Search results for: innovation maturity models

Authors: Raninder Kaur Dhillon, Mayur Jethwa, Hardeep Singh Rai

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Information technology has made a pivotal progress across disparate disciplines, one of which is AEC (Architecture, Engineering and Construction) industry. CAD is a form of computer-aided building modulation that architects, engineers and contractors use to create and view two- and three-dimensional models. The AEC industry also uses building information modeling (BIM), a newer computerized modeling system that can create four-dimensional models; this software can greatly increase productivity in the AEC industry. BIM models generate open source IFC (Industry Foundation Classes) files which aim for interoperability for exchanging information throughout the project lifecycle among various disciplines. The methods developed in previous studies require either an IFC schema or MVD and software applications, such as an IFC model server or a Building Information Modeling (BIM) authoring tool, to extract a partial or complete IFC instance model. This paper proposes an efficient algorithm for extracting a partial and total model from an Industry Foundation Classes (IFC) instance model without an IFC schema or a complete IFC model view definition (MVD).

Keywords: BIM, CAD, IFC, MVD

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Authors: Daniel Fulus Fom, Gau Patrick Damulak

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In this study, the Autoregressive Fractional Integrated Moving Average (ARFIMA) and Jordan Recurrent Neural Network (JRNN) models were employed to model the forecasting performance of the daily turbidity flow of White Clay Creek (WCC). The two methods were applied to the log difference series of the daily turbidity flow series of WCC. The measurements of error employed to investigate the forecasting performance of the ARFIMA and JRNN models are the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). The outcome of the investigation revealed that the forecasting performance of the JRNN technique is better than the forecasting performance of the ARFIMA technique in the mean square error sense. The results of the ARFIMA and JRNN models were obtained by the simulation of the models using MATLAB version 8.03. The significance of using the log difference series rather than the difference series is that the log difference series stabilizes the turbidity flow series than the difference series on the ARFIMA and JRNN.

Keywords: auto regressive, mean absolute error, neural network, root square mean error

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Authors: Marina Sofia, Alexios Giannakopoulos, Antonia Touloudi, Dimitris C Chatzopoulos, Zoi Athanasakopoulou, Vassiliki Spyrou, Charalambos Billinis

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Fertility of dairy cattle can be affected by heat stress when the ambient temperature increases above 30°C and the relative humidity ranges from 35% to 50%. The present study was conducted on dairy cattle farms during summer months in Greece and aimed to identify the serological profile against pathogens that could affect fertility and to associate the positive serological results at herd level with temperature factors. A total of 323 serum samples were collected from clinically healthy dairy cows of 8 herds, located in Central and Northern Greece. ELISA tests were performed to detect antibodies against selected pathogens that affect fertility, namely Chlamydophila abortus, Coxiella burnetii, Neospora caninum, Toxoplasma gondii and Infectious Bovine Rhinotracheitis Virus (IBRV). Eleven climatic variables were derived from the WorldClim version 1.4. and ArcGIS V.10.1 software was used for analysis of the spatial information. Five different MaxEnt models were applied to associate the temperature variables with the locations of seropositive Chl. abortus, C. burnetii, N. caninum, T. gondii and IBRV herds (one for each pathogen). The logistic outputs were used for the interpretation of the results. ROC analyses were performed to evaluate the goodness of fit of the models’ predictions. Jackknife tests were used to identify the variables with a substantial contribution to each model. The seropositivity rates of pathogens varied among the 8 herds (0.85-4.76% for Chl. abortus, 4.76-62.71% for N. caninum, 3.8-43.47% for C. burnetii, 4.76-39.28% for T. gondii and 47.83-78.57% for IBRV). The variables of annual temperature range, mean diurnal range and maximum temperature of the warmest month gave a contribution to all five models. The regularized training gains, the training AUCs and the unregularized training gains were estimated. The mean diurnal range gave the highest gain when used in isolation and decreased the gain the most when it was omitted in the two models for seropositive Chl.abortus and IBRV herds. The annual temperature range increased the gain when used alone and decreased the gain the most when it was omitted in the models for seropositive C. burnetii, N. caninum and T. gondii herds. In conclusion, antibodies against Chl. abortus, C. burnetii, N. caninum, T. gondii and IBRV were detected in most herds suggesting circulation of pathogens that could cause infertility. The results of the spatial analyses demonstrated that the annual temperature range, mean diurnal range and maximum temperature of the warmest month could affect positively the possible pathogens’ presence. Acknowledgment: This research has been co‐financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH–CREATE–INNOVATE (project code: T1EDK-01078).

Keywords: dairy cows, seropositivity, spatial analysis, temperature factors

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Authors: Po-Kun Tsai

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The issue of climate change is essential in the 21st century. State governments have launched types of strategic industrial policies to encourage more widespread R&D in green technology. Research also indicates that technology is an essential tool to mitigate some of extreme situations. However, one could learn from several prominent cases in international trade area that they have been easily argued and disputed by the foreign counterparts. Thus, how to justify the public sector’s R&D measures under the current world trading system and how to promote the transfer of environmentally sound technologies (EST) to developing states are crucial. The study is to undertake a preliminary examination of the current R&D research area in green technology in Taiwan. Through selective interviews and comparative approach, it tries to identify the loopholes under the current legal framework in Taiwan. It would be, as a basis, for further legal and policy recommendations for the benefits of mankind.

Keywords: government, R&D, innovation, environmentally sound technology (EST)

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Authors: Cheikh Bamba Dione, Alla Lo, Elhadji Mamadou Nguer, Siley O. Ba

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In this paper, we propose two neural machine translation (NMT) systems (French-to-Wolof and Wolof-to-French) based on sequence-to-sequence with attention and transformer architectures. We trained our models on a parallel French-Wolof corpus of about 83k sentence pairs. Because of the low-resource setting, we experimented with advanced methods for handling data sparsity, including subword segmentation, back translation, and the copied corpus method. We evaluate the models using the BLEU score and find that transformer outperforms the classic seq2seq model in all settings, in addition to being less sensitive to noise. In general, the best scores are achieved when training the models on word-level-based units. For subword-level models, using back translation proves to be slightly beneficial in low-resource (WO) to high-resource (FR) language translation for the transformer (but not for the seq2seq) models. A slight improvement can also be observed when injecting copied monolingual text in the target language. Moreover, combining the copied method data with back translation leads to a substantial improvement of the translation quality.

Keywords: backtranslation, low-resource language, neural machine translation, sequence-to-sequence, transformer, Wolof

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Authors: Peyman Aela, Lu Zong, Guoqing Jing

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Recently, there has been growing interest in numerical modeling of ballast railway tracks. A commonly used mechanistic modeling approach for ballast is the discrete element method (DEM). Up to now, the effects of the contact model on ballast particle behavior have not been precisely examined. In this regard, selecting the appropriate contact model is mainly associated with the particle characteristics and the loading condition. Since ballast is cohesionless material, different contact models, including the linear spring, Hertz-Mindlin, and Hysteretic models, could be used to calculate particle-particle or wall-particle contact forces. Moreover, the simulation of a dynamic test is vital to investigate the effect of damping parameters on the ballast deformation. In this study, ballast box tests were simulated by DEM to examine the influence of different contact models on the mechanical behavior of the ballast layer under cyclic loading. This paper shows how the contact model can affect the deformation and damping of a ballast layer subjected to cyclic loading in a ballast box.

Keywords: ballast, contact model, cyclic loading, DEM

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Authors: Stuart Bradley, Mathew Legg, Lilyan Panton

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Ultrasonic remote sensing is a useful tool for assessing the interior structure of complex targets. For these methods, significantly enhanced spatial resolution is obtained if the pulse is coded, for example using a linearly changing frequency during the pulse duration. Such pulses have a time-dependent spectral structure. Interpretation of the backscatter from targets is, therefore, complicated if the scattering is frequency-dependent. While analytic models are well established for steady sinusoidal excitations applied to simple shapes such as spheres, such models do not generally exist for temporally evolving excitations. Therefore, models are developed in the current paper for handling such signals so that the properties of the targets can be quantitatively evaluated while maintaining very high spatial resolution. Laboratory measurements on simple shapes are used to confirm the validity of the models.

Keywords: linear FM chirp, time-dependent acoustic scattering, ultrasonic remote sensing, ultrasonic scattering

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Authors: Zoran Jovanovic, Zoran Masonicic, S. Dragutinovic, Z. Sakota

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In this paper, results concerning flame propagation of various fuels in a particular combustion chamber with four tilted valves were elucidated. Flame propagation was represented by the evolution of spatial distribution of temperature in various cut-planes within combustion chamber while the flame front location was determined by dint of zones with maximum temperature gradient. The results presented are only a small part of broader on-going scrutinizing activity in the field of multidimensional modeling of reactive flows in combustion chambers with complicated geometries encompassing various models of turbulence, different fuels and combustion models. In the case of turbulence two different models were applied i.e. standard k-ε model of turbulence and k-ξ-f model of turbulence. In this paper flame propagation results were analyzed and presented for two different hydrocarbon fuels, such as CH4 and C8H18. In the case of combustion all differences ensuing from different turbulence models, obvious for non-reactive flows are annihilated entirely. Namely the interplay between fluid flow pattern and flame propagation is invariant as regards turbulence models and fuels applied. Namely the interplay between fluid flow pattern and flame propagation is entirely invariant as regards fuel variation indicating that the flame propagation through unburned mixture of CH4 and C8H18 fuels is not chemically controlled.

Keywords: automotive flows, flame propagation, combustion modelling, CNG

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Authors: Violeta Kostadinova, Emilia Tsankova

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The purpose of this research is to explore and analyze the integration of the younger generation with Bulgarian folklore through various innovative forms, methods and approaches. A survey was conducted among students to evaluate its success. The results prove the relevance of the problem as well and the necessity of innovation to keep the national and promotion of Bulgarian folklore. Innovation in music education is a good way to create positive motivation and raise interest in folk art, which is especially pronounced when the folk songs have contemporary arrangement. Modern interpretation of Bulgarian folk music makes the music more approachable to young people and makes it more likely that folk music will become an integral part of our modern culture. The research would help the implementation of appropriate innovations for more effective training and upgrading for music students in all middle and high schools in the country.

Keywords: Bulgarian folklore, innovative forms, methods and approaches, motivation, music education

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Authors: Michele Paleologo, Marta Acampora, Serena Barello, Guendalina Graffigna

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Introduction: In the face of increasing demands for higher agricultural productivity with minimal environmental impact, participatory research approaches emerge as promising means to promote innovation. However, the complexities and ambiguities surrounding these approaches in both theory and practice present challenges. This Scoping Review seeks to bridge these gaps by mapping participatory approaches in agricultural contexts, analyzing their characteristics, and identifying indicators of success. Methods: Following PRISMA guidelines, we conducted a systematic Scoping Review, searching Scopus and Web of Science databases. Our review encompassed 34 projects from diverse geographical regions and farming contexts. Thematic analysis was employed to explore the types of innovation promoted and the categories of participants involved. Results: The identified innovation types encompass technological advancements, sustainable farming practices, and market integration, forming 5 main themes: climate change, cultivar, irrigation, pest and herbicide, and technical improvement. These themes represent critical areas where participatory research drives innovation to address pressing agricultural challenges. Participants were categorized as citizens, experts, NGOs, private companies, and public bodies. Understanding their roles is vital for designing effective participatory initiatives that embrace diverse stakeholders. The review also highlighted 27 theoretical frameworks underpinning participatory projects. Clearer guidelines and reporting standards are crucial for facilitating the comparison and synthesis of findings across studies, thereby enhancing the robustness of future participatory endeavors. Furthermore, we identified three main categories of barriers and facilitators: pragmatic/behavioral, emotional/relational, and cognitive. These insights underscore the significance of participant engagement and collaborative decision-making for project success beyond theoretical considerations. Regarding participation, projects were classified as contributory (5 cases), where stakeholders contributed insights; collaborative (10 cases), with active co-designing of solutions; and co-created (19 cases), featuring deep stakeholder involvement from ideation to implementation, resulting in joint ownership of outcomes. Such diverse participation modes highlight the adaptability of participatory approaches to varying agricultural contexts. Discussion: In conclusion, this Scoping Review demonstrates the potential of participatory research in driving transformative changes in farmers' practices, fostering sustainability and innovation in agriculture. Understanding the diverse landscape of participatory approaches, theoretical frameworks, and participant engagement strategies is essential for designing effective and context-specific interventions. Collaborative efforts among researchers, practitioners, and stakeholders are pivotal in harnessing the full potential of participatory approaches and driving positive change in agricultural settings worldwide. The identified themes of innovation and participation modes provide valuable insights for future research and targeted interventions in agricultural innovation.

Keywords: participatory research, co-creation, agricultural innovation, stakeholders' engagement

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Authors: Jessemyn Modiini, Timothy Lynar, Elena Sitnikova

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We present a novel enhanced compartmental model for malware spread analysis in cyber security. This paper applies cyber security data features to epidemiological compartmental models to model the infectious potential of malware. Compartmental models are most efficient for calculating the infectious potential of a disease. In this paper, we discuss and profile epidemiologically relevant data features from a Domain Name System (DNS) dataset. We then apply these features to epidemiological compartmental models to network traffic features. This paper demonstrates how epidemiological principles can be applied to the novel analysis of key cybersecurity behaviours and trends and provides insight into threat modelling above that of kill-chain analysis. In applying deterministic compartmental models to a cyber security use case, the authors analyse the deficiencies and provide an enhanced stochastic model for cyber epidemiology. This enhanced compartmental model (SUEICRN model) is contrasted with the traditional SEIR model to demonstrate its efficacy.

Keywords: cybersecurity, epidemiology, cyber epidemiology, malware

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Authors: Minji Seo

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Fashion stakeholders are becoming increasingly aware of technological innovation in manufacturing. In 2020, the COVID-19 pandemic caused transformations in working patterns, such as working remotely rather thancommuting. To enable smooth remote working, 3D fashion design software is being adoptedas the latest trend in design and production. The majority of fashion designers, however, are still resistantto this change. Previous studies on 3D fashion design software solely highlighted the beneficial and detrimental factors of adopting design innovations. They lacked research on the relationship between resistance factors and the adoption of innovation. These studies also fell short of exploringthe perspectives of users of these innovations. This paper aims to investigate the key drivers and barriers of employing 3D fashion design software as wellas to explore the challenges faced by designers.It also toucheson the governmental support for digital manufacturing in Seoul, South Korea, and London, the United Kingdom. By conceptualising local support, this study aims to provide a new path for industrial clusters to optimise digital apparel manufacturing. The study uses a mixture of quantitative and qualitative approaches. Initially, it reflects a survey of 350 samples, fashion designers, on innovation resistance factors of 3D fashion design software and the effectiveness of local support. In-depth interviews with 30 participants provide a better understanding of designers’ aspects of the benefits and obstacles of employing 3D fashion design software. The key findings of this research are the main barriers to employing 3D fashion design software in fashion production. The cultural characteristics and interviews resultsare used to interpret the survey results. The findings of quantitative data examine the main resistance factors to adopting design innovations. The dominant obstacles are: the cost of software and its complexity; lack of customers’ interest in innovation; lack of qualified personnel, and lack of knowledge. The main difference between Seoul and London is the attitudes towards government support. Compared to the UK’s fashion designers, South Korean designers emphasise that government support is highly relevant to employing 3D fashion design software. The top-down and bottom-up policy implementation approach distinguishes the perception of government support. Compared to top-down policy approaches in South Korea, British fashion designers based on employing bottom-up approaches are reluctant to receive government support. The findings of this research will contribute to generating solutions for local government and the optimisation of use of 3D fashion design software in fashion industrial clusters.

Keywords: digital apparel production, industrial clusters, innovation resistance, 3D fashion design software, manufacturing, innovation, technology, digital manufacturing, innovative fashion design process

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Authors: Pattra Pukdeekiat, Siriluk Ruangrungrote

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This work was originated to precisely determine direct solar radiation by using atmospheric physics models since the accurate prediction of solar radiation is necessary and useful for solar energy applications including atmospheric research. The possible models and techniques for a calculation of regional direct solar radiation were challenging and compulsory for the case of unavailable instrumental measurement. The investigation was mathematically governed by six astronomical parameters i.e. declination (δ), hour angle (ω), solar time, solar zenith angle (θz), extraterrestrial radiation (Iso) and eccentricity (E0) along with two atmospheric parameters i.e. air mass (mr) and dew point temperature at Bangna meteorological station (13.67° N, 100.61° E) in Bangkok, Thailand. Analyses of five models of solar radiation determination with the assumption of clear sky were applied accompanied by three statistical tests: Mean Bias Difference (MBD), Root Mean Square Difference (RMSD) and Coefficient of determination (R2) in order to validate the accuracy of obtainable results. The calculated direct solar radiation was in a range of 491-505 Watt/m2 with relative percentage error 8.41% for winter and 532-540 Watt/m2 with relative percentage error 4.89% for summer 2014. Additionally, dataset of seven continuous days, representing both seasons were considered with the MBD, RMSD and R2 of -0.08, 0.25, 0.86 and -0.14, 0.35, 3.29, respectively, which belong to Kumar model for winter and CSR model for summer. In summary, the determination of direct solar radiation based on atmospheric models and empirical equations could advantageously provide immediate and reliable values of the solar components for any site in the region without a constraint of actual measurement.

Keywords: atmospheric physics models, astronomical parameters, atmospheric parameters, clear sky condition

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Authors: Makram Ben Jeddou

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The ABC classification is widely used by managers for inventory control. The classical ABC classification is based on the Pareto principle and according to the criterion of the annual use value only. Single criterion classification is often insufficient for a closely inventory control. Multi-criteria inventory classification models have been proposed by researchers in order to take into account other important criteria. From these models, we will consider the ZF model in order to make a sensitive analysis on the composite score calculated for each item. In fact, this score based on a normalized average between a good and a bad optimized index can affect the ABC items classification. We will then focus on the weights assigned to each index and propose a classification compromise.

Keywords: ABC classification, multi criteria inventory classification models, ZF-model

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Authors: Vinayak Bassi, Rajpreet Singh

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Over the years, with the emergence of sophisticated computers and algorithms, finance has been quantified using computational prowess. Asset valuation has been one of the key components of quantitative finance. In fact, it has become one of the embryonic steps in determining risk related to a portfolio, the main goal of quantitative finance. This study comprises a drawing comparison between valuation output generated by two stochastic dynamic models, namely Black-Scholes and Dupire’s bi-dimensionality model. Both of these models are formulated for computing the valuation function for a portfolio of European options using Monte Carlo simulation methods. Although Monte Carlo algorithms have a slower convergence rate than calculus-based simulation techniques (like FDM), they work quite effectively over high-dimensional dynamic models. A fidelity gap is analyzed between the static (historical) and stochastic inputs for a sample portfolio of underlying assets. In order to enhance the performance efficiency of the model, the study emphasized the use of variable reduction methods and customizing random number generators to implement parallelization. An attempt has been made to further implement the Dupire’s model on a GPU to achieve higher computational performance. Furthermore, ideas have been discussed around the performance enhancement and bottleneck identification related to the implementation of options-pricing models on GPUs.

Keywords: monte carlo, stochastic models, computational finance, parallel programming, scientific computing

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Authors: Fidelia A. Orji, Julita Vassileva

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This research aims to develop machine learning models for students' academic performance and study strategy prediction, which could be generalized to all courses in higher education. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) used in building the models are chosen based on prior studies, which revealed that the attributes are essential in students’ learning process. Previous studies revealed the individual effects of each of these attributes on students’ learning progress. However, few studies have investigated the combined effect of the attributes in predicting student study strategy and academic performance to reduce the dropout rate. To bridge this gap, we used Scikit-learn in python to build five machine learning models (Decision Tree, K-Nearest Neighbour, Random Forest, Linear/Logistic Regression, and Support Vector Machine) for both regression and classification tasks to perform our analysis. The models were trained, evaluated, and tested for accuracy using 924 university dentistry students' data collected by Chilean authors through quantitative research design. A comparative analysis of the models revealed that the tree-based models such as the random forest (with prediction accuracy of 94.9%) and decision tree show the best results compared to the linear, support vector, and k-nearest neighbours. The models built in this research can be used in predicting student performance and study strategy so that appropriate interventions could be implemented to improve student learning progress. Thus, incorporating strategies that could improve diverse student learning attributes in the design of online educational systems may increase the likelihood of students continuing with their learning tasks as required. Moreover, the results show that the attributes could be modelled together and used to adapt/personalize the learning process.

Keywords: classification models, learning strategy, predictive modeling, regression models, student academic performance, student motivation, supervised machine learning

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Authors: Imene Skhakhfa, Lahbaci Ouerdachi

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To ensure the overall coherence of simulated results, it is necessary to develop a robust validation process. In many applications, it is no longer content to calibrate and validate the model only in relation to the hydro graph measured at the outlet, but we try to better simulate the functioning of the watershed in space. Therefore the timing also performs compared to other variables such as water level measurements in intermediate stations or groundwater levels. As part of this work, we limit ourselves to modeling flood of short duration for which the process of evapotranspiration is negligible. The main parameters to identify the models are related to the method of unit hydro graph (HU). Three different models were tested: SNYDER, CLARK and SCS. These models differ in their mathematical structure and parameters to be calibrated while hydrological data are the same, the initial water content and precipitation. The models are compared on the basis of their performance in terms six objective criteria, three global criteria and three criteria representing volume, peak flow, and the mean square error. The first type of criteria gives more weight to strong events whereas the second considers all events to be of equal weight. The results show that the calibrated parameter values are dependent and also highlight the problems associated with the simulation of low flow events and intermittent precipitation.

Keywords: model calibration, intensity, runoff, hydrograph

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Authors: Abdunaser Abduelmula, Maria Luisa M. Bastos, José A. Gonçalves

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The modeling of the earth's surface and evaluation of urban environment, with 3D models, is an important research topic. New stereo capabilities of high-resolution optical satellites images, such as the tri-stereo mode of Pleiades, combined with new image matching algorithms, are now available and can be applied in urban area analysis. In addition, photogrammetry software packages gained new, more efficient matching algorithms, such as SGM, as well as improved filters to deal with shadow areas, can achieve denser and more precise results. This paper describes a comparison between 3D data extracted from tri-stereo and dual stereo satellite images, combined with pixel based matching and Wallis filter. The aim was to improve the accuracy of 3D models especially in urban areas, in order to assess if satellite images are appropriate for a rapid evaluation of urban environments. The results showed that 3D models achieved by Pleiades tri-stereo outperformed, both in terms of accuracy and detail, the result obtained from a Geo-eye pair. The assessment was made with reference digital surface models derived from high-resolution aerial photography. This could mean that tri-stereo images can be successfully used for the proposed urban change analyses.

Keywords: 3D models, environment, matching, pleiades

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Authors: Sayani Chatterjee, Kusum Lata Pangtey, Sarita Singh, Harvir Singh

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Biodegradation leads to a systematic alteration of the chemical and physical properties of crude oil showing sequential depletion of n-alkane, cycloalkanes, aromatic which increases its specific gravity, viscosity and the abundance of heteroatom-containing compounds. The biodegradation leads to a change in the molecular fingerprints and geochemical parameters of degraded oils, thus make source and maturity identification inconclusive or ambiguous. Asphaltene is equivalent to the most labile part of the respective kerogen and generally has high molecular weight. Its complex chemical structure with substantial microporous units makes it suitable to occlude the hydrocarbon expelled from the source. The occluded molecules are well preserved by the macromolecular structure and thus prevented from secondary alterations. They retain primary organic geochemical information over the geological time. The present study involves the extraction of this occluded hydrocarbon from the asphaltene cage through mild oxidative degradation using mild oxidative reagents like Hydrogen Peroxide (H₂O₂) and Acetic Acid (CH₃COOH) on purified asphaltene of the biodegraded oils of Mansa, Lanwa and Santhal fields in Cambay Basin. The study of these extracted occluded hydrocarbons was carried out for establishing oil to oil and oil to source correlation in the Mehsana block of Cambay Basin. The n-alkane and biomarker analysis through GC and GC-MS of these occluded hydrocarbons show similar biomarker imprint as the normal oil in the area and hence correlatable with them. The abundance of C29 steranes, presence of Oleanane, Gammacerane and 4-Methyl sterane depicts that the oils are derived from terrestrial organic matter deposited in the stratified saline water column in the marine environment with moderate maturity (VRc 0.6-0.8). The oil source correlation study suggests that the oils are derived from Jotana-Warosan Low area. The developed geochemical technique to extract the occluded hydrocarbon has effectively resolved the ambiguity that resulted from the inconclusive fingerprint of the biodegraded oil and the method can be also applied in other biodegraded oils as well.

Keywords: asphaltene, biomarkers, correlation, mild oxidation, occluded hydrocarbon

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Authors: Gonzalo García-Reyes

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Conformastat spherically symmetric exact solutions of Einstein's field equations representing matter distributions made of fluid both perfect and anisotropic from given solutions of Poisson's equation of Newtonian gravity are investigated. The approach is used in the construction of new relativistic models of thick spherical shells and three-component models of galaxies (bulge, disk, and dark matter halo), writing, in this case, the metric in cylindrical coordinates. In addition, the circular motion of test particles (rotation curves) along geodesics on the equatorial plane of matter configurations and the stability of the orbits against radial perturbations are studied. The models constructed satisfy all the energy conditions.

Keywords: general relativity, exact solutions, spherical symmetry, galaxy, kinematics and dynamics, dark matter

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Authors: Subhan Ahmad, Pradeep Bhargava, Ajay Chourasia

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Shear failure in reinforced concrete beams without shear reinforcement leads to loss of property and life since a very little or no warning occurs before failure as in case of flexural failure. Shear strength of reinforced concrete beams decreases as its depth increases. This phenomenon is generally called as the size effect. In this paper, a comparative analysis is performed to estimate the performance of shear strength models in capturing the size effect of reinforced concrete beams made with conventional concrete, self-compacting concrete, and recycled aggregate concrete. Four shear strength models that account for the size effect in shear are selected from the literature and applied on the datasets of slender reinforced concrete beams. Beams prepared with conventional concrete, self-compacting concrete, and recycled aggregate concrete are considered for the analysis. Results showed that all the four models captured the size effect in shear effectively and produced conservative estimates of the shear strength for beams made with normal strength conventional concrete. These models yielded unconservative estimates for high strength conventional concrete beams with larger effective depths ( > 450 mm). Model of Bazant and Kim (1984) captured the size effect precisely and produced conservative estimates of shear strength of self-compacting concrete beams at all the effective depths. Also, shear strength models considered in this study produced unconservative estimates of shear strength for recycled aggregate concrete beams at all effective depths.

Keywords: reinforced concrete beams; shear strength; prediction models; size effect

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Authors: Hamidreza Ghazvinian, Khosro Ghazvinian, Touba Khodaiean

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The importance of evaporation estimation in water resources and agricultural studies is undeniable. Pan evaporation are used as an indicator to determine the evaporation of lakes and reservoirs around the world due to the ease of interpreting its data. In this research, intelligent models were investigated in estimating pan evaporation on a daily basis. Shahroud and Mayamey were considered as the studied cities. These two cities are located in Semnan province in Iran. The mentioned cities have dry weather conditions that are susceptible to high evaporation potential. Meteorological data of 11 years of synoptic stations of Shahrood and Mayamey cities were used. The intelligent models used in this study are Artificial Neural Network (ANN), Least Squares Support Vector Machine (LSSVM), and M5 tree models. Meteorological parameters of minimum and maximum air temperature (Tmax, Tmin), wind speed (WS), sunshine hours (SH), air pressure (PA), relative humidity (RH) as selected input data and evaporation data from pan (EP) to The output data was considered. 70% of data is used at the education level, and 30 % of the data is used at the test level. Models used with explanation coefficient evaluation (R2) Root of Mean Squares Error (RMSE) and Mean Absolute Error (MAE). The results for the two Shahroud and Mayamey stations showed that the above three models' operations are rather appropriate.

Keywords: pan evaporation, intelligent methods, shahroud, mayamey

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Authors: Ernest Nweke, Enkhtuya Bavuudorj

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The harsh realities of the scandalous failure of several notable corporations in the past two decades have inextricably resulted in a surge in corporate governance studies. Nevertheless, little or no attention has been paid to corporate governance studies in Mongolian firms and much less to the comprehension of the correlation among ownership structure, corporate governance mechanisms and trend of innovative activities. Innovation is the bed rock of enterprise success. However, the funding and support for innovative activities in many firms are to a great extent determined by the incentives provided by the firm’s internal and external governance mechanisms. Mongolia is an East Asian country currently undergoing a fast-paced transition from socialist to democratic system and it is a widely held view that private ownership as against public ownership fosters innovation. Hence, following the privatization policy of Mongolian Government which has led to the transfer of the ownership of hitherto state controlled and state directed firms to private individuals and organizations, expectations are high that sufficient motivation would be provided for firm managers to engage in innovative activities. This research focuses on the relationship between ownership structure, corporate governance on one hand and the level of innovation on the hand. The paper is empirical in nature and derives data from both reliable secondary and primary sources. Secondary data for the study was in respect of ownership structure of Mongolian listed firms and innovation trend in Mongolia generally. These were analyzed using tables, charts, bars and percentages. Personal interviews and surveys were held to collect primary data. Primary data was in respect of corporate governance practices in Mongolian firms and were collected using structured questionnaire. Out of a population of three hundred and twenty (320) companies listed on the Mongolian Stock Exchange (MSE), a sample size of thirty (30) randomly selected companies was utilized for the study. Five (5) management level employees were surveyed in each selected firm giving a total of one hundred and fifty (150) respondents. Data collected were analyzed and research hypotheses tested using Chi-Square test statistic. Research results showed that corporate governance mechanisms were better and have significantly improved overtime in privately held as opposed to publicly owned firms. Consequently, the levels of innovation in privately held firms were considerably higher. It was concluded that a significant and positive relationship exists between private ownership and good corporate governance on one hand and the level of funding provided for innovative activities in Mongolian firms on the other hand.

Keywords: corporate governance, innovation, ownership structure, stock exchange

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Authors: Awol Seid Ebrie

Abstract:

HIV results as an incurable disease, AIDS. After a person is infected with virus, the virus gradually destroys all the infection fighting cells called CD4 cells and makes the individual susceptible to opportunistic infections which cause severe or fatal health problems. Several studies show that the CD4 cells count is the most determinant indicator of the effectiveness of the treatment or progression of the disease. The objective of this paper is to investigate the progression of the disease over time among patient under HAART treatment. Two main approaches of the generalized multilevel ordinal models; namely the proportional odds model and the nonproportional odds model have been applied to the HAART data. Also, the multilevel part of both models includes random intercepts and random coefficients. In general, four models are explored in the analysis and then the models are compared using the deviance information criteria. Of these models, the random coefficients nonproportional odds model is selected as the best model for the HAART data used as it has the smallest DIC value. The selected model shows that the progression of the disease increases as the time under the treatment increases. In addition, it reveals that gender, baseline clinical stage and functional status of the patient have a significant association with the progression of the disease.

Keywords: nonproportional odds model, proportional odds model, random coefficients model, random intercepts model

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Authors: Selin Tosun

Abstract:

The improvements in technology have started to transform the way we live, work, play, and commute in our cities. The emerging smart city understanding has been paving the way for more efficient, more useful, and more profitable cities. Smart sensors, smart lighting, smart waste, water and electricity management, smart transportation and communication systems are introduced to cities at a rapid pace. In today's world, innovation is often correlated with start-up companies and technological pioneers seeking broader economic objectives such as production and competitiveness. The government's position is primarily that of an enabler, with creativity mostly coming from the private sector. The paper argues that to achieve sustainable development, the ways in which smart and sustainable city approaches are being applied to cities need to be redefined. The research aims to address common discussions in the discourse of smart and sustainable cities criticizing the priority of lifestyle sterilization over human-centered sustainable interventions and social innovation strategies. The dichotomy between the fact that smart cities are mostly motivated by the competitive global market and the fact that the delocalization is, in fact, their biggest problem in the way of becoming authentic, sustainable cities is the main challenge that we face today. In other words, the key actors in smart cities have different and somewhat conflicting interests and demands. By reviewing the roles of the public and private actors in smart city making, the paper aspires to reconceptualize the understanding of “smartness” in achieving sustainable development in which the “smartness” is understood as a multi-layered complex phenomenon that can be channeled through different dynamics. The case cities around the world are explored and compared in terms of their technological innovations, governance and policy innovations, public-private stakeholder relationships, and the understanding of the public realm. The study aims to understand the current trends and general dynamics in the field, key issues that are being addressed, the scale that is preferred to reflect upon and the projects that are designed for the particular issues.

Keywords: smart city, sustainable development, technological innovation, social innovation

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Authors: Abhishek Saha, Serene Tay, Gerard Pijcke

Abstract:

The availability of high-resolution topography and rainfall information in urban areas has made it necessary to revise modeling approaches used for simulating flood risk assessments. Lidar derived elevation models that have 1m or lower resolutions are becoming widely accessible. The classical approaches of 1D-2D flow models where channel flow is simulated and coupled with a coarse resolution 2D overland flow models may not fully utilize the information provided by high-resolution data. In this context, a study was undertaken to compare three different modeling approaches to simulate flooding in an urban area. The first model used is the base model used is Sobek, which uses 1D model formulation together with hydrologic boundary conditions and couples with an overland flow model in 2D. The second model uses a full 2D model for the entire area with shallow water equations at the resolution of the digital elevation model (DEM). These models are compared against another shallow water equation solver in 2D, which uses a subgrid method for grid refinement. These models are simulated for different horizontal resolutions of DEM varying between 1m to 5m. The results show a significant difference in inundation extents and water levels for different DEMs. They are also sensitive to the different numerical models with the same physical parameters, such as friction. The study shows the importance of having reliable field observations of inundation extents and levels before a choice of model and data can be made for spatial flood risk assessments.

Keywords: flooding, DEM, shallow water equations, subgrid

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Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas

Abstract:

To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.

Keywords: building energy prediction, data mining, demand response, electricity market

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Authors: Sandeep Kaur, Jenny Vuong, Marcel Julliard, Sean O'Donoghue

Abstract:

Time-series data are useful for modelling as they can enable model-evaluation. However, when reconstructing models from phosphoproteomic data, often non-exact methods are utilised, as the knowledge regarding the network structure, such as, which kinases and phosphatases lead to the observed phosphorylation state, is incomplete. Thus, such reactions are often hypothesised, which gives rise to uncertainty. Here, we propose a framework, implemented via a web-based tool (as an extension to Minardo), which given time-series phosphoproteomic datasets, can generate κ models. The incompleteness and uncertainty in the generated model and reactions are clearly presented to the user via the visual method. Furthermore, we demonstrate, via a toy EGF signalling model, the use of algorithmic verification to verify κ models. Manually formulated requirements were evaluated with regards to the model, leading to the highlighting of the nodes causing unsatisfiability (i.e. error causing nodes). We aim to integrate such methods into our web-based tool and demonstrate how the identified erroneous nodes can be presented to the user via the visual method. Thus, in this research we present a framework, to enable a user to explore phosphorylation proteomic time-series data in the context of models. The observer can visualise which reactions in the model are highly uncertain, and which nodes cause incorrect simulation outputs. A tool such as this enables an end-user to determine the empirical analysis to perform, to reduce uncertainty in the presented model - thus enabling a better understanding of the underlying system.

Keywords: κ-models, model verification, time-series phosphoproteomic datasets, uncertainty and error visualisation

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Authors: Cristobal Miralles Insa

Abstract:

The network of chairs of the "Valencian Public System of Social Services" (VPSSS) is sponsored by the Valencian Institute of Training, Research, and Quality in Social Services and aims to promote research, dissemination, and evaluation of the needs that arise in the field of the public system of social services. It also seeks to transfer knowledge to foster the development of public policies in this field. Given that it is an Interuniversity Chair among the five public universities in Valencia, there is coordination of complementary themes and roles for this objective, with Universitat Politènica de València focusing primarily on promoting innovation and social entrepreneurship to address multiple social challenges through its platform INSSPIRA. This approach is aimed at the entire university community and its various interest groups, carrying out research, teaching, and dissemination activities that promote social inclusion, personal development, and autonomy for groups in situations of vulnerability, lack of protection, dependence, or social urgency. Although it focuses on the Valencian context, both the issues in this context and the tools in process to address them, often have a universal and scalable character and has been inspiring for the innovation system of UPV. This entrepreneurial incubator goes along from early stages of students on the campus until the so-called “StartUPV” system, where students are challenged with social problems that require creative solutions. Therefore, the Chair is conceived with a holistic spirit and an inspiring vocation that engages the whole university community. In this communication, it is described the entities and individuals participating in this UPV Chair of VPSSS, followed by the presentation of different work lines and objectives for the chair. Subsequently, a description of various activities undertaken to promote innovation in social services are described, where support to teaching and extracurricular activities in this field are exposed. It must be noted that some awareness and dissemination of activities are carried out in a transversal mode as they contribute to different objectives simultaneously; with special focus on Learning-Service approaches that achieved very good results which are also summarized.

Keywords: social innovation, entrepeneurship, university, vulnerable sectors

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Authors: Yananda Siraphatthada

Abstract:

The objectives of this research were to study the level of influencing factors, leadership, supply chain management, innovation, competitive advantages, business success, and affecting factors to the business success of the mobile phone system service providers in Bangkok Metropolitan. This research was done by the quantitative approach and the qualitative approach. The quantitative approach was used for questionnaires to collect data from the 331 mobile service shop managers franchised by AIS, Dtac and TrueMove. The mobile phone system service providers/shop managers were randomly stratified and proportionally allocated into subgroups exclusive to the number of the providers in each network. In terms of qualitative method, there were in-depth interviews of 6 mobile service providers/managers of Telewiz and Dtac and TrueMove shop to find the agreement or disagreement with the content analysis method. Descriptive Statistics, including Frequency, Percentage, Means and Standard Deviation were employed; also, the Structural Equation Model (SEM) was used as a tool for data analysis. The content analysis method was applied to identify key patterns emerging from the interview responses. The two data sets were brought together for comparing and contrasting to make the findings, providing triangulation to enrich result interpretation. It revealed that the level of the influencing factors – leadership, innovation management, supply chain management, and business competitiveness had an impact at a great level, but that the level of factors, innovation and the business, financial success and nonbusiness financial success of the mobile phone system service providers in Bangkok Metropolitan, is at the highest level. Moreover, the business influencing factors, competitive advantages in the business of mobile system service providers which were leadership, supply chain management, innovation management, business advantages, and business success, had statistical significance at .01 which corresponded to the data from the interviews.

Keywords: mobile service providers, facilitating factors, Bangkok Metropolitan, business success

Procedia PDF Downloads 341