Search results for: neural tube defects

Authors: Hichem Sassi

Abstract:

Pneumonia, being a respiratory infection, has garnered global attention due to its rapid transmission and relatively high mortality rates. Timely detection and treatment play a crucial role in significantly reducing mortality associated with pneumonia. Presently, X-ray diagnosis stands out as a reasonably effective method. However, the manual scrutiny of a patient's X-ray chest radiograph by a proficient practitioner usually requires 5 to 15 minutes. In situations where cases are concentrated, this places immense pressure on clinicians for timely diagnosis. Relying solely on the visual acumen of imaging doctors proves to be inefficient, particularly given the low speed of manual analysis. Therefore, the integration of artificial intelligence into the clinical image diagnosis of pneumonia becomes imperative. Additionally, AI recognition is notably rapid, with convolutional neural networks (CNNs) demonstrating superior performance compared to human counterparts in image identification tasks. To conduct our study, we utilized a dataset comprising chest X-ray images obtained from Kaggle, encompassing a total of 5216 training images and 624 test images, categorized into two classes: normal and pneumonia. Employing five mainstream network algorithms, we undertook a comprehensive analysis to classify these diseases within the dataset, subsequently comparing the results. The integration of artificial intelligence, particularly through improved network architectures, stands as a transformative step towards more efficient and accurate clinical diagnoses across various medical domains.

Keywords: deep learning, computer vision, pneumonia, models, comparative study

Procedia PDF Downloads 34

Authors: Reza Nematirad, Anil Pahwa, Balasubramaniam Natarajan

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Time series data consist of successive data points collected over a period of time. Accurate prediction of future values is essential for informed decision-making in several real-world applications, including electricity load demand forecasting, lifetime estimation of industrial machinery, traffic planning, weather prediction, and the stock market. Due to their critical relevance and wide application, there has been considerable interest in time series forecasting in recent years. However, the proliferation of sensors and IoT devices, real-time monitoring systems, and high-frequency trading data introduce significant intricate temporal variations, rapid changes, noise, and non-linearities, making time series forecasting more challenging. Classical methods such as Autoregressive integrated moving average (ARIMA) and Exponential Smoothing aim to extract pre-defined temporal variations, such as trends and seasonality. While these methods are effective for capturing well-defined seasonal patterns and trends, they often struggle with more complex, non-linear patterns present in real-world time series data. In recent years, deep learning has made significant contributions to time series forecasting. Recurrent Neural Networks (RNNs) and their variants, such as Long short-term memory (LSTMs) and Gated Recurrent Units (GRUs), have been widely adopted for modeling sequential data. However, they often suffer from the locality, making it difficult to capture local trends and rapid fluctuations. Convolutional Neural Networks (CNNs), particularly Temporal Convolutional Networks (TCNs), leverage convolutional layers to capture temporal dependencies by applying convolutional filters along the temporal dimension. Despite their advantages, TCNs struggle with capturing relationships between distant time points due to the locality of one-dimensional convolution kernels. Transformers have revolutionized time series forecasting with their powerful attention mechanisms, effectively capturing long-term dependencies and relationships between distant time points. However, the attention mechanism may struggle to discern dependencies directly from scattered time points due to intricate temporal patterns. Lastly, Multi-Layer Perceptrons (MLPs) have also been employed, with models like N-BEATS and LightTS demonstrating success. Despite this, MLPs often face high volatility and computational complexity challenges in long-horizon forecasting. To address intricate temporal variations in time series data, this study introduces Times2D, a novel framework that parallelly integrates 2D spectrogram and derivative heatmap techniques. The spectrogram focuses on the frequency domain, capturing periodicity, while the derivative patterns emphasize the time domain, highlighting sharp fluctuations and turning points. This 2D transformation enables the utilization of powerful computer vision techniques to capture various intricate temporal variations. To evaluate the performance of Times2D, extensive experiments were conducted on standard time series datasets and compared with various state-of-the-art algorithms, including DLinear (2023), TimesNet (2023), Non-stationary Transformer (2022), PatchTST (2023), N-HiTS (2023), Crossformer (2023), MICN (2023), LightTS (2022), FEDformer (2022), FiLM (2022), SCINet (2022a), Autoformer (2021), and Informer (2021) under the same modeling conditions. The initial results demonstrated that Times2D achieves consistent state-of-the-art performance in both short-term and long-term forecasting tasks. Furthermore, the generality of the Times2D framework allows it to be applied to various tasks such as time series imputation, clustering, classification, and anomaly detection, offering potential benefits in any domain that involves sequential data analysis.

Keywords: derivative patterns, spectrogram, time series forecasting, times2D, 2D representation

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Authors: Raymond Xu, Ashley Hua, Andrew Wang, Yuru Lin

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During COVID-19, the depression rate has increased dramatically. Young adults are most vulnerable to the mental health effects of the pandemic. Lower-income families have a higher ratio to be diagnosed with depression than the general population, but less access to clinics. This research aims to achieve early depression detection at low cost, large scale, and high accuracy with an interdisciplinary approach by incorporating clinical practices defined by American Psychiatric Association (APA) as well as multimodal AI framework. The proposed approach detected the nine depression symptoms with Natural Language Processing sentiment analysis and a symptom-based Lexicon uniquely designed for young adults. The experiments were conducted on the multimedia survey results from adolescents and young adults and unbiased Twitter communications. The result was further aggregated with the facial emotional cues analyzed by the Convolutional Neural Network on the multimedia survey videos. Five experiments each conducted on 10k data entries reached consistent results with an average accuracy of 88.31%, higher than the existing natural language analysis models. This approach can reach 300+ million daily active Twitter users and is highly accessible by low-income populations to promote early depression detection to raise awareness in adolescents and young adults and reveal complementary cues to assist clinical depression diagnosis.

Keywords: artificial intelligence, COVID-19, depression detection, psychiatric disorder

Procedia PDF Downloads 108

Authors: Pakize Neslihan Taslı, Alev Cumbul, Gul Merve Yalcın, Fikrettin Sahin

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A key experimental trial in the regeneration of large oral and craniofacial defects is the neogenesis of osseous and ligamentous interfacial structures. Currently, oral regenerative medicine strategies are unpredictable for repair of tooth supporting tissues destroyed as a consequence of trauma, chronic infection or surgical resection. A different approach combining the gel-casting method with Hydroxy Apatite HA-based scaffold and different cell lineages as a hybrid system leads to successively mimic the early stage of tooth development, in vitro. HA is widely accepted as a bioactive material for guided bone and tooth regeneration. In this study, it was reported that, HA porous scaffold preparation, characterization and evaluation of structural and chemical properties. HA is the main factor that exists in tooth and it is in harmony with structural, biological, and mechanical characteristics. Here, this study shows mimicking immature tooth at the late bell stage design and construction of HA scaffolds for cell transplantation of human Adipose Stem Cells (hASCs), human Bone Marrow Stem Cells (hBMSCs) and Gingival Epitelial cells for the formation of human tooth dentin-pulp-enamel complexes in vitro. Scaffold characterization was demonstrated by SEM, FTIR and pore size and density measurements. The biological contraction of dental tissues against each other was demonstrated by mRNA gene expressions, histopatologic observations and protein release profile by ELISA tecnique. The tooth shaped constructs with a pore size ranging from 150 to 300 µm arranged by gathering right amounts of materials provide interconnected macro-porous structure. The newly formed tissue like structures that grow and integrate within the HA designed constructs forming tooth cementum like tissue, pulp and bone structures. These findings are important as they emphasize the potential biological effect of the hybrid scaffold system. In conclusion, this in vitro study clearly demonstrates that designed 3D scaffolds shaped as a immature tooth at the late bell stage were essential to form enamel-dentin-pulp interfaces with an appropriate cell and biodegradable material combination. The biomimetic architecture achieved here is providing a promising platform for dental tissue engineering.

Keywords: tooth regeneration, tissue engineering, adipose stem cells, hydroxyapatite tooth engineering, porous scaffold

Procedia PDF Downloads 209

Authors: Said S. Almaiuof

Abstract:

Integration Program Through Physical Education Lessons for The Influence of Some Physical Fitness Health-Related Components in Visual Handicapped Children. Propose of the study: The aim of this study is to design and evaluate a program for the physical education lesson to improve the cardio-respiratory endurance, considering the lesson of physical education as one of the educational means to achieve the society aims toward health and fitness, concerning the importance of the cardio-respiratory endurance as an essential one of the physical fitness health-related components. And this program tried to give them hand of help as a part of the society having the same rights as the rest of the society. Methodology: The researcher used the experimental method as a suitable method for this study and tested its hypothesis. The subject of the research selected randomly of the two classes from primary education in TRIPOLI-LIBYA, it was 28 boys (14-15) years old, then divided into groups, experimental group, n=13, which practiced the special exercises program and control group, n=15, which only practiced the normal school program. The selected child subjected to a medical examination in order to make sure that they are healthy, and the Vo2max measured by cycle ergometry and test some physical fitness makers before and after the program. Results: The study cleared that the experimental group developed in all physical variation in comparison with the control group which has a little development in a general balance only. Results are following: 1. The experimental group was successful more than the control group in all the research variation. 2. There were some improvements in time of keeping Balance in control group only. Discussion /conclusion: According to statistical analysis of data related to the values of the variables in this study; the suggested exercise program according to development the cardio respiratory endurance (CRE), and some physical fitness more than the program which already implemented in the school was less effective, just there is developing on keep balancing. It’s statistically significant (p<0.05) after applying this program. Improving the experimental group on (CRE), balance, running, sit and reach, from the suggested exercise program of what indicate the partaking program positive in improve those physical variables, and the selected exercises may contributed in improving the (CRE) for visual impairment and its pivotal in visual impairment children’s life. A health-related physical education curriculum can provide students with substantially more physical activity during physical education classes. The results show that the physical rehabilitation program for visual impairment children helped them in developing their defects due to the injury which means that this program should be in every organization having this part of people to increase their production and give them hand of help as a part of the society having the same rights as the rest of the society and to establish the idea of sport for all.

Keywords: visual handicapped, cardio-respiratory endurance, health, ergometry, education

Procedia PDF Downloads 414

Authors: Sh. Sousani, Z. Shadrokh, M. Hofbauerová, J. Kollár, M. Jergel, P. Nádaždy, M. Omastová, E. Majková

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Perovskite quantum dots (PQDs) have the potential to increase the performance of the perovskite solar cell (PSCs). The integration of PQDs into PSCs can extend the absorption range and enhance photon harvesting and device efficiency. In addition, PQDs can stabilize the device structure by passivating surface defects and traps in the perovskite layer and enhance its stability. The integration of PQDs into PSCs is strongly affected by the type of ligands on the surface of PQDs. The ligands affect the charge transport properties of PQDs, as well as the formation of well-defined interfaces and stability of PSCs. In this work, the CsPbBr₃ QDs were synthesized by the conventional hot-injection method using cesium oleate, PbBr₂ and two different ligands, namely oleic acid (OA) oleylamine (OAm) and didodecyldimethylammonium bromide (DDAB). The STEM confirmed regular shape and relatively monodisperse cubic structure with an average size of about 10-14 nm of the prepared CsPbBr₃ QDs. Further, the photoluminescent (PL) properties of the PQDs/perovskite bilayer with the ligand OA, OAm and DDAB were studied. For this purpose, ITO/PQDs as well as ITO/PQDs/MAPI perovskite structures were prepared by spin coating and the effect of the ligand and oxygen plasma treatment was analyzed. The plasma treatment of the PQDs layer could be beneficial for the deposition of the MAPI perovskite layer and the formation of a well-defined PQDs/MAPI interface. The absorption edge in UV-Vis absorption spectra for OA, OAm CsPbBr₃ QDs is placed around 513 nm (the band gap 2.38 eV); for DDAB CsPbBr₃ QDs, it is located at 490 nm (the band gap 2.33 eV). The photoluminescence (PL) spectra of CsPbBr₃ QDs show two peaks located around 514 nm (503 nm) and 718 nm (708 nm) for OA, OAm (DDAB). The peak around 500 nm corresponds to the PL of PQDs, and the peak close to 710 nm belongs to the surface states of PQDs for both types of ligands. These surface states are strongly affected by the O₃ plasma treatment. For PQDs with DDAB ligand, the O₃ exposure (5, 10, 15 s) results in the blue shift of the PQDs peak and a non-monotonous change of the amplitude of the surface states' peak. For OA, OAm ligand, the O₃ exposition did not cause any shift of the PQDs peak, and the intensity of the PL peak related to the surface states is lower by one order of magnitude in comparison with DDAB, being affected by O₃ plasma treatment. The PL results indicate the possibility of tuning the position of the PL maximum by the ligand of the PQDs. Similar behavior of the PQDs layer was observed for the ITO/QDs/MAPI samples, where an additional strong PL peak at 770 nm coming from the perovskite layer was observed; for the sample with PQDs with DDAB ligands, a small blue shift of the perovskite PL maximum was observed independently of the plasma treatment. These results suggest the possibility of affecting the PL maximum position and the surface states of the PQDs by the combination of a suitable ligand and the O₃ plasma treatment.

Keywords: perovskite quantum dots, photoluminescence, O₃ plasma., Perovskite Solar Cells

Procedia PDF Downloads 41

Authors: Sh. Sousani, Z. Shadrokh, M. Hofbauerová, J. Kollár, M. Jergel, P. Nádaždy, M. Omastová, E. Majková

Abstract:

Perovskite quantum dots (PQDs) have the potential to increase the performance of the perovskite solar cells (PSCs). The integration of PQDs into PSCs can extend the absorption range and enhance photon harvesting and device efficiency. In addition, PQDs can stabilize the device structure by passivating surface defects and traps in the perovskite layer and enhance its stability. The integration of PQDs into PSCs is strongly affected by the type of ligands on the surface of PQDs. The ligands affect the charge transport properties of PQDs, as well as the formation of well-defined interfaces and stability of PSCs. In this work, the CsPbBr₃ QDs were synthesized by the conventional hot-injection method using cesium oleate, PbBr₂, and two different ligands, namely oleic acid (OA)@oleylamine (OAm) and didodecyldimethylammonium bromide (DDAB). The STEM confirmed regular shape and relatively monodisperse cubic structure with an average size of about 10-14 nm of the prepared CsPbBr₃ QDs. Further, the photoluminescent (PL) properties of the PQDs/perovskite bilayer with the ligand OA@OAm and DDAB were studied. For this purpose, ITO/PQDs, as well as ITO/PQDs/MAPI perovskite structures, were prepared by spin coating, and the effect of the ligand and oxygen plasma treatment was analysed. The plasma treatment of the PQDs layer could be beneficial for the deposition of the MAPI perovskite layer and the formation of a well-defined PQDs/MAPI interface. The absorption edge in UV-Vis absorption spectra for OA@OAm CsPbBr₃ QDs is placed around 513 nm (the band gap 2.38 eV); for DDAB CsPbBr₃ QDs, it is located at 490 nm (the band gap 2.33 eV). The photoluminescence (PL) spectra of CsPbBr₃ QDs show two peaks located around 514 nm (503 nm) and 718 nm (708 nm) for OA@OAm (DDAB). The peak around 500 nm corresponds to the PL of PQDs, and the peak close to 710 nm belongs to the surface states of PQDs for both types of ligands. These surface states are strongly affected by the O₃ plasma treatment. For PQDs with DDAB ligand, the O₃ exposure (5, 10, 15 s) results in the blue shift of the PQDs peak and a non-monotonous change of the amplitude of the surface states' peak. For OA@OAm ligand, the O₃ exposition did not cause any shift of the PQDs peak, and the intensity of the PL peak related to the surface states is lower by one order of magnitude in comparison with DDAB, being affected by O₃ plasma treatment. The PL results indicate the possibility of tuning the position of the PL maximum by the ligand of the PQDs. Similar behaviour of the PQDs layer was observed for the ITO/QDs/MAPI samples, where an additional strong PL peak at 770 nm coming from the perovskite layer was observed; for the sample with PQDs with DDAB ligands, a small blue shift of the perovskite PL maximum was observed independently of the plasma treatment. These results suggest the possibility of affecting the PL maximum position and the surface states of the PQDs by the combination of a suitable ligand and the O₃ plasma treatment.

Keywords: perovskite quantum dots, photoluminescence, O₃ plasma., perovskite solar cells

Procedia PDF Downloads 42

Authors: G. Shahzadi, A. Soulaimani

Abstract:

This research work aims to identify the physical parameters of the constitutive soil model in the design of a rockfill dam by inverse analysis. The best parameters of the constitutive soil model, are those that minimize the objective function, defined as the difference between the measured and numerical results. The Finite Element code (Plaxis) has been utilized for numerical simulation. Polynomial and neural network-based response surfaces have been generated to analyze the relationship between soil parameters and displacements. The performance of surrogate models has been analyzed and compared by evaluating the root mean square error. A comparative study has been done based on objective functions and optimization techniques. Objective functions are categorized by considering measured data with and without uncertainty in instruments, defined by the least square method, which estimates the norm between the predicted displacements and the measured values. Hydro Quebec provided data sets for the measured values of the Romaine-2 dam. Stochastic optimization, an approach that can overcome local minima, and solve non-convex and non-differentiable problems with ease, is used to obtain an optimum value. Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) are compared for the minimization problem, although all these techniques take time to converge to an optimum value; however, PSO provided the better convergence and best soil parameters. Overall, parameter identification analysis could be effectively used for the rockfill dam application and has the potential to become a valuable tool for geotechnical engineers for assessing dam performance and dam safety.

Keywords: Rockfill dam, parameter identification, stochastic analysis, regression, PLAXIS

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Authors: Gabrielle Peck, Ryan Hayes

Abstract:

Current large scale fire tests focus on flammability and heat release measurements. Smoke toxicity isn’t considered despite it being a leading cause of death and injury in unwanted fires. A key reason could be that the practical difficulties associated with quantifying individual toxic components present in a fire effluent often require specialist equipment and expertise. Fire effluent contains a mixture of unreactive and reactive gases, water, organic vapours and particulate matter, which interact with each other. This interferes with the operation of the analytical instrumentation and must be removed without changing the concentration of the target analyte. To mitigate the need for expensive equipment and time-consuming analysis, a portable gas analysis system was designed, constructed and tested for use in large-scale fire tests as a simpler and more robust alternative to online FTIR measurements. The novel equipment aimed to be easily portable and able to run on battery or mains electricity; be able to be calibrated at the test site; be capable of quantifying CO, CO2, O2, HCN, HBr, HCl, NOx and SO2 accurately and reliably; be capable of independent data logging; be capable of automated switchover of 7 bubblers; be able to withstand fire effluents; be simple to operate; allow individual bubbler times to be pre-set; be capable of being controlled remotely. To test the analysers functionality, it was used alongside the ISO/TS 19700 Steady State Tube Furnace (SSTF). A series of tests were conducted to assess the validity of the box analyser measurements and the data logging abilities of the apparatus. PMMA and PA 6.6 were used to assess the validity of the box analyser measurements. The data obtained from the bench-scale assessments showed excellent agreement. Following this, the portable analyser was used to monitor gas concentrations during large-scale testing using the ISO 9705 room corner test. The analyser was set up, calibrated and set to record smoke toxicity measurements in the doorway of the test room. The analyser was successful in operating without manual interference and successfully recorded data for 12 of the 12 tests conducted in the ISO room tests. At the end of each test, the analyser created a data file (formatted as .csv) containing the measured gas concentrations throughout the test, which do not require specialist knowledge to interpret. This validated the portable analyser’s ability to monitor fire effluent without operator intervention on both a bench and large-scale. The portable analyser is a validated and significantly more practical alternative to FTIR, proven to work for large-scale fire testing for quantification of smoke toxicity. The analyser is a cheaper, more accessible option to assess smoke toxicity, mitigating the need for expensive equipment and specialist operators.

Keywords: smoke toxicity, large-scale tests, iso 9705, analyser, novel equipment

Procedia PDF Downloads 50

Authors: Mohd Iz'aan Paiz Bin Zamri, Anis Salwa Mohd Khairuddin, Norrima Mokhtar, Rubiyah Yusof

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Effective statistical feature extraction and classification are important in image-based automatic inspection and analysis. An automatic wood species recognition system is designed to perform wood inspection at custom checkpoints to avoid mislabeling of timber which will results to loss of income to the timber industry. The system focuses on analyzing the statistical pores properties of the wood images. This paper proposed a fuzzy-based feature extractor which mimics the experts’ knowledge on wood texture to extract the properties of pores distribution from the wood surface texture. The proposed feature extractor consists of two steps namely pores extraction and fuzzy pores management. The total number of statistical features extracted from each wood image is 38 features. Then, a backpropagation neural network is used to classify the wood species based on the statistical features. A comprehensive set of experiments on a database composed of 5200 macroscopic images from 52 tropical wood species was used to evaluate the performance of the proposed feature extractor. The advantage of the proposed feature extraction technique is that it mimics the experts’ interpretation on wood texture which allows human involvement when analyzing the wood texture. Experimental results show the efficiency of the proposed method.

Keywords: classification, feature extraction, fuzzy, inspection system, image analysis, macroscopic images

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Authors: Nikolaos Katsoulas, Sofia Faliagka, George Kountrias, Eleni Dimitriou, Eleftheria Pechlivani

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The challenges to feed the world in 2050 are becoming more and more apparent. This calls for producing more with fewer inputs (most of them under scarcity), higher resource efficiency, minimum or zero effect on the environment, and higher sustainability. Therefore, increasing the circularity of production systems is highly significant for their sustainability. Protected horticulture offers opportunities for maximum resource efficiency across various levels within and between farms and at the regional level), high-quality production, and contributes significantly to the nutrition security as part of the world food production. In greenhouses, closed soilless cultivation systems give the opportunity to increase the water and nutrient use efficiency and reduce the environmental impact of the cultivation system by the reuse of the drained water and nutrients. However, due to the low quality of the water used in the Mediterranean countries, a completely closed system is not feasible. Partial discharge of the drainage nutrient solution when the levels of electrical conductivity (EC) or of the toxic ions in the system are reached is still a necessity. Thus, in the frame of the circular economy concept, this work presents the utilisation of the drainage solution of soilless cultivation systems for microalgae and biofertilisers production. The system includes a greenhouse equipped with a soilless cultivation system, a drainage solution collection tank, a closed bioreactor for microalgae production, and a biocatalysis tank. The bioreactor tested in the frame of this work includes two closed tube loops of a capacity of 1000 L each where, after the initial inoculation, the microalgae is developed using as a growth medium the drainage solution collected from the greenhouse crops. The bioreactor includes light and temperature control while pH is still manually regulated. As soon as the microalgae culture reaches a certain density level, 20% of the culture is harvested, and the culture system is refiled by a drainage nutrient solution. The microalgae produced goes through a biocatalysis process, which leads to the production of a rich aminoacids (and nitrogen) biofertiliser. The produced biofertiliser is then used for the fertilisation of greenhouse crops. The complete production cycle along with the effects of the biofertiliser produced on crop growth and yield are presented and discussed in this manuscript. Acknowledgment: This work was carried out under the PestNu project that has received funding from the European Union’s Horizon 2020 research and innovation programme under the Green Deal grant agreement No. 101037128 — PestNu.

Keywords: soilless, water use efficiency, nutrients use efficiency, biostimulant

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Authors: Roya Vakili, Lekaa Elhajjmoussa, Barzin Omidi-Shal, Kim Blake

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Objective: The purpose of this case report is to highlight the successful treatment of a patient with Coloboma, Heart defect, Atresia choanae, Retarded growth and development, Genital hypoplasia, Ear anomalies/deafness, (CHARGE syndrome) using a vagal nerve stimulator (VNS). Background: This is the first documented case report, to the authors' best knowledge, for a patient with CHARGE syndrome, epilepsy, autism, and postural orthostatic tachycardia syndrome (POTS) that was successfully treated with an implanted VNS therapeutic device. Methodology: The study is a case report. Results: This is the case of a 24-year-old female patient with CHARGE syndrome (non-random association of anomalies Coloboma, Heart defect, Atresia choanae, Retarded growth and development, Genital hypoplasia, Ear anomalies/deafness) and several other comorbidities including refractory epilepsy, Patent Ductus Arteriosus (PDA) and POTS who had significant improvement of her symptoms after VNS implantation. She was a VNS candidate given her longstanding history of drug-resistant epilepsy and current disposition secondary to CHARGE syndrome. Prior to VNS implantation, she experienced three generalized seizures a year and daily POTS-related symptoms. She was having frequent lightheadedness and syncope spells due to a rapid heart rate and low blood pressure. The VNS device was set to detect a rapid heart rate and send appropriate stimulation anytime the heart rate exceeded 20% of the patient’s normal baseline. The VNS device demonstrated frequent elevated heart rates and concurrent VNS release every 8 minutes in addition to the programmed events. Following VNS installation, the patient became more active, alert, and communicative and was able to verbally communicate with words she was unable to say prior. Her GI symptoms also improved, as she was able to tolerate food better orally in addition to her G and J tube, likely another result of the vagal nerve stimulation. Additionally, the patient’s seizures and POTS-related cardiac events appeared to be well controlled. She had prolonged electroencephalogram (EEG) testing, showing no significant change in epileptiform activity. Improvements in the patient’s disposition are believed to be secondary to parasympathetic stimulation, adequate heart rate control, and GI stimulation, in addition to behavioral changes and other benefits via her implanted VNS. Conclusion: VNS showed promising results in improving the patient's quality of life and managing her diverse symptoms, including dysautonomia, POTs, gastrointestinal mobility, cognitive functioning as well seizure control.

Keywords: autism, POTs, CHARGE, VNS

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Authors: A. L. Verret, N. Prince, Y. Jerome, A. Bras

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The effects of housing design on the health and well-being of populations are quite intangible. In fact, healthy housing parameters are generally difficult to establish scientifically. It is often unclear the direction of a cause-and-effect relationship between health variables and housing. However, the lack of clear and definite measurements does not entail the absence of relationship between housing, health, and well-being. Research has thus been conducted. It has mostly aimed the physical rather than the psychological or social well-being of a population, given the difficulties to establish cause-effect relationships because of the subjectivity of the psychological symptoms and of the challenge in determining the influence of other factors. That said, a strong relationship has been exposed between light and physiology. Both the nervous and endocrine systems, amongst others, are affected by different wavelengths of natural light within a building. Daylight in the workplace is indeed associated to decreased absenteeism, errors and product defects, fatigue, eyestrain, increased productivity and positive attitude. Similar associations can also be made to residential housing. Lower levels of sunlight within the home have been proven to result in impaired cognition in depressed participants of a cross-sectional case study. Moreover, minimum space (area and volume) has been linked to healthy housing and quality of life, resulting in norms and regulations for such parameters for home constructions. As a matter of fact, it is estimated that people spend the two-thirds of their lives within the home and its immediate environment. Therefore, it is possible to deduct that the health and well-being of the occupants are potentially at risk in an unhealthy housing situation. While the impact of architecture on health and well-being is acknowledged and considered somewhat crucial in various countries of the north and the south, this issue is barely raised in Haiti. In fact, little importance is given to architecture for many reasons (lack of information, lack of means, societal reflex, poverty…). However, the middle-class is known for its residential strategies and trajectories in search of better-quality homes and environments. For this reason, it would be pertinent to use this group and its strategies and trajectories to isolate the impact of housing design on the overall health and well-being. This research aims to analyze the impact of housing architecture on the health and well-being of middle-class families in the metropolitan region of Port-au-Prince. It is a case study which uses semi-structured interviews and observations as research methods. Although at an early stage, this research anticipates that homes affect their occupants both psychologically and physiologically, and consequently, public policies and the population should take into account the architectural design in the planning and construction of housing and, furthermore, cities.

Keywords: architectural design, health and well-being, middle-class housing, Port-au-Prince, Haiti

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Authors: Jacintha Menezes, Ramalingam Dharmalingam, Palaiahnakote Shivakumara

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In the recent years, Omani acid lime cultivation and production has been affected by Citrus greening or Huanglongbing (HLB) disease. HLB disease is one of the most destructive diseases for citrus, with no remedies or countermeasures to stop the disease. Currently used Polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA) HLB detection tests require lengthy and labor-intensive laboratory procedures. Furthermore, the equipment and staff needed to carry out the laboratory procedures are frequently specialized hence making them a less optimal solution for the detection of the disease. The current research uses hyperspectral imaging technology for automatic detection of citrus trees with HLB disease. Omani citrus tree leaf images were captured through portable Specim IQ hyperspectral camera. The research considered healthy, nutrition deficient, and HLB infected leaf samples based on the Polymerase chain reaction (PCR) test. The highresolution image samples were sliced to into sub cubes. The sub cubes were further processed to obtain RGB images with spatial features. Similarly, RGB spectral slices were obtained through a moving window on the wavelength. The resized spectral-Spatial RGB images were given to Convolution Neural Networks for deep features extraction. The current research was able to classify a given sample to the appropriate class with 92.86% accuracy indicating the effectiveness of the proposed techniques. The significant bands with a difference in three types of leaves are found to be 560nm, 678nm, 726 nm and 750nm.

Keywords: huanglongbing (HLB), hyperspectral imaging (HSI), · omani citrus, CNN

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Authors: Chul Lee, Seoae Cho, Erich D. Jarvis, Heebal Kim

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Vocal learning, the ability to imitate vocalizations based on auditory experience, is a homoplastic character state observed in different independent lineages of animals such as songbirds, parrots, hummingbirds and human. It has now become possible to perform genome-wide molecular analyses across vocal learners and vocal non-learners with the recent expansion of avian genome data. It was analyzed the whole genomes of human and 48 avian species including those belonging to the three avian vocal learning lineages, to determine if behavior and neural convergence are associated with molecular convergence in divergent species of vocal learners. Analyses of 8295 orthologous genes across bird species revealed 141 genes with amino acid substitutions specific to vocal learners. Out of these, 25 genes have vocal learner specific genetic homoplasies, and their functions were enriched for learning. Several sites in these genes are estimated under convergent evolution and positive selection. A potential role for a subset of these genes in vocal learning was supported by associations with gene expression profiles in vocal learning brain regions of songbirds and human disease that cause language dysfunctions. The key candidate gene with multiple independent lines of the evidences specific to vocal learners was DRD5. Our findings suggest cAMP-based learning pathway in avian vocal learners, indicating molecular homoplastic changes associated with a complex behavioral trait, vocal learning.

Keywords: amino acid substitutions, convergent evolution, positive selection, vocal learning

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Authors: Qasim Munye, Juned Islam, Haseeb Qureshi, Syed Jung

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Lung nodules are commonly identified in computed tomography (CT) scans by experienced radiologists at a relatively late stage. Early diagnosis can greatly increase survival. We propose using a pix2pix conditional generative adversarial network to generate realistic images simulating early-stage lung nodule growth. We have applied deep images prior to 2341 slices from 895 computed tomography (CT) scans from the Lung Image Database Consortium (LIDC) dataset to generate pseudo-healthy medical images. From these images, 819 were chosen to train a pix2pix network. We observed that for most of the images, the pix2pix network was able to generate images where the nodule increased in size and intensity across epochs. To evaluate the images, 400 generated images were chosen at random and shown to a medical student beside their corresponding original image. Of these 400 generated images, 384 were defined as satisfactory - meaning they resembled a nodule and were visually similar to the corresponding image. We believe that this generated dataset could be used as training data for neural networks to detect lung nodules at an early stage or to improve the accuracy of such networks. This is particularly significant as datasets containing the growth of early-stage nodules are scarce. This project shows that the combination of deep image prior and generative models could potentially open the door to creating larger datasets than currently possible and has the potential to increase the accuracy of medical classification tasks.

Keywords: medical technology, artificial intelligence, radiology, lung cancer

Procedia PDF Downloads 44

Authors: Burçak Boztemur, Yue Xu, Laima Luo, M. Lütfi Öveçoğlu, Duygu Ağaoğulları

Abstract:

Tungsten (W) is used chiefly as plasma-facing material. However, it has some problems, such as brittleness after plasma exposure. High-entropy alloys (RHEAs) are a new opportunity for this deficiency. So, the neutron shielding behavior of WNbMoVTa and WNbMoVTaCr₀.₅Al compositions were examined against He⁺ irradiation in this study. The mechanical and irradiation properties of the WNbMoVTa base composition were investigated by adding the Al and Cr elements. The mechanical alloying (MA) for 6 hours was applied to obtain RHEA powders. According to the X-ray diffraction (XRD) method, the body-centered cubic (BCC) phase and NbTa phase with a small amount of WC impurity that comes from vials and balls were determined after 6 h MA. Also, RHEA powders were consolidated with the spark plasma sintering (SPS) method (1500 ºC, 30 MPa, and 10 min). After the SPS method, (Nb,Ta)C and W₂C₀.₈₅ phases were obtained with the decomposition of WC and stearic acid that is added during MA based on XRD results. Also, the BCC phase was obtained for both samples. While the Al₂O₃ phase with a small intensity was seen for the WNbMoVTaCr₀.₅Al sample, the Ta₂VO₆ phase was determined for the base sample. These phases were observed as three different regions according to scanning electron microscopy (SEM). All elements were distributed homogeneously on the white region by measuring an electron probe micro-analyzer (EPMA) coupled with a wavelength dispersive spectroscope (WDS). Also, the grey region of the WNbMoVTa sample was rich in Ta, V, and O elements. However, the amount of Al and O elements was higher for the grey region of the WNbMoVTaCr₀.₅Al sample. The high amount of Nb, Ta, and C elements were determined for both samples. Archimedes’ densities that were measured with alcohol media were closer to the theoretical densities of RHEAs. These values were important for the microhardness and irradiation resistance of compositions. While the Vickers microhardness value of the WNbMoVTa sample was measured as ~11 GPa, this value increased to nearly 13 GPa with the WNbMoVTaCr₀.₅Al sample. These values were compatible with the wear behavior. The wear volume loss was decreased to 0.16×10⁻⁴ from 1.25×10⁻⁴ mm³ by the addition of Al and Cr elements to the WNbMoVTa. The He⁺ irradiation was conducted on the samples to observe surface damage. After irradiation, the XRD patterns were shifted to the left because of defects and dislocations. He⁺ ions were infused under the surface, so they created the lattice expansion. The peak shifting of the WNbMoVTaCr₀.₅Al sample was less than the WNbMoVTa base sample, thanks to less impact. A small amount of fuzz was observed for the base sample. This structure was removed and transformed into a wavy structure with the addition of Cr and Al elements. Also, the deformation hardening was actualized after irradiation. A lower amount of hardening was obtained with the WNbMoVTaCr₀.₅Al sample based on the changing microhardness values. The surface deformation was decreased in the WNbMoVTaCr₀.₅Al sample.

Keywords: refractory high entropy alloy, microhardness, wear resistance, He⁺ irradiation

Procedia PDF Downloads 48

Authors: Seung Ju Yeon, Seul Ki Min, Jun Sang Park, Yeo Seon Kwon, Hoo Cheol Lee, Hyun Jung Shim, Il-Doo Kim, Ja Kyeong Lee, Hwa Sung Shin

Abstract:

In brain injury, depleting oxidative stress is the most effective way to reduce the brain infarct size. C-phycocyanin (C-Pc) is a well-known antioxidant protein that has neuroprotective effects obtained from green microalgae. Astrocyte is glial cell that supports the nerve cell such as neuron, which account for a large portion of the brain. In brain injury, such as ischemia and reperfusion, astrocyte has an important rule that overcomes the oxidative stress and protect from brain reactive oxygen species (ROS) injury. However little is known about how C-Pc regulates the anti-oxidants effects of astrocyte. In this study, when the C-Pc was treated in oxidized astrocyte, we confirmed that inflammatory factors Interleukin-6 and Interleukin-3 were increased and antioxidants enzyme, Superoxide dismutase (SOD) and catalase was upregulated, and neurotrophic factors, brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF) was alleviated. Also, it was confirmed to reduce infarct size of the brain in ischemia and reperfusion because C-Pc has anti-oxidant effects in middle cerebral artery occlusion (MCAO) animal model. These results show that C-Pc can help astrocytes lead neuroprotective activities in the oxidative stressed environment of the brain. In summary, the C-PC protects astrocytes from oxidative stress and has anti-oxidative, anti-inflammatory, neurotrophic effects under ischemic situations.

Keywords: c-phycocyanin, astrocyte, reactive oxygen species, ischemia and reperfusion, neuroprotective effect

Procedia PDF Downloads 295

Authors: Manali Chakraborty

Abstract:

In the developing countries the most prevalent pathological state is malnutrition and under nutrition due to deficiency of essential nutrients. This condition is more seen between the woman population, especially in the adolescent girls. It is causing childhood deaths along with others cognitive, degenerative diseases. Born of low weight babies and stillbirth are also major problems associated with the malnutrition. Along with the increased level of population, people not only should concern about their quantity of food but also for the quality of the food to be healthy. Lethargy, depression caused due to iron deficiency often quoted as normal or unimportant issues. Children of malnourished women are more likely associated with cognitive impairment, immune dysfunction leading to a higher risk of being attacked by diseases. Malnourishment also affects the productivity of women. Low social status, lack of proper nutritional education is an important cause of this nutrient deficiency among women. Iron deficiency and anemia are mostly famous nutritional deficiencies among women worldwide. Mostly women from below poverty lined area are anemic due to less consumption of iron-rich foods or having foods that might inhibit the iron absorption. Growing females like adolescents or lactating females need more iron supply. Less supplement causes iron deficiency. Though malaria also might cause anemia, it is more likely endemic one in some specific areas. Folate deficiency in females also may cause neurological defects in the infants. Other Vitamin B, A deficiencies along with low iodine level is also noted in malnourished women. According to tradition, still in some areas in developing countries females have their food at the end. According to some survey and collected data, these females are often into the risk zone of being malnourished. Regularly they have the very lesser amount, or food sometimes may start to lose its nutrients. Women are the one who maintains the responsibility to cook foods in the home. Lack of proper nutritional education and proper food preparation not only make those foods lose the nutrients leading to impairment in proper nutrient intake of the family members but also impairing own nutritional status. Formulation and development of food products from iron or other nutrient affluent sources viz., traditional herbs can be helpful in the prevention of such malnutrition condition in females. Keeping low cost and smooth maintenance of the food product development from the natural sources like pulses, cereals or other vegetation also can be beneficial to sustain socio-economic condition. Consumption of such kind of foodstuff is much healthier rather than taking continuous supplements like capsules. Utilization of proper scientific and cost-effective techniques for this food product development and their distribution among rural women population might be an enormous initiative.

Keywords: anemia, food supplements, malnutrition, rural places, women population

Procedia PDF Downloads 97

Authors: Yan Huang

Abstract:

Event-related potentials (ERPs) technique helps researchers to make continuous measures on the whole process of language comprehension, with an excellent temporal resolution at the level of milliseconds. The research on sentence processing has developed from the behavioral level to the neuropsychological level, which brings about a variety of sentence processing theories and models. However, the applicability of these models to L2 learners is still under debate. Therefore, the present study aims to investigate the neural mechanisms underlying English subjunctive mood processing by Chinese ESL learners. To this end, English subject clauses with subjunctive moods are used as the stimuli, all of which follow the same syntactic structure, “It is + adjective + that … + (should) do + …” Besides, in order to examine the role that language proficiency plays on L2 processing, this research deals with two groups of Chinese ESL learners (18 males and 22 females, mean age=21.68), namely, high proficiency group (Group H) and low proficiency group (Group L). Finally, the behavioral and neurophysiological data analysis reveals the following findings: 1) Syntax and semantics interact with each other on the SECOND phase (300-500ms) of sentence processing, which is partially in line with the Three-phase Sentence Model; 2) Language proficiency does affect L2 processing. Specifically, for Group H, it is the syntactic processing that plays the dominant role in sentence processing while for Group L, semantic processing also affects the syntactic parsing during the THIRD phase of sentence processing (500-700ms). Besides, Group H, compared to Group L, demonstrates a richer native-like ERPs pattern, which further demonstrates the role of language proficiency in L2 processing. Based on the research findings, this paper also provides some enlightenment for the L2 pedagogy as well as the L2 proficiency assessment.

Keywords: Chinese ESL learners, English subjunctive mood, ERPs, L2 processing

Procedia PDF Downloads 109

Authors: Soo-Yong Cho, Chong-Hyun Cho, Chae-Whan Rim, Sang-Kyu Choi, Jin-Gyun Kim, Ju-Seok Nam

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Recently, vertical axis wind turbines (VAWT) have been widely used to produce electricity even in urban. They have several merits such as low sound noise, easy installation of the generator and simple structure without yaw-control mechanism and so on. However, their blades are operated under the influence of the trailing vortices generated by the preceding blades. This phenomenon deteriorates its output power and makes difficulty predicting correctly its performance. In order to improve the performance of VAWT, wind power towers can be applied. Usually, the wind power tower can be constructed as a multi-story building to increase the frontal area of the wind stream. Hence, multiple sets of the VAWT can be installed within the wind power tower, and they can be operated at high elevation. Many different types of wind power tower can be used in the field. In this study, a wind power tower with circular column shape was applied, and the VAWT was installed at the center of the wind power tower. Seven guide walls were used as a strut between the floors of the wind power tower. These guide walls were utilized not only to increase the wind velocity within the wind power tower but also to adjust the wind direction for making a better working condition on the VAWT. Hence, some important design variables, such as the distance between the wind turbine and the guide wall, the outer diameter of the wind power tower, the direction of the guide wall against the wind direction, should be considered to enhance the output power on the VAWT. A numerical analysis was conducted to find the optimum dimension on design variables by using the computational fluid dynamics (CFD) among many prediction methods. The CFD could be an accurate prediction method compared with the stream-tube methods. In order to obtain the accurate results in the CFD, it needs the transient analysis and the full three-dimensional (3-D) computation. However, this full 3-D CFD could be hard to be a practical tool because it requires huge computation time. Therefore, the reduced computational domain is applied as a practical method. In this study, the computations were conducted in the reduced computational domain and they were compared with the experimental results in the literature. It was examined the mechanism of the difference between the experimental results and the computational results. The computed results showed this computational method could be an effective method in the design methodology using the optimization algorithm. After validation of the numerical method, the CFD on the wind power tower was conducted with the important design variables affecting the performance of VAWT. The results showed that the output power of the VAWT obtained using the wind power tower was increased compared to them obtained without the wind power tower. In addition, they showed that the increased output power on the wind turbine depended greatly on the dimension of the guide wall.

Keywords: CFD, performance, VAWT, wind power tower

Procedia PDF Downloads 360

Authors: Chi Wu, Dean Wu, Wen-Te Liu, Cheng-Yu Tsai, Shin-Mei Hsu, Yin-Tzu Lin, Ru-Yin Yang

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Background: The results of previous studies compared the benefits and risks of receiving insomnia medication. However, the effects between hypnotic drugs used and enhancement of sleep quality were still unclear. Objective: The aim of this study is to establish a prediction model for hypnotic drugs' dosage used for insomnia subjects and associated the relationship between sleep stage ratio change and drug types. Methodologies: According to American Academy of Sleep Medicine (AASM) guideline, sleep stages were classified and transformed to hypnogram via the polysomnography (PSG) in a hospital in New Taipei City (Taiwan). The subjects with diagnosis for insomnia without receiving hypnotic drugs treatment were be set as the comparison group. Conversely, hypnotic drugs dosage within the past three months was obtained from the clinical registration for each subject. Furthermore, the collecting subjects were divided into two groups for training and testing. After training convolution neuron network (CNN) to predict types of hypnotics used and dosages are taken, the test group was used to evaluate the accuracy of classification. Results: We recruited 76 subjects in this study, who had been done PSG for transforming hypnogram from their sleep stages. The accuracy of dosages obtained from confusion matrix on the test group by CNN is 81.94%, and accuracy of hypnotic drug types used is 74.22%. Moreover, the subjects with high ratio of wake stage were correctly classified as requiring medical treatment. Conclusion: CNN with hypnogram was potentially used for adjusting the dosage of hypnotic drugs and providing subjects to pre-screening the types of hypnotic drugs taken.

Keywords: convolution neuron network, hypnotic drugs, insomnia, polysomnography

Procedia PDF Downloads 166

Authors: Rensson Homero Celiz Ygnacio, Marco Aurélio Schiavo Novaes, Lucy Vanessa Sulca Ñaupas, Ana Paula Ribeiro Rodrigues

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The cryopreservation of testicular tissue emerges as an alternative for the preservation of the reproductive potential of individuals who still cannot produce sperm; however, they will undergo treatments that may affect their fertility (e.g., chemotherapy). Therefore, the present work aims to compare two cryopreservation methods (slow freezing and vitrification) in testicular tissue of prepubertal lambs. For that, to obtain the testicular tissue, the animals were castrated and the testicles were collected immediately in a physiological solution supplemented with antibiotics. In the laboratory, the testis was split into small pieces. The total size of the testicular fragments was 3×3x1 mm³ and was placed in a dish contained in Minimum Essential Medium (MEM-HEPES). The fragments were distributed randomly into non-cryopreserved (fresh control), slow freezing (SF), and vitrified. To SF procedures, two fragments from a given male were then placed in a 2,0 mL cryogenic vial containing 1,0 mL MEM-HEPES supplemented with 20% fetal bovine serum (FBS) and 20% dimethylsulfoxide (DMSO). Tubes were placed into a Mr. Frosty™ Freezing container with isopropyl alcohol and transferred to a -80°C freezer for overnight storage. On the next day, each tube was plunged into liquid nitrogen (NL). For vitrification, the ovarian tissue cryosystem (OTC) device was used. Testicular fragments were placed in the OTC device and exposed to the first vitrification solution composed of MEM-HEPES supplemented with 10 mg/mL Bovine Serum Albumin (BSA), 0.25 M sucrose, 10% Ethylene glycol (EG), 10% DMSO and 150 μM alpha-lipoic acid for four min. The VS1 was discarded and then the fragments were submerged into a second vitrification solution (VS2) containing the same composition of VS1 but 20% EG and 20% DMSO. VS2 was then discarded and each OTC device containing up to four testicular fragments was closed and immersed in NL. After the storage period, the fragments were removed from the NL, kept at room temperature for one min and then immersed at 37 °C in a water bath for 30 s. Samples were warmed by sequentially immersing in solutions of MEM-HEPES supplemented with 3 mg/mL BSA and decreasing concentrations of sucrose. Hematoxylin-eosin staining to analyze the tissue architecture was used. The score scale used was from 0 to 3, classified with a score 0 representing normal morphologically, and 3 were considered a lot of alteration. The histomorphological evaluation of the testicular tissue shows that when evaluating the nuclear alteration (distinction of nucleoli and condensation of nuclei), there are no differences when using slow freezing with respect to the control. However, vitrification presents greater damage (p <0.05). On the other hand, when evaluating the epithelial alteration, we observed that the freezing showed scores statistically equal to the control in variables such as retraction of the basement membrane, formation of gaps and organization of the peritubular cells. The results of the study demonstrated that cryopreservation using the slow freezing method is an excellent tool for the preservation of pubertal testicular tissue.

Keywords: cryopreservation, slow freezing, vitrification, testicular tissue, lambs

Procedia PDF Downloads 150

Authors: E. Tuğba Topçu, Ebru E. Kazan, Erhan Büken

Abstract:

Emergency nurses are the first health care professional to generally observe the patients, communicate patients’ family or relatives, touch the properties of patients and contact to laboratory sample of patients. Also, they are the encounter incidents related crime, people who engage in violence or suspicious injuries frequently. So, documentation of patients’ condition came to the hospital and conservation of evidence are important in the inquiry of forensic medicine. The aim of the study was to determine the knowledge level of healthcare professional working at the emergency services regarding their approaches to common forensic cases. The study was comprised of 404 healthcare professional working (nurse, emergency medicine technician, health officer) at the emergency services of 6 state hospitals, 6 training and 6 research hospitals and 3 university hospitals in Ankara. Data was collected using questionnaire form which was developed by researches in the direction of literature. Questionnaire form is comprised of two sections. The first section includes 17 questions related demographic information about health care professional and 4 questions related Turkish laws. The second section includes 43 questions to the determination of knowledge level of health care professional’s working in the emergency department, about approaches to frequently encountered forensic cases. For the data evaluation of the study; Mann Whitney U test, Bonferroni correction Kruskal Wallis H test and Chi Square tests have been used. According to study, it’s said that there is no forensic medicine expert in the foundation by 73.4% of health care professionals. Two third (66%) of participants’ in emergency department reported daily average 7 or above forensic cases applied to the emergency department and 52.1% of participants did not evaluate incidents came to the emergency department as a forensic case. Most of the participants informed 'duty of preservation of evidence' is health care professionals duty related forensic cases. In result, we determinated that knowledge level of health care professional working in the emergency department, about approaches to frequently encountered forensic cases, is not the expected level. Because we found that most of them haven't received education about forensic nursing.Postgraduates participants, educated health professional about forensic nursing, staff who applied to sources about forensic nursing and staff who evaluated emergency department cases as forensic cases have significantly higher level of knowledge. Moreover, it’s found that forensic cases diagnosis score is the highest in health officer and university graduated. Health care professional’s deficiency in knowledge about forensic cases can cause defects in operation of the forensic process because of mistakes in collecting and conserving of evidence. It is obvious that training about the approach to forensic nursing should be arranged.

Keywords: emergency nurses, forensic case, forensic nursing, level of knowledge

Procedia PDF Downloads 265

Authors: Md. Mahadi Hasan Sany, Mumenunnessa Keya, Sharun Khushbu, Sheikh Abujar

Abstract:

Since the beginning of China's economic reform, trade between the U.S. and China has grown rapidly, and has increased since China's accession to the World Trade Organization in 2001. The US imports more than it exports from China, reducing the trade war between China and the U.S. for the 2019 trade deficit, but in 2020, the opposite happens. In international and U.S. trade, Washington launched a full-scale trade war against China in March 2016, which occurred a catastrophic epidemic. The main goal of our study is to measure and predict trade relations between China and the U.S., before and after the arrival of the COVID epidemic. The ML model uses different data as input but has no time dimension that is present in the time series models and is only able to predict the future from previously observed data. The LSTM (a well-known Recurrent Neural Network) model is applied as the best time series model for trading forecasting. We have been able to create a sustainable forecasting system in trade between China and the US by closely monitoring a dataset published by the State Website NZ Tatauranga Aotearoa from January 1, 2015, to April 30, 2021. Throughout the survey, we provided a 180-day forecast that outlined what would happen to trade between China and the US during COVID-19. In addition, we have illustrated that the LSTM model provides outstanding outcome in time series data analysis rather than RFR and SVR (e.g., both ML models). The study looks at how the current Covid outbreak affects China-US trade. As a comparative study, RMSE transmission rate is calculated for LSTM, RFR and SVR. From our time series analysis, it can be said that the LSTM model has given very favorable thoughts in terms of China-US trade on the future export situation.

Keywords: RFR, China-U.S. trade war, SVR, LSTM, deep learning, Covid-19, export value, forecasting, time series analysis

Procedia PDF Downloads 172

Authors: ChunYi Peng, Wei Hsuan CHeng, Shyh Kuang Ueng

Abstract:

This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

Procedia PDF Downloads 23

Authors: Nick Weir, Robert Stevens, Alan Hargreaves, Martin McGinnity, Chris Tinsley

Abstract:

Hydrophobic materials have previously demonstrated the ability to elevate cell-cell interactions and promote the formation of neural networks whilst aligned nanofibers demonstrate the ability to induce extensive neurite outgrowth in an aligned manner. Hydrophobic materials typically elicit an immune response upon implantation and thus materials used for implantation are typically hydrophilic. Poly-L-lactic acid (PLLA) is a hydrophobic, non-immunogenic, FDA approved material that can be electrospun to form aligned nanofibers. Primary rat cortical neurons cultured for 10 days on aligned PLLA nanofibers formed 3D cell clusters, approximately 800 microns in diameter. Neurites that extended from these clusters were highly aligned due to the alignment of the nanofibers they were cultured upon and fasciculation was also evident. Plasma treatment of the PLLA nanofibers prior to seeding of cells significantly reduced the hydrophobicity and abolished the cluster formation and neurite fasciculation, whilst reducing the extent and directionality of neurite outgrowth; it is proposed that hydrophobicity induces the changes to cellular behaviors. Aligned PLLA nanofibers induced the formation of a structure that mimics the grey-white matter compartmentalization that is observed in vivo and thus represents a step forward in generating organoids or biomaterial-based implants. Upon implantation into the brain, the biomaterial architectures described here may provide a useful platform for both brain repair and brain remodeling initiatives.

Keywords: hydrophobicity, nanofibers, neurite fasciculation, neurite outgrowth, PLLA

Procedia PDF Downloads 133

Authors: Haidy S. Ghali, Engy Serag, A. Samer Ezeldin

Abstract:

Copper is an essential raw material used in the construction industry. During the year 2021 and the first half of 2022, the global market suffered from a significant fluctuation in copper raw material prices due to the aftermath of both the COVID-19 pandemic and the Russia-Ukraine war, which exposed its consumers to an unexpected financial risk. Thereto, this paper aims to develop two ANN-LSTM price prediction models, using Python, that can forecast the average monthly copper prices traded in the London Metal Exchange; the first model is a multivariate model that forecasts the copper price of the next 1-month and the second is a univariate model that predicts the copper prices of the upcoming three months. Historical data of average monthly London Metal Exchange copper prices are collected from January 2009 till July 2022, and potential external factors are identified and employed in the multivariate model. These factors lie under three main categories: energy prices and economic indicators of the three major exporting countries of copper, depending on the data availability. Before developing the LSTM models, the collected external parameters are analyzed with respect to the copper prices using correlation and multicollinearity tests in R software; then, the parameters are further screened to select the parameters that influence the copper prices. Then, the two LSTM models are developed, and the dataset is divided into training, validation, and testing sets. The results show that the performance of the 3-Month prediction model is better than the 1-Month prediction model, but still, both models can act as predicting tools for diverse economic situations.

Keywords: copper prices, prediction model, neural network, time series forecasting

Procedia PDF Downloads 86

Authors: George Mahalu

Abstract:

The Chua diode has been configured over time in various ways, using electronic structures like operational amplifiers (AOs) or devices with gas or semiconductors. When discussing the use of semiconductor devices, tunnel diodes (Esaki diodes) are most often considered, and more recently, transistorized configurations such as lambda diodes. The paperwork proposed here uses in the modeling a lambda diode type configuration consisting of two junction field effect transistors (JFET). The original scheme is created in the MULTISIM electronic simulation environment and is analyzed in order to identify the conditions for the appearance of evolutionary unpredictability specific to nonlinear dynamic systems with chaos-induced behavior. The chaotic deterministic oscillator is one autonomous type, a fact that places it in the class of Chua’s type oscillators, the only significant and most important difference being the presence of a nonlinear device like the one mentioned structure above. The chaotic behavior is identified both by means of strange attractor-type trajectories and visible during the simulation and by highlighting the hypersensitivity of the system to small variations of one of the input parameters. The results obtained through simulation and the conclusions drawn are useful in the further research of ways to implement such constructive electronic solutions in theoretical and practical applications related to modern small signal amplification structures, to systems for encoding and decoding messages through various modern ways of communication, as well as new structures that can be imagined both in modern neural networks and in those for the physical implementation of some requirements imposed by current research with the aim of obtaining practically usable solutions in quantum computing and quantum computers.

Keywords: chua, diode, memristor, chaos

Procedia PDF Downloads 62

Authors: Peng, Chun-Yi, Chen, Wei-Hsuan, Ueng, Shyh-Kuang

Abstract:

This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

Procedia PDF Downloads 29