Search results for: neural stem/precursor cells

Authors: Debajyoti Mukherjee, Arathy B. S., Arpita Sahu, Saranga P. Pogula

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A functional model to mimic the functional correctness of a neural network compute accelerator IP is very crucial for design validation. Neural network workloads are based on a Brain Floating Point (BF-16) data type. The major challenge we were facing was the incompatibility of GCC compilers to the BF-16 datatype, which we addressed with a native BF-16 generator integrated into our functional model. Moreover, working with big GEMM (General Matrix Multiplication) or SpMM (Sparse Matrix Multiplication) Work Loads (Dense or Sparse) and debugging the failures related to data integrity is highly painstaking. In this paper, we are addressing the quality challenge of such a complex neural network accelerator design by proposing a functional model-based scoreboard or software model using SystemC. The proposed functional model executes the assembly code based on the ISA of the processor IP, decodes all instructions, and executes as expected to be done by the DUT. The said model would give a lot of visibility and debug capability in the DUT, bringing up micro-steps of execution.

Keywords: ISA, neural network, Brain Float-16, DUT

Procedia PDF Downloads 87

Authors: El Mahdi Barrah, Said Safi, Abdessamad Malaoui

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In this paper, we present an algorithm for reconstruction from incomplete 2D scans for tifinagh characters. This algorithm is based on using correlation between the lost block and its neighbors. This system proposed contains three main parts: pre-processing, features extraction and recognition. In the first step, we construct a database of tifinagh characters. In the second step, we will apply “shape analysis algorithm”. In classification part, we will use Neural Network. The simulation results demonstrate that the proposed method give good results.

Keywords: Tifinagh character recognition, neural networks, local cost computation, ANN

Procedia PDF Downloads 329

Authors: Tharini N. de Silva, Xiao Zhibo, Zhao Rui, Mao Kezhi

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Causal relation identification is a crucial task in information extraction and knowledge discovery. In this work, we present two approaches to causal relation identification. The first is a classification model trained on a set of knowledge-based features. The second is a deep learning based approach training a model using convolutional neural networks to classify causal relations. We experiment with several different convolutional neural networks (CNN) models based on previous work on relation extraction as well as our own research. Our models are able to identify both explicit and implicit causal relations as well as the direction of the causal relation. The results of our experiments show a higher accuracy than previously achieved for causal relation identification tasks.

Keywords: causal realtion extraction, relation extracton, convolutional neural network, text representation

Procedia PDF Downloads 723

Authors: Dimitra Sygkridou, Dimitrios Karageorgopoulos, Elias Stathatos, Evangelos Vitoratos

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Transparent nickel doped cobalt sulfide was fabricated on a SnO2:F electrode and tested as an efficient electrocatalyst and as an alternative to the expensive platinum counter electrode. In order to investigate how this electrode could affect the electrical characteristics of a dye-sensitized solar cell, we manufactured cells with the same TiO2 photoanode sensitized with dye (N719) and employing the same quasi-solid electrolyte, altering only the counter electrode used. The cells were electrically and electrochemically characterized and it was observed that the ones with the Ni doped CoS2 outperformed the efficiency of the cells with the Pt counter electrode (3.76% and 3.44% respectively). Particularly, the higher efficiency of the cells with the Ni doped CoS2 counter electrode (CE) is mainly because of the enhanced photocurrent density which is attributed to the enhanced electrocatalytic ability of the CE and the low charge transfer resistance at the CE/electrolyte interface.

Keywords: nickel doped cobalt sulfide, counter electrodes, dye-sensitized solar cells, quasi-solid state electrolyte, hybrid organic-inorganic materials

Procedia PDF Downloads 753

Authors: Harsh Sadawarti, Kamal Malik

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Humans are the unique and the most powerful creature on this planet just because of the high level of intelligence gifted by nature. Computational Intelligence is highly influenced by the term natural intelligence, neurosciences and mathematics. To deal with the in-depth study of computational intelligence and to utilize it in real-life applications, it is quite important to understand its simulation with the human brain. In this paper, the three important parts, Frontal Lobe, Occipital Lobe and Parietal Lobe of the human brain, are compared with the ANN(Artificial Neural Network), CNN(Convolutional Neural network), and RNN(Recurrent Neural Network), respectively. Intelligent computational systems are created by combining deductive reasoning, logical concepts and high-level algorithms with the simulation and study of the human brain. Human brain is a combination of Physiology, Psychology, emotions, calculations and many other parameters which are of utmost importance that determines the overall intelligence. To create intelligent algorithms, smart machines and to simulate the human brain in an effective manner, it is quite important to have an insight into the human brain and the basic concepts of biological neurosciences.

Keywords: computational intelligence, neurosciences, convolutional neural network, recurrent neural network, artificial neural network, frontal lobe, occipital lobe, parietal lobe

Procedia PDF Downloads 105

Authors: Guillaume Schuchardt, Solenn Berson, Gerard Perrier

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Multi-junction solar cell configurations, where two sub-cells with complementary absorption are stacked and connected in series, offer an exciting approach to tackle the single junction limitations of organic solar cells and improve their power conversion efficiency. However, the augmentation of the number of layers has, as a consequence, to increase the risk of reducing the lifetime of the cell due to the ageing phenomena present at the interfaces. In this work, we study the intrinsic degradation mechanisms, under continuous illumination AM1.5G, inert atmosphere and room temperature, in single and tandem organic solar cells using Impedance Spectroscopy, IV Curves, External Quantum Efficiency, Steady-State Photocarrier Grating, Scanning Kelvin Probe and UV-Visible light.

Keywords: single and tandem organic solar cells, intrinsic degradation mechanisms, characterization: SKP, EQE, SSPG, UV-Visible, Impedance Spectroscopy, optical simulation

Procedia PDF Downloads 358

Authors: Habtemariam Alemu

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It has been known that the performance of position and force control is highly affected by both robot dynamic and environment stiffness uncertainties. In this paper, joint space hybrid force and position control strategy with self-selecting matrix using artificial neural network compensator is proposed. The objective of the work is to improve controller robustness by applying a neural network technique in order to compensate the effect of uncertainties in the robot model. Simulation results for a 6 degree of freedom (6-DoF) manipulator and different types of environments showed the effectiveness of the suggested approach. 6-DoF Puma 560 family robot manipulator is chosen as industrial robot and its efficient dynamic model is designed using Matlab/SimMechanics library.

Keywords: robot manipulator, force/position control, artificial neural network, Matlab/Simulink

Procedia PDF Downloads 510

Authors: Kevin D. Manalo, Jumelyn L. Torres, Noel B. Linsangan

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This paper focuses on a wireless myoelectric prosthesis of the upper-limb that uses a Multilayer Perceptron Neural network with back propagation. The algorithm is widely used in pattern recognition. The network can be used to train signals and be able to use it in performing a function on their own based on sample inputs. The paper makes use of the Neural Network in classifying the electromyography signal that is produced by the muscle in the amputee’s skin surface. The gathered data will be passed on through the Classification Stage wirelessly through Zigbee Technology. The signal will be classified and trained to be used in performing the arm positions in the prosthesis. Through programming using Verilog and using a Field Programmable Gate Array (FPGA) with Zigbee, the EMG signals will be acquired and will be used for classification. The classified signal is used to produce the corresponding Hand Movements (Open, Pick, Hold, and Grip) through the Zigbee controller. The data will then be processed through the MLP Neural Network using MATLAB which then be used for the surface myoelectric prosthesis. Z-test will be used to display the output acquired from using the neural network.

Keywords: field programmable gate array, multilayer perceptron neural network, verilog, zigbee

Procedia PDF Downloads 386

Authors: Javier Fernandez, Mayte Medina, Rafael Fernandez de Canete, Nuria Alcain, Juan Carlos Ramos-Diaz

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At present, the experimentation of anesthetic drugs on patients requires a regulation protocol, and the response of each patient to several doses of entry drug must be well known. Therefore, the development of pharmacological dose control systems is a promising field of research in anesthesiology. In this paper, it has been developed a non-linear compartmental the pharmacokinetic-pharmacodynamical model which describes the anesthesia depth effect in a sufficiently reliable way over a set of patients with the depth effect quantified by the Bi-Spectral Index. Afterwards, an Artificial Neural Network (ANN) predictive controller has been designed based on the depth of anesthesia model so as to keep the patient in the optimum condition while he undergoes surgical treatment. For the purpose of quantifying the efficiency of the neural predictive controller, a classical proportional-integral-derivative controller has also been developed to compare both strategies. Results show the superior performance of predictive neural controller during BiSpectral Index reference tracking.

Keywords: anesthesia, bi-spectral index, neural network control, pharmacokinetic-pharmacodynamical model

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Authors: Ateeqa Aslam, Hans J. Nauwynck

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Porcine hemagglutinating encephalomyelitis virus (PHEV) is a betacoronavirus that has been studied in the past as a cause of vomiting and wasting disease (VWD) in young piglets (<3 weeks). Nowadays, the virus is still circulating on most farms in Belgium, but there are no descriptions anymore of VWD. Therefore, we are interested in differences between the old and new strains. We compared the replication kinetics of the old well-studied strain VW572 (1972) and the recent isolate P412 (2020) in a susceptible continuous cell line (RPD cells) and in primary porcine respiratory epithelial cells (PoRECs). The RPD cell line was inoculated with each PHEV strain at an m.o.i. of 1 the supernatant was collected, and the cells were fixed at different time points post-inoculation. The supernatant was titrated (extracellular virus titer), and the infected cells were revealed by immunofluorescence staining and quantitated by fluorescence microscopy. We found that VW572 replicated better in the RPD cell line at earlier time points when compared to P412. Porcine respiratory epithelial cells (PoREC) were isolated, grown at air-liquid interphase in transwells and inoculated with both strains of PHEV at a virus titer of 106.6TCID50 per 200 µl either at the apical side or at the basal side of the cells. At different time points after inoculation, the transwells were fixed and stained for infected cells. VW572 preferentially infected the epithelial cells via the basolateral side of porcine nasal epithelial cells, whereas P412 preferred the apical side. These findings suggest that there has been an evolution of PHEV in its interaction with the respiratory epithelial cells. In the future, more virus strains will be enclosed and the tropism of the strains for different neuronal cell types will be examined for the change in virus neurotropism.

Keywords: porcine hemagglutinating encephalomyelitis virus (PHEV), primary porcine respiratory epithelial cells (PoRECs), virus tropism, vomiting and wasting disease (VWD)

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Authors: Yunhui Chen, Damon Kent, Matthew Dargusch

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Synthetic scaffolds are a highly promising new approach to replace both autografts and allografts to repair and remodel damaged bone tissue. Biocompatible porous titanium scaffold was manufactured through a powder metallurgy approach. Magnesium powder was used as space holder material which was compacted with titanium powder and removed during sintering. Evaluation of the porosity and mechanical properties showed a high level of compatibility with human bone. Interconnectivity between pores is higher than 95% for porosity as low as 30%. The elastic moduli are 39 GPa, 16 GPa and 9 GPa for 30%, 40% and 50% porosity samples which match well to that of natural bone (4-30 GPa). The yield strengths for 30% and 40% porosity samples of 315 MPa and 175 MPa are superior to that of human bone (130-180 MPa). In-vitro cell culture tests on the scaffold samples using Human Mesenchymal Stem Cells (hMSCs) demonstrated their biocompatibility and indicated osseointegration potential. The scaffolds allowed cells to adhere and spread both on the surface and inside the pore structures. With increasing levels of porosity/interconnectivity, improved cell proliferation is obtained within the pores. It is concluded that samples with 30% porosity exhibit the best biocompatibility. The results suggest that porous titanium scaffolds generated using this manufacturing route have excellent potential for hard tissue engineering applications.

Keywords: scaffolds, MG-63 cell culture, titanium, space holder

Procedia PDF Downloads 234

Authors: Homayon Ahmad Panahi, Seyed Mehdi Seyed Nejad, Elham Moniri

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A novel bio-adsorbent is fabricated by attaching a cibacron blue to yeast cells. The modified bio-sorbent has been characterized by some techniques like Fourier transform infrared spectroscopy (FT-IR) and elemental analysis (CHN) and applied for the preconcentration and determination of samarium from aqueous water samples. The best pH value for adsorption of the brilliant crecyle blue by yeast cells- cibacron blue was 7. The sorption capacity of modified biosorbent was 18.5 mg. g⁻¹. A recovery of 95.3% was obtained for Sm(III) when eluted with 0.5 M nitric acid. The method was applied for Sm(III) preconcentration and determination in river water sample.

Keywords: samarium, solid phase extraction, yeast cells, water sample, removal

Procedia PDF Downloads 250

Authors: Frank Kuebler, Rolf Steinhilper

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Artificial neural networks (ANN) as well as Design of Experiments (DOE) based regression analysis (RA) are mainly used for modeling of complex systems. Both methodologies are commonly applied in process and quality control of manufacturing processes. Due to the fact that resource efficiency has become a critical concern for manufacturing companies, these models needs to be extended to predict resource-consumption of manufacturing processes. This paper describes an approach to use neural networks as well as DOE based regression analysis for predicting resource consumption of manufacturing processes and gives a comparison of the achievable results based on an industrial case study of a turning process.

Keywords: artificial neural network, design of experiments, regression analysis, resource efficiency, manufacturing process

Procedia PDF Downloads 520

Authors: Nasrin Bakhshizadeh, Ashkan Forootan

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A major problem that affects the quality control of polymer in the industrial polymerization is the lack of suitable on-line measurement tools to evaluate the properties of the polymer such as melt and density indices. Controlling the polymerization in ordinary method is performed manually by taking samples, measuring the quality of polymer in the lab and registry of results. This method is highly time consuming and leads to producing large number of incompatible products. An online application for estimating melt index and density proposed in this study is a neural network based on the input-output data of the polyethylene production plant. Temperature, the level of reactors' bed, the intensity of ethylene mass flow, hydrogen and butene-1, the molar concentration of ethylene, hydrogen and butene-1 are used for the process to establish the neural model. The neural network is taught based on the actual operational data and back-propagation and Levenberg-Marquart techniques. The simulated results indicate that the neural network process model established with three layers (one hidden layer) for forecasting the density and the four layers for the melt index is able to successfully predict those quality properties.

Keywords: polyethylene, polymerization, density, melt index, neural network

Procedia PDF Downloads 141

Authors: Seonggil Kim, Jeonghun Nam, Chae Seung Lim

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Rapid diagnosis of fungal infections is critical for rapid antifungal therapy. However, it is difficult to detect extremely low concentration fungi in blood sample. To address the limitation, separation and concentration of fungi in blood sample are required to enhance the sensitivity of PCR analysis. In this study, we demonstrated a sheathless separation and concentration of fungi, candida cells using a viscoelastic fluid. To validate the performance of the device, microparticle mixture (2 and 13 μm) was used, and those particles were successfully separated based on the size difference at high flow rate of 100 μl/min. For the final application, successful separation of the Candida cells from the white blood cells (WBCs) was achieved. Based on the viscoelastic lateral migration toward the equilibrium position, Candida cells were separated and concentrated by center focusing, while WBCs were removed by patterning into two streams between the channel center and the sidewalls. By flow cytometric analysis, the separation efficiency and the purity were evaluated as ~99% and ~ 97%, respectively. From the results, the device can be the powerful tool for detecting extremely rare disease-related cells.

Keywords: candida cells, concentration, separation, viscoelastic fluid

Procedia PDF Downloads 195

Authors: Chutamas Thepmalee, Aussara Panya, Mutita Junking, Jatuporn Sujjitjoon, Nunghathai Sawasdee, Pa-Thai Yenchitsomanus

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Cholangiocarcinoma (CCA) is an aggressive malignancy of bile duct epithelial cells in which the standard treatments, including surgery, radiotherapy, chemotherapy, and targeted therapy are partially effective. Many solid tumors including CCA escape host immune responses by creating tumor microenvironment and generating immunosuppressive cytokines such as interleukin-10 (IL-10) and transforming growth factor-β (TGF-β). These cytokines can inhibit dendritic cell (DC) differentiation and function, leading to decreased activation and response of effector CD4+ and CD8+ T cells for cancer cell elimination. To overcome the effects of these immunosuppressive cytokines and to increase ability of DC to activate effector CD4+ and CD8+ T cells, we generated self-differentiated DCs (SD-DCs) with down-regulation of IL-10 and TGF-β receptors for activation of effector CD4+ and CD8+ T cells. Human peripheral blood monocytes were initially transduced with lentiviral particles containing the genes encoding GM-CSF and IL-4 and then secondly transduced with lentiviral particles containing short-hairpin RNAs (shRNAs) to knock-down mRNAs of IL-10 and TGF-β receptors. The generated SD-DCs showed up-regulation of MHC class II (HLA-DR) and co-stimulatory molecules (CD40 and CD86), comparable to those of DCs generated by convention method. Suppression of IL-10 and TGF-β receptors on SD-DCs by specific shRNAs significantly increased levels of IFN-γ and also increased cytolytic activity of DC-activated effector T cells against CCA cell lines (KKU-213 and KKU-100), but it had little effect to immortalized cholangiocytes (MMNK-1). Thus, SD-DCs with down-regulation of IL-10 and TGF-β receptors increased activation of effector T cells, which is a recommended method to improve DC function for the preparation of DC-activated effector T cells for adoptive T-cell therapy.

Keywords: cholangiocarcinoma, IL-10 receptor, self-differentiated dendritic cells, TGF-β receptor

Procedia PDF Downloads 136

Authors: Hossain A, Hossain S.

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Introduction: In a small cohort, we sought to assess the magnetic resonance spectroscopy's (MRS) ability to predict the presence of metastatic lesions. Method: A Popular Diagnostic Centre Limited enrolled patients with neuroepithelial tumors. The 1H CSI MRS of the brain allows us to detect changes in the concentration of specific metabolites caused by metastatic lesions. Among these metabolites are N-acetyl-aspartate (NNA), creatine (Cr), and choline (Cho). For Cho, NAA, Cr, and Cr₂, the metabolic ratio was calculated using the division method. Results: The NAA values were 0.63 and 5.65 for tumor cells, 1.86 and 5.66 for normal cells, and 1.86 and 5.66 for normal cells 2. NAA values for normal cells 1 were 1.84, 10.6, and 1.86 for normal cells 2, respectively. Cho levels were as low as 0.8 and 10.53 in the tumor cell, compared to 1.12 and 2.7 in the normal cell 1 and 1.24 and 6.36 in the normal cell 2. Cho/Cr₂ barely distinguished itself from the other ratios in terms of significance. For tumor cells, the ratios of Cho/NAA, Cho/Cr₂, NAA/Cho, and NAA/Cr₂ were significant. Normal cell 1 had significant Cho/NAA, Cho/Cr, NAA/Cho, and NAA/Cr ratios. Conclusion: The clinical result can be improved by using 1H-MRSI to guide the size of resection for metastatic lesions. Even though it is non-invasive and doesn't present any difficulties during the procedure, MRS has been shown to predict the detection of metastatic lesions.

Keywords: metabolite ratio, MRI images, metastatic lesion, MR spectroscopy, N-acetyl-aspartate

Procedia PDF Downloads 91

Authors: Z. Zerdoumi, D. Benatia, , D. Chicouche

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This paper investigates the application of artificial neural network to the problem of nonlinear channel equalization. The difficulties caused by channel distortions such as inter symbol interference (ISI) and nonlinearity can overcome by nonlinear equalizers employing neural networks. It has been shown that multilayer perceptron based equalizer outperform significantly linear equalizers. We present a multilayer perceptron based equalizer with decision feedback (MLP-DFE) trained with the back propagation algorithm. The capacity of the MLP-DFE to deal with nonlinear channels is evaluated. From simulation results it can be noted that the MLP based DFE improves significantly the restored signal quality, the steady state mean square error (MSE), and minimum Bit Error Rate (BER), when comparing with its conventional counterpart.

Keywords: Artificial Neural Network, signal restoration, Nonlinear Channel equalization, equalization

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Authors: Jennifer Gallup, Beverly Ray, Esther Ntuli

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Many students such as students from linguistically or culturally diverse backgrounds and those with a disability remain chronically underrepresented in higher level science and mathematics disciplines as well as many hands-on-lab-based activities due to the need for remedial reading and mathematics instruction. Makerspace labs can be a conduit for supporting inclusive learning for these students through hands-on active learning strategies that support equitable access to STEM disciplines. Makerspace is a physical space where individuals gather to create, invent, innovate, and learn while using hands-on materials such as 2D and 3D printers, software programs, electronics, and other tools and supplies. Makerspaces are emerging across many P-12 settings; however, many teachers enter the field not prepared to harness the power inherent in a makerspace, especially for those with disabilities and differing needs. This paper offers suggestions on teaching pre-service teachers and practicing teachers how to incorporate a makerspace into their professional practice through guided instruction and hands-on practice. Recommendations for interested stakeholders are included as well.

Keywords: STEM learning, technology, autism, students with disabilities, makerspace

Procedia PDF Downloads 91

Authors: Aarti Sharma, Sarita Kumar, Pushplata Tripathi

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Aedes aegypti L. is one of the most important insect vectors in the world transmitting several diseases of concern; dengue fever, dengue haemorrhagic fever and yellow fever. Though since ages the control of dengue vector is primarily relied upon the use of synthetic chemical insecticides, the continued and indiscriminate use of insecticides for their control has received wide public apprehension because of multifarious problems including insecticide resistance, resurgence of pest species, environmental pollution, toxic hazards to humans and non-target organisms. These problems have necessitated the need to explore and develop alternative strategies using eco-friendly and bio-degradable plant products. Bio-insecticides, despite being the focus of research nowadays, have not been investigated much regarding their physiological effects on the mosquitoes. Thus, the present studies were carried out to investigate the anti-mosquito potential of the leaf and stem hexane extracts of Achyranthes aspera against early fourth instars of Aedes aegypti L and their effects on the histological architecture of their midgut. The larvicidal bioassays conducted with the A. aspera leaf hexane extracts revealed the respective LC30, LC50 and LC90 values of 66.545 ppm, 82.555 ppm, 139.817 ppm while the assays with stem hexane extracts resulted in respective values of 54.982 ppm, 68.133 ppm, 115.075 ppm. The studies clearly indicate the efficacy of extracts as larvicidal agents against Ae. aegypti, the stem extracts being found more effective than the leaf extracts. When the larvae assayed with extracts were investigated for the modifications in the histo-architecture of the midgut, the studies showed significant damage, shrinkage, distortion and vacuolization of gut tissues and peritrophic membrane causing disintegration of epithelial cells and cytoplasmic organelles; extent of toxicity and damage varied depending upon the concentration and exposure time period. These changes revealed appreciable stomach poison potential of A. aspera extracts against Ae. aegypti larvae, which may have also caused adverse impact on the growth and development of larvae. These effects were also found to be more pronounced with the stem extract than the leaf extract. Our findings may prove significant suggesting the use of A. aspera extract as a bio-insecticide against early fourth instar larvae of Ae. aegypti. Further studies are needed to identify the bioactive component in the extracts and to ascertain the use of component in the fields as anti-mosquito control agent.

Keywords: Achyranthes aspera, Aedes aegypti, histological architecture, larvicidal, midgut, stomach poison

Procedia PDF Downloads 288

Authors: Sello Mokwena, Monyepao Thabang

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In this study, we propose a comprehensive approach to address the challenges in person re-identification models. By combining a centroid tracking algorithm with a Siamese convolutional neural network model, our method excels in detecting, tracking, and capturing robust person features across non-overlapping camera views. The algorithm efficiently identifies individuals in the camera network, while the neural network extracts fine-grained global features for precise cross-image comparisons. The approach's effectiveness is further accentuated by leveraging the camera network topology for guidance. Our empirical analysis on benchmark datasets highlights its competitive performance, particularly evident when background subtraction techniques are selectively applied, underscoring its potential in advancing person re-identification techniques.

Keywords: camera network, convolutional neural network topology, person tracking, person re-identification, siamese

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Authors: Stephanie J. Sedberry, William J. Gerace, Ian D. Beatty, Michael J. Kane

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Increasing the number of US university students who attain degrees in STEM and enter the STEM workforce is a national priority. Demographic groups vary in their rates of participation in STEM, and the US produces just 10% of the world’s science and engineering degrees (2014 figures). To address these gaps, we have developed and tested a practical, 30-minute, single-session classroom-based intervention to improve students’ self-efficacy and academic performance in University STEM courses. Self-efficacy is a psychosocial construct that strongly correlates with academic success. Self-efficacy is a construct that is internal and relates to the social, emotional, and psychological aspects of student motivation and performance. A compelling body of research demonstrates that university students’ self-efficacy beliefs are strongly related to their selection of STEM as a major, aspirations for STEM-related careers, and persistence in science. The development of an intervention to increase students’ self-efficacy is motivated by research showing that short, social-psychological interventions in education can lead to large gains in student achievement. Our intervention addresses STEM self-efficacy via two strong, but previously separate, lines of research into attitudinal/affect variables that influence student success. The first is ‘attributional retraining,’ in which students learn to attribute their successes and failures to internal rather than external factors. The second is ‘mindset’ about fixed vs. growable intelligence, in which students learn that the brain remains plastic throughout life and that they can, with conscious effort and attention to thinking skills and strategies, become smarter. Extant interventions for both of these constructs have significantly increased academic performance in the classroom. We developed a 34-item questionnaire (Likert scale) to measure STEM Self-efficacy, Perceived Academic Control, and Growth Mindset in a University STEM context, and validated it with exploratory factor analysis, Rasch analysis, and multi-trait multi-method comparison to coded interviews. Four iterations of our 42-week research protocol were conducted across two academic years (2017-2018) at three different Universities in North Carolina, USA (UNC-G, NC A&T SU, and NCSU) with varied student demographics. We utilized a quasi-experimental prospective multiple-group time series research design with both experimental and control groups, and we are employing linear modeling to estimate the impact of the intervention on Self-Efficacy,wth-Mindset, Perceived Academic Control, and final course grades (performance measure). Preliminary results indicate statistically significant effects of treatment vs. control on Self-Efficacy, Growth-Mindset, Perceived Academic Control. Analyses are ongoing and final results pending. This intervention may have the potential to increase student success in the STEM classroom—and ownership of that success—to continue in a STEM career. Additionally, we have learned a great deal about the complex components and dynamics of self-efficacy, their link to performance, and the ways they can be impacted to improve students’ academic performance.

Keywords: academic performance, affect variables, growth mindset, intervention, perceived academic control, psycho-social variables, self-efficacy, STEM, university classrooms

Procedia PDF Downloads 126

Authors: A. Djoudi, R. Djibaou, H. A. Reguieg Yssaad

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Our study focuses on the study of a bacteria belonging to Pseudomonas solubilizing tricalcium phosphate. They were isolated from rhizosphere of a variety of broad bean grown in salt-affected soils (electrical conductivity between 4 and 8 mmhos/cm) of the irrigated perimeter of Mina in northwestern Algeria. Isolates which have advantageous results in the calcium phosphate solubilization index test were subjected to identification using API20 then used to re-inoculate the same soil in pots experimentation to assess the effects of inoculation on the growth of the broad bean (Vicia faba). Based on the results obtained from the in-vitro tests, two isolates P5 and P8 showed a significant effect on the solubilization of tricalcium phosphate with an index I estimated at 314% and 283% sequentially. According to the results of in-vivo tests, the inoculation of the soil with P5 and P8 were significantly and positively influencing the growth in biometric parameters of the broad bean. Inoculation with strain P5 has promoted the growth of the broad bean in stem height, stem fresh weight and stem dry weight of 108.59%, 115.28%, 104.33%, respectively. Inoculation with strain P8 has fostered the growth of the broad bean stem fresh weight of 112.47%. The effect of Pseudomonas on the development of Vicia faba is considered as an interesting process by which PGPR can increase biological production and crop protection.

Keywords: Pseudomonas, Vicia faba, promoting of plant growth, solubilization tricalcium phosphate

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Authors: S. M. Anowarul Haque, Md. Asiful Islam

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Electrical load forecasting is considered to be one of the most indispensable parts of a modern-day electrical power system. To ensure a reliable and efficient supply of electric energy, special emphasis should have been put on the predictive feature of electricity supply. Artificial Neural Network-based approaches have emerged to be a significant area of interest for electric load forecasting research. This paper proposed an Artificial Neural Network model based on the particle swarm optimization algorithm for improved electric load forecasting for Mymensingh, Bangladesh. The forecasting model is developed and simulated on the MATLAB environment with a large number of training datasets. The model is trained based on eight input parameters including historical load and weather data. The predicted load data are then compared with an available dataset for validation. The proposed neural network model is proved to be more reliable in terms of day-wise load forecasting for Mymensingh, Bangladesh.

Keywords: load forecasting, artificial neural network, particle swarm optimization

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Authors: Osama Elsarrar, Marjorie Darrah, Richard Devin

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This paper discusses the idea of capturing an expert’s knowledge in the form of human understandable rules and then inserting these rules into a dynamic cell structure (DCS) neural network. The DCS is a form of self-organizing map that can be used for many purposes, including classification and prediction. This particular neural network is considered to be a topology preserving network that starts with no pre-structure, but assumes a structure once trained. The DCS has been used in mission and safety-critical applications, including adaptive flight control and health-monitoring in aerial vehicles. The approach is to insert expert knowledge into the DCS before training. Rules are translated into a pre-structure and then training data are presented. This idea has been demonstrated using the well-known Iris data set and it has been shown that inserting the pre-structure results in better accuracy with the same training.

Keywords: neural network, self-organizing map, rule extraction, rule insertion

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Authors: Tatiana A. Dolenko, Sergey A. Burikov, Alexander O. Efitorov, Sergey A. Dolenko

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In this study, a comparative analysis of the approaches associated with the use of neural network algorithms for effective solution of a complex inverse problem – the problem of identifying and determining the individual concentrations of inorganic salts in multicomponent aqueous solutions by the spectra of Raman scattering of light – is performed. It is shown that application of artificial neural networks provides the average accuracy of determination of concentration of each salt no worse than 0.025 M. The results of comparative analysis of input data compression methods are presented. It is demonstrated that use of uniform aggregation of input features allows decreasing the error of determination of individual concentrations of components by 16-18% on the average.

Keywords: inverse problems, multi-component solutions, neural networks, Raman spectroscopy

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Authors: Homayon Ahmad Panahi, Niusha Mohseni Darabi, Elham Moniri

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A new biosorbent is prepared by coupling a cibacron blue to yeast cells. The modified yeast cells with cibacron blue has been characterized by Fourier transform infrared spectroscopy (FT-IR) and elemental analysis and applied for the preconcentration and solid phase extraction of trace cadmium ion from water samples. The optimum pH value for sorption of the cadmium ions by yeast cells- cibacron blue was 5.5. The sorption capacity of modified biosorbent was 45 mg. g−1. A recovery of 98.2% was obtained for Cd(II) when eluted with 0.5 M nitric acid. The method was applied for Cd(II) preconcentration and determination in sea water sample.

Keywords: solid phase extraction, yeast cells, Nickl, isotherm study

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Authors: Po-Jen Chen, Jian-Jiun Ding, Hung-Wei Hsu, Chien-Yao Wang, Jia-Ching Wang

Abstract:

A more accurate scene classification algorithm using label relations and the hierarchy neural network was developed in this work. In many classification algorithms, it is assumed that the labels are mutually exclusive. This assumption is true in some specific problems, however, for scene classification, the assumption is not reasonable. Because there are a variety of objects with a photo image, it is more practical to assign multiple labels for an image. In this paper, two label relations, which are exclusive relation and hierarchical relation, were adopted in the classification process to achieve more accurate multiple label classification results. Moreover, the hierarchy neural network (hierarchy NN) is applied to classify the image and the directed acyclic graph structure is used for predicting a more reasonable result which obey exclusive and hierarchical relations. Simulations show that, with these techniques, a much more accurate scene classification result can be achieved.

Keywords: convolutional neural network, label relation, hierarchy neural network, scene classification

Procedia PDF Downloads 453

Authors: Diana Islam, Juan Fang, Vito Fanelli, Bing Han, Julie Khang, Jianfeng Wu, Arthur S. Slutsky, Haibo Zhang

Abstract:

Introduction: MSC delivery in preclinical models of ARDS has demonstrated significant improvements in lung function and recovery from acute injury. However, the role of MSC delivery in ARDS associated pulmonary fibrosis is not well understood. Some animal studies using bleomycin, asbestos, and silica-induced pulmonary fibrosis show that MSC delivery can suppress fibrosis. While other animal studies using radiation induced pulmonary fibrosis, liver, and kidney fibrosis models show that MSC delivery can contribute to fibrosis. Hypothesis: The beneficial and deleterious effects of MSC in ARDS are modulated by the lung microenvironment at the time of MSC delivery. Methods: To induce ARDS a two-hit mouse model of Hydrochloric acid (HCl) aspiration (day 0) and mechanical ventilation (MV) (day 2) was used. HCl and injurious MV generated fibrosis within 14-28 days. 0.5x106 mouse MSCs were delivered (via both intratracheal and intravenous routes) either in the active inflammatory phase (day 2) or during the remodeling phase (day 14) of ARDS (mouse fibroblasts or PBS used as a control). Lung injury accessed using inflammation score and elastance measurement. Pulmonary fibrosis was accessed using histological score, tissue collagen level, and collagen expression. In addition alveolar epithelial (E) and mesenchymal (M) marker expression profile was also measured. All measurements were taken at day 2, 14, and 28. Results: MSC delivery 2 days after HCl exacerbated lung injury and fibrosis compared to HCl alone, while the day 14 delivery showed protective effects. However in the absence of HCl, MSC significantly reduced the injurious MV-induced fibrosis. HCl injury suppressed E markers and up-regulated M markers. MSC delivery 2 days after HCl further amplified M marker expression, indicating their role in myofibroblast proliferation/activation. While with 14-day delivery E marker up-regulation was observed indicating their role in epithelial restoration. Conclusions: Early MSC delivery can be protective of injurious MV. Late MSC delivery during repair phase may also aid in recovery. However, early MSC delivery during the exudative inflammatory phase of HCl-induced ARDS can result in pro-fibrotic profiles. It is critical to understand the interaction between MSC and the lung microenvironment before MSC-based therapies are utilized for ARDS.

Keywords: acute respiratory distress syndrome (ARDS), mesenchymal stem cells (MSC), hydrochloric acid (HCl), mechanical ventilation (MV)

Procedia PDF Downloads 666

Authors: Hong Li, Tingxian Li, Xudong Wang, Weibo Yang

Abstract:

Co-limitation of iron (Fe) deficiency in legume nitrogen fixation process is not well understood. Our objectives were to examine how peanut plants cope with Fe deficiency with the rhizobial inoculants and N-nutrient treatments. The study was conducted in the tropical Hainan Island during 2012-2013. The soil was strongly acidic (pH 4.6±0.7) and deficient in Fe (9.2±2.3 mg/kg). Peanut plants were intercropped with cassava. The inoculants and N treatments were arranged in a split-plot design with three blocks. Peanut root nodulation, stem allantoin, amino acids and plant N derived from fixation (P) reduced with declining soil Fe concentrations. The treatment interactions were significant on relative ureide % and peanut yields (P<0.05). Residual fixed N from peanut plants was beneficial to cassava plants. It was concluded that co-variance of Fe deficiency could influence peanut N fixation efficiency and rhizobia and N inputs could help improving peanut tolerance to Fe deficiency stress.

Keywords: amino acids, plant N derived from N fixation, root nodulation, soil Fe co-variance, stem ureide, peanuts, cassava

Procedia PDF Downloads 292