Search results for: adaptive thermal comfort model

Authors: A. P. Joshi, H. V. Warrior, J. P. Panda

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This paper is a continuation of the work carried out by various turbulence modelers in Oceanography on the topic of oceanic turbulent mixing. It evaluates the evolution of ocean water temperature and salinity by the appropriate modeling of turbulent mixing utilizing proper prescription of eddy viscosity. Many modelers in past have suggested including terms like shear, buoyancy and vorticity to be the parameters that decide the slow pressure strain correlation. We add to it the fact that dissipation anisotropy also modifies the correlation through eddy viscosity parameterization. This recalibrates the established correlation constants slightly and gives improved results. This anisotropization of dissipation implies that the critical Richardson’s number increases much beyond unity (to 1.66) to accommodate enhanced mixing, as is seen in reality. The model is run for a couple of test cases in the General Ocean Turbulence Model (GOTM) and the results are presented here.

Keywords: Anisotropy, GOTM, pressure-strain correlation, Richardson critical number

Procedia PDF Downloads 166

Authors: Parastou Kharazmi

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Failure and leakage of metallic components because of corrosion in infrastructure structures is a considerably problematic and expensive issue and the traditional solution of replacing the component is costly and time-consuming. Rehabilitation techniques by using advanced polymeric materials are an alternative solution towards this problem. This paper provides a summary of analyses on relined rehabilitated metallic samples after exposure in practice and real condition to study the composite material performance when it is exposed to water, heat and chemicals in real condition. The study was carried out by using different test methods such as microscopy, thermal and chemical as well as mechanical analyses.

Keywords: composite, material, rehabilitation, structure

Procedia PDF Downloads 232

Authors: Xiangtuo Chen, Paul-Henry Cournéde

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Crop yield prediction is a paramount issue in agriculture. The main idea of this paper is to find out efficient way to predict the yield of corn based meteorological records. The prediction models used in this paper can be classified into model-driven approaches and data-driven approaches, according to the different modeling methodologies. The model-driven approaches are based on crop mechanistic modeling. They describe crop growth in interaction with their environment as dynamical systems. But the calibration process of the dynamic system comes up with much difficulty, because it turns out to be a multidimensional non-convex optimization problem. An original contribution of this paper is to propose a statistical methodology, Multi-Scenarios Parameters Estimation (MSPE), for the parametrization of potentially complex mechanistic models from a new type of datasets (climatic data, final yield in many situations). It is tested with CORNFLO, a crop model for maize growth. On the other hand, the data-driven approach for yield prediction is free of the complex biophysical process. But it has some strict requirements about the dataset. A second contribution of the paper is the comparison of these model-driven methods with classical data-driven methods. For this purpose, we consider two classes of regression methods, methods derived from linear regression (Ridge and Lasso Regression, Principal Components Regression or Partial Least Squares Regression) and machine learning methods (Random Forest, k-Nearest Neighbor, Artificial Neural Network and SVM regression). The dataset consists of 720 records of corn yield at county scale provided by the United States Department of Agriculture (USDA) and the associated climatic data. A 5-folds cross-validation process and two accuracy metrics: root mean square error of prediction(RMSEP), mean absolute error of prediction(MAEP) were used to evaluate the crop prediction capacity. The results show that among the data-driven approaches, Random Forest is the most robust and generally achieves the best prediction error (MAEP 4.27%). It also outperforms our model-driven approach (MAEP 6.11%). However, the method to calibrate the mechanistic model from dataset easy to access offers several side-perspectives. The mechanistic model can potentially help to underline the stresses suffered by the crop or to identify the biological parameters of interest for breeding purposes. For this reason, an interesting perspective is to combine these two types of approaches.

Keywords: crop yield prediction, crop model, sensitivity analysis, paramater estimation, particle swarm optimization, random forest

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Authors: Qais H. Alsafasfeh

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The aim of this paper is to provide a forecast of the peak demand for the next 15 years for electrical distribution companies. The proposed methodology provides both the peak demand and its location for the next 15 years. This paper describes the Spatial Load Forecasting model used, the information provided by electrical distribution company in Jordan, the workflow followed, the parameters used and the assumptions made to run the model. The aim of this paper is to provide a forecast of the peak demand for the next 15 years for electrical distribution companies. The proposed methodology provides both the peak demand and its location for the next 15 years. This paper describes the Spatial Load Forecasting model used, the information provided by electrical distribution company in Jordan, the workflow followed, the parameters used and the assumptions made to run the model.

Keywords: load forecast, peak demand, spatial load, electrical distribution

Procedia PDF Downloads 492

Authors: Ioannis Manikas

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The increased consumer demand for environmentally friendly production and distribution practices and the stricter environmental regulations turned environmental aspects into important criteria in business decision-making. On the other hand, Food and Nutrition Security (FNS) has evolved dramatically during the last decades in theory and practice serving as a reference point for exchanging experiences among all agents involved in programs and projects to fostering policy and strategy development. Global pressures make it more important than ever to gain a better understanding of the contribution that agrifood businesses make to FNS and to examine ways to make them more resilient in an increasingly globalized and uncertain world. This study extends the standard three-dimensional model of sustainability to include two more dimensions: A technological dimension and a policy/political dimension. Apart from the economic, environmental and social dimensions regularly used in sustainability literature, the extended model will accurately represent the measures and policies addressing food and nutrition security.

Keywords: food and nutrition security, sustainability, food safety, resilience

Procedia PDF Downloads 333

Authors: Camila Hurtado, Maria A. Morales, Diego Torres, L.H. Reyes, Alejandro Maranon, Alicia Porras

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This work presents the physical and chemical characterization of beer brand fibers and the properties of novel composite materials made of these fibers and polylactic acid (PLA). Treated and untreated fibers were physically characterized in terms of their moisture content (ASTM D1348), density, and particle size (ASAE S319.2). A chemical analysis following TAPPI standards was performed to determine ash, extractives, lignin, and cellulose content on fibers. Thermal stability was determined by TGA analysis, and an FTIR was carried out to check the influence of the alkali treatment in fiber composition. An alkali treatment with NaOH (5%) of fibers was performed for 90 min, with the objective to improve the interfacial adhesion with polymeric matrix in composites. Composite materials based on either treated or untreated beer brand fibers and polylactic acid (PLA) were developed characterized in tension (ASTM D638), bending (ASTM D790) and impact (ASTM D256). Before composites manufacturing, PLA and brand beer fibers (10 wt.%) were mixed in a twin extruder with a temperature profile between 155°C and 180°C. Coupons were manufactured by compression molding (110 bar) at 190°C. Physical characterization showed that alkali treatment does not affect the moisture content (6.9%) and the density (0.48 g/cm³ for untreated fiber and 0.46 g/cm³ for the treated one). Chemical and FTIR analysis showed a slight decrease in ash and extractives. Also, a decrease of 47% and 50% for lignin and hemicellulose content was observed, coupled with an increase of 71% for cellulose content. Fiber thermal stability was improved with the alkali treatment at about 10°C. Tensile strength of composites was found to be between 42 and 44 MPa with no significant statistical difference between coupons with either treated or untreated fibers. However, compared to neat PLA, composites with beer bran fibers present a decrease in tensile strength of 27%. Young modulus increases by 10% with treated fiber, compared to neat PLA. Flexural strength decreases in coupons with treated fiber (67.7 MPa), while flexural modulus increases (3.2 GPa) compared to neat PLA (83.3 MPa and 2.8 GPa, respectively). Izod impact test results showed an improvement of 99.4% in coupons with treated fibers - compared with neat PLA.

Keywords: beer bran, characterization, green composite, polylactic acid, surface treatment

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Authors: H. Barati, M. Wu, A. Kharicha, A. Ludwig

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A transient model for nozzle clogging has been developed and successfully validated against a laboratory experiment. Key steps of clogging are considered: transport of particles by turbulent flow towards the nozzle wall; interactions between fluid flow and nozzle wall, and the adhesion of the particle on the wall; the growth of the clog layer and its interaction with the flow. The current paper is to investigate the mesh (size and type) sensitivity of the model in both two and three dimensions. It is found that the algorithm for clog growth alone excluding the flow effect is insensitive to the mesh type and size, but the calculation including flow becomes sensitive to the mesh quality. The use of 2D meshes leads to overestimation of the clog growth because the 3D nature of flow in the boundary layer cannot be properly solved by 2D calculation. 3D simulation with tetrahedron mesh can also lead to an error estimation of the clog growth. A mesh-independent result can be achieved with hexahedral mesh, or at least with triangular prism (inflation layer) for near-wall regions.

Keywords: clogging, continuous casting, inclusion, simulation, submerged entry nozzle

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Authors: Shuo Tian, Xuepiao Bai, Jianqin Shang, Pengtao Gai, Yuansong Zeng

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Shot peen forming is an essential forming process for aircraft metal wing panel. With the development of computer simulation technology, scholars have proposed a numerical simulation method of shot peen forming based on stress field. Three shot peen forming indexes of crater diameter, shot speed and surface coverage are required as simulation parameters in the stress field method. It is necessary to establish the relationship between simulation and experimental process parameters in order to simulate the deformation under different shot peen forming parameters. The shot peen forming tests of the 2024-T351 aluminum alloy workpieces were carried out using uniform test design method, and three factors of air pressure, feed rate and shot flow were selected. The second-order response surface model between simulation parameters and uniform test factors was established by stepwise regression method using MATLAB software according to the results. The response surface model was combined with the stress field method to simulate the shot peen forming deformation of the workpiece. Compared with the experimental results, the simulated values were smaller than the corresponding test values, the maximum and average errors were 14.8% and 9%, respectively.

Keywords: shot peen forming, process parameter, response surface model, numerical simulation

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Authors: Aber Salem Aboalgasm, Rupert Ward

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This study investigated some results of the use of digital art tools by junior school children in order to discover if these tools could promote artistic ability and creativity. The study considers the ease of use and usefulness of the tools as well as how to assess artwork produced by digital means. As the use of these tools is a relatively new development in Art education, this study may help educators in their choice of which tools to use and when to use them. The study also aims to present a model for the assessment of students’ artistic development and creativity by studying their artistic activity. This model can help in determining differences in students’ creative ability and could be useful both for teachers, as a means of assessing digital artwork, and for students, by providing the motivation to use the tools to their fullest extent. Sixteen students aged nine to ten years old were observed and recorded while they used the digital drawing tools. The study found that, according to the students’ own statements, it was not the ease of use but the successful effects the tools provided which motivated the children to use them.

Keywords: artistic ability, creativity, drawing digital tool, TAM model, psychomotor domain

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Authors: E. A. Kiridi, A. O. Ogunlela

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Pollutants from aquacultural practices constitute environmental problems and phytoremediation could offer cheaper environmentally sustainable alternative since equipment using advanced treatment for fish tank effluent is expensive to import, install, operate and maintain, especially in developing countries. The main objective of this research was, therefore, to develop a mathematical model for phytoremediation by aquatic plants in aquaculture wastewater. Other objectives were to evaluate the retention times on phytoremediation rates using the model and to measure the nutrient level of the aquaculture effluent and phytoremediation rates of three aquatic macrophytes, namely; water hyacinth (Eichornia crassippes), water lettuce (Pistial stratoites) and morning glory (Ipomea asarifolia). A completely randomized experimental design was used in the study. Approximately 100 g of each macrophyte were introduced into the hydroponic units and phytoremediation indices monitored at 8 different intervals from the first to the 28th day. The water quality parameters measured were pH and electrical conductivity (EC). Others were concentration of ammonium–nitrogen (NH₄⁺ -N), nitrite- nitrogen (NO₂⁻ -N), nitrate- nitrogen (NO₃⁻ -N), phosphate –phosphorus (PO₄³⁻ -P), and biomass value. The biomass produced by water hyacinth was 438.2 g, 600.7 g, 688.2 g and 725.7 g at four 7–day intervals. The corresponding values for water lettuce were 361.2 g, 498.7 g, 561.2 g and 623.7 g and for morning glory were 417.0 g, 567.0 g, 642.0 g and 679.5g. Coefficient of determination was greater than 80% for EC, TDS, NO₂⁻ -N, NO₃⁻ -N and 70% for NH₄⁺ -N using any of the macrophytes and the predicted values were within the 95% confidence interval of measured values. Therefore, the model is valuable in the design and operation of phytoremediation systems for aquaculture effluent.

Keywords: aquaculture effluent, macrophytes, mathematical model, phytoremediation

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Authors: Jackson Simon Lusagalika, Lai Hsin-Chih, Dai Yu-Tung

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Particles with an aerodynamic diameter of 2.5 meters or less are referred to as "fine particulate matter" (PM₂.₅) are easily inhaled and can go deeper into the lungs than other particles in the atmosphere, where it may have detrimental health consequences. In this study, we use a hybrid model that combined CMAQ and AERMOD as well as initial meteorological fields from the Weather Research and Forecasting (WRF) model to study the impact of power plant PM₂.₅ emissions in South Taiwan since it frequently experiences higher PM₂.₅ levels. A specific date of March 3, 2022, was chosen as a result of a power outage that prompted the bulk of power plants to shut down. In some way, it is not conceivable anywhere in the world to turn off the power for the sole purpose of doing research. Therefore, this catastrophe involving a power outage and the shutdown of power plants offers a great occasion to evaluate the impact of air pollution driven by this power sector. As a result, four numerical experiments were conducted in the study using the Continuous Emission Data System (CEMS), assuming that the power plants continued to function normally after the power outage. The hybrid model results revealed that power plants have a minor impact in the study region. However, we examined the accumulation of PM₂.₅ in the study and discovered that once the vortex at 925hPa was established and moved to the north of Taiwan's coast, the study region experienced higher observed PM₂.₅ concentrations influenced by meteorological factors. This study recommends that decision-makers take into account not only control techniques, specifically emission reductions, but also the atmospheric and meteorological implications for future investigations.

Keywords: PM₂.₅ concentration, powerplants, hybrid air quality model, CEMS, Vorticity

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Authors: Sihem Chaibeddra, Fattoum Kharchi, Fahim Kahlouche, Youcef Benna

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In the recent years, a considerable level of interest has been developed on the use of earth in construction, led by its rediscovery as an environmentally building material. The Stabilized Earth Concrete (SEC) is a good alternative to the cement concrete, thanks to its thermal and moisture regulating features. Many parameters affect the behavior of stabilized earth concrete. This article presents research results related to the influence of the compacting nature on some SEC properties namely: The mechanical behavior, capillary absorption, shrinkage and sustainability to water erosion, and this, basing on two types of compacting: Manual and semi-automatic.

Keywords: behavior, compacting, manual, SEC, semi-automatic

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Authors: Dongping Fang, Lian Duan, Xiaojing Yuan, Mike Xu, Allyn Klunder, Kevin Tan, Suiting Cao, Yeqing Ji

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Accurate prediction of a medical condition with straight clinical evidence is a long-sought topic in the medical management and health insurance field. Although great progress has been made with machine learning algorithms, the medical community is still, to a certain degree, suspicious about the model's accuracy and interpretability. This paper presents an innovative hierarchical attention deep learning model to achieve good prediction and clear interpretability that can be easily understood by medical professionals. This deep learning model uses a hierarchical attention structure that matches naturally with the medical history data structure and reflects the member’s encounter (date of service) sequence. The model attention structure consists of 3 levels: (1) attention on the medical code types (diagnosis codes, procedure codes, lab test results, and prescription drugs), (2) attention on the sequential medical encounters within a type, (3) attention on the medical codes within an encounter and type. This model is applied to predict the occurrence of stage 3 chronic kidney disease (CKD3), using three years’ medical history of Medicare Advantage (MA) members from a top health insurance company. The model takes members’ medical events, both claims and electronic medical record (EMR) data, as input, makes a prediction of CKD3 and calculates the contribution from individual events to the predicted outcome. The model outcome can be easily explained with the clinical evidence identified by the model algorithm. Here are examples: Member A had 36 medical encounters in the past three years: multiple office visits, lab tests and medications. The model predicts member A has a high risk of CKD3 with the following well-contributed clinical events - multiple high ‘Creatinine in Serum or Plasma’ tests and multiple low kidneys functioning ‘Glomerular filtration rate’ tests. Among the abnormal lab tests, more recent results contributed more to the prediction. The model also indicates regular office visits, no abnormal findings of medical examinations, and taking proper medications decreased the CKD3 risk. Member B had 104 medical encounters in the past 3 years and was predicted to have a low risk of CKD3, because the model didn’t identify diagnoses, procedures, or medications related to kidney disease, and many lab test results, including ‘Glomerular filtration rate’ were within the normal range. The model accurately predicts members A and B and provides interpretable clinical evidence that is validated by clinicians. Without extra effort, the interpretation is generated directly from the model and presented together with the occurrence date. Our model uses the medical data in its most raw format without any further data aggregation, transformation, or mapping. This greatly simplifies the data preparation process, mitigates the chance for error and eliminates post-modeling work needed for traditional model explanation. To our knowledge, this is the first paper on an interpretable deep-learning model using a 3-level attention structure, sourcing both EMR and claim data, including all 4 types of medical data, on the entire Medicare population of a big insurance company, and more importantly, directly generating model interpretation to support user decision. In the future, we plan to enrich the model input by adding patients’ demographics and information from free-texted physician notes.

Keywords: deep learning, interpretability, attention, big data, medical conditions

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Authors: Francesco Marotti de Sciarra, Raffaele Barretta

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Starting from nonlocal continuum mechanics, a thermodynamically new nonlocal model of Euler-Bernoulli nanobeams is provided. The nonlocal variational formulation is consistently provided and the governing differential equation for transverse displacement are presented. Higher-order boundary conditions are then consistently derived. An example is contributed in order to show the effectiveness of the proposed model.

Keywords: Bernoulli-Euler beams, nanobeams, nonlocal elasticity, closed-form solutions

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Authors: Kathaleeya Chanda, Panupong Chanplin, Suppara Charoenpoom

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This purposes of the development of creative instruction model through digital media are to: 1) enable learners to learn from instruction media application; 2) help learners implementing instruction media correctly and appropriately; and 3) facilitate learners to apply technology for searching information and practicing skills to implement technology creatively. The sample group consists of 130 cases of secondary students studying in Bo Kluea School, Bo Kluea Nuea Sub-district, Bo Kluea District, Nan Province. The probability sampling was selected through the simple random sampling and the statistics used in this research are percentage, mean, standard deviation and one group pretest – posttest design. The findings are summarized as follows: The congruence index of instruction media for occupation and technology subjects is appropriate. By comparing between learning achievements before implementing the instruction media and learning achievements after implementing the instruction media, it is found that the posttest achievements are higher than the pretest achievements with statistical significance at the level of .05. For the learning achievements from instruction media implementation, pretest mean is 16.24 while posttest mean is 26.28. Besides, pretest and posttest results are compared and differences of mean are tested, the test results show that the posttest achievements are higher than the pretest achievements with statistical significance at the level of .05. This can be interpreted that the learners achieve better learning progress.

Keywords: teaching learning model, digital media, creative instruction model, Bo Kluea school

Procedia PDF Downloads 139

Authors: J.P. Henriques, E. R. Neto, G. Almeida, G. Ribeiro, J.V. Coutinho, A.B. Lugli

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Industry 4.0, or the Fourth Industrial Revolution, is transforming the relationship between people and machines. In this scenario, some technologies such as Cloud Computing, Internet of Things, Augmented Reality, Artificial Intelligence, Additive Manufacturing, among others, are making industries and devices increasingly intelligent. One of the most powerful technologies of this new revolution is the Digital Twin, which allows the virtualization of a real system or process. In this context, the present paper addresses the linear and nonlinear dynamic study of a didactic level plant using Digital Twin. In the first part of the work, the level plant is identified at a fixed point of operation, BY using the existing method of least squares means. The linearized model is embedded in a Digital Twin using Automation Studio® from Famous Technologies. Finally, in order to validate the usage of the Digital Twin in the linearized study of the plant, the dynamic response of the real system is compared to the Digital Twin. Furthermore, in order to develop the nonlinear model on a Digital Twin, the didactic level plant is identified by using the method proposed by Hammerstein. Different steps are applied to the plant, and from the Hammerstein algorithm, the nonlinear model is obtained for all operating ranges of the plant. As for the linear approach, the nonlinear model is embedded in the Digital Twin, and the dynamic response is compared to the real system in different points of operation. Finally, yet importantly, from the practical results obtained, one can conclude that the usage of Digital Twin to study the dynamic systems is extremely useful in the industrial environment, taking into account that it is possible to develop and tune controllers BY using the virtual model of the real systems.

Keywords: industry 4.0, digital twin, system identification, linear and nonlinear models

Procedia PDF Downloads 139

Authors: Burak Oksuzoglu, Savas Yildirim, Ferhat Kutlu

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Opinion mining (OM) is one of the natural language processing (NLP) problems to determine the polarity of opinions, mostly represented on a positive-neutral-negative axis. The data for OM is usually collected from various social media platforms. In an era where social media has considerable control over companies’ futures, it’s worth understanding social media and taking actions accordingly. OM comes to the fore here as the scale of the discussion about companies increases, and it becomes unfeasible to gauge opinion on individual levels. Thus, the companies opt to automize this process by applying machine learning (ML) approaches to their data. For the last two decades, OM or sentiment analysis (SA) has been mainly performed by applying ML classification algorithms such as support vector machines (SVM) and Naïve Bayes to a bag of n-gram representations of textual data. With the advent of deep learning and its apparent success in NLP, traditional methods have become obsolete. Transfer learning paradigm that has been commonly used in computer vision (CV) problems started to shape NLP approaches and language models (LM) lately. This gave a sudden rise to the usage of the pretrained language model (PTM), which contains language representations that are obtained by training it on the large datasets using self-supervised learning objectives. The PTMs are further fine-tuned by a specialized downstream task dataset to produce efficient models for various NLP tasks such as OM, NER (Named-Entity Recognition), Question Answering (QA), and so forth. In this study, the traditional and modern NLP approaches have been evaluated for OM by using a sizable corpus belonging to a large private company containing about 76,000 comments in Turkish: SVM with a bag of n-grams, and two chosen pre-trained models, multilingual universal sentence encoder (MUSE) and bidirectional encoder representations from transformers (BERT). The MUSE model is a multilingual model that supports 16 languages, including Turkish, and it is based on convolutional neural networks. The BERT is a monolingual model in our case and transformers-based neural networks. It uses a masked language model and next sentence prediction tasks that allow the bidirectional training of the transformers. During the training phase of the architecture, pre-processing operations such as morphological parsing, stemming, and spelling correction was not used since the experiments showed that their contribution to the model performance was found insignificant even though Turkish is a highly agglutinative and inflective language. The results show that usage of deep learning methods with pre-trained models and fine-tuning achieve about 11% improvement over SVM for OM. The BERT model achieved around 94% prediction accuracy while the MUSE model achieved around 88% and SVM did around 83%. The MUSE multilingual model shows better results than SVM, but it still performs worse than the monolingual BERT model.

Keywords: BERT, MUSE, opinion mining, pretrained language model, SVM, Turkish

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Authors: Harish Rajak, Swati Singh

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A series of novel pyrimidine based semicarbazone were designed and synthesized on the basis of semicarbazone based pharmacophoric model to satisfy the structural prerequisite crucial for antiepileptic activity. The semicarbazones based pharmacophoric model consists of following four essential binding sites: (i) An aryl hydrophobic binding site with halo substituent; (ii) A hydrogen bonding domain; (iii) An electron donor group and (iv) Another hydrophobic-hydrophilic site controlling the pharmacokinetic features of the anticonvulsant. The aryl semicarbazones has been recognized as a structurally novel class of compounds with remarkable anticonvulsant activity. In the present study, all the test semicarbazones were subjected to molecular docking using Glide v5.8. Some of the compounds were found to interact with ARG192, GLU270 and THR353 residues of 1OHV protein, present in GABA-AT receptor. The chemical structures of the synthesized molecules were characterized by elemental and spectral (IR, 1H NMR, 13C NMR and MS) analysis. The anticonvulsant activities of the compounds were investigated using maximal electroshock seizure (MES) and subcutaneous pentylenetrtrazole (scPTZ) models. The neurotoxicity was evaluated in mice by the rotorod test. The attempts were also made to establish structure-activity relationships among synthesized compounds. The results of the present study confirmed that the pharmacophore model with four binding sites is essential for antiepileptic activity.

Keywords: pyrimidine, semicarbazones, anticonvulsant activity, neurotoxicity

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Authors: Yaru Meng

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The study contributes to the small but growing literature around computerized approaches to dynamic assessment (C-DA), wherein individual items are accompanied by mediating prompts. Mediation in the current computerized dynamic listening assessment (CDLA) was informed by an attribute-based mediation model (AMM) that identified the underlying L2 listening cognitive abilities and associated descriptors. The AMM served to focus mediation during C-DA on particular cognitive abilities with a goal of specifying areas of learner difficulty. 86 low-intermediate L2 English learners from a university in China completed three listening assessments, with an experimental group receiving the CLDA system and a control group a non-dynamic assessment. As an assessment, the use of the AMM in C-DA generated detailed diagnoses for each learner. In addition, both within- and between-group repeated ANOVA found greater gains at the level of specific attributes among C-DA learners over the course of a 5-week study. Directions for future research are discussed.

Keywords: computerized dynamic assessment, effectiveness, English as foreign language listening, attribute-based mediation model

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Authors: F. Jiménez, R. Jódar, M. Martín, G. Sánchez, G. Sciavicco

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Abstract—Attribute or feature selection is one of the basic strategies to improve the performances of data classification tasks, and, at the same time, to reduce the complexity of classifiers, and it is a particularly fundamental one when the number of attributes is relatively high. Its application to unsupervised classification is restricted to a limited number of experiments in the literature. Evolutionary computation has already proven itself to be a very effective choice to consistently reduce the number of attributes towards a better classification rate and a simpler semantic interpretation of the inferred classifiers. We present a feature selection wrapper model composed by a multi-objective evolutionary algorithm, the clustering method Expectation-Maximization (EM), and the classifier C4.5 for the unsupervised classification of data extracted from a psychological test named BASC-II (Behavior Assessment System for Children - II ed.) with two objectives: Maximizing the likelihood of the clustering model and maximizing the accuracy of the obtained classifier. We present a methodology to integrate feature selection for unsupervised classification, model evaluation, decision making (to choose the most satisfactory model according to a a posteriori process in a multi-objective context), and testing. We compare the performance of the classifier obtained by the multi-objective evolutionary algorithms ENORA and NSGA-II, and the best solution is then validated by the psychologists that collected the data.

Keywords: evolutionary computation, feature selection, classification, clustering

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Authors: Harish Rajak, Preeti Patel

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HDAC inhibitors can reactivate gene expression and inhibit the growth and survival of cancer cells. The 3D-QSAR and Pharmacophore modeling studies were performed to identify important pharmacophoric features and correlate 3D-chemical structure with biological activity. The pharmacophore hypotheses were developed using e-pharmacophore script and phase module. Pharmacophore hypothesis represents the 3D arrangement of molecular features necessary for activity. A series of 55 compounds with well-assigned HDAC inhibitory activity was used for 3D-QSAR model development. Best 3D-QSAR model, which is a five PLS factor model with good statistics and predictive ability, acquired Q2 (0.7293), R2 (0.9811) and standard deviation (0.0952). Molecular docking were performed using Histone Deacetylase protein (PDB ID: 1t69) and prepared series of hydroxamic acid based HDAC inhibitors. Docking study of compound 43 show significant binding interactions Ser 276 and oxygen atom of dioxine cap region, Gly 151 and amino group and Asp 267 with carboxyl group of CONHOH, which are essential for anticancer activity. On docking, most of the compounds exhibited better glide score values between -8 to -10.5. We have established structure activity correlation using docking, energetic based pharmacophore modelling, pharmacophore and atom based 3D QSAR model. The results of these studies were further used for the design and testing of new HDAC analogs.

Keywords: Docking, e-pharmacophore, HDACIs, QSAR, Suberoylanilidehydroxamic acid.

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Authors: hadi kargar, bahador shabani

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In this article, 16 cylinder motor combustion CFD modeling with a diameter of 165 mm and 195 mm along the way to help the FIRE software to optimize its function to work. A three-dimensional model of the processes that formed inside the cylinder made that involves mixing the fuel and air, ignition and spraying. In this three-dimensional model, all chemical species, density of air fuel spraying and spray with full profile intended to detailed results from mixing the fuel and air, igniting the ignition advance, spray, and mixed media in different times and get fit by moving the piston. Optimal selection of the model for the shape of the piston and spraying fuel specifications (including the management of spraying, the number of azhneh hole, start time of spraying and spraying angle) to achieve the best fuel consumption and minimal pollution. The spray hole 6 and 7 in three different configurations with five spraying and gives the best geometry and various performances in the simulation. 6 hole spray angle, finally spraying 72.5 degrees and two forms of spraying a better performance in comparison with other items of their own.

Keywords: spray, FIRE, CFD, optimize, diesel engine

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Authors: Ruoyu Mao

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Exploring the relationship between urban green development and regional integration level is of great significance for realising regional high quality and sustainable development. Based on the Tapio decoupling model and the theoretical framework of urban green development and regional integration, this paper builds an analysis system, makes a quantitative analysis of urban green development and regional integration level in a certain period, and discusses the relationship between the two. It also takes China's Yangtze River Delta urban agglomeration as an example to study the degree of decoupling, the type of decoupling, and the trend of the evolution of the spatio-temporal pattern of decoupling between the level of urban green development and the level of regional integration in the period of 2014-2021, with the aim of providing a useful reference for the future development of the region.

Keywords: regional integration, urban green development, Tapio decoupling model, Yangtze River Delta urban agglomeration

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Authors: A. T. Kuda, J. J. Dayya, A. Jimoh

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This paper investigates the fault detection and Isolation technique of measurement data sets from a three tank system using analytical model-based temporal redundancy which is based on residual generation using parity equations/space approach. It further briefly outlines other approaches of model-based residual generation. The basic idea of parity space residual generation in temporal redundancy is dynamic relationship between sensor outputs and actuator inputs (input-output model). These residuals where then used to detect whether or not the system is faulty and indicate the location of the fault when it is faulty. The method obtains good results by detecting and isolating faults from the considered data sets measurements generated from the system.

Keywords: fault detection, fault isolation, disturbing influences, system failure, parity equation/relation, structured parity equations

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Authors: J. Junk, M. Eickermann

Abstract:

The Small Hive Beetle, Aethina tumida (Coleoptera: Nitidulidae) is a parasite for honey bee colonies, Apis mellifera, and was recently introduced to the European continent, accidentally. Based on the literature, a model was developed by using regional meteorological variables (daily values of minimum, maximum and mean air temperature as well as mean soil temperature at 50 mm depth) to calculate the time-point of hive invasion by A. tumida in springtime, the development duration of pupae as well as the number of generations of A. tumida per year. Luxembourg was used as a test region for our model for 2005 to 2013. The model output indicates a successful surviving of the Small Hive Beetle in Luxembourg with two up to three generations per year. Additionally, based on our meteorological data sets a first migration of SHB to apiaries can be expected from mid of March up to April. Our approach can be transferred easily to other countries to estimate the risk potential for a successful introduction and spreading of A. tumida in Western Europe.

Keywords: Aethina tumida, air temperature, larval development, soil temperature

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Authors: Safa'a M. Rasheed, Saba A. Gheni, Wadood T. Mohamed

Abstract:

Phenolic compounds are the most carcinogenic pollutants in waste water in effluents of refineries and pulp industry. Catalytic wet air oxidation is an efficient industrial treatment process to oxidize phenolic compounds into unharmful organic compounds. Mode of flow of the fluid to be treated is a dominant factor in determining effectiveness of the catalytic process. The present study aims to obtain a mathematical model describing the conversion of phenolic compounds as a function of the process variables; mode of flow (trickling and pulsing), temperature, pressure, along with a high concentration of phenols and a platinum supported alumina catalyst. The model was validated with the results of experiments obtained in a fixed bed reactor. High pressure and temperature were employed at 8 bar and 140 °C. It has been found that conversion of phenols is highly influenced by mode of flow and the change is caused by changes occurred in hydrodynamic regime at the time of pulsing flow mode, thereby a temporal variation in wetting efficiency of platinum prevails; which in turn increases and/or decreases contact time with phenols in wastewater. The model obtained was validated with experimental results, and it is found that the model is a good agreement with the experimental results.

Keywords: wastewater, phenol, pulsing flow, wet oxidation, high pressure

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Authors: Amr S. Fahil, Eman Ghoneim

Abstract:

Egypt made significant efforts during the past few years to discover significant renewable energy sources. Regions in Egypt that have been identified for geothermal potential investigation include the Gulf of Suez and the Western Desert. One of the most promising sites for the development of Egypt's Northern Western Desert is Siwa Oasis. The geological setting of the oasis, a tectonically generated depression situated in the northernmost region of the Western desert, supports the potential for substantial geothermal resources. Field data obtained from 27 deep oil wells along the Western Desert included bottom-hole temperature (BHT) depth to basement measurements, and geological maps; data were utilized in this study. The major lithological units, elevation, surface gradient, lineaments density, and remote sensing multispectral and topographic were mapped together to generate the related physiographic variables. Eleven thematic layers were integrated in a geographic information system (GIS) to create geothermal maps to aid in the detection of significant potential geothermal spots along the Siwa Oasis and its vicinity. The contribution of total magnetic intensity data with reduction to the pole (RTP) to the first investigation of the geothermal potential in Siwa Oasis is applied in this work. The integration of geospatial data with magnetic field measurements showed a clear correlation between areas of high heat flow and magnetic anomalies. Such anomalies can be interpreted as related to the existence of high geothermal energy and dense rock, which also have high magnetic susceptibility. The outcomes indicated that the study area has a geothermal gradient ranging from 18 to 42 °C/km, a heat flow ranging from 24.7 to 111.3 m.W. k−1, a thermal conductivity of 1.3–2.65 W.m−1.k−1 and a measured amplitude temperature maximum of 100.7 °C. The southeastern part of the Siwa Oasis, and some sporadic locations on the eastern section of the oasis were found to have significant geothermal potential; consequently, this location is suitable for future geothermal investigation. The adopted method might be applied to identify significant prospective geothermal energy locations in other regions of Egypt and East Africa.

Keywords: magnetic data, SRTM, depth to basement, remote sensing, GIS, geothermal gradient, heat flow, thermal conductivity

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Authors: Isa Ahmadi, Ramin Khamedi

Abstract:

In this paper, the elasto-plastic and cyclic torsion of a shaft is studied using a finite element method. The Prager kinematic hardening theory of plasticity with the Ramberg and Osgood stress-strain equation is used to evaluate the cyclic loading behavior of the shaft under the torsional loading. The material of shaft is assumed to follow the non-linear strain hardening property based on the Prager model. The finite element method with C1 continuity is developed and used for solution of the governing equations of the problem. The successive substitution iterative method is used to calculate the distribution of stresses and plastic strains in the shaft due to cyclic loads. The shear stress, effective stress, residual stress and elastic and plastic shear strain distribution are presented in the numerical results.

Keywords: cyclic loading, finite element analysis, Prager kinematic hardening model, torsion of shaft

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Authors: Rolandas Strazdas, Jurate Cerneviciute

Abstract:

Creative industries are defined as those industries which produce tangible or intangible artistic and creative output and have a potential for income generation by exploitingcultural assets and producing knowledge-based goods and services (both traditional and contemporary). With the emergence of an entire sector of creative industriestriggered by the development of creative products managingcreativity-based business processes becomes a critical issue. Diverse managerial practices and models on effective management of creativity have beenexamined in scholarly literature. Even thoughthese studies suggest how creativity in organisations can be nourished, they do not sufficiently relate the proposed practices to the underlying business processes. The article analyses a range of business process improvement methods such as PDCA, DMAIC, DMADV and TOC. The strengths and weaknesses of these methods aimed to improvethe innovation development process are identified. Based on the analysis of the existing improvement methods, a continuous improvement model was developed and presented in the article.

Keywords: continuous improvement, creative industries, improvement model, process mapping

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Authors: T. Chinyoka

Abstract:

We present the Johnson-Segalman constitutive model to capture certain fluid flow phenomena that has been experimentally observed in the flow of complex polymeric fluids. In particular, experimentally observed phenomena such as shear banding, spurt and slip are explored and/or explained in terms of the non-monotonic shear-stress versus shear-rate relationships. We also explore the effects of the inclusion of physical flow aspects such as wall porosity on shear banding. We similarly also explore the effects of the inclusion of mathematical modelling aspects such as stress diffusion into the stress constitutive models in order to predict shear-stress (or shear-rate) paths. We employ semi-implicit finite difference methods for all the computational solution procedures.

Keywords: Johnson-Segalman model, diffusive Johnson-Segalman model, shear banding, finite difference methods, complex fluid flow

Procedia PDF Downloads 363