Search results for: constriction factor based particle swarm optimization (CPSO)

Authors: Dorcas Matowe

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Inequality in health care for racial and ethnic minorities continues to be a growing concern for many Americans. Some of the barriers hindering the elimination of health disparities include lack of insurance, socioeconomic status (SES), and racism. This study will specifically focus on the association between some of these factors- health care access, which includes insurance coverage and frequency of doctor visits, race, ethnicity, and health status. The purpose of this study will be to address the following questions: is having health insurance associated with increased doctor visits? Are racial and ethnic minorities with health insurance more or less likely to see a doctor? Is the association between having health insurance moderated by being an ethnic minority? Given the current implications of the 2010 Affordable Care Act, this study will highlight the need to prioritize health care access for minorities and confront institutional racism. Critical Race Theory (CRT) will demonstrate how racism has reinforced these health disparities. This quantitative study design will analyze secondary data from the 2015 Behavioral Risk Factor Surveillance System (BRFSS) questionnaire, a telephone survey conducted annually in all 50 states and three US territories by state health departments in conjunction with the Center for Disease Control (CDC). Non-identifying health-related data is gathered annually from over 400,000 adults 18 years and above about their health status and use of preventative services. Through Structural Equation Modeling (SEM), the relationship between the predictor variables of health care access, race, and ethnicity, the criterion variable of health status, and the latent variables of emotional support and life satisfaction will be examined. It is hypothesized that there will be an interaction between certain racial and ethnic minorities who went to see a doctor, had insurance coverage, experienced racism, and the quality of their health status, emotional support, and life satisfaction.

Keywords: ethnic minorities, health disparities, health access, racism

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Authors: Pattarachaya Preechakasedkit, Kota Osada, Koji Suzuki, Daniel Citterio, Orawon Chailapakul

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A paper-based lateral flow device for screening thyroid stimulating hormone (TSH) is reported. A sandwich immunoassay was performed using two mouse monoclonal TSH antibodies (anti-hTSH 5403 and 5404) as immobilized and labeled antibodies for capturing TSH samples. Test (anti-hTSH 5403) and control (goat anti-Mouse IgG) lines were fabricated on nitrocellulose membrane (NCM) using ballpoint pen printed with a speed of 3 cm/s and thickness setting of 1. The novel gold nanoparticles europium complex (AuNPs@Eu) was used as fluorescence label compared to conventional AuNPs label. The results obtained with this device can be visually assessed by the naked eyes and under UV hand lamps, and quantitative analysis can be performed using the ImageJ program. The limit of detection (LOD) under UV hand lamps (0.1 µIU/mL) provided 50-fold greater sensitivity than AuNPs (5 µIU/mL), which is suitable for both hypothyroidism and hyperthyroidism screening within 30 min. A linear relationship between the red intensity and the logarithmic concentrations of TSH was observed with a good correlation (R²=0.992). Furthermore, the device can be effectively applied for screening TSH in the spiked human serum with recovery range of 96.80-104.45% and RSD of 2.18-3.63%. Therefore, the developed device is an alternative method for TSH screening which provides a lot of advantages including low cost, short time analysis, ease of use, disposability, portability, and on-site measurement.

Keywords: thyroid stimulating hormone, paper-based lateral flow, hypothyroidism, hyperthyroidism

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Authors: Samuel Asiedu-Addo, Jonathan Annan, Yarhands Dissou Arthur

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The study aims to explore the relational effect of lecturers’ personal attribute on student’s interest in statistics. In this study personal attributes of lecturers’ such as lecturer’s dynamism, communication strategies and rapport in the classroom as well as applied knowledge during lecture were examined. Here, exploratory research design was used to establish the effect of lecturer’s personal attributes on student’s interest. Data were analyzed by means of confirmatory factor analysis and structural equation modeling (SEM) using the SmartPLS 3 program. The study recruited 376 students from the faculty of technical and vocational education of the University of Education Winneba Kumasi campus, and Ghana Technology University College as well as Kwame Nkrumah University of science and Technology. The results revealed that personal attributes of an effective lecturer were lecturer’s dynamism, rapport, communication and applied knowledge contribute (52.9%) in explaining students interest in statistics. Our regression analysis and structural equation modeling confirm that lecturers personal attribute contribute effectively by predicting student’s interest of 52.9% and 53.7% respectively. The paper concludes that the total effect of a lecturer’s attribute on student’s interest is moderate and significant. While a lecturer’s communication and dynamism were found to contribute positively to students’ interest, they were insignificant in predicting students’ interest. We further showed that a lecturer’s personal attributes such as applied knowledge and rapport have positive and significant effect on tertiary student’s interest in statistic, whilst lecturers’ communication and dynamism do not significantly affect student interest in statistics; though positively related.

Keywords: student interest, effective teacher, personal attributes, regression and SEM

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Authors: Rashima Mahajan, Dipali Bansal, Shweta Singh

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Real time non-invasive Brain Computer Interfaces have a significant progressive role in restoring or maintaining a quality life for medically challenged people. This manuscript provides a comprehensive review of emerging research in the field of cognitive/affective computing in context of human neural responses. The perspectives of different emotion assessment modalities like face expressions, speech, text, gestures, and human physiological responses have also been discussed. Focus has been paid to explore the ability of EEG (Electroencephalogram) signals to portray thoughts, feelings, and unspoken words. An automated workflow-based protocol to design an EEG-based real time Brain Computer Interface system for analysis and classification of human emotions elicited by external audio/visual stimuli has been proposed. The front end hardware includes a cost effective and portable Emotive EEG Neuroheadset unit, a personal computer and a set of external stimulators. Primary signal analysis and processing of real time acquired EEG shall be performed using MATLAB based advanced brain mapping toolbox EEGLab/BCILab. This shall be followed by the development of MATLAB based self-defined algorithm to capture and characterize temporal and spectral variations in EEG under emotional stimulations. The extracted hybrid feature set shall be used to classify emotional states using artificial intelligence tools like Artificial Neural Network. The final system would result in an inexpensive, portable and more intuitive Brain Computer Interface in real time scenario to control prosthetic devices by translating different brain states into operative control signals.

Keywords: brain computer interface, electroencephalogram, EEGLab, BCILab, emotive, emotions, interval features, spectral features, artificial neural network, control applications

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Authors: Cristy Stagnar, Nina Hubig, Diana Ivankovic

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The epigenome is a compelling candidate for mediating long-term responses to environmental effects modifying disease risk. The main goal of this research is to develop a machine learning-based DNA methylation score, which will be valuable in delineating the unique contribution of paternal epigenetic modifications to the germline impacting childhood health outcomes. It will also be a useful tool in validating self-reports of nonsmoking and in adjusting epigenome-wide DNA methylation association studies for this early-life exposure. Using secondary data from two population-based methylation profiling studies, our DNA methylation score is based on CpG DNA methylation measurements from cord blood gathered from children whose fathers smoked pre- and peri-conceptually. Each child’s mother and father fell into one of three class labels in the accompanying questionnaires -never smoker, former smoker, or current smoker. By applying different machine learning algorithms to the accessible resource for integrated epigenomic studies (ARIES) sub-study of the Avon longitudinal study of parents and children (ALSPAC) data set, which we used for training and testing of our model, the best-performing algorithm for classifying the father smoker and mother never smoker was selected based on Cohen’s κ. Error in the model was identified and optimized. The final DNA methylation score was further tested and validated in an independent data set. This resulted in a linear combination of methylation values of selected probes via a logistic link function that accurately classified each group and contributed the most towards classification. The result is a unique, robust DNA methylation score which combines information on DNA methylation and early life exposure of offspring to paternal smoking during pregnancy and which may be used to examine the paternal contribution to offspring health outcomes.

Keywords: epigenome, health outcomes, paternal preconception environmental exposures, supervised machine learning

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Authors: Janet Holland

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Eye tracking is a great way to triangulate multiple data sources for deeper, more complete knowledge of how instructional materials are really being used and emotional connections made. Using sensor based biometrics provides a detailed local analysis in real time expanding our ability to collect science based data for a more comprehensive level of understanding, not previously possible, for teaching and learning. The knowledge gained will be used to make future improvements to instructional materials, tools, and interactions. The literature has been examined and a preliminary pilot test was implemented to develop a methodology for research in Instructional Design and Technology. Eye tracking now offers the addition of objective metrics obtained from eye tracking and other biometric data collection with analysis for a fresh perspective.

Keywords: area of interest, eye tracking, biometrics, fixation, fixation count, fixation sequence, fixation time, gaze points, heat map, saccades, time to first fixation

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Authors: O. Anan, D. Böhning, A. Maruotti

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The purpose of the study is to estimate the elusive target population size under a truncated count model that accounts for heterogeneity. The purposed estimator is based on the generalized Poisson distribution (GPD), which extends the Poisson distribution by adding a dispersion parameter. Thus, it becomes an useful model for capture-recapture data where concurrent events are not homogeneous. In addition, it can account for over-dispersion and under-dispersion. The ratios of neighboring frequency counts are used as a tool for investigating the validity of whether generalized Poisson or Poisson distribution. Since capture-recapture approaches do not provide the zero counts, the estimated parameters can be achieved by modifying the EM-algorithm technique for the zero-truncated generalized Poisson distribution. The properties and the comparative performance of proposed estimator were investigated through simulation studies. Furthermore, some empirical examples are represented insights on the behavior of the estimators.

Keywords: capture, recapture methods, ratio plot, heterogeneous population, zero-truncated count

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Authors: Yafri Hazbi, Zaenal Bachruddin, Nafiatul Umami, Lies Mira Yusiati

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The objective of this study was to identify the benefits of total mixture concentrate based on tofu waste silage (TMC-TWS) as ration containing lactic acid bacteria on performance of lambs. Fifteen weaning lambs (2-3 months old) were randomly divided into two treatment groups, treatment group I (TI) was fed with TMC-TWS as ration and treatment group II (TII) was fed with TMC-TWS fresh (without silage fermentation). The performance of lambs was evaluated on day 0, 15, and 30 to have data of body weight per day. Meanwhile, blood sampling and feces were made on the 30th day to get an analysis on the blood profile (erythrocytes (mill/ml), hemoglobin (g/dL), packed cell volume (%), and leukocytes (mill/ml)) and the number of worm eggs in feces. The results of this study showed no significant difference between the effect of different feed on the blood profile (erythrocytes (mill/ml), hemoglobin (g/dL), packed cell volume (%), as well as the number of worm eggs in the feces. However the results showed significant differences if it is low (P<0.05) due to the treatment group based on sex on body weight gain per day, feed conversion rate and the number of erythrocytes.

Keywords: lambs, total mixture concentrate, silage, acid lactid bacteria, blood profile, eggs worm in feces

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Authors: Agustyas Tjiptaningrum, Intanri Kurniati, Fadilah Fadilah, Linda Erlina, Tiwuk Susantiningsih

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Coronavirus disease-19 (COVID-19) is still one of the health problems. It can be a severe condition that is caused by a cytokine storm. In a cytokine storm, several proinflammatory cytokines are released massively. It destroys epithelial cells, and subsequently, it can cause death. The anti-inflammatory agent can be used to decrease the number of severe Covid-19 conditions. Melaleuca cajuputi is a plant that has antiviral, antibiotic, antioxidant, and anti-inflammatory activities. This study was carried out to analyze the prediction mechanism of the M. cajuputi extract from Lampung, Indonesia, as an anti-inflammatory agent for COVID-19. This study constructed a database of protein host target that was involved in the inflammation process of COVID-19 using data retrieval from GeneCards with the keyword “SARS-CoV2”, “inflammation,” “cytokine storm,” and “acute respiratory distress syndrome.” Subsequent protein-protein interaction was generated by using Cytoscape version 3.9.1. It can predict the significant target protein. Then the analysis of the Gene Ontology (GO) and KEGG pathways was conducted to generate the genes and components that play a role in COVID-19. The result of this study was 30 nodes representing significant proteins, namely NF-κβ, IL-6, IL-6R, IL-2RA, IL-2, IFN2, C3, TRAF6, IFNAR1, and DOX58. From the KEGG pathway, we obtained the result that NF-κβ has a role in the production of proinflammatory cytokines, which play a role in the COVID-19 cytokine storm. It is an important factor for macrophage transcription; therefore, it will induce inflammatory gene expression that encodes proinflammatory cytokines such as IL-6, TNF-α, and IL-1β. In conclusion, the blocking of NF-κβ is the prediction mechanism of the M. cajuputi extract as an anti-inflammation agent for COVID-19.

Keywords: antiinflammation, COVID-19, cytokine storm, NF-κβ, M. cajuputi

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Authors: Ikaputra

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This paper will focus on the concept of social capital for especially housing reconstruction Post Disaster. The context of the study is Indonesia and Yogyakarta Post Earthquake 2006 as a case, but it is expected that the concept can be adopted in general post disaster reconstruction. The discussion will begin by addressing issues on House Reconstruction Post Disaster in Indonesia and Yogyakarta; defining Social Capital as a concept for effective management capacity based on community; Social Capital Post Java Earthquake utilizing Gotong Royong—community mutual self-help, and Approach and Strategy towards Community-based Reconstruction.

Keywords: community empowerment, Gotong Royong, post disaster, reconstruction, social capital, Yogyakarta-Indonesia

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Authors: Taezoon Park, Wonil Hwang

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This study aims to build a design guideline for the effective visual display used for consumer products considering user characteristics; gender and age. Although a number of basic experiments identified the limits of human visual perception, the findings remain fragmented and many times in an unfriendly form. This study compiled a design cases along with tables aggregated from the experimental result of visual perception; brightness/contrast, useful field of view, color sensitivity. Visual design elements commonly used for consumer product, were selected and appropriate guidelines were developed based on the experimental result. Since the provided data with case example suggests a feasible design space, it will save time for a product designer to find appropriate design alternatives.

Keywords: design guideline, consumer product, visual design element, visual perception, emotional design

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Authors: Gałka Aleksandra, Jelińska Justyna, Masiak Albert, Walentukiewicz Krzysztof

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An offline signature is well-known however not the safest way to verify identity. Nowadays, to ensure proper authentication, i.e. in banking systems, multimodal verification is more widely used. In this paper the online signature analysis based on dynamic time warping (DTW) coupled with machine learning approaches has been presented. In our research signatures made with biometric pens were gathered. Signature features as well as their forgeries have been described. For verification of authenticity various methods were used including convolutional neural networks using DTW matrix and multilayer perceptron using sums of DTW matrix paths. System efficiency has been evaluated on signatures and signature forgeries collected on the same day. Results are presented and discussed in this paper.

Keywords: dynamic time warping, handwritten signature verification, feature-based recognition, online signature

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Authors: Joao A. Labrincha, Rui M. Novais, L. Senff, J. Carvalheiras

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The development of multifunctional materials to tackle the energy consumption and improve the hygrothermal performance of buildings is very relevant. This work reports the development of porous geopolymers or bi-layered composites, composed by a highly porous top-layer and a dense bottom-layer, showing high ability to reduce the temperature swings inside buildings and simultaneously buffer the humidity levels. The use of phase change materials (PCM) strongly reduces the indoor thermal fluctuation (up to 5 °C). The potential to modulate indoor humidity is demonstrated by the very high practical MBV (2.71 g/m2 Δ%HR). Since geopolymer matrixes are produced from wastes (biomass fly ash, red mud) the developed solutions contribute to sustainable and energy efficient and healthy building.

Keywords: waste-based geopolymers, thermal insulation, temperature regulation, moisture buffer

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Authors: Larry Powell, Seth Polsley, Drew Casey, Tracy Hammond

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Human activity recognition (HAR) systems have shown positive performance when recognizing repetitive activities like walking, running, and sleeping. Water-based activities are a reasonably new area for activity recognition. However, water-based activity recognition has largely focused on supporting the elite and competitive swimming population, which already has amazing coordination and proper form. Beginner swimmers are not perfect, and activity recognition needs to support the individual motions to help beginners. Activity recognition algorithms are traditionally built around short segments of timed sensor data. Using a time window input can cause performance issues in the machine learning model. The window’s size can be too small or large, requiring careful tuning and precise data segmentation. In this work, we present a method that uses a time window as the initial segmentation, then separates the data based on the change in the sensor value. Our system uses a multi-phase segmentation method that pulls all peaks and valleys for each axis of an accelerometer placed on the swimmer’s lower back. This results in high recognition performance using leave-one-subject-out validation on our study with 20 beginner swimmers, with our model optimized from our final dataset resulting in an F-Score of 0.95.

Keywords: time window, peak/valley segmentation, feature extraction, beginner swimming, activity recognition

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Authors: Carolina S. Correia, Thalita V. Thomé, Andersen Boniolo, Dhayana I. Veiga

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Evidence-based practices (EBP) in education is a set of principles and practices used to raise educational policy, it involves the integration of professional expertise in education with the best empirical evidence in making decisions about how to deliver instruction. The purpose of this presentation is to describe and characterize studies about EBP in education in elementary classroom setting. Data here presented is part of an ongoing systematic review research. Articles were searched and selected from four academic databases: ProQuest, Scielo, Science Direct and Capes. The search terms were evidence-based practices or program effectiveness, and education or teaching or teaching practices or teaching methods. Articles were included according to the following criteria: The studies were explicitly described as evidence-based or discussed the most effective practices in education, they discussed teaching practices in classroom context in elementary school level. Document excerpts were extracted and recorded in Excel, organized by reference, descriptors, abstract, purpose, setting, participants, type of teaching practice, study design and main results. The total amount of articles selected were 1.185, 569 articles from Proquest Research Library; 216 from CAPES; 251 from ScienceDirect and 149 from Scielo Library. The potentially relevant references were 178, from which duplicates were removed. The final number of articles analyzed was 140. From 140 articles, are 47 theoretical studies and 93 empirical articles. The following research design methods were identified: longitudinal intervention study, cluster-randomized trial, meta-analysis and pretest-posttest studies. From 140 articles, 103 studies were about regular school teaching and 37 were on special education teaching practices. In several studies, used as teaching method: active learning, content acquisition podcast (CAP), precision teaching (PT), mediated reading practice, speech therapist programs and peer-assisted learning strategies (PALS). The countries of origin of the studies were United States of America, United Kingdom, Panama, Sweden, Scotland, South Korea, Argentina, Chile, New Zealand and Brunei. The present study in is an ongoing project, so some representative findings will be discussed, providing further acknowledgment on the best teaching practices in elementary classroom setting.

Keywords: best practices, children, evidence-based education, elementary school, teaching methods

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Authors: Faramarz Ashenai Ghasemi, Ismail Ghasemi, Sajad Daneshpayeh

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In this study, tensile strength and elastic modulus of nanocomposites based on polypropylene/ linear low density polyethylene/ nano titanium dioxide (PP/LLDPE/TiO2) were studied. The samples were produced using a co-rotating twin screw extruder including 0, 2, 4 Wt .% of nano particles, and 20, 40, 60 Wt.% of LLDPE. The styrene-ethylene-butylene-styrene (SEBS) was used as comptabiliser. Tensile strength and elastic modulus were evaluated. The results showed that modulus was increased by 7% with addition of nano particles in comparison to PP/LLDPE. In addition, tensile strength was decreased.

Keywords: PP/LLDPE/TiO2, nanocomposites, elastic modulus, tensile strength

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Authors: Isaac Dery

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Masculinity studies have gained much purchase globally in recent decades, especially the sense in which they have produced discursive space for interdisciplinary investigations. In the light of this, there is increasing consensus among commentators that different masculinities co-exist within a particular social space. There is also a growing recognition and awareness of the merits in examining the conceptual underpinnings of masculinity (especially hegemonic masculinity) its variously contested meanings, and values, and how it contributes to violent behaviours by men. The consequences of hegemonic masculinity and its violent and traumatic impacts on men and women have been evident. The emerging call to imagine more egalitarian and complex masculinities among men has been at the centre of various discussions on the fight against violence. Some theorists argue that this violence emanates from men’s drive to live up to impossible ideals of “masculinity.” Seeking to make the connections between masculinity and gender-based violence, this paper discusses the imperative and possibilities of engaging men/boys as key actors in the campaign against violence. It is worth re-examining the ways in which men’s embodiment and performance of dangerous masculinities contribute towards violence. This paper therefore argues that empowering men to understand the implications of certain behaviours is the key in an attempt to arrest violence and its traumatic cost. This paper is situated within the thesis that there is a relationship between men’s embodiment and performance of dominant forms of masculinities, on the one hand, and violence against women and other men, on the other. Based on research conducted in northern Ghana on domestic violence, it is the argument of this paper that in order to contain violence against women, conditions of gender construction need to be problematized in a manner that will transform fundamental understandings of gender relations in society.

Keywords: violence against women, masculinities, Ghana, gender

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Authors: Yasser F. Hassan

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Deep learning structure is a branch of machine learning science and greet achievement in research and applications. Cellular neural networks are regarded as array of nonlinear analog processors called cells connected in a way allowing parallel computations. The paper discusses how to use deep learning structure for representing neural cellular automata model. The proposed learning technique in cellular automata model will be examined from structure of deep learning. A deep automata neural cellular system modifies each neuron based on the behavior of the individual and its decision as a result of multi-level deep structure learning. The paper will present the architecture of the model and the results of simulation of approach are given. Results from the implementation enrich deep neural cellular automata system and shed a light on concept formulation of the model and the learning in it.

Keywords: cellular automata, neural cellular automata, deep learning, classification

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Authors: Zhidong Zhang, Zhi-Chao Zhang, Georgiana Duarte

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This study explored early adolescents’ anxious and depressed syndromes in Northeast China. Specifically, the study examined anxious and depressed behaviors and the relationship to education environments. The purpose is to examine how the elements of educational environments and the early adolescents’ behaviors as independent variables influence and possibly predict the early adolescents’ anxious/depressed problems. Achenbach System of Empirically Based Assessment (ASEBA), was the instrument, used in collection of data. A stratified sampling method was utilized to collect data from 2532 participants in seven schools. The results indicated that several background variables influenced anxious/depressed problem. Specifically, age, grade, sports activities and hobbies had a relationship with the anxious/depressed variable.

Keywords: anxious/depressed problems, CBCL, empirically-based assessment, internalizing problems

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Authors: Adisorn Banhan, Toansakul Santiboon, Prasong Saihong

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Using the Socio-Scientific Issues-Based Learning Method is to compare of the blended instruction of STEM education with a sample consisted of 84 students in 2 classes at the 11th grade level in Sarakham Pittayakhom School. The 2-instructional models were managed of five instructional lesson plans in the context of electronic wave issue. These research procedures were designed of each instructional method through two groups, the 40-experimental student group was designed for the instructional STEM education (STEMe) and 40-controlling student group was administered with the Socio-Scientific Issues-Based Learning (SSIBL) methods. Associations between students’ learning achievements of each instructional method and their science attitudes of their predictions to their exploring activities toward physics with the STEMe and SSIBL methods were compared. The Measuring Reasonable Ethics Test (MRET) was assessed students’ reasonable ethics with the STEMe and SSIBL instructional design methods on two each group. Using the pretest and posttest technique to monitor and evaluate students’ performances of their reasonable ethics on electromagnetic wave issue in the STEMe and SSIBL instructional classes were examined. Students were observed and gained experience with the phenomena being studied with the Socio-Scientific Issues-Based Learning method Model. To support with the STEM that it was not just teaching about Science, Technology, Engineering, and Mathematics; it is a culture that needs to be cultivated to help create a problem solving, creative, critical thinking workforce for tomorrow in physics. Students’ attitudes were assessed with the Test Of Physics-Related Attitude (TOPRA) modified from the original Test Of Science-Related Attitude (TOSRA). Comparisons between students’ learning achievements of their different instructional methods on the STEMe and SSIBL were analyzed. Associations between students’ performances the STEMe and SSIBL instructional design methods of their reasonable ethics and their science attitudes toward physics were associated. These findings have found that the efficiency of the SSIBL and the STEMe innovations were based on criteria of the IOC value higher than evidence as 80/80 standard level. Statistically significant of students’ learning achievements to their later outcomes on the controlling and experimental groups with the SSIBL and STEMe were differentiated between students’ learning achievements at the .05 level. To compare between students’ reasonable ethics with the SSIBL and STEMe of students’ responses to their instructional activities in the STEMe is higher than the SSIBL instructional methods. Associations between students’ later learning achievements with the SSIBL and STEMe, the predictive efficiency values of the R2 indicate that 67% and 75% for the SSIBL, and indicate that 74% and 81% for the STEMe of the variances were attributable to their developing reasonable ethics and science attitudes toward physics, consequently.

Keywords: socio-scientific issues-based learning method, STEM education, science attitudes, measurement, reasonable ethics, physics classes

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Authors: Zhidi Lei, Xixi Cai, Jue Ding, Peifen Weng, Xiaowei Li

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In the process of preparing thin films by chemical solution method, the uniformity of gel coating has a great influence on the subsequent film thickness. Based on a coating device, the research tracks the interface development of gas-liquid flow by volume of fluid method (VOF). The effects of fluid viscosity and wall wetting property for the shape and position of the coating window are discussed in the process of slot die coating. The result shows that downstream contact lines gets closer to the corner with the increase of fluid viscosity. When the viscosity increases from 0.2Pa∙s to 0.3Pa∙s, 18.2% of the vortex region area will be reduced. With the static contact angle of upper die head surface (θ_sd) increasing, X_u decreased gradually which cause the instability changes of upstream surface. Also, θ_sd increasing brings the reduction of vortex region.

Keywords: film growth, vortex, VOF, slot die coating

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Authors: Hassane Ouazzani Chahdi, Omar Helli, Bourzgui Nour Eddine, Hassan Maher, Ali Soltani

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The real-time knowledge of the NO, NO₂ concentration at high temperature, would allow manufacturers of automobiles to meet the upcoming stringent EURO7 anti-pollution measures for diesel engines. Knowledge of the concentration of each of these species will also enable engines to run leaner (i.e., more fuel efficient) while still meeting the anti-pollution requirements. Our proposed technology is promising in the field of automotive sensors. It consists of nanostructured semiconductors based on gallium nitride and zirconia dioxide. The development of new technologies for selective detection of NO and NO₂ gas species would be a critical enabler of superior depollution. The current response was well correlated to the NO concentration in the range of 0–2000 ppm, 0-2500 ppm NO₂, and 0-300 ppm NH₃ at a temperature of 600.

Keywords: NOₓ sensors, HEMT transistor, anti-pollution, gallium nitride, gas sensor

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Authors: Nassib Abdallah, Pierre Chauvet, Abd El Salam Hajjar, Bassam Daya

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In this paper, we propose an optimized brain computer interface (BCI) system for unspoken speech recognition, based on the fact that the constructions of unspoken words rely strongly on the Wernicke area, situated in the temporal lobe. Our BCI system has four modules: (i) the EEG Acquisition module based on a non-invasive headset with 14 electrodes; (ii) the Preprocessing module to remove noise and artifacts, using the Common Average Reference method; (iii) the Features Extraction module, using Wavelet Packet Transform (WPT); (iv) the Classification module based on a one-hidden layer artificial neural network. The present study consists of comparing the recognition accuracy of 5 Arabic words, when using all the headset electrodes or only the 4 electrodes situated near the Wernicke area, as well as the selection effect of the subbands produced by the WPT module. After applying the articial neural network on the produced database, we obtain, on the test dataset, an accuracy of 83.4% with all the electrodes and all the subbands of 8 levels of the WPT decomposition. However, by using only the 4 electrodes near Wernicke Area and the 6 middle subbands of the WPT, we obtain a high reduction of the dataset size, equal to approximately 19% of the total dataset, with 67.5% of accuracy rate. This reduction appears particularly important to improve the design of a low cost and simple to use BCI, trained for several words.

Keywords: brain-computer interface, speech recognition, artificial neural network, electroencephalography, EEG, wernicke area

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Authors: Ijaz Ahmed, Anwar Zada, Muhammad Waqas, M. U. Ashraf

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We demonstrate the performance of a very efficient tagger applies on hadronically decaying top quark pairs as signal based on deep neural network algorithms and compares with the QCD multi-jet background events. A significant enhancement of performance in boosted top quark events is observed with our limited computing resources. We also compare modern machine learning approaches and perform a multivariate analysis of boosted top-pair as well as single top quark production through weak interaction at √s = 14 TeV proton-proton Collider. The most relevant known background processes are incorporated. Through the techniques of Boosted Decision Tree (BDT), likelihood and Multlayer Perceptron (MLP) the analysis is trained to observe the performance in comparison with the conventional cut based and count approach

Keywords: top tagger, multivariate, deep learning, LHC, single top

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Authors: Alhusein Almahjoub, Dongyu Qiu

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This paper deals with the random access procedure in next-generation networks and presents the solution to reduce total service time (TST) which is one of the most important performance metrics in current and future internet of things (IoT) based networks. The proposed solution focuses on the calculation of optimal transmission probability which maximizes the success probability and reduces TST. It uses the information of several idle preambles in every time slot, and based on it, it estimates the number of backlogged IoT devices using Naïve Bayes estimation which is a type of supervised learning in the machine learning domain. The estimation of backlogged devices is necessary since optimal transmission probability depends on it and the eNodeB does not have information about it. The simulations are carried out in MATLAB which verify that the proposed solution gives excellent performance.

Keywords: random access, LTE/LTE-A, 5G, machine learning, Naïve Bayes estimation

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Authors: Karen Bell

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It is vital to integrate a working-class perspective into the just transition to an inclusive and sustainable society because of the particular expertise and interests that working-class people bring to the debates and actions. In class societies, those who are not well represented in the current structures of power can find it easier to see when the system is not working. They are also more likely to be impacted by the environmental crises because wealthier people can change their dwelling places, jobs and other aspects of their lives in the face of risks. Therefore, challenging the ‘post-material values thesis’, this paper argues that, if enabled to do so, working-class people are more likely to identify what needs to be addressed and changed in transition and can be more motivated to make the changes necessary than other social groups. However, they are often excluded from environmental decision-making and environmental social movements. The paper is based on a mixed methodology; drawing on secondary data, interview material, participant observation and documentary analysis. It is based on years of research and activism on environmental issues in working-class communities. The analysis and conclusion discusses the seven kinds of change required to address this problem: 1) organizational change - participatory practice (2) legislative change - make class an equalities and human rights issue (3) policy change - reduce inequality (4) social movement change - radicalize the environmental movement and support the environmental working-class (5) political change - create an eco-social state based on sharing (6) cultural change - integrate social and environmental justice, and (7) revolutionary change - dismantle capitalism.

Keywords: environmentalism, just transition, sustainability, working class

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Authors: Deeman Yousif Mahmood, Mohammed Abdullah Hussein

Abstract:

The goal of a network-based intrusion detection system is to classify activities of network traffics into two major categories: normal and attack (intrusive) activities. Nowadays, data mining and machine learning plays an important role in many sciences; including intrusion detection system (IDS) using both supervised and unsupervised techniques. However, one of the essential steps of data mining is feature selection that helps in improving the efficiency, performance and prediction rate of proposed approach. This paper applies unsupervised K-means clustering algorithm with information gain (IG) for feature selection and reduction to build a network intrusion detection system. For our experimental analysis, we have used the new NSL-KDD dataset, which is a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. With a split of 60.0% for the training set and the remainder for the testing set, a 2 class classifications have been implemented (Normal, Attack). Weka framework which is a java based open source software consists of a collection of machine learning algorithms for data mining tasks has been used in the testing process. The experimental results show that the proposed approach is very accurate with low false positive rate and high true positive rate and it takes less learning time in comparison with using the full features of the dataset with the same algorithm.

Keywords: information gain (IG), intrusion detection system (IDS), k-means clustering, Weka

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Authors: Halonen

Abstract:

Covid-19 pandemic and the succeeding crises have profoundly shaken the accustomed ways of interaction and thereby challenged the customary engagement patterns among entrepreneurs Consequently, this has led to the experience of lack of collegial interaction for some. Networks and relationships are a crucial factor to strengthening resilience, being especially significant in non-ordinary times. This study aims to shed light on entrepreneurs’ resilience development in and through entrepreneurs’ communal and training space. The context for research is a communal training space in a municipality in Finland of which goal is to help entrepreneurs to experience of peer support and community as part of the "tribe" is strengthened, the entrepreneurs' well-being at work, resilience, ability to change, innovativeness and general life management is strengthened. This communal space is regarded as an example of a physical community of practice (CoP) of entrepreneurs. The research aims to highlight the importance of rediscovering the “new normal” communality as itself but as a key building block of resilience. The initial research questions of the study are: RQ1: What is the role of entrepreneurs’ CoP and communal space in nurturing resilience development among them? RQ2: What positive entrepreneurial outcomes can be achieved through established CoP. The data will be gathered starting from the launch of the communality space in September 2023 onwards. It includes participatory observations of training gatherings, interviews with entrepreneurs and utilizes action research as the method. The author has an active role in participating and facilitating the development. The full paper will be finalized by the fall 2024. The idea of the new normal communality in a CoP among entrepreneurs is to be rediscovered due to its positive impact on entrepreneur’s resilience and business success. The other implications of study can extend to wider entrepreneurial ecosystem and other key stakeholders. Especially emphasizing the potential of communality in CoP for fostering entrepreneurs’ resilience and well-being ensuing business growth, community-driven entrepreneurship development and vitality of the case municipality.

Keywords: resilience, resilience development, communal space, community of practice (CoP)

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Authors: Agatha Swasti Ayuning Tyas

Abstract:

Hyperglycemia in Diabetes Mellitus (DM) is an important factor in cellular and vascular damage, which is caused by activation of C Protein Kinase, polyol and hexosamine track, and production of Advanced Glycation End-Products (AGE). Those mentioned before causes the accumulation of Reactive Oxygen Species (ROS). Oxidative stress increases the expression of proinflammatory factors IL-6 as one of many signs of endothelial disfunction. Genistein in soy milk has a high immunomodulator potential. Goat milk contains amino acids which have antioxidative potential. Fermented kefir has an anti-inflammatory activity which believed will also contribute in potentiating goat milk and soy milk. This study is a quasi-experimental posttest-only research to 30 Wistar mice. This study compared the levels of IL-6 between healthy Wistar mice group (G1) and 4 DM Wistar mice with intervention and grouped as follows: mice without treatment (G2), mice treated with 100% goat milk kefir (G3), mice treated with combination of 50% goat milk kefir and 50% soy milk kefir (G4), and mice treated with 100% soy milk kefir (G5). DM animal models were induced with Streptozotocin & Nicotinamide to achieve hyperglycemic condition. Goat milk kefir and soy milk kefir are given at a dose of 2 mL/kg body weight/day for four weeks to intervention groups. Blood glucose was analyzed by the GOD-POD principle. IL-6 was analyzed by enzyme-linked sandwich ELISA. The level of IL-6 in DM untreated control group (G2) showed a significant difference from the group treated with the combination of 50% goat milk kefir and 50% soy milk kefir (G3) (p=0,006) and the group treated with 100% soy milk kefir (G5) (p=0,009). Whereas the difference of IL-6 in group treated with 100% goat milk kefir (G3) was not significant (p=0,131). There is also synergism between glucose level and IL-6 in intervention groups treated with combination of 50% goat milk kefir and 50% soy milk kefir (G3) and the group treated with 100% soy milk kefir (G5). Combination of 50 % goat milk kefir and 50% soy milk kefir and administration of 100% soy milk kefir alone can control the level of IL-6 remained low in DM Wistar mice induced with streptozocin and nicotinamide.

Keywords: diabetes mellitus, goat milk kefir, soy milk kefir, interleukin 6

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Authors: Fasil Wagnew, Kerri Viney, Kefyalew Addis Alene, Matthew Kelly, Darren Gray

Abstract:

Background: Undernutrition is a risk factor for tuberculosis (TB), including poor treatment outcomes. However, evidence regarding the effect of undernutrition on TB treatment outcomes is not well understood. We aimed to evaluate the effect of undernutrition on sputum culture conversion and treatment outcomes among people with multi-drug resistance (MDR)-TB. Methods: We searched for publications in the Medline, Embase, Scopus, and Web of Science databases without restrictions on geography or year of publication. We conducted a random-effect meta-analysis to estimate the effects of undernutrition on sputum culture conversion and treatment outcomes. Two reviewers independently assessed the study eligibility, extracted the necessary information, and assessed the risk of bias. Depending on the nature of the data, odds ratio (OR) and hazard ratio (HR) with 95% confidence intervals (CIs) were used to summarize the effect estimates. Potential publication bias was checked using funnel plots and Egger’s tests. Results: Of 2358 records screened, 59 studies comprising a total of 31,254 people with MDR-TB were included. Undernutrition was significantly associated with a lower sputum culture conversion rate (HR 0·7, 95% CI 0·6–0·9, I2=67·1%) and a higher rate of mortality (OR 2·9, 95%CI 2·1–3·8, I2=23·7%) and unfavourable treatment outcomes (OR 1·8, 95%CI 1·5–2·0, I2=72·7%). There was no statistically significant publication bias in the included studies. Three studies were low, forty-two studies were moderate, and fourteen studies were high quality. Interpretations: Undernutrition was significantly associated with unfavourable treatment outcomes, including mortality and lower sputum culture conversion among people with MDR-TB. These findings have implications for supporting targeted nutritional interventions alongside standardised second-line TB drugs.

Keywords: undernutrition, MDR-TB, sputum culture conversion, treatment outcomes, meta-analysis

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