Search results for: scientific models

Authors: Rasha Aljaryian, Lalit Kumar

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Climate is undergoing continuous change and this trend will affect the cultivation areas ofmost crops, including wheat (Triticum aestivum L.), in the future. The current suitable cultivation areas may become unsuitable climatically. Countries that depend on wheat cultivation and export may suffer an economic loss because of production decline. On the other hand, some regions of the world could gain economically by increasing cultivation areas. This study models the potential future climatic suitability of wheat by using CLIMEX software. Two different global climate models (GCMs) were used, CSIRO-Mk3.0 (CS) and MIROC-H (MR), with two emission scenarios (A2, A1B). The results of this research indicate that the suitable climatic areas for wheat in the southern hemisphere, such as Australia, are expected to contract by the end of this century. However, some unsuitable or marginal areas will become climatically suitable under future climate scenarios. In North America and Europe further expansion inland could occur. Also, the results illustrate that heat and dry stresses as abiotic climatic factors will play an important role in wheat distribution in the future. Providing sufficient information about future wheat distribution will be useful for agricultural ministries and organizations to manage the shift in production areas in the future. They can minimize the expected harmful economic consequences by preparing strategic plans and identifying new areas for wheat cultivation.

Keywords: Climate change, Climate modelling, CLIMEX, Triticum aestivum, Wheat

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Authors: Kien-Trinh Thi Bui, Cuong Manh Nguyen

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In order to simulate and reproduce the operational characteristics of a dam visually, it is necessary to capture the displacement at different measurement points and analyze the observed movement data promptly to forecast the dam safety. The accuracy of forecasts is further improved by applying machine learning methods to data analysis progress. In this study, the horizontal displacement monitoring data of the Ialy hydroelectric dam was applied to machine learning algorithms: Gaussian processes, multi-layer perceptron neural networks, and the M5-rules algorithm for modelling and forecasting of horizontal displacement of the Ialy hydropower dam (Vietnam), respectively, for analysing. The database which used in this research was built by collecting time series of data from 2006 to 2021 and divided into two parts: training dataset and validating dataset. The final results show all three algorithms have high performance for both training and model validation, but the MLPs is the best model. The usability of them are further investigated by comparison with a benchmark models created by multi-linear regression. The result show the performance which obtained from all the GP model, the MLPs model and the M5-Rules model are much better, therefore these three models should be used to analyze and predict the horizontal displacement of the dam.

Keywords: Gaussian processes, horizontal displacement, hydropower dam, Ialy dam, M5-Rules, multi-layer perception neural networks

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Authors: M. Khalid, W. Rashmi, L. L. Kwan

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This work provides an overview on the experimental and numerical simulations of various nanofluids and their flow and heat transfer behavior. It was further extended to study the effect of nanoparticle concentration, fluid flow rates and thermo-physical properties on the heat transfer enhancement of Al2O3/water nanofluid in a turbulent flow circular conduit using ANSYS FLUENT™ 14.0. Single-phase approximation (homogeneous model) and two-phase (mixture and Eulerian) models were used to simulate the nanofluid flow behavior in the 3-D horizontal pipe. The numerical results were further validated with experimental correlations reported in the literature. It was found that heat transfer of nanofluids increases with increasing particle volume concentration and Reynolds number, respectively. Results showed good agreement (~9% deviation) with the experimental correlations, especially for a single-phase model with constant properties. Among two-phase models, mixture model (~14% deviation) showed better prediction compared to Eulerian-dispersed model (~18% deviation) when temperature independent properties were used. Non-drag forces were also employed in the Eulerian two-phase model. However, the two-phase mixture model with temperature dependent nanofluid properties gave slightly closer agreement (~12% deviation).

Keywords: nanofluid, CFD, heat transfer, forced convection, circular conduit

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Authors: Noora Al-Shanfari, M. Mazharul Islam

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The cumulative incidence function (CIF) is a fundamental approach for analyzing survival data in the presence of competing risks, which estimates the marginal probability for each competing event. Parametric modeling of CIF has the advantage of fitting various shapes of CIF and estimates the impact of covariates with maximum efficiency. To calculate the total CIF's covariate influence using a parametric model., it is essential to parametrize the baseline of the CIF. As the CIF is an improper function by nature, it is necessary to utilize an improper distribution when applying parametric models. The Gompertz distribution, which is an improper distribution, is limited in its applicability as it only accounts for monotone hazard shapes. The generalized Gompertz distribution, however, can adapt to a wider range of hazard shapes, including unimodal, bathtub, and monotonic increasing or decreasing hazard shapes. In this paper, the generalized Gompertz distribution is used to parametrize the baseline of the CIF, and the parameters of the proposed model are estimated using the maximum likelihood approach. The proposed model is compared with the existing Gompertz model using the Akaike information criterion. Appropriate statistical test procedures and model-fitting criteria will be used to test the adequacy of the model. Both models are applied to the ‘colon’ dataset, which is available in the “biostat3” package in R.

Keywords: competing risks, cumulative incidence function, improper distribution, parametric modeling, survival analysis

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Authors: Sabyasachi Mukhopadhyay, Joseph Ogutu, Hans-Peter Piepho

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We aim to model dynamics of wildlife or pastoral livestock population for understanding of their population change and hence for wildlife conservation and promoting human welfare. The study is motivated by an age-sex structured population counts in different regions of Serengeti-Mara during the period 1989-2003. Developing reliable and realistic models for population dynamics of large herbivore population can be a very complex and challenging exercise. However, the Bayesian statistical domain offers some flexible computational methods that enable the development and efficient implementation of complex population dynamics models. In this work, we have used a novel Bayesian state-space model to analyse the dynamics of topi and hartebeest populations in the Serengeti-Mara Ecosystem of East Africa. The state-space model involves survival probabilities of the animals which further depend on various factors like monthly rainfall, size of habitat, etc. that cause recent declines in numbers of the herbivore populations and potentially threaten their future population viability in the ecosystem. Our study shows that seasonal rainfall is the most important factors shaping the population size of animals and indicates the age-class which most severely affected by any change in weather conditions.

Keywords: bayesian state-space model, Markov Chain Monte Carlo, population dynamics, conservation

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Authors: Selin Guney, Andres Riquelme

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Among the main purposes of optimal and efficient forecasts of agricultural commodity prices is to guide the firms to advance the economic decision making process such as planning business operations and marketing decisions. Governments are also the beneficiaries and suppliers of agricultural price forecasts. They use this information to establish a proper agricultural policy, and hence, the forecasts affect social welfare and systematic errors in forecasts could lead to a misallocation of scarce resources. Various empirical approaches have been applied to forecast commodity prices that have used different methodologies. Most commonly-used approaches to forecast commodity sectors depend on classical time series models that assume values of the response variables are precise which is quite often not true in reality. Recently, this literature has mostly evolved to a consideration of fuzzy time series models that provide more flexibility in terms of the classical time series models assumptions such as stationarity, and large sample size requirement. Besides, fuzzy modeling approach allows decision making with estimated values under incomplete information or uncertainty. A number of fuzzy time series models have been developed and implemented over the last decades; however, most of them are not appropriate for forecasting repeated and nonconsecutive transitions in the data. The modeling scheme used in this paper eliminates this problem by introducing Markov modeling approach that takes into account both the repeated and nonconsecutive transitions. Also, the determination of length of interval is crucial in terms of the accuracy of forecasts. The problem of determining the length of interval arbitrarily is overcome and a methodology to determine the proper length of interval based on the distribution or mean of the first differences of series to improve forecast accuracy is proposed. The specific purpose of this paper is to propose and investigate the potential of a new forecasting model that integrates methodologies for determining the proper length of interval based on the distribution or mean of the first differences of series and Fuzzy Time Series- Markov Chain model. Moreover, the accuracy of the forecasting performance of proposed integrated model is compared to different univariate time series models and the superiority of proposed method over competing methods in respect of modelling and forecasting on the basis of forecast evaluation criteria is demonstrated. The application is to daily corn and soybean prices observed at three commercially important North Carolina markets; Candor, Cofield and Roaring River for corn and Fayetteville, Cofield and Greenville City for soybeans respectively. One main conclusion from this paper is that using fuzzy logic improves the forecast performance and accuracy; the effectiveness and potential benefits of the proposed model is confirmed with small selection criteria value such MAPE. The paper concludes with a discussion of the implications of integrating fuzzy logic and nonarbitrary determination of length of interval for the reliability and accuracy of price forecasts. The empirical results represent a significant contribution to our understanding of the applicability of fuzzy modeling in commodity price forecasts.

Keywords: commodity, forecast, fuzzy, Markov

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Authors: Cristiane R. Magalhaes

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Advances in Information and Communication Technologies (ICT) have led to changes in different sectors particularly in architecture, engineering, construction, and operation (AECO) industry. In this context, the advent of BIM (Building Information Modeling) has brought a number of opportunities in the field of the digital architectural design process bringing integrated design concepts that impact on the development, elaboration, coordination, and management of ventures. The project scope has begun to contemplate, from its original stage, the third dimension, by means of virtual environments (VEs), composed of models containing different specialties, substituting the two-dimensional products. The possibility to simulate the construction process of a venture in a VE starts at the beginning of the design process offering, through new technologies, many possibilities beyond geometrical digital modeling. This is a significant change and relates not only to form, but also to how information is appropriated in architectural and engineering models and exchanged among professionals. In order to achieve the main objective of this work, the Design Science Research Method will be adopted to elaborate an artifact containing strategies for the application and use of ICTs from BIM flows, with pre-construction cut-off to the execution of the building. This article intends to discuss and investigate how BIM can be extended to the site acting as a protagonist and link between the Virtual Environments and the Real-World, as well as its contribution to the integration of the value chain and the consequent increase of efficiency in the production of the building. The virtualization of the design process has reached high levels of development through the use of BIM. Therefore it is essential that the lessons learned with the virtual models be transposed to the actual building production increasing precision and efficiency. Thus, this paper discusses how the Fourth Industrial Revolution has impacted on property developments and how BIM could be the propellant acting as the main fuel and link between the virtual environment and the real production for the structuring of flows, information management and efficiency in this process. The results obtained are partial and not definite up to the date of this publication. This research is part of a doctoral thesis development, which focuses on the discussion of the impact of digital transformation in the construction of residential buildings in Brazil.

Keywords: building information modeling, building production, digital transformation, ICT

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Authors: Margarida Franca

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The influence of the ‘cultural turn’ movement and the consequent deconstruction of scientific thought allowed geography and other social sciences to open or deepen their studies based on the analysis of multiple identities, on singularities, on what is particular or what marks the difference between individuals. In the context of postmodernity, the geography of religion has gained a favorable scientific, thematic and methodological focus for the qualitative and subjective interpretation of various religious identities, sacred places, territories of belonging, religious communities, among others. In the context of ‘late modernity’ or ‘net modernity’, sacred places and the definition of a network of sacred territories allow believers to attain the ‘ontological security’. The integration on a religious group or a local community, particularly a religious community, allows human beings to achieve a sense of belonging, familiarity or solidarity and to overcome, in part, some of the risks or fears that society has discovered. The importance of sacred places comes not only from their inherent characteristics (eg transcendent, mystical and mythical, respect, intimacy and abnegation), but also from the possibility of adding and integrating members of the same community, creating bonds of belonging, reference and individual and collective memory. In addition, the formation of different networks of sacred places, with multiple scales and dimensions, allows the human being to identify and structure his times and spaces of daily life. Thus, each individual, due to his unique identity and life and religious paths, creates his own network of sacred places. The territorial expression of religious identity allows to draw a variable and unique geography of sacred places. Through the case study of the practicing Catholic population in the diocese of Coimbra (Portugal), the aim is to study the territorial expression of the religious identity of the different local communities of this city. Through a survey of six parishes in the city, we sought to identify which factors, qualitative or not, define the different territorial expressions on a local, national and international scale, with emphasis on the socioeconomic profile of the population, the religious path of the believers, the religious group they belong to and the external interferences, religious or not. The analysis of these factors allows us to categorize the communities of the city of Coimbra and, for each typology or category, to identify the specific elements that unite the believers to the sacred places, the networks and religious territories that structure the religious practice and experience and also the non-representational landscape that unifies and creates memory. We conclude that an apparently homogeneous group, the Catholic community, incorporates multitemporalities and multiterritorialities that are necessary to understand the history and geography of a whole country and of the Catholic communities in particular.

Keywords: geography of religion, sacred places, territoriality, Catholic Church

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

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The relationship between antibacterial activity of eighteen different substituted benzimidazole derivatives and their molecular characteristics was studied using chemometric QSAR (Quantitative Structure–Activity Relationships) approach. QSAR analysis has been carried out on inhibitory activity towards Staphylococcus aureus, by using molecular descriptors, as well as minimal inhibitory activity (MIC). Molecular descriptors were calculated from the optimized structures. Principal component analysis (PCA) followed by hierarchical cluster analysis (HCA) and multiple linear regression (MLR) was performed in order to select molecular descriptors that best describe the antibacterial behavior of the compounds investigated, and to determine the similarities between molecules. The HCA grouped the molecules in separated clusters which have the similar inhibitory activity. PCA showed very similar classification of molecules as the HCA, and displayed which descriptors contribute to that classification. MLR equations, that represent MIC as a function of the in silico molecular descriptors were established. The statistical significance of the estimated models was confirmed by standard statistical measures and cross-validation parameters (SD = 0.0816, F = 46.27, R = 0.9791, R2CV = 0.8266, R2adj = 0.9379, PRESS = 0.1116). These parameters indicate the possibility of application of the established chemometric models in prediction of the antibacterial behaviour of studied derivatives and structurally very similar compounds.

Keywords: antibacterial, benzimidazole, molecular descriptors, QSAR

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Authors: Mohsen Shirani, Micaela Demichela

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During the last decades, a number of food crises such as Bovine Spongiform Encephalopathy (BSE), Mad-Cow disease, Dioxin in chicken food, Food-and-Mouth Disease (FMD), have certainly inflicted the reliability of the food industry. Consequently, the trend in applying different scientific methods of risk assessment in food safety has obtained more attentions in the academic and practice. However, lack of practical approach considering entire food supply chain is tangible in the academic literature. In this regard, this paper aims to apply risk assessment tool (FMEA) with integration of Human Factor along the entire supply chain of food production and test the method in a case study of Diary production, and analyze its results.

Keywords: FMEA, food supply chain, risk assessment, human factor

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Authors: Saji George, Eng Khuan Seng, Christof Luda

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Nanotechnology has been recognized as an important tool for modern agriculture and has the potential to overcome some of the pressing challenges faced by aquaculture industry. A strategy for optimizing nanotechnology-based therapeutic delivery platform for immunizing farmed fish was developed. Accordingly, a compositional library of nanomaterials of natural chemistry (Halloysite (clay), Chitosan, Hydroxyapatite, Mesoporous Silica and a composite material of clay-chitosan) was screened for their toxicity and efficiency in delivering models antigens in cellular and zebrafish embryo models using high throughput screening platforms. Through multi-parametric optimization, chitosan modified halloysite (clay) nanomaterial was identified as an optimal vaccine delivery platform. Further, studies conducted in juvenile seabass showed the potential of clay-chitosan in delivering outer membrane protein of Tenacibaculum maritimum- TIMA (pathogenic bacteria) to and its efficiency in eliciting immune responses in fish. In short, as exemplified by this work, the strategy of using compositional nanomaterial libraries and their biological profiling using high-throughput screening platform could fasten the discovery process of nanomaterials with potential applications in food and agriculture.

Keywords: nanotechnology, fish-vaccine, drug-delivery, halloysite-chitosan

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Authors: Kwabena Asare, Ezekiel N. N. Nortey, Felix O. Mettle

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Modelling of extreme events has always been of interest in fields such as hydrology and meteorology. However, after the recent global financial crises, appropriate models for modelling of such rare events leading to these crises have become quite essential in the finance and risk management fields. This paper models the extreme values of the Ghana Stock Exchange All-Shares indices (2000-2010) by applying the Extreme Value Theory to fit a model to the tails of the daily stock returns data. A conditional approach of the EVT was preferred and hence an ARMA-GARCH model was fitted to the data to correct for the effects of autocorrelation and conditional heteroscedastic terms present in the returns series, before EVT method was applied. The Peak Over Threshold (POT) approach of the EVT, which fits a Generalized Pareto Distribution (GPD) model to excesses above a certain selected threshold, was employed. Maximum likelihood estimates of the model parameters were obtained and the model’s goodness of fit was assessed graphically using Q-Q, P-P and density plots. The findings indicate that the GPD provides an adequate fit to the data of excesses. The size of the extreme daily Ghanaian stock market movements were then computed using the Value at Risk (VaR) and Expected Shortfall (ES) risk measures at some high quantiles, based on the fitted GPD model.

Keywords: extreme value theory, expected shortfall, generalized pareto distribution, peak over threshold, value at risk

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Authors: Diana Cunha, Catarina Coelho, Ana Paula Relvas, Elisabeth Kastenholz

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Context: Tourist experiences have a recognized impact on the well-being of individuals. However, studies on the specific benefits of tourist experiences for families are scattered across different disciplines. This study aims to systematically review the literature to synthesize the evidence on the benefits of tourist experiences for families. Research Aim: The main objective is to systematize the evidence in the literature regarding the benefits of tourist experiences for families. Methodology: A systematic literature review was conducted using Nvivo, analyzing 33 scientific studies obtained from various databases. The search terms used were "family"/ "couple" and "tourist experience". The studies included quantitative, qualitative, mixed methods, and literature reviews. All works prior to the year 2000 were excluded, and the search was restricted to full text. A language filter was also used, considering articles in Portuguese, English, and Spanish. For NVivo analysis, information was coded based on both deductive and inductive perspectives. To minimize the subjectivity of the selection and coding process, two of the authors discussed the process and agreed on criteria that would make the coding more objective. Once the coding process in NVivo was completed, the data relating to the identification/characterization of the works were exported to the Statistical Package for the Social Sciences (SPPS), to characterize the sample. Findings: The results highlight that tourist experiences have several benefits for family systems, including the strengthening of family and marital bonds, the creation of family memories, and overall well-being and life satisfaction. These benefits contribute to both immediate relationship quality improvement and long-term family identity construction and transgenerational transmission. Theoretical Importance: This study emphasizes the systemic nature of the effects and relationships within family systems. It also shows that no harm was reported within these experiences, with only some challenges related to positive outcomes. Data Collection and Analysis Procedures: The study collected data from 33 scientific studies published predominantly after 2013. The data were analyzed using Nvivo, employing a systematic review approach. Question Addressed: The study addresses the question of the benefits of tourist experiences for families and how these experiences contribute to family functioning and individual well-being. Conclusion: Tourist experiences provide opportunities for families to enhance their interpersonal relationships and create lasting memories. The findings suggest that formal interventions based on evidence could further enhance the potential benefits of these experiences and be a valuable preventive tool in therapeutic interventions.

Keywords: family systems, individual and family well-being, marital satisfaction, tourist experiences

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Authors: Reinhold Kosfeld, Andreas Gohs

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In the reduction of CO₂ emissions, the transition to environmentally friendly transport modes has a high significance. In Germany, the climate protection programme 2030 includes various measures for promoting electromobility. Although electric cars at present hold a market share of just over one percent, its stock more than doubled in the past two years. Special measures like tax incentives and a buyer’s premium have been put in place to promote the shift towards electric cars and boost their diffusion. Knowledge of the future expansion of electric cars is required for planning purposes and adaptation measures. With a view of these objectives, we particularly investigate the effect of spatial spillovers on forecasting performance. For this purpose, time series econometrics and panel econometric models are designed for pure electric cars and hybrid cars for Germany. Regional forecasting models with spatial interactions are consistently estimated by using spatial econometric techniques. Regional data on the stocks of electric cars and their determinants at the district level (NUTS 3 regions) are available from the Federal Motor Transport Authority (Kraftfahrt-Bundesamt) for the period 2017 - 2019. A comparative examination of aggregated regional and national predictions provides quantitative information on accuracy gains by allowing for spatial spillovers in forecasting electric mobility.

Keywords: electric mobility, forecasting market diffusion, regional panel data model, spatial interaction

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Authors: Hooman Torabifard

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In the last few years, with the progress of technology and computers and artificial intelligence entry into all kinds of scientific and industrial fields, the lifestyles of human life have changed and in general, the way of humans live on earth has many changes and development. Until now, some of the changes has occurred in the context of digital images and image processing and still continues. However, besides all the benefits, there have been disadvantages. One of these disadvantages is the multiplicity of images with high volume and data; the focus of this paper is on improving and developing a method for summarizing and enhancing the productivity of these images. The general method used for this purpose in this paper consists of a set of methods based on data obtained from image processing and using the PSO (Particle swarm optimization) algorithm. In the remainder of this paper, the method used is elaborated in detail.

Keywords: image summarization, particle swarm optimization, image threshold, image processing

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Authors: Prateek Goyal, Archini Paruthi, Superb K. Misra

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Contamination of water resources has been a major concern, which has drawn attention to the need to develop new material models for treatment of effluents. Existing conventional waste water treatment methods remain ineffective sometimes and uneconomical in terms of remediating contaminants like heavy metal ions (mercury, arsenic, lead, cadmium and chromium); organic matter (dyes, chlorinated solvents) and high salt concentration, which makes water unfit for consumption. We believe that nanotechnology based strategy, where we use nanoparticles as a tool to remediate a class of pollutants would prove to be effective due to its property of high surface area to volume ratio, higher selectivity, sensitivity and affinity. In recent years, scientific advancement has been made to study the application of photocatalytic (ZnO, TiO2 etc.) nanomaterials and magnetic nanomaterials in remediating contaminants (like heavy metals and organic dyes) from water/wastewater. Our study focuses on the synthesis and monitoring remediation efficiency of ZnO, Fe3O4 and Fe3O4 coated ZnO nanoparticulate system for the removal of heavy metals and dyes simultaneously. Multitude of ZnO nanostructures (spheres, rods and flowers) using multiple routes (microwave & hydrothermal approach) offers a wide range of light active photo catalytic property. The phase purity, morphology, size distribution, zeta potential, surface area and porosity in addition to the magnetic susceptibility of the particles were characterized by XRD, TEM, CPS, DLS, BET and VSM measurements respectively. Further on, the introduction of crystalline defects into ZnO nanostructures can also assist in light activation for improved dye degradation. Band gap of a material and its absorbance is a concrete indicator for photocatalytic activity of the material. Due to high surface area, high porosity and affinity towards metal ions and availability of active surface sites, iron oxide nanoparticles show promising application in adsorption of heavy metal ions. An additional advantage of having magnetic based nanocomposite is, it offers magnetic field responsive separation and recovery of the catalyst. Therefore, we believe that ZnO linked Fe3O4 nanosystem would be efficient and reusable. Improved photocatalytic efficiency in addition to adsorption for environmental remediation has been a long standing challenge, and the nano-composite system offers the best of features which the two individual metal oxides provide for nanoremediation.

Keywords: adsorption, nanocomposite, nanoremediation, photocatalysis

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Authors: Anna Czarczynska

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Creativity is taken as a requirement for a personal anti-fragile career path in the context of regional competitive advantage in the terms of socio-economics creative environment. At the personal level, the competence and value-based approach to creativity are proposed, is an elaboration of the resource-based view of the group of individuals selected from given country. Entrepreneurial creativity competence (measured by the Schein anchor questionnaire) is based on an independent way of thinking and empowerment presents one aspect of creative capability, however quickly verified by the market, that’s why we treat this as a basic exemplification of average creative attitude combine with the entrepreneurial attitude. This introductory instrument enables further scientific research based on the same group in the context of multi-cultural external creative or the non-creative environment.

Keywords: creativity, value-based approach, entrepreneurship, regional culture

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Authors: Maria Tsouroufli, Andrea Mondokova, Subashini Suresh

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Background: The under-representation of women and consequent failure to fulfil their potential contribution to Science, Technology, Engineering, Maths, and Medicine (STEMM) subjects in the UK is an issue that the Higher Education sector is being encouraged to address. Focus: The aim of this research is to investigate the barriers, facilitators, and incentives that influence diverse groups of women who have embarked upon a related career in STEMM subjects. The project will address a number of interconnected research questions: 1. How do participants perceive the barriers, facilitators and incentives for women in terms of research, teaching and management/leadership at each stage of their development towards forging a career in STEMM? 2. How might gender intersect with ethnicity, pregnancy/maternity and academic grade in the career experiences of women in STEMM? 3. How do participants perceive the example of female role models in emulating them as a career model? 4. How do successful females in STEMM see themselves as role models and what strategies do they employ to promote their careers? 5. How does institutional culture manifest itself as a barrier or facilitator for women in STEMM subjects in the institution? Methodology and Theoretical framework: A mixed-methodology will be employed in a case study of one university. The study will draw on extant quantitative data for context and involve conducting a qualitative inquiry to discover the perceptions of staff and students around the key concepts under study (career progression, sense of belonging and tenure, role-models, personal satisfaction, perceived gender in/equality, institutional culture). The analysis will be informed by an intersectionality framework, feminist and gender theory, and organisational psychology and human resource management perspectives. Implications: Preliminary findings will be collected in 2017. Conclusions will be drawn and used to inform recruitment and retention, and the development and implementation of initiatives to enhance the experiences and outcomes of women working and studying in STEMM subjects in Higher Education.

Keywords: under-representation, women, STEMM subjects, intersectionality

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Authors: Dávid Kovács, Bálint Csanády, Dániel Boros, Iván Ivkovic, Lóránt Nagy, Dalma Tóth-Lakits, László Márkus, András Lukács

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The modeling practice of financial instruments has seen significant change over the last decade due to the recognition of time-dependent and stochastically changing correlations among the market prices or the prices and market characteristics. To represent this phenomenon, the Stochastic Correlation Process (SCP) has come to the fore in the joint modeling of prices, offering a more nuanced description of their interdependence. This approach has allowed for the attainment of realistic tail dependencies, highlighting that prices tend to synchronize more during intense or volatile trading periods, resulting in stronger correlations. Evidence in statistical literature suggests that, similarly to the volatility, the SCP of certain stock prices follows rough paths, which can be described using fractional differential equations. However, estimating parameters for these equations often involves complex and computation-intensive algorithms, creating a necessity for alternative solutions. In this regard, the Fractional Ornstein-Uhlenbeck (fOU) process from the family of fractional processes offers a promising path. We can effectively describe the rough SCP by utilizing certain transformations of the fOU. We employed neural networks to understand the behavior of these processes. We had to develop a fast algorithm to generate a valid and suitably large sample from the appropriate process to train the network. With an extensive training set, the neural network can estimate the process parameters accurately and efficiently. Although the initial focus was the fOU, the resulting model displayed broader applicability, thus paving the way for further investigation of other processes in the realm of financial mathematics. The utility of SCP extends beyond its immediate application. It also serves as a springboard for a deeper exploration of fractional processes and for extending existing models that use ordinary Wiener processes to fractional scenarios. In essence, deploying both SCP and fractional processes in financial models provides new, more accurate ways to depict market dynamics.

Keywords: fractional Ornstein-Uhlenbeck process, fractional stochastic processes, Heston model, neural networks, stochastic correlation, stochastic differential equations, stochastic volatility

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Authors: Pierre-Marie Basset, Gabriel Reboul, Binh DangVu, Sébastien Mercier

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Rotorcraft provides invaluable services thanks to their Vertical Take-Off and Landing (VTOL), hover and low speed capabilities. Yet their use is still often limited by their cost and environmental impact, especially noise and energy consumption. One of the main brakes to the expansion of the use of rotorcraft for urban missions is the environmental impact. The first main concern for the population is the noise. In order to develop the transversal competency to assess the rotorcraft environmental footprint, a collaboration has been launched between six research departments within ONERA. The progress in terms of models and methods are capitalized into the numerical workshop C.R.E.A.T.I.O.N. “Concepts of Rotorcraft Enhanced Assessment Through Integrated Optimization Network”. A typical mission for which the environmental impact issue is of great relevance has been defined. The first milestone is to perform the pre-sizing of a reference helicopter for this mission. In a second milestone, an alternate rotorcraft concept has been defined: a tandem rotorcraft with optional propulsion. The key design trends are given for the pre-sizing of this rotorcraft aiming at a significant reduction of the global environmental impact while still giving equivalent flight performance and safety with respect to the reference helicopter. The models and methods have been improved for catching sooner and more globally, the relative variations on the environmental impact when changing the rotorcraft architecture, the pre-design variables and the operation parameters.

Keywords: environmental impact, flight performance, helicopter, multi objectives multidisciplinary optimization, rotorcraft

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Authors: Abhijeet Ostawal, Parmjit Lall

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The run times for a large strategic model that we were managing had become too long leading to delays in project delivery, increased costs and loss in productivity. Software developers are continuously working towards developing more efficient tools by changing their algorithms and processes. The issue faced by our team was how do you apply the latest technologies on validated existing models which are based on much older versions of software that do not have the latest software capabilities. The multi-model transport model that we had could only be run in sequential assignment order. Recent upgrades to the software now allowed the assignment to be run in parallel, a concept called parallelization. Parallelization is a Python script working only within the latest version of the software. A full model transfer to the latest version was not possible due to time, budget and the potential changes in trip assignment. This article is to show the method to adapt and update the Python script in such a way that it can be used in older software versions by calling the latest version and then recalling the old version for assignment model without affecting the results. Through a process of trial-and-error run time savings of up to 30-40% have been achieved. Assignment results were maintained within the older version and through this learning process we’ve applied this methodology to other even older versions of the software resulting in huge time savings, more productivity and efficiency for both client and consultant.

Keywords: model run time, demand model, parallelisation, python scripting

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Authors: Seyed Esmail Seyedi Bariran, Khairul Salleh Mohamed Sahari

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In robotics literature, the simultaneous localization and mapping (SLAM) is commonly associated with a priori-posteriori problem. The autonomous vehicle needs a neutral map to spontaneously track its local position, i.e., “localization” while at the same time a precise path estimation of the environment state is required for effective route planning and obstacle avoidance. On the other hand, the environmental noise factors can significantly intensify the inherent uncertainties in using odometry information and measurements obtained from the robot’s exteroceptive sensor which in return directly affect the overall performance of the corresponding SLAM. Therefore, the current work is primarily dedicated to provide a diagnostic analysis of six SLAM algorithms including FastSLAM, L-SLAM, GraphSLAM, Grid SLAM and DP-SLAM. A SLAM simulated environment consisting of two sets of landmark locations and robot waypoints was set based on modified EKF and UKF in MATLAB using two separate maps for indoor and outdoor route planning subject to natural and artificial obstacles. The simulation results are expected to provide an unbiased platform to compare the estimation performances of the five SLAM models as well as on the reliability of each SLAM model for indoor and outdoor applications.

Keywords: route planning, obstacle, estimation performance, FastSLAM, L-SLAM, GraphSLAM, Grid SLAM, DP-SLAM

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Authors: M. Soleimani, D. Cree, C. Chafe, L. Bates

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The accidental release of petroleum products into the environment could have harmful consequences to our ecosystem. Different techniques such as mechanical separation, membrane filtration, incineration, treatment processes using enzymes and dispersants, bioremediation, and sorption process using sorbents have been applied for oil spill remediation. Most of the techniques investigated are too costly or do not have high enough efficiency. This study was conducted to determine the sorption performance of hemp byproducts (cellulosic materials) in terms of sorption capacity and kinetics for hydrophobic and hydrophilic fluids. In this study, heavy oil, light oil, diesel fuel, and water/water vapor were used as sorbate fluids. Hemp stalk in different forms, including loose material (hammer milled (HM) and shredded (Sh) with low bulk densities) and densified forms (pellet form (P) and crumbled pellets (CP)) with high bulk densities, were used as sorbents. The sorption/retention tests were conducted according to ASTM 726 standard. For a quick-purpose application of the sorbents, the sorption tests were conducted for 15 min, and for an ideal sorption capacity of the materials, the tests were carried out for 24 h. During the test, the sorbent material was exposed to the fluid by immersion, followed by filtration through a stainless-steel wire screen. Water vapor adsorption was carried out in a controlled environment chamber with the capability of controlling relative humidity (RH) and temperature. To determine the kinetics of sorption for each fluid and sorbent, the retention capacity also was determined intervalley for up to 24 h. To analyze the kinetics of sorption, pseudo-first-order, pseudo-second order and intraparticle diffusion models were employed with the objective of minimal deviation of the experimental results from the models. The results indicated that HM and Sh materials had the highest sorption capacity for the hydrophobic fluids with approximately 6 times compared to P and CP materials. For example, average retention values of heavy oil on HM and Sh was 560% and 470% of the mass of the sorbents, respectively. Whereas, the retention of heavy oil on P and CP was up to 85% of the mass of the sorbents. This lower sorption capacity for P and CP can be due to the less exposed surface area of these materials and compacted voids or capillary tubes in the structures. For water uptake application, HM and Sh resulted in at least 40% higher sorption capacity compared to those obtained for P and CP. On average, the performance of sorbate uptake from high to low was as follows: water, heavy oil, light oil, diesel fuel. The kinetic analysis indicated that the second-pseudo order model can describe the sorption process of the oil and diesel better than other models. However, the kinetics of water absorption was better described by the pseudo-first-order model. Acetylation of HM materials could improve its oil and diesel sorption to some extent. Water vapor adsorption of hemp fiber was a function of temperature and RH, and among the models studied, the modified Oswin model was the best model in describing this phenomenon.

Keywords: environment, fiber, petroleum, sorption

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Authors: Zh. Saffari, A. Naeimi, M. S. Ekrami-Kakhki, Kh. Khandan-Barani

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The textile industries are becoming a major source of environmental contamination because an alarming amount of dye pollutants are generated during the dyeing processes. Organic dyes are one of the largest pollutants released into wastewater from textile and other industrial processes, which have shown severe impacts on human physiology. Nano-structure compounds have gained importance in this category due their anticipated high surface area and improved reactive sites. In recent years several novel adsorbents have been reported to possess great adsorption potential due to their enhanced adsorptive capacity. Nano-MnO2 has great potential applications in environment protection field and has gained importance in this category because it has a wide variety of structure with large surface area. The diverse structures, chemical properties of manganese oxides are taken advantage of in potential applications such as adsorbents, sensor catalysis and it is also used for wide catalytic applications, such as degradation of dyes. In this study, adsorption of Methyl Violet (MV) dye from aqueous solutions onto MnO2 nanoparticles (MNP) has been investigated. The surface characterization of these nano particles was examined by Particle size analysis, Scanning Electron Microscopy (SEM), Fourier Transform Infrared (FTIR) spectroscopy and X-Ray Diffraction (XRD). The effects of process parameters such as initial concentration, pH, temperature and contact duration on the adsorption capacities have been evaluated, in which pH has been found to be most effective parameter among all. The data were analyzed using the Langmuir and Freundlich for explaining the equilibrium characteristics of adsorption. And kinetic models like pseudo first- order, second-order model and Elovich equation were utilized to describe the kinetic data. The experimental data were well fitted with Langmuir adsorption isotherm model and pseudo second order kinetic model. The thermodynamic parameters, such as Free energy of adsorption (ΔG°), enthalpy change (ΔH°) and entropy change (ΔS°) were also determined and evaluated.

Keywords: MnO2 nanoparticles, adsorption, methyl violet, isotherm models, kinetic models, surface chemistry

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Authors: Bunakiye R. Japheth, Ogude U. Cyril

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Since the process of transforming user requirements to modeling constructs are not very well supported by domain-specific frameworks, it became necessary to integrate domain requirements with the specific architectures to achieve an integrated customizable solutions space via artifact orientation. Domain-specific modeling language specifications of model-driven engineering technologies focus more on requirements within a particular domain, which can be tailored to aid the domain expert in expressing domain concepts effectively. Modeling processes through domain-specific language formalisms are highly volatile due to dependencies on domain concepts or used process models. A capable solution is given by artifact orientation that stresses on the results rather than expressing a strict dependence on complicated platforms for model creation and development. Based on this premise, domain-specific methods for producing artifacts without having to take into account the complexity and variability of platforms for model definitions can be integrated to support customizable development. In this paper, we discuss methods for the integration capabilities and necessities within a common structure and semantics that contribute a metamodel for artifact-orientation, which leads to a reusable software layer with concrete syntax capable of determining design intents from domain expert. These concepts forming the language formalism are established from models explained within the oil and gas pipelines industry.

Keywords: control process, metrics of engineering, structured abstraction, semantic model

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Authors: Hui Zhang, Sherif Beskhyroun

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Structural Health monitoring systems (SHM) are commonly used to identify and assess structural damage. In terms of damage detection, SHM needs to periodically collect data from sensors placed in the structure as damage-sensitive features. This includes abnormal changes caused by the strain field and abnormal symptoms of the structure, such as damage and deterioration. Currently, deploying sensors on a large scale in a building structure is a challenge. In this study, a highly stretchable strain sensors are used in this study to collect data sets of strain generated on the surface of full-size reinforced concrete (RC) frames under extreme cyclic load application. This sensing sheet can be switched freely between the test bending strain and the axial strain to achieve two different configurations. On this basis, the deep neural network prediction model of the frame beam and frame column is established. The training results show that the method can accurately predict the strain value and has good generalization ability. The two deep neural network prediction models will also be deployed in the SHM system in the future as part of the intelligent strain sensor system.

Keywords: strain sensing sheets, deep neural networks, strain measurement, SHM system, RC frames

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Authors: Khalid Ali, Manar Jammal

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In order to meet the stringent latency and reliability requirements of the upcoming 5G networks, Open Radio Access Networks (O-RAN) have been proposed. The virtualization of O-RAN has allowed it to be treated as a Network Function Virtualization (NFV) architecture, while its components are considered Virtualized Network Functions (VNFs). Hence, intelligent Machine Learning (ML) based solutions can be utilized to apply different resource management and allocation techniques on O-RAN. However, intelligently allocating resources for O-RAN VNFs can prove challenging due to the dynamicity of traffic in mobile networks. Network providers need to dynamically scale the allocated resources in response to the incoming traffic. Elastically allocating resources can provide a higher level of flexibility in the network in addition to reducing the OPerational EXpenditure (OPEX) and increasing the resources utilization. Most of the existing elastic solutions are reactive in nature, despite the fact that proactive approaches are more agile since they scale instances ahead of time by predicting the incoming traffic. In this work, we propose and evaluate traffic forecasting models based on the ML algorithm. The algorithms aim at predicting future O-RAN traffic by using previous traffic data. Detailed analysis of the traffic data was carried out to validate the quality and applicability of the traffic dataset. Hence, two ML models were proposed and evaluated based on their prediction capabilities.

Keywords: O-RAN, traffic forecasting, NFV, ARIMA, LSTM, elasticity

Procedia PDF Downloads 226

Authors: Bruce G. Wilson

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The collection of municipal solid waste at the curbside is a complex operation that is repeated daily under varying circumstances around the world. There have been several attempts to develop Monte Carlo simulation models of the waste collection process dating back almost 50 years. Despite this long history, the use of simulation modeling as a planning or optimization tool for waste collection is still extremely limited in practice. Historically, simulation modeling of waste collection systems has been hampered by the limitations of computer hardware and software and by the availability of representative input data. This paper outlines the development of a Monte Carlo simulation model that overcomes many of the limitations contained in previous models. The model uses a general purpose simulation software program that is easily capable of modeling an entire waste collection network. The model treats the stops on a waste collection route as a queue of work to be processed by a collection vehicle (or server). Input data can be collected from a variety of sources including municipal geographic information systems, global positioning system recorders on collection vehicles, and weigh scales at transfer stations or treatment facilities. The result is a flexible model that is sufficiently robust that it can model the collection activities in a large municipality, while providing the flexibility to adapt to changing conditions on the collection route.

Keywords: modeling, queues, residential waste collection, Monte Carlo simulation

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Authors: Amirhosein Moonesi Shabestary, Eckhard Krepper, Dirk Lucas

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The current PhD project comprises CFD-modeling and simulation of condensation and heat transfer inside horizontal pipes. Condensation plays an important role in emergency cooling systems of reactors. The emergency cooling system consists of inclined horizontal pipes which are immersed in a tank of subcooled water. In the case of an accident the water level in the core is decreasing, steam comes in the emergency pipes, and due to the subcooled water around the pipe, this steam will start to condense. These horizontal pipes act as a strong heat sink which is responsible for a quick depressurization of the reactor core when any accident happens. This project is defined in order to model all these processes which happening in the emergency cooling systems. The most focus of the project is on detection of different morphologies such as annular flow, stratified flow, slug flow and plug flow. This project is an ongoing project which has been started 1 year ago in Helmholtz Zentrum Dresden Rossendorf (HZDR), Fluid Dynamics department. In HZDR most in cooperation with ANSYS different models are developed for modeling multiphase flows. Inhomogeneous MUSIG model considers the bubble size distribution and is used for modeling small-scaled dispersed gas phase. AIAD (Algebraic Interfacial Area Density Model) is developed for detection of the local morphology and corresponding switch between them. The recent model is GENTOP combines both concepts. GENTOP is able to simulate co-existing large-scaled (continuous) and small-scaled (polydispersed) structures. All these models are validated for adiabatic cases without any phase change. Therefore, the start point of the current PhD project is using the available models and trying to integrate phase transition and wall condensing models into them. In order to simplify the idea of condensation inside horizontal tubes, 3 steps have been defined. The first step is the investigation of condensation inside a horizontal tube by considering only direct contact condensation (DCC) and neglect wall condensation. Therefore, the inlet of the pipe is considered to be annular flow. In this step, AIAD model is used in order to detect the interface. The second step is the extension of the model to consider wall condensation as well which is closer to the reality. In this step, the inlet is pure steam, and due to the wall condensation, a liquid film occurs near the wall which leads to annular flow. The last step will be modeling of different morphologies which are occurring inside the tube during the condensation via using GENTOP model. By using GENTOP, the dispersed phase is able to be considered and simulated. Finally, the results of the simulations will be validated by experimental data which will be available also in HZDR.

Keywords: wall condensation, direct contact condensation, AIAD model, morphology detection

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Authors: Libena Cernohorska, Jan Cernohorsky

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This article aims to evaluate the impact of loans provided within the Czech banking sector on the growth of the Czech economy. The article is based on research of current scientific findings in respect to bank loans and economic development. The paper is based on data taken from the Czech Statistical Office on the development of the gross domestic product and data from the Czech National Bank on the development of loans from the period 2004-2015. Links between selected variables are tested using Granger causality tests. The results calculated confirm the hypothesis of the impact of the loans on economic growth, with a six-month delay. The results thus correspond to the standard economic findings and results of most previous studies.

Keywords: bank, business cycle, economic growth, loans

Procedia PDF Downloads 126