Search results for: neural stem cells

Authors: Leila Ayat, Afak Meftah

Abstract:

Amorphous-silicon alloys have great promise as low cost solar cell materials. They have excellent photo-conductivity and high optical absorption to sunlight. Now PIN a-Si:H based solar cells are widely used in power generation modules. However, to improve the performance of these cells further, a better fundamental under-standing of the factors limiting cell performance in the homo junction PIN structure is necessary. In this paper we discuss the sensitivity of light J-V characteristics to various device and material parameters in PIN homo junction solar cells. This work is a numerical simulation of the output parameters of a PIN a-Si:H solar cell under AM1.5 spectrum. These parameters are the short circuit current (Jsc), the open circuit voltage (Voc), the fill factor (FF), the conversion efficiency. The simulation was performed with SCAPS-1D software version 3.3 developed at ELIS in Belgium by Marc Burgelman et al. The obtained results are in agreement with experiment. In addition, the effect of the thickness, doping density, capture cross sections of the gap states and the band microscopic mobilities on the output parameters of the cell are also presented.

Keywords: amorphous silicon p-i-n junctions, thin film, solar cells, sensitivity

Procedia PDF Downloads 516

Authors: Hien Thi Thu Le, Nuno Rafael Candeias, Olli Yli-Harja, Meenakshisundaram Kandhavelu

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Purinergic receptor 1 (P2Y1R) is the potential therapeutic target for inducing prostate cancer (PCa) cell death. Recently, 1-indolinoalkyl 2-phenolic derivative, HIC, was identified as a P2Y1R agonist that increases apoptosis and inhibits cell proliferation of PCa. However, the biological effects of HIC have not been extensively studied at the molecular level. In the present study, we have investigated the anticancer effects of HIC and the molecular mechanisms underlying in PCa cells. Half maximal inhibitory concentration (IC₅₀) of HIC was measured as 15.98 μM and 15.64 μM for DU145 and PC3 cells, respectively. In addition, we found that HIC inhibited cell growth and metastasis of PC3 and DU145 cells colonies, spheroid areas, and migrated cells. RNA seq analysis revealed significant changes of over 3000 genes (p value < 0.05) upon HIC treatment in PC3 and DU145 cells. Genes involved in DNA damage, apoptosis, cell cycle arrest at G1/S phase were modulated by HIC treatment. MAPK and NF-κB protein array revealed the increased expression of ERK1/2, JNK1/2, p53 phosphorylation, and p53 protein. ERK1/2 and JNK1/2 activations are known to increase the stabilization of p53, a tumor suppressor protein, which is required to arrest the cell cycle at G1/S phase and cause cell death of PCa cells. Overall, our results suggest that HIC can serve as a multi-dimensional chemotherapeutic agent possessing strong cytotoxic, anti-cancer, and anti-metastasis against PCa growth.

Keywords: prostate cancer, P2Y1 receptor, apoptosis, metastasis

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Authors: Maria Amodeo, Pasquale Arpaia, Marco Buzio, Vincenzo Di Capua, Francesco Donnarumma

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Different deep neural network architectures have been compared and tested to predict magnetic hysteresis in the context of pulsed electromagnets for experimental physics applications. Modelling quasi-static or dynamic major and especially minor hysteresis loops is one of the most challenging topics for computational magnetism. Recent attempts at mathematical prediction in this context using Preisach models could not attain better than percent-level accuracy. Hence, this work explores neural network approaches and shows that the architecture that best fits the measured magnetic field behaviour, including the effects of hysteresis and eddy currents, is the nonlinear autoregressive exogenous neural network (NARX) model. This architecture aims to achieve a relative RMSE of the order of a few 100 ppm for complex magnetic field cycling, including arbitrary sequences of pseudo-random high field and low field cycles. The NARX-based architecture is compared with the state-of-the-art, showing better performance than the classical operator-based and differential models, and is tested on a reference quadrupole magnetic lens used for CERN particle beams, chosen as a case study. The training and test datasets are a representative example of real-world magnet operation; this makes the good result obtained very promising for future applications in this context.

Keywords: deep neural network, magnetic modelling, measurement and empirical software engineering, NARX

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Authors: Mariyam Arif, Ye Liu, Israr Ul Haq, Ahsan Ashfaq

Abstract:

High cost of fossil fuels and intensifying installations of alternate energy generation sources are intimidating main challenges in power systems. Making accurate load forecasting an important and challenging task for optimal energy planning and management at both distribution and generation side. There are many techniques to forecast load but each technique comes with its own limitation and requires data to accurately predict the forecast load. Artificial Neural Network (ANN) is one such technique to efficiently forecast the load. Comparison between two different ranges of input datasets has been applied to dynamic ANN technique using MATLAB Neural Network Toolbox. It has been observed that selection of input data on training of a network has significant effects on forecasted results. Day-wise input data forecasted the load accurately as compared to year-wise input data. The forecasted load is then distributed among the six generators by using the linear programming to get the optimal point of generation. The algorithm is then verified by comparing the results of each generator with their respective generation limits.

Keywords: artificial neural networks, demand-side management, economic dispatch, linear programming, power generation dispatch

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Authors: Bekir Turedi

Abstract:

Exploiting the potential of perovskite single-crystal solar cells in optoelectronic applications necessitates overcoming a significant challenge: the low charge collection efficiency at increased thickness, which has restricted their deployment in radiation detectors and nuclear batteries. Our research details a promising approach to this problem, wherein we have successfully fabricated single-crystal MAPbI3 solar cells employing a space-limited inverse temperature crystallization (ITC) methodology. Remarkably, these cells, up to 400-fold thicker than current-generation perovskite polycrystalline films, maintain a high charge collection efficiency even without external bias. The crux of this achievement lies in the long electron diffusion length within these cells, estimated to be around 0.45 mm. This extended diffusion length ensures the conservation of high charge collection and power conversion efficiencies, even as the thickness of the cells increases. Fabricated cells at 110, 214, and 290 µm thickness manifested power conversion efficiencies (PCEs) of 20.0, 18.4, and 14.7% respectively. The single crystals demonstrated nearly optimal charge collection, even when their thickness exceeded 200 µm. Devices of thickness 108, 214, and 290 µm maintained 98.6, 94.3, and 80.4% of charge collection efficiency relative to their maximum theoretical short-circuit current value, respectively. Additionally, we have proposed an innovative, self-consistent technique for ascertaining the electron-diffusion length in perovskite single crystals under operational conditions. The computed electron-diffusion length approximated 446 µm, significantly surpassing previously reported values for this material. In conclusion, our findings underscore the feasibility of fabricating halide perovskite single-crystal solar cells of hundreds of micrometers in thickness while preserving high charge extraction efficiency and PCE. This advancement paves the way for developing perovskite-based optoelectronics necessitating thicker active layers, such as X-ray detectors and nuclear batteries.

Keywords: perovskite, solar cell, single crystal, diffusion length

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Authors: R. Alramyan, S. Alsalamah, R. Alrashed, R. Alakel, F. Altheyeb, M. Alessa

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Background: Nutritional support with total parenteral nutrition (TPN) is usually commenced with hematopoietic stem cell transplantation (HSCT) patients. However, it has its benefits and risks. Complications related to central venous catheter such as infections, and metabolic disturbances, including abnormal liver function, is usually of concern in such patients. Methods: A retrospective charts review of all pediatric patients who underwent HSCT between the period 2015-2018 in a tertiary hospital in Riyadh, Saudi Arabia. Patients' demographics, types of conditioning, type of nutrition, and patients' outcomes were collected. Statistical analysis was conducted using SPSS version 22. Frequencies and percentages were used to describe categorical variables. Mean, and standard deviation were used for continuous variables. A P value of less than 0.05 was considered as statically significant. Results: a total of 162 HSCTs were identified during the period mentioned. Indication of allogenic transplant included hemoglobinopathy in 50 patients (31%), acute lymphoblastic leukemia in 21 patients (13%). TPN was used in 96 patients (59.30%) for a median of 14 days, nasogastric tube feeding (NGT) in 16 (9.90%) patients for a median of 11 days, and 71 of patients (43.80%) were able to tolerate oral feeding. Out of the 96 patients (59.30%) who were dependent on TPN, 64 patients (66.7%) had severe mucositis in comparison to 17 patients (25.8%) who were either on NGT or tolerated oral intake. (P-value= 0.00). Sinusoidal obstruction syndrome (SOS) was seen in 14 patients (14.6%) who were receiving TPN compared to none in non-TPN patients (P=value 0.001). Moreover, majority of patients who had SOS received myeloablative conditioning therapy for non-malignant disease (hemoglobinopathy). However, there were no statistically significant differences in Graft-vs-Host Disease (both acute and chronic), bacteremia, and patient outcome between both groups. Conclusions: Nutritional support using TPN is used in majority of patients, especially post-myeloablative conditioning associated with severe mucositis. TPN was associated with VOD, especially in hemoglobinopathy patients who received myeloablative therapy. This may emphasize on use of preventative measures such as fluid restriction, use of diuretics, or defibrotide in high-risk patients.

Keywords: hematopoeitic stem cell transplant, HSCT, stem cell transplant, sinusoidal obstruction syndrome, total parenteral nutrition

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Authors: Carlos Vera, Tetyana Baydyk, Ernst Kussul, Graciela Velasco, Miguel Aparicio

Abstract:

This article describes the Random Subspace Neural Classifier (RSC) for the recognition of meteors in the night sky. We used images of meteors entering the atmosphere at night between 8:00 p.m.-5: 00 a.m. The objective of this project is to classify meteor and star images (with stars as the image background). The monitoring of the sky and the classification of meteors are made for future applications by scientists. The image database was collected from different websites. We worked with RGB-type images with dimensions of 220x220 pixels stored in the BitMap Protocol (BMP) format. Subsequent window scanning and processing were carried out for each image. The scan window where the characteristics were extracted had the size of 20x20 pixels with a scanning step size of 10 pixels. Brightness, contrast and contour orientation histograms were used as inputs for the RSC. The RSC worked with two classes and classified into: 1) with meteors and 2) without meteors. Different tests were carried out by varying the number of training cycles and the number of images for training and recognition. The percentage error for the neural classifier was calculated. The results show a good RSC classifier response with 89% correct recognition. The results of these experiments are presented and discussed.

Keywords: contour orientation histogram, meteors, night sky, RSC neural classifier, stars

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Authors: A. Clementking, C. Jothi Venkateswaran

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Mathematical and computational functionalities such as descriptive mining, optimization, and predictions are espoused to resolve natural resource planning. The water quality prediction and its attributes influence determinations are adopted optimization techniques. The water properties are tainted while merging water resource one with another. This work aimed to predict influencing water resource distribution connectivity in accordance to water quality and sediment using an innovative proposed master-slave neural network back-propagation model. The experiment results are arrived through collecting water quality attributes, computation of water quality index, design and development of neural network model to determine water quality and sediment, master–slave back propagation neural network back-propagation model to determine variations on water quality and sediment attributes between the water resources and the recommendation for connectivity. The homogeneous and parallel biochemical reactions are influences water quality and sediment while distributing water from one location to another. Therefore, an innovative master-slave neural network model [M (9:9:2)::S(9:9:2)] designed and developed to predict the attribute variations. The result of training dataset given as an input to master model and its maximum weights are assigned as an input to the slave model to predict the water quality. The developed master-slave model is predicted physicochemical attributes weight variations for 85 % to 90% of water quality as a target values.The sediment level variations also predicated from 0.01 to 0.05% of each water quality percentage. The model produced the significant variations on physiochemical attribute weights. According to the predicated experimental weight variation on training data set, effective recommendations are made to connect different resources.

Keywords: master-slave back propagation neural network model(MSBPNNM), water quality analysis, multivariate analysis, environmental mining

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Authors: Sana Rezig, Yosr Ben Mlik, Mounir Jaouadi, Foued Khoffi, Slah Msahli, Bernard Durand

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Increasing interest in environmental concerns, natural fibers are once again being considered as reinforcements for polymer composites. The main objective of this study is to explore another natural resource, Typha fiber; which is renewable without production cost and available abundantly in nature. The aim of this study was to study the flexural properties of composite resin with and without reinforcing Typha leaf and stem fibers. The specimens were made by the hand-lay-up process using polyester matrix. In our work, we focused on the effect of various treatment conditions (sea water, alkali treatment and a combination of the two treatments), as a surface modifier, on the flexural properties of the Typha fibers reinforced polyester composites. Moreover, weight ratio of Typha leaf or stem fibers was investigated. Besides, both fibers from leaf and stem of Typha plant were used to evaluate the reinforcing effect. Another parameter, which is reinforcement structure, was investigated. In fact, a first composite was made with air-laid nonwoven structure of fibers. A second composite was with a mixture of fibers and resin for each kind of treatment. Results show that alkali treatment and combined process provided better mechanical properties of composites in comparison with fiber treated by sea water. The fiber weight ratio influenced the flexural properties of composites. Indeed, a maximum value of flexural strength of 69.8 and 62,32 MPa with flexural modulus of 6.16 and 6.34 GPawas observed respectively for composite reinforced with leaf and stem fibers for 12.6 % fiber weight ratio. For the different treatments carried out, the treatment using caustic soda, whether alone or after retting seawater, show the best results because it improves adhesion between the polyester matrix and the fibers of reinforcement. SEM photographs were made to ascertain the effects of the surface treatment of the fibers. By varying the structure of the fibers of Typha, the reinforcement used in bulk shows more effective results as that used in the non-woven structure. In addition, flexural strength rises with about (65.32 %) in the case of composite reinforced with a mixture of 12.6% leaf fibers and (27.45 %) in the case of a composite reinforced with a nonwoven structure of 12.6 % of leaf fibers. Thus, to better evaluate the effect of the fiber origin, the reinforcing structure, the processing performed and the reinforcement factor on the performance of composite materials, a statistical study was performed using Minitab. Thus, ANOVA was used, and the patterns of the main effects of these parameters and interaction between them were established. Statistical analysis, the fiber treatment and reinforcement structure seem to be the most significant parameters.

Keywords: flexural properties, fiber treatment, structure and weight ratio, SEM photographs, Typha leaf and stem fibers

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Authors: P. S. BEDI* , Neavty Thakur, Balvınder Sıngh

Abstract:

In the present study an attempt has been made to prepare the crude extracts of leaves and stem of ‘Girardinia heterophylla’ by using various solvents like petroleum ether, ethanol and double distilled water. The samples were given the code NGLS 1, NGLS 2, NGLS 3, NGSS 1, NGSS 2 and NGSS 3 respectively. All the extracts were used to study their antimicrobial activity against gram positive bacteria eg. Bacillus subtilis, Gram negative bacteria eg. E. coli, K. pneumonia and antifungal activity against Aspergillus niger. The results of the antimicrobial activity showed that all the crude extracts of the plant posseses antibacterial activity. Maximum antibacterial activity was shown by NGLS 2, NGLS 3 and NGSS 3 against K. pneumonia. The growth of fungus A. niger was also inhibited by all the crude extracts. Maximum inhibition was shown by NGSS 2 followed by NGSS 1.

Keywords: Girardinia heterophylla, leaves and stem extracts, Antibacterial activity, antifungal activity.

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Authors: M. S. Hasni, J. Guan, K. Yakimchuk, M. Berglund, B. Sander, G. Enblad, R. M. Amini, S. Okret

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Lymphomas are generally not considered as endocrine-related cancers. However, most lymphoid malignancies show gender differences in incidence and show prognosis with males being more affected. Furthermore, some epidemiological data indicate a protective role of estrogens against Non-Hodgkin lymphomas. Recent studies have demonstrated estrogen receptor β (ERβ) to be the major ER expressed in normal and malignant cells of lymphoid origin. We have analyzed the effects of estradiol and selective ERα and ERβ agonists on lymphoma growth in culture and in vivo. Treating lymphoma cells with estradiol or ERα selective agonist had minor or no effect on cell growth while selective ERβ agonist treatment showed an antiproliferative effect. When grafting mice with murine T lymphoma cells, male mice developed larger tumors compared to female mice, a difference that was abolished following ovariectomy, demonstrating estrogen-dependent growth in vivo. When subcutaneously grafting lymphoma cells to mice, so far growth of all tested human B lymphoma tumors (Raji and Ramos Burkitt lymphoma, SU.DHL4 (GC) and U2932 (ABC) DLBCL, Granta-519, Maver1 and Z138 MCL cells), were reduced following treatment with ERβ selective agonist (ref. 2 and unpublished). Moreover, the number and size of liver foci of disseminating Raji cells was reduced. We have identified target genes and mechanism that could explain the above effects of ERβ agonists. This included effects on angio and lymphangiogenesis. Now we have further analyzed effects of ERβ agonists on Ibrutinib-sensitive and -insensitive MCL cells in xenograft experiments as well as ERβ expression in primary lymphoma material (DLBCL). Preliminary statistical analysis has been done correlating ERβ expression to other biomarkers and clinical data.

Keywords: lymphomas, estrogen receptors, cancer, liver foci

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Authors: Sokol Isaraj, Lorela Bendo

Abstract:

Scars resulting from surgery, injury, or burns have a physical and psychological impact on the affected patient. Although a number of treatments are available, nano fat grafting is an effective treatment for scars. Nano fat is a liquid suspension rich in stem cells obtained by mechanical emulsification. Nano fat grafting was performed in 10 cases to correct rhytides, surgical scars, and post-burn scars between January 2022 and April 2022. Fat was aspirated from the lower abdomen or trochanteric region. After emulsification and filtration protocol, the resulting nano fat liquid was injected intradermally and subdermally. All patients filled out a questionnaire at three months post-treatment, which consisted of questions regarding the grade of improvement of skin and recommendation of the procedure. The clinical results were apparent between 2 and 3 weeks after the treatment. All patients confirmed an improvement in skin texture and quality. The most significant improvement was seen in pigmentation and pliability. No complications were reported. Nano fat seems to be a safe and effective treatment in scar treatment and skin rejuvenation.

Keywords: fat grafting, fat transfer, micro fat, nano fat

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Authors: Natalia Koltovaya, Nadezhda Zhuchkina, Ksenia Lyubimova

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We study the biological effects induced by ionizing radiation in view of therapeutic exposure and the idea of space flights beyond Earth's magnetosphere. In particular, we examine the differences between base pair substitution induction by ionizing radiation in model haploid and diploid yeast Saccharomyces cerevisiae cells. Such mutations are difficult to study in higher eukaryotic systems. In our research, we have used a collection of six isogenic trp5-strains and 14 isogenic haploid and diploid cyc1-strains that are specific markers of all possible base-pair substitutions. These strains differ from each other only in single base substitutions within codon-50 of the trp5 gene or codon-22 of the cyc1 gene. Different mutation spectra for two different haploid genetic trp5- and cyc1-assays and different mutation spectra for the same genetic cyc1-system in cells with different ploidy — haploid and diploid — have been obtained. It was linear function for dose-dependence in haploid and exponential in diploid cells. We suggest that the differences between haploid yeast strains reflect the dependence on the sequence context, while the differences between haploid and diploid strains reflect the different molecular mechanisms of mutations.

Keywords: base pair substitutions, γ-rays, haploid and diploid cells, yeast Saccharomyces cerevisiae

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Authors: Priyanka Chowdhury, Asitikantha Sarma, Utpal Ghosh

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Hadron therapy using high Linear Energy Transfer (LET) ion beam is producing promising clinical results worldwide. The major advantages are its ability to kill radio-resistant tumor and its anti-metastatic activity. Poly(ADP-ribose) polymerase-1 (PARP-1) inhibitors have been widely used as radiosensitizer, but its role in metastasis is unknown. The purpose of our study was to investigate the effect of PARP-1 depletion in combination with either Carbon Ion Beam (CIB) or gamma irradiation on metastatic potential of cultured cancerous cells. A549 cells were irradiated with CIB (0-4Gy) or gamma (0, 2, 4, 6 and 10 Gy) with and without PARP-1 inhibition. The metastatic potential of the cells was determined by cell migratory assay, expression, and activity of MMP-2 and MMP-9, expression of Cadherin, Fibronectin, and Vimentin. CIB exposure reduced migratory property and activity of MMP-2 and MMP-9 significantly. CIB with PARP-1 inhibition reduced cell migration and Matrix Metalloproteinase (MMPs) activity in a synergistic manner. Expression of MMPs was also down-regulated in CIB and combined treatment. On the contrary, MMP- 2 and MMP-9 activity was significantly increased in gamma irradiated cells but decreased upon combined treatment of gamma and PARP-1 inhibitor. MMPs expression and migration was reduced when gamma irradiation was combined with PARP-1 inhibition. Thus, our study clearly demonstrates that PARP-1 inhibition in combination with either high or low LET can significantly suppress metastatic potential in cancer cells and thereby can be a promising tool in controlling metastatic cancers.

Keywords: high LET, low LET, matrix metalloproteinase (MMP), PARP-1

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Authors: D. G. Badagha, C. D. Modhera, S. A. Vasanwala

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There is a great challenge for civil engineering field to contribute in environment prevention by finding out alternatives of cement and natural aggregates. There is a problem of global warming due to cement utilization in concrete, so it is necessary to give sustainable solution to produce concrete containing waste. It is very difficult to produce designated grade of concrete containing different ingredient and water cement ratio including waste to achieve desired fresh and harden properties of concrete as per requirement and specifications. To achieve the desired grade of concrete, a number of trials have to be taken, and then after evaluating the different parameters at long time performance, the concrete can be finalized to use for different purposes. This research work is carried out to solve the problem of time, cost and serviceability in the field of construction. In this research work, artificial neural network introduced to fix proportion of concrete ingredient with 50% waste replacement for M20, M25, M30, M35, M40, M45, M50, M55 and M60 grades of concrete. By using the neural network, mix design of high performance concrete was finalized, and the main basic mechanical properties were predicted at 3 days, 7 days and 28 days. The predicted strength was compared with the actual experimental mix design and concrete cube strength after 3 days, 7 days and 28 days. This experimentally and neural network based mix design can be used practically in field to give cost effective, time saving, feasible and sustainable high performance concrete for different types of structures.

Keywords: artificial neural network, high performance concrete, rebound hammer, strength prediction

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Authors: Nkechinyere Joy Olawuyi, Babajide Samuel Afolabi, Bola Ibitoye

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This study collected a preprocessed dataset of chest radiographs and formulated a deep neural network model for detecting abnormalities. It also evaluated the performance of the formulated model and implemented a prototype of the formulated model. This was with the view to developing a deep neural network model to automatically classify abnormalities in chest radiographs. In order to achieve the overall purpose of this research, a large set of chest x-ray images were sourced for and collected from the CheXpert dataset, which is an online repository of annotated chest radiographs compiled by the Machine Learning Research Group, Stanford University. The chest radiographs were preprocessed into a format that can be fed into a deep neural network. The preprocessing techniques used were standardization and normalization. The classification problem was formulated as a multi-label binary classification model, which used convolutional neural network architecture to make a decision on whether an abnormality was present or not in the chest radiographs. The classification model was evaluated using specificity, sensitivity, and Area Under Curve (AUC) score as the parameter. A prototype of the classification model was implemented using Keras Open source deep learning framework in Python Programming Language. The AUC ROC curve of the model was able to classify Atelestasis, Support devices, Pleural effusion, Pneumonia, A normal CXR (no finding), Pneumothorax, and Consolidation. However, Lung opacity and Cardiomegaly had a probability of less than 0.5 and thus were classified as absent. Precision, recall, and F1 score values were 0.78; this implies that the number of False Positive and False Negative is the same, revealing some measure of label imbalance in the dataset. The study concluded that the developed model is sufficient to classify abnormalities present in chest radiographs into present or absent.

Keywords: transfer learning, convolutional neural network, radiograph, classification, multi-label

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Authors: Chaitanya Chawla, Divya Panwar, Gurneesh Singh Anand, M. P. S Bhatia

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This paper presents a deep-learning mechanism for classifying computer generated images and photographic images. The proposed method accounts for a convolutional layer capable of automatically learning correlation between neighbouring pixels. In the current form, Convolutional Neural Network (CNN) will learn features based on an image's content instead of the structural features of the image. The layer is particularly designed to subdue an image's content and robustly learn the sensor pattern noise features (usually inherited from image processing in a camera) as well as the statistical properties of images. The paper was assessed on latest natural and computer generated images, and it was concluded that it performs better than the current state of the art methods.

Keywords: image forensics, computer graphics, classification, deep learning, convolutional neural networks

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Authors: Lauren Beth Birney

Abstract:

BOP-CCERS is a consortium of scientists, K-16 New York City students, faculty, academicians, teachers, stakeholders, STEM Industry professionals, CBO’s, NPO’s, citizen scientists, and local businesses working in partnership to restore New York Harbor’s oyster populations while at the same time providing clean water in New York Harbor. BOP-CCERS gives students an opportunity to learn hands-on about environmental stewardship as well as environmental law and policy by giving students real responsibility. The purpose of this REU will allow for the BOP CCERS Project to further broaden its parameters into the focus of environmental law and policy where further change can be affected. Creating opportunities for undergraduates to work collaboratively with graduate students in law and policy and envision themselves in STEM careers in the field of law continues to be of importance in this project. More importantly, creating opportunities for underrepresented students to pursue careers in STEM Education has been a goal of the project over the last ten years. By raising the level of student interest in community-based citizen science integrated into environmental law and policy, a more diversified workforce will be fostered through the momentum of this dynamic program. The continuing climate crisis facing our planet calls for 21st-century skill development that includes learning and innovation skills derived from critical thinking, which will help REU students address the issues of climate change facing our planet. The demand for a climate-friendly workforce will continue to be met through this community-based citizen science effort. Environmental laws and policies play a crucial role in protecting humans, animals, resources, and habitats. Without these laws, there would be no regulations concerning pollution or contamination of our waterways. Environmental law serves as a mechanism to protect the land, air, water, and soil of our planet. To protect the environment, it is crucial that future policymakers and legal experts both understand and value the importance of environmental protection. The Environmental Law and Policy REU provides students with the opportunity to learn, through hands-on work, the skills, and knowledge needed to help foster a legal workforce centered around environmental protection while participating alongside the BOP CCERS researchers in order to gain research experience. Broadening this area to law and policy will further increase these opportunities and permit students to ultimately affect and influence larger-scale change on a global level while further diversifying the STEM workforce. Students’ findings will be shared at the annual STEM Institute at Pace University in August 2022. Basic research methodologies include qualitative and quantitative analysis performed by the research team. Early findings indicate that providing students with an opportunity to experience, explore and participate in environmental science programs such as these enhances their interests in pursuing STEM careers in Law and Policy, with the focus being on providing opportunities for underserved, marginalized, and underrepresented populations.

Keywords: environmental restoration science, citizen science, environmental law and policy, STEM education

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Authors: Ulas Vural, M. Ergun Okay, E. Mesut Yildiz

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Telecommunication service providers demand accurate and precise prediction of customer churn probabilities to increase the effectiveness of their customer relation services. The large amount of customer data owned by the service providers is suitable for analysis by machine learning methods. In this study, expenditure data of customers are analyzed by using an artificial neural network (ANN). The ANN model is applied to the data of customers with different billing duration. The proposed model successfully predicts the churn probabilities at 83% accuracy for only three months expenditure data and the prediction accuracy increases up to 89% when the nine month data is used. The experiments also show that the accuracy of ANN model increases on an extended feature set with information of the changes on the bill amounts.

Keywords: customer relationship management, churn prediction, telecom industry, deep learning, artificial neural networks

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Authors: Parisa Golbayani, Dan Wang, Ionut¸ Florescu

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In this work we implement machine learning techniques to financial statement reports in order to asses company’s credit rating. Specifically, the work analyzes the performance of four neural network architectures (MLP, CNN, CNN2D, LSTM) in predicting corporate credit rating as issued by Standard and Poor’s. The paper focuses on companies from the energy, financial, and healthcare sectors in the US. The goal of this analysis is to improve application of machine learning algorithms to credit assessment. To accomplish this, the study investigates three questions. First, we investigate if the algorithms perform better when using a selected subset of important features or whether better performance is obtained by allowing the algorithms to select features themselves. Second, we address the temporal aspect inherent in financial data and study whether it is important for the results obtained by a machine learning algorithm. Third, we aim to answer if one of the four particular neural network architectures considered consistently outperforms the others, and if so under which conditions. This work frames the problem as several case studies to answer these questions and analyze the results using ANOVA and multiple comparison testing procedures.

Keywords: convolutional neural network, long short term memory, multilayer perceptron, credit rating

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Authors: Sokol Isaraj, Lorela Bendo

Abstract:

Scars resulting from surgery, injury, or burns have a physical and psychological impact on the affected patient. Although a number of treatments are available, nano fat grafting is an effective treatment for scars. Nano fat is a liquid suspension rich in stem cells obtained by mechanical emulsification. Nano fat grafting was performed in 10 cases to correct rhytides, surgical scars and post-burn scars between January 2022 and April 2022. Fat was aspirated from the lower abdomen or trochanteric region. After emulsification and filtration protocol, the resulting nano fat liquid was injected intradermally and subdermally. All patients filled out a questionnaire at 3 months post-treatment which consisted of questions regarding the grade of improvement of skin and recommendation of the procedure. The clinical results were apparent between 2 and 3 weeks after the treatment. All patients confirmed an improvement in skin texture and quality. The most significant improvement was seen in pigmentation and pliability. No complications were reported. Nano fat seems to be a safe and effective treatment for scar treatment and skin rejuvenation.

Keywords: fat grafting, fat transfer, microfat, nanofat

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Authors: Chad Brown

Abstract:

This paper establishes general conditions for the convergence rates of nonparametric sieve estimators with dependent data. We present two key results: one for nonstationary data and another for stationary mixing data. Previous theoretical results often lack practical applicability to deep neural networks (DNNs). Using these conditions, we derive convergence rates for DNN sieve estimators in nonparametric regression settings with both nonstationary and stationary mixing data. The DNN architectures considered adhere to current industry standards, featuring fully connected feedforward networks with rectified linear unit activation functions, unbounded weights, and a width and depth that grows with sample size.

Keywords: sieve extremum estimates, nonparametric estimation, deep learning, neural networks, rectified linear unit, nonstationary processes

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Authors: Mohamed Raouf Benmakrelouf, Wafa Karouche, Joseph Rynkiewicz

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In this paper, we propose an alternative method to construct a Bayesian Network (BN). This method relies on a convolutional neural network (CNN classifier), which determinates the edges of the network skeleton. We train a CNN on a normalized empirical probability density distribution (NEPDF) for predicting causal interactions and relationships. We have to find the optimal Bayesian network structure for causal inference. Indeed, we are undertaking a search for pair-wise causality, depending on considered causal assumptions. In order to avoid unreasonable causal structure, we consider a blacklist and a whitelist of causality senses. We tested the method on real data to assess the influence of education on the voting intention for the extreme right-wing party. We show that, with this method, we get a safer causal structure of variables (Bayesian Network) and make to identify a variable that satisfies the backdoor criterion.

Keywords: Bayesian network, structure learning, optimal search, convolutional neural network, causal inference

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Authors: Naif Al-Jomah, Falah H Al-Mohanna, Abdelilah Aboussekhra

Abstract:

Active breast cancer-associated fibroblasts (CAFs), the most influential cells in breast tumor microenvironment, express/secrete high levels of the proinvasive/metastatic interleukin-6 (IL-6). Therefore, we have tested here the effect of the IL-6 receptor (IL-6R) inhibitor tocilizumab (TCZ; Actemra) on different active breast CAFs. We have shown that TCZ potently and persistently suppresses the expression of various CAF biomarkers, namely α-SMA, SDF-1 as well as the STAT3 pathway and its downstream target AUF1. TCZ also inhibited the proliferation, migration and invasion abilities of active breast CAF cells. Additionally, TCZ repressed the ability of CAF cells in promoting epithelial-to-mesenchymal transition, and enhancing the migratory/invasive and proliferative capacities of breast cancer cells in vitro. Importantly, these findings were confirmed in orthotopic humanized breast tumors in mice. Furthermore, TCZ suppressed the expression of the pro-angiogenic factor VEGF-A and its transactivator HIF-1α in CAF cells, and consequently inhibited the angiogenic-promoting effect of active CAFs both in vitro and in orthotopic tumor xenografts. These results indicate that inhibition of the IL-6/STAT3/AUF1 pathway by TCZ can normalize active breast CAFs and suppress their paracrine pro-carcinogenic effects, which paves the way toward development of specific CAF-targeting therapy, badly needed for more efficient breast cancer treatments.

Keywords: angiogenesis, interleukin-6, paracrine, cancer-associated fibroblasts

Procedia PDF Downloads 89

Authors: Sung Hyun Yoo, In Hwan Choi, Jun Ho Jung, Choon Ki Ahn, Myo Taeg Lim

Abstract:

Home energy management system (HEMS) has been issued to reduce the power consumption. The HEMS performs electric power control for the indoor electric device. However, HEMS commonly treats the smart devices. In this paper, we suggest the output estimation of home legacy device using the artificial neural fuzzy inference system (ANFIS). This paper discusses the overview and the architecture of the system. In addition, accurate performance of the output estimation using the ANFIS inference system is shown via a numerical example.

Keywords: artificial neural fuzzy inference system (ANFIS), home energy management system (HEMS), smart device, legacy device

Procedia PDF Downloads 537

Authors: Aydin Teymourifar, Gurkan Ozturk

Abstract:

In this paper, a distributed hybrid algorithm is proposed for solving the job shop scheduling problem. The suggested method executes different artificial neural networks, heuristics and meta-heuristics simultaneously on more than one machine. The neural networks are used to control the constraints of the problem while the meta-heuristics search the global space and the heuristics are used to prevent the premature convergence. To attain an efficient distributed intelligent method for solving big and distributed job shop scheduling problems, Apache Spark and Hadoop frameworks are used. In the algorithm implementation and design steps, new approaches are applied. Comparison between the proposed algorithm and other efficient algorithms from the literature shows its efficiency, which is able to solve large size problems in short time.

Keywords: distributed algorithms, Apache Spark, Hadoop, job shop scheduling, neural network

Procedia PDF Downloads 383

Authors: Yike Lamu, Jieyu Tang, Jialin Wu, Jianyun Huang

Abstract:

With the development of economy and science and technology, the urban heat island effect becomes more and more serious. Taking Beijing city as an example, this paper divides the value of each influence index of heat island intensity and establishes a mathematical model – neural network system based on the fuzzy comprehensive evaluation index of heat island effect. After data preprocessing, the algorithm of weight of each factor affecting heat island effect is generated, and the data of sex indexes affecting heat island intensity of Shenyang City and Shanghai City, Beijing, and Hangzhou City are input, and the result is automatically output by the neural network system. It is of practical significance to show the intensity of heat island effect by visual method, which is simple, intuitive and can be dynamically monitored.

Keywords: heat island effect, neural network, comprehensive evaluation, visualization

Procedia PDF Downloads 132

Authors: Kyu Chul Lee, Sung Hyun Yoo, Choon Ki Ahn, Myo Taeg Lim

Abstract:

Recent experimental evidences have shown that because of a fast convergence and a nice accuracy, neural networks training via extended Kalman filter (EKF) method is widely applied. However, as to an uncertainty of the system dynamics or modeling error, the performance of the method is unreliable. In order to overcome this problem in this paper, a new finite impulse response (FIR) filter based learning algorithm is proposed to train radial basis function neural networks (RBFN) for nonlinear function approximation. Compared to the EKF training method, the proposed FIR filter training method is more robust to those environmental conditions. Furthermore, the number of centers will be considered since it affects the performance of approximation.

Keywords: extended Kalman filter, classification problem, radial basis function networks (RBFN), finite impulse response (FIR) filter

Procedia PDF Downloads 453

Authors: Amel Ourici

Abstract:

An electrical machine failure that is not identified in an initial stage may become catastrophic and it may suffer severe damage. Thus, undetected machine faults may cascade in it failure, which in turn may cause production shutdowns. Such shutdowns are costly in terms of lost production time, maintenance costs, and wasted raw materials. Doubly fed induction generators are used mainly for wind energy conversion in MW power plants. This paper presents a detection of an inter turn stator fault in a doubly fed induction machine whose stator and rotor are supplied by two pulse width modulation (PWM) inverters. The method used in this article to detect this fault, is based on Park’s Vector Approach, using a neural network.

Keywords: doubly fed induction machine, PWM inverter, inter turn stator fault, Park’s vector approach, neural network

Procedia PDF Downloads 604

Authors: Abdulkader Helwan

Abstract:

Iris cancer, so called intraocular melanoma is a cancer that starts in the iris; the colored part of the eye that surrounds the pupil. There is a need for an accurate and cost-effective iris cancer detection system since the available techniques used currently are still not efficient. The combination of the image processing and artificial neural networks has a great efficiency for the diagnosis and detection of the iris cancer. Image processing techniques improve the diagnosis of the cancer by enhancing the quality of the images, so the physicians diagnose properly. However, neural networks can help in making decision; whether the eye is cancerous or not. This paper aims to develop an intelligent system that stimulates a human visual detection of the intraocular melanoma, so called iris cancer. The suggested system combines both image processing techniques and neural networks. The images are first converted to grayscale, filtered, and then segmented using prewitt edge detection algorithm to detect the iris, sclera circles and the cancer. The principal component analysis is used to reduce the image size and for extracting features. Those features are considered then as inputs for a neural network which is capable of deciding if the eye is cancerous or not, throughout its experience adopted by many training iterations of different normal and abnormal eye images during the training phase. Normal images are obtained from a public database available on the internet, “Mile Research”, while the abnormal ones are obtained from another database which is the “eyecancer”. The experimental results for the proposed system show high accuracy 100% for detecting cancer and making the right decision.

Keywords: iris cancer, intraocular melanoma, cancerous, prewitt edge detection algorithm, sclera

Procedia PDF Downloads 501