Search results for: dataset generation

Authors: Xu Jiaqiao

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Text sentiment analysis is an important branch of natural language processing. This technology is widely used in public opinion analysis and web surfing recommendations. At present, the mainstream sentiment analysis methods include three parts: sentiment analysis based on a sentiment dictionary, based on traditional machine learning, and based on deep learning. This paper mainly analyzes and compares the advantages and disadvantages of the SVM method of traditional machine learning and the Long Short-term Memory (LSTM) method of deep learning in the field of Chinese sentiment analysis, using Chinese comments on Sina Microblog as the data set. Firstly, this paper classifies and adds labels to the original comment dataset obtained by the web crawler, and then uses Jieba word segmentation to classify the original dataset and remove stop words. After that, this paper extracts text feature vectors and builds document word vectors to facilitate the training of the model. Finally, SVM and LSTM models are trained respectively. After accuracy calculation, it can be obtained that the accuracy of the LSTM model is 85.80%, while the accuracy of SVM is 91.07%. But at the same time, LSTM operation only needs 2.57 seconds, SVM model needs 6.06 seconds. Therefore, this paper concludes that: compared with the SVM model, the LSTM model is worse in accuracy but faster in processing speed.

Keywords: sentiment analysis, support vector machine, long short-term memory, Chinese microblog comments

Procedia PDF Downloads 94

Authors: Robi Lou Logarta, Meliz Ann Marilag, Kristyl Lee Nisnisan, Felipe Lula Jr.

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Nowadays, alcohol is consumed widely around the globe for plenty of reasons. College years are the time that the students really decide if whether they will or will not engage into alcohol, although alcohol drinking begins before students arrive at college. The reasons on why college students consume alcohol vary in many categories. The norms on alcohol drinking are addiction, emotional pain reliever, popularity purposes, socialization, and a medium of euphoria for most students; college students in particular are most likely to feel this need. After tons of requirements to be complied and courses to be reviewed, they felt a need for celebration and relaxation which ends up in drinking with college mates and a few old friends. A lot of reasons consist the consumption of alcohol and this research determined the reasons behind the students’ onset for alcohol consumption; the main reason for such action and the experiences they encountered after in-take, furthermore, the correlation of alcohol drinking to the average allowance of the involved participants; Mindanao State University-Iligan Institute of Technology Students whether it affects their spending towards alcohol or not. This study assumes that alcohol drinking for MSU-IIT students’ is done to relieve emotional pain caused by flunking in particular subjects as well as dealing with romance, as part of the student body, these acts are noticeable enough which made this hypothesis be formulated. Selected MSU-IIT students were asked about their opinions regarding reasons of alcohol consumption. There were 100 respondents consisting of first year to fifth-year students aging 17-23 years old. Choices were given to the students to mark their most favorable reason for drinking that is adult influence, curiosity, family/personal problems, peer pressure, stress. Using the bar and pie chart illustrations, the collected data was then analyzed and among the given choices, the result has invalidated the hypothesis. The outcome shows that curiosity is the topmost reason why students start to drink and not due to emotional pain. With this, another hypothesis is formulated stating that millennial is a curious generation; this generation has changed the norm of drinking. One of the characteristics of the Y generation is being adventurous which correlates to how they get curious about things and the same goes for alcohol consumption, compared to the latter, this generation can be considered early drinkers in this manner. Therefore, it is concluded that MSU-IIT students which are part of the generation Y are adventurous enough to try unfamiliar beverages to satisfy their curious minds.

Keywords: adult influence, curiosity, family/personal problems, peer pressure, stress

Procedia PDF Downloads 261

Authors: Joseph M. Rich, Vinay A. Duddalwar, Assad A. Oberai

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Prostate adenocarcinoma is the most common cancer in males, with osseous metastases as the commonest site of metastatic prostate carcinoma (mPC). Treatment monitoring is based on the evaluation and characterization of lesions on multiple imaging studies, including Computed Tomography (CT). Monitoring of the osseous disease burden, including follow-up of lesions and identification and characterization of new lesions, is a laborious task for radiologists. Deep learning algorithms are increasingly used to perform tasks such as identification and segmentation for osseous metastatic disease and provide accurate information regarding metastatic burden. Here, nnUNet was used to produce a model which can segment CT scan images of prostate adenocarcinoma vertebral bone metastatic lesions. nnUNet is an open-source Python package that adds optimizations to deep learning-based UNet architecture but has not been extensively combined with transfer learning techniques due to the absence of a readily available functionality of this method. The IRB-approved study data set includes imaging studies from patients with mPC who were enrolled in clinical trials at the University of Southern California (USC) Health Science Campus and Los Angeles County (LAC)/USC medical center. Manual segmentation of metastatic lesions was completed by an expert radiologist Dr. Vinay Duddalwar (20+ years in radiology and oncologic imaging), to serve as ground truths for the automated segmentation. Despite nnUNet’s success on some medical segmentation tasks, it only produced an average Dice Similarity Coefficient (DSC) of 0.31 on the USC dataset. DSC results fell in a bimodal distribution, with most scores falling either over 0.66 (reasonably accurate) or at 0 (no lesion detected). Applying more aggressive data augmentation techniques dropped the DSC to 0.15, and reducing the number of epochs reduced the DSC to below 0.1. Datasets have been identified for transfer learning, which involve balancing between size and similarity of the dataset. Identified datasets include the Pancreas data from the Medical Segmentation Decathlon, Pelvic Reference Data, and CT volumes with multiple organ segmentations (CT-ORG). Some of the challenges of producing an accurate model from the USC dataset include small dataset size (115 images), 2D data (as nnUNet generally performs better on 3D data), and the limited amount of public data capturing annotated CT images of bone lesions. Optimizations and improvements will be made by applying transfer learning and generative methods, including incorporating generative adversarial networks and diffusion models in order to augment the dataset. Performance with different libraries, including MONAI and custom architectures with Pytorch, will be compared. In the future, molecular correlations will be tracked with radiologic features for the purpose of multimodal composite biomarker identification. Once validated, these models will be incorporated into evaluation workflows to optimize radiologist evaluation. Our work demonstrates the challenges of applying automated image segmentation to small medical datasets and lays a foundation for techniques to improve performance. As machine learning models become increasingly incorporated into the workflow of radiologists, these findings will help improve the speed and accuracy of vertebral metastatic lesions detection.

Keywords: deep learning, image segmentation, medicine, nnUNet, prostate carcinoma, radiomics

Procedia PDF Downloads 96

Authors: Yehjune Heo

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Fingerprint Anti-Spoofing approaches have been actively developed and applied in real-world applications. One of the main problems for Fingerprint Anti-Spoofing is not robust to unseen samples, especially in real-world scenarios. A possible solution will be to generate artificial, but realistic fingerprint samples and use them for training in order to achieve good generalization. This paper contains experimental and comparative results with currently popular GAN based methods and uses realistic synthesis of fingerprints in training in order to increase the performance. Among various GAN models, the most popular StyleGAN is used for the experiments. The CNN models were first trained with the dataset that did not contain generated fake images and the accuracy along with the mean average error rate were recorded. Then, the fake generated images (fake images of live fingerprints and fake images of spoof fingerprints) were each combined with the original images (real images of live fingerprints and real images of spoof fingerprints), and various CNN models were trained. The best performances for each CNN model, trained with the dataset of generated fake images and each time the accuracy and the mean average error rate, were recorded. We observe that current GAN based approaches need significant improvements for the Anti-Spoofing performance, although the overall quality of the synthesized fingerprints seems to be reasonable. We include the analysis of this performance degradation, especially with a small number of samples. In addition, we suggest several approaches towards improved generalization with a small number of samples, by focusing on what GAN based approaches should learn and should not learn.

Keywords: anti-spoofing, CNN, fingerprint recognition, GAN

Procedia PDF Downloads 184

Authors: Subhro Sarkar, Umesh Mishra

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Agartala Municipal Council (AMC) is the municipal body which regulates and governs the Agartala city. MSW management may be proclaimed as a tool which rests on the principles of public health, economy, engineering and other aesthetic or environmental factors by dealing with the controlled generation, collection, transport, processing and disposal of MSW. Around 220-250 MT of solid waste per day is collected by AMC out of which 12-14 MT is plastic and is disposed of in Devendra Chandra Nagar dumping ground (33 acres), nearly 12-15 km from the city. A survey was performed to list down the prevailing operations conducted by the AMC which includes road sweeping, garbage lifting, carcass removal, biomedical waste collection, dumping, and incineration. Different types of vehicles are engaged to carry out these operations. Door to door collection of garbage is done from the houses with the help of 220 tricycles issued by 53 NGOs. The location of the dustbin containers were earmarked which consisted of 4.5 cum, 0.6 cum containers and 0.1 cum containers, placed at various locations within the city. The total household waste was categorized as organic, recyclable and other wastes. It was found that East Pratapgarh ward produced 99.3% organic waste out of the total MSW generated in that ward which is maximum among all the wards. A comparison of the waste generation versus the family size has been made. A questionnaire for the survey of MSW from household and market place was prepared. The average waste generated (in kg) per person per day was found out for each of the wards. It has been noted that East Jogendranagar ward had a maximum per person per day waste generation of 0.493 kg/day.In view of the studies made, it has been found that AMC has failed to implement MSWM in an effective way because of the unavailability of suitable facilities for treatment and disposal of the large amount of MSW. It has also been noted that AMC is not following the standard procedures of handling MSW. Transportation system has also been found less effective leading to waste of time, money and manpower.

Keywords: MSW, waste generation, solid waste disposal, management

Procedia PDF Downloads 317

Authors: M. Habab, C. Benachaiba, B. Mazari, H. Madi, C. Benoudjafer

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The use of Distributed Generation (DG) has been increasing in recent years to fill the gap between energy supply and demand. This paper presents the grid connected wind energy system with UPQC based on fuzzy controller to compensate for voltage and current disturbances. The proposed system can improve power quality at the point of installation on power distribution systems. Simulation results show the capability of the DG-UPQC intelligent system to compensate sags voltage and current harmonics at the Point of Common Coupling (PCC).

Keywords: shunt active filter, series active filter, UPQC, power quality, sags voltage, distributed generation, wind turbine

Procedia PDF Downloads 407

Authors: Mohammad Didar Hossain

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Background/Purpose: The purpose of this study was to examine the associations between productive engagements and the psychological well-being of older adults in the U.S by analyzing cross-sectional data from a secondary dataset. Specifically, this paper analyzed the associations of 4 different types of productive engagements, including current work status, caregiving to the family members, volunteering and religious strengths with the psychological well-being as an outcome variable. Methods: Data and sample: The study used the data from the Health and Retirement Study (HRS). The HRS is a nationally representative prospective longitudinal cohort study that has been conducting biennial surveys since 1992 to community-dwelling individuals 50 years of age or older on diverse issues. This analysis was based on the 2016 wave (cross-sectional) of the HRS dataset and the data collection period was April 2016 through August 2017. The samples were recruited from a multistage, national area-clustered probability sampling frame. Measures: Four different variables were considered as the predicting variables in this analysis. Firstly, current working status was a binary variable that measured by 0=Yes and 1= No. The second and third variables were respectively caregiving and volunteering, and both of them were measured by; 0=Regularly, 1= Irregularly. Finally, find in strength was measured by 0= Agree and 1= Disagree. Outcome (Wellbeing) variable was measured by 0= High level of well-being, 1= Low level of well-being. Control variables including age were measured in years, education in the categories of 0=Low level of education, 1= Higher level of education and sex r in the categories 0=male, 1= female. Analysis and Results: Besides the descriptive statistics, binary logistic regression analyses were applied to examine the association between independent and dependent variables. The results showed that among the four independent variables, three of them including working status (OR: .392, p<.001), volunteering (OR: .471, p<.003) and strengths in religion (OR .588, p<.003), were significantly associated with psychological well-being while controlling for age, gender and education factors. Also, no significant association was found between the caregiving engagement of older adults and their psychological well-being outcome. Conclusions and Implications: The findings of this study are mostly consistent with the previous studies except for the caregiving engagements and their impact on older adults’ well-being outcomes. Therefore, the findings support the proactive initiatives from different micro to macro levels to facilitate opportunities for productive engagements for the older adults, and all of these may ultimately benefit their psychological well-being and life satisfaction in later life.

Keywords: productive engagements, older adults, psychological wellbeing, productive aging

Procedia PDF Downloads 155

Authors: Hyunjoo Park, Taehyun Kim, Taehyun Kim

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The government that declared 'de-nuclearization' pushes up renewable energy policies such as solar power and wind power as an alternative to nuclear power generation. However, local residents who are concerned about the development and natural disasters have been hit by opposition, and related businesses around the country are experiencing difficulties. There is also a voice saying that installing a large wind power generator will cause landslides, low frequencies and noise, which will have a bad influence. Renewal is only a harmful and disgusting facility for the residents. In this way, it is expected that extreme social conflicts will occur in the decision making process related to the locally unwanted land-use (LULU). The government's efforts to solve this problem have been steadily progressing, but the systematic methodology for bringing in active participation and opinion gathering of the residents has not yet been established except for the simple opinion poll or referendum. Therefore, it is time to identify the factors that concern the local residents about the wind power generation facilities, and to find ways to make policy decision-making possible. In this study, we analyze the perception of people about offshore and onshore wind power facilities through questionnaires or interviews, and examine quantitative and qualitative precedent studies to analyze them. In addition, the study evaluates what factors affect the local acceptance of wind power facilities. As a result of the factor analysis of the questionnaire items, factors affecting the residents' acceptance of the wind power facility were extracted from four factors such as environmental, economic, risk, social, and management factor. The study also found that the influence of the determinants of local acceptance on the regional acceptability differs according to the demographic characteristics such as gender and income level. This study will contribute to minimizing the conflict on the installation of wind power facilities through communication among the local residents.

Keywords: factor analysis, local acceptance, locally unwanted land-use, LULU, wind power generation facilities

Procedia PDF Downloads 156

Authors: Young-Kwan Choi, Gang-Wook Shin, Sung-Taek Hong

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Water supply system consumes large amount of power load during water treatment and transportation of purified water. Many energy conserving high efficiency materials such as DC motor and LED light have recently been introduced to water supply system for energy conservation. This paper performed empirical analysis on BLDC, AC motors, and comparatively analyzed the change in power according to DC power supply ratio in order to conserve energy of a next-generation water treatment system called vertical water treatment system. In addition, a DC distribution system linked with photovoltaic generation was simulated to analyze the energy conserving effect of DC load.

Keywords: vertical water treatment system, DC power supply, energy efficiency, BLDC

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Authors: M. Akhlaque Ahmed, Sidra A. Shaikh, Maliha A. Siddiqui

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Global and diffuse solar radiation on horizontal surface of Lower Sindh, namely Karachi, Hyderabad, Nawabshah were carried out using sunshine hour data of the area to assess the feasibility of solar energy utilization for power generation in Sindh province. The results obtained show a large variation in the direct and diffuse component of solar radiation in summer and winter months in Lower Sindh (50% direct and 50% diffuse for Karachi and Hyderabad). In Nawabshah area, the contribution of diffuse solar radiation is low during the monsoon months, July and August. The K_T value of Nawabshah indicates a clear sky throughout almost the entire year. The percentage of diffuse radiation does not exceed more than 20%. In Nawabshah, the appearance of cloud is rare even during the monsoon months. The estimated values indicate that Nawabshah has high solar potential, whereas Karachi and Hyderabad have low solar potential. During the monsoon months the Lower part of Sindh can utilize the hybrid system with wind power. Near Karachi and Hyderabad, the wind speed ranges between 6.2 m/sec to 6.9 m/sec. A wind corridor exists near Karachi, Hyderabad, Gharo, Keti Bander and Shah Bander. The short fall of solar can be compensated by wind because in the monsoon months of July and August, wind speeds are higher in the Lower region of Sindh.

Keywords: hybrid power system, lower Sindh, power generation, solar and wind energy potential

Procedia PDF Downloads 252

Authors: Maíra O. Palm, Cintia Marangoni, Ozair Souza, Noeli Sellin

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Wastes from a vegetal extracts industry (cocoa, oak, Guarana and mate) were characterized by particle size, proximate and ultimate analysis, lignocellulosic fractions, high heating value, thermal analysis (Thermogravimetric analysis – TGA, and Differential thermal analysis - DTA) and energy density to evaluate their potential as biomass in the form of briquettes for power generation. All wastes presented adequate particle sizes to briquettes production. The wastes showed high moisture content, requiring previous drying for use as briquettes. Cocoa and oak wastes had the highest volatile matter contents with maximum mass loss at 310 ºC and 450 ºC, respectively. The solvents used in the aroma extraction process influenced in the moisture content of the wastes, which was higher for mate due to water has been used as solvent. All wastes showed an insignificant loss mass after 565 °C, hence resulting in low ash content. High carbon and hydrogen contents and low sulfur and nitrogen contents were observed ensuring a low generation of sulfur and nitrous oxides. Mate and cocoa exhibited the highest carbon and lignin content, and high heating value. The dried wastes had high heating value, from 17.1 MJ/kg to 20.8 MJ/kg. The results indicate the energy potential of wastes for use as fuel in power generation.

Keywords: agro-industrial waste, biomass, briquettes, combustion

Procedia PDF Downloads 206

Authors: Sreto Boljevic

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In the absence of considerable investment in electricity generation, transmission and distribution network (DN) capacity, the demand for electrical energy will quickly strain the capacity of the existing electrical power network. With anticipated growth and proliferation of Electric vehicles (EVs) and Heat pump (HPs) identified the likelihood that the additional load from EV changing and the HPs operation will require capital investment in the DN. While an area-wide implementation of EVs and HPs will contribute to the decarbonization of the energy system, they represent new challenges for the existing low-voltage (LV) network. Distributed energy resources (DER), operating both as part of the DN and in the off-network mode, have been offered as a means to meet growing electricity demand while maintaining and ever-improving DN reliability, resiliency and power quality. DN planning has traditionally been done by forecasting future growth in demand and estimating peak load that the network should meet. However, new problems are arising. These problems are associated with a high degree of proliferation of EVs and HPs as load imposes on DN. In addition to that, the promotion of electricity generation from renewable energy sources (RES). High distributed generation (DG) penetration and a large increase in load proliferation at low-voltage DNs may have numerous impacts on DNs that create issues that include energy losses, voltage control, fault levels, reliability, resiliency and power quality. To mitigate negative impacts and at a same time enhance positive impacts regarding the new operational state of DN, CHP system integration can be seen as best action to postpone/reduce capital investment needed to facilitate promotion and maximize benefits of EVs, HPs and RES integration in low-voltage DN. The aim of this paper is to generate an algorithm by using an analytical approach. Algorithm implementation will provide a way for optimal placement of the CHP system in the DN in order to maximize the integration of RES and increase in proliferation of EVs and HPs.

Keywords: combined heat & power (CHP), distribution networks, EVs, HPs, RES

Procedia PDF Downloads 202

Authors: Hichem Sassi

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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 64

Authors: Adel Edwar Waheeb Louka

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deeplearning, image processing, machine learningSarapin, intraarticular, chronic knee pain, osteoarthritisFNS, trauma, hip, neck femur fracture, minimally invasive surgery

Procedia PDF Downloads 73

Authors: Surachai Ampawasuvan, Supornchai Utainarumol

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Most of fuel station’ customers, who drive on the major arterial road wants to use the stations to fill fuel to their vehicle during their journey to destinations. According to the survey of traffic volume of the vehicle using fuel stations by video cameras, automatic counting tools, or questionnaires, it was found that most users prefer to use fuel stations on holiday rather than on working day. They also prefer to use fuel stations in the morning rather than in the evening. When comparing the ratio of the distribution pattern of traffic volume of the vehicle using fuel stations by video cameras, automatic counting tools, there is no significant difference. However, when comparing the ratio of peak hour (peak hour rate) of the results from questionnaires at 13 to 14 percent with the results obtained by using the methods of the Institute of Transportation Engineering (ITE), it is found that the value is similar. However, it is different from a survey by video camera and automatic traffic counting at 6 to 7 percent of about half. So, this study suggests that in order to forecast trip generation of vehicle using fuel stations on major arterial road which is mostly characterized by Though Traffic, it is recommended to use the value of half of peak hour rate, which would make the forecast for trips generation to be more precise and accurate and compatible to surrounding environment.

Keywords: peak rate, trips generation, fuel station, arterial road

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Authors: I. V. Pinto, M. R. Sooriyarachchi

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It can be frequently observed that the data arising in our environment have a hierarchical or a nested structure attached with the data. Multilevel modelling is a modern approach to handle this kind of data. When multilevel modelling is combined with a binary response, the estimation methods get complex in nature and the usual techniques are derived from quasi-likelihood method. The estimation methods which are compared in this study are, marginal quasi-likelihood (order 1 & order 2) (MQL1, MQL2) and penalized quasi-likelihood (order 1 & order 2) (PQL1, PQL2). A statistical model is of no use if it does not reflect the given dataset. Therefore, checking the adequacy of the fitted model through a goodness-of-fit (GOF) test is an essential stage in any modelling procedure. However, prior to usage, it is also equally important to confirm that the GOF test performs well and is suitable for the given model. This study assesses the suitability of the GOF test developed for binary response multilevel models with respect to the method used in model estimation. An extensive set of simulations was conducted using MLwiN (v 2.19) with varying number of clusters, cluster sizes and intra cluster correlations. The test maintained the desirable Type-I error for models estimated using PQL2 and it failed for almost all the combinations of MQL. Power of the test was adequate for most of the combinations in all estimation methods except MQL1. Moreover, models were fitted using the four methods to a real-life dataset and performance of the test was compared for each model.

Keywords: goodness-of-fit test, marginal quasi-likelihood, multilevel modelling, penalized quasi-likelihood, power, quasi-likelihood, type-I error

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Authors: Basma Hamdi, Lakdar Kairouani, Ezzedine Nahdi

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The objective of this work is to present a thermodynamic analysis of low temperature solar Rankine cycle with ejector for power generation using zeotropic mixtures. Based on theoretical calculation, effects of zeotropic mixtures compositions on the performance of solar Rankine cycle with ejector are discussed and compared with corresponding pure fluids. Variations of net power output, thermal efficiency were calculating with changing evaporation temperature. The ejector coefficient had analyzed as independent variable. The result show that (R245fa/R152a) has a higher thermal efficiency than using pure fluids.

Keywords: zeotropic mixture, thermodynamic analysis, ejector, low-temperature solar rankine cycle

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Authors: Janos Juhasz, Sandor Pongor, Balazs Ligeti

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Next-generation sequencing, especially whole genome shotgun sequencing, is becoming a common approach to gain insight into the microbiomes in a culture-independent way, even in clinical practice. It does not only give us information about the species composition of an environmental sample but opens the possibility to detect antimicrobial resistance and novel, or currently unknown, pathogens. Accurately and reliably detecting the microbial strains is a challenging task. Here we present a sensitive approach for detecting pathogens in metagenomics samples with special regard to detecting novel variants of known pathogens. We have developed a pipeline that uses fast, short read aligner programs (i.e., Bowtie2/BWA) and comprehensive nucleotide databases. Taxonomic binning is based on the lowest common ancestor (LCA) principle; each read is assigned to a taxon, covering the most significantly hit taxa. This approach helps in balancing between sensitivity and running time. The program was tested both on experimental and synthetic data. The results implicate that our method performs as good as the state-of-the-art BLAST-based ones, furthermore, in some cases, it even proves to be better, while running two orders magnitude faster. It is sensitive and capable of identifying taxa being present only in small abundance. Moreover, it needs two orders of magnitude less reads to complete the identification than MetaPhLan2 does. We analyzed an experimental anthrax dataset (B. anthracis strain BA104). The majority of the reads (96.50%) was classified as Bacillus anthracis, a small portion, 1.2%, was classified as other species from the Bacillus genus. We demonstrate that the evaluation of high-throughput sequencing data is feasible in a reasonable time with good classification accuracy.

Keywords: metagenomics, taxonomy binning, pathogens, microbiome, B. anthracis

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Authors: Sean W. T. Bayly, Daniel Glover, Don Horrell, Simon Horrocks, Barnes Callum, Stuart Gibson, Mac Misuira

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We assess suitability of recent, ∼7B parameter, instruction-tuned Language Models for Generative Named Entity Recognition (GNER). Alongside Retrieval Augmented Generation (RAG), and supported by task-specific prompting, our proposed Multi-Level Information Retrieval method achieves notable improvements over finetuned entity-level and sentence-level methods. We conclude that language models directed toward this task are highly capable when distinguishing between positive classes (precision). However, smaller models seem to struggle to find all entities (recall). Poorly defined classes such as ”Miscellaneous” exhibit substantial declines in performance, likely due to the ambiguity it introduces to the prompt. This is partially resolved through a self-verification method using engineered prompts containing knowledge of the stricter class definitions, particularly in areas where their boundaries are in danger of overlapping, such as the conflation between the location ”Britain” and the nationality ”British”. Finally, we explore correlations between model performance on the GNER task with performance on relevant academic benchmarks.

Keywords: generative named entity recognition, information retrieval, lightweight artificial intelligence, prompt engineering, personal information identification, retrieval augmented generation, self verification

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Authors: Arwa Alzughaibi

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Human Motion detection is a challenging task due to a number of factors including variable appearance, posture and a wide range of illumination conditions and background. So, the first need of such a model is a reliable feature set that can discriminate between a human and a non-human form with a fair amount of confidence even under difficult conditions. By having richer representations, the classification task becomes easier and improved results can be achieved. The Aim of this paper is to investigate the reliable and accurate human motion detection models that are able to detect the human motions accurately under varying illumination levels and backgrounds. Different sets of features are tried and tested including Histogram of Oriented Gradients (HOG), Deformable Parts Model (DPM), Local Decorrelated Channel Feature (LDCF) and Aggregate Channel Feature (ACF). However, we propose an efficient and reliable human motion detection approach by combining Histogram of oriented gradients (HOG) and local phase quantization (LPQ) as the feature set, and implementing search pruning algorithm based on optical flow to reduce the number of false positive. Experimental results show the effectiveness of combining local phase quantization descriptor and the histogram of gradient to perform perfectly well for a large range of illumination conditions and backgrounds than the state-of-the-art human detectors. Areaunder th ROC Curve (AUC) of the proposed method achieved 0.781 for UCF dataset and 0.826 for CDW dataset which indicates that it performs comparably better than HOG, DPM, LDCF and ACF methods.

Keywords: human motion detection, histograms of oriented gradient, local phase quantization, local phase quantization

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Authors: Elmo Thiago Lins Cöuras Ford, Valentina Alessandra Carvalho do Vale

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The sun is an inexhaustible source and harnessing its potential both for heating and for power generation is one of the most promising and necessary alternatives, mainly due to environmental issues. However, it should be noted that this has always been present in the generation of energy on the planet, only indirectly, as it is responsible for virtually all other energy sources, such as: Generates the evaporation source of the water cycle, which allows the impoundment and the consequent generation of electricity (hydroelectricity); Winds are caused by large-scale atmospheric induction caused by solar radiation; Oil, coal and natural gas were generated from waste plants and animals that originally obtained the energy needed for its development of solar radiation. Thus, the idea of using solar energy for practical purposes for the benefit of man is not new, as it accompanies the story since the beginning of time, which means that the sun was always of utmost importance in the design of shelters, or homes is, constructed by taking into consideration the use of sunlight, practicing what was being lost through the centuries, until a time when the buildings started to be designed completely independent of the sun. However, the climatic rigors still needed to be fought, only artificially and today seen as unsustainable, with additional facilities fueled by energy consumption. This paper presents a study on the feasibility of using solar energy for heating water in homes, developing a simplified methodology covering the mode of operation of solar water heaters, solar potential existing alternative systems of Brazil, the international market, and barriers encountered.

Keywords: solar energy, solar heating, solar project, water heating

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Authors: Awatef Ahmed Ben Ramadan, Aqsa Arshad

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Background: There is a huge Muslim migration movement to North America and Europe for several reasons, primarily refuge from war areas and partly to search for better work and educational chances. There are always concerns regarding first-Generation Immigrant women's health and computer literacy, an adequate understanding of the health systems, and the use of the existing healthcare technology and services effectively and efficiently. Language proficiency level, preference for cultural and traditional remedies, socioeconomic factors, fear of stereotyping, limited accessibility to health services, and general unfamiliarity with the existing health services and resources are familiar variables among these women. Aims: The current study aims to assess the health and digital literacy of first-generation Muslim women in Atlanta city. Also, the study aims to examine how the COVID-19 pandemic has encouraged the use of health information technology and increased technology awareness among the targeted women. Methods: The study design is cross-sectional correlational research. The study will be conducted to produce preliminary results that the investigators want to have to supplement an NIH grant application about leveraging information technology to reduce the health inequalities amongst the first-generation immigrant Muslim women in Atlanta City. The investigators will collect the study data in two phases using different tools. Phase one was conducted in June 2022; the investigators used tools to measure health and digital literacy amongst 42 first-generation immigrant Muslim women. Phase two was conducted in November 2022; the investigators measured the Knowledge, Attitude, and Practice (KAP) of using health information technology such as telehealth from a sample of 45 first-generation Muslim immigrant women in Atlanta; in addition, the investigators measured how the current pandemic has affected their KAP to use telemedicine and telehealth services. Both phases' study participants were recruited using convenience sampling methodology. The investigators collected around 40 of 18 years old or older first-generation Muslim immigrant women for both study phases. The study excluded Immigrants who hold work visas and second-generation immigrants. Results: At the point of submitting this abstract, the investigators are still analyzing the study data to produce preliminary results to apply for an NIH grant entitled "Leveraging Health Information Technology (Health IT) to Address and Reduce Health Care Disparities (R01 Clinical Trial Optional)". This research will be the first step of a comprehensive research project to assess and measure health and digital literacy amongst a vulnerable community group. The targeted group might have different points of view from the U.S.-born inhabitants on how to: promote their health, gain healthy lifestyles and habits, screen for diseases, adhere to health treatment and follow-up plans, perceive the importance of using available and affordable technology to communicate with their providers and improve their health, and help in making serious decisions for their health. The investigators aim to develop an educational and instructional health mobile application considering the language and cultural factors that affect immigrants' ability to access different health and social support sources, know their health rights and obligations in their communities, and improve their health behavior and behavior lifestyles.

Keywords: first-generation immigrant Muslim women, telehealth, COVID-19 pandemic, health information technology, health and digital literacy

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Authors: Aisultan Shoiynbek, Darkhan Kuanyshbay, Paulo Menezes, Akbayan Bekarystankyzy, Assylbek Mukhametzhanov, Temirlan Shoiynbek

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Speech emotion recognition (SER) has received increasing research interest in recent years. It is a common practice to utilize emotional speech collected under controlled conditions recorded by actors imitating and artificially producing emotions in front of a microphone. There are four issues related to that approach: emotions are not natural, meaning that machines are learning to recognize fake emotions; emotions are very limited in quantity and poor in variety of speaking; there is some language dependency in SER; consequently, each time researchers want to start work with SER, they need to find a good emotional database in their language. This paper proposes an approach to create an automatic tool for speech emotion extraction based on facial emotion recognition and describes the sequence of actions involved in the proposed approach. One of the first objectives in the sequence of actions is the speech detection issue. The paper provides a detailed description of the speech detection model based on a fully connected deep neural network for Kazakh and Russian. Despite the high results in speech detection for Kazakh and Russian, the described process is suitable for any language. To investigate the working capacity of the developed model, an analysis of speech detection and extraction from real tasks has been performed.

Keywords: deep neural networks, speech detection, speech emotion recognition, Mel-frequency cepstrum coefficients, collecting speech emotion corpus, collecting speech emotion dataset, Kazakh speech dataset

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Authors: Nirmal Kumar Verma, Pallavi Jha

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Terahertz (THz) radiation generation by propagation of two-color laser pulses in plasma is an active area of research due to its potential applications in various areas, including security screening, material characterization and spectroscopic techniques. Due to non ionizing nature and the ability to penetrate several millimeters, THz radiation is suitable for diagnosis of cancerous cells. Traditional THz emitters like optically active crystals when irradiated with high power laser radiation, are subject to material breakdown and hence low conversion efficiencies. This problem is not encountered in laser - plasma based THz radiation sources. The present paper is devoted to the simulation study of the enhanced THz radiation generation by propagation of two-color, linearly polarized laser pulses through magnetized plasma. The two laser pulses orthogonally polarized are co-propagating along the same direction. The direction of the external magnetic field is such that one of the two laser pulses propagates in the ordinary mode, while the other pulse propagates in the extraordinary mode through homogeneous plasma. A transverse electromagnetic wave with frequency in the THz range is generated due to the presence of the static magnetic field. It is observed that larger amplitude terahertz can be generated by mixing of ordinary and extraordinary modes of two-color laser pulses as compared with a single laser pulse propagating in the extraordinary mode.

Keywords: two-color laser pulses, terahertz radiation, magnetized plasma, ordinary and extraordinary mode

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Authors: Z. Jovanovic, Z. Masonicic, S. Dragutinovic, Z. Sakota

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In this paper, a methodology for noise reduction of motor vehicles in use is presented. The methodology relies on synergic model of noise generation as a function of time. The arbitrary number of motor vehicle noise sources act in concert yielding the generation of the overall noise level of motor vehicle thereafter. The number of noise sources participating in the overall noise level of motor vehicle is subjected to the constraint of the calculation of the acoustic potential of each noise source under consideration. It is the prerequisite condition for the calculation of the acoustic potential of the whole vehicle. The recast form of pertinent set of equations describing the synergic model is laid down and solved by dint of Gauss method. The bunch of results emerged and some of them i.e. those ensuing from model application to MDD FAP Priboj motor vehicle in use are particularly elucidated.

Keywords: noise abatement, MV noise sources, noise source identification, muffler

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Authors: M. Akhlaque Ahmed, Sidra A. Shaikh, Maliha A. Siddiqui, Adeel Tahir

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Global and diffuse solar radiation on horizontal surface of southern sindh namely Karachi, Hyderabad, Nawabshah were carried out using sunshine hour data of the area to asses the feasibility of solar Energy utilization at Sindh province for power generation. From the observation, result is derived which shows a drastic variation in the diffuse and direct component of solar radiation for summer and winter for Southern Sindh that is both contributes 50% for Karachi and Hyderabad. In Nawabshah area, the contribution of diffuse solar radiation is low in monsoon months, July and August. The Kᴛ value of Nawabshah indicates a clear sky almost throughout the year. The percentage of diffuse radiation does not exceed more than 20%. In Nawabshah, the appearance of cloud is rare even in monsoon months. The estimated values indicate that Nawabshah has high solar potential whereas Karachi and Hyderabad has low solar potential. During the monsoon months, the southern part of Sind can utilize the hybrid system with wind power. Near Karachi and Hyderabad, the wind speed ranges between 6.2 to 6.9 m/sec. There exist a wind corridor near Karachi, Hyderabad, Gharo, Keti Bander and Shah Bander. The short fall of solar can be compensated by wind because in monsoon months July and August the wind speed are higher in the southern region of Sindh.

Keywords: hybrid power system, power generation, solar and wind energy potential, southern Sindh

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Authors: Betty Chiyangwa

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The subject of second-generation migrant youth is under-researched in the context of South Africa. Thus, their opinions and views have been marginalised in social science research. This paper addresses this gap by exploring the complexities of second-generation Mozambican migrant youth’s lived experiences in how they construct their identities and develop a sense of belonging in post-apartheid South Africa, specifically in Bushbuckridge. Bushbuckridge was among the earliest districts to accommodate Mozambican refugees to South Africa in the 1970s and remains associated with large numbers of Mozambicans. Drawing on Crenshaw’s (1989) intersectionality approach, the study contributes to knowledge on South-to-South migration by demonstrating how this approach is operationalised to understand the complex lived experiences of a disadvantaged group in life and possibly in death. In conceptualising the notion of identity among second-generation migrant youth, this paper explores the history and present of first and second-generation Mozambican migrants in South Africa to reveal how being born to migrant parents and raised in a hosting country poses life-long complications in one’s identity and sense of belonging. In the quest to form their identities and construct a sense of belonging, migrant youth employ precariously means to navigate the terrane. This is a case study informed by semi-structured interviews and narrative data gathered from 22 second-generation Mozambican migrant youth between 18 and 34 years who were born to at least one Mozambican parent living in Bushbuckridge and raised in South Africa. Views of two key informants from the South African Department of Home Affairs and the local tribal authority provided additional perspectives on second-generation migrant youth’s lived experiences in Bushbuckridge, which were explored thematically and narratively through Braun and Clarke’s (2012) six-step framework for analysing qualitative data. In exploring the interdependency and interconnectedness of social categories and social systems in Bushbuckridge, the findings revealed that participants’ experiences of identity formation and development of a sense of belonging were marginalised in complex, intersectional and precarious ways where they constantly (re)negotiated their daily experiences, which were largely shaped by their paradoxical migrant status in a host country. This study found that, in the quest for belonging, migrant youths were not a perfectly integrated category but evolved from almost daily lived experiences of creating a living that gave them an identity and a sense of belonging in South Africa. The majority of them shared feelings of living in limbo since childhood and fear of possibly dying in limbo with no clear (solid) sense of belonging to either South Africa or Mozambique. This study concludes that there is a strong association between feelings of identity, sense of belonging and levels of social integration. It recommends the development and adoption of a multilayer comprehensive model for understanding second-generation migrant youth identity and belonging in South Africa which encourages a collaborative effort among individual migrant youth, their family members, neighbours, society, and regional and national institutional structures for migrants to enhance and harness their capabilities and improve their wellbeing in South Africa.

Keywords: bushbuckridge, limbo, mozambican migrants, second-generation

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Authors: Norshazmira Mat Azmi, Sayidal El Fatimah Masnan, Shatirah Akib

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Boundless renewable energy, such as tidal energy, tidal current energy, wave energy, thermal energy and chemical energy are covered and possessed by oceans. The hybrid system helps in improving the economic and environmental sustainability of renewable energy systems to fulfill the energy demand. The objective and concept of hybridizing renewable energy is to meet the desired system requirements, with the lowest value of the energy cost. This paper reviews applications of using hybrid power generation system for remote area. It also highlights the future directions to investigate the impacts of surface waves on turbine design and performance. The importance of understanding the site-specific wave conditions could also been explored.

Keywords: hybrid, marine current energy, tidal turbine, wave turbine

Procedia PDF Downloads 362

Authors: Boudene Maamar, Xu Mai

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Optical Intersatellite Links (OISLs) are wireless communications using optical signals to interconnect satellites. It is expected to be the next generation wireless communication technology according to its inherent characteristics like: an increased bandwidth, a high data rate, a data transmission security, an immunity to interference, and an unregulated spectrum etc. Optical space links are the best choice for the classical communication schemes due to its distinctive properties; high frequency, small antenna diameter and lowest transmitted power, which are critical factors to define a space communication. This paper discusses the development of free space technology and analyses the parameters and factors to establish a reliable intersatellite links using an optical signal to exchange data between satellites.

Keywords: optical intersatellite links, optical wireless communications, free space optical communications, next generation wireless communication

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Authors: Himanshu Shekhar Maharana, S. K .Dash

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Economic Load Dispatch (ELD) proves to be a vital optimization process in electric power system for allocating generation amongst various units to compute the cost of generation, the cost of emission involving global warming gases like sulphur dioxide, nitrous oxide and carbon monoxide etc. In this dissertation, we emphasize ramp rate constriction factor based particle swarm optimization (RRCPSO) for analyzing various performance objectives, namely cost of generation, cost of emission, and a dual objective function involving both these objectives through the experimental simulated results. A 6-unit 30 bus IEEE test case system has been utilized for simulating the results involving improved weight factor advanced ramp rate limit constraints for optimizing total cost of generation and emission. This method increases the tendency of particles to venture into the solution space to ameliorate their convergence rates. Earlier works through dispersed PSO (DPSO) and constriction factor based PSO (CPSO) give rise to comparatively higher computational time and less good optimal solution at par with current dissertation. This paper deals with ramp rate and constriction factor based well defined ramp rate PSO to compute various objectives namely cost, emission and total objective etc. and compares the result with DPSO and weight improved PSO (WIPSO) techniques illustrating lesser computational time and better optimal solution. 

Keywords: economic load dispatch (ELD), constriction factor based particle swarm optimization (CPSO), dispersed particle swarm optimization (DPSO), weight improved particle swarm optimization (WIPSO), ramp rate and constriction factor based particle swarm optimization (RRCPSO)

Procedia PDF Downloads 382