Search results for: one side class algorithm

Authors: Myungkyu Park, Minkun Kim, Sunghoon Kim, Samgon Ryu

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Recently, research for reactive fabrics which have the characteristics of CWA (Chemical Warfare Agent) decomposition is being performed actively. The performance level of decomposition for CWA decomposition in various environmental condition is one of the critical factors in applicability as protective materials for NBC (Nuclear, Biological, and Chemical) protective clothing. In this study, results of performance test for CWA decomposition by reactive fabric made of electrospinning web and reactive particle are presented. Currently, the MOF (metal organic framework) type of UiO-66-NH₂ is frequently being studied as material for decomposing CWA especially blister agent HD [Bis(2-chloroethyl) sulfide]. When we test decomposition rate with electrospinning web made of PVB (Polyvinyl Butiral) polymer and UiO-66-NH₂ particle, we can get very high protective performance than the case other particles are applied. Furthermore, if the repellant surface fabric is added on reactive material as the component of protective fabric, the performance of layer by layered reactive fabric could be approached to the level of current NBC protective fabric for HD decomposition rate. Reactive fabric we used in this study is manufactured by electrospinning process of polymer which contains the reactive particle of UiO-66-NH₂, and we performed crystalizing process once again on that polymer fiber web in solvent systems as a second step for manufacturing reactive fabric. Three kinds of polymer materials are used in this process, but PVB was most suitable as an electrospinning fiber polymer considering the shape of product. The density of particle on fiber web and HD decomposition rate is enhanced by secondary crystallization compared with the results which are not processed. The amount of HD penetration by 24hr AVLAG (Aerosol Vapor Liquid Assessment Group) swatch test through the reactive fabrics with secondary crystallization and without crystallization is 24 and 146μg/cm² respectively. Even though all of the reactive fiber webs for this test are combined with repellant surface layer at outer side of swatch, the effects of secondary crystallization of particle for the reactive fiber web are remarkable.

Keywords: CWA, Chemical Warfare Agent, gas decomposition, particle growth, protective clothing, reactive fabric, swatch test

Procedia PDF Downloads 295

Authors: Thewodros K. Geberemariam

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The rapid physical expansion of urbanization coupled with aging infrastructure presents a unique decision and management challenges for many big city municipalities. Cities must therefore upgrade and maintain the existing aging urban drainage infrastructure systems to keep up with the demands. Given the overall contribution of assets to municipal revenue and the importance of infrastructure to the success of a livable city, many municipalities are currently looking for a robust and smart urban drainage infrastructure asset management solution that combines management, financial, engineering and technical practices. This robust decision-making shall rely on sound, complete, current and relevant data that enables asset valuation, impairment testing, lifecycle modeling, and forecasting across the multiple asset portfolios. On this paper, predictive computational intelligence (CI) and multi-class machine learning (ML) coupled with online, offline, and historical record data that are collected from an array of multi-parameter sensors are used for the extraction of different operational and non-conforming patterns hidden in structured and unstructured data to determine and produce actionable insight on the current and future states of the network. This paper aims to improve the strategic decision-making process by identifying all possible alternatives; evaluate the risk of each alternative, and choose the alternative most likely to attain the required goal in a cost-effective manner using historical and near real-time urban drainage infrastructure data for urban drainage infrastructures assets that have previously not benefited from computational intelligence and machine learning advancements.

Keywords: computational intelligence, machine learning, urban drainage infrastructure, machine learning, classification, prediction, asset management space

Procedia PDF Downloads 152

Authors: Hao Cheng, Zhiwu Wang, Guozheng Yan, Pingping Jiang, Shijia Qin, Shuai Kuang

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Autofluorescence Imaging (AFI) is a technology for detecting early carcinogenesis of the gastrointestinal tract in recent years. Compared with traditional white light endoscopy (WLE), this technology greatly improves the detection accuracy of early carcinogenesis, because the colors of normal tissues are different from cancerous tissues. Thus, edge detection can distinguish them in grayscale images. In this paper, based on the traditional Sobel edge detection method, optimization has been performed on this method which considers the environment of the gastrointestinal, including adaptive threshold and morphological processing. All of the processes are implemented on our self-designed system based on the image sensor OV6930 and Field Programmable Gate Array (FPGA), The system can capture the gastrointestinal image taken by the lens in real time and detect edges. The final experiments verified the feasibility of our system and the effectiveness and accuracy of the edge detection algorithm.

Keywords: AFI, edge detection, adaptive threshold, morphological processing, OV6930, FPGA

Procedia PDF Downloads 230

Authors: Yuxuan Tang

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Cognitive diagnostic assessment has received much attention in second language education, and assessment for it can provide pedagogically useful feedback for language learners. However, there is a lack of research on how students interpret and use cognitive diagnostic feedback. Thus the present study aims to adopt a mixed-method approach mainly to explore the relationship between the goal-orientation and students' response to cognitive diagnostic feedback. Almost 200 Chinese undergraduates from two universities in Xi'an, China, will be invited to do a cognitive diagnostic reading test, and each student will receive specialized cognitive diagnostic feedback, comprising of students' reading attributes mastery level generated by applying a well-selected cognitive diagnostic model, students' perceived reading ability assessed by a self-assessing questionnaire and students’ level position in the whole class. And a goal-orientation questionnaire and a self-generated questionnaire on the perception of feedback will be given to students the moment they receive feedback. In addition, interviews of students will be conducted on their future plans to see whether they have awareness of carrying out studying plans. The study aims to find a new perspective towards how students use and interpret cognitive diagnostic feedback in terms of their different goal-orientation (self-based, task-based, and other-based goals) by applying the newest goal orientation model, which is an important construct of motivation in psychology, seldom researched under language learning area. And the study is expected to provide evidence on how diagnostic feedback promotes students' learning under the educational belief of assessment for learning. Practically speaking, according to the personalized diagnostic feedback, students can take remedial self-learning more purposefully, and teachers can target students' weaknesses to adjust teaching methods and carry out tailored teaching.

Keywords: assessment for learning, cognitive diagnostic assessment, goal-orientation, personalized feedback

Procedia PDF Downloads 133

Authors: Ahmed Chetouani

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Corrosion is a natural occurring process where it can be defined as the deterioration of materials properties due to its interaction with its environment. Corrosion can lead to failures in plant infrastructure and machines which are usually costly to repair. In terms of loss of contaminated products which will cause environmental damage and possibly costly in terms of human health. The driving force that causes metals to corrode is due to the natural consequence of their temporary existence in metallic form. There is a growing trend in utilizing plant extracts and pharmaceutical compounds as corrosion inhibitors. Exquisite identification of the essential oil of aerial parts of Pelargonium was obtained using hydrodistillation and identification using GC (gas chromatography) and GC/MS (gas chromatography-mass spectrometry). The oil was predominated by Citronellol (22.8%). The inhibitory effect of essential oil and extract of Pelargonium was estimated on the corrosion of mild steel in 1M hydrochloric acid (HCl) using weight loss, Electrochemical Impedance Spectroscopy (EIS) and Tafel polarization curves. Inhibition was found to increase with increasing concentration of the essential oil and extract of Pelargonium. The effect of temperature on the corrosion behaviour of mild steel in 1M HCl with addition of essential oil and extract was also studied and the thermodynamic parameters were determined and discussed. Values of inhibition efficiency were calculated from weight loss, Tafel polarization curves, and EIS. All results are in good agreement. Polarization curves showed that essential oil and extract of Pelargonium behave as mixed type inhibitors in hydrochloric acid. The results obtained showed that the essential oil and extract of Pelargonium could serve as an effective inhibitor of the corrosion of mild steel in Hydrochloric acid solution. To avoid any surprise of toxicity, the majority compounds have been studied by using POM analyses.

Keywords: corrosion inhibition, mild steel, pelargonium oil, extract, electrochemical system, hydrodistillation, side effects, POM Analyses

Procedia PDF Downloads 401

Authors: John Pomerat, Aviv Segev

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Recently, deep learning has had many theoretical breakthroughs. For deep learning to be successful in the industry, however, there need to be practical algorithms capable of handling many real-world hiccups preventing the immediate application of a learning algorithm. Although AI promises to revolutionize the healthcare industry, getting access to patient data in order to train learning algorithms has not been easy. One proposed solution to this is data- sharing. In this paper, we propose an alternative protocol, based on multi-party computation, to train deep learning models while maintaining both the privacy and security of training data. We examine three methods of training neural networks in this way: Transfer learning, average ensemble learning, and series network learning. We compare these methods to the equivalent model obtained through data-sharing across two different experiments. Additionally, we address the security concerns of this protocol. While the motivating example is healthcare, our findings regarding multi-party computation of neural network training are purely theoretical and have use-cases outside the domain of healthcare.

Keywords: neural network aggregation, multi-party computation, transfer learning, average ensemble learning

Procedia PDF Downloads 162

Authors: Uranus Saadat

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ChatGPT, as one of the well-known artificial intelligence (AI) tools, has recently been integrated into English language education and has had several impacts on learners. Accordingly, concerns regarding the overuse of this tool among EFL/ESL learners are rising, which could lead to several disadvantages in their writing skills development. The use of ChatGPT in facilitating writing skills is a novel concept that demands further studies in different contexts and learners. In this study, a qualitative case study is applied to investigate the impact of ChatGPT on the writing skills of a group of EFL bachelor’s students from Uzbekistan studying Teaching English as the Second Language (TESL) at a private university in Malaysia. The data was collected through the triangulation of document analysis, semi-structured interviews, classroom observations, and focus group discussions. Subsequently, the data was analyzed by using thematic analysis. Some of the emerging themes indicated that ChatGPT is helpful in engaging students by reducing their anxiety in class and providing them with constructive feedback and support. Conversely, certain emerging themes revealed excessive reliance on ChatGPT, resulting in a decrease in students’ creativity and critical thinking skills, memory span, and tolerance for ambiguity. The study suggests a number of strategies to alleviate its negative impacts, such as peer review activities, workshops for familiarizing students with AI, and gradual withdrawal of AI support activities. This study emphasizes the need for cautious AI integration into English language education to cultivate independent learners with higher-order thinking skills.

Keywords: ChatGPT, EFL/ESL learners, English writing skills, artificial intelligence tools, critical thinking skills

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Authors: Carlos Gonzales, Zaida Quiroz, Marcos Prates

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Several spatial variables collected at the same location that share a common spatial distribution can be modeled simultaneously through a multivariate geostatistical model that takes into account the correlation between these variables and the spatial autocorrelation. The main goal of this model is to perform spatial prediction of these variables in the region of study. Here we focus on a geostatistical multivariate formulation that relies on sharing common spatial random effect terms. In particular, the first response variable can be modeled by a mean that incorporates a shared random spatial effect, while the other response variables depend on this shared spatial term, in addition to specific random spatial effects. Each spatial random effect is defined through a Gaussian process with a valid covariance function, but in order to improve the computational efficiency when the data are large, each Gaussian process is approximated to a Gaussian random Markov field (GRMF), specifically to the block nearest neighbor Gaussian process (Block-NNGP). This approach involves dividing the spatial domain into several dependent blocks under certain constraints, where the cross blocks allow capturing the spatial dependence on a large scale, while each individual block captures the spatial dependence on a smaller scale. The multivariate geostatistical model belongs to the class of Latent Gaussian Models; thus, to achieve fast Bayesian inference, it is used the integrated nested Laplace approximation (INLA) method. The good performance of the proposed model is shown through simulations and applications for massive data.

Keywords: Block-NNGP, geostatistics, gaussian process, GRMF, INLA, multivariate models.

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Authors: Mohammed S. Al-Rawi, J. Bastos, J. Rodriguez

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Object detection and object recognition are essential components of every computer vision system. Despite the high computational complexity and other problems related to numerical stability and accuracy, Zernike moments of 2D images (ZMs) have shown resilience when used in object recognition and have been used in various image analysis applications. In this work, we propose a novel method for computing ZMs via Fast Fourier Transform (FFT). Notably, this is the first algorithm that can generate ZMs up to extremely high orders accurately, e.g., it can be used to generate ZMs for orders up to 1000 or even higher. Furthermore, the proposed method is also simpler and faster than the other methods due to the availability of FFT software and/or hardware. The accuracies and numerical stability of ZMs computed via FFT have been confirmed using the orthogonality property. We also introduce normalizing ZMs with Neumann factor when the image is embedded in a larger grid, and color image reconstruction based on RGB normalization of the reconstructed images. Astonishingly, higher-order image reconstruction experiments show that the proposed methods are superior, both quantitatively and subjectively, compared to the q-recursive method.

Keywords: Chebyshev polynomial, fourier transform, fast algorithms, image recognition, pseudo Zernike moments, Zernike moments

Procedia PDF Downloads 265

Authors: Ninlawat Phuangchoke, Waraporn Viyanon, Setta Sasananan

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The most important process of the water treatment plant process is the coagulation using alum and poly aluminum chloride (PACL), and the value of usage per day is a hundred thousand baht. Therefore, determining the dosage of alum and PACL are the most important factors to be prescribed. Water production is economical and valuable. This research applies an artificial neural network (ANN), which uses the Levenberg–Marquardt algorithm to create a mathematical model (Soft Jar Test) for prediction chemical dose used to coagulation such as alum and PACL, which input data consists of turbidity, pH, alkalinity, conductivity, and, oxygen consumption (OC) of Bangkhen water treatment plant (BKWTP) Metropolitan Waterworks Authority. The data collected from 1 January 2019 to 31 December 2019 cover changing seasons of Thailand. The input data of ANN is divided into three groups training set, test set, and validation set, which the best model performance with a coefficient of determination and mean absolute error of alum are 0.73, 3.18, and PACL is 0.59, 3.21 respectively.

Keywords: soft jar test, jar test, water treatment plant process, artificial neural network

Procedia PDF Downloads 166

Authors: Lule Basha, Eralda Gjika

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The exchange rate is one of the most important economic variables, especially for a small, open economy such as Albania. Its effect is noticeable in one country's competitiveness, trade and current account, inflation, wages, domestic economic activity, and bank stability. This study investigates the fluctuation of Albania’s exchange rates using monthly average foreign currency, Euro (Eur) to Albanian Lek (ALL) exchange rate with a time span from January 2008 to June 2021, and the macroeconomic factors that have a significant effect on the exchange rate. Initially, the Random Forest Regression algorithm is constructed to understand the impact of economic variables on the behavior of monthly average foreign currencies exchange rates. Then the forecast of macro-economic indicators for 12 months was performed using time series models. The predicted values received are placed in the random forest model in order to obtain the average monthly forecast of the Euro to Albanian Lek (ALL) exchange rate for the period July 2021 to June 2022.

Keywords: exchange rate, random forest, time series, machine learning, prediction

Procedia PDF Downloads 104

Authors: Bachir Bentouati, Lakhdar Chaib, Saliha Chettih, Gai-Ge Wang

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The problem of economic dispatch (ED) is the basic problem of power framework, its main goal is to find the most favorable generation dispatch to generate each unit, reduce the whole power generation cost, and meet all system limitations. A heuristic algorithm, recently developed called Stud Krill Herd (SKH), has been employed in this paper to treat non-convex ED problems. The proposed KH has been modified using Stud selection and crossover (SSC) operator, to enhance the solution quality and avoid local optima. We are demonstrated SKH effects in two case study systems composed of 13-unit and 40-unit test systems to verify its performance and applicability in solving the ED problems. In the above systems, SKH can successfully obtain the best fuel generator and distribute the load requirements for the online generators. The results showed that the use of the proposed SKH method could reduce the total cost of generation and optimize the fulfillment of the load requirements.

Keywords: stud krill herd, economic dispatch, crossover, stud selection, valve-point effect

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Authors: Nadia Masood Khan, Muhammad Salman Khan, Gul Muhammad Khan

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Cardiologists perform cardiac auscultation to detect abnormalities in heart sounds. Since accurate auscultation is a crucial first step in screening patients with heart diseases, there is a need to develop computer-aided detection/diagnosis (CAD) systems to assist cardiologists in interpreting heart sounds and provide second opinions. In this paper different algorithms are implemented for automated heart sound classification using unsegmented phonocardiogram (PCG) signals. Support vector machine (SVM), artificial neural network (ANN) and cartesian genetic programming evolved artificial neural network (CGPANN) without the application of any segmentation algorithm has been explored in this study. The signals are first pre-processed to remove any unwanted frequencies. Both time and frequency domain features are then extracted for training the different models. The different algorithms are tested in multiple scenarios and their strengths and weaknesses are discussed. Results indicate that SVM outperforms the rest with an accuracy of 73.64%.

Keywords: pattern recognition, machine learning, computer aided diagnosis, heart sound classification, and feature extraction

Procedia PDF Downloads 263

Authors: Hadeer E. Eliwa, Naglaa S. Bazan, Ebtissam A. Darweesh, Nagwa A. Sabri

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Background: Generic substitution of brand ivabradine prescriptions can reduce drug expenditures and improve adherence. However, the distrust of generic medicines by practitioners and patients due to doubts regarding their quality and fear of counterfeiting compromise the acceptance of this practice. Aim: The goal of this study is to compare the therapeutic equivalence of brand product versus the generic product of ivabradine in adult patients with chronic heart failure with reduced ejection fraction (≤ 40%) (HFrEF). Methodology: Thirty-two Egyptian patients with chronic heart failure with reduced ejection fraction (HFrEF) were treated with branded ivabradine (Procrolan ©) and generic (Bradipect ©) during 24 (2x12) weeks. Primary outcomes were resting heart rate (HR), NYHA FC, Quality of life (QoL) using Minnesota Living with Heart Failure (MLWHF) and EF. Secondary outcomes were the number of hospitalizations for worsening HFrEF and adverse effects. The washout period was not allowed. Findings: At the 12th week, the reduction in HR was comparable in the two groups (90.13±7.11 to 69±11.41 vs 96.13±17.58 to 67.31±8.68 bpm in brand and generic groups, respectively). Also, the increase in EF was comparable in the two groups (27.44 ±4.59 to 33.38±5.62 vs 32±5.96 to 39.31±8.95 in brand and generic groups, respectively). The improvement in NYHA FC was comparable in both groups (87.5% in brand group vs 93.8% in the generic group). The mean value of the QOL improved from 31.63±15.8 to 19.6±14.7 vs 35.68±17.63 to 22.9±15.1 for the brand and generic groups, respectively. Similarly, at end of 24 weeks, no significant changes were observed from data observed at 12th week regarding HR, EF, QoL and NYHA FC. Only minor side effects, mainly phosphenes, and a comparable number of hospitalizations were observed in both groups. Conclusion: The study revealed no statistically significant differences in the therapeutic effect and safety between generic and branded ivabradine. We assume that practitioners can safely interchange between them for economic reasons.

Keywords: bradipect©, heart failure, ivabradine, Procrolan ©, therapeutic equivalence

Procedia PDF Downloads 458

Authors: Edit Xhajanka, Gresa Baboci, Irene Malagnino, Mimoza Canga, Vito Antonio Malagnino

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Purpose: This study aimed at identifying dental caries increment or reduction, based on factors such as smoking, the scaling of teeth, BMI before and during pregnancy, carbohydrates consumption in relation to childbirth. Material and method: In this observational study, the sample included a total of 98 pregnant women and their age class was 18-45 years old, with a median age of 31.5 years. The setting of the participants resides in Vlora –Albania. Moreover, 64.4% were from the city and 35.6% were from the nearby villages. The study was conducted in the time period January 2018 –June 2021. Body mass index (BMI) was calculated using the standard formula (kg/m²). Maternal pre, during and post-pregnancy BMI was collected by using a validated questionnaire. Statistical analysis was performed using IBM SPSS Statistics 23.0. The significance level (α) was set at 0.05, whereas P-value and analysis of variance (ANOVA) were used to analyze the data. Results: Based on the data analysis, 44.4% of the sample declared that they did smoke before pregnancy and 55.6% not smoked during their pregnancy. As a result, no association was found between smoking and length of pregnancy P=0.95. There is also a strong relation (P=0.000) between the number of teeth with caries before pregnancy and the number of teeth with caries during pregnancy. There is a significant relationship between the scaling of teeth and childbirth, P=0.05. BMI before and during pregnancy in relation to carbohydrates consumption have a significant correlation P=0.004 and P=0.002. The values of BMI before and during pregnancy in relation to childbirth have a strong correlation: P=0.043 and P=0.040, respectively. As a result, obesity was associated with preterm birth. The percentage of children born during 34-36 weeks of pregnancy was 69%, and children born during 32-34 weeks of pregnancy were 31%. CONCLUSION: There was a positive association between dental caries experience, BMI and carbohydrates consumption. Obesity in pregnancy is increasing worldwide; that is why this study suggests the importance of an appropriate weight before and during pregnancy.

Keywords: BMI, dental caries, pregnancy, scaling, smoking

Procedia PDF Downloads 198

Authors: Rashmi Yadawad, Disha Narayanan, Ravi Gautam

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Through Wall Radar Imaging is gaining increasing importance now a days in the field of Defense and one of the most important criteria that forms the basis for the image quality obtained is the Cross-Range resolution of the image. In this research paper, the Bilateral Back projection algorithm has been implemented for Through Wall Radar Imaging. The sole purpose is to enhance the resolution in the cross range direction of the obtained Back projection image. Synthetic Data is generated for two targets which are placed at various locations in a room of dimensions 8 m by 6m. Two algorithms namely, simple back projection and Bilateral Back projection have been implemented, images are obtained and the obtained images are compared. Numerical simulations have been coded in MATLAB and experimental results of the two algorithms have been shown. Based on the comparison between the two images, it can be clearly seen that the ringing effect and chess board effect have been heavily reduced in the bilaterally back projected image and hence promising results are obtained giving a relatively sharper image with relatively well defined edges.

Keywords: through wall radar imaging, bilateral back projection, cross range resolution, synthetic data

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Authors: Xiongxiong You, Zhanwen Niu

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Choosing an appropriate surrogate model plays an important role in surrogates-assisted evolutionary algorithms (SAEAs) since there are many types and different kernel functions in the surrogate model. In this paper, an adaptive selection of the best suitable surrogate model method is proposed to solve different kinds of expensive optimization problems. Firstly, according to the prediction residual error sum of square (PRESS) and different model selection strategies, the excellent individual surrogate models are integrated into multiple ensemble models in each generation. Then, based on the minimum root of mean square error (RMSE), the best suitable surrogate model is selected dynamically. Secondly, two methods with dynamic number of models and selection strategies are designed, which are used to show the influence of the number of individual models and selection strategy. Finally, some compared studies are made to deal with several commonly used benchmark problems, as well as a rotor system optimization problem. The results demonstrate the accuracy and robustness of the proposed method.

Keywords: adaptive selection, expensive optimization, rotor system, surrogates assisted evolutionary algorithms

Procedia PDF Downloads 141

Authors: Adeline Meriaux, Claire Gaiani, Jennifer Burgain, Frantz Fournier, Lionel Muniglia, Jérémy Petit

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Spray-drying process is widely used for the production of dairy powders for food and pharmaceuticals industries. It involves the atomization of a liquid feed into fine droplets, which are subsequently dried through contact with a hot air flow. The resulting powders permit transportation cost reduction and shelf life increase but can also exhibit various interesting functionalities (flowability, solubility, protein modification or acid gelation), depending on operating conditions and milk composition. Indeed, particles porosity, surface composition, lactose crystallization, protein denaturation, protein association or crust formation may change. Links between spray-drying conditions and physicochemical and functional properties of powders were investigated by a design of experiment methodology and analyzed by principal component analysis. Quadratic models were developed, and multicriteria optimization was carried out by the use of genetic algorithm. At the time of abstract submission, verification spray-drying trials are ongoing. To perform experiments, milk from dairy farm was collected, skimmed, froze and spray-dried at different air pressure (between 1 and 3 bars) and outlet temperature (between 75 and 95 °C). Dry matter, minerals content and proteins content were determined by standard method. Solubility index, absorption index and hygroscopicity were determined by method found in literature. Particle size distribution were obtained by laser diffraction granulometry. Location of the powder color in the Cielab color space and water activity were characterized by a colorimeter and an aw-value meter, respectively. Flow properties were characterized with FT4 powder rheometer; in particular compressibility and shearing test were performed. Air pressure and outlet temperature are key factors that directly impact the drying kinetics and powder characteristics during spray-drying process. It was shown that the air pressure affects the particle size distribution by impacting the size of droplet exiting the nozzle. Moreover, small particles lead to more cohesive powder and less saturated color of powders. Higher outlet temperature results in lower moisture level particles which are less sticky and can explain a spray-drying yield increase and the higher cohesiveness; it also leads to particle with low water activity because of the intense evaporation rate. However, it induces a high hygroscopicity, thus, powders tend to get wet rapidly if they are not well stored. On the other hand, high temperature provokes a decrease of native serum proteins which is positively correlated to gelation properties (gel point and firmness). Partial denaturation of serum proteins can improve functional properties of powder. The control of air pressure and outlet temperature during the spray-drying process significantly affects the physicochemical and functional properties of powder. This study permitted to better understand the links between physicochemical and functional properties of powder, to identify correlations between air pressure and outlet temperature. Therefore, mathematical models have been developed and the use of genetic algorithm will allow the optimization of powder functionalities.

Keywords: dairy powders, spray-drying, powders functionalities, design of experiment

Procedia PDF Downloads 93

Authors: Francis O. Nwawuru, Jeremiah N. Ezeora

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In this paper, a new algorithm for solving the system of split equilibrium and fixed point problems in real Hilbert spaces is considered. The equilibrium bifunction involves a nite family of pseudo-monotone mappings, which is an improvement over monotone operators. More so, it turns out that the solution of the finite family of nonexpansive mappings. The regularized parameters do not depend on Lipschitz constants. Also, the computations of the stepsize, which plays a crucial role in the convergence analysis of the proposed method, do require prior knowledge of the norm of the involved bounded linear map. Furthermore, to speed up the rate of convergence, an inertial term technique is introduced in the proposed method. Under standard assumptions on the operators and the control sequences, using a modified Halpern iteration method, we establish strong convergence, a desired result in applications. Finally, the proposed scheme is applied to solve some optimization problems. The result obtained improves numerous results announced earlier in this direction.

Keywords: equilibrium, Hilbert spaces, fixed point, nonexpansive mapping, extragradient method, regularized equilibrium

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Authors: Adriana Duarte, Sandra Sampaio, Catia Ferreira, Jaime I. N. R. Gomes

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With the fashion of faded worn garments the textile industry has moved from indigo and pigments to dyes that are fixed by cationization, with products that can be toxic, and that can show this effect after washing down the dye with friction and/or treating with enzymes in a subsequent operation. Increasingly they are treated with bleaches, such as hypochlorite and permanganate, both toxic substances. An alternative process is presented in this work for both garment and jet dyeing processes, without the use of pre-cationization and the alternative use of “particle-dyes”. These are hybrid products, made up by an inorganic particle and an organic dye. With standard soluble dyes, it is not possible to avoid diffusion into the inside of the fiber unless using previous cationization. Only in this way can diffusion be avoided keeping the centre of the fibres undyed so as to produce the faded effect by removing the surface dye and showing the white fiber beneath. With “particle-dyes”, previous cationization is avoided. By applying low temperatures, the dye does not diffuse completely into the inside of the fiber, since it is a particle and not a soluble dye, being then able to give the faded effect. Even though bleaching can be used it can also be avoided, by the use of friction and enzymes they can be used just as for other dyes. This fashion brought about new ways of applying reactive dyes by the use of previous cationization of cotton, lowering the salt, and temperatures that reactive dyes usually need for reacting and as a side effect the application of a more environmental process. However, cationization is a process that can be problematic in applying it outside garment dyeing, such as jet dyeing, being difficult to obtain level dyeings. It also should be applied by a pad-fix or Pad-batch process due to the low affinity of the pre-cationization products making it a more expensive process, and the risk of unlevelness in processes such as jet dyeing. Wit particle-dyes, since no pre-cationizartion is necessary, they can be applied in jet dyeing. The excess dye is fixed by a fixing agent, fixing the insoluble dye onto the surface of the fibers. By applying the fixing agent only one to 1-3 rinses in water at room temperature are necessary, saving water and improving the washfastness.

Keywords: denim, garment dyeing, worn look, eco-fashion

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Authors: Sharon Cayzer-Haller, Shir Ginosar-Yaari, Ariel Knafo-Noam

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The 21st century emerged as the digital century, and it is marked by a wide range of technological developments and changes, followed by potential changes in human communication skills. This technological revolution has changed human means of communication in many different ways: children and adolescents are spending much of their time in front of screens, participating in all sorts of online activities (even more so since the outbreak of COVID-19). The current study focuses on the role of values in adolescents' online bystanders' behavior. Values are cognitive, abstract representations of desirable goals that motivate behavior, and we hypothesized finding significant associations between specific values and differential online bystanders' feelings and behavior. Data was collected through online questionnaires that measured the participants' values, using Schwartz's short version of the Portrait Values Questionnaire (Schwartz, 2012). Participants’ online behavior was assessed in a questionnaire addressing reactions to situations of cyber shaming and cyberbullying, and specifically positive feelings and pro-social behavior (e.g., more supportive reactions) toward the victims, as opposed to different offensive behavioral reactions (such as laughing at the victim or ignoring the situation). Participants were recruited with a commercial research panel company, and 308 Israeli adolescents' values and online behavior were examined (mean age 15.2). As hypothesized, results show significant associations between self-transcendence values (universalism and benevolence) and conservation values (conformity, tradition, and security). These two groups of values were positively correlated with pro-social bystanders' feelings and behavior. On the opposite side of the values scale, the value of power was negatively associated with the participants' pro-social behavior, and positively associated with offensive behavioral reactions. Further research is needed, but we conclude that values serve as crucial guiding factors in directing adolescents' online feelings and behavior.

Keywords: adolescents, values, cyberbullying, online behavior, power

Procedia PDF Downloads 66

Authors: Oyebade K. Oyedotun

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The cardiotocogram is a technical recording of the heartbeat rate and uterine contractions of a fetus during pregnancy. During pregnancy, several complications can occur to both the mother and the fetus; hence it is very crucial that medical experts are able to find technical means to check the healthiness of the mother and especially the fetus. It is very important that the fetus develops as expected in stages during the pregnancy period; however, the task of monitoring the health status of the fetus is not that which is easily achieved as the fetus is not wholly physically available to medical experts for inspection. Hence, doctors have to resort to some other tests that can give an indication of the status of the fetus. One of such diagnostic test is to obtain cardiotocograms of the fetus. From the analysis of the cardiotocograms, medical experts can determine the status of the fetus, and therefore necessary medical interventions. Generally, medical experts classify examined cardiotocograms into ‘normal’, ‘suspect’, or ‘pathological’. This work presents an artificial neural network based decision support system which can filter cardiotocograms data, producing the corresponding statuses of the fetuses. The capability of artificial neural network to explore the cardiotocogram data and learn features that distinguish one class from the others has been exploited in this research. In this research, feedforward and radial basis neural networks were trained on a publicly available database to classify the processed cardiotocogram data into one of the three classes: ‘normal’, ‘suspect’, or ‘pathological’. Classification accuracies of 87.8% and 89.2% were achieved during the test phase of the trained network for the feedforward and radial basis neural networks respectively. It is the hope that while the system described in this work may not be a complete replacement for a medical expert in fetus status evaluation, it can significantly reinforce the confidence in medical diagnosis reached by experts.

Keywords: decision support, cardiotocogram, classification, neural networks

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Authors: Gokcen Akyurek, Hulya Kayihan, Deniz Yuce, Selen Yilmaz

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The stigma towards mental illness is still very rooted in our society, despite the number of studies, campaigns, and anti-stigma programs developed in recent years. Stigma represents a serious obstacle to recovery and social integration for people who experience a mental illness, affecting directly their well-being and quality of life. It implies that these persons have to deal with many other barriers apart from the disease symptoms (1-5). Convergent, recent literature suggests that less positive attitudes by mental health professionals interfere with the self-determination and recovery process (4-10).The aim of this study was to translate the Attribution Questionnaire-27 (AQ-27) to the Turkish language (AQ-27-T), and to examine the reliability and validity of this new Turkish version. Cultural adaptation was implemented according to the internationally suggested method. To determine the understandability and appropriateness of this measure for the Turkish culture, a pretest was administered and the final form was generated. Then, 424 randomly chosen people took part in the study. Participant’s mean age was 36.9±12.7 years and %52 of them female. Cronbach's alpha and intra-class coefficients were used to estimate instrument reliability. The AQ-27-T was assessed again 14 days later for test retest reliability. The AQ-27-T demonstrated acceptable internal consistency, with a Cronbach's alpha of 0.88 for the total scale and ranging between 0.86 and 0.89 for the items. The test-retest reliability was good, with Pearson correlation coefficients of 0.79 for the total scale and ranging between 0.35 and 0.77 for the items (p<0.05). Correlation between subscales was moderate-good, with Pearson correlation coefficients of 0.18-0.88 (p<0.05). Fit indices of the model supported the factor structure and paths. The AQ-27-T is a reliable measure to assess stigmatizing attitudes in Turkish.

Keywords: attribution questionnaire, validity, reliability, stigma

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Authors: Huy Hieu Pham, Houssam Salmane, Louahdi Khoudour, Alain Crouzil, Pablo Zegers, Sergio Velastin

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We present a deep learning-based multitask framework for joint 3D human pose estimation and action recognition from color video sequences. Our approach proceeds along two stages. In the first, we run a real-time 2D pose detector to determine the precise pixel location of important key points of the body. A two-stream neural network is then designed and trained to map detected 2D keypoints into 3D poses. In the second, we deploy the Efficient Neural Architecture Search (ENAS) algorithm to find an optimal network architecture that is used for modeling the Spatio-temporal evolution of the estimated 3D poses via an image-based intermediate representation and performing action recognition. Experiments on Human3.6M, Microsoft Research Redmond (MSR) Action3D, and Stony Brook University (SBU) Kinect Interaction datasets verify the effectiveness of the proposed method on the targeted tasks. Moreover, we show that our method requires a low computational budget for training and inference.

Keywords: human action recognition, pose estimation, D-CNN, deep learning

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Authors: Gülcan Taşkıran, Ayla Bayık Temel

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Background: Florence Nightingale, the modern founder of the nursing, is most famous for her role as a nurse. But not so much known about her contributions as a mathematician and statistician. Aim: In this conceptual article it is aimed to examine Florence Nightingale's statistics education, how she used her passion for statistics and applied statistical data in nursing care and her scientific contributions to statistical science. Design: Literature review method was used in the study. The databases of Istanbul University Library Search Engine, Turkish Medical Directory, Thesis Scanning Center of Higher Education Council, PubMed, Google Scholar, EBSCO Host, Web of Science were scanned to reach the studies. The keywords 'statistics' and 'Florence Nightingale' have been used in Turkish and English while being screened. As a result of the screening, totally 41 studies were examined from the national and international literature. Results: Florence Nightingale has interested in mathematics and statistics at her early ages and has received various training in these subjects. Lessons learned by Nightingale in a cultured family environment, her talent in mathematics and numbers, and her religious beliefs played a crucial role in the direction of the statistics. She was influenced by Quetelet's ideas in the formation of the statistical philosophy and received support from William Farr in her statistical studies. During the Crimean War, she applied statistical knowledge to nursing care, developed many statistical methods and graphics, so that she made revolutionary reforms in the health field. Conclusions: Nightingale's interest in statistics, her broad vision, the statistical ideas fused with religious beliefs, the innovative graphics she has developed and the extraordinary statistical projects that she carried out has been influential on the basis of her professional achievements. Florence Nightingale has also become a model for women in statistics. Today, using and teaching of statistics and research in nursing care practices and education programs continues with the light she gave.

Keywords: Crimean war, Florence Nightingale, nursing, statistics

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Authors: Ira Solomatina

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This paper looks at the role of fashion and clothes in the context of the late nineteenth-century Indian zenana. It suggests that fashion and clothes served as tools for self-assertion and empowerment among the zenana women, allowing them to negotiate between tradition and modernity and establish themselves as modern subjects. In pre-Independence India and in upper-class Indians households, zenana was women's part of the house, where women lived separately from men and in seclusion (purdah). To male colonial scholars and officials, zenana remained impenetrable, inviting speculations about the position of the zenana women. In the colonial imagination, the Indian woman was not only the helpless victim, oppressed by the Indian man but also the agent of deviant sexuality. Consequently, in the colonial British scholarship, zenana was portrayed as a space of idleness, perverse sexuality, ignorance, and illness. Contrary to the dominating ideas about zenana, some Western women writers presented more varied accounts of the zenana life, noting on the good education, dignified manners, and sophisticated fashion choices of the women in the zenana. Contemporary research by postcolonial scholars shows that zenana women in purdah travelled, had access to education and political power. The history of India has examples of women rulers in purdah and more than enough instances of zenana women influencing politics and culture. Zenana, in short, was not an ahistorical, dark realm of idleness but the space of culture and a space impacted by modernity. The paper proves that in the context of zenana, clothes, and fashion provided a visual vocabulary for the women to establish themselves as modern subjects and negotiate between modernity and tradition. To do so, it relies on photographs of zenana women and written accounts about and from the nineteenth-century zenana.

Keywords: woman's fashion, colonial India, modernity, zenana

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Authors: Dawit Thomas

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Feeding and weaning practices vary from culture to culture and depend on different child-rearing values. The socio-cultural dimensions that influence the acceptable infant feeding practices are varied and complex. Understanding cultural differences in beliefs and practices relating to infant feeding is important to enhance designing programs for delivering successful psychological, social, physiological and economic well-being of mothers and infants. The main purpose of this study was exploring mothers infant feeding practices in the context of Hadiyya culture. After purposively selecting Harche Huyaya Uyaya Kebele eight infant feeding mothers were selected using snowball sampling technique. The study employed interviews and focus group discussion. The study found out early initiation and prolonged breastfeeding and early complementary feeding in some instances immediately after birth. In addition, infants were not forced to wean unless the mothers encounter pressing issues like pregnancy and health related problems. Furthermore, the main weaning techniques were putting unpleasant materials on the tip of nipples and sending infants to grandparents home. The study also found out gender difference in weaning, i.e., early initiation of weaning for girls. This can be indicative of gender-based bias on weaning practice. Finally, health extension workers, office of women and children affairs and Hadiyya Zone Tourism office should organize awareness raising programs to preserve vital infant feeding practices like prolonged breastfeeding and length of weaning. In addition, the offices should raise awareness among communities on negative side effects of sending infant to grandparents home that may weaken infant-mothers attachment and create favorable ground for the development of phobia.

Keywords: feeding, infant, practices, weaning

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Authors: Muhammad Zaheer Babar, Amer Kashif, Muhammad Rizwan Javed

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Nowadays distributed generation integration is best way to overcome the increasing load demand. Optimal allocation of distributed generation plays a vital role in reducing system losses and improves voltage profile. In this paper, a Meta heuristic technique is proposed for allocation of DG in order to reduce power losses and improve voltage profile. The proposed technique is based on Multi Objective Particle Swarm optimization. Fewer control parameters are needed in this algorithm. Modification is made in search space of PSO. The effectiveness of proposed technique is tested on IEEE 33 bus test system. Single DG as well as multiple DG scenario is adopted for proposed method. Proposed method is more effective as compared to other Meta heuristic techniques and gives better results regarding system losses and voltage profile.

Keywords: Distributed generation (DG), Multi Objective Particle Swarm Optimization (MOPSO), particle swarm optimization (PSO), IEEE standard Test System

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Authors: Carla O'Connor, Shanta' Robinson, Alaina Neal, Elan Hope, Adam Hengen, Samantha Drotar

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In this paper, we provide a preliminary analysis of the stories teachers tell about their Black students in their efforts to make sense of and professionally resolve the underperformance of Black students in their district. The teachers themselves hail from three demographically distinct districts that participate in the state coordinated inter-district school choice system. The districts are Varuna Hills (a pseudonym, as are all other names in this manuscript), a district that serves a predominantly White and affluent community; Newport, a district that serves a socioeconomically diverse but still majority White population; and Aspen, a district in which the student body is predominantly Black and predominantly working to lower middle class. Relying upon teacher focus group interviews in each of these districts which share a common reform context, we show how teachers’ everyday and narrative discourse makes meaning of the bodies and achievement of Black students and their families. More specifically, we show that these discourses construct Black students as interlopers, as suffering from extraordinary neediness, and in dire need of proper parenting. Our analysis reveals that there are nuances by which the teachers articulate these discourses with the nuances being a function of how the schools of choice reform context intersects with the demographics of each school and beliefs about the demographics of the schools of choice population. We unpack the racialized and classed nature of these narratives and the implications for teachers’ personal practical knowledge.

Keywords: black achievement, educational access and opportunity, race and schooling, teacher knowledge and education

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Authors: Alireza Ansariyar, Mansoureh Jeihani

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Light Detection and Ranging (LiDAR) sensors are capable of recording traffic data including the number of passing vehicles and bicyclists, the speed of vehicles and bicyclists, and the number of conflicts among both road users. In order to collect real-time traffic data and investigate the safety of different road users, a LiDAR sensor was installed at Cold Spring Ln – Hillen Rd intersection in Baltimore City. The frequency and severity of collected real-time conflicts were analyzed and the results highlighted that 122 conflicts were recorded over a 10-month time interval from May 2022 to February 2023. By using an innovative image-processing algorithm, a new safety Measure of Effectiveness (MOE) was proposed to recognize the critical zones for bicyclists entering each zone. Considering the trajectory of conflicts, the results of the analysis demonstrated that conflicts in the northern approach (zone N) are more frequent and severe. Additionally, sunny weather is more likely to cause severe vehicle-bike conflicts.

Keywords: LiDAR sensor, post encroachment time threshold (PET), vehicle-bike conflicts, a measure of effectiveness (MOE), weather condition

Procedia PDF Downloads 237