and accuracy of the problem. Recently, solutions of complicated mathematical problems with statistical methods based on randomization of naturally occurring phenomena have gained significant importance. Photon bundles with discrete energy can be replicated with random numbers describing the emission, absorption, and scattering processes. Photon Monte Carlo (PMC) is a simple, yet powerful technique, to solve radiative transfer problems in complicated geometries with arbitrary participating medium. The method, on the one hand, increases the accuracy of estimation, and on the other hand, increases the computational cost. The participating media -generally a gas, such as CO₂, CO, and H₂O- present complex emission and absorption spectra. To model the emission/absorption accurately with random numbers requires a weighted sampling as different sections of the spectrum carries different importance. Importance sampling (IS) was implemented to sample random photon of arbitrary wavelength, and the sampled data provided unbiased training of MC estimators for better results. A better replacement to uniform random numbers is using deterministic, quasi-random sequences. Halton, Sobol, and Faure Low-Discrepancy Sequences are used in this study. They possess better space-filling performance than the uniform random number generator and gives rise to a low variance, stable Quasi-Monte Carlo (QMC) estimators with faster convergence. An optimal supervised learning scheme was further considered to reduce the computation costs of the PMC simulation. A one-dimensional plane-parallel slab problem with participating media was formulated. The history of some randomly sampled photon bundles is recorded to train an Artificial Neural Network (ANN), back-propagation model. The flux was calculated using the standard quasi PMC and was considered to be the training target. Results obtained with the proposed model for the one-dimensional problem are compared with the exact analytical and PMC model with the Line by Line (LBL) spectral model. The approximate variance obtained was around 3.14%. Results were analyzed with respect to time and the total flux in both cases. A significant reduction in variance as well a faster rate of convergence was observed in the case of the QMC method over the standard PMC method. However, the results obtained with the ANN method resulted in greater variance (around 25-28%) as compared to the other cases. There is a great scope of machine learning models to help in further reduction of computation cost once trained successfully. Multiple ways of selecting the input data as well as various architectures will be tried such that the concerned environment can be fully addressed to the ANN model. Better results can be achieved in this unexplored domain.

Keywords: radiative heat transfer, Monte Carlo Method, pseudo-random numbers, low discrepancy sequences, artificial neural networks

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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: Martin Alexander Eder, Sergei Semenov

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Many glass-epoxy composite structures, such as large utility wind turbine rotor blades (WTBs), comprise of adhesive joints with typically thick bond lines used to connect the different components during assembly. Performance optimization of rotor blades to increase power output by simultaneously maintaining high stiffness-to-low-mass ratios entails intricate geometries in conjunction with complex anisotropic material behavior. Consequently, adhesive joints in WTBs are subject to multiaxial stress states with significant stress gradients depending on the local joint geometry. Moreover, the dynamic aero-elastic interaction of the WTB with the airflow generates non-proportional, variable amplitude stress histories in the material. Empiricism shows that a prominent failure type in WTBs is high cycle fatigue failure of adhesive bond line interfaces, which in fact over time developed into a design driver as WTB sizes increase rapidly. Structural optimization employed at an early design stage, therefore, sets high demands on computationally efficient interface fatigue models capable of predicting the critical locations prone for interface failure. The numerical stress-based interface fatigue model presented in this work uses the Drucker-Prager criterion to compute three different damage indices corresponding to the two interface shear tractions and the outward normal traction. The two-parameter Drucker-Prager model was chosen because of its ability to consider shear strength enhancement under compression and shear strength reduction under tension. The governing interface damage index is taken as the maximum of the triple. The damage indices are computed through the well-known linear Palmgren-Miner rule after separate rain flow-counting of the equivalent shear stress history and the equivalent pure normal stress history. The equivalent stress signals are obtained by self-similar scaling of the Drucker-Prager surface whose shape is defined by the uniaxial tensile strength and the shear strength such that it intersects with the stress point at every time step. This approach implicitly assumes that the damage caused by the prevailing multiaxial stress state is the same as the damage caused by an amplified equivalent uniaxial stress state in the three interface directions. The model was implemented as Python plug-in for the commercially available finite element code Abaqus for its use with solid elements. The model was used to predict the interface damage of an adhesively bonded, tapered glass-epoxy composite cantilever I-beam tested by LM Wind Power under constant amplitude compression-compression tip load in the high cycle fatigue regime. Results show that the model was able to predict the location of debonding in the adhesive interface between the webfoot and the cap. Moreover, with a set of two different constant life diagrams namely in shear and tension, it was possible to predict both the fatigue lifetime and the failure mode of the sub-component with reasonable accuracy. It can be concluded that the fidelity, robustness and computational efficiency of the proposed model make it especially suitable for rapid fatigue damage screening of large 3D finite element models subject to complex dynamic load histories.

Keywords: adhesive, fatigue, interface, multiaxial stress

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