Search results for: computational modeling

Authors: Lukasz Grabowski, Marcin Szlachetka, Tytus Tulwin

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The main task of the engine cooling system is to maintain its average operating temperatures within strictly defined limits. Too high or too low average temperatures result in accelerated wear or even damage to the engine or its individual components. In order to avoid local overheating or significant temperature gradients, leading to high stresses in the component, the aim is to ensure an even flow of air. In the case of analyses related to heat exchange, one of the main problems is the comparison of temperature fields because standard measuring instruments such as thermocouples or thermistors only provide information about the course of temperature at a given point. Thermal imaging tests can be helpful in this case. With appropriate camera settings and taking into account environmental conditions, we are able to obtain accurate temperature fields in the form of thermograms. Emission of heat from the engine to the engine compartment is an important issue when designing a cooling system. Also, in the case of liquid cooling, the main sources of heat in the form of emissions from the engine block, cylinders, etc. should be identified. It is important to redesign the engine compartment ventilation system. Ensuring proper cooling of aircraft reciprocating engine is difficult not only because of variable operating range but mainly because of different cooling conditions related to the change of speed or altitude of flight. Engine temperature also has a direct and significant impact on the properties of engine oil, which under the influence of this parameter changes, in particular, its viscosity. Too low or too high, its value can be a result of fast wear of engine parts. One of the ways to determine the temperatures occurring on individual parts of the engine is the use of thermal imaging measurements. The article presents the results of preliminary thermal imaging tests of aircraft piston diesel engine with a maximum power of about 100 HP. In order to perform the heat emission tests of the tested engine, the ThermaCAM S65 thermovision monitoring system from FLIR (Forward-Looking Infrared) together with the ThermaCAM Researcher Professional software was used. The measurements were carried out after the engine warm up. The engine speed was 5300 rpm The measurements were taken for the following environmental parameters: air temperature: 17 °C, ambient pressure: 1004 hPa, relative humidity: 38%. The temperatures distribution on the engine cylinder and on the exhaust manifold were analysed. Thermal imaging tests made it possible to relate the results of simulation tests to the real object by measuring the rib temperature of the cylinders. The results obtained are necessary to develop a CFD (Computational Fluid Dynamics) model of heat emission from the engine bay. The project/research was financed in the framework of the project Lublin University of Technology-Regional Excellence Initiative, funded by the Polish Ministry of Science and Higher Education (contract no. 030/RID/2018/19).

Keywords: aircraft, piston engine, heat, emission

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Authors: Mehwish Jawaad

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Purpose: The concept of GrSCM (Green Supply Chain Management) in the academic and research field is still thought to be in the development stage especially in Asian Emerging Economies. The purpose of this paper is to contribute significantly to the first wave of empirical investigation on GrSCM Practices and Firm Performance measures in Pakistan. The aim of this research is to develop a more holistic approach towards investigating the impact of Green Supply Chain Management Practices (Ecodesign, Internal Environmental Management systems, Green Distribution, Green Purchasing and Cooperation with Customers) on multiple dimensions of Firm Performance Measures (Economic Performance, Environmental Performance and Operational Performance) with a mediating role of Investment Recovery and Competitiveness. This paper also serves as an initiative to identify if the relationship between Investment Recovery and Firm Performance Measures is mediated by Competitiveness. Design/ Methodology/Approach: This study is based on survey Data collected from 272, ISO (14001) Certified Textile Firms Based in Lahore, Faisalabad, and Karachi which are involved in Spinning, Dyeing, Printing or Bleaching. A Theoretical model was developed incorporating the constructs representing Green Activities and Firm Performance Measures of a firm. The data was analyzed using Partial Least Square Structural Equation Modeling. Senior and Mid-level managers provided the data reflecting the degree to which their organizations deal with both internal and external stakeholders to improve the environmental sustainability of their supply chain. Findings: Of the 36 proposed Hypothesis, 20 are considered valid and significant. The statistics result reveal that GrSCM practices positively impact Environmental Performance followed by Economic and Operational Performance. Investment Recovery acts as a strong mediator between Intra organizational Green activities and performance outcomes. The relationship of Reverse Logistics influencing outcomes is significantly mediated by Competitiveness. The pressure originating from customers exert significant positive influence on the firm to adopt Green Practices consequently leading to higher outcomes. Research Contribution/Originality: Underpinning the Resource dependence theory and as a first wave of investigating the impact of Green Supply chain on performance outcomes in Pakistan, this study intends to make a prominent mark in the field of research. Investment and Competitiveness together are tested as a mediator for the first time in this arena. Managerial implications: Practitioner is provided with a framework for assessing the synergistic impact of GrSCM practices on performance. Upgradation of Accreditations and Audit Programs on regular basis are the need of the hour. Making the processes leaner with the sale of excess inventories and scrap helps the firm to work more efficiently and productively.

Keywords: economic performance, environmental performance, green supply chain management practices, operational performance, sustainability, a textile sector of Pakistan

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Authors: Yuri Laevsky, Tatyana Nosova

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The phenomenon of filtration gas combustion (FGC) had been discovered experimentally at the beginning of 80’s of the previous century. It has a number of important applications in such areas as chemical technologies, fire-explosion safety, energy-saving technologies, oil production. From the physical point of view, FGC may be defined as the propagation of region of gaseous exothermic reaction in chemically inert porous medium, as the gaseous reactants seep into the region of chemical transformation. The movement of the combustion front has different modes, and this investigation is focused on the low-velocity regime. The main characteristic of the process is the velocity of the combustion front propagation. Computation of this characteristic encounters substantial difficulties because of the strong heterogeneity of the process. The mathematical model of FGC is formed by the energy conservation laws for the temperature of the porous medium and the temperature of gas and the mass conservation law for the relative concentration of the reacting component of the gas mixture. In this case the homogenization of the model is performed with the use of the two-temperature approach when at each point of the continuous medium we specify the solid and gas phases with a Newtonian heat exchange between them. The construction of a computational scheme is based on the principles of mixed finite element method with the usage of a regular mesh. The approximation in time is performed by an explicit–implicit difference scheme. Special attention was given to determination of the combustion front propagation velocity. Straight computation of the velocity as grid derivative leads to extremely unstable algorithm. It is worth to note that the term ‘front propagation velocity’ makes sense for settled motion when some analytical formulae linking velocity and equilibrium temperature are correct. The numerical implementation of one of such formulae leading to the stable computation of instantaneous front velocity has been proposed. The algorithm obtained has been applied in subsequent numerical investigation of the FGC process. This way the dependence of the main characteristics of the process on various physical parameters has been studied. In particular, the influence of the combustible gas mixture consumption on the front propagation velocity has been investigated. It also has been reaffirmed numerically that there is an interval of critical values of the interfacial heat transfer coefficient at which a sort of a breakdown occurs from a slow combustion front propagation to a rapid one. Approximate boundaries of such an interval have been calculated for some specific parameters. All the results obtained are in full agreement with both experimental and theoretical data, confirming the adequacy of the model and the algorithm constructed. The presence of stable techniques to calculate the instantaneous velocity of the combustion wave allows considering the semi-Lagrangian approach to the solution of the problem.

Keywords: filtration gas combustion, low-velocity regime, mixed finite element method, numerical simulation

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Authors: Simran Baweja, Bhavika Kalal, Surajit Maity

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Photoinduced tautomerization reactions have been the centre of attention among the scientific community over the past several decades because of their significance in various biological systems. 7-azaindole (7AI) is considered a model system for DNA base pairing and to understand the role of such tautomerization reactions in mutations. To the best of our knowledge, extensive studies have been carried out on 7-azaindole and its solvent clusters exhibiting proton/ hydrogen transfer in both solution as well as gas phases. Derivatives of the above molecule, like 2,7- and 2,6-diazaindoles are proposed to have even better photophysical properties due to the presence of -aza group on the 2nd position. However, there are studies in the solution phase that suggest the relevance of these molecules, but there are no experimental studies reported in the gas phase yet. In our current investigation, we present the first gas phase spectroscopic data of 2,7-diazaindole (2,7-DAI) and its solvent cluster (2,7-DAI-H2O). In this, we have employed state-of-the-art laser spectroscopic methods such as fluorescence excitation (LIF), dispersed fluorescence (DF), resonant two-photon ionization-time of flight mass spectrometry (2C-R2PI), photoionization efficiency spectroscopy (PIE), IR-UV double resonance spectroscopy, i.e., fluorescence-dip infrared spectroscopy (FDIR) and resonant ion-dip infrared spectroscopy (IDIR) to understand the electronic structure of the molecule. The origin band corresponding to the S1 ← S0 transition of the bare 2,7-DAI is found to be positioned at 33910 cm-1, whereas the origin band corresponding to S1 ← S0 transition of the 2,7-DAI-H2O is positioned at 33074 cm-1. The red-shifted transition in the case of solvent cluster suggests the enhanced feasibility of excited state hydrogen/ proton transfer. The ionization potential for the 2,7-DAI molecule is found to be 8.92 eV which is significantly higher than the previously reported 7AI (8.11 eV) molecule, making it a comparatively complex molecule to study. The ionization potential is reduced by 0.14 eV in the case of 2,7-DAI-H2O (8.78 eV) cluster compared to that of 2,7-DAI. Moreover, on comparison with the available literature values of 7AI, we found the origin band of 2,7-DAI and 2,7-DAI-H2O to be red-shifted by -729 and -280 cm-1 respectively. The ground and excited state N-H stretching frequencies of the 27DAI molecule were determined using fluorescence-dip infrared spectra (FDIR) and resonant ion dip infrared spectroscopy (IDIR), obtained at 3523 and 3467 cm-1, respectively. The lower value of vNH in the electronically excited state of 27DAI implies the higher acidity of the group compared to the ground state. Moreover, we have done extensive computational analysis, which suggests that the energy barrier in the excited state reduces significantly as we increase the number of catalytic solvent molecules (S= H2O, NH3) as well as the polarity of solvent molecules. We found that the ammonia molecule is a better candidate for hydrogen transfer compared to water because of its higher gas-phase basicity. Further studies are underway to understand the excited state dynamics and photochemistry of such N-rich chromophores.

Keywords: excited state hydrogen transfer, supersonic expansion, gas phase spectroscopy, IR-UV double resonance spectroscopy, laser induced fluorescence, photoionization efficiency spectroscopy

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Authors: Marina Shakleina, Konstantin Shaklein, Stanislav Yakiro

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The digitalization of modern human life over the last decade has facilitated the acquisition, storage, and processing of data, which are used to detect changes in consumer preferences and to improve the internal efficiency of the production process. This emerging trend has attracted academic interest in the use of big data in research. The study focuses on modeling the social comfort of the Russian population for the period 2010-2021 using big data. Big data provides enormous opportunities for understanding human interactions at the scale of society with plenty of space and time dynamics. One of the most popular big data sources is Google Trends. The methodology for assessing social comfort using big data involves several steps: 1. 574 words were selected based on the Harvard IV-4 Dictionary adjusted to fit the reality of everyday Russian life. The set of keywords was further cleansed by excluding queries consisting of verbs and words with several lexical meanings. 2. Search queries were processed to ensure comparability of results: the transformation of data to a 10-point scale, elimination of popularity peaks, detrending, and deseasoning. The proposed methodology for keyword search and Google Trends processing was implemented in the form of a script in the Python programming language. 3. Block and summary integral indicators of social comfort were constructed using the first modified principal component resulting in weighting coefficients values of block components. According to the study, social comfort is described by 12 blocks: ‘health’, ‘education’, ‘social support’, ‘financial situation’, ‘employment’, ‘housing’, ‘ethical norms’, ‘security’, ‘political stability’, ‘leisure’, ‘environment’, ‘infrastructure’. According to the model, the summary integral indicator increased by 54% and was 4.631 points; the average annual rate was 3.6%, which is higher than the rate of economic growth by 2.7 p.p. The value of the indicator describing social comfort in Russia is determined by 26% by ‘social support’, 24% by ‘education’, 12% by ‘infrastructure’, 10% by ‘leisure’, and the remaining 28% by others. Among 25% of the most popular searches, 85% are of negative nature and are mainly related to the blocks ‘security’, ‘political stability’, ‘health’, for example, ‘crime rate’, ‘vulnerability’. Among the 25% most unpopular queries, 99% of the queries were positive and mostly related to the blocks ‘ethical norms’, ‘education’, ‘employment’, for example, ‘social package’, ‘recycling’. In conclusion, the introduction of the latent category ‘social comfort’ into the scientific vocabulary deepens the theory of the quality of life of the population in terms of the study of the involvement of an individual in the society and expanding the subjective aspect of the measurements of various indicators. Integral assessment of social comfort demonstrates the overall picture of the development of the phenomenon over time and space and quantitatively evaluates ongoing socio-economic policy. The application of big data in the assessment of latent categories gives stable results, which opens up possibilities for their practical implementation.

Keywords: big data, Google trends, integral indicator, social comfort

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Authors: Sumedha Weerasekara, Morgan Miles, Mark Morrison, Branka Krivokapic-Skoko

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This paper is aimed at exploring the interrelationships among entrepreneurial ecosystem factors and entrepreneurial cognition in regional and metropolitan ecosystems. Entrepreneurial ecosystem factors examined include: culture, infrastructure, access to finance, informal networks, support services, access to universities, and the depth and breadth of the talent pool. Using a multivariate approach we explore the impact of these ecosystem factors or elements on entrepreneurial cognition. In doing so, the existing body of knowledge from the literature on entrepreneurial ecosystem and cognition have been blended to explore the relationship between entrepreneurial ecosystem factors and cognition in a way not hitherto investigated. The concept of the entrepreneurial ecosystem has received increased attention as governments, universities and communities have started to recognize the potential of integrated policies, structures, programs and processes that foster entrepreneurship activities by supporting innovation, productivity and employment growth. The notion of entrepreneurial ecosystems has evolved and grown with the advancement of theoretical research and empirical studies. Importance of incorporating external factors like culture, political environment, and the economic environment within a single framework will enhance the capacity of examining the whole systems functionality to better understand the interaction of the entrepreneurial actors and factors within a single framework. The literature on clusters underplays the role of entrepreneurs and entrepreneurial management in creating and co-creating organizations, markets, and supporting ecosystems. Entrepreneurs are only one actor following a limited set of roles and dependent upon many other factors to thrive. As a consequence, entrepreneurs and relevant authorities should be aware of the other actors and factors with which they engage and rely, and make strategic choices to achieve both self and also collective objectives. The study uses stratified random sampling method to collect survey data from 12 different regions in regional and metropolitan regions of NSW, Australia. A questionnaire was administered online among 512 Small and medium enterprise owners operating their business in selected 12 regions in NSW, Australia. Data were analyzed using descriptive analyzing techniques and partial least squares - structural equation modeling. The findings show that even though there is a significant relationship between each and every entrepreneurial ecosystem factors, there is a weak relationship between most entrepreneurial ecosystem factors and entrepreneurial cognition. In the metropolitan context, the availability of finance and informal networks have the largest impact on entrepreneurial cognition while culture, infrastructure, and support services having the smallest impact and the talent pool and universities having a moderate impact on entrepreneurial cognition. Interestingly, in a regional context, culture, availability of finance, and the talent pool have the highest impact on entrepreneurial cognition, while informal networks having the smallest impact and the remaining factors – infrastructure, universities, and support services have a moderate impact on entrepreneurial cognition. These findings suggest the need for a location-specific strategy for supporting the development of entrepreneurial cognition.

Keywords: academic achievement, colour response card, feedback

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Authors: Hanan Begali, Shahi Dost, Annett Ziegler, Markus Cornberg, Maria-Esther Vidal, Anke R. M. Kraft

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Chronic hepatitis B virus (HBV) infection can be treated with nucleot(s)ide analog (NA), for example, which inhibits HBV replication. However, they have hardly any influence on the functional cure of HBV, which is defined by hepatitis B surface antigen (HBsAg) loss. NA needs to be taken life-long, which is not available for all patients worldwide. Additionally, NA-treated patients are still at risk of developing cirrhosis, liver failure, or hepatocellular carcinoma (HCC). Although each patient has the same components of the immune system, immune responses vary between patients. Therefore, a deeper understanding of the immune response against HBV in different patients is necessary to understand the parameters leading to HBV cure and to use this knowledge to optimize HBV therapies. This requires seamless integration of an enormous amount of diverse and fine-grained data from viral markers, e.g., hepatitis B core-related antigen (HBcrAg) and hepatitis B surface antigen (HBsAg). The data integration system relies on the assumption that profiling human immune systems requires the analysis of various variables (e.g., demographic data, treatments, pre-existing conditions, immune cell response, or HLA-typing) rather than only one. However, the values of these variables are collected independently. They are presented in a myriad of formats, e.g., excel files, textual descriptions, lab book notes, and images of flow cytometry dot plots. Additionally, patients can be identified differently in these analyses. This heterogeneity complicates the integration of variables, as data management techniques are needed to create a unified view in which individual formats and identifiers are transparent when profiling the human immune systems. The proposed study (HBsRE) aims at integrating heterogeneous data sets of 87 chronically HBV-infected patients, e.g., clinical data, immune cell response, and HLA-typing, with knowledge encoded in biomedical ontologies and open-source databases into a knowledge-driven framework. This new technique enables us to harmonize and standardize heterogeneous datasets in the defined modeling of the data integration system, which will be evaluated in the knowledge graph (KG). KGs are data structures that represent the knowledge and data as factual statements using a graph data model. Finally, the analytic data model will be applied on top of KG in order to develop a deeper understanding of the immune profiles among various patients and to evaluate factors playing a role in a holistic profile of patients with HBsAg level loss. Additionally, our objective is to utilize this unified approach to stratify patients for new effective treatments. This study is developed in the context of the project “Transforming big data into knowledge: for deep immune profiling in vaccination, infectious diseases, and transplantation (ImProVIT)”, which is a multidisciplinary team composed of computer scientists, infection biologists, and immunologists.

Keywords: chronic hepatitis B infection, immune response, knowledge graphs, ontology

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Authors: Michael Mederle, Heinz Bernhardt

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The Management of machine fleets or autonomous vehicle control will considerably increase efficiency in future agricultural production. Especially entire process chains, e.g. harvesting complexes with several interacting combine harvesters, grain carts, and removal trucks, provide lots of optimization potential. Organization and pre-planning ensure to get these efficiency reserves accessible. One way to achieve this is to optimize infield path planning. Particularly autonomous machinery requires precise specifications about infield logistics to be navigated effectively and process optimized in the fields individually or in machine complexes. In the past, a lot of theoretical optimization has been done regarding infield logistics, mainly based on field geometry. However, there are reasons why farmers often do not apply the infield strategy suggested by mathematical route planning tools. To make the computational optimization more useful for farmers this study focuses on these influencing factors by expert interviews. As a result practice-oriented navigation not only to the field but also within the field will be possible. The survey study is intended to cover the entire range of German agriculture. Rural mixed farms with simple technology equipment are considered as well as large agricultural cooperatives which farm thousands of hectares using track guidance and various other electronic assistance systems. First results show that farm managers using guidance systems increasingly attune their infield-logistics on direction giving obstacles such as power lines. In consequence, they can avoid inefficient boom flippings while doing plant protection with the sprayer. Livestock farmers rather focus on the application of organic manure with its specific requirements concerning road conditions, landscape terrain or field access points. Cultivation of sugar beets makes great demands on infield patterns because of its particularities such as the row crop system or high logistics demands. Furthermore, several machines working in the same field simultaneously influence each other, regardless whether or not they are of the equal type. Specific infield strategies always are based on interactions of several different influences and decision criteria. Single working steps like tillage, seeding, plant protection or harvest mostly cannot be considered each individually. The entire production process has to be taken into consideration to detect the right infield logistics. One long-term objective of this examination is to integrate the obtained influences on infield strategies as decision criteria into an infield navigation tool. In this way, path planning will become more practical for farmers which is a basic requirement for automatic vehicle control and increasing process efficiency.

Keywords: autonomous vehicle control, infield logistics, path planning, process optimizing

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Authors: Natalia V. Naryzhnaya, Alexandr V. Mukhomedzyanov, Ivan A. Derkachev, Boris K. Kurbatov, Leonid N. Maslov

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Background: Techniques of adaptation to hypoxia and remote postconditioning (RPost) have great prospects for use in the clinic. However, recent studies have shown low efficacy of remote postconditioning in patients with AMI. We hypothesize that the reasons for this inefficiency may be metabolic disorders, which are very common, especially in patients with cardiovascular disease, and age of patients. The purpose of the study was to reveal the effectiveness of adaptation to chronic hypoxia and RPost. To determine the possible relationship between the decrease in the effectiveness of projective impacts and disorders of carbohydrate and lipid metabolism. Design: The study was carried out on Wistar rats 60 day old. MetS was induced by high-carbohydrate, high-fat diet (HСHFD). Modeling MS led to the formation of obesity, hypertension, impaired lipid and carbohydrate metabolism, hyperleptinemia, and moderate stress. Groups with adaptation to chronic hypoxia were subjected to hypoxia for 21 days at 12% O2 and 0.3% CO2 after complete of HСHFD. All animals were subjected to 45 min coronary occlusion and 120 min reperfusion. Groups with RPost, immediately after the end of ischemia, tourniquets were applied to the hind limbs in the area of the hip joint (3 times in the mode of 5 min ischemia, 5 min reperfusion). Results: RPost led to a twofold reduction of infarct size in rats with intact metabolism (р < 0.0001), while in rats with MetS, a decrease in infarct size during RPost was 25 % (p = 0.00003). A direct correlation was found between of infarct size during RPost and the serum leptin level of rats with MetC (r = 0.85). The presented data suggested that a decrease in the efficiency of remote postconditioning in rats with diet-induced metabolic syndrome depends on serum leptin. Chronic hypoxia resulted in a 38% reduced in infarct size in metabolically intact rats. The decrease of cardioprotection was observed in rats with chronic hypoxia and MetS. Infarct size showed a direct correlation with impaired glucose tolerance (AUC, glucose tolerance test, r = 0.034) and serum triglyceride levels (r = 0.39). Our study showed the dependence of cardioprotection in rats with metabolic syndrome during chronic hypoxia and DPost on opioids in the blood serum and myocardium, protein kinase C and NO synthase activity. Conclusion: The results obtained showed that the infarct-limiting efficiency of adaptation to hypoxia and remote postconditioning is reduced or completely absent in animals with metabolic syndrome. The increase in the infarction, in this case, directly depends on the disturbances in carbohydrate. lipid metabolism and opioids signaling. Funding: Investigation of effectiveness of chronic hypoxia at the metabolic syndrome was carried out within the support of Russian Science Foundation Grant 22-15-00048. Studies of the mechanisms of arterial hypertension in induced metabolic syndrome were carried out within the framework of the state assignment (122020300042-4). The work was performed using the Center for Collective Use "Medical Genomics".

Keywords: chronic hypoxia, opioids, remote postconditioning, metabolic syndrome

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Authors: Danny Barash

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Riboswitches are RNA genetic control elements that were originally discovered in bacteria and provide a unique mechanism of gene regulation. They work without the participation of proteins and are believed to represent ancient regulatory systems in the evolutionary timescale. One of the biggest challenges in riboswitch research is that many are found in prokaryotes but only a small percentage of known riboswitches have been found in certain eukaryotic organisms. The few examples of eukaryotic riboswitches were identified using sequence-based bioinformatics search methods that include some slight structural considerations. These pattern-matching methods were the first ones to be applied for the purpose of riboswitch detection and they can also be programmed very efficiently using a data structure called affix arrays, making them suitable for genome-wide searches of riboswitch patterns. However, they are limited by their ability to detect harder to find riboswitches that deviate from the known patterns. Several methods have been developed since then to tackle this problem. The most commonly used by practitioners is Infernal that relies on Hidden Markov Models (HMMs) and Covariance Models (CMs). Profile Hidden Markov Models were also carried out in the pHMM Riboswitch Scanner web application, independently from Infernal. Other computational approaches that have been developed include RMDetect by the use of 3D structural modules and RNAbor that utilizes Boltzmann probability of structural neighbors. We have tried to incorporate more sophisticated secondary structure considerations based on RNA folding prediction using several strategies. The first idea was to utilize window-based methods in conjunction with folding predictions by energy minimization. The moving window approach is heavily geared towards secondary structure consideration relative to sequence that is treated as a constraint. However, the method cannot be used genome-wide due to its high cost because each folding prediction by energy minimization in the moving window is computationally expensive, enabling to scan only at the vicinity of genes of interest. The second idea was to remedy the inefficiency of the previous approach by constructing a pipeline that consists of inverse RNA folding considering RNA secondary structure, followed by a BLAST search that is sequence-based and highly efficient. This approach, which relies on inverse RNA folding in general and our own in-house fragment-based inverse RNA folding program called RNAfbinv in particular, shows capability to find attractive candidates that are missed by Infernal and other standard methods being used for riboswitch detection. We demonstrate attractive candidates found by both the moving-window approach and the inverse RNA folding approach performed together with BLAST. We conclude that structure-based methods like the two strategies outlined above hold considerable promise in detecting riboswitches and other conserved RNAs of functional importance in a variety of organisms.

Keywords: riboswitches, RNA folding prediction, RNA structure, structure-based methods

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Authors: Mostafa Shahriari, Sergio Rojas, David Pardo, Angel Rodriguez- Rozas, Shaaban A. Bakr, Victor M. Calo, Ignacio Muga

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Logging-While Drilling (LWD) is a technique to record down-hole logging measurements while drilling the well. Nowadays, LWD devices (e.g., nuclear, sonic, resistivity) are mostly used commercially for geo-steering applications. Modern borehole resistivity tools are able to measure all components of the magnetic field by incorporating tilted coils. The depth of investigation of LWD tools is limited compared to the thickness of the geological layers. Thus, it is a common practice to approximate the Earth’s subsurface with a sequence of 1D models. For a 1D model, we can reduce the dimensionality of the problem using a Hankel transform. We can solve the resulting system of ordinary differential equations (ODEs) either (a) analytically, which results in a so-called semi-analytic method after performing a numerical inverse Hankel transform, or (b) numerically. Semi-analytic methods are used by the industry due to their high performance. However, they have major limitations, namely: -The analytical solution of the aforementioned system of ODEs exists only for piecewise constant resistivity distributions. For arbitrary resistivity distributions, the solution of the system of ODEs is unknown by today’s knowledge. -In geo-steering, we need to solve inverse problems with respect to the inversion variables (e.g., the constant resistivity value of each layer and bed boundary positions) using a gradient-based inversion method. Thus, we need to compute the corresponding derivatives. However, the analytical derivatives of cross-bedded formation and the analytical derivatives with respect to the bed boundary positions have not been published to the best of our knowledge. The main contribution of this work is to overcome the aforementioned limitations of semi-analytic methods by solving each 1D model (associated with each Hankel mode) using an efficient multi-scale finite element method. The main idea is to divide our computations into two parts: (a) offline computations, which are independent of the tool positions and we precompute only once and use them for all logging positions, and (b) online computations, which depend upon the logging position. With the above method, (a) we can consider arbitrary resistivity distributions along the 1D model, and (b) we can easily and rapidly compute the derivatives with respect to any inversion variable at a negligible additional cost by using an adjoint state formulation. Although the proposed method is slower than semi-analytic methods, its computational efficiency is still high. In the presentation, we shall derive the mathematical variational formulation, describe the proposed multi-scale finite element method, and verify the accuracy and efficiency of our method by performing a wide range of numerical experiments and comparing the numerical solutions to semi-analytic ones when the latest are available.

Keywords: logging-While-Drilling, resistivity measurements, multi-scale finite elements, Hankel transform

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Authors: Chaitanya H. Acharya, Pavan Kumar P., Gopalakrishna Narayana

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In the realm of aerodynamics, numerous vehicles incorporate moving components to enhance their performance. For instance, airliners deploy hydraulically operated flaps and ailerons during take-off and landing, while Formula 1 racing cars utilize hydraulic tubes and actuators for various components, including the Drag Reduction System (DRS). The DRS, consisting of a rear wing and adjustable flaps, plays a crucial role in overtaking manoeuvres. The DRS has two positions: the default position with the flaps down, providing high downforce, and the lifted position, which reduces drag, allowing for increased speed and aiding in overtaking. Swift deployment of the DRS during races is essential for overtaking competitors. The fluid flow over the rear wing flap becomes intricate during deployment, involving flow reversal and operational changes, leading to unsteady flow physics that significantly influence aerodynamic characteristics. Understanding the drag and downforce during DRS deployment is crucial for determining race outcomes. While experiments can yield accurate aerodynamic data, they can be expensive and challenging to conduct across varying speeds. Computational Fluid Dynamics (CFD) emerges as a cost-effective solution to predict drag and downforce across a range of speeds, especially with the rapid deployment of the DRS. This study employs the finite volume-based solver Ansys Fluent, incorporating dynamic mesh motions and a turbulent model to capture the complex flow phenomena associated with the moving rear wing flap. A dedicated section for the rare wing-flap is considered in the present simulations, and the aerodynamics of these sections closely resemble S1223 aerofoils. Before delving into the simulations of the rare wing-flap aerofoil, numerical results undergo validation using experimental data from an NLR flap aerofoil case, encompassing different flap angles at two distinct angles of attack was carried out. The increase in flap angle as increase in lift and drag is observed for a given angle of attack. The simulation methodology for the rare-wing-flap aerofoil case involves specific time durations before lifting the flap. During this period, drag and downforce values are determined as 330 N and 1800N, respectively. Following the flap lift, a noteworthy reduction in drag to 55 % and a decrease in downforce to 17 % are observed. This understanding is critical for making instantaneous decisions regarding the deployment of the Drag Reduction System (DRS) at specific speeds, thereby influencing the overall performance of the Formula 1 racing car. Hence, this work emphasizes the utilization of dynamic mesh motion methodology to predict the aerodynamic characteristics during the deployment of the DRS in a Formula 1 racing car.

Keywords: DRS, CFD, drag, downforce, dynamics mesh motion

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Authors: Simona Maria Ignat

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Bullying online has been an increasing studied topic during the last years. Different approaches, psychological, linguistic, or computational, have been applied. To our best knowledge, a definition and a set of characteristics of phenomenon agreed internationally as a common framework are still waiting for answers. Thus, the objectives of this paper are the identification of bullying utterances on Twitter and their algorithms. This research paper is focused on the identification of words or groups of words, categorized as “utterances”, with bullying effect, from Twitter platform, extracted on a set of legislative criteria. This set is the result of analysis followed by synthesis of law documents on bullying(online) from United States of America, European Union, and Ireland. The outcome is a linguistic corpus with approximatively 10,000 entries. The methods applied to the first objective have been the following. The discourse analysis has been applied in identification of keywords with bullying effect in texts from Google search engine, Images link. Transcription and anonymization have been applied on texts grouped in CL1 (Corpus linguistics 1). The keywords search method and the legislative criteria have been used for identifying bullying utterances from Twitter. The texts with at least 30 representations on Twitter have been grouped. They form the second corpus linguistics, Bullying utterances from Twitter (CL2). The entries have been identified by using the legislative criteria on the the BoW method principle. The BoW is a method of extracting words or group of words with same meaning in any context. The methods applied for reaching the second objective is the conversion of parts of speech to alphabetical and numerical symbols and writing the bullying utterances as algorithms. The converted form of parts of speech has been chosen on the criterion of relevance within bullying message. The inductive reasoning approach has been applied in sampling and identifying the algorithms. The results are groups with interchangeable elements. The outcomes convey two aspects of bullying: the form and the content or meaning. The form conveys the intentional intimidation against somebody, expressed at the level of texts by grammatical and lexical marks. This outcome has applicability in the forensic linguistics for establishing the intentionality of an action. Another outcome of form is a complex of graphemic variations essential in detecting harmful texts online. This research enriches the lexicon already known on the topic. The second aspect, the content, revealed the topics like threat, harassment, assault, or suicide. They are subcategories of a broader harmful content which is a constant concern for task forces and legislators at national and international levels. These topic – outcomes of the dataset are a valuable source of detection. The analysis of content revealed algorithms and lexicons which could be applied to other harmful contents. A third outcome of content are the conveyances of Stylistics, which is a rich source of discourse analysis of social media platforms. In conclusion, this corpus linguistics is structured on legislative criteria and could be used in various fields.

Keywords: corpus linguistics, cyberbullying, legislation, natural language processing, twitter

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Authors: Mehrnaz Mostafavi

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The assessment and categorization of incidental lung nodules present a considerable challenge in healthcare, often necessitating resource-intensive multiple computed tomography (CT) scans for growth confirmation. This research addresses this issue by introducing a distinct computational approach leveraging radiomics and deep-learning methods. However, understanding local services is essential before implementing these advancements. With diverse tracking methods in place, there is a need for efficient and accurate identification approaches, especially in the context of managing lung nodules alongside pre-existing cancer scenarios. This study explores the integration of text-based algorithms in medical data curation, indicating their efficacy in conjunction with machine learning and deep-learning models for identifying lung nodules. Combining medical images with text data has demonstrated superior data retrieval compared to using each modality independently. While deep learning and text analysis show potential in detecting previously missed nodules, challenges persist, such as increased false positives. The presented research introduces a Structured-Query-Language (SQL) algorithm designed for identifying pulmonary nodules in a tertiary cancer center, externally validated at another hospital. Leveraging natural language processing (NLP) and machine learning, the algorithm categorizes lung nodule reports based on sentence features, aiming to facilitate research and assess clinical pathways. The hypothesis posits that the algorithm can accurately identify lung nodule CT scans and predict concerning nodule features using machine-learning classifiers. Through a retrospective observational study spanning a decade, CT scan reports were collected, and an algorithm was developed to extract and classify data. Results underscore the complexity of lung nodule cohorts in cancer centers, emphasizing the importance of careful evaluation before assuming a metastatic origin. The SQL and NLP algorithms demonstrated high accuracy in identifying lung nodule sentences, indicating potential for local service evaluation and research dataset creation. Machine-learning models exhibited strong accuracy in predicting concerning changes in lung nodule scan reports. While limitations include variability in disease group attribution, the potential for correlation rather than causality in clinical findings, and the need for further external validation, the algorithm's accuracy and potential to support clinical decision-making and healthcare automation represent a significant stride in lung nodule management and research.

Keywords: lung cancer diagnosis, structured-query-language (SQL), natural language processing (NLP), machine learning, CT scans

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Authors: Pawel Magryta, Mateusz Paszko

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In today's world, a big problem of individual mobility, especially in large urban areas, occurs. Commonly used grand way of transport such as buses, trains or cars do not fulfill their tasks, i.e. they are not able to meet the increasing mobility needs of the growing urban population. Additional to that, the limitations of civil infrastructure construction in the cities exist. Nowadays the most common idea is to transfer the part of urban transport on the level of air transport. However to do this, there is a need to develop an individual flying transport vehicle. The biggest problem occurring in this concept is the type of the propulsion system from which the vehicle will obtain a lifting force. Standard propeller drives appear to be too noisy. One of the ideas is to provide the required take-off and flight power by the machine using the innovative ejector system. This kind of the system will be designed through a suitable choice of the three-dimensional geometric structure with special shape of nozzle in order to generate overpressure. The authors idea is to make a device that would allow to cumulate the overpressure using the a five-sided geometrical structure that will be limited on the one side by the blowing flow of air jet. In order to test this hypothesis a computer simulation study of aerodynamic drag of such 3D structures have been made. Based on the results of these studies, the tests on real model were also performed. The final stage of work was a comparative analysis of the results of simulation and real tests. The CFD simulation studies of air flow was conducted using the Star CD - Star Pro 3.2 software. The design of virtual model was made using the Catia v5 software. Apart from the objective to obtain advanced aviation propulsion system, all of the tests and modifications of 3D structures were also aimed at achieving high efficiency of this device while maintaining the ability to generate high value of overpressures. This was possible only in case of a large mass flow rate of air. All these aspects have been possible to verify using CFD methods for observing the flow of the working medium in the tested model. During the simulation tests, the distribution and size of pressure and velocity vectors were analyzed. Simulations were made with different boundary conditions (supply air pressure), but with a fixed external conditions (ambient temp., ambient pressure, etc.). The maximum value of obtained overpressure is 2 kPa. This value is too low to exploit the power of this device for the individual transport vehicle. Both the simulation model and real object shows a linear dependence of the overpressure values obtained from the different geometrical parameters of three-dimensional structures. Application of computational software greatly simplifies and streamlines the design and simulation capabilities. This work has been financed by the Polish Ministry of Science and Higher Education.

Keywords: aviation propulsion, CFD, 3d structure, aerodynamic drag

Procedia PDF Downloads 285

Authors: Mohkammad Nur Cahyadi, Imam Wahyu Farid, Ronny Mardianto, Agung Budi Cahyono, Eko Yuli Handoko, Daud Wahyu Imani, Arizal Bawazir, Luki Adi Triawan

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Unmanned aerial vehicle (UAV) technology has cost efficiency and data retrieval time advantages. Using technologies such as UAV, GNSS, and LiDAR will later be combined into one of the newest technologies to cover each other's deficiencies. This integration system aims to increase the accuracy of calculating the volume of the land stockpile of PT. Garam (Salt Company). The use of UAV applications to obtain geometric data and capture textures that characterize the structure of objects. This study uses the Taror 650 Iron Man drone with four propellers, which can fly for 15 minutes. LiDAR can classify based on the number of image acquisitions processed in the software, utilizing photogrammetry and structural science principles from Motion point cloud technology. LiDAR can perform data acquisition that enables the creation of point clouds, three-dimensional models, Digital Surface Models, Contours, and orthomosaics with high accuracy. LiDAR has a drawback in the form of coordinate data positions that have local references. Therefore, researchers use GNSS, LiDAR, and drone multi-sensor technology to map the stockpile of salt on open land and warehouses every year, carried out by PT. Garam twice, where the previous process used terrestrial methods and manual calculations with sacks. Research with LiDAR needs to be combined with UAV to overcome data acquisition limitations because it only passes through the right and left sides of the object, mainly when applied to a salt stockpile. The UAV is flown to assist data acquisition with a wide coverage with the help of integration of the 200-gram LiDAR system so that the flying angle taken can be optimal during the flight process. Using LiDAR for low-cost mapping surveys will make it easier for surveyors and academics to obtain pretty accurate data at a more economical price. As a survey tool, LiDAR is included in a tool with a low price, around 999 USD; this device can produce detailed data. Therefore, to minimize the operational costs of using LiDAR, surveyors can use Low-Cost LiDAR, GNSS, and UAV at a price of around 638 USD. The data generated by this sensor is in the form of a visualization of an object shape made in three dimensions. This study aims to combine Low-Cost GPS measurements with Low-Cost LiDAR, which are processed using free user software. GPS Low Cost generates data in the form of position-determining latitude and longitude coordinates. The data generates X, Y, and Z values to help georeferencing process the detected object. This research will also produce LiDAR, which can detect objects, including the height of the entire environment in that location. The results of the data obtained are calibrated with pitch, roll, and yaw to get the vertical height of the existing contours. This study conducted an experimental process on the roof of a building with a radius of approximately 30 meters.

Keywords: LiDAR, unmanned aerial vehicle, low-cost GNSS, contour

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Authors: A. Odoom, A. Salama, H. Ibrahim

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Hydrogen is a clean source of energy for power production and transportation. The main source of hydrogen in this research is biodiesel. Glycerol also called glycerine is a by-product of biodiesel production by transesterification of vegetable oils and methanol. This is a reliable and environmentally-friendly source of hydrogen production than fossil fuels. A typical composition of crude glycerol comprises of glycerol, water, organic and inorganic salts, soap, methanol and small amounts of glycerides. Crude glycerol has limited industrial application due to its low purity thus, the usage of crude glycerol can significantly enhance the sustainability and production of biodiesel. Reforming techniques is an approach for hydrogen production mainly Steam Reforming (SR), Autothermal Reforming (ATR) and Partial Oxidation Reforming (POR). SR produces high hydrogen conversions and yield but is highly endothermic whereas POR is exothermic. On the downside, PO yields lower hydrogen as well as large amount of side reactions. ATR which is a fusion of partial oxidation reforming and steam reforming is thermally neutral because net reactor heat duty is zero. It has relatively high hydrogen yield, selectivity as well as limits coke formation. The complex chemical processes that take place during the production phases makes it relatively difficult to construct a reliable and robust numerical model. Numerical model is a tool to mimic reality and provide insight into the influence of the parameters. In this work, we introduce a finite volume numerical study for an 'in-house' lab-scale experiment of ATR. Previous numerical studies on this process have considered either using Comsol or nodal finite difference analysis. Since Comsol is a commercial package which is not readily available everywhere and lab-scale experiment can be considered well mixed in the radial direction. One spatial dimension suffices to capture the essential feature of ATR, in this work, we consider developing our own numerical approach using MATLAB. A continuum fixed bed reactor is modelled using MATLAB with both pseudo homogeneous and heterogeneous models. The drawback of nodal finite difference formulation is that it is not locally conservative which means that materials and momenta can be generated inside the domain as an artifact of the discretization. Control volume, on the other hand, is locally conservative and suites very well problems where materials are generated and consumed inside the domain. In this work, species mass balance, Darcy’s equation and energy equations are solved using operator splitting technique. Therefore, diffusion-like terms are discretized implicitly while advection-like terms are discretized explicitly. An upwind scheme is adapted for the advection term to ensure accuracy and positivity. Comparisons with the experimental data show very good agreements which build confidence in our modeling approach. The models obtained were validated and optimized for better results.

Keywords: autothermal reforming, crude glycerol, hydrogen, numerical model

Procedia PDF Downloads 121

Authors: Roberto de Lieto Vollaro, Emanuele de Lieto Vollaro, Gianluca Coltrinari

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The building sector is responsible, in many industrialized countries, for about 40% of the total energy requirements, so it seems necessary to devote some efforts in this area in order to achieve a significant reduction of energy consumption and of greenhouse gases emissions. The paper presents a study aiming at providing a design methodology able to identify the best configuration of the system building/plant, from a technical, economic and environmentally point of view. Normally, the classical approach involves a building's energy loads analysis under steady state conditions, and subsequent selection of measures aimed at improving the energy performance, based on previous experience made by architects and engineers in the design team. Instead, the proposed approach uses a sequence of two well known scientifically validated calculation methods (TRNSYS and RETScreen), that allow quite a detailed feasibility analysis. To assess the validity of the calculation model, an existing, historical building in Central Italy, that will be the object of restoration and preservative redevelopment, was selected as a case-study. The building is made of a basement and three floors, with a total floor area of about 3,000 square meters. The first step has been the determination of the heating and cooling energy loads of the building in a dynamic regime by means of TRNSYS, which allows to simulate the real energy needs of the building in function of its use. Traditional methodologies, based as they are on steady-state conditions, cannot faithfully reproduce the effects of varying climatic conditions and of inertial properties of the structure. With TRNSYS it is possible to obtain quite accurate and reliable results, that allow to identify effective combinations building-HVAC system. The second step has consisted of using output data obtained with TRNSYS as input to the calculation model RETScreen, which enables to compare different system configurations from the energy, environmental and financial point of view, with an analysis of investment, and operation and maintenance costs, so allowing to determine the economic benefit of possible interventions. The classical methodology often leads to the choice of conventional plant systems, while RETScreen provides a financial-economic assessment for innovative energy systems and low environmental impact. Computational analysis can help in the design phase, particularly in the case of complex structures with centralized plant systems, by comparing the data returned by the calculation model RETScreen for different design options. For example, the analysis performed on the building, taken as a case study, found that the most suitable plant solution, taking into account technical, economic and environmental aspects, is the one based on a CCHP system (Combined Cooling, Heating, and Power) using an internal combustion engine.

Keywords: energy, system, building, cooling, electrical

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Authors: Oibar Martinez, Clara Oliver, Jose Miguel Miranda

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In this paper, we discuss the standard improvements which can be made to reduce the earth resistance in difficult terrains for optimum lightning protection, what are the practical limitations, and how the modeling can be refined for accurate diagnostics and ground resistance minimization. Ground resistance minimization can be made via three different approaches: burying vertical electrodes connected in parallel, burying horizontal conductive plates or meshes, or modifying the own terrain, either by changing the entire terrain material in a large volume or by adding earth-enhancing compounds. The use of vertical electrodes connected in parallel pose several practical limitations. In order to prevent loss of effectiveness, it is necessary to keep a minimum distance between each electrode, which is typically around five times larger than the electrode length. Otherwise, the overlapping of the local equipotential lines around each electrode reduces the efficiency of the configuration. The addition of parallel electrodes reduces the resistance and facilitates the measurement, but the basic parallel resistor formula of circuit theory will always underestimate the final resistance. Numerical simulation of equipotential lines around the electrodes overcomes this limitation. The resistance of a single electrode will always be proportional to the soil resistivity. The electrodes are usually installed with a backfilling material of high conductivity, which increases the effective diameter. However, the improvement is marginal, since the electrode diameter counts in the estimation of the ground resistance via a logarithmic function. Substances that are used for efficient chemical treatment must be environmentally friendly and must feature stability, high hygroscopicity, low corrosivity, and high electrical conductivity. A number of earth enhancement materials are commercially available. Many are comprised of carbon-based materials or clays like bentonite. These materials can also be used as backfilling materials to reduce the resistance of an electrode. Chemical treatment of soil has environmental issues. Some products contain copper sulfate or other copper-based compounds, which may not be environmentally friendly. Carbon-based compounds are relatively inexpensive and they do have very low resistivities, but they also feature corrosion issues. Typically, the carbon can corrode and destroy a copper electrode in around five years. These compounds also have potential environmental concerns. Some earthing enhancement materials contain cement, which, after installation acquire properties that are very close to concrete. This prevents the earthing enhancement material from leaching into the soil. After analyzing different configurations, we conclude that a buried conductive ring with vertical electrodes connected periodically should be the optimum baseline solution for the grounding of a large size structure installed on a large resistivity terrain. In order to show this, a practical example is explained here where we simulate the ground resistance of a conductive ring buried in a terrain with a resistivity in the range of 1 kOhm·m.

Keywords: grounding improvements, large scale scientific instrument, lightning risk assessment, lightning standards

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Authors: Gabriel Malgaresi, Sara Borazjani, Hadi Madani, Pavel Bedrikovetsky

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Suspension-Colloidal flow in porous media occurs in numerous engineering fields, such as industrial water treatment, the disposal of industrial wastes into aquifers with the propagation of contaminants and low salinity water injection into petroleum reservoirs. The main effects are particle mobilization and captured by the porous rock, which can cause pore plugging and permeability reduction which is known as formation damage. Various factors such as fluid salinity, pH, temperature, and rock properties affect particle detachment. Formation damage is unfavorable specifically near injection and production wells. One way to control formation damage is pre-treatment of the rock with nanoparticles. Adsorption of nanoparticles on fines and rock surfaces alters zeta-potential of the surfaces and enhances the attachment force between the rock and fine particles. The main objective of this study is to develop a two-stage mathematical model for (1) flow and adsorption of nanoparticles on the rock in the pre-treatment stage and (2) fines migration and permeability reduction during the water production after the pre-treatment. The model accounts for adsorption and desorption of nanoparticles, fines migration, and kinetics of particle capture. The system of equations allows for the exact solution. The non-self-similar wave-interaction problem was solved by the Method of Characteristics. The analytical model is new in two ways: First, it accounts for the specific boundary and initial condition describing the injection of nanoparticle and production from the pre-treated porous media; second, it contains the effect of nanoparticle sorption hysteresis. The derived analytical model contains explicit formulae for the concentration fronts along with pressure drop. The solution is used to determine the optimal injection concentration of nanoparticle to avoid formation damage. The mathematical model was validated via an innovative laboratory program. The laboratory study includes two sets of core-flood experiments: (1) production of water without nanoparticle pre-treatment; (2) pre-treatment of a similar core with nanoparticles followed by water production. Positively-charged Alumina nanoparticles with the average particle size of 100 nm were used for the rock pre-treatment. The core was saturated with the nanoparticles and then flushed with low salinity water; pressure drop across the core and the outlet fine concentration was monitored and used for model validation. The results of the analytical modeling showed a significant reduction in the fine outlet concentration and formation damage. This observation was in great agreement with the results of core-flood data. The exact solution accurately describes fines particle breakthroughs and evaluates the positive effect of nanoparticles in formation damage. We show that the adsorbed concentration of nanoparticle highly affects the permeability of the porous media. For the laboratory case presented, the reduction of permeability after 1 PVI production in the pre-treated scenario is 50% lower than the reference case. The main outcome of this study is to provide a validated mathematical model to evaluate the effect of nanoparticles on formation damage.

Keywords: nano-particles, formation damage, permeability, fines migration

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Authors: Andrzej Szewczak

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Study of the effect of high temperatures on polymer coatings represents an important field of research of their properties. Polymers, as materials with numerous features (chemical resistance, ease of processing and recycling, corrosion resistance, low density and weight) are currently the most widely used modern building materials, among others in the resin concrete, plastic parts, and hydrophobic coatings. Unfortunately, the polymers have also disadvantages, one of which decides about their usage - low resistance to high temperatures and brittleness. This applies in particular thin and flexible polymeric coatings applied to other materials, such a steel and concrete, which degrade under varying thermal conditions. Research about improvement of this state includes methods of modification of the polymer composition, structure, conditioning conditions, and the polymerization reaction. At present, ways are sought to reflect the actual environmental conditions, in which the coating will be operating after it has been applied to other material. These studies are difficult because of the need for adopting a proper model of the polymer operation and the determination of phenomena occurring at the time of temperature fluctuations. For this reason, alternative methods are being developed, taking into account the rapid modeling and the simulation of the actual operating conditions of polymeric coating’s materials in real conditions. The nature of a duration is typical for the temperature influence in the environment. Studies typically involve the measurement of variation one or more physical and mechanical properties of such coating in time. Based on these results it is possible to determine the effects of temperature loading and develop methods affecting in the improvement of coatings’ properties. This paper contains a description of the stability studies of silicone coatings deposited on the surface of a ceramic brick. The brick’s surface was hydrophobized by two types of inorganic polymers: nano-polymer preparation based on dialkyl siloxanes (Series 1 - 5) and an aqueous solution of the silicon (series 6 - 10). In order to enhance the stability of the film formed on the brick’s surface and immunize it to variable temperature and humidity loading, the nano silica was added to the polymer. The right combination of the polymer liquid phase and the solid phase of nano silica was obtained by disintegration of the mixture by the sonification. The changes of viscosity and surface tension of polymers were defined, which are the basic rheological parameters affecting the state and the durability of the polymer coating. The coatings created on the brick’s surfaces were then subjected to a temperature loading of 100° C and moisture by total immersion in water, in order to determine any water absorption changes caused by damages and the degradation of the polymer film. The effect of moisture and temperature was determined by measurement (at specified number of cycles) of changes in the surface hardness (using a Vickers’ method) and the absorption of individual samples. As a result, on the basis of the obtained results, the degradation process of polymer coatings related to their durability changes in time was determined.

Keywords: silicones, siloxanes, surface hardness, temperature, water absorption

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Authors: Julia Ive, Lucia Specia

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Understanding and detecting toxicity is an important problem to support safer human interactions online. Our work focuses on the important problem of contextual toxicity detection, where automated classifiers are tasked with determining whether a short textual segment (usually a sentence) is toxic within its conversational context. We use “toxicity” as an umbrella term to denote a number of variants commonly named in the literature, including hate, abuse, offence, among others. Detecting toxicity in context is a non-trivial problem and has been addressed by very few previous studies. These previous studies have analysed the influence of conversational context in human perception of toxicity in controlled experiments and concluded that humans rarely change their judgements in the presence of context. They have also evaluated contextual detection models based on state-of-the-art Deep Learning and Natural Language Processing (NLP) techniques. Counterintuitively, they reached the general conclusion that computational models tend to suffer performance degradation in the presence of context. We challenge these empirical observations by devising better contextual predictive models that also rely on NLP data augmentation techniques to create larger and better data. In our study, we start by further analysing the human perception of toxicity in conversational data (i.e., tweets), in the absence versus presence of context, in this case, previous tweets in the same conversational thread. We observed that the conclusions of previous work on human perception are mainly due to data issues: The contextual data available does not provide sufficient evidence that context is indeed important (even for humans). The data problem is common in current toxicity datasets: cases labelled as toxic are either obviously toxic (i.e., overt toxicity with swear, racist, etc. words), and thus context does is not needed for a decision, or are ambiguous, vague or unclear even in the presence of context; in addition, the data contains labeling inconsistencies. To address this problem, we propose to automatically generate contextual samples where toxicity is not obvious (i.e., covert cases) without context or where different contexts can lead to different toxicity judgements for the same tweet. We generate toxic and non-toxic utterances conditioned on the context or on target tweets using a range of techniques for controlled text generation(e.g., Generative Adversarial Networks and steering techniques). On the contextual detection models, we posit that their poor performance is due to limitations on both of the data they are trained on (same problems stated above) and the architectures they use, which are not able to leverage context in effective ways. To improve on that, we propose text classification architectures that take the hierarchy of conversational utterances into account. In experiments benchmarking ours against previous models on existing and automatically generated data, we show that both data and architectural choices are very important. Our model achieves substantial performance improvements as compared to the baselines that are non-contextual or contextual but agnostic of the conversation structure.

Keywords: contextual toxicity detection, data augmentation, hierarchical text classification models, natural language processing

Procedia PDF Downloads 143

Authors: Daniel Martin Fellows, Sean P. Walton, Jennifer Thompson, Oubay Hassan, Ella Quigley, Kevin Tinkham

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Fire performance is a crucial aspect to consider when designing cladding products, and testing this performance is extremely expensive. Appropriate use of numerical simulation of fire performance has the potential to reduce the total number of fire tests required when designing a product by eliminating poor-performing design ideas early in the design phase. Due to the complexity of fire and the large spectrum of failures it can cause, multi-disciplinary models are needed to capture the complex fire behavior and its structural effects on its surroundings. Working alongside Tata Steel U.K., the authors have focused on completing a coupled CFD-FEA simulation model suited to test Polyisocyanurate (PIR) based sandwich panel products to gain confidence before costly experimental standards testing. The sandwich panels are part of a thermally insulating façade system primarily for large non-domestic buildings. The work presented in this paper compares two coupling methodologies of a replicated physical experimental standards test LPS 1181-1, carried out by Tata Steel U.K. The two coupling methodologies that are considered within this research are; one-way and two-way. A one-way coupled analysis consists of importing thermal data from the CFD solver into the FEA solver. A two-way coupling analysis consists of continuously importing the updated changes in thermal data, due to the fire's behavior, to the FEA solver throughout the simulation. Likewise, the mechanical changes will also be updated back to the CFD solver to include geometric changes within the solution. For CFD calculations, a solver called Fire Dynamic Simulator (FDS) has been chosen due to its adapted numerical scheme to focus solely on fire problems. Validation of FDS applicability has been achieved in past benchmark cases. In addition, an FEA solver called ABAQUS has been chosen to model the structural response to the fire due to its crushable foam plasticity model, which can accurately model the compressibility of PIR foam. An open-source code called FDS-2-ABAQUS is used to couple the two solvers together, using several python modules to complete the process, including failure checks. The coupling methodologies and experimental data acquired from Tata Steel U.K are compared using several variables. The comparison data includes; gas temperatures, surface temperatures, and mechanical deformation of the panels. Conclusions are drawn, noting improvements to be made on the current coupling open-source code FDS-2-ABAQUS to make it more applicable to Tata Steel U.K sandwich panel products. Future directions for reducing the computational cost of the simulation are also considered.

Keywords: fire engineering, numerical coupling, sandwich panels, thermo fluids

Procedia PDF Downloads 66

Authors: Ntandazo Dlatu, Benjamin Longo-Mbenza, Andre Renzaho, Ruffin Appalata, Yolande Yvonne Valeria Matoumona Mavoungou, Mbenza Ben Longo, Kenneth Ekoru, Blaise Makoso, Gedeon Longo Longo

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Background: The gap between complexities related to the integration of Tuberculosis /HIV control and evidence-based knowledge motivated the initiation of the study. Therefore, the objective of this study was to explore correlations between national TB management guidelines, multiple deprivation indexes, quantiles, components and levels of Tuberculosis control programme using mathematical modeling in rural O.R. Tambo District Health Facilities, South Africa. Methods: The study design used mixed secondary data analysis and cross-sectional analysis between 2009 and 2013 across O.R Tambo District, Eastern Cape, South Africa using univariate/ bivariate analysis, linear multiple regression models, and multivariate discriminant analysis. Health inequalities indicators and component of an impediment to the tuberculosis control programme were evaluated. Results: In total, 62 400 records for TB notification were analyzed for the period 2009-2013. There was a significant but negative between Financial Year Expenditure (r= -0.894; P= 0.041) Seropositive HIV status(r= -0.979; P= 0.004), Population Density (r = -0.881; P= 0.048) and the number of TB defaulter in all TB cases. It was shown unsuccessful control of TB management program through correlations between numbers of new PTB smear positive, TB defaulter new smear-positive, TB failure all TB, Pulmonary Tuberculosis case finding index and deprivation-concentration-dispersion index. It was shown successful TB program control through significant and negative associations between declining numbers of death in co-infection of HIV and TB, TB deaths all TB and SMIAD gradient/ deprivation-concentration-dispersion index. The multivariate linear model was summarized by unadjusted r of 96%, adjusted R2 of 95 %, Standard Error of estimate of 0.110, R2 changed of 0.959 and significance for variance change for P=0.004 to explain the prediction of TB defaulter in all TB with equation y= 8.558-0.979 x number of HIV seropositive. After adjusting for confounding factors (PTB case finding the index, TB defaulter new smear-positive, TB death in all TB, TB defaulter all TB, and TB failure in all TB). The HIV and TB death, as well as new PTB smear positive, were identified as the most important, significant, and independent indicator to discriminate most deprived deprivation index far from other deprivation quintiles 2-5 using discriminant analysis. Conclusion: Elimination of poverty such as overcrowding, lack of sanitation and environment of highest burden of HIV might end the TB threat in O.R Tambo District, Eastern Cape, South Africa. Furthermore, ongoing adequate budget comprehensive, holistic and collaborative initiative towards Sustainable Developmental Goals (SDGs) is necessary for complete elimination of TB in poor O.R Tambo District.

Keywords: tuberculosis, HIV/AIDS, success, failure, control program, health inequalities, South Africa

Procedia PDF Downloads 139

Authors: Muhammad Fayyaz Nazir, Ellen Wayenberg, Shahzadaah Faahed Qureshi

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Introduction: The study is based on the Contextual Interaction Theory (CIT) constructs to explore the role of policy actors in implementing the COVID-19 Disease Prevention and Control (DP&C) Policy. The study analyzes the role of healthcare workers' contextual factors, such as cognition, motives, and resources, and their interactions in implementing Social Distancing (SD). In this way, we test a two actors policy implementation theory, i.e., the CIT in a three-actor context. Methods: Data was collected through document analysis and semi-structured interviews. For a qualitative study design, interviews were conducted with questions on cognition, motives, and resources from the healthcare workers involved in implementing SD in the local context in Multan – Pakistan. The possible interactions resulting from contextual factors of the policy actors – healthcare workers were identified through framework analysis protocol guided by CIT and supported by trustworthiness criterion and data saturation. Results: This inquiry resulted in theory application, addition, and enrichment. The theoretical application in the three actor's contexts illustrates the different levels of motives, cognition, and resources of healthcare workers – senior administrators, managers, and healthcare professionals. The senior administrators working in National Command and Operations Center (NCOC), Provincial Technical Committees (PTCs), and Districts Covid Teams (DCTs) were playing their role with high motivation. They were fully informed about the policy and moderately resourceful. The policy implementors: healthcare managers working on implementing the SD within their respective hospitals were playing their role with high motivation and were fully informed about the policy. However, they lacked the required resources to implement SD. The target medical and allied healthcare professionals were moderately motivated but lack of resources and information. The interaction resulted in cooperation and the need for learning to manage the future healthcare crisis. However, the lack of resources created opposition to the implementation of SD. Objectives of the Study: The study aimed to apply a two actors theory in a multi actors context. We take this as an opportunity to qualitatively test the theory in a novel situation of the Covid-19 pandemic and make way for its quantitative application by designing a survey instrument so that implementation researchers can apply CIT through multivariate analyses or higher-order statistical modeling. Conclusion: Applying two actors' implementation theory in exploring a complex case of healthcare intervention in three actors context is a unique work that has never been done before, up to the best of our knowledge. So, the work will contribute to the policy implementation studies by applying, extending, and enriching an implementation theory in a novel case of the Covi-19 pandemic, ultimately fulfilling the gap in implementation literature. Policy institutions and other low or middle-income countries can learn from this research and improve SD implementation by working on the variables with weak significance levels.

Keywords: COVID-19, disease prevention and control policy, implementation, policy actors, social distancing

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Authors: R. Yuan, S. Sivasankaran, N. Dutta, K. Ebrahimi

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This paper investigates the software capability and computer-aided engineering (CAE) method of modelling transient heat transfer process occurred in the vehicle underhood region during vehicle thermal soak phase. The heat retention from the soak period will be beneficial to the cold start with reduced friction loss for the second 14°C worldwide harmonized light-duty vehicle test procedure (WLTP) cycle, therefore provides benefits on both CO₂ emission reduction and fuel economy. When vehicle undergoes soak stage, the airflow and the associated convective heat transfer around and inside the engine bay is driven by the buoyancy effect. This effect along with thermal radiation and conduction are the key factors to the thermal simulation of the engine bay to obtain the accurate fluids and metal temperature cool-down trajectories and to predict the temperatures at the end of the soak period. Method development has been investigated in this study on a light-duty passenger vehicle using coupled aerodynamic-heat transfer thermal transient modelling method for the full vehicle under 9 hours of thermal soak. The 3D underhood flow dynamics were solved inherently transient by the Lattice-Boltzmann Method (LBM) method using the PowerFlow software. This was further coupled with heat transfer modelling using the PowerTHERM software provided by Exa Corporation. The particle-based LBM method was capable of accurately handling extremely complicated transient flow behavior on complex surface geometries. The detailed thermal modelling, including heat conduction, radiation, and buoyancy-driven heat convection, were integrated solved by PowerTHERM. The 9 hours cool-down period was simulated and compared with the vehicle testing data of the key fluid (coolant, oil) and metal temperatures. The developed CAE method was able to predict the cool-down behaviour of the key fluids and components in agreement with the experimental data and also visualised the air leakage paths and thermal retention around the engine bay. The cool-down trajectories of the key components obtained for the 9 hours thermal soak period provide vital information and a basis for the further development of reduced-order modelling studies in future work. This allows a fast-running model to be developed and be further imbedded with the holistic study of vehicle energy modelling and thermal management. It is also found that the buoyancy effect plays an important part at the first stage of the 9 hours soak and the flow development during this stage is vital to accurately predict the heat transfer coefficients for the heat retention modelling. The developed method has demonstrated the software integration for simulating buoyancy-driven heat transfer in a vehicle underhood region during thermal soak with satisfying accuracy and efficient computing time. The CAE method developed will allow integration of the design of engine encapsulations for improving fuel consumption and reducing CO₂ emissions in a timely and robust manner, aiding the development of low-carbon transport technologies.

Keywords: ATCT/WLTC driving cycle, buoyancy-driven heat transfer, CAE method, heat retention, underhood modeling, vehicle thermal soak

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Authors: Md. Afroz Ansari

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The Bengal basin is a spectacular example of a peripheral foreland basin formed by the convergence of the Indian plate beneath the Eurasian and Burmese plates. The basin is embraced on three sides; north, west and east by different fault-controlled tectonic features whereas released in the south where the rivers are drained into the Bay of Bengal. The Bengal basin in the eastern part of the Indian subcontinent constitutes the largest fluvio-deltaic to shallow marine sedimentary basin in the world today. This continental basin coupled with the offshore Bengal Fan under the Bay of Bengal forms the biggest sediment dispersal system. The continental basin is continuously receiving the sediments by the two major rivers Ganga and Brahmaputra (known as Jamuna in Bengal), and Meghna (emerging from the point of conflux of the Ganga and Brahmaputra) and large number of rain-fed, small tributaries originating from the eastern Indian Shield. The drained sediments are ultimately delivered into the Bengal fan. The significance of the present study is to delineate the variations in thicknesses of the sediments, different crustal structures, and the mantle lithosphere throughout the onshore-offshore Bengal basin. In the present study, the different crustal/geological units and the shallower mantle lithosphere were delineated by analyzing the Bouguer Gravity Anomaly (BGA) data along two long traverses South-North (running from Bengal fan cutting across the transition offshore-onshore of the Bengal basin and intersecting the Main Frontal Thrust of India-Himalaya collision zone in Sikkim-Bhutan Himalaya) and West-East (running from the Peninsular Indian Shield across the Bengal basin to the Chittagong–Tripura Fold Belt). The BGA map was derived from the analysis of topex data after incorporating Bouguer correction and all terrain corrections. The anomaly map was compared with the available ground gravity data in the western Bengal basin and the sub-continents of India for consistency of the data used. Initially, the anisotropy associated with the thicknesses of the different crustal units, crustal interfaces and moho boundary was estimated through spectral analysis of the gravity data with varying window size over the study area. The 2D density sections along the traverses were finalized after a number of iterations with the acceptable root mean square (RMS) errors. The estimated thicknesses of the different crustal units and dips of the Moho boundary along both the profiles are consistent with the earlier results. Further the results were encouraged by examining the earthquake database and focal mechanism solutions for better understanding the geodynamics. The earthquake data were taken from the catalogue of US Geological Survey, and the focal mechanism solutions were compiled from the Harvard Centroid Moment Tensor Catalogue. The concentrations of seismic events at different depth levels are not uncommon. The occurrences of earthquakes may be due to stress accumulation as a result of resistance from three sides.

Keywords: anisotropy, interfaces, seismicity, spectrum analysis

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Authors: Cynthia Sau Chun Yip, Richard Fielding

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High meat and especially red meat intakes are significantly and positively associated with a multiple burden of diseases and also high greenhouse gas (GHG) emissions. This study investigated population meat intake patterns in Hong Kong. It quantified the burden of disease and GHG emission outcomes by modeling to adjust Hong Kong population meat intakes to recommended healthy levels. It compared age- and sex-specific population meat, fruit and vegetable intakes obtained from a population survey among adults aged 20 years and over in Hong Kong in 2005-2007, against intake recommendations suggested in the Modelling System to Inform the Revision of the Australian Guide to Healthy Eating (AGHE-2011-MS) technical document. This study found that meat and meat alternatives, especially red meat intakes among Hong Kong males aged 20+ years and over are significantly higher than recommended. Red meat intakes among females aged 50-69 years and other meat and alternatives intakes among aged 20-59 years are also higher than recommended. Taking the 2005-07 age- and sex-specific population meat intake as baselines, three counterfactual scenarios of adjusting Hong Kong adult population meat intakes to AGHE-2011-MS and Pre-2011 AGHE recommendations by the year 2030 were established. Consequent energy intake gaps were substituted with additional legume, fruit and vegetable intakes. To quantify the consequent GHG emission outcomes associated with Hong Kong meat intakes, Cradle-to-ready-to-eat lifecycle assessment emission outcome modelling was used. Comparative risk assessment of burden of disease model was used to quantify the health outcomes. This study found adjusting meat intakes to recommended levels could reduce Hong Kong GHG emission by 17%-44% when compared against baseline meat intake emissions, and prevent 2,519 to 7,012 premature deaths in males and 53 to 1,342 in females, as well as multiple burden of diseases when compared to the baseline meat intake scenario. Comparing lump sum meat intake reduction and outcome measures across the entire population, and using emission factors, and relative risks from individual studies in previous co-benefit studies, this study used age- and sex-specific input and output measures, emission factors and relative risks obtained from high quality meta-analysis and meta-review respectively, and has taken government dietary recommendations into account. Hence evaluations in this study are of better quality and more reflective of real life practices. Further to previous co-benefit studies, this study pinpointed age- and sex-specific population and meat-type-specific intervention points and leverages. When compared with similar studies in Australia, this study also showed that intervention points and leverages among populations in different geographic and cultural background could be different, and that globalization also globalizes meat consumption emission effects. More regional and cultural specific evaluations are recommended to promote more sustainable meat consumption and enhance global food security.

Keywords: burden of diseases, greenhouse gas emissions, Hong Kong diet, sustainable meat consumption

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Authors: Simran Baweja, Bhavika Kalal, Surajit Maity

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Photoinduced tautomerization reactions have been the centre of attention among scientific community over past several decades because of their significance in various biological systems. 7-azaindole (7AI) is considered as a model system for DNA base pairing and to understand the role of such tautomerization reactions in mutations. To the best of our knowledge, extensive studies have been carried on 7-azaindole and its solvent clusters exhibiting proton/ hydrogen transfer in both solution as well as gas phase. Derivatives of above molecule, like 2,7- and 2,6-diazaindoles are proposed to have even better photophysical properties due to the presence of -aza group on the 2nd position. However, there are a few studies in the solution phase which suggest the relevance of these molecules, but there are no experimental studies reported in the gas phase yet. In our current investigation, we present the first gas phase spectroscopic data of 2,7-diazaindole (2,7-DAI) and its solvent cluster (2,7-DAI-H2O). In this, we have employed state-of-the-art laser spectroscopic methods such as fluorescence excitation (LIF), dispersed fluorescence (DF), resonant two-photon ionization time of flight mass spectrometry (2C-R2PI), photoionization efficiency spectroscopy (PIE), IR-UV double resonance spectroscopy i.e. fluorescence-dip infrared spectroscopy (FDIR) and resonant ion-dip infrared spectroscopy (IDIR) to understand the electronic structure of the molecule. The origin band corresponding to S1 ← S0 transition of the bare 2,7-DAI is found to be positioned at 33910 cm-1 whereas the origin band corresponding to S1 ← S0 transition of the 2,7-DAI-H2O is positioned at 33074 cm-1. The red shifted transition in case of solvent cluster suggests the enhanced feasibility of excited state hydrogen/ proton transfer. The ionization potential for the 2,7-DAI molecule is found to be 8.92 eV, which is significantly higher that the previously reported 7AI (8.11 eV) molecule, making it a comparatively complex molecule to study. The ionization potential is reduced by 0.14 eV in case of 2,7-DAI-H2O (8.78 eV) cluster compared to that of 2,7-DAI. Moreover, on comparison with the available literature values of 7AI, we found the origin band of 2,7-DAI and 2,7-DAI-H2O to be red shifted by -729 and -280 cm-1 respectively. The ground and excited state N-H stretching frequencies of the 27DAI molecule were determined using fluorescence-dip infrared spectra (FDIR) and resonant ion dip infrared spectroscopy (IDIR), obtained at 3523 and 3467 cm-1, respectively. The lower value of vNH in the electronic excited state of 27DAI implies the higher acidity of the group compared to the ground state. Moreover, we have done extensive computational analysis, which suggests that the energy barrier in excited state reduces significantly as we increase the number of catalytic solvent molecules (S= H2O, NH3) as well as the polarity of solvent molecules. We found that the ammonia molecule is a better candidate for hydrogen transfer compared to water because of its higher gas-phase basicity. Further studies are underway to understand the excited state dynamics and photochemistry of such N-rich chromophores.

Keywords: photoinduced tautomerization reactions, gas phse spectroscopy, ), IR-UV double resonance spectroscopy, resonant two-photon ionization time of flight mass spectrometry (2C-R2PI)

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Authors: U. S. Putro, L. Mayangsari, M. Siallagan, N. P. Tjahyani

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National Collective Management Institute (LKMN) in Indonesia stated that legal music products were about 77.552.008 unit while illegal music products were about 22.0688.225 unit in 1996 and this number keeps getting worse every year. Consequently, Indonesia named as one of the countries with high piracy levels in 2005. This study models people decision toward unlawful behavior, music content piracy in particular, using agent-based modeling and simulation (ABMS). The classification of actors in the model constructed in this study are legal consumer, illegal consumer, and neutral consumer. The decision toward piracy among the actors is a manifestation of the social norm which attributes are social pressure, peer pressure, social approval, and perceived prevalence of piracy. The influencing attributes fluctuate depending on the majority of surrounding behavior called social network. There are two main interventions undertaken in the model, campaign and peer influence, which leads to scenarios in the simulation: positively-framed descriptive norm message, negatively-framed descriptive norm message, positively-framed injunctive norm with benefits message, and negatively-framed injunctive norm with costs message. Using NetLogo, the model is simulated in 30 runs with 10.000 iteration for each run. The initial number of agent was set 100 proportion of 95:5 for illegal consumption. The assumption of proportion is based on the data stated that 95% sales of music industry are pirated. The finding of this study is that negatively-framed descriptive norm message has a worse reversed effect toward music piracy. The study discovers that selecting the context-based campaign is the key process to reduce the level of intention toward music piracy as unlawful behavior by increasing the compliance awareness. The context of Indonesia reveals that that majority of people has actively engaged in music piracy as unlawful behavior, so that people think that this illegal act is common behavior. Therefore, providing the information about how widespread and big this problem is could make people do the illegal consumption behavior instead. The positively-framed descriptive norm message scenario works best to reduce music piracy numbers as it focuses on supporting positive behavior and subject to the right perception on this phenomenon. Music piracy is not merely economical, but rather social phenomenon due to the underlying motivation of the actors which has shifted toward community sharing. The indication of misconception of value co-creation in the context of music piracy in Indonesia is also discussed. This study contributes theoretically that understanding how social norm configures the behavior of decision-making process is essential to breakdown the phenomenon of unlawful behavior in music industry. In practice, this study proposes that reward-based and context-based strategy is the most relevant strategy for stakeholders in music industry. Furthermore, this study provides an opportunity that findings may generalize well beyond music piracy context. As an emerging body of work that systematically constructs the backstage of law and social affect decision-making process, it is interesting to see how the model is implemented in other decision-behavior related situation.

Keywords: music piracy, social norm, behavioral decision-making, agent-based model, value co-creation

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