Search results for: root uptake models

Authors: Azadeh Jafari, Robert G. Owens

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In this study, a geometrical multiscale approach which means coupling together the 2-D Navier-Stokes equations, constitutive equations and 0-D lumped parameter models is investigated. A multiscale approach, suggest a natural way of coupling detailed local models (in the flow domain) with coarser models able to describe the dynamics over a large part or even the whole cardiovascular system at acceptable computational cost. In this study we introduce a new velocity correction scheme to decouple the velocity computation from the pressure one. To evaluate the capability of our new scheme, a comparison between the results obtained with Neumann outflow boundary conditions on the velocity and Dirichlet outflow boundary conditions on the pressure and those obtained using coupling with the lumped parameter model has been performed. Comprehensive studies have been done based on the sensitivity of numerical scheme to the initial conditions, elasticity and number of spectral modes. Improvement of the computational algorithm with stable convergence has been demonstrated for at least moderate Weissenberg number. We comment on mathematical properties of the reduced model, its limitations in yielding realistic and accurate numerical simulations, and its contribution to a better understanding of microvascular blood flow. We discuss the sophistication and reliability of multiscale models for computing correct boundary conditions at the outflow boundaries of a section of the cardiovascular system of interest. In this respect the geometrical multiscale approach can be regarded as a new method for solving a class of biofluids problems, whose application goes significantly beyond the one addressed in this work.

Keywords: geometrical multiscale models, haemorheology model, coupled 2-D navier-stokes 0-D lumped parameter modeling, computational fluid dynamics

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Authors: Areej A. Alzarqaa, Shahira S. Roushdy, Ali A. Alderfasi, Fahad A. AL-Yahya, Ahmed A. Dawaba

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The resistance of mungbean (Vigna radiata L. Wilczeck) and its physiological responses to drought stress was studied in a greenhouse pot experiment. A randomized complete block Design (RCBD) with factorial arrangement having three replications of each treatment was used. Treatments included three water deficit samples (80%, 40% and 20% of field capacity), two mungbean genotypes (Kawmay-1 and VC2010) and two root-knot nematode (Meloidogyne javanica) infection levels (infected and non-infected). Results showed that water deficit stress significantly hampered most of the studied parameters, except for the shoot water content, whereas genotypes showed highly significant differences for stomatal conductance, shoot dry weight and leaf area. Shoot water content was found to be non-significant in relation to chlorophyll b, shoot dry weight and leaf area, whereas highly significant but negatively correlated with chlorophyll a and stomatal conductance. However, all other possible correlations among studied parameters were found to be highly and positively significant. Results also showed that VC 2010 surpassed Kawmay-1 in most of studied characteristics. In the present study, genotypic variation was observed for these parameters and can be used as a basis for selection of the most promising variety under drought conditions.

Keywords: drought stress, Meloidogyne javanica, mungbean, stomatal conductivity, leaf area, root-knot nematode, shoot water content

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Authors: Zhihan Li

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Nowadays, most universities use the course enrollment system considering students’ registration orders. However, the students’ preference level to certain courses is also one important factor to consider. In this research, the possibility of applying a preference-first system has been discussed and analyzed compared to the order-first system. A bipartite graph is applied to resemble the relationship between students and courses they tend to register. With the graph set up, we apply Ford-Fulkerson (F.F.) Algorithm to maximize parings between two sets of nodes, in our case, students and courses. Two models are proposed in this paper: the one considered students’ order first, and the one considered students’ preference first. By comparing and contrasting the two models, we highlight the usability of models which potentially leads to better designs for school course registration systems.

Keywords: bipartite graph, Ford-Fulkerson (F.F.) algorithm, graph theory, maximum matching

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Authors: Karen Koellner, Nanette Seago, Jennifer Jacobs, Helen Garnier

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Although studies of teacher professional development (PD) are prevalent, surprisingly most have only produced incremental shifts in teachers’ learning and their impact on students. There is a critical need to understand what teachers take up and use in their classroom practice after attending PD and why we often do not see greater changes in learning and practice. This paper is based on a mixed methods efficacy study of the Learning and Teaching Geometry (LTG) video-based mathematics professional development materials. The extent to which the materials produce a beneficial impact on teachers’ mathematics knowledge, classroom practices, and their students’ knowledge in the domain of geometry through a group-randomized experimental design are considered. Included is a close-up examination of a small group of teachers to better understand their interpretations of the workshops and their classroom uptake. The participants included 103 secondary mathematics teachers serving grades 6-12 from two US states in different regions. Randomization was conducted at the school level, with 23 schools and 49 teachers assigned to the treatment group and 18 schools and 54 teachers assigned to the comparison group. The case study examination included twelve treatment teachers. PD workshops for treatment teachers began in Summer 2016. Nine full days of professional development were offered to teachers, beginning with the one-week institute (Summer 2016) and four days of PD throughout the academic year. The same facilitator-led all of the workshops, after completing a facilitator preparation process that included a multi-faceted assessment of fidelity. The overall impact of the LTG PD program was assessed from multiple sources: two teacher content assessments, two PD embedded assessments, pre-post-post videotaped classroom observations, and student assessments. Additional data were collected from the case study teachers including additional videotaped classroom observations and interviews. Repeated measures ANOVA analyses were used to detect patterns of change in the treatment teachers’ content knowledge before and after completion of the LTG PD, relative to the comparison group. No significant effects were found across the two groups of teachers on the two teacher content assessments. Teachers were rated on the quality of their mathematics instruction captured in videotaped classroom observations using the Math in Common Observation Protocol. On average, teachers who attended the LTG PD intervention improved their ability to engage students in mathematical reasoning and to provide accurate, coherent, and well-justified mathematical content. In addition, the LTG PD intervention and instruction that engaged students in mathematical practices both positively and significantly predicted greater student knowledge gains. Teacher knowledge was not a significant predictor. Twelve treatment teachers self-selected to serve as case study teachers to provide additional videotapes in which they felt they were using something from the PD they learned and experienced. Project staff analyzed the videos, compared them to previous videos and interviewed the teachers regarding their uptake of the PD related to content knowledge, pedagogical knowledge and resources used. The full paper will include the case study of Ana to illustrate the factors involved in what teachers take up and use from participating in the LTG PD.

Keywords: geometry, mathematics professional development, pedagogical content knowledge, teacher learning

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Authors: Nikolett Deutsch, Éva Pintér, Péter Bagó, Miklós Hetényi

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Strategic technology management has emerged and evolved parallelly with strategic management paradigms. It focuses on the opportunity for organizations operating mainly in technology-intensive industries to explore and exploit technological capabilities upon which competitive advantage can be obtained. As strategic technology management involves multifunction within an organization, requires broad and diversified knowledge, and must be developed and implemented with business objectives to enable a firm’s profitability and growth, excellence in strategic technology management provides unique opportunities for organizations in terms of building a successful future. Accordingly, a framework supporting the evaluation of the technological readiness level of management can significantly contribute to developing organizational competitiveness through a better understanding of strategic-level capabilities and deficiencies in operations. In the last decade, several innovation maturity assessment models have appeared and become designated management tools that can serve as references for future practical approaches expected to be used by corporate leaders, strategists, and technology managers to understand and manage technological capabilities and capacities. The aim of this paper is to provide a comprehensive review of the state-of-the-art innovation maturity frameworks, to investigate the critical lessons learned from their application, to identify the similarities and differences among the models, and identify the main aspects and elements valid for the field and critical functions of technology management. To this end, a systematic literature review was carried out considering the relevant papers and articles published in highly ranked international journals around the 27 most widely known innovation maturity models from four relevant digital sources. Key findings suggest that despite the diversity of the given models, there is still room for improvement regarding the common understanding of innovation typologies, the full coverage of innovation capabilities, and the generalist approach to the validation and practical applicability of the structure and content of the models. Furthermore, the paper proposes an initial structure by considering the maturity assessment of the technological capacities and capabilities - i.e., technology identification, technology selection, technology acquisition, technology exploitation, and technology protection - covered by strategic technology management.

Keywords: innovation capabilities, innovation maturity models, technology audit, technology management, technology management maturity models

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Authors: Mohamed S. Sasi, Adel M. Mlitan, Abdulfattah M. Alkherraz

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This research project aims to investigate difference in relative rates concerning phosphoryl transfer relevant to biological catalysis of DNA and RNA in the pH-independent reactions. Activated Models of DNA and RNA for alkyl-aryl phosphate diesters (with 4-nitrophenyl as a good leaving group) have successfully been prepared to gather kinetic parameters. Eyring plots for the pH–independent hydrolysis of 1 and 2 were established at different temperatures in the range 100–160 °C. These measurements have been used to provide a better estimate for the difference in relative rates between the reactivity of DNA and RNA cleavage. Eyring plot gave an extrapolated rate of kH2O = 1 × 10-10 s -1 for 1 (RNA model) and 2 (DNA model) at 25°C. Comparing the reactivity of RNA model and DNA model shows that the difference in relative rates in the pH-independent reactions is surprisingly very similar at 25°. This allows us to obtain chemical insights into how biological catalysts such as enzymes may have evolved to perform their current functions.

Keywords: DNA and RNA models, relative rates, reactivity, phosphoryl transfe

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Authors: Diego Tancara, Raul Coto, Ariel Norambuena, Hoseein T. Dinani, Felipe Fanchini

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Quantum machine learning is a growing research field that aims to perform machine learning tasks assisted by a quantum computer. Kernel-based quantum machine learning models are paradigmatic examples where the kernel involves quantum states, and the Gram matrix is calculated from the overlapping between these states. With the kernel at hand, a regular machine learning model is used for the learning process. In this paper we investigate the quantum support vector machine and quantum kernel ridge models to predict the degree of non-Markovianity of a quantum system. We perform digital quantum simulation of amplitude damping and phase damping channels to create our quantum dataset. We elaborate on different kernel functions to map the data and kernel circuits to compute the overlapping between quantum states. We observe a good performance of the models.

Keywords: quantum, machine learning, kernel, non-markovianity

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Authors: Mohammad Abdollahi

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Tomato (Lycopersicon esculentum Mill.) is one of the most popular, widely grown and the second most important vegetable crop, after potatoes. Nematodes have been identified as one of the major pests affecting tomato production throughout the world. The most destructive nematodes are the genus Meloidogyne. Most widespread and devastating species of this genus are M. incognita, M. javanica, and M. arenaria. These species can cause complete crop loss under adverse growing conditions. There are several potential methods for management of the root knot nematodes. Although the chemicals are widely used against the phytonematodes, because of hazardous effects of these compounds on non-target organisms and on the environment, there is a need to develop other control strategies. Nowadays, non-chemical measures are widely used to control the plant parasitic nematodes. Biocontrol of phytonematodes is an important method among environment-friendly measures of nematode management. There are some soil-inhabiting fungi that have biocontrol potential on phytonematodes, which can be used in nematode management program. The fungus Metarhizium anisopliae, originally is an entomopathogenic bioagent. Biocontrol potential of this fungus on some phytonematodes has been reported earlier. Recently, use of organic soil amendments as well as the use of bioagents is under special attention in sustainable agriculture. This research aimed to reduce the pesticide use in control of root-knot nematode, Meloidogyne javanica in tomato. The effects of M. anisopliae IMI 330189 and different levels of oak tree debris on M. javanica were determined. The combination effect of the fungus as well as the different rates of soil amendments was determined. Pots were filled with steam pasteurized soil mixture and the six leaf tomato seedlings were inoculated with 3000 second stage larvae of M. javanica/kg of soil. After eight weeks, plant growth parameters and nematode reproduction factors were compared. Based on the results of our experiment, combination of M. anisopliae IMI 330189 and oak debris caused more than 90% reduction in reproduction factor of nematode, at the rates of 100 and 150 g/kg soil (P ≤ 0.05). As compared to control, the reduction in number of galls was 76%. It was 86% for nematode reproduction factor, showing the significance of combined effect of both tested agents. Our results showed that plant debris can increase the biological activity of the tested bioagent. It was also proved that there was no adverse effect of oak debris, which potentially has antimicrobial activity, on antagonistic power of applied bioagent.

Keywords: biological control, nematode management, organic soil, Quercus branti, root knot nematode, soil amendment

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Authors: O. U. Nwosu, C. O. Osuagwu, N. Nnawugwu, C. T. Amanze

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Phytoremediation is a green and sustainable approach to decontaminate and restore contaminated sites while maintaining the biological activity and physical structure of soils. A pot experiment was conducted for a period of 70 days to evaluate the remediation potentials of Vetiveira zizanioides, Heianthus annuus L., Zea mays, and Glycine max in concurrent removal of anthracene and Pb in co-contaminated soil. Sandy loam soils were polluted with Pb chloride salt and anthracene at three different levels (50mg/kg of Pb, 100mg/kg of Pb, and 100mg/kg of Pb+100mg/kg of anthracene) and laid out in a completely randomized design with three replicates. Shoot dry matter weight was significantly reduced (p≤0.05) in comparison to control treatments by 33%, 32%, 40%, and 6.7% when exposed to 100mg kg⁻¹ of Pb, respectively in G.max, H.annuus, Z.mays, and vetiver. There was 42%, 41%, 48%, and 7.1% growth inhibition of shoot dry matter weight of G.max, H.annuus, Z.mays, and vetiver relative to control treatments when 100 mg Pb kg⁻¹ was mixed with 100 mgkg⁻¹ anthracene. Root and shoot metal concentration in G.max, H.annuus, Z.mays, and vetiver increased with increasing concentration of Pb. Translocation factor (TF < 1) obtained for G.max, Z.mays, and vetiver suggests that these plant species predominantly retain Pb in the root portion, while the TF value (TF≥1) obtained for H.annuus suggests that it predominantly retains Pb in the shoot portion. The extractable anthracene decreased significantly (p ≤ 0.05) in soil planted with G.max, H.annuus, Z.mays, and vetiver, as well as in pots without plants. This accounted for 53% to 71% of anthracene dissipation in planted soil and 40% dissipation in unplanted soil. This result suggested that the plant species used are a promising candidate for phytoremediation.

Keywords: phytoremediation, heavy metals, polyaromatic hydrocarbon, co-contaminated soil

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Authors: Solene Masloh, Anne Chevrel, Maxime Culot, Leonardo Scapozza, Magali Zeisser-Labouebe

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The limited stability of clinically proven therapeutic antibodies limits their administration by the parenteral route. However, oral administration remains the best alternative as it is the most convenient and less invasive one. Obtaining a targeted treatment based on biologics, which can be orally administered, would, therefore, be an ideal situation to improve patient adherence and compliance. Nevertheless, the delivery of macromolecules through the intestine remains challenging because of their sensitivity to the harsh conditions of the gastrointestinal tract and their low permeability across the intestinal mucosa. To address this challenge, this project aims to demonstrate that targeting receptor-mediated endocytosis followed by transcytosis could maximize the intestinal uptake and transport of large molecules, such as Nanofitins. These affinity proteins of 7 kDa with binding properties similar to antibodies have already demonstrated retained stability in the digestive tract and local efficiency. However, their size does not allow passive diffusion through the intestinal barrier. Nanofitins having a controlled affinity for membrane receptors involved in the transcytosis mechanism used naturally for the transport of large molecules in humans were generated. Proteins were expressed using ribosome display and selected based on affinity to the targeted receptor and other characteristics. Their uptake and transport ex vivo across viable porcine intestines were investigated using an Ussing chambers system. In this paper, we will report the results achieved while addressing the different challenges linked to this study. To validate the ex vivo model, first, we proved the presence of the receptors targeted in humans on the porcine intestine. Then, after the identification of an optimal way of detection of Nanofitins, transport experiments were performed on porcine intestines with viability followed during the time of the experiment. The results, showing that the physiological process of transcytosis is capable of being triggered by the binding of Nanofitins on their target, will be reported here. In conclusion, the results show that Nanofitins can be transported across the intestinal barrier by triggering the receptor-mediated transcytosis and that the ex vivo model is an interesting technique to assess biologics absorption through the intestine.

Keywords: ex-vivo, Nanofitins, oral administration, transcytosis

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Authors: Shaomei Li, Yawen Wang, Chao Gao

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To improve tracking drift which often occurs in adaptive tracking, an algorithm based on the fusion of tracking and detection is proposed in this paper. Firstly, object tracking is posed as a binary classification problem and is modeled by partial least squares (PLS) analysis. Secondly, tracking object frame by frame via particle filtering. Thirdly, validating the tracking reliability based on both positive and negative models matching. Finally, relocating the object based on SIFT features matching and voting when drift occurs. Object appearance model is updated at the same time. The algorithm cannot only sense tracking drift but also relocate the object whenever needed. Experimental results demonstrate that this algorithm outperforms state-of-the-art algorithms on many challenging sequences.

Keywords: object tracking, tracking drift, partial least squares analysis, positive and negative models matching

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Authors: Baeza S. Roberto

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The knitted fabric suffers a deformation in its dimensions due to stretching and tension factors, transverse and longitudinal respectively, during the process in rectilinear knitting machines so it performs a dry relaxation shrinkage procedure and thermal action of prefixed to obtain stable conditions in the knitting. This paper presents a dry relaxation shrinkage prediction of Bordeaux fiber using a feed forward neural network and linear regression models. Six operational alternatives of shrinkage were predicted. A comparison of the results was performed finding neural network models with higher levels of explanation of the variability and prediction. The presence of different reposes are included. The models were obtained through a neural toolbox of Matlab and Minitab software with real data in a knitting company of Southern Guanajuato. The results allow predicting dry relaxation shrinkage of each alternative operation.

Keywords: neural network, dry relaxation, knitting, linear regression

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Authors: Broccolo Cinzia, Grigaliunaite Ruta, Saint-Nom Cloé, Savasta Guido

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The present article analyses the root causes of under-reporting and under-recording of hate crimes against Muslim women in Italy. The main findings emerged from the survey conducted between May and September 2022 within the framework of the TRUST project (co-funded by the CERV programme (CERV-2021-EQUAL) of the European Union) with relevant practitioners and members of the Muslim community, including first-generation and second-generation Muslim women residing in Italy. The findings reveal that multiple factors contribute to the low reporting rate as well as to the flaws in recording episodes of intolerance and hatred against the above-mentioned group. Lack of trust in the judiciary or the police may represent one of the main causes of under-reporting; however, the phenomenon is not limited to such aspects, and additional factors and sources of discrimination paving the way to under-recording have been identified during the survey. The significant “tendency” to not report a case of intolerance as the difficulties in identifying the discriminatory nature of the crime are two faces of the same coin and are particularly intertwined; despite this, at first, both issues need to be assessed and analysed separately in order to take their own specificities into duly consideration. By contrast, the potential solution to low recording and reporting trends should be found collectively, namely by involving all the relevant parties and bodies facing the above-mentioned issues. In this regard, a participatory and multi-agency approach may curb the root causes leading Muslim women not to report and, besides this, support law enforcement officials as well as public authorities in providing a more effective service to the victims of hatred, whether offline or online.

Keywords: hate crime, under-reporting, under-recording, Islamophobia, Muslim women

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Authors: Seyed Esmail Seyedi Bariran, Khairul Salleh Mohamed Sahari

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In multi-echelon supply chain networks, optimal supplier selection significantly depends on the accuracy of suppliers’ performance prediction. Different methods of multi criteria decision making such as ANN, GA, Fuzzy, AHP, etc have been previously used to predict the supplier performance but the “black-box” characteristic of these methods is yet a major concern to be resolved. Therefore, the primary objective in this paper is to implement an artificial intelligence-based gene expression programming (GEP) model to compare the prediction accuracy with that of ANN. A full factorial design with %95 confidence interval is initially applied to determine the appropriate set of criteria for supplier performance evaluation. A test-train approach is then utilized for the ANN and GEP exclusively. The training results are used to find the optimal network architecture and the testing data will determine the prediction accuracy of each method based on measures of root mean square error (RMSE) and correlation coefficient (R2). The results of a case study conducted in Supplying Automotive Parts Co. (SAPCO) with more than 100 local and foreign supply chain members revealed that, in comparison with ANN, gene expression programming has a significant preference in predicting supplier performance by referring to the respective RMSE and R-squared values. Moreover, using GEP, a mathematical function was also derived to solve the issue of ANN black-box structure in modeling the performance prediction.

Keywords: Supplier Performance Prediction, ANN, GEP, Automotive, SAPCO

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Authors: M. O. Oke, T. S. Workneh

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Drying behaviour of blanched sweet potato in a cabinet dryer using different five air temperatures (40-80oC) and ten sweet potato varieties sliced to 5 mm thickness were investigated. The drying data were fitted to eight models. The Modified Henderson and Pabis model gave the best fit to the experimental moisture ratio data obtained during the drying of all the varieties while Newton (Lewis) and Wang and Singh models gave the least fit. The values of Deff obtained for Bophelo variety (1.27 x 10-9 to 1.77 x 10-9 m2/s) was the least while that of S191 (1.93 x 10-9 to 2.47 x 10-9 m2/s) was the highest which indicates that moisture diffusivity in sweet potato is affected by the genetic factor. Activation energy values ranged from 0.27-6.54 kJ/mol. The lower activation energy indicates that drying of sweet potato slices requires less energy and is hence a cost and energy saving method. The drying behavior of blanched sweet potato was investigated in a cabinet dryer. Drying time decreased considerably with increase in hot air temperature. Out of the eight models fitted, the Modified Henderson and Pabis model gave the best fit to the experimental moisture ratio data on all the varieties while Newton, Wang and Singh models gave the least. The lower activation energy (0.27-6.54 kJ/mol) obtained indicates that drying of sweet potato slices requires less energy and is hence a cost and energy saving method.

Keywords: sweet potato slice, drying models, moisture ratio, moisture diffusivity, activation energy

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Authors: Saheed A. Rufai

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Courses in Pedagogy and Curriculum Development expectedly occupy a core place in the professional education components of teacher education at Lagos, Lagos State, and Sokoto State Universities. This is in keeping with the National Teacher Education Policy statement that stipulates that for student teachers to learn effectively teacher education institutions must be equipped to prepare them adequately. However, there is a growing concern over the unfaithfulness of some of the dominant Nigerian models of teacher education, to this policy statement on teacher educators’ knowledge and skills. The purpose of this paper is to comparatively evaluate both the curricular provisions and the manpower for the pedagogy and curriculum development sub-components of the Lagos, Lagos State, and Sokoto State models of teacher preparation. The paper employs a combination of quantitative and qualitative methods. Preliminary analysis revealed a new trend in teacher educators’ pedagogical knowledge and understanding, with regard to the two intertwined sub-components. The significance of such a study lies in its potential to determine the degree of conformity of each of the three models to the stipulated standards. The paper’s contribution to scholarship lies in its correlation of deficiencies in teacher educators’ professional knowledge and skills and articulation of the implications of such deficiencies for the professional knowledge and skills of the prospective teachers, with a view to providing a framework for reforms.

Keywords: curriculum development, pedagogy, teacher education, dominant Nigerian teacher preparation models

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Authors: Peyman Sheikholharam Mashhadi

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Difficulties in analyzing real world images in classical image processing and machine vision framework have motivated researchers towards considering the biology-based vision. It is a common belief that mammalian visual cortex has been adapted to the statistics of the real world images through the evolution process. There are two well-known successful models of mammalian visual cortical cells: Independent Component Analysis (ICA) and Independent Subspace Analysis (ISA). In this paper, we statistically analyze the dependencies which remain in the components after applying these models to the natural images. Also, we investigate the response of feature detectors to gratings with various parameters in order to find optimal parameters of the feature detectors. Finally, the selectiveness of feature detectors to phase, in both models is considered.

Keywords: statistics, independent component analysis, independent subspace analysis, phase, natural images

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Authors: Sidika McNeil

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Borderline personality disorder (BPD) is a personality disorder that has been found to have strong origins in childhood trauma. One of the key symptoms of BPD is an association with irregular moods swings, as well as suicidal ideation (SI). Owing to the typically severe trauma patients experience during childhood, it is hard for them to control their emotions and thus makes it hard to emotionally regulate. It is then very common for those suffering from BPD to turn to unhealthy coping mechanisms, such as substance use, unhealthy relationships, and more, often unsuccessfully creating experiences that facilitate safety which leads to further negative experiences. With the high suicide rating among children, adolescents, and teens, and an ever-increasing number of children being diagnosed with BPD, it is very important that more research is done to find further treatments for patients who are currently suffering. Methods: Utilizing data found in prior studies, this paper will analyze the literature to focus on a comprehensive treatment plan for those with DBT. It is currently suggested that with the use of dialectical behavioral therapy (DBT), a therapy that focuses on changing negative thinking patterns and pushes for more positive ones is helpful for treatment for those with BPD. Though this therapy is not a cure to BPD, it does help mitigate the risk; this essay will explore other options that can further the treatment process, such as cognitive analytical therapy (CAT), which focuses on delving into the past to find the root causes of an issue to create coping strategies and harm reduction, a type of therapy used to aid patients in lowering the use of substances without complete cessation. Results: The research provides enough evidence to link between the treatment of BPD with the utilization of CAT.

Keywords: borderline personality disorder, cognitive analytical therapy, dialectical behavioral therapy, harm reduction, suicidal ideation

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Authors: Jiun Jeon, Byung-Ju Yi

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This paper investigates modeling and shape prediction of elastic kinematic chains such as colonoscopy. 2D and 3D models of elastic kinematic chains are suggested and their behaviors are demonstrated through simulation. To corroborate the effectiveness of those models, experimental work is performed using a magnetic sensor system.

Keywords: elastic kinematic chain, shape prediction, colonoscopy, modeling

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Authors: Agus Masrukhin

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This research aims to find and analyze the model of character building in the Police Headquarters in Bali with a case study of Muslim members in improving the quality of the morality of its members. The formation of patterns of thinking, behavior, mentality, and police officers noble character, later can be used as a solution to reduce the hedonistic nature of the challenges in the era of globalization. The benefit of this study is expected to be a positive recommendation to find a constructive character building models of police officers in the Republic of Indonesia, especially Bali Police. For the long term, the discovery of the character building models can be developed for the entire police force in Indonesia. The type of research that would apply in this study researchers mix the qualitative research methods based on the narrative between the subject and the concrete experience of field research and quantitative research methods with 92 respondents from the police regional police Bali. This research used a descriptive analysis and SWOT analysis then it is presented in the FGD (focus group discussion). The results of this research indicate that the variable modeling the leadership of the police and variable police offices culture have significant influence on the implementation of spiritual development.

Keywords: positive constructive, hedonistic, character models, morality

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Authors: Zhuoneng Li, Zeeshan A. Rana, Karl W. Jenkins

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In industry, to validate a case, often a multitude of simulation are required and in order to demonstrate confidence in the process where users tend to use a coarser mesh. Therefore, it is imperative to establish the coarsest mesh that could be used while keeping reasonable simulation accuracy. To date, the two most reliable, affordable and broadly used advanced simulations are the hybrid RANS (Reynolds Averaged Navier Stokes)/LES (Large Eddy Simulation) and wall modelled LES. The potentials in these two simulations will still be developed in the next decades mainly because the unaffordable computational cost of a DNS (Direct Numerical Simulation). In the wall modelled LES, the turbulence model is applied as a sub-grid scale model in the most inner layer near the wall. The RANS turbulence models cover the entire boundary layer region in a hybrid RANS/LES (Detached Eddy Simulation) and its variants, therefore, the RANS still has a very important role in the state of art simulations. This research focuses on the turbulence model mesh sensitivity analysis where various turbulence models such as the S-A (Spalart-Allmaras), SSG (Speziale-Sarkar-Gatski), K-Omega transitional SST (Shear Stress Transport), K-kl-Omega, γ-Reθ transitional model, v2f are evaluated within the OpenFOAM. The simulations are conducted on a fully developed turbulent flow over a flat plate where the skin friction coefficient as well as velocity profiles are obtained to compare against experimental values and DNS results. A concrete conclusion is made to clarify the mesh sensitivity for different turbulence models.

Keywords: mesh sensitivity, turbulence models, OpenFOAM, RANS

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Authors: Niya Chen, Jennifer Chan

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In the financial market, risk management helps to minimize potential loss and maximize profit. There are two ways to assess risks; the first way is to calculate the risk directly based on the volatility. The most common risk measurements are Value at Risk (VaR), sharp ratio, and beta. Alternatively, we could look at the quantile of the return to assess the risk. Popular return models such as GARCH and stochastic volatility (SV) focus on modeling the mean of the return distribution via capturing the volatility dynamics; however, the quantile/expectile method will give us an idea of the distribution with the extreme return value. It will allow us to forecast VaR using return which is direct information. The advantage of using these non-parametric methods is that it is not bounded by the distribution assumptions from the parametric method. But the difference between them is that expectile uses a second-order loss function while quantile regression uses a first-order loss function. We consider several quantile functions, different volatility measures, and estimates from some volatility models. To estimate the expectile of the model, we use Realized Conditional Autoregressive Expectile (CARE) model with the bayesian method to achieve this. We would like to see if our proposed models outperform existing models in cryptocurrency, and we will test it by using Bitcoin mainly as well as Ethereum.

Keywords: expectile, CARE Model, CARR Model, quantile, cryptocurrency, Value at Risk

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Authors: Marzieh Fotovvat, Farzaneh Najafi, Ramazan Ali Khavari-Nejad, Daryush Talei, Farhad Rejali

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Salvia leriifolia Benth. is one of the valuable and perennial medicinal plants of the Lamiaceae family, geographically growing in the south and tropical regions of Khorassan and Semnan provinces in Iran. In recent years, several medicinal properties such as antimicrobial, antifungal, anti-diabetic, analgesic, and anti-inflammatory effects have been reported from this plant. The use of elicitors such as chitosan and Arbuscular mycorrhizal fungi (AMF) symbiosis are the main methods for increasing the production of secondary metabolites, growth, and physiological factors in plants. The main aim of this study was to investigate the effects of foliar spraying applications by chitosan and/or the contribution of AMF (Glomus interaradices) on some growth factors and chlorophyll content of S. leriifolia under glasshouse conditions. The sterilized seeds were germinated by placing them into a cocopeat. After one month, seedlings that were in the 2-4 leaf stage were transferred to plastic pots (garden soil and pumice at 2:1) with or without mycorrhizal fungi. Chitosan (0, 50, 100, 200, and 400 mg L-1) was sprayed four times in the fourth month of the vegetative period. The results showed that fresh leaf weight, fresh root weight, root height, and chlorophyll content could change in the plant treated with chitosan and AMF symbiosis. So that the highest chlorophyll content and fresh weight of roots and leaves were observed in the interaction of chitosan and G. interaradices. In general, by optimizing the chitosan concentration and the use of appropriate AMF symbiosis, it is possible to improve the growth and quality of the medicinal plant S. leriifolia.

Keywords: chitosan, chlorophyll, growth factors, mycorrhiza

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Authors: Alireza Ansariyar, Safieh Laaly

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Over the last decades, the vehicle industry has shown increased interest in integrating autonomous, connected, and electrical technologies in vehicle design with the primary hope of improving mobility and road safety while reducing transportation’s environmental impact. Using the State of Maryland (M.D.) in the United States as a pilot study, this research investigates CAVs’ fuel consumption and air pollutants (C.O., PM, and NOx) and utilizes meaningful linear regression models to predict CAV’s environmental effects. Maryland transportation network was simulated in VISUM software, and data on a set of variables were collected through a comprehensive survey. The number of pollutants and fuel consumption were obtained for the time interval 2010 to 2021 from the macro simulation. Eventually, four linear regression models were proposed to predict the amount of C.O., NOx, PM pollutants, and fuel consumption in the future. The results highlighted that CAVs’ pollutants and fuel consumption have a significant correlation with the income, age, and race of the CAV customers. Furthermore, the reliability of four statistical models was compared with the reliability of macro simulation model outputs in the year 2030. The error of three pollutants and fuel consumption was obtained at less than 9% by statistical models in SPSS. This study is expected to assist researchers and policymakers with planning decisions to reduce CAV environmental impacts in M.D.

Keywords: connected and autonomous vehicles, statistical model, environmental effects, pollutants and fuel consumption, VISUM, linear regression models

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Authors: Atucungwiire Rwebiita

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Introduction: In Uganda, provision of adolescent sexual reproductive health and rights (SRHR) services for key population is still hindered by negative attitudes, stigma and discrimination (S&D) at both the community and facility levels. To address this barrier, Integrated Community Based Initiatives (ICOBI) with support from SIDA is currently implementing a quality improvement (QI) innovative approach for strengthening the capacity of key population (KP) peer leaders and health workers to deliver friendly SRHR services without S&D. Methods: Our innovative approach involves continuous mentorship and coaching of 8 QI teams at 8 health facilities and their catchment areas. Each of the 8 teams (comprised of 5 health workers and 5 KP peer leaders) are facilitated twice a month by two QI Mentors in a 2-hour mentorship session over a period of 4 months. The QI mentors were provided a 2-weeks training on QI approaches for reducing S&D against young key populations in the delivery of SRHR Services. The mentorship sessions are guided by a manual where teams base to analyse root causes of S&D and develop key performance indicators (KPIs) in the 1st and 2nd second sessions respectively. The teams then develop action plans in the 3rd session and review implementation progress on KPIs at the end of subsequent sessions. The KPIs capture information on the attitude of health workers and peer leaders and the general service delivery setting as well as clients’ experience. A dashboard is developed to routinely track the KPIs for S&D across all the supported health facilities and catchment areas. After 4 months, QI teams share documented QI best practices and tested change packages on S&D in a learning and exchange session involving all the teams. Findings: The implementation of this approach is showing positive results. So far, QI teams have already identified the root causes of S&D against key populations including: poor information among health workers, fear of a perceived risk of infection, perceived links between HIV and disreputable behaviour. Others are perceptions that HIV & STIs are divine punishment, sex work and homosexuality are against religion and cultural values. They have also noted the perception that MSM are mentally sick and a danger to everyone. Eight QI teams have developed action plans to address the root causes of S&D. Conclusion: This approach is promising, offers a novel and scalable means to implement stigma-reduction interventions in facility and community settings.

Keywords: key populations, sexual reproductive health and rights, stigma and discrimination , quality improvement approach

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Authors: Carl van Walraven, Meltem Tuna

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Background: Network meta-analysis (NMA) quantifies the relative efficacy of 3 or more interventions from studies containing a subgroup of interventions. This study applied the analytical approach of NMA to quantify the relative accuracy of prediction models with distinct inclusion criteria that are evaluated on a common population (‘concurrent external validation’). Methods: We simulated binary events in 5000 patients using a known risk function. We biased the risk function and modified its precision by pre-specified amounts to create 15 prediction models with varying accuracy and distinct patient applicability. Prediction model accuracy was measured using the Scaled Brier Score (SBS). Overall prediction model accuracy was measured using fixed-effects methods that accounted for model applicability patterns. Prediction model accuracy was summarized as the Network Relative Model Accuracy (NeRMA) Score which ranges from -∞ through 0 (accuracy of random guessing) to 1 (accuracy of most accurate model in concurrent external validation). Results: The unbiased prediction model had the highest SBS. The NeRMA score correctly ranked all simulated prediction models by the extent of bias from the known risk function. A SAS macro and R-function was created to implement the NeRMA Score. Conclusions: The NeRMA Score makes it possible to quantify the accuracy of binomial prediction models having distinct inclusion criteria in a concurrent external validation.

Keywords: prediction model accuracy, scaled brier score, fixed effects methods, concurrent external validation

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Authors: Nureni O. Adeboye, Dawud A. Agunbiade

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This research attempts to investigate the effects of heteroscedasticity and periodicity in a Panel Data Regression Model (PDRM) by extending previous works on balanced panel data estimation within the context of fitting PDRM for Banks audit fee. The estimation of such model was achieved through the derivation of Joint Lagrange Multiplier (LM) test for homoscedasticity and zero-serial correlation, a conditional LM test for zero serial correlation given heteroscedasticity of varying degrees as well as conditional LM test for homoscedasticity given first order positive serial correlation via a two-way error component model. Monte Carlo simulations were carried out for 81 different variations, of which its design assumed a uniform distribution under a linear heteroscedasticity function. Each of the variation was iterated 1000 times and the assessment of the three estimators considered are based on Variance, Absolute bias (ABIAS), Mean square error (MSE) and the Root Mean Square (RMSE) of parameters estimates. Eighteen different models at different specified conditions were fitted, and the best-fitted model is that of within estimator when heteroscedasticity is severe at either zero or positive serial correlation value. LM test results showed that the tests have good size and power as all the three tests are significant at 5% for the specified linear form of heteroscedasticity function which established the facts that Banks operations are severely heteroscedastic in nature with little or no periodicity effects.

Keywords: audit fee lagrange multiplier test, heteroscedasticity, lagrange multiplier test, Monte-Carlo scheme, periodicity

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Authors: Sahar Abbaszadeh, Sharifah Rafidah Wan Alwi, Colin Webb, Nahid Ghasemi, Ida Idayu Muhamad

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Toxic heavy metal discharges into environment due to rapid industrialization is a serious pollution problem that has drawn global attention towards their adverse impacts on both the structure of ecological systems as well as human health. Lead as toxic and bio-accumulating elements through the food chain, is regularly entering to water bodies from discharges of industries such as plating, mining activities, battery manufacture, paint manufacture, etc. The application of conventional methods to degrease and remove Pb(II) ion from wastewater is often restricted due to technical and economic constrains. Therefore, the use of various agro-wastes as low-cost bioadsorbent is found to be attractive since they are abundantly available and cheap. In this study, activated carbon of papaya peel (AC-PP) (as locally available agricultural waste) was employed to evaluate its Pb(II) uptake capacity from single-solute solutions in sets of batch mode experiments. To assess the surface characteristics of the adsorbents, the scanning electron microscope (SEM) coupled with energy disperse X-ray (EDX), and Fourier transform infrared spectroscopy (FT-IR) analysis were utilized. The removal amount of Pb(II) was determined by atomic adsorption spectrometry (AAS). The effects of pH, contact time, the initial concentration of Pb(II) and adsorbent dosage were investigated. The pH value = 5 was observed as optimum solution pH. The optimum initial concentration of Pb(II) in the solution for AC-PP was found to be 200 mg/l where the amount of Pb(II) removed was 36.42 mg/g. At the agitating time of 2 h, the adsorption processes using 100 mg dosage of AC-PP reached equilibrium. The experimental results exhibit high capability and metal affinity of modified papaya peel waste with removal efficiency of 93.22 %. The evaluation results show that the equilibrium adsorption of Pb(II) was best expressed by Freundlich isotherm model (R2 > 0.93). The experimental results confirmed that AC-PP potentially can be employed as an alternative adsorbent for Pb(II) uptake from industrial wastewater for the design of an environmentally friendly yet economical wastewater treatment process.

Keywords: activated carbon, bioadsorption, lead removal, papaya peel, wastewater treatment

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Authors: Aloysius Hardoko, Susilo

Abstract:

The root cause of environmental damage post coal mining activities as determined by the province of East Kalimantan as a corridor of economic activity masterplan acceleration of economic development expansion (MP3EI) is the behavior of adults. Adult behavior can be changed through knowledge management and attitude. Based on the root of the problem, the objective of the research is to apply knowledge management and attitude based on holistic approach in learning andragogy as an effort to solve environmental problems after coal mining activities. Research methods to achieve the objective of using quantitative research with pretest posttest group design. Knowledge management and attitudes based on a holistic approach in adult learning are applied through initial learning activities, core and case-based cover of environmental damage. The research instrument is a description of the case of environmental damage. The data analysis uses t-test to see the effect of knowledge management attitude based on holistic approach before and after adult learning. Location and sample of representative research of adults as many as 20 people in Kutai Kertanegara District, one of the districts in East Kalimantan province, which suffered the worst environmental damage. The conclusion of the research result is the application of knowledge management and attitude in adult learning influence to adult knowledge and attitude to overcome environmental problem post coal mining activity.

Keywords: knowledge management and attitude, holistic approach, andragogy learning, environmental damage

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Authors: Praveen S. Muthukumarana, Achala C. Aponso

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A large percentage of global crop harvest is lost due to crop diseases. Timely identification and treatment of crop diseases is difficult in many developing nations due to insufficient trained professionals in the field of agriculture. Many crop diseases can be accurately diagnosed by visual symptoms. In the past decade, deep learning has been successfully utilized in domains such as healthcare but adoption in agriculture for plant disease detection is rare. The literature shows that models trained with popular datasets such as PlantVillage does not generalize well on real world images. This paper attempts to find out how to make plant disease identification models that generalize well with real world images.

Keywords: agriculture, convolutional neural network, deep learning, plant disease classification, plant disease detection, plant disease diagnosis

Procedia PDF Downloads 146