Search results for: prediction modeling

Authors: Maryam Hajibaba, Mohsen Soltanpour, Mehrnoosh Abbasian, S. Abbas Haghshenas

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This paper aims to evaluate the main role of mangroves forests on the unique hydrodynamic characteristics of the Khuran Strait (KS) in the Persian Gulf. Investigation of hydrodynamic conditions of KS is vital to predict and estimate sedimentation and erosion all over the protected areas north of Qeshm Island. KS (or Tang-e-Khuran) is located between Qeshm Island and the Iranian mother land and has a minimum width of approximately two kilometers. Hydrodynamics of the strait is dominated by strong tidal currents of up to 2 m/s. The bathymetry of the area is dynamic and complicated as 1) strong currents do exist in the area which lead to seemingly sand dune movements in the middle and southern parts of the strait, and 2) existence a vast area with mangrove coverage next to the narrowest part of the strait. This is why ordinary modeling schemes with normal mesh resolutions are not capable for high accuracy estimations of current fields in the KS. A comprehensive set of measurements were carried out with several components, to investigate the hydrodynamics and morpho-dynamics of the study area, including 1) vertical current profiling at six stations, 2) directional wave measurements at four stations, 3) water level measurements at six stations, 4) wind measurements at one station, and 5) sediment grab sampling at 100 locations. Additionally, a set of periodic hydrographic surveys was included in the program. The numerical simulation was carried out by using Delft3D – Flow Module. Model calibration was done by comparing water levels and depth averaged velocity of currents against available observational data. The results clearly indicate that observed data and simulations only fit together if a realistic perspective of the mangrove area is well captured by the model bathymetry data. Generating unstructured grid by using RGFGRID and QUICKIN, the flow model was driven with water level time-series at open boundaries. Adopting the available field data, the key role of mangrove area on the hydrodynamics of the study area can be studied. The results show that including the accurate geometry of the mangrove area and consideration of its sponge-like behavior are the key aspects through which a realistic current field can be simulated in the KS.

Keywords: Khuran Strait, Persian Gulf, tide, current, Delft3D

Procedia PDF Downloads 170

Authors: Ehsan Moradi, Parisa Paikhaste, Amir Alam Beigi, Seyedeh Somayeh Bathaei

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The need for entrepreneurial sustainability is as important as the entrepreneurship category itself. By transferring competencies in a sustainable entrepreneurship framework, entrepreneurship education can make a significant contribution to the effectiveness of businesses, especially for start-up entrepreneurs. This study analyzes the essential competencies of students in the development of sustainable entrepreneurship. It is an applied causal study in terms of nature and field in terms of data collection. The main purpose of this research project is to study and explain the dimensions of sustainability entrepreneurship competencies among agricultural students. The statistical population consists of 730 junior and senior undergraduate students of the Campus of Agriculture and Natural Resources, University of Tehran. The sample size was determined to be 120 using the Cochran's formula, and the convenience sampling method was used. Face validity, structure validity, and diagnostic methods were used to evaluate the validity of the research tool and Cronbach's alpha and composite reliability to evaluate its reliability. Structural equation modeling (SEM) was used by the confirmatory factor analysis (CFA) method to prepare a measurement model for data processing. The results showed that seven key dimensions play a role in shaping sustainable entrepreneurial development competencies: systems thinking competence (STC), embracing diversity and interdisciplinary (EDI), foresighted thinking (FTC), normative competence (NC), action competence (AC), interpersonal competence (IC), and strategic management competence (SMC). It was found that acquiring skills in SMC by creating the ability to plan to achieve sustainable entrepreneurship in students through the relevant mechanisms can improve entrepreneurship in students by adopting a sustainability attitude. While increasing students' analytical ability in the field of social and environmental needs and challenges and emphasizing curriculum updates, AC should pay more attention to the relationship between the curriculum and its content in the form of entrepreneurship culture promotion programs. In the field of EDI, it was found that the success of entrepreneurs in terms of sustainability and business sustainability of start-up entrepreneurs depends on their interdisciplinary thinking. It was also found that STC plays an important role in explaining the relationship between sustainability and entrepreneurship. Therefore, focusing on these competencies in agricultural education to train start-up entrepreneurs can lead to sustainable entrepreneurship in the agricultural higher education system.

Keywords: sustainable entrepreneurship, entrepreneurship education, competency, agricultural higher education

Procedia PDF Downloads 115

Authors: Armin Nabaei, M. Omair Ahmad, M. N. S. Swamy

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Many facial expression and emotion recognition methods in the traditional approaches of using LDA, PCA, and EBGM have been proposed. In recent years deep learning models have provided a unique platform addressing by automatically extracting the features for the detection of facial expression and emotions. However, deep networks require large training datasets to extract automatic features effectively. In this work, we propose an efficient emotion detection algorithm using face images when only small datasets are available for training. We design a deep network whose feature extraction capability is enhanced by utilizing several parallel modules between the input and output of the network, each focusing on the extraction of different types of coarse features with fined grained details to break the symmetry of produced information. In fact, we leverage long range dependencies, which is one of the main drawback of CNNs. We develop this work by introducing a Dynamic Soft-Margin SoftMax.The conventional SoftMax suffers from reaching to gold labels very soon, which take the model to over-fitting. Because it’s not able to determine adequately discriminant feature vectors for some variant class labels. We reduced the risk of over-fitting by using a dynamic shape of input tensor instead of static in SoftMax layer with specifying a desired Soft- Margin. In fact, it acts as a controller to how hard the model should work to push dissimilar embedding vectors apart. For the proposed Categorical Loss, by the objective of compacting the same class labels and separating different class labels in the normalized log domain.We select penalty for those predictions with high divergence from ground-truth labels.So, we shorten correct feature vectors and enlarge false prediction tensors, it means we assign more weights for those classes with conjunction to each other (namely, “hard labels to learn”). By doing this work, we constrain the model to generate more discriminate feature vectors for variant class labels. Finally, for the proposed optimizer, our focus is on solving weak convergence of Adam optimizer for a non-convex problem. Our noteworthy optimizer is working by an alternative updating gradient procedure with an exponential weighted moving average function for faster convergence and exploiting a weight decay method to help drastically reducing the learning rate near optima to reach the dominant local minimum. We demonstrate the superiority of our proposed work by surpassing the first rank of three widely used Facial Expression Recognition datasets with 93.30% on FER-2013, and 16% improvement compare to the first rank after 10 years, reaching to 90.73% on RAF-DB, and 100% k-fold average accuracy for CK+ dataset, and shown to provide a top performance to that provided by other networks, which require much larger training datasets.

Keywords: computer vision, facial expression recognition, machine learning, algorithms, depp learning, neural networks

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Authors: Valery Gulyayev, Sergey Glazunov, Elena Andrusenko, Nataliya Shlyun

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Enhancement of technology and techniques for drilling deep directed oil and gas bore-wells are of essential industrial significance because these wells make it possible to increase their productivity and output. Generally, they are used for drilling in hard and shale formations, that is why their drivage processes are followed by the emergency and failure effects. As is corroborated by practice, the principal drilling drawback occurring in drivage of long curvilinear bore-wells is conditioned by the need to obviate essential force hindrances caused by simultaneous action of the gravity, contact and friction forces. Primarily, these forces depend on the type of the technological regime, drill string stiffness, bore-hole tortuosity and its length. They can lead to the Eulerian buckling of the drill string and its sticking. To predict and exclude these states, special mathematic models and methods of computer simulation should play a dominant role. At the same time, one might note that these mechanical phenomena are very complex and only simplified approaches (‘soft string drag and torque models’) are used for their analysis. Taking into consideration that now the cost of directed wells increases essentially with complication of their geometry and enlargement of their lengths, it can be concluded that the price of mistakes of the drill string behavior simulation through the use of simplified approaches can be very high and so the problem of correct software elaboration is very urgent. This paper deals with the problem of simulating the regimes of drilling deep curvilinear bore-wells with prescribed imperfect geometrical trajectories of their axial lines. On the basis of the theory of curvilinear flexible elastic rods, methods of differential geometry, and numerical analysis methods, the 3D ‘stiff-string drag and torque model’ of the drill string bending and the appropriate software are elaborated for the simulation of the tripping in and out regimes and drilling operations. It is shown by the computer calculations that the contact and friction forces can be calculated and regulated, providing predesigned trouble-free modes of operation. The elaborated mathematic models and software can be used for the emergency situations prognostication and their exclusion at the stages of the drilling process design and realization.

Keywords: curvilinear drilling, drill string tripping in and out, contact forces, resistance forces

Procedia PDF Downloads 120

Authors: Yangzhi Li

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Network transfer of information and performance customization is now a viable method of digital industrial production in the era of Industry 4.0. Robot platforms and network platforms have grown more important in digital design and construction. The pressing need for novel building techniques is driven by the growing labor scarcity problem and increased awareness of construction safety. Robotic approaches in construction research are regarded as an extension of operational and production tools. Several technological theories related to robot autonomous recognition, which include high-performance computing, physical system modeling, extensive sensor coordination, and dataset deep learning, have not been explored using intelligent construction. Relevant transdisciplinary theory and practice research still has specific gaps. Optimizing high-performance computing and autonomous recognition visual guidance technologies improves the robot's grasp of the scene and capacity for autonomous operation. Intelligent vision guidance technology for industrial robots has a serious issue with camera calibration, and the use of intelligent visual guiding and identification technologies for industrial robots in industrial production has strict accuracy requirements. It can be considered that visual recognition systems have challenges with precision issues. In such a situation, it will directly impact the effectiveness and standard of industrial production, necessitating a strengthening of the visual guiding study on positioning precision in recognition technology. To best facilitate the handling of complicated components, an approach for the visual recognition of parts utilizing machine learning algorithms is proposed. This study will identify the position of target components by detecting the information at the boundary and corner of a dense point cloud and determining the aspect ratio in accordance with the guidelines for the modularization of building components. To collect and use components, operational processing systems assign them to the same coordinate system based on their locations and postures. The RGB image's inclination detection and the depth image's verification will be used to determine the component's present posture. Finally, a virtual environment model for the robot's obstacle-avoidance route will be constructed using the point cloud information.

Keywords: robotic construction, robotic assembly, visual guidance, machine learning

Procedia PDF Downloads 59

Authors: Torsten Hermanns, Thoufik Al Khawli, Wolfgang Schulz

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In laser cutting as well as in long pulsed laser drilling of metals, it can be demonstrated that the ablation shape (the shape of cut faces respectively the hole shape) that is formed approaches a so-called asymptotic shape such that it changes only slightly or not at all with further irradiation. These findings are already known from the ultrashort pulse (USP) ablation of dielectric and semiconducting materials. The explanation for the occurrence of an asymptotic shape in laser cutting and long pulse drilling of metals is identified, its underlying mechanism numerically implemented, tested and clearly confirmed by comparison with experimental data. In detail, there now is a model that allows the simulation of the temporal (pulse-resolved) evolution of the hole shape in laser drilling as well as the final (asymptotic) shape of the cut faces in laser cutting. This simulation especially requires much less in the way of resources, such that it can even run on common desktop PCs or laptops. Individual parameters can be adjusted using sliders – the simulation result appears in an adjacent window and changes in real time. This is made possible by an application-specific reduction of the underlying ablation model. Because this reduction dramatically decreases the complexity of calculation, it produces a result much more quickly. This means that the simulation can be carried out directly at the laser machine. Time-intensive experiments can be reduced and set-up processes can be completed much faster. The high speed of simulation also opens up a range of entirely different options, such as metamodeling. Suitable for complex applications with many parameters, metamodeling involves generating high-dimensional data sets with the parameters and several evaluation criteria for process and product quality. These sets can then be used to create individual process maps that show the dependency of individual parameter pairs. This advanced simulation makes it possible to find global and local extreme values through mathematical manipulation. Such simultaneous optimization of multiple parameters is scarcely possible by experimental means. This means that new methods in manufacturing such as self-optimization can be executed much faster. However, the software’s potential does not stop there; time-intensive calculations exist in many areas of industry. In laser welding or laser additive manufacturing, for example, the simulation of thermal induced residual stresses still uses up considerable computing capacity or is even not possible. Transferring the principle of reduced models promises substantial savings there, too.

Keywords: asymptotic ablation shape, interactive process simulation, laser drilling, laser cutting, metamodeling, reduced modeling

Procedia PDF Downloads 194

Authors: Kumar Shashvat, Amol P. Bhondekar

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In the midst of the five senses, odor is the most reminiscent and least understood. Odor testing has been mysterious and odor data fabled to most practitioners. The delinquent of recognition and classification of odor is important to achieve. The facility to smell and predict whether the artifact is of further use or it has become undesirable for consumption; the imitation of this problem hooked on a model is of consideration. The general industrial standard for this classification is color based anyhow; odor can be improved classifier than color based classification and if incorporated in machine will be awfully constructive. For cataloging of odor for peas, trees and cashews various discriminative approaches have been used Discriminative approaches offer good prognostic performance and have been widely used in many applications but are incapable to make effectual use of the unlabeled information. In such scenarios, generative approaches have better applicability, as they are able to knob glitches, such as in set-ups where variability in the series of possible input vectors is enormous. Generative models are integrated in machine learning for either modeling data directly or as a transitional step to form an indeterminate probability density function. The algorithms or models Linear Discriminant Analysis and Naive Bayes Classifier have been used for classification of the odor of cashews. Linear Discriminant Analysis is a method used in data classification, pattern recognition, and machine learning to discover a linear combination of features that typifies or divides two or more classes of objects or procedures. The Naive Bayes algorithm is a classification approach base on Bayes rule and a set of qualified independence theory. Naive Bayes classifiers are highly scalable, requiring a number of restraints linear in the number of variables (features/predictors) in a learning predicament. The main recompenses of using the generative models are generally a Generative Models make stronger assumptions about the data, specifically, about the distribution of predictors given the response variables. The Electronic instrument which is used for artificial odor sensing and classification is an electronic nose. This device is designed to imitate the anthropological sense of odor by providing an analysis of individual chemicals or chemical mixtures. The experimental results have been evaluated in the form of the performance measures i.e. are accuracy, precision and recall. The investigational results have proven that the overall performance of the Linear Discriminant Analysis was better in assessment to the Naive Bayes Classifier on cashew dataset.

Keywords: odor classification, generative models, naive bayes, linear discriminant analysis

Procedia PDF Downloads 357

Authors: Huseyin Guler, Cigdem Kosar

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The aim of this study is to propose Bridge estimator for seasonal unit root tests. Seasonality is an important factor for many economic time series. Some variables may contain seasonal patterns and forecasts that ignore important seasonal patterns have a high variance. Therefore, it is very important to eliminate seasonality for seasonal macroeconomic data. There are some methods to eliminate the impacts of seasonality in time series. One of them is filtering the data. However, this method leads to undesired consequences in unit root tests, especially if the data is generated by a stochastic seasonal process. Another method to eliminate seasonality is using seasonal dummy variables. Some seasonal patterns may result from stationary seasonal processes, which are modelled using seasonal dummies but if there is a varying and changing seasonal pattern over time, so the seasonal process is non-stationary, deterministic seasonal dummies are inadequate to capture the seasonal process. It is not suitable to use seasonal dummies for modeling such seasonally nonstationary series. Instead of that, it is necessary to take seasonal difference if there are seasonal unit roots in the series. Different alternative methods are proposed in the literature to test seasonal unit roots, such as Dickey, Hazsa, Fuller (DHF) and Hylleberg, Engle, Granger, Yoo (HEGY) tests. HEGY test can be also used to test the seasonal unit root in different frequencies (monthly, quarterly, and semiannual). Another issue in unit root tests is the lag selection. Lagged dependent variables are added to the model in seasonal unit root tests as in the unit root tests to overcome the autocorrelation problem. In this case, it is necessary to choose the lag length and determine any deterministic components (i.e., a constant and trend) first, and then use the proper model to test for seasonal unit roots. However, this two-step procedure might lead size distortions and lack of power in seasonal unit root tests. Recent studies show that Bridge estimators are good in selecting optimal lag length while differentiating nonstationary versus stationary models for nonseasonal data. The advantage of this estimator is the elimination of the two-step nature of conventional unit root tests and this leads a gain in size and power. In this paper, the Bridge estimator is proposed to test seasonal unit roots in a HEGY model. A Monte-Carlo experiment is done to determine the efficiency of this approach and compare the size and power of this method with HEGY test. Since Bridge estimator performs well in model selection, our approach may lead to some gain in terms of size and power over HEGY test.

Keywords: bridge estimators, HEGY test, model selection, seasonal unit root

Procedia PDF Downloads 302

Authors: Ursula Das, Manavjit Singh Dhindsa, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

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Wildland fires, also known as forest fires or wildfires, are exhibiting an alarming surge in frequency in recent times, further adding to its perennial global concern. Forest fires often lead to devastating consequences ranging from loss of healthy forest foliage and wildlife to substantial economic losses and the tragic loss of human lives. Despite the existence of substantial literature on the detection of active forest fires, numerous potential research avenues in forest fire management, such as preventative measures and ancillary effects of forest fires, remain largely underexplored. This paper undertakes a systematic review of these underexplored areas in forest fire research, meticulously categorizing them into distinct phases, namely pre-fire, during-fire, and post-fire stages. The pre-fire phase encompasses the assessment of fire risk, analysis of fuel properties, and other activities aimed at preventing or reducing the risk of forest fires. The during-fire phase includes activities aimed at reducing the impact of active forest fires, such as the detection and localization of active fires, optimization of wildfire suppression methods, and prediction of the behavior of active fires. The post-fire phase involves analyzing the impact of forest fires on various aspects, such as the extent of damage in forest areas, post-fire regeneration of forests, impact on wildlife, economic losses, and health impacts from byproducts produced during burning. A comprehensive understanding of the three stages is imperative for effective forest fire management and mitigation of the impact of forest fires on both ecological systems and human well-being. Artificial intelligence and machine learning (AI/ML) methods have garnered much attention in the cyber-physical systems domain in recent times leading to their adoption in decision-making in diverse applications including disaster management. This paper explores the current state of AI/ML applications for managing the activities in the aforementioned phases of forest fire. While conventional machine learning and deep learning methods have been extensively explored for the prevention, detection, and management of forest fires, a systematic classification of these methods into distinct AI research domains is conspicuously absent. This paper gives a comprehensive overview of the state of forest fire research across more recent and prominent AI/ML disciplines, including big data, classical machine learning, computer vision, explainable AI, generative AI, natural language processing, optimization algorithms, and time series forecasting. By providing a detailed overview of the potential areas of research and identifying the diverse ways AI/ML can be employed in forest fire research, this paper aims to serve as a roadmap for future investigations in this domain.

Keywords: artificial intelligence, computer vision, deep learning, during-fire activities, forest fire management, machine learning, pre-fire activities, post-fire activities

Procedia PDF Downloads 40

Authors: Massimiliano Biagini, Marco Spinsanti, Gabriella De Angelis, Sara Tomei, Ilaria Ferlenghi, Maria Scarselli, Alessia Biolchi, Alessandro Muzzi, Brunella Brunelli, Silvana Savino, Marzia M. Giuliani, Isabel Delany, Paolo Costantino, Rino Rappuoli, Vega Masignani, Nathalie Norais

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The gram-negative bacterium Neisseria meningitidis serogroup B (MenB) is an exclusively human pathogen representing the major cause of meningitides and severe sepsis in infants and children but also in young adults. This pathogen is usually present in the 30% of healthy population that act as a reservoir, spreading it through saliva and respiratory fluids during coughing, sneezing, kissing. Among surface-exposed protein components of this diplococcus, factor H binding protein is a lipoprotein proved to be a protective antigen used as a component of the recently licensed Bexsero vaccine. fHbp is a highly variable meningococcal protein: to reflect its remarkable sequence variability, it has been classified in three variants (or two subfamilies), and with poor cross-protection among the different variants. Furthermore, the level of fHbp expression varies significantly among strains, and this has also been considered an important factor for predicting MenB strain susceptibility to anti-fHbp antisera. Different methods have been used to assess fHbp expression on meningococcal strains, however, all these methods use anti-fHbp antibodies, and for this reason, the results are affected by the different affinity that antibodies can have to different antigenic variants. To overcome the limitations of an antibody-based quantification, we developed a quantitative Mass Spectrometry (MS) approach. Selected Reaction Monitoring (SRM) recently emerged as a powerful MS tool for detecting and quantifying proteins in complex mixtures. SRM is based on the targeted detection of ProteoTypicPeptides (PTPs), which are unique signatures of a protein that can be easily detected and quantified by MS. This approach, proven to be highly sensitive, quantitatively accurate and highly reproducible, was used to quantify the absolute amount of fHbp antigen in total extracts derived from 105 clinical isolates, evenly distributed among the three main variant groups and selected to be representative of the fHbp circulating subvariants around the world. We extended the study at the genetic level investigating the correlation between the differential level of expression and polymorphisms present within the genes and their promoter sequences. The implications of fHbp expression on the susceptibility of the strain to killing by anti-fHbp antisera are also presented. To date this is the first comprehensive fHbp expression profiling in a large panel of Neisseria meningitidis clinical isolates driven by an antibody-independent MS-based methodology, opening the door to new applications in vaccine coverage prediction and reinforcing the molecular understanding of released vaccines.

Keywords: quantitative mass spectrometry, Neisseria meningitidis, vaccines, bexsero, molecular epidemiology

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Authors: Armana Huq, Wahidur Rahman, Sanwar Kader

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Inland waterways in Bangladesh play an important role in providing comfortable and low-cost transportation. However, a maritime accident takes away many lives and creates unwanted hazards every year. This article deals with a comprehensive review of inland waterway accidents in Bangladesh. Additionally, it includes a comparative study between international and local inland research studies based on maritime accidents. Articles from inland waterway areas are analyzed in-depth to make a comprehensive overview of the nature of the academic work, accident and risk management process and different statistical analyses. It is found that empirical analysis based on the available statistical data dominates the research domain. For this study, major maritime accident-related works in the last four decades in Bangladesh (1981-2020) are being analyzed for preparing a bibliometric analysis. A study of maritime accidents of passenger's vessels during (1995-2005) indicates that the predominant causes of accidents in the inland waterways of Bangladesh are collision and adverse weather (77%), out of which collision due to human error alone stands (56%) of all accidents. Another study refers that the major causes of waterway accidents are the collision (60.3%) during 2005-2015. About 92% of this collision occurs due to direct contact with another vessel during this period. Rest 8% of the collision occurs by contact with permanent obstruction on waterway roots. The overall analysis of another study from the last 25 years (1995-2019) shows that one of the main types of accidents is collisions, with about 50.3% of accidents being caused by collisions. The other accident types are cyclone or storm (17%), overload (11.3%), physical failure (10.3%), excessive waves (5.1%), and others (6%). Very few notable works are available in testing or comparing the methods, proposing new methods for risk management, modeling, uncertainty treatment. The purpose of this paper is to provide an overview of the evolution of marine accident-related research domain regarding inland waterway of Bangladesh and attempts to introduce new ideas and methods to abridge the gap between international and national inland maritime-related work domain which can be a catalyst for a safer and sustainable water transportation system in Bangladesh. Another fundamental objective of this paper is to navigate various national maritime authorities and international organizations to implement risk management processes for shipping accident prevention in waterway areas.

Keywords: inland waterways, safety, bibliometric analysis, risk management, accidents

Procedia PDF Downloads 163

Authors: A. Schuchter, M. Promegger

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The outbreak of the COVID-19 pandemic has shown us that online education is so much more than just a cool feature for teachers – it is an essential part of modern teaching. In online math teaching, it is common to use tools to share screens, compute and calculate mathematical examples, while the students can watch the process. On the other hand, flipped classroom models are on the rise, with their focus on how students can gather knowledge by watching videos and on the teacher’s use of technological tools for information transfer. This paper proposes a co-educational teaching approach for coding and engineering subjects with the help of drone-building to spark interest in technology and create a platform for knowledge transfer. The project combines aspects from mathematics (matrices, vectors, shaders, trigonometry), physics (force, pressure and rotation) and coding (computational thinking, block-based programming, JavaScript and Python) and makes use of collaborative-shared 3D Modeling with clara.io, where students create mathematics knowhow. The instructor follows a problem-based learning approach and encourages their students to find solutions in their own time and in their own way, which will help them develop new skills intuitively and boost logically structured thinking. The collaborative aspect of working in groups will help the students develop communication skills as well as structural and computational thinking. Students are not just listeners as in traditional classroom settings, but play an active part in creating content together by compiling a Handbook of Knowledge (called “open book”) with examples and solutions. Before students start calculating, they have to write down all their ideas and working steps in full sentences so other students can easily follow their train of thought. Therefore, students will learn to formulate goals, solve problems, and create a ready-to use product with the help of “reverse engineering”, cross-referencing and creative thinking. The work on drones gives the students the opportunity to create a real-life application with a practical purpose, while going through all stages of product development.

Keywords: flipped classroom, co-creational education, coding, making, drones, co-education, ARCS-model, problem-based learning

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Authors: Catherine Peyrols Wu

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Intercultural interactions are becoming increasingly common in organizations and life. These interactions are often the stage of miscommunication and conflict. In management research, these problems are commonly attributed to cultural differences in values and interactional norms. As a result, the notion that intercultural competence can minimize these challenges is widely accepted. Cultural differences, however, are not the only source of a challenge during intercultural interactions. The need to rely on a lingua franca – or common language between people who have different mother tongues – is another important one. In theory, a lingua franca can improve communication and ease coordination. In practice however, disparities in people’s ability and confidence to communicate in the language can exacerbate tensions and generate inefficiencies. In this study, we draw on power theory to develop a model of disparities in language competence and conflict in a multicultural work context. Specifically, we hypothesized that differences in language competence between interaction partners would be positively related to conflict such that people would report greater conflict with partners who have more dissimilar levels of language competence and lesser conflict with partners with more similar levels of language competence. Furthermore, we proposed that cultural intelligence (CQ) an intercultural competence that denotes an individual’s capability to be effective in intercultural situations, would weaken the relationship between disparities in language competence and conflict such that people would report less conflict with partners who have more dissimilar levels of language competence when the interaction partner has high CQ and more conflict when the partner has low CQ. We tested this model with a sample of 135 undergraduate students working in multicultural teams for 13 weeks. We used a round-robin design to examine conflict in 646 dyads nested within 21 teams. Results of analyses using social relations modeling provided support for our hypotheses. Specifically, we found that in intercultural dyads with large disparities in language competence, partners with the lowest level of language competence would report higher levels of interpersonal conflict. However, this relationship disappeared when the partner with higher language competence was also high in CQ. These findings suggest that communication in a lingua franca can be a source of conflict in intercultural collaboration when partners differ in their level of language competence and that CQ can alleviate these effects during collaboration with partners who have relatively lower levels of language competence. Theoretically, this study underscores the benefits of CQ as a complement to language competence for intercultural effectiveness. Practically, these results further attest to the benefits of investing resources to develop language competence and CQ in employees engaged in multicultural work.

Keywords: cultural intelligence, intercultural interactions, language competence, multicultural teamwork

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Authors: Fikret Yalcinkaya, Hamza Unsal

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To understand how neurons work, it is required to combine experimental studies on neural science with numerical simulations of neuron models in a computer environment. In this regard, the simplicity and applicability of spiking neuron modeling functions have been of great interest in computational neuron science and numerical neuroscience in recent years. Spiking neuron models can be classified by exhibiting various neuronal behaviors, such as spiking and bursting. These classifications are important for researchers working on theoretical neuroscience. In this paper, three different spiking neuron models; Izhikevich, Adaptive Exponential Integrate Fire (AEIF) and Hindmarsh Rose (HR), which are based on first order differential equations, are discussed and compared. First, the physical meanings, derivatives, and differential equations of each model are provided and simulated in the Matlab environment. Then, by selecting appropriate parameters, the models were visually examined in the Matlab environment and it was aimed to demonstrate which model can simulate well-known biological neuron behaviours such as Tonic Spiking, Tonic Bursting, Mixed Mode Firing, Spike Frequency Adaptation, Resonator and Integrator. As a result, the Izhikevich model has been shown to perform Regular Spiking, Continuous Explosion, Intrinsically Bursting, Thalmo Cortical, Low-Threshold Spiking and Resonator. The Adaptive Exponential Integrate Fire model has been able to produce firing patterns such as Regular Ignition, Adaptive Ignition, Initially Explosive Ignition, Regular Explosive Ignition, Delayed Ignition, Delayed Regular Explosive Ignition, Temporary Ignition and Irregular Ignition. The Hindmarsh Rose model showed three different dynamic neuron behaviours; Spike, Burst and Chaotic. From these results, the Izhikevich cell model may be preferred due to its ability to reflect the true behavior of the nerve cell, the ability to produce different types of spikes, and the suitability for use in larger scale brain models. The most important reason for choosing the Adaptive Exponential Integrate Fire model is that it can create rich ignition patterns with fewer parameters. The chaotic behaviours of the Hindmarsh Rose neuron model, like some chaotic systems, is thought to be used in many scientific and engineering applications such as physics, secure communication and signal processing.

Keywords: Izhikevich, adaptive exponential integrate fire, Hindmarsh Rose, biological neuron behaviours, spiking neuron models

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Authors: Najeh Lakhoua

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Introduction: Scientific developments and techniques for the systemic approach generate several names to the systemic approach: systems analysis, systems analysis, structural analysis. The main purpose of these reflections is to find a multi-disciplinary approach which organizes knowledge, creates universal language design and controls complex sets. In fact, system analysis is structured sequentially by steps: the observation of the system by various observers in various aspects, the analysis of interactions and regulatory chains, the modeling that takes into account the evolution of the system, the simulation and the real tests in order to obtain the consensus. Thus the system approach allows two types of analysis according to the structure and the function of the system. The purpose of this paper is to present an application of system analysis of Unmanned Aerial Vehicle (UAV) components in order to represent the architecture of this system. Method: There are various analysis methods which are proposed, in the literature, in to carry out actions of global analysis and different points of view as SADT method (Structured Analysis and Design Technique), Petri Network. The methodology adopted in order to contribute to the system analysis of an Unmanned Aerial Vehicle has been proposed in this paper and it is based on the use of SADT. In fact, we present a functional analysis based on the SADT method of UAV components Body, power supply and platform, computing, sensors, actuators, software, loop principles, flight controls and communications). Results: In this part, we present the application of SADT method for the functional analysis of the UAV components. This SADT model will be composed exclusively of actigrams. It starts with the main function ‘To analysis of the UAV components’. Then, this function is broken into sub-functions and this process is developed until the last decomposition level has been reached (levels A1, A2, A3 and A4). Recall that SADT techniques are semi-formal; however, for the same subject, different correct models can be built without having to know with certitude which model is the good or, at least, the best. In fact, this kind of model allows users a sufficient freedom in its construction and so the subjective factor introduces a supplementary dimension for its validation. That is why the validation step on the whole necessitates the confrontation of different points of views. Conclusion: In this paper, we presented an application of system analysis of Unmanned Aerial Vehicle components. In fact, this application of system analysis is based on SADT method (Structured Analysis Design Technique). This functional analysis proved the useful use of SADT method and its ability of describing complex dynamic systems.

Keywords: system analysis, unmanned aerial vehicle, functional analysis, architecture

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Authors: Yi-Tung Lin

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Researchers commonly assess implicit self-esteem with the Implicit Association Test (IAT). The IAT’s measure, often referred to as the IAT effect, indicates the strengths of automatic preferences for the self relative to others, which is often considered an index of implicit self-esteem. However, based on the Dual-process theory, the IAT does not rely entirely on the automatic process; it is also influenced by a controlled process. The present study, therefore, analyzed the IAT data with the Quad model, separating four processes on the IAT performance: the likelihood that automatic association is activated by the stimulus in the trial (AC); that a correct response is discriminated in the trial (D); that the automatic bias is overcome in favor of a deliberate response (OB); and that when the association is not activated, and the individual fails to discriminate a correct answer, there is a guessing or response bias drives the response (G). The AC and G processes are automatic, while the D and OB processes are controlled. The AC parameter is considered as the strength of the association activated by the stimulus, which reflects what implicit measures of social cognition aim to assess. The stronger the automatic association between self and positive valence, the more likely it will be activated by a relevant stimulus. Therefore, the AC parameter was used as the index of implicit self-esteem in the present study. Meanwhile, the relationship between implicit self-esteem and depression is not fully investigated. In the cognitive theory of depression, it is assumed that the negative self-schema is crucial in depression. Based on this point of view, implicit self-esteem would be negatively associated with depression. However, the results among empirical studies are inconsistent. The aims of the present study were to examine the psychometric properties of the AC (i.e., test-retest reliability and its correlations with explicit self-esteem and depression) and compare it with that of the IAT effect. The present study had 105 patients with depressive disorders completing the Rosenberg Self-Esteem Scale, Beck Depression Inventory-II and the IAT on the pretest. After at least 3 weeks, the participants completed the second IAT. The data were analyzed by the latent-trait multinomial processing tree model (latent-trait MPT) with the TreeBUGS package in R. The result showed that the latent-trait MPT had a satisfactory model fit. The effect size of test-retest reliability of the AC and the IAT effect were medium (r = .43, p < .0001) and small (r = .29, p < .01) respectively. Only the AC showed a significant correlation with explicit self-esteem (r = .19, p < .05). Neither of the two indexes was correlated with depression. Collectively, the AC parameter was a satisfactory index of implicit self-esteem compared with the IAT effect. Also, the present study supported the results that implicit self-esteem was not correlated with depression.

Keywords: cognitive modeling, implicit association test, implicit self-esteem, quad model

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Authors: Bilel Chalghaf, Mathieu Varin

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Forest characterization in Quebec, Canada, is usually assessed based on photo-interpretation at the stand level. For species identification, this often results in a lack of precision. Very high spatial resolution imagery, such as DigitalGlobe, and Light Detection and Ranging (LiDAR), have the potential to overcome the limitations of aerial imagery. To date, few studies have used that data to map a large number of species at the tree level using machine learning techniques. The main objective of this study is to map 11 individual high tree species ( > 17m) at the tree level using an object-based approach in the broadleaf forest of Kenauk Nature, Quebec. For the individual tree crown segmentation, three canopy-height models (CHMs) from LiDAR data were assessed: 1) the original, 2) a filtered, and 3) a corrected model. The corrected CHM gave the best accuracy and was then coupled with imagery to refine tree species crown identification. When compared with photo-interpretation, 90% of the objects represented a single species. For modeling, 313 variables were derived from 16-band WorldView-3 imagery and LiDAR data, using radiance, reflectance, pixel, and object-based calculation techniques. Variable selection procedures were employed to reduce their number from 313 to 16, using only 11 bands to aid reproducibility. For classification, a global approach using all 11 species was compared to a semi-hierarchical hybrid classification approach at two levels: (1) tree type (broadleaf/conifer) and (2) individual broadleaf (five) and conifer (six) species. Five different model techniques were used: (1) support vector machine (SVM), (2) classification and regression tree (CART), (3) random forest (RF), (4) k-nearest neighbors (k-NN), and (5) linear discriminant analysis (LDA). Each model was tuned separately for all approaches and levels. For the global approach, the best model was the SVM using eight variables (overall accuracy (OA): 80%, Kappa: 0.77). With the semi-hierarchical hybrid approach, at the tree type level, the best model was the k-NN using six variables (OA: 100% and Kappa: 1.00). At the level of identifying broadleaf and conifer species, the best model was the SVM, with OA of 80% and 97% and Kappa values of 0.74 and 0.97, respectively, using seven variables for both models. This paper demonstrates that a hybrid classification approach gives better results and that using 16-band WorldView-3 with LiDAR data leads to more precise predictions for tree segmentation and classification, especially when the number of tree species is large.

Keywords: tree species, object-based, classification, multispectral, machine learning, WorldView-3, LiDAR

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Authors: Imran Aziz Tunio

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Islamkot is an underdeveloped sub-district (Taluka) in the Tharparkar district Sindh province of Pakistan located between latitude 24°25'19.79"N to 24°47'59.92"N and longitude 70° 1'13.95"E to 70°32'15.11"E. The Islamkot has an arid desert climate and the region is generally devoid of perennial rivers, canals, and streams. It is highly dependent on rainfall which is not considered a reliable surface water source and groundwater is the only key source of water for many centuries. To assess groundwater’s potential, an electrical resistivity survey (ERS) was conducted in Islamkot Taluka. Groundwater investigations for 128 Vertical Electrical Sounding (VES) were collected to determine the groundwater potential and obtain qualitatively and quantitatively layered resistivity parameters. The PASI Model 16 GL-N Resistivity Meter was used by employing a Schlumberger electrode configuration, with half current electrode spacing (AB/2) ranging from 1.5 to 100 m and the potential electrode spacing (MN/2) from 0.5 to 10 m. The data was acquired with a maximum current electrode spacing of 200 m. The data processing for the delineation of dune sand aquifers involved the technique of data inversion, and the interpretation of the inversion results was aided by the use of forward modeling. The measured geo-electrical parameters were examined by Interpex IX1D software, and apparent resistivity curves and synthetic model layered parameters were mapped in the ArcGIS environment using the inverse Distance Weighting (IDW) interpolation technique. Qualitative interpretation of vertical electrical sounding (VES) data shows the number of geo-electrical layers in the area varies from three to four with different resistivity values detected. Out of 128 VES model curves, 42 nos. are 3 layered, and 86 nos. are 4 layered. The resistivity of the first subsurface layers (Loose surface sand) varied from 16.13 Ωm to 3353.3 Ωm and thickness varied from 0.046 m to 17.52m. The resistivity of the second subsurface layer (Semi-consolidated sand) varied from 1.10 Ωm to 7442.8 Ωm and thickness varied from 0.30 m to 56.27 m. The resistivity of the third subsurface layer (Consolidated sand) varied from 0.00001 Ωm to 3190.8 Ωm and thickness varied from 3.26 m to 86.66 m. The resistivity of the fourth subsurface layer (Silt and Clay) varied from 0.0013 Ωm to 16264 Ωm and thickness varied from 13.50 m to 87.68 m. The Dar Zarrouk parameters, i.e. longitudinal unit conductance S is from 0.00024 to 19.91 mho; transverse unit resistance T from 7.34 to 40080.63 Ωm2; longitudinal resistance RS is from 1.22 to 3137.10 Ωm and transverse resistivity RT from 5.84 to 3138.54 Ωm. ERS data and Dar Zarrouk parameters were mapped which revealed that the study area has groundwater potential in the subsurface.

Keywords: electrical resistivity survey, GIS & RS, groundwater potential, environmental assessment, VES

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

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Rainfall is a critical component of climate governing vegetation growth and production, forage availability and quality for herbivores. However, reliable rainfall measurements are not always available, making it necessary to predict rainfall values for particular locations through time. Predicting rainfall in space and time can be a complex and challenging task, especially where the rain gauge network is sparse and measurements are not recorded consistently for all rain gauges, leading to many missing values. Here, we develop a flexible Bayesian model for predicting rainfall in space and time and apply it to Narok County, situated in southwestern Kenya, using data collected at 23 rain gauges from 1965 to 2015. Narok County encompasses the Maasai Mara ecosystem, the northern-most section of the Mara-Serengeti ecosystem, famous for its diverse and abundant large mammal populations and spectacular migration of enormous herds of wildebeest, zebra and Thomson's gazelle. The model incorporates geographical and meteorological predictor variables, including elevation, distance to Lake Victoria and minimum temperature. We assess the efficiency of the model by comparing it empirically with the established Gaussian process, Kriging, simple linear and Bayesian linear models. We use the model to predict total monthly rainfall and its standard error for all 5 * 5 km grid cells in Narok County. Using the Monte Carlo integration method, we estimate seasonal and annual rainfall and their standard errors for 29 sub-regions in Narok. Finally, we use the predicted rainfall to predict large herbivore biomass in the Maasai Mara ecosystem on a 5 * 5 km grid for both the wet and dry seasons. We show that herbivore biomass increases with rainfall in both seasons. The model can handle data from a sparse network of observations with many missing values and performs at least as well as or better than four established and widely used models, on the Narok data set. The model produces rainfall predictions consistent with expectation and in good agreement with the blended station and satellite rainfall values. The predictions are precise enough for most practical purposes. The model is very general and applicable to other variables besides rainfall.

Keywords: non-stationary covariance function, gaussian process, ungulate biomass, MCMC, maasai mara ecosystem

Procedia PDF Downloads 262

Authors: Tatsuya Yamazaki, Kazuya Miyakawa, Tomohiko Sugiyama, Toshitaka Iwatani

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The introduction of sensor technologies into agriculture is a necessary step to realize Precision Agriculture. Although sensing methodologies themselves have been prevailing owing to miniaturization and reduction in costs of sensors, there are some difficulties to analyze and understand the sensing data. Targeting at pears ’Le Lectier’, which is particular to Niigata in Japan, cultivation environmental data have been collected at pear fields by eight sorts of sensors: field temperature, field humidity, rain gauge, soil water potential, soil temperature, soil moisture, inner-bag temperature, and inner-bag humidity sensors. With regard to the inner-bag temperature and humidity sensors, they are used to measure the environment inside the fruit bag used for pre-harvest bagging of pears. In this experiment, three kinds of fruit bags were used for the pre-harvest bagging. After over 100 days continuous measurement, volumes of sensing data have been collected. Firstly, correlation analysis among sensing data measured by respective sensors reveals that one sensor can replace another sensor so that more efficient and cost-saving sensing systems can be proposed to pear farmers. Secondly, differences in characteristic and performance of the three kinds of fruit bags are clarified by the measurement results by the inner-bag environmental sensing. It is found that characteristic and performance of the inner-bags significantly differ from each other by statistical analysis. Lastly, a relational model between the sensing data and the pear outlook quality is established by use of Structural Equation Model (SEM). Here, the pear outlook quality is related with existence of stain, blob, scratch, and so on caused by physiological impair or diseases. Conceptually SEM is a combination of exploratory factor analysis and multiple regression. By using SEM, a model is constructed to connect independent and dependent variables. The proposed SEM model relates the measured sensing data and the pear outlook quality determined on the basis of farmer judgement. In particularly, it is found that the inner-bag humidity variable relatively affects the pear outlook quality. Therefore, inner-bag humidity sensing might help the farmers to control the pear outlook quality. These results are supported by a large quantity of inner-bag humidity data measured over the years 2014, 2015, and 2016. The experimental and analytical results in this research contribute to spreading Precision Agriculture technologies among the farmers growing ’Le Lectier’.

Keywords: precision agriculture, pre-harvest bagging, sensor fusion, structural equation model

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Authors: Panagiota Katsikouli, William Hamilton Byrne, Thomas Gammeltoft-Hansen, Tijs Slaats

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An individual who demonstrates a well-founded fear of persecution or faces real risk of being subjected to torture is eligible for asylum. In Danish law, the exact legal thresholds reflect those established by international conventions, notably the 1951 Refugee Convention and the 1950 European Convention for Human Rights. These international treaties, however, remain largely silent when it comes to how states should assess asylum claims. As a result, national authorities are typically left to determine an individual’s legal eligibility on a narrow basis consisting of an oral testimony, which may itself be hampered by several factors, including imprecise language interpretation, insecurity or lacking trust towards the authorities among applicants. The leaky ground, on which authorities must assess their subjective perceptions of asylum applicants' credibility, questions whether, in all cases, adjudicators make the correct decision. Moreover, the subjective element in these assessments raises questions on whether individual asylum cases could be afflicted by implicit biases or stereotyping amongst adjudicators. In fact, recent studies have uncovered significant correlations between decision outcomes and the experience and gender of the assigned judge, as well as correlations between asylum outcomes and entirely external events such as weather and political elections. In this study, we analyze a publicly available dataset containing approximately 8,000 summaries of asylum cases, initially rejected, and re-tried by the Refugee Appeals Board (RAB) in Denmark. First, we look for variations in the recognition rates, with regards to a number of applicants’ features: their country of origin/nationality, their identified gender, their identified religion, their ethnicity, whether torture was mentioned in their case and if so, whether it was supported or not, and the year the applicant entered Denmark. In order to extract those features from the text summaries, as well as the final decision of the RAB, we applied natural language processing and regular expressions, adjusting for the Danish language. We observed interesting variations in recognition rates related to the applicants’ country of origin, ethnicity, year of entry and the support or not of torture claims, whenever those were made in the case. The appearance (or not) of significant variations in the recognition rates, does not necessarily imply (or not) bias in the decision-making progress. None of the considered features, with the exception maybe of the torture claims, should be decisive factors for an asylum seeker’s fate. We therefore investigate whether the decision can be predicted on the basis of these features, and consequently, whether biases are likely to exist in the decisionmaking progress. We employed a number of machine learning classifiers, and found that when using the applicant’s country of origin, religion, ethnicity and year of entry with a random forest classifier, or a decision tree, the prediction accuracy is as high as 82% and 85% respectively. tentially predictive properties with regards to the outcome of an asylum case. Our analysis and findings call for further investigation on the predictability of the outcome, on a larger dataset of 17,000 cases, which is undergoing.

Keywords: asylum adjudications, automated decision-making, machine learning, text mining

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Authors: Mballa Abanda Luc Aurelien Serge, Henda Gnakate Biba, Kuate Guemo Romaric, Akono Rufine Nicole, Zabotom Yaya Fadel Biba, Petfiang Sidonie, Bella Suzane Jenifer

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The main hypotheses proposed around the definition of the syllable and of music, of the common origin of music and language, should lead the reader to reflect on the cross-cutting questions raised by the debate on the notion of universals in linguistics and musicology. These are objects of controversy, and there lies its interest: the debate raises questions that are at the heart of theories on language. It is an inventive, original, and innovative research thesis. A contribution to the theoretical, musicological, ethno musicological, and linguistic conceptualization of languages, giving rise to the practice of interlocution between the social and cognitive sciences, the activities of artistic creation, and the question of modeling in the human sciences: mathematics, computer science, translation automation, and artificial intelligence. When you apply this theory to any text of a folksong of a world-tone language, you do not only piece together the exact melody, rhythm, and harmonies of that song as if you knew it in advance but also the exact speaking of this language. The author believes that the issue of the disappearance of tonal languages and their preservation has been structurally resolved, as well as one of the greatest cultural equations related to the composition and creation of tonal, polytonal, and random music. The experimentation confirming the theorization, I designed a semi-digital, semi-analog application that translates the tonal languages of Africa (about 2,100 languages) into blues, jazz, world music, polyphonic music, tonal and anatonal music, and deterministic and random music). To test this application, I use music reading and writing software that allows me to collect the data extracted from my mother tongue, which is already modeled in the musical staves saved in the ethnographic (semiotic) dictionary for automatic translation ( volume 2 of the book). The translation is done (from writing to writing, from writing to speech, and from writing to music). Mode of operation: you type a text on your computer, a structured song (chorus-verse), and you command the machine a melody of blues, jazz, and world music or variety, etc. The software runs, giving you the option to choose harmonies, and then you select your melody.

Keywords: language, music, sciences, quantum entenglement

Procedia PDF Downloads 52

Authors: Tzu-Ting Hu, Jung-Der Wang, Ming-Yeng Lin, Jin-Luh Chen, Perng-Jy Tsai

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The vinyl chloride monomer (VCM) has been classified as group 1 (human) carcinogen by the IARC. Workers exposed to VCM are known associated with the development of the liver cancer and hence might cause economical and health losses. Particularly, for those work for the petrochemical industry have been seriously concerned in the environmental and occupational health field. Considering assessing workers’ health risks and their resultant economical and health losses requires the establishment of long-term VCM exposure data for any similar exposure group (SEG) of interest, the development of suitable technologies has become an urgent and important issue. In the present study, VCM exposures for petrochemical industry workers were determined firstly based on the database of the 'Workplace Environmental Monitoring Information Systems (WEMIS)' provided by Taiwan OSHA. Considering the existence of miss data, the reconstruction of historical exposure techniques were then used for completing the long-term exposure data for SEGs with routine operations. For SEGs with non-routine operations, exposure modeling techniques, together with their time/activity records, were adopted for determining their long-term exposure concentrations. The Bayesian decision analysis (BDA) was adopted for conducting exposure and health risk assessments for any given SEG in the petrochemical industry. The resultant excessive cancer risk was then used to determine the corresponding loss of quality-adjusted life expectancy (QALE). Results show that low average concentrations can be found for SEGs with routine operations (e.g., VCM rectification 0.0973 ppm, polymerization 0.306 ppm, reaction tank 0.33 ppm, VCM recovery 1.4 ppm, control room 0.14 ppm, VCM storage tanks 0.095 ppm and wastewater treatment 0.390 ppm), and the above values were much lower than that of the permissible exposure limit (PEL; 3 ppm) of VCM promulgated in Taiwan. For non-routine workers, though their high exposure concentrations, their low exposure time and frequencies result in low corresponding health risks. Through the consideration of exposure assessment results, health risk assessment results, and QALE results simultaneously, it is concluded that the proposed method was useful for prioritizing SEGs for conducting exposure abatement measurements. Particularly, the obtained QALE results further indicate the importance of reducing workers’ VCM exposures, though their exposures were low as in comparison with the PEL and the acceptable health risk.

Keywords: exposure assessment, health risk assessment, petrochemical industry, quality-adjusted life years, vinyl chloride monomer

Procedia PDF Downloads 169

Authors: Jineetkumar Gawad, Chandrakant Bonde

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Tuberculosis (TB) is a major worldwide concern whose control has been exacerbated by HIV, the rise of multidrug-resistance (MDR-TB) and extensively drug resistance (XDR-TB) strains of Mycobacterium tuberculosis. The interest for newer and faster acting antitubercular drugs are more remarkable than any time. To search potent compounds is need and challenge for researchers. Here, we tried to design lead for inhibition of Decaprenyl phosphoryl-β-D-ribose 2′-epimerase (DprE1) enzyme. Arabinose is an essential constituent of mycobacterial cell wall. DprE1 is a flavoenzyme that converts decaprenylphosphoryl-D-ribose into decaprenylphosphoryl-2-keto-ribose, which is intermediate in biosynthetic pathway of arabinose. Latter, DprE2 converts keto-ribose into decaprenylphosphoryl-D-arabinose. We had a selection of 23 compounds from azaindole series for computational study, and they were drawn using marvisketch. Ligands were prepared using Maestro molecular modeling interface, Schrodinger, v10.5. Common pharmacophore hypotheses were developed by applying dataset thresholds to yield active and inactive set of compounds. There were 326 hypotheses were developed. On the basis of survival score, ADRRR (Survival Score: 5.453) was selected. Selected pharmacophore hypotheses were subjected to virtual screening results into 1000 hits. Hits were prepared and docked with protein 4KW5 (oxydoreductase inhibitor) was downloaded in .pdb format from RCSB Protein Data Bank. Protein was prepared using protein preparation wizard. Protein was preprocessed, the workspace was analyzed using force field OPLS 2005. Glide grid was generated by picking single atom in molecule. Prepared ligands were docked with prepared protein 4KW5 using Glide docking. After docking, on the basis of glide score top-five compounds were selected, (5223, 5812, 0661, 0662, and 2945) and the glide docking score (-8.928, -8.534, -8.412, -8.411, -8.351) respectively. There were interactions of ligand and protein, specifically HIS 132, LYS 418, TRY 230, ASN 385. Pi-pi stacking was observed in few compounds with basic Imidazo [4,5-b] pyridine ring. We had basic azaindole ring in parent compounds, but after glide docking, we received compounds with Imidazo [4,5-b] pyridine as a basic ring. That might be the new lead in the process of drug discovery.

Keywords: DprE1 inhibitors, in silico drug designing, imidazo [4, 5-b] pyridine, lead, tuberculosis

Procedia PDF Downloads 129

Authors: Calister Wingang Makebe, Wilson Agwanande Ambindei, Zangue Steve Carly Desobgo, Abraham Billu, Emmanuel Jong Nso, P. Nisha

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Traditional liquid-state fermentation processes of Annona muricata L. juice can result in fluctuating product quality and quantity due to difficulties in control and scale up. This work describes a laboratory-scale batch fermentation process to produce a probiotic Annona muricata L. enzymatically extracted juice, which was modeled using the Doehlert design with independent extraction factors being incubation time, temperature, and enzyme concentration. It aimed at a better understanding of the traditional process as an initial step for future optimization. Annona muricata L. juice was fermented with L. acidophilus (NCDC 291) (LA), L. casei (NCDC 17) (LC), and a blend of LA and LC (LCA) for 72 h at 37 °C. Experimental data were fitted into mathematical models (Monod, Logistic and Luedeking and Piret models) using MATLAB software, to describe biomass growth, sugar utilization, and organic acid production. The optimal fermentation time was obtained based on cell viability, which was 24 h for LC and 36 h for LA and LCA. The model was particularly effective in estimating biomass growth, reducing sugar consumption, and lactic acid production. The values of the determination coefficient, R2, were 0.9946, 0.9913 and 0.9946, while the residual sum of square error, SSE, was 0.2876, 0.1738 and 0.1589 for LC, LA and LCA, respectively. The growth kinetic parameters included the maximum specific growth rate, µm, which was 0.2876 h-1, 0.1738 h-1 and 0.1589 h-1, as well as the substrate saturation, Ks, with 9.0680 g/L, 9.9337 g/L and 9.0709 g/L respectively for LC, LA and LCA. For the stoichiometric parameters, the yield of biomass based on utilized substrate (YXS) was 50.7932, 3.3940 and 61.0202, and the yield of product based on utilized substrate (YPS) was 2.4524, 0.2307 and 0.7415 for LC, LA, and LCA, respectively. In addition, the maintenance energy parameter (ms) was 0.0128, 0.0001 and 0.0004 with respect to LC, LA and LCA. With the kinetic model proposed by Luedeking and Piret for lactic acid production rate, the growth associated and non-growth associated coefficients were determined as 1.0028 and 0.0109, respectively. The model was demonstrated for batch growth of LA, LC, and LCA in Annona muricata L. juice. The present investigation validates the potential of Annona muricata L. based medium for heightened economical production of a probiotic medium.

Keywords: L. acidophilus, L. casei, fermentation, modelling, kinetics

Procedia PDF Downloads 44

Authors: Meenakshi Barsaul Chauhan, Aastha Chauhan, Shilpa Hurmade, Rajeev Sen, Jyotsna Sen, Monika Dalal

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Introduction: Ovarian masses are common forms of neoplasm in women and represent 2/3rd of gynaecological malignancies. A pre-operative suggestion of malignancy can guide the gynecologist to refer women with suspected pelvic mass to a gynecological oncologist for appropriate therapy and optimized treatment, which can improve survival. In the younger age group preoperative differentiation into benign or malignant pathology can decide for conservative or radical surgery. Imaging modalities have a definite role in establishing the diagnosis. By using International Ovarian Tumor Analysis (IOTA) classification with sonography, costly radiological methods like Magnetic Resonance Imaging (MRI) / computed tomography (CT) scan can be reduced, especially in developing countries like India. Thus, this study is being undertaken to evaluate the role of clinical methods and sonography for diagnosis of the nature of the ovarian tumor. Material And Methods: This prospective observational study was conducted on 40 patients presenting with ovarian masses, in the Department of Obstetrics and Gynaecology, at a tertiary care center in northern India. Functional cysts were excluded. Ultrasonography and color Doppler were performed on all the cases.IOTA rules were applied, which take into account locularity, size, presence of solid components, acoustic shadow, dopper flow etc . Magnetic Resonance Imaging (MRI) / computed tomography (CT) scans abdomen and pelvis were done in cases where sonography was inconclusive. In inoperable cases, Fine needle aspiration cytology (FNAC) was done. The histopathology report after surgery and cytology report after FNAC was correlated statistically with the pre-operative diagnosis made clinically and sonographically using IOTA rules. Statistical Analysis: Descriptive measures were analyzed by using mean and standard deviation and the Student t-test was applied and the proportion was analyzed by applying the chi-square test. Inferential measures were analyzed by sensitivity, specificity, negative predictive value, and positive predictive value. Results: Provisional diagnosis of the benign tumor was made in 16(42.5%) and of the malignant tumor was made in 24(57.5%) patients on the basis of clinical findings. With IOTA simple rules on sonography, 15(37.5%) were found to be benign, while 23 (57.5%) were found to be malignant and findings were inconclusive in 2 patients (5%). FNAC/Histopathology reported that benign ovarian tumors were 14 (35%) and 26(65%) were malignant, which was taken as the gold standard. The clinical finding alone was found to have a sensitivity of 66.6% and a specificity of 90.9%. USG alone had a sensitivity of 86% and a specificity of 80%. When clinical findings and IOTA simple rules of sonography were combined (excluding inconclusive masses), the sensitivity and specificity were 83.3% and 92.3%, respectively. While including inconclusive masses, sensitivity came out to be 91.6% and specificity was 89.2. Conclusion: IOTA's simple sonography rules are highly sensitive and specific in the prediction of ovarian malignancy and also easy to use and easily reproducible. Thus, combining clinical examination with USG will help in the better management of patients in terms of time, cost and better prognosis. This will also avoid the need for costlier modalities like CT, and MRI.

Keywords: benign, international ovarian tumor analysis classification, malignant, ovarian tumours, sonography

Procedia PDF Downloads 52

Authors: Abulbasit G. Abdulsayid, Zinab F. Abdulla, Asma A. Omer

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On the ground that the orifice plate is relatively inexpensive, requires very little maintenance and only calibrated during the occasion of plant turnaround, the orifice plate has turned to be in a real prevalent use in gas industry. Inaccuracy of measurement in the fiscal metering stations may highly be accounted to be the most vital factor for mischarges in the natural gas industry in Libya. A very trivial error in measurement can add up a fast escalating financial burden to the custodian transactions. The unaccounted gas quantity transferred annually via orifice plates in Libya, could be estimated in an extent of multi-million dollars. As the oil and gas wealth is the solely source of income to Libya, every effort is now being exerted to improve the accuracy of existing orifice metering facilities. Discharge coefficient has become pivotal in current researches undertaken in this regard. Hence, increasing the knowledge of the flow field in a typical orifice meter is indispensable. Recently and in a drastic pace, the CFD has become the most time and cost efficient versatile tool for in-depth analysis of fluid mechanics, heat and mass transfer of various industrial applications. Getting deeper into the physical phenomena lied beneath and predicting all relevant parameters and variables with high spatial and temporal resolution have been the greatest weighing pros counting for CFD. In this paper, flow phenomena for air passing through an orifice meter were numerically analyzed with CFD code based modeling, giving important information about the effect of orifice plate specifications on the discharge coefficient for three different tappings locations, i.e., flange tappings, D and D/2 tappings compared with vena contracta tappings. Discharge coefficients were paralleled with discharge coefficients estimated by ISO 5167. The influences of orifice plate bore thickness, orifice plate thickness, beveled angle, perpendicularity and buckling of the orifice plate, were all duly investigated. A case of an orifice meter whose pipe diameter of 2 in, beta ratio of 0.5 and Reynolds number of 91100, was taken as a model. The results highlighted that the discharge coefficients were highly responsive to the variation of plate specifications and under all cases, the discharge coefficients for D and D/2 tappings were very close to that of vena contracta tappings which were believed as an ideal arrangement. Also, in general sense, it was appreciated that the standard equation in ISO 5167, by which the discharge coefficient was calculated, cannot capture the variation of the plate specifications and thus further thorough considerations would be still needed.

Keywords: CFD, discharge coefficients, orifice meter, orifice plate specifications

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Authors: Takuya Suzuki, Atsushi Shirai, Takashi Seki

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Acupuncture therapy is one of the treatments in traditional Chinese medicine. Recently, some reports have shown the effectiveness of acupuncture. However, its full acceptance has been hindered by the lack of understanding on mechanism of the therapy. Acupuncture applied to Zusanli (ST-36) enhances blood flow volume in superior mesenteric artery (SMA), yielding peripheral vascular resistance – regulated blood flow of SMA dominated by the parasympathetic system and inhibition of sympathetic system. In this study, a lumped-parameter approximation model of blood flow in the systemic arteries was developed. This model was extremely simple, consisting of the aorta, carotid arteries, arteries of the four limbs and SMA, and their peripheral vascular resistances. Here, the individual artery was simplified to a tapered tube and the resistances were modelled by a linear resistance. We numerically investigated contribution of the peripheral vascular resistance of SMA to the systemic blood distribution using this model. In addition to the upstream end of the model, which correlates with the left ventricle, two types of boundary condition were applied; mean left ventricular pressure which correlates with blood pressure (BP) and mean cardiac output which corresponds to cardiac index (CI). We examined it to reproduce the experimentally obtained hemodynamic change, in terms of the ratio of the aforementioned hemodynamic parameters from their initial values before the acupuncture, by regulating the peripheral vascular resistances and the upstream boundary condition. First, only the peripheral vascular resistance of SMA was changed to show contribution of the resistance to the change in blood flow volume in SMA, expecting reproduction of the experimentally obtained change. It was found, however, this was not enough to reproduce the experimental result. Then, we also changed the resistances of the other arteries together with the value given at upstream boundary. Here, the resistances of the other arteries were changed simultaneously in the same amount. Consequently, we successfully reproduced the hemodynamic change to find that regulation of the upstream boundary condition to the value experimentally obtained after the stimulation is necessary for the reproduction, though statistically significant changes in BP and CI were not observed in the experiment. It is generally known that sympathetic and parasympathetic tones take part in regulation of whole the systemic circulation including the cardiac function. The present result indicates that stimulation to ST-36 could induce vasodilation of peripheral circulation of SMA and vasoconstriction of that of other arteries. In addition, it implies that experimentally obtained small changes in BP and CI induced by the acupuncture may be involved in the therapeutic response.

Keywords: acupuncture, hemodynamics, lumped-parameter approximation, modeling, systemic vascular resistance

Procedia PDF Downloads 205

Authors: Babak Forouraghi

Abstract:

A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations of previous approaches, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with attention mechanism. In a previous work on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: transformers, generative ai, gene expression design, classification

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Authors: Alfredo Fortunato, Carmelofrancesco Origlia, Sara Laurita, Rossella Nicoletti

Abstract:

In the post-pandemic recovery phase of tourism, the role of a Destination Management Organization (DMO) as a coordinated management system of all the elements that make up a destination (attractions, access, marketing, human resources, brand, pricing, etc.) is also becoming relevant for local territories. The objective of a DMO is to maximize the visitor's perception of value and quality while ensuring the competitiveness and sustainability of the destination, as well as the long-term preservation of its natural and cultural assets, and to catalyze benefits for the local economy and residents. In carrying out the multiple functions to which it is called, the DMO can leverage a collective intelligence that comes from the ability to pool information, explicit and tacit knowledge, and relationships of the various stakeholders: policymakers, public managers and officials, entrepreneurs in the tourism supply chain, researchers, data journalists, schools, associations and committees, citizens, etc. The DMO potentially has at its disposal large volumes of data and many of them at low cost, that need to be properly processed to produce value. Based on these assumptions, the paper presents a conceptual framework for building an information system to support the DMO in the intelligent management of a tourist destination tested in an area of southern Italy. The approach adopted is data-informed and consists of four phases: (1) formulation of the knowledge problem (analysis of policy documents and industry reports; focus groups and co-design with stakeholders; definition of information needs and key questions); (2) research and metadatation of relevant sources (reconnaissance of official sources, administrative archives and internal DMO sources); (3) gap analysis and identification of unconventional information sources (evaluation of traditional sources with respect to the level of consistency with information needs, the freshness of information and granularity of data; enrichment of the information base by identifying and studying web sources such as Wikipedia, Google Trends, Booking.com, Tripadvisor, websites of accommodation facilities and online newspapers); (4) definition of the set of indicators and construction of the information base (specific definition of indicators and procedures for data acquisition, transformation, and analysis). The framework derived consists of 6 thematic areas (accommodation supply, cultural heritage, flows, value, sustainability, and enabling factors), each of which is divided into three domains that gather a specific information need to be represented by a scheme of questions to be answered through the analysis of available indicators. The framework is characterized by a high degree of flexibility in the European context, given that it can be customized for each destination by adapting the part related to internal sources. Application to the case study led to the creation of a decision support system that allows: •integration of data from heterogeneous sources, including through the execution of automated web crawling procedures for data ingestion of social and web information; •reading and interpretation of data and metadata through guided navigation paths in the key of digital story-telling; •implementation of complex analysis capabilities through the use of data mining algorithms such as for the prediction of tourist flows.

Keywords: collective intelligence, data framework, destination management, smart tourism

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