Search results for: energetic losses models

Authors: Bhavita S. Dave, Jaimin Vaidya, Chandresh H. Solanki, Atul K.

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To measure the permeability of a soil specimen, one of the basic assumptions of Darcy's law is that the soil sample should be saturated. Unlike saturated soils, the permeability of unsaturated soils cannot be found using conventional methods as it does not follow Darcy's law. Many empirical models, such as the Van Genuchten Model and Fredlund-Xing Model were suggested to predict permeability value for unsaturated soil. Such models use data from the soil-freezing characteristic curve to find fitting parameters for frozen unsaturated soils. In this study, soil specimens were subjected to 0, 1, 3, and 5 freezing-thawing (F-T) cycles for different degrees of saturation to have a wide range of suction, and its soil freezing characteristic curves were formulated for all F-T cycles. Changes in fitting parameters and relative permeability with subsequent F-T cycles are presented in this paper for both models.

Keywords: frozen unsaturated soil, Fredlund Xing model, soil-freezing characteristic curve, Van Genuchten model

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Authors: O. Behar, A. Khellaf, K. Mohammedi, S. Ait Kaci

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Up to now, most validation studies have been based on the MBE and RMSE, and therefore, focused only on long and short terms performance to test and classify solar radiation models. This traditional analysis does not take into account the quality of modeling and linearity. In our analysis we have tested 22 solar radiation models that are capable to provide instantaneous direct and global radiation at any given location Worldwide. We introduce a new indicator, which we named Global Accuracy Indicator (GAI) to examine the linear relationship between the measured and predicted values and the quality of modeling in addition to long and short terms performance. Note that the quality of model has been represented by the T-Statistical test, the model linearity has been given by the correlation coefficient and the long and short term performance have been respectively known by the MBE and RMSE. An important founding of this research is that the use GAI allows avoiding default validation when using traditional methodology that might results in erroneous prediction of solar power conversion systems performances.

Keywords: solar radiation model, parametric model, performance analysis, Global Accuracy Indicator (GAI)

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Authors: E. Harrer, M. Mauerhofer, T. Werginz

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As a result of intensive competition, the building sector is suffering from a high degree of rivalry. Furthermore, there can be observed an unbalanced distribution of project risks. Clients are aimed to shift their own risks into the sphere of the constructors or planners. The consequence of this is that the number of conflicts between the involved parties is inordinately high or even increasing; an alternative approach to counter on that developments are cooperative project forms in the construction sector. This research compares conventional contract models and models with partnering agreements to examine the influence on project risks by an early integration of the involved parties. The goal is to show up deviations in different project stages from the design phase to the project transfer phase. These deviations are evaluated by a survey of experts from the three spheres: clients, contractors and planners. By rating the influence of the participants on specific risk factors it is possible to identify factors which are relevant for a smooth project execution.

Keywords: building projects, contract models, partnering, project risks

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Authors: Peter Kress, Alexander Pflaum, Ulrich Loewen

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The number of Internet-of-Things (IoT) platforms is steadily increasing across various industries, especially for smart factories, smart homes and smart mobility. Also in the manufacturing industry, the number of Industrial-IoT platforms is growing. Both IT players, start-ups and increasingly also established industry players and small-and-medium-enterprises introduce offerings for the connection of industrial equipment on platforms, enabled by advanced information and communication technology. Beside the offered functionalities, the established ecosystem of partners around a platform is one of the key differentiators to generate a competitive advantage. The key question is how platform operators design the business model around their platform to attract a high number of customers and partners to co-create value for the entire ecosystem. The present research tries to answer this question by determining the key characteristics of business models of successful platforms in the manufacturing industry. To achieve that, the authors selected an explorative qualitative research approach and created an inductive comparative case study. The authors generated valuable descriptive insights of the business model elements (e.g., value proposition, pricing model or partnering model) of various established platforms. Furthermore, patterns across the various cases were identified to derive propositions for the successful design of business models of platforms in the manufacturing industry.

Keywords: industrial-internet-of-things, business models, platforms, ecosystems, case study

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Authors: O. P. Tripathi, P. L. Verma

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Coronal Mass Ejections (CMEs) are mostly reached on Earth from 1 to 5 days from the Sun. As a consequence, slow CMEs are accelerated toward the speed of solar wind and fast CMEs are decelerated toward the speed of the solar wind. Coronal mass ejections (CMEs) are bursts of solar material i.e. clouds of plasma and magnetic fields that shoot off the sun’s surface. Other solar events include solar wind streams that come from the coronal holes on the Sun and solar energetic particles that are primarily released by CMEs. We have studied geomagnetic storms (DST ≤ - 80nT) during 1997-2012 with halo and partial halo coronal mass ejections and found that 73.28% CMEs (halo and partial halo coronal mass ejections) are associated with geomagnetic storms. The association rate of halo and partial halo coronal mass ejections are found 67.06% and 32.94% with geomagnetic storms respectively. We have also determined positive co-relation between magnitude of geomagnetic storms and speed of coronal mass ejection with correlation co-efficient 0.23.

Keywords: geomagnetic storms, coronal mass ejections (CMEs), disturbance storm time (Dst), interplanetary magnetic field (IMF)

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Authors: Hazel Pettifor, Charlie Wilson, David Mccollum, Oreane Edelenbosch

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Vehicle purchase is a technology adoption decision that will strongly influence future energy and emission outcomes. Global integrated assessment models (IAMs) provide valuable insights into the medium and long terms effects of socio-economic development, technological change and climate policy. In this paper we present a unique and transparent approach for improving the behavioural representation of these models by incorporating social influence effects to more accurately represent consumer choice. This work draws together strong conceptual thinking and robust empirical evidence to introduce heterogeneous and interconnected consumers who vary in their aversion to new technologies. Focussing on vehicle choice, we conduct novel empirical research to parameterise consumer risk aversion and how this is shaped by social and cultural influences. We find robust evidence for social influence effects, and variation between countries as a function of cultural differences. We then formulate an approach to modelling social influence which is implementable in both simulation and optimisation-type models. We use two global integrated assessment models (IMAGE and MESSAGE) to analyse four scenarios that introduce social influence and cultural differences between regions. These scenarios allow us to explore the interactions between consumer preferences and social influence. We find that incorporating social influence effects into global models accelerates the early deployment of electric vehicles and stimulates more widespread deployment across adopter groups. Incorporating cultural variation leads to significant differences in deployment between culturally divergent regions such as the USA and China. Our analysis significantly extends the ability of global integrated assessment models to provide policy-relevant analysis grounded in real-world processes.

Keywords: behavioural realism, electric vehicles, social influence, vehicle choice

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Authors: Gautier Viaud, Paul-Henry Cournède

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Plant growth models have been used extensively for the prediction of the phenotypic performance of plants. However, they remain most often calibrated for a given genotype and therefore do not take into account genotype by environment interactions. One way of achieving such an objective is to consider Bayesian hierarchical models. Three levels can be identified in such models: The first level describes how a given growth model describes the phenotype of the plant as a function of individual parameters, the second level describes how these individual parameters are distributed within a plant population, the third level corresponds to the attribution of priors on population parameters. Thanks to the Bayesian framework, choosing appropriate priors for the population parameters permits to derive analytical expressions for the full conditional distributions of these population parameters. As plant growth models are of a nonlinear nature, individual parameters cannot be sampled explicitly, and a Metropolis step must be performed. This allows for the use of a hybrid Gibbs--Metropolis sampler. A generic approach was devised for the implementation of both general state space models and estimation algorithms within a programming platform. It was designed using the Julia language, which combines an elegant syntax, metaprogramming capabilities and exhibits high efficiency. Results were obtained for Arabidopsis thaliana on both simulated and real data. An organ-scale Greenlab model for the latter is thus presented, where the surface areas of each individual leaf can be simulated. It is assumed that the error made on the measurement of leaf areas is proportional to the leaf area itself; multiplicative normal noises for the observations are therefore used. Real data were obtained via image analysis of zenithal images of Arabidopsis thaliana over a period of 21 days using a two-step segmentation and tracking algorithm which notably takes advantage of the Arabidopsis thaliana phyllotaxy. Since the model formulation is rather flexible, there is no need that the data for a single individual be available at all times, nor that the times at which data is available be the same for all the different individuals. This allows to discard data from image analysis when it is not considered reliable enough, thereby providing low-biased data in large quantity for leaf areas. The proposed model precisely reproduces the dynamics of Arabidopsis thaliana’s growth while accounting for the variability between genotypes. In addition to the estimation of the population parameters, the level of variability is an interesting indicator of the genotypic stability of model parameters. A promising perspective is to test whether some of the latter should be considered as fixed effects.

Keywords: bayesian, genotypic differentiation, hierarchical models, plant growth models

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Authors: Francesca Croce

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Governmental development strategies aimed at entrepreneurship as a major resource for economic development and poverty reduction of indigenous people. As initiatives and programs are local based, there is a need to better understand the contextual factors of indigenous entrepreneurial models. The purpose of this paper is, therefore, to analyze and integrated the indigenous entrepreneurship literature in order to identify the main models of indigenous entrepreneurship. To answer this need, a systematic literature review was conducted. Relevant articles were identified in selected electronic databases (ABI/Inform Global, Business Source Premier, Web of Science; International Bibliography of the Social Sciences, Academic Search, Sociological Abstract, Entrepreneurial Studies Sources and Bibliography of Native North America) and in selected electronic review. Beginning to 1st January 1995 (first International Day of the World’s Indigenous People), 59 academic articles were selected from 1411. Through systematic analysis of the cultural, social and organizational variables, the paper highlights that a typology of indigenous entrepreneurial models is possible thought the concept of entrepreneurial ecosystem, which includes the geographical position and the environment of the indigenous communities. The results show three models of indigenous entrepreneurship: the urban indigenous entrepreneurship, the semi-urban indigenous entrepreneurship, and rural indigenous entrepreneurship. After the introduction, the paper is organized as follows. In the first part theoretical and practical needs of a systematic literature review on indigenous entrepreneurship are provided. In the second part, the methodology, the selection process and evaluation of the articles are explained. In the third part, findings are presented and each indigenous entrepreneurial model characteristics are discussed. The results of this study bring a new theorization about indigenous entrepreneurship and may be useful for scientists in the field in search of overcoming the cognitive border of Indigenous business models still too little known. Also, the study is addressed to policy makers in charge of indigenous entrepreneurial development strategies more focused on contextual factors studies.

Keywords: community development, entrepreneurial ecosystem, indigenous entrepreneurship model, indigenous people, systematic literature review

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Authors: Sanja O. Podunavac-Kuzmanović, Lidija R. Jevrić, Strahinja Z. Kovačević

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Benzimidazoles are a group of compounds with significant antibacterial, antifungal and anticancer activity. The studied compounds consist of the main benzimidazole structure with different combinations of substituens. This study is based on the two-dimensional and three-dimensional molecular modeling and calculation of molecular descriptors (physicochemical and lipophilicity descriptors) of structurally diverse benzimidazoles. Molecular modeling was carried out by using ChemBio3D Ultra version 14.0 software. The obtained 3D models were subjected to energy minimization using molecular mechanics force field method (MM2). The cutoff for structure optimization was set at a gradient of 0.1 kcal/Åmol. The obtained set of molecular descriptors was used in principal component analysis (PCA) of possible similarities and dissimilarities among the studied derivatives. After the molecular modeling, the quantitative structure-property relationship (QSPR) analysis was applied in order to get the mathematical models which can be used in prediction of pKb values of structurally similar benzimidazoles. The obtained models are based on statistically valid multiple linear regression (MLR) equations. The calculated cross-validation parameters indicate the high prediction ability of the established QSPR models. This study is financially supported by COST action CM1306 and the project No. 114-451-347/2015-02, financially supported by the Provincial Secretariat for Science and Technological Development of Vojvodina.

Keywords: benzimidazoles, chemometrics, molecular modeling, molecular descriptors, QSPR

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Authors: Marcio S. da Silva, Jose Mauricio de B. Bezerra, Antonio E. de A. Nogueira

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This paper aims to make a performance analysis of shield wire transmission lines against atmospheric discharges when it is made the option of sectioning the shield wire and verify if the tolerability of the change. As a goal of this work, it was established to make complete modeling of a transmission line in the ATPDraw program with shield wire grounded in all the towers and in some towers. The methodology used to make the proposed evaluation was to choose an actual transmission line that served as a case study. From the choice of transmission line and verification of all its topology and materials, complete modeling of the line using the ATPDraw software was performed. Then several atmospheric discharges were simulated by striking the grounded shield wires in each tower. These simulations served to identify the behavior of the existing line against atmospheric discharges. After this first analysis, the same line was reconsidered with shield wire segmentation. The shielding wire segmentation technique aims to reduce induced losses in shield wires and is adopted in some transmission lines in Brazil. With the same conditions of atmospheric discharge the transmission line, this time with shield wire segmentation was again evaluated. The results obtained showed that it is possible to obtain similar performances against atmospheric discharges between a shield wired line in multiple towers and the same line with shield wire segmentation if some precautions are adopted as verification of the ground resistance of the wire segmented shield, adequacy of the maximum length of the segmented gap, evaluation of the separation length of the electrodes of the insulator spark, among others. As a conclusion, it is verified that since the correct assessment and adopted the correct criteria of adjustment a transmission line with shielded wire segmentation can perform very similar to the traditional use with multiple earths. This solution contributes in a very important way to the reduction of energy losses in transmission lines.

Keywords: atmospheric discharges, ATPDraw, shield wire, transmission lines

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Authors: Gangmin Li, Fan Yang

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Personalized recommendation is crucial for any recommendation system. One of the techniques for personalized recommendation is to identify the intention. Traditional user intention identification uses the user’s selection when facing multiple items. This modeling relies primarily on historical behaviour data resulting in challenges such as the cold start, unintended choice, and failure to capture intention when items are new. Motivated by recent advancements in Large Language Models (LLMs) like ChatGPT, we present an approach for user intention identification by embracing LLMs with Chain-of-Thought (CoT) prompting. We use the initial user profile as input to LLMs and design a collection of prompts to align the LLM's response through various recommendation tasks encompassing rating prediction, search and browse history, user clarification, etc. Our tests on real-world datasets demonstrate the improvements in recommendation by explicit user intention identification and, with that intention, merged into a user model.

Keywords: personalized recommendation, generative user modelling, user intention identification, large language models, chain-of-thought prompting

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Authors: Ugbede Onalo, Mohd Lizam, Ahmad Kaseri, Otache Innocent

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Giving the paucity of studies on IFRS adoption and quality of banks accounting quality, particularly in emerging economies, this study is motivated to investigate whether the Nigeria decision to adopt IFRS beginning from 1 January 2012 is associated with high quality accounting measures. Consistent with prior literatures, this study measure quality of financial statement information using earnings measurement, timeliness of loss recognition and value relevance. A total of twenty Nigeria banks covering a period of six years (2008-2013) divided equally into three years each (2008, 2009, 2010) pre adoption period and (2011, 2012, 2013) post adoption period were investigated. Following prior studies eight models were in all employed to investigate earnings management, timeliness of loss recognition and value relevance of Nigeria bank accounting quality for the different reporting regimes. Results suggest that IFRS adoption is associated with minimal earnings management, timely recognition of losses and high value relevance of accounting information. Summarily, IFRS adoption engenders higher quality of banks financial statement information compared to local GAAP. Hence, this study recommends the global adoption of IFRS and that Nigeria banks should embrace good corporate governance practices.

Keywords: IFRS, SAS, quality of accounting information, earnings measurement, discretionary accruals, non-discretionary accruals, total accruals, Jones model, timeliness of loss recognition, value relevance

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Authors: Isaac K. E. Ampomah, Seong-Bae Park, Sang-Jo Lee

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Over the past decade, there have been promising developments in Natural Language Processing (NLP) with several investigations of approaches focusing on Recognizing Textual Entailment (RTE). These models include models based on lexical similarities, models based on formal reasoning, and most recently deep neural models. In this paper, we present a sentence encoding model that exploits the sentence-to-sentence relation information for RTE. In terms of sentence modeling, Convolutional neural network (CNN) and recurrent neural networks (RNNs) adopt different approaches. RNNs are known to be well suited for sequence modeling, whilst CNN is suited for the extraction of n-gram features through the filters and can learn ranges of relations via the pooling mechanism. We combine the strength of RNN and CNN as stated above to present a unified model for the RTE task. Our model basically combines relation vectors computed from the phrasal representation of each sentence and final encoded sentence representations. Firstly, we pass each sentence through a convolutional layer to extract a sequence of higher-level phrase representation for each sentence from which the first relation vector is computed. Secondly, the phrasal representation of each sentence from the convolutional layer is fed into a Bidirectional Long Short Term Memory (Bi-LSTM) to obtain the final sentence representations from which a second relation vector is computed. The relations vectors are combined and then used in then used in the same fashion as attention mechanism over the Bi-LSTM outputs to yield the final sentence representations for the classification. Experiment on the Stanford Natural Language Inference (SNLI) corpus suggests that this is a promising technique for RTE.

Keywords: deep neural models, natural language inference, recognizing textual entailment (RTE), sentence-to-sentence relation

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Authors: M. Jerold, V. Sivasubramanian

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In this study, nano zero valent iron Sargassum swartzii (nZVI-SS) biocomposite a marine algal based biosorbent was used for the removal of simulated crystal violet (CV) in batch and continuous fixed bed operation. The Box-Behnen design (BBD) experimental results revealed the biosoprtion was maximum at pH 7.5, biosorbent dosage 0.1 g/L and initial CV concentration of 100 mg/L. The effect of various column parameters like bed depth (3, 6 and 9 cm), flow rate (5, 10 and 15 mL/min) and influent CV concentration (5, 10 and 15 mg/L) were investigated. The exhaustion time increased with increase of bed depth, influent CV concentration and decrease of flow rate. Adam-Bohart, Thomas and Yoon-Nelson models were used to predict the breakthrough curve and to evaluate the model parameters. Out of these models, Thomas and Yoon-Nelson models well described the experimental data. Therefore, the result implies that nZVI-SS biocomposite is a cheap and most promising biosorbent for the removal of CV from wastewater.

Keywords: algae, biosorption, zero-valent, dye, wastewater

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Authors: Minna-Maari Harmaala, Hanna Harilainen

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Estimates of the economic value of transitioning to circular economy models vary but it has been estimated to represent $1 trillion worth of new business into the global economy. In Europe alone, estimates claim that adopting circular-economy principles could not only have environmental and social benefits but also generate a net economic benefit of €1.8 trillion by 2030. Proponents of a circular economy argue that it offers a major opportunity to increase resource productivity, decrease resource dependence and waste, and increase employment and growth. A circular system could improve competitiveness and unleash innovation. Yet, most companies are not capturing these opportunities and thus the even abundant circular opportunities remain uncaptured even though they would seem inherently profitable. Service design in broad terms relates to developing an existing or a new service or service concept with emphasis and focus on the customer experience from the onset of the development process. Service design may even mean starting from scratch and co-creating the service concept entirely with the help of customer involvement. Service design methodologies provide a structured way of incorporating customer understanding and involvement in the process of designing better services with better resonance to customer needs. A business model is a depiction of how the company creates, delivers, and captures value; i.e. how it organizes its business. The process of business model development and adjustment or modification is also called business model innovation. Innovating business models has become a part of business strategy. Our hypothesis is that in addition to linear models still being easier to adopt and often with lower threshold costs, companies lack an understanding of how circular models can be adopted into their business and how customers will be willing and ready to adopt the new circular business models. In our research, we use robust service design methodology to develop circular economy solutions with two case study companies. The aim of the process is to not only develop the service concepts and portfolio, but to demonstrate the willingness to adopt circular solutions exists in the customer base. In addition to service design, we employ business model innovation methods to develop, test, and validate the new circular business models further. The results clearly indicate that amongst the customer groups there are specific customer personas that are willing to adopt and in fact are expecting the companies to take a leading role in the transition towards a circular economy. At the same time, there is a group of indifferents, to whom the idea of circularity provides no added value. In addition, the case studies clearly show what changes adoption of circular economy principles brings to the existing business model and how they can be integrated.

Keywords: business model innovation, circular economy, circular economy business models, service design

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Authors: Noura Kichou, Manel Tafergguenit, Nabila Ghechtouli, Zakia Hank

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The aim of this work is to synthesize, characterize and evaluate the biological activity of two Ligands : glyoxime and dimethylglyoxime, and their metal Ni(II) chelates. The newly chelates were characterized by elemental analysis, IR, EPR, nuclear magnetic resonances (1H and 13C), and biological activity. The antibacterial and antifungal activities of the ligands and its metal complexes were screened against bacterial species (Staphylococcus aureus, Bacillus subtilis, and Escherichia coli) and fungi (Candida albicans). Ampicillin and amphotericin were used as references for antibacterial and antifungal studies. The activity data show that the metal complexes have a promising biological activity comparable with parent free ligand against bacterial and fungal species. A structural, energetic, and electronic theoretical study was carried out using the DFT method, with the functional B3LYP and the gaussian program 09. A complete optimization of geometries was made, followed by a calculation of the frequencies of the normal modes of vibration. The UV spectrum was also interpreted. The theoretical results were compared with the experimental data.

Keywords: glyoxime, dimetylglyoxime, nickel, antibacterial activity

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Authors: Noura Kichou, Manel Tafergguenit, Nabila Ghechtouli, Zakia Hank

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The aim of this work is to synthesize, characterize and evaluate the biological activity of two Ligands: glyoxime and dimethylglyoxime, and their metal Ni(II) chelates. The newly chelates were characterized by elemental analysis, IR, EPR, nuclear magnetic resonances (1H and 13C), and biological activity. The antibacterial and antifungal activities of the ligands and its metal complexes were screened against bacterial species (Staphylococcus aureus, Bacillus subtilis, and Escherichia coli) and fungi (Candida albicans). Ampicillin and amphotericin were used as references for antibacterial and antifungal studies. The activity data show that the metal complexes have a promising biological activity comparable with parent free ligand against bacterial and fungal species. A structural, energetic, and electronic theoretical study was carried out using the DFT method, with the functional B3LYP and the gaussian program 09. A complete optimization of geometries was made, followed by a calculation of the frequencies of the normal modes of vibration. The UV spectrum was also interpreted. The theoretical results were compared with the experimental data.

Keywords: glyoxime, dimetylglyoxime, nickel, antibacterial activity

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Authors: Elhameh Narimani, Mikhail Sorin, Philippe Micheau, Hakim Nesreddine

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Numerical models of the heat exchangers in ejector refrigeration system (ERS) were developed and validated with the experimental data. The models were based on the switched heat exchangers model using the moving boundary method, which were capable of estimating the zones’ lengths, the outlet temperatures of both sides and the heat loads at various experimental points. The developed models were utilized to investigate the influence of the primary flow pressure on the performance of an R245fa ERS based on its coefficient of performance (COP) and exergy efficiency. It was illustrated numerically and proved experimentally that increasing the primary flow pressure slightly reduces the COP while the exergy efficiency goes through a maximum before decreasing.

Keywords: Coefficient of Performance, COP, Ejector Refrigeration System, ERS, exergy efficiency (ηII), heat exchangers modeling, moving boundary method

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Authors: Fu Jia

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The effects of soil-structure interaction (SSI) are often studied using axial-symmetric three-dimensional (3D) models to avoid the high computational cost of the more realistic, fully 3D models, which require 2-3 orders of magnitude more computer time and storage. This paper analyzes the error and presents correction factors for system frequency, system damping, and peak amplitude of structural response computed by axisymmetric models, embedded in uniform or layered half-space. The results are compared with those for fully 3D rectangular foundations of different aspect ratios. Correction factors are presented for a range of the model parameters, such as fixed-base frequency, structure mass, height and length-to-width ratio, foundation embedment, soil-layer stiffness and thickness. It is shown that the errors are larger for stiffer, taller and heavier structures, deeper foundations and deeper soil layer. For example, for a stiff structure like Millikan Library (NS response; length-to-width ratio 1), the error is 6.5% in system frequency, 49% in system damping and 180% in peak amplitude. Analysis of a case study shows that the NEHRP-2015 provisions for reduction of base shear force due to SSI effects may be unsafe for some structures and need revision. The presented correction factor diagrams can be used in practical design and other applications.

Keywords: 3D soil-structure interaction, correction factors for axisymmetric models, length-to-width ratio, NEHRP-2015 provisions for reduction of base shear force, rectangular embedded foundations, SSI system frequency, SSI system damping

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Authors: Misho Matsankov, Stoyan Petrov

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The paper presents the methodology and the obtained mathematical models for determining the value of the grounding resistance of a disconnected conductor in a three-phase power line, for which the contact voltage is safe, by taking into account the potentials, induced by the non-disconnected phase conductors. The mathematical models have been obtained by implementing the experimental design techniques.

Keywords: contact voltage, experimental design, induced voltage, safety

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Authors: Nathaniel Deboever, Malcolm Breeze, Adrian Sheen

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Basic surgical and suturing techniques are a fundamental requirement for all doctors. In order to gain confidence and competence, doctors in training need to obtain sufficient teaching and just as importantly: practice. Young doctors with an apt level of expertise on these simple surgical skills, which are often used in the Emergency Department, can help alleviate some pressure during a busy evening. Unfortunately, learning these skills can be quite difficult during medical school or even during junior doctor years. The aim of this project was to adequately train medical students attending University of Sydney’s Nepean Clinical School through a series of workshops highlighting practical skills, with hopes to further extend this program to junior doctors in the hospital. The sessions instructed basic skills via tutorials, demonstrations, and lastly, the sessions cemented these proficiencies with practical sessions. During such an endeavor, it is fundamental to employ models that appropriately resemble what students will encounter in the clinical setting. The sustainability of workshops is similarly important to the continuity of such a program. To address both these challenges, the authors have developed models including suturing platforms, knot tying, and vessel ligation stations, as well as a shave and punch biopsy models and ophthalmologic foreign body device. The unique aspect of this work is that we utilized hands-on teaching sessions, to address a gap in doctors-in-training and junior doctor curriculum. Presented to you through this poster are our approaches to creating models that do not employ animal products and therefore do not necessitate particular facilities or discarding requirements. Covering numerous skills that would be beneficial to all young doctors, these models are easily replicable and affordable. This exciting work allows for countless sessions at low cost, providing enough practice for students to perform these skills confidently as it has been shown through attendee questionnaires.

Keywords: medical education, surgical models, surgical simulation, surgical skills education

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Authors: Saud Hassan

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This paper shows simulation for the prediction of the flow around the backlight angle of the passenger vehicle. The CFD simulations are carried out on different car models. The Ahmed model “bluff body” used as the stander model to study aerodynamics of the backlight angle. This paper described the airflow over the different car models with different backlight angles and also on the Ahmed model to determine the trailing vortices with the varying backlight angle of a passenger vehicle body. The CFD simulation is carried out with the Ahmed body which has simplified car model mainly used in automotive industry to investigate the flow over the car body surface. The main goal of the simulation is to study the behavior of trailing vortices of these models. In this paper the air flow over the slant angle of 0,5o, 12.5o, 20o, 30o, 40o are considered. As investigating on the rear backlight angle two dimensional flows occurred at the rear slant, on the other hand when the slant angle is 30o the flow become three dimensional. Above this angle sudden drop occurred in drag.

Keywords: aerodynamics, Ahemd vehicle , backlight angle, finite element method

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Authors: Pius Marthin, Nihal Ata Tutkun

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Survival analysis has become one of the paramount procedures in the modeling of time-to-event data. When we encounter complex survival problems, the traditional approach remains limited in accounting for the complex correlational structure between the covariates and the outcome due to the strong assumptions that limit the inference and prediction ability of the resulting models. Several studies exist on the deep learning approach to survival modeling; moreover, the application for the case of complex survival problems still needs to be improved. In addition, the existing models need to address the data structure's complexity fully and are subject to noise and redundant information. In this study, we design a deep learning technique (CmpXRnnSurv_AE) that obliterates the limitations imposed by traditional approaches and addresses the above issues to jointly predict the risk-specific probabilities and survival function for recurrent events with competing risks. We introduce the component termed Risks Information Weights (RIW) as an attention mechanism to compute the weighted cumulative incidence function (WCIF) and an external auto-encoder (ExternalAE) as a feature selector to extract complex characteristics among the set of covariates responsible for the cause-specific events. We train our model using synthetic and real data sets and employ the appropriate metrics for complex survival models for evaluation. As benchmarks, we selected both traditional and machine learning models and our model demonstrates better performance across all datasets.

Keywords: cumulative incidence function (CIF), risk information weight (RIW), autoencoders (AE), survival analysis, recurrent events with competing risks, recurrent neural networks (RNN), long short-term memory (LSTM), self-attention, multilayers perceptrons (MLPs)

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Authors: Itamir de Morais Barroca Filho, Cephas A. S. Barreto, Ramon Malaquias, Cezar Miranda Paula de Souza, Arthur Costa Gorgônio, João C. Xavier-Júnior, Mateus Firmino, Fellipe Matheus Costa Barbosa

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Information and Communication Technologies (ICT) in healthcare are crucial for efficiently delivering medical healthcare services to patients. These ICTs are also known as e-health and comprise technologies such as electronic record systems, telemedicine systems, and personalized devices for diagnosis. The focus of e-health is to improve the quality of health information, strengthen national health systems, and ensure accessible, high-quality health care for all. All the data gathered by these technologies make it possible to help clinical staff with automated decisions using machine learning. In this context, we collected patient data, such as heart rate, oxygen saturation (SpO2), blood pressure, respiration, and others. With this data, we were able to develop machine learning models for patients’ risk of death and estimate the length of stay in ICU beds. Thus, this paper presents the methodology for applying machine learning techniques to develop these models. As a result, although we implemented these models on an IoT healthcare platform, helping clinical staff in healthcare in an ICU, it is essential to create a robust clinical validation process and monitoring of the proposed models.

Keywords: ICT, e-health, machine learning, ICU, healthcare

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Authors: Mohd Aftar Abu Bakar, Noratiqah Mohd Ariff, Abdul Aziz Jemain

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Storm Event Analysis (SEA) provides a method to define rainfalls events as storms where each storm has its own amount and duration. By modelling daily probability of different types of storms, the onset, offset and cycle of rainfall seasons can be determined and investigated. Furthermore, researchers from the field of meteorology will be able to study the dynamical characteristics of rainfalls and make predictions for future reference. In this study, four categories of storms; short, intermediate, long and very long storms; are introduced based on the length of storm duration. Daily probability models of storms are built for these four categories of storms in Peninsular Malaysia. The models are constructed by using Bernoulli distribution and by applying linear regression on the first Fourier harmonic equation. From the models obtained, it is found that daily probability of storms at the Eastern part of Peninsular Malaysia shows a unimodal pattern with high probability of rain beginning at the end of the year and lasting until early the next year. This is very likely due to the Northeast monsoon season which occurs from November to March every year. Meanwhile, short and intermediate storms at other regions of Peninsular Malaysia experience a bimodal cycle due to the two inter-monsoon seasons. Overall, these models indicate that Peninsular Malaysia can be divided into four distinct regions based on the daily pattern for the probability of various storm events.

Keywords: daily probability model, monsoon seasons, regions, storm events

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Authors: Luanna B. Prevost, Kelli Carter, Margaurete Romero, Kirsti Martinez

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Writing is an essential scientific practice, yet, in several countries, the increasing university science class-size limits the use of written assessments. Written assessments allow students to demonstrate their learning in their own words and permit the faculty to evaluate students’ understanding. However, the time and resources required to grade written assessments prohibit their use in large-enrollment science courses. This study examined the use of machine learning algorithms to automatically analyze student writing and provide timely feedback to the faculty about students' writing in biology. Written responses to questions about matter and energy transformation were collected from large-enrollment undergraduate introductory biology classrooms. Responses were analyzed using the LightSide text mining and classification software. Cohen’s Kappa was used to measure agreement between the LightSide models and human raters. Predictive models achieved agreement with human coding of 0.7 Cohen’s Kappa or greater. Models captured that when writing about matter-energy transformation at the ecosystem level, students focused on primarily on the concepts of heat loss, recycling of matter, and conservation of matter and energy. Models were also produced to capture writing about processes such as decomposition and biochemical cycling. The models created in this study can be used to provide automatic feedback about students understanding of these concepts to biology faculty who desire to use formative written assessments in larger enrollment biology classes, but do not have the time or personnel for manual grading.

Keywords: machine learning, written assessment, biology education, text mining

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Authors: Antonio Horta, Renato Marinho, Raimir Holanda

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Considered as an evolution of the Blockchain, the Ethereum platform, besides allowing transactions of its cryptocurrency named Ether, it allows the programming of decentralised applications (DApps) and smart contracts. However, this functionality into blockchains has raised other types of threats, and the exploitation of smart contracts vulnerabilities has taken companies to experience big losses. This research intends to figure out the number of contracts that are under risk of being drained. Through a deep investigation, more than two hundred thousand smart contracts currently available in the Ethereum platform were scanned and estimated how much money is at risk. The experiment was based in a query run on Google Big Query in July 2022 and returned 50,707,133 contracts published on the Ethereum platform. After applying the filtering criteria, the experimentgot 430,584 smart contracts to download and analyse. The filtering criteria consisted of filtering out: ERC20 and ERC721 contracts, contracts without transactions, and contracts without balance. From this amount of 430,584 smart contracts selected, only 268,103 had source codes published on Etherscan, however, we discovered, using a hashing process, that there were contracts duplication. Removing the duplicated contracts, the process ended up with 20,417 source codes, which were analysed using the open source SAST tool smartbugswith oyente and securify algorithms. In the end, there was nearly $100,000 at risk of being drained from the potentially vulnerable smart contracts. It is important to note that the tools used in this study may generate false positives, which may interfere with the number of vulnerable contracts. To address this point, our next step in this research is to develop an application to test the contract in a parallel environment to verify the vulnerability. Finally, this study aims to alert users and companies about the risk on not properly creating and analysing their smart contracts before publishing them into the platform. As any other application, smart contracts are at risk of having vulnerabilities which, in this case, may result in direct financial losses.

Keywords: blockchain, reentrancy, static application security testing, smart contracts

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Authors: B. A. Alabadan, E. S. Ajayi, C. A. Okolo

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The storage losses recorded globally in maize (Zea mays) especially in the developing countries is worrisome. Certain degenerating changes in the physical characteristics (PC) of the grain occur due to the interaction between the stored maize and the immediate environment especially during long storage period. There has been tremendous reduction in the storage losses since the evolution of metallic silos. This study was carried out to assess the physical quality attributes of maize stored in 2500 MT and 1 MT metallic silos for a period of eight months. The PC evaluated includes percentage moisture content MC, insect damage ID, foreign matters FM, hectolitre weight HC, mould M and germinability VG. The evaluation of data obtained was done using statistical package for social sciences (SPSS 20) for windows evaluation version to determine significant levels and trend of deterioration (P < 0.05) for all the values obtained using Multiple Analysis of Variance (MANOVA) and Duncan’s multivariate test. The result shows that the PC are significant with duration of storage at (P < 0.05) except MI and FM that are significant at (P > 0.05) irrespective of the size of the metallic silos. The average mean deviation for physical properties from the control in respect to duration of storage are as follows: MC 10.0 ±0.00%, HC 72.9 ± 0.44% ID 0.29 ± 0.00%, BG 0.55±0.05%, MI 0.00 ± 0.65%, FM 0.80± 0.20%, VG 100 ± 0.03%. The variables that were found to be significant (p < 0.05) with the position of grain in the bulk are VG, MI and ID while others are insignificant at (p > 0.05). Variables were all significant (p < 0.05) with the duration of storage with (0.00) significant levels, irrespective of the size of the metallic silos, but were insignificant with the position of the grain in the bulk (p > 0.05). From the results, it can be concluded that there is a slight decrease of the following variables, with time, HC, MC, and V, probably due to weather fluctuations and grain respiration, while FM, BG, ID and M were found to increase slightly probably due to insect activity in the bigger silos and loss of moisture. The size of metallic silos has no remarkable influence on the PC of stored maize (Zea mays). Germinability was found to be better with the 1 MT silos probably due to its hermetic nature. Smaller size metallic silos are preferred for storage of seeds but bigger silos largely depend on the position of the grains in the bulk.

Keywords: maize, storage, silo, physical characteristics

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Authors: Chokri Slim

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The relative failure of simultaneous equation models in the seventies has led researchers to turn to other approaches that take into account the dynamics of economic and financial systems. In this paper, we use an approach based on vector autoregressive model that is widely used in recent years. Their popularity is due to their flexible nature and ease of use to produce models with useful descriptive characteristics. It is also easy to use them to test economic hypotheses. The standard econometric techniques assume that the series studied are stable over time (stationary hypothesis). Most economic series do not verify this hypothesis, which assumes, when one wishes to study the relationships that bind them to implement specific techniques. This is cointegration which characterizes non-stationary series (integrated) with a linear combination is stationary, will also be presented in this paper. Since the work of Johansen, this approach is generally presented as part of a multivariate analysis and to specify long-term stable relationships while at the same time analyzing the short-term dynamics of the variables considered. In the empirical part, we have applied these concepts to study the dynamics of of the exchange rate in Tunisia, which is one of the most important economic policy of a country open to the outside. According to the results of the empirical study by the cointegration method, there is a cointegration relationship between the exchange rate and its determinants. This relationship shows that the variables have a significant influence in determining the exchange rate in Tunisia.

Keywords: stationarity, cointegration, dynamic models, causality, VECM models

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Authors: Víctor Cecilio, Roberto Gómez, J. Alberto Escobar, Héctor Guerrero

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The study of the structural behavior of a support/pier of an elevated viaduct in Mexico City is presented. Detection of foundation piles with uncertain integrity prompted the review of possible situations that could jeopardy the structural safety of the pier. The objective of this paper is to evaluate the structural conditions of the support, taking into account the type of anomaly reported and the depth at which it is located, the position of the pile with uncertain integrity in the foundation system, the stratigraphy of the surrounding soil and the geometry and structural characteristics of the pier. To carry out the above, dynamic analysis, spectral modal, and step-by-step, with elastic and inelastic material models, were performed. Results were evaluated in accordance with the standards used for the design of the original structural project and with the Construction Regulations for Mexico’s Federal District (RCDF-2017, 2017). Comments on the response of the analyzed models are issued, and the conclusions are presented from a structural point of view.

Keywords: dynamic analysis, inelastic models, dubious foundation, bridge pier

Procedia PDF Downloads 121