Search results for: generative models

Authors: Paulo Cesar Pereira Junior, Alexandre Monteiro, Rafael da Luz Ribeiro, Antonio Carlos Sobieranski, Aldo von Wangenheim

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In this paper, we present a comparison between convolutional neural networks and classical computer vision approaches, for the specific precision agriculture problem of weed mapping on sugarcane fields aerial images. A systematic literature review was conducted to find which computer vision methods are being used on this specific problem. The most cited methods were implemented, as well as four models of convolutional neural networks. All implemented approaches were tested using the same dataset, and their results were quantitatively and qualitatively analyzed. The obtained results were compared to a human expert made ground truth for validation. The results indicate that the convolutional neural networks present better precision and generalize better than the classical models.

Keywords: convolutional neural networks, deep learning, digital image processing, precision agriculture, semantic segmentation, unmanned aerial vehicles

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Authors: Yuxiang Pan, Yong Qiu, Chenlei Gu, Ping Wang

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In the past decade, three-dimensional (3D) tumor cell models have attracted increasing interest in the field of drug screening due to their great advantages in simulating more accurately the heterogeneous tumor behavior in vivo. Drug sensitivity testing based on 3D tumor cell models can provide more reliable in vivo efficacy prediction. The gold standard fluorescence staining is hard to achieve the real-time and label-free monitoring of the viability of 3D tumor cell models. In this study, micro-groove impedance sensor (MGIS) was specially developed for dynamic and non-invasive monitoring of 3D cell viability. 3D tumor cells were trapped in the micro-grooves with opposite gold electrodes for the in-situ impedance measurement. The change of live cell number would cause inversely proportional change to the impedance magnitude of the entire cell/matrigel to construct and reflect the proliferation and apoptosis of 3D cells. It was confirmed that 3D cell viability detected by the MGIS platform is highly consistent with the standard live/dead staining. Furthermore, the accuracy of MGIS platform was demonstrated quantitatively using 3D lung cancer model and sophisticated drug sensitivity testing. In addition, the parameters of micro-groove impedance chip processing and measurement experiments were optimized in details. The results demonstrated that the MGIS and 3D cell-based biosensor and would be a promising platform to improve the efficiency and accuracy of cell-based anti-cancer drug screening in vitro.

Keywords: micro-groove impedance sensor, 3D cell-based biosensors, 3D cell viability, micro-electromechanical systems

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Authors: Nurhayati Br Tarigan, Rizkita Rachmi Esyanti

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Stevia (Stevia rebaudiana Bertoni) has a great potential to be used as a natural sweetener because it contains steviol glycoside, which is approximately 100 - 300 times sweeter than sucrose, yet low calories. Vegetative and generative propagation of S. rebaudiana is inefficient to produce stevia biomass and stevioside. One of alternative for stevia propagation is in vitro shoot culture. This research was conducted to optimize the best medium for shoot growth and to compare the bioconversion efficiency and stevioside production of S. rebaudiana shoot culture cultivated in thin layer culture (TLC), recipient for automated temporary immersion system (TIS RITA®) bioreactor, and bubble column bioreactor. The result showed that 1 ppm of Kinetin produced a healthy shoot and the highest number of leaves compared to BAP. Shoots were then cultivated in TLC, TIS RITA® bioreactor, and bubble column bioreactor. Growth medium efficiency was determined by yield and productivity. TLC produced the highest growth medium efficiency of S. rebaudiana, the yield was 0.471 ± 0.117 gbiomass.gsubstrate-1, and the productivity was 0.599 ± 0.122 gbiomass.Lmedium-1.day-1. While TIS RITA® bioreactor produced the lowest yield and productivity, 0.182 ± 0.024 gbiomass.gsubstrate-1 and 0.041 ± 0.0002 gbiomass.Lmedium-1.day-1 respectively. The yield of bubble column bioreactor was 0.354 ± 0.204 gbiomass.gsubstrate-1 and the productivity was 0,099 ± 0,009 gbiomass.Lmedium-1.day-1. The stevioside content from the highest to the lowest was obtained from stevia shoot which was cultivated on TLC, TIS RITA® bioreactor, and bubble column bioreactor; the content was 93,44 μg/g, 42,57 μg/g, and 23,03 μg/g respectively. All three systems could be used to produce stevia shoot biomass, but optimization on the number of nutrition and oxygen intake was required in each system.

Keywords: bubble column, growth medium efficiency, Stevia rebaudiana, stevioside, TIS RITA®, TLC

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Authors: Danièle Peto

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A qualitative research based on twenty-eight semi-structured interviews of students in Social Work, in Brussels (Belgium), showed specific results for the Arab and Muslim students: second and third generations immigrants build their identity on the basis of a mix of differentiation with and recognition of their parents' culture of origin. Building a bridge between Modernity and Tradition, they claim active citizenship; at the same time they show and live by values and religious believes which reinforce the link to their parents’ origins. But they present those values and believes as their own rational choices among other choices, all available and rich for our multicultural society. The way they speak of themselves is highly modern. But, they still have to build a third way to find a place for themselves in society: one allowing them to live their religion as a partially public matter (when the Occidental society leaves no such place for religion) while ensuring, at the same time, the development of independent critical thought. On this basis, other semi-structured interviews are being laid with Social workers working with families from diverse ethnic backgrounds. They will verify the reality of those identity and cultural bricolages when those young adults of second and third generations build their own family. In between the theoretical models of traditional family and modern family, shall we find a new model, hybrid and more or less stable, combining some aspects of the former and the latter? The exploratory research phase focuses on three aspects of building a family life in this context : the way those generations play, discursively or not, in between their parents and the society in which they grew up; the importance of intercultural dialogue in this process of building; and testing the hypothesis that some families, in our society, show a special way of courting Modernity.

Keywords: family models, identity bricolages, intercultural, modernity and tradition

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Authors: Hairin Taha, Aditya Arya, M. A. Hapipah, A. M. Mustafa

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Plants have been an important source of medicine since ancient times. Pseuduvaria monticola is a rare montane forest species from the Annonaceae family. Traditionally, the plant was used to cure symptoms of fever, inflammation, stomach-ache and also to reduce the elevated levels of blood glucose. Scientifically, we have evaluated the antidiabetic potential of the Pseuduvaria monticola bark methanolic extract on certain in vitro cell based assays, followed by in vivo study. Results from in vitro models displayed PMm upregulated glucose uptake and insulin secretion in mouse pancreatic β-cells. In vivo study demonstrated the PMm down-regulated hyperglycaemia, oxidative stress and elevated levels of pro-inflammatory cytokines in type 2 diabetic rat models. Altogether, the study revealed that Pseuduvaria monticola might be used as a potential candidate for the management of type 2 diabetes and its related complications.

Keywords: type 2 diabetes, Pseuduvaria monticola, insulin secretion, glucose uptake

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Authors: Sergio Paez Moncaleano, Alvaro Mauricio Montenegro

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This paper presents a novel statistical methodology for measuring and founding constructs in Latent Regression Analysis. This approach uses the qualities of Factor Analysis in binary data with interpretations on Item Response Theory (IRT). In addition, based on the fundamentals of submodel theory and with a convergence of many ideas of IRT, we propose an algorithm not just to solve the dimensionality problem (nowadays an open discussion) but a new research field that promises more fear and realistic qualifications for examiners and a revolution on IRT and educational research. In the end, the methodology is applied to a set of real data set presenting impressive results for the coherence, speed and precision. Acknowledgments: This research was financed by Colciencias through the project: 'Multidimensional Item Response Theory Models for Practical Application in Large Test Designed to Measure Multiple Constructs' and both authors belong to SICS Research Group from Universidad Nacional de Colombia.

Keywords: item response theory, dimensionality, submodel theory, factorial analysis

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Authors: Richard Roberts, Anna Kravtcova

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Introduction: The realisation that personality can change over the course of a lifetime has led to a new companion model to the Big Five, the behavioural, emotional, and social skills approach (BESSA). BESSA hypothesizes that this set of skills represents how the individual is thinking, feeling, and behaving when the situation calls for it, as opposed to traits, which represent how someone tends to think, feel, and behave averaged across situations. The five major skill domains share parallels with the Big Five Factor (BFF) model creativity and innovation (openness), self-management (conscientiousness), social engagement (extraversion), cooperation (agreeableness), and emotional resilience (emotional stability) skills. We point to noteworthy limitations in the current operationalisation of BESSA skills (i.e., via Likert-type items) and offer up a different measurement approach: forced choice. Method: In this forced-choice paradigm, individuals were given three skill items (e.g., managing my time) and asked to select one response they believed they were “worst at” and “best at”. The Thurstonian IRT models allow these to be placed on a normative scale. Two multivariate studies (N = 1178) were conducted with a 22-item forced-choice version of the BESSA, a published measure of the BFF, and various criteria. Findings: Confirmatory factor analysis of the forced-choice assessment showed acceptable model fit (RMSEA<0.06), while reliability estimates were reasonable (around 0.70 for each construct). Convergent validity evidence was as predicted (correlations between 0.40 and 0.60 for corresponding BFF and BESSA constructs). Notable was the extent the forced-choice BESSA assessment improved upon test-criterion relationships over and above the BFF. For example, typical regression models find BFF personality accounting for 25% of the variance in life satisfaction scores; both studies showed incremental gains over the BFF exceeding 6% (i.e., BFF and BESSA together accounted for over 31% of the variance in both studies). Discussion: Forced-choice measurement models offer up the promise of creating equated test forms that may unequivocally measure skill gains and are less prone to fakability and reference bias effects. Implications for practitioners are discussed, especially those interested in selection, succession planning, and training and development. We also discuss how the forced choice method can be applied to other constructs like emotional immunity, cross-cultural competence, and self-estimates of cognitive ability.

Keywords: Big Five, forced-choice method, BFF, methods of measurements

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Authors: Saif Al Ghafri, Thomas Hughes, Armand Karimi, Kumarini Seneviratne, Jordan Oakley, Michael Johns, Eric F. May

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Knowledge of the thermophysical properties of complex mixtures at extreme conditions of pressure and temperature have always been essential to the Liquefied Natural Gas (LNG) industry’s evolution because of the tremendous technical challenges present at all stages in the supply chain from production to liquefaction to transport. Each stage is designed using predictions of the mixture’s properties, such as density, viscosity, surface tension, heat capacity and phase behaviour as a function of temperature, pressure, and composition. Unfortunately, currently available models lead to equipment over-designs of 15% or more. To achieve better designs that work more effectively and/or over a wider range of conditions, new fundamental property data are essential, both to resolve discrepancies in our current predictive capabilities and to extend them to the higher-pressure conditions characteristic of many new gas fields. Furthermore, innovative experimental techniques are required to measure different thermophysical properties at high pressures and over a wide range of temperatures, including near the mixture’s critical points where gas and liquid become indistinguishable and most existing predictive fluid property models used breakdown. In this work, we present a wide range of experimental measurements made for different binary and ternary mixtures relevant to LNG processing, with a particular focus on viscosity, surface tension, heat capacity, bubble-points and density. For this purpose, customized and specialized apparatus were designed and validated over the temperature range (200 to 423) K at pressures to 35 MPa. The mixtures studied were (CH4 + C3H8), (CH4 + C3H8 + CO2) and (CH4 + C3H8 + C7H16); in the last of these the heptane contents was up to 10 mol %. Viscosity was measured using a vibrating wire apparatus, while mixture densities were obtained by means of a high-pressure magnetic-suspension densimeter and an isochoric cell apparatus; the latter was also used to determine bubble-points. Surface tensions were measured using the capillary rise method in a visual cell, which also enabled the location of the mixture critical point to be determined from observations of critical opalescence. Mixture heat capacities were measured using a customised high-pressure differential scanning calorimeter (DSC). The combined standard relative uncertainties were less than 0.3% for density, 2% for viscosity, 3% for heat capacity and 3 % for surface tension. The extensive experimental data gathered in this work were compared with a variety of different advanced engineering models frequently used for predicting thermophysical properties of mixtures relevant to LNG processing. In many cases the discrepancies between the predictions of different engineering models for these mixtures was large, and the high quality data allowed erroneous but often widely-used models to be identified. The data enable the development of new or improved models, to be implemented in process simulation software, so that the fluid properties needed for equipment and process design can be predicted reliably. This in turn will enable reduced capital and operational expenditure by the LNG industry. The current work also aided the community of scientists working to advance theoretical descriptions of fluid properties by allowing to identify deficiencies in theoretical descriptions and calculations.

Keywords: LNG, thermophysical, viscosity, density, surface tension, heat capacity, bubble points, models

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Authors: Baldev Ram Khandoliyan, Ram Kishor

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India has a classical tradition of Sanskrit Lexicons. Research work has been done on the study of Indian lexicography. India has seen amazing strides in Information and Communication Technology (ICT) applications for Indian languages in general and for Sanskrit in particular. Since Machine Translation from Sanskrit to other Indian languages is often the desired goal, traditional Sanskrit lexicography has attracted a lot of attention from the ICT and Computational Linguistics community. From Nighaŋţu and Nirukta to Amarakośa and Medinīkośa, Sanskrit owns a rich history of lexicography. As these kośas do not follow the same typology or standard in the selection and arrangement of the words and the information related to them, several types of Kośa-styles have emerged in this tradition. The model of a grammar given by Aṣṭādhyāyī is well appreciated by Indian and western linguists and grammarians. But the different models provided by lexicographic tradition also have importance. The general usefulness of Sanskrit traditional Kośas is well discussed by some scholars. That is most of the matter made available in the text. Some also have discussed the good arrangement of lexica. This paper aims to discuss some more use of the different models of Sanskrit lexicography especially focusing on its computational modeling and its use in different computational operations.

Keywords: computational lexicography, Sanskrit Lexicons, nighanṭu, kośa, Amarkosa

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Authors: Jazlah Majeed Sulaiman, Lakshmi P.

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Concrete slabs are considered to be very ductile structural members. Openings in reinforced slabs are necessary so as to install the mechanical, electrical and pumping (MEP) conduits and ducts. However, these openings reduce the load-carrying capacity, stiffness, energy, and ductility of the slabs. To resolve the undesirable effects of openings in the slab behavior, it is significant to achieve the desired strength against the loads acting on it. The use of Basalt Fiber Reinforcement Polymers (BFRP) as reinforcement has become a valid sustainable option as they produce less greenhouse gases, resist corrosion and have higher tensile strength. In this paper, five slab models are analyzed using non-linear static analysis in ANSYS Workbench to study the effect of openings on slabs reinforced with basalt bars. A parametric numerical study on the loading condition and the shape and size of the opening is conducted, and their load and displacement values are compared. One of the models is validated experimentally.

Keywords: concrete slabs, openings, BFRP, sustainable, corrosion resistant, non-linear static analysis, ANSYS

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Authors: C. V. Pietreanu, S. E. Zaharia, C. Dinu

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The present research is built on three major pillars, commencing by making some considerations on accident investigation methods and pointing out both defining aspects and differences between linear and non-linear analysis. The traditional linear focus on accident analysis describes accidents as a sequence of events, while the latest systemic models outline interdependencies between different factors and define the processes evolution related to a specific (normal) situation. Linear and non-linear accident analysis methods have specific limitations, so the second point of interest is mirrored by the aim to discover the drawbacks of systemic models which becomes a starting point for developing new directions to identify risks or data closer to the cause of incidents/accidents. Since communication represents a critical issue in the interaction of human factor and has been proved to be the answer of the problems made by possible breakdowns in different communication procedures, from this focus point, on the third pylon a new error-modeling instrument suitable for risk assessment/accident analysis will be elaborated.

Keywords: accident analysis, multi-factorial error modeling, risk, systemic methods

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Authors: M. Sadeghi, H. Pezeshk, R. Tusserkani, A. Sharifi Zarchi, A. Malekpour, M. Foroughmand, S. Goliaei, M. Totonchi, N. Ansari–Pour

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The role and relative importance of intrinsic and extrinsic factors in the development of complex diseases such as cancer still remains a controversial issue. Determining the amount of variation explained by these factors needs experimental data and statistical models. These models are nevertheless based on the occurrence and accumulation of random mutational events during stem cell division, thus rendering cancer development a stochastic outcome. We demonstrate that not only individual genome sequencing is uninformative in determining cancer risk, but also assigning a unique genome sequence to any given individual (healthy or affected) is not meaningful. Current whole-genome sequencing approaches are therefore unlikely to realize the promise of personalized medicine. In conclusion, since genome sequence differs from cell to cell and changes over time, it seems that determining the risk factor of complex diseases based on genome sequence is somewhat unrealistic, and therefore, the resulting data are likely to be inherently uninformative.

Keywords: cancer risk, extrinsic factors, genome sequencing, intrinsic factors

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Authors: Arya Pirooz

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The effects of different fuels (DME, RME B100, and SME B100) on barrel engines were studied as a general, single dimensional investigation for characterization of these types of engines. A base computational model was created as reference point to be used as a point of comparison with different cases. The models were computed using the commercial computational fluid dynamics program, Diesel-RK. The base model was created using basic dimensions of the PAMAR-3 engine with inline unit injectors. Four fuel cases were considered. Optimized models were also considered for diesel and DME cases with respect to injection duration, fuel, injection timing, exhaust and intake port opening, CR, angular offset. These factors were optimized for highest BMEP, combined PM and NOx emissions, and highest SFC. Results included mechanical efficiency (eta_m), efficiency and power, emission characteristics, combustion characteristics. DME proved to have the highest performing characteristics in relation to diesel and RME fuels for this type of barrel engine.

Keywords: DME, RME, Diesel-RK, characterization, inline unit injector

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Authors: A. A. Okafor, C. H. Achebe, J. L. Chukwuneke, C. G. Ozoegwu

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The performance of an engine whose basic design parameters are known can be predicted with the assistance of simulation programs into the less time, cost and near value of actual. This paper presents a comprehensive mathematical model of the performance parameters of four stroke spark ignition engine. The essence of this research work is to develop a mathematical model for the analysis of engine performance parameters of four stroke spark ignition engine before embarking on full scale construction, this will ensure that only optimal parameters are in the design and development of an engine and also allow to check and develop the design of the engine and it’s operation alternatives in an inexpensive way and less time, instead of using experimental method which requires costly research test beds. To achieve this, equations were derived which describe the performance parameters (sfc, thermal efficiency, mep and A/F). The equations were used to simulate and optimize the engine performance of the model for various engine speeds. The optimal values obtained for the developed bivariate mathematical models are: sfc is 0.2833kg/kwh, efficiency is 28.77% and a/f is 20.75.

Keywords: bivariate models, engine performance, injector engine, optimization, performance parameters, simulation, spark ignition

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Authors: Yeo Kyeong Lee, Hae Won Min, Ji Yeon Kang, Hee Sun Kim, Yeong Soo Shin

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Fire incidents have been steadily increased over the last year according to national emergency management agency of South Korea. Even though most of the fire incidents with property damage have been occurred in building, rehabilitation has not been properly done with consideration of structure safety. Therefore, this study aims at evaluating rehabilitation effects on fire damaged normal strength concrete beams through experiments and finite element analyses. For the experiments, reinforced concrete beams were fabricated having designed concrete strength of 21 MPa. Two different cover thicknesses were used as 40 mm and 50 mm. After cured, the fabricated beams were heated for 1hour or 2hours according to ISO-834 standard time-temperature curve. Rehabilitation was done by removing the damaged part of cover thickness and filling polymeric mortar into the removed part. Both fire damaged beams and rehabilitated beams were tested with four point loading system to observe structural behaviors and the rehabilitation effect. To verify the experiment, finite element (FE) models for structural analysis were generated using commercial software ABAQUS 6.10-3. For the rehabilitated beam models, integrated temperature-structural analyses were performed in advance to obtain geometries of the fire damaged beams. In addition to the fire damaged beam models, rehabilitated part was added with material properties of polymeric mortar. Three dimensional continuum brick elements were used for both temperature and structural analyses. The same loading and boundary conditions as experiments were implemented to the rehabilitated beam models and non-linear geometrical analyses were performed. Test results showed that maximum loads of the rehabilitated beams were 8~10% higher than those of the non-rehabilitated beams and even 1~6 % higher than those of the non-fire damaged beam. Stiffness of the rehabilitated beams were also larger than that of non-rehabilitated beams but smaller than that of the non-fire damaged beams. In addition, predicted structural behaviors from the analyses also showed good rehabilitation effect and the predicted load-deflection curves were similar to the experimental results. From this study, both experiments and analytical results demonstrated good rehabilitation effect on the fire damaged normal strength concrete beams. For the further, the proposed analytical method can be used to predict structural behaviors of rehabilitated and fire damaged concrete beams accurately without suffering from time and cost consuming experimental process.

Keywords: fire, normal strength concrete, rehabilitation, reinforced concrete beam

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Authors: Ammarah Irum, Muhammad Ali Tahir

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Document-level sentiment analysis in Urdu is a challenging Natural Language Processing (NLP) task due to the difficulty of working with lengthy texts in a language with constrained resources. Deep learning models, which are complex neural network architectures, are well-suited to text-based applications in addition to data formats like audio, image, and video. To investigate the potential of deep learning for Urdu sentiment analysis, we implemented five different deep learning models, including Bidirectional Long Short Term Memory (BiLSTM), Convolutional Neural Network (CNN), Convolutional Neural Network with Bidirectional Long Short Term Memory (CNN-BiLSTM), and Bidirectional Encoder Representation from Transformer (BERT). In this study, we developed a hybrid deep learning model called BiLSTM-Single Layer Multi Filter Convolutional Neural Network (BiLSTM-SLMFCNN) by fusing BiLSTM and CNN architecture. The proposed and baseline techniques are applied on Urdu Customer Support data set and IMDB Urdu movie review data set by using pre-trained Urdu word embedding that are suitable for sentiment analysis at the document level. Results of these techniques are evaluated and our proposed model outperforms all other deep learning techniques for Urdu sentiment analysis. BiLSTM-SLMFCNN outperformed the baseline deep learning models and achieved 83%, 79%, 83% and 94% accuracy on small, medium and large sized IMDB Urdu movie review data set and Urdu Customer Support data set respectively.

Keywords: urdu sentiment analysis, deep learning, natural language processing, opinion mining, low-resource language

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Authors: Hermann Kuehnle

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Developments in ICT totally reshape manufacturing as machines, objects and equipment on the shop floors will be smart and online. Interactions with virtualizations and models of a manufacturing unit will appear exactly as interactions with the unit itself. These virtualizations may be driven by providers with novel ICT services on demand that might jeopardize even well established business models. Context aware equipment, autonomous orders, scalable machine capacity or networkable manufacturing unit will be the terminology to get familiar with in manufacturing and manufacturing management. Such newly appearing smart abilities with impact on network behavior, collaboration procedures and human resource development will make distributed manufacturing a preferred model to produce. Computing miniaturization and smart devices revolutionize manufacturing set ups, as virtualizations and atomization of resources unwrap novel manufacturing principles. Processes and resources obey novel specific laws and have strategic impact on manufacturing and major operational implications. Mechanisms from distributed manufacturing engaging interacting smart manufacturing units and decentralized planning and decision procedures already demonstrate important effects from this shift of focus towards collaboration and interoperability.

Keywords: autonomous unit, networkability, smart manufacturing unit, virtualization

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Authors: Hayder H. Kareem, Shunqi Pan

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The geological environment where the groundwater is collected represents the most important element that affects the behaviour of groundwater aquifer. As groundwater is a worldwide vital resource, it requires knowing the parameters that affect this source accurately so that the conceptualized mathematical models would be acceptable to the broadest ranges. Therefore, groundwater models have recently become an effective and efficient tool to investigate groundwater aquifer behaviours. Groundwater aquifer may contain aquitards, aquicludes, or interfaces within its geological formations. Aquitards and aquicludes have geological formations that forced the modellers to include those formations within the conceptualized groundwater models, while interfaces are commonly neglected from the conceptualization process because the modellers believe that the interface has no effect on aquifer behaviour. The current research highlights the impact of an interface existing in a real unconfined groundwater aquifer called Dibdibba, located in Al-Najaf City, Iraq where it has a river called the Euphrates River that passes through the eastern part of this city. Dibdibba groundwater aquifer consists of two types of soil layers separated by an interface soil layer. A groundwater model is built for Al-Najaf City to explore the impact of this interface. Calibration process is done using PEST 'Parameter ESTimation' approach and the best Dibdibba groundwater model is obtained. When the soil interface is conceptualized, results show that the groundwater tables are significantly affected by that interface through appearing dry areas of 56.24 km² and 6.16 km² in the upper and lower layers of the aquifer, respectively. The Euphrates River will also leak water into the groundwater aquifer of 7359 m³/day. While these results are changed when the soil interface is neglected where the dry area became 0.16 km², the Euphrates River leakage became 6334 m³/day. In addition, the conceptualized models (with and without interface) reveal different responses for the change in the recharge rates applied on the aquifer through the uncertainty analysis test. The aquifer of Dibdibba in Al-Najaf City shows a slight deficit in the amount of water supplied by the current pumping scheme and also notices that the Euphrates River suffers from stresses applied to the aquifer. Ultimately, this study shows a crucial need to represent the interface soil layer in model conceptualization to be the intended and future predicted behaviours more reliable for consideration purposes.

Keywords: Al-Najaf City, groundwater aquifer behaviour, groundwater modelling, interface soil layer, Visual MODFLOW

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Authors: Irene Yi

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Machine learning, natural language processing, and neural network models of language are becoming more and more prevalent in the fields of technology and linguistics today. Training data for machines are at best, large corpora of human literature and at worst, a reflection of the ugliness in society. Machines have been trained on millions of human books, only to find that in the course of human history, derogatory and sexist adjectives are used significantly more frequently when describing females in history and literature than when describing males. This is extremely problematic, both as training data, and as the outcome of natural language processing. As machines start to handle more responsibilities, it is crucial to ensure that they do not take with them historical sexist and misogynistic notions. This paper gathers data and algorithms from neural network models of language having to deal with syntax, semantics, sociolinguistics, and text classification. Results are significant in showing the existing intentional and unintentional misogynistic notions used to train machines, as well as in developing better technologies that take into account the semantics and syntax of text to be more mindful and reflect gender equality. Further, this paper deals with the idea of non-binary gender pronouns and how machines can process these pronouns correctly, given its semantic and syntactic context. This paper also delves into the implications of gendered grammar and its effect, cross-linguistically, on natural language processing. Languages such as French or Spanish not only have rigid gendered grammar rules, but also historically patriarchal societies. The progression of society comes hand in hand with not only its language, but how machines process those natural languages. These ideas are all extremely vital to the development of natural language models in technology, and they must be taken into account immediately.

Keywords: gendered grammar, misogynistic language, natural language processing, neural networks

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Authors: Lilla Dorina Habsz, Marta Rado

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Popularity is affected by a variety of factors in the primary school such as academic achievement and ethnicity. The main goal of our study was to analyse whether acting white exists in Hungarian elementary schools. In other words, we observed whether Roma students penalize those in-group members who obtain the high academic achievement. Furthermore, to show how popularity is influenced by changes in academic achievement in inter-ethnic relations. The empirical basis of our research was the 'competition and negative networks' longitudinal dataset, which was collected by the MTA TK 'Lendület' RECENS research group. This research followed 11 and 12-year old students for a two-year period. The survey was analysed using fixed and random effect models. Overall, we found a positive correlation between grades and popularity, but no evidence for the acting white effect. However, better grades were more positively evaluated within the majority group than within the minority group, which may further increase inequalities.

Keywords: academic achievement, elementary school, ethnicity, popularity

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Authors: Alireza Fallahfard, Ludwig Vinches, Stephane Halle

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In the workplace, the exposure level of airborne contaminants should be evaluated due to health and safety issues. It can be done by numerical models or experimental measurements, but the numerical approach can be useful when it is challenging to perform experiments. One of the simplest models is the well-mixed room (WMR) model, which has shown its usefulness to predict inhalation exposure in many situations. However, since the WMR is limited to gases and vapors, it cannot be used to predict exposure to aerosols. The main objective is to modify the WMR model to expand its application to exposure scenarios involving aerosols. To reach this objective, the standard WMR model has been modified to consider the deposition of particles by gravitational settling and Brownian and turbulent deposition. Three deposition models were implemented in the model. The time-dependent concentrations of airborne particles predicted by the model were compared to experimental results conducted in a 0.512 m3 chamber. Polystyrene particles of 1, 2, and 3 µm in aerodynamic diameter were generated with a nebulizer under two air changes per hour (ACH). The well-mixed condition and chamber ACH were determined by the tracer gas decay method. The mean friction velocity on the chamber surfaces as one of the input variables for the deposition models was determined by computational fluid dynamics (CFD) simulation. For the experimental procedure, the particles were generated until reaching the steady-state condition (emission period). Then generation stopped, and concentration measurements continued until reaching the background concentration (decay period). The results of the tracer gas decay tests revealed that the ACHs of the chamber were: 1.4 and 3.0, and the well-mixed condition was achieved. The CFD results showed the average mean friction velocity and their standard deviations for the lowest and highest ACH were (8.87 ± 0.36) ×10-2 m/s and (8.88 ± 0.38) ×10-2 m/s, respectively. The numerical results indicated the difference between the predicted deposition rates by the three deposition models was less than 2%. The experimental and numerical aerosol concentrations were compared in the emission period and decay period. In both periods, the prediction accuracy of the modified model improved in comparison with the classic WMR model. However, there is still a difference between the actual value and the predicted value. In the emission period, the modified WMR results closely follow the experimental data. However, the model significantly overestimates the experimental results during the decay period. This finding is mainly due to an underestimation of the deposition rate in the model and uncertainty related to measurement devices and particle size distribution. Comparing the experimental and numerical deposition rates revealed that the actual particle deposition rate is significant, but the deposition mechanisms considered in the model were ten times lower than the experimental value. Thus, particle deposition was significant and will affect the airborne concentration in occupational settings, and it should be considered in the airborne exposure prediction model. The role of other removal mechanisms should be investigated.

Keywords: aerosol, CFD, exposure assessment, occupational settings, well-mixed room model, zonal model

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Authors: Yuhanis Yusof, Zuriani Mustaffa, Siti Sakira Kamaruddin

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Data mining is a fundamental technique in identifying patterns from large data sets. The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical. Prior to that, data are consolidated so that the resulting mining process may be more efficient. This study investigates the effect of different data normalization techniques, which are Min-max, Z-score, and decimal scaling, on Swarm-based forecasting models. Recent swarm intelligence algorithms employed includes the Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC). Forecasting models are later developed to predict the daily spot price of crude oil and gasoline. Results showed that GWO works better with Z-score normalization technique while ABC produces better accuracy with the Min-Max. Nevertheless, the GWO is more superior that ABC as its model generates the highest accuracy for both crude oil and gasoline price. Such a result indicates that GWO is a promising competitor in the family of swarm intelligence algorithms.

Keywords: artificial bee colony, data normalization, forecasting, Grey Wolf optimizer

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Authors: Jakub Zembrzuski, Bartosz Sobczyk, Mikołaj MIśkiewicz

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Besides designing new constructions, engineers all over the world must face another problem – maintenance, repairs, and assessment of the technical condition of existing bridges. To solve more complex issues, it is necessary to be familiar with the theory of finite element method and to have access to the software that provides sufficient tools which to enable create of sometimes significantly advanced numerical models. The paper includes a brief assessment of the technical condition, a description of the in situ non-destructive testing carried out and the FEM models created for global and local analysis. In situ testing was performed using strain gauges and displacement sensors. Numerical models were created using various software and numerical modeling techniques. Particularly noteworthy is the method of modeling riveted joints of the crossbeam of the viaduct. It is a simplified method that consists of the use of only basic numerical tools such as beam and shell finite elements, constraints, and simplified boundary conditions (fixed support and symmetry). The results of the numerical analyses were presented and discussed. It is clearly explained why the structure did not fail, despite the fact that the weld of the deck plate completely failed. A further research problem that was solved was to determine the cause of the rapid increase in values on the stress diagram in the cross-section of the transverse section. The problems were solved using the solely mentioned, simplified method of modeling riveted joints, which demonstrates that it is possible to solve such problems without access to sophisticated software that enables to performance of the advanced nonlinear analysis. Moreover, the obtained results are of great importance in the field of assessing the operation of bridge structures with an orthotropic plate.

Keywords: bridge, diagnostics, FEM simulations, failure, NDT, in situ testing

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Authors: Elizaveta S. Prokofyeva, Svetlana V. Maltseva, Roman D. Zaitsev

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Rapidly evolving modern data analysis technologies in healthcare play a large role in understanding the operation of the system and its characteristics. Nowadays, one of the key tasks in urban healthcare is to optimize the resource allocation. Thus, the application of data analysis in medical institutions to solve optimization problems determines the significance of this study. The purpose of this research was to establish the dependence between the indicators of the effectiveness of the medical institution and its resources. Hospital discharges by diagnosis; hospital days of in-patients and in-patient average length of stay were selected as the performance indicators and the demand of the medical facility. The hospital beds by type of care, medical technology (magnetic resonance tomography, gamma cameras, angiographic complexes and lithotripters) and physicians characterized the resource provision of medical institutions for the developed models. The data source for the research was an open database of the statistical service Eurostat. The choice of the source is due to the fact that the databases contain complete and open information necessary for research tasks in the field of public health. In addition, the statistical database has a user-friendly interface that allows you to quickly build analytical reports. The study provides information on 28 European for the period from 2007 to 2016. For all countries included in the study, with the most accurate and complete data for the period under review, predictive models were developed based on historical panel data. An attempt to improve the quality and the interpretation of the models was made by cluster analysis of the investigated set of countries. The main idea was to assess the similarity of the joint behavior of the variables throughout the time period under consideration to identify groups of similar countries and to construct the separate regression models for them. Therefore, the original time series were used as the objects of clustering. The hierarchical agglomerate algorithm k-medoids was used. The sampled objects were used as the centers of the clusters obtained, since determining the centroid when working with time series involves additional difficulties. The number of clusters used the silhouette coefficient. After the cluster analysis it was possible to significantly improve the predictive power of the models: for example, in the one of the clusters, MAPE error was only 0,82%, which makes it possible to conclude that this forecast is highly reliable in the short term. The obtained predicted values of the developed models have a relatively low level of error and can be used to make decisions on the resource provision of the hospital by medical personnel. The research displays the strong dependencies between the demand for the medical services and the modern medical equipment variable, which highlights the importance of the technological component for the successful development of the medical facility. Currently, data analysis has a huge potential, which allows to significantly improving health services. Medical institutions that are the first to introduce these technologies will certainly have a competitive advantage.

Keywords: data analysis, demand modeling, healthcare, medical facilities

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Authors: M. S. Wickramarachchi, L. S. Nawarathna, C. M. B. Dematawewa

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Hatchery performance is critical for the profitability of poultry breeder operations. Some extrinsic parameters of eggs and breeders cause to increase or decrease the hatchability. This study aims to identify the affecting extrinsic parameters on the commercial hatchability of local chicken's eggs and determine the most efficient classification model with a hatchability rate greater than 90%. In this study, seven extrinsic parameters were considered: egg weight, moisture loss, breeders age, number of fertilised eggs, shell width, shell length, and shell thickness. Multiple linear regression was performed to determine the most influencing variable on hatchability. First, the correlation between each parameter and hatchability were checked. Then a multiple regression model was developed, and the accuracy of the fitted model was evaluated. Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), k-Nearest Neighbors (kNN), Support Vector Machines (SVM) with a linear kernel, and Random Forest (RF) algorithms were applied to classify the hatchability. This grouping process was conducted using binary classification techniques. Hatchability was negatively correlated with egg weight, breeders' age, shell width, shell length, and positive correlations were identified with moisture loss, number of fertilised eggs, and shell thickness. Multiple linear regression models were more accurate than single linear models regarding the highest coefficient of determination (R²) with 94% and minimum AIC and BIC values. According to the classification results, RF, CART, and kNN had performed the highest accuracy values 0.99, 0.975, and 0.972, respectively, for the commercial hatchery process. Therefore, the RF is the most appropriate machine learning algorithm for classifying the breeder outcomes, which are economically profitable or not, in a commercial hatchery.

Keywords: classification models, egg weight, fertilised eggs, multiple linear regression

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Authors: Saif Obaid Alkaabi, Nabil Ayad

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Governments constantly seek to offer faster, more secure, efficient and effective services for their citizens. Recent changes and developments to communication services and technologies, mainly due the Internet, have led to immense improvements in the way governments of advanced countries carry out their interior operations Therefore, advances in e-government services have been broadly adopted and used in various developed countries, as well as being adapted to developing countries. The implementation of advances depends on the utilization of the most innovative structures of data techniques, mainly in web dependent applications, to enhance the main functions of governments. These functions, in turn, have spread to mobile and wireless techniques, generating a new advanced direction called m-government. This paper discusses a selection of available m-government applications and several business modules and frameworks in various fields. Practically, the m-government models, techniques and methods have become the improved version of e-government. M-government offers the potential for applications which will work better, providing citizens with services utilizing mobile communication and data models incorporating several government entities. Developing countries can benefit greatly from this innovation due to the fact that a large percentage of their population is young and can adapt to new technology and to the fact that mobile computing devices are more affordable. The use of models of mobile transactions encourages effective participation through the use of mobile portals by businesses, various organizations, and individual citizens. Although the application of m-government has great potential, it does have major limitations. The limitations include: the implementation of wireless networks and relative communications, the encouragement of mobile diffusion, the administration of complicated tasks concerning the protection of security (including the ability to offer privacy for information), and the management of the legal issues concerning mobile applications and the utilization of services.

Keywords: e-government, m-government, system dependability, system security, trust

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Authors: Ivan Sosa Gomez

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Digitalization has impacted the entire insurance value chain, and the growing movement towards P2P platforms and the collaborative economy is also beginning to have a significant impact. P2P insurance is defined as innovation, enabling policyholders to pool their capital, self-organize, and self-manage their own insurance. In this context, new InsurTech start-ups are emerging as peer-to-peer (P2P) providers, based on a model that differs from traditional insurance. As a result, although P2P platforms do not change the fundamental basis of insurance, they do enable potentially more efficient business models to be established in terms of ensuring the coverage of risk. It is therefore relevant to determine whether p2p innovation can have substantial effects on the future of the insurance sector. For this purpose, it is considered necessary to develop P2P innovation from a business perspective, as well as to build a comparison between a traditional model and a P2P model from an actuarial perspective. Objectives: The objectives are (1) to represent P2P innovation in the business model compared to the traditional insurance model and (2) to establish a comparison between a traditional model and a P2P model from an actuarial perspective. Methodology: The research design is defined as action research in terms of understanding and solving the problems of a collectivity linked to an environment, applying theory and best practices according to the approach. For this purpose, the study is carried out through the participatory variant, which involves the collaboration of the participants, given that in this design, participants are considered experts. For this purpose, prolonged immersion in the field is carried out as the main instrument for data collection. Finally, an actuarial model is developed relating to the calculation of premiums that allows for the establishment of projections of future scenarios and the generation of conclusions between the two models. Main Contributions: From an actuarial and business perspective, we aim to contribute by developing a comparison of the two models in the coverage of risk in order to determine whether P2P innovation can have substantial effects on the future of the insurance sector.

Keywords: Insurtech, innovation, business model, P2P, insurance

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Authors: Behzad Behnia, Noah LaRussa-Trott

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In recent years, fibers have been successfully used as an additive to reinforce asphalt concrete materials and to enhance the sustainability and resiliency of transportation infrastructure. Roads covered with fiber-reinforced asphalt concrete (FRAC) require less frequent maintenance and tend to have a longer lifespan. The present work investigates the application of sasobit-coated aramid fibers in asphalt pavements and employs machine learning to develop prediction models to evaluate the cracking performance of FRAC materials. For the experimental part of the study, the effects of several important parameters such as fiber content, fiber length, and testing temperature on fracture characteristics of FRAC mixtures were thoroughly investigated. Two mechanical performance tests, i.e., the disk-shaped compact tension [DC(T)] and indirect tensile [ID(T)] strength tests, as well as the non-destructive acoustic emission test, were utilized to experimentally measure the cracking behavior of the FRAC material in both macro and micro level, respectively. The experimental results were used to train the supervised machine learning approach in order to establish prediction models for fracture performance of the FRAC mixtures in the field. Experimental results demonstrated that adding fibers improved the overall fracture performance of asphalt concrete materials by increasing their fracture energy, tensile strength and lowering their 'embrittlement temperature'. FRAC mixtures containing long-size fibers exhibited better cracking performance than regular-size fiber mixtures. The developed prediction models of this study could be easily employed by pavement engineers in the assessment of the FRAC pavements.

Keywords: fiber reinforced asphalt concrete, machine learning, cracking performance tests, prediction model

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Authors: Parminder Kaur Heer, Alexei Lapkin

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Due to global challenges of increased competition and demand for more sustainable products/processes, there is a rising pressure on the industry to develop innovative processes. Through Process Intensification (PI) the existing and new processes may be able to attain higher efficiency. However, very few PI options are generally considered. This is because processes are typically analysed at a unit operation level, thus limiting the search space for potential process options. PI performed at more detailed levels of a process can increase the size of the search space. The different levels at which PI can be achieved is unit operations, functional and phenomena level. Physical/chemical phenomena form the lowest level of aggregation and thus, are expected to give the highest impact because all the intensification options can be described by their enhancement. The objective of the current work is thus, generation of numerous process alternatives based on phenomena, and development of their corresponding computer aided models. The methodology comprises: a) automated generation of process options, and b) automated generation of process models. The process under investigation is disintegrated into functions viz. reaction, separation etc., and these functions are further broken down into the phenomena required to perform them. E.g., separation may be performed via vapour-liquid or liquid-liquid equilibrium. A list of phenomena for the process is formed and new phenomena, which can overcome the difficulties/drawbacks of the current process or can enhance the effectiveness of the process, are added to the list. For instance, catalyst separation issue can be handled by using solid catalysts; the corresponding phenomena are identified and added. The phenomena are then combined to generate all possible combinations. However, not all combinations make sense and, hence, screening is carried out to discard the combinations that are meaningless. For example, phase change phenomena need the co-presence of the energy transfer phenomena. Feasible combinations of phenomena are then assigned to the functions they execute. A combination may accomplish a single or multiple functions, i.e. it might perform reaction or reaction with separation. The combinations are then allotted to the functions needed for the process. This creates a series of options for carrying out each function. Combination of these options for different functions in the process leads to the generation of superstructure of process options. These process options, which are formed by a list of phenomena for each function, are passed to the model generation algorithm in the form of binaries (1, 0). The algorithm gathers the active phenomena and couples them to generate the model. A series of models is generated for the functions, which are combined to get the process model. The most promising process options are then chosen subjected to a performance criterion, for example purity of product, or via a multi-objective Pareto optimisation. The methodology was applied to a two-step process and the best route was determined based on the higher product yield. The current methodology can identify, produce and evaluate process intensification options from which the optimal process can be determined. It can be applied to any chemical/biochemical process because of its generic nature.

Keywords: Phenomena, Process intensification, Process models , Process options

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Authors: Zhaoxin Luo, Michael Zhu

Abstract:

In natural languages, there are always complex semantic hierarchies. Obtaining the feature representation based on these complex semantic hierarchies becomes the key to the success of the model. Several RNN models have recently been proposed to use latent indicators to obtain the hierarchical structure of documents. However, the model that only uses a single-layer latent indicator cannot achieve the true hierarchical structure of the language, especially a complex language like Chinese. In this paper, we propose a deep layered model that stacks arbitrarily many RNN layers equipped with latent indicators. After using EM and training it hierarchically, our model solves the computational problem of stacking RNN layers and makes it possible to stack arbitrarily many RNN layers. Our deep hierarchical model not only achieves comparable results to large pre-trained models on the Chinese short text classification problem but also achieves state of art results on the Chinese long text classification problem.

Keywords: nature language processing, recurrent neural network, hierarchical structure, document classification, Chinese

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