Search results for: bit error probability

Authors: Maryam Kheirollahpour, Mahmoud Danaee, Amir Faisal Merican, Asma Ahmad Shariff

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Background: The presence of nonlinearity among the risk factors of eating behavior causes a bias in the prediction models. The accuracy of estimation of eating behaviors risk factors in the primary prevention of obesity has been established. Objective: The aim of this study was to explore the potential of a hybrid model of structural equation modeling (SEM) and Artificial Neural Networks (ANN) to predict eating behaviors. Methods: The Partial Least Square-SEM (PLS-SEM) and a hybrid model (SEM-Artificial Neural Networks (SEM-ANN)) were applied to evaluate the factors affecting eating behavior patterns among university students. 340 university students participated in this study. The PLS-SEM analysis was used to check the effect of emotional eating scale (EES), body shape concern (BSC), and body appreciation scale (BAS) on different categories of eating behavior patterns (EBP). Then, the hybrid model was conducted using multilayer perceptron (MLP) with feedforward network topology. Moreover, Levenberg-Marquardt, which is a supervised learning model, was applied as a learning method for MLP training. The Tangent/sigmoid function was used for the input layer while the linear function applied for the output layer. The coefficient of determination (R²) and mean square error (MSE) was calculated. Results: It was proved that the hybrid model was superior to PLS-SEM methods. Using hybrid model, the optimal network happened at MPLP 3-17-8, while the R² of the model was increased by 27%, while, the MSE was decreased by 9.6%. Moreover, it was found that which one of these factors have significantly affected on healthy and unhealthy eating behavior patterns. The p-value was reported to be less than 0.01 for most of the paths. Conclusion/Importance: Thus, a hybrid approach could be suggested as a significant methodological contribution from a statistical standpoint, and it can be implemented as software to be able to predict models with the highest accuracy.

Keywords: hybrid model, structural equation modeling, artificial neural networks, eating behavior patterns

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Authors: Jinhoa Lee

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The relationships between environmental quality, energy use and economic output have created growing attention over the past decades among researchers and policy makers. Focusing on the empirical aspects of the role of CO2 emissions and energy use in affecting the economic output, this paper is an effort to fulfill the gap in a comprehensive case study at a country level using modern econometric techniques. To achieve the goal, this country-specific study examines the short-run and long-run relationships among energy consumption (using disaggregated energy sources: crude oil, coal, natural gas, electricity), carbon dioxide (CO2) emissions and gross domestic product (GDP) for Israel using time series analysis from the year 1980-2010. To investigate the relationships between the variables, this paper employs the Phillips–Perron (PP) test for stationarity, Johansen maximum likelihood method for cointegration and a Vector Error Correction Model (VECM) for both short- and long-run causality among the research variables for the sample. The long-run equilibrium in the VECM suggests significant positive impacts of coal and natural gas consumptions on GDP in Israel. In the short run, GDP positively affects coal consumption. While there exists a positive unidirectional causality running from coal consumption to consumption of petroleum products and the direct combustion of crude oil, there exists a negative unidirectional causality running from natural gas consumption to consumption of petroleum products and the direct combustion of crude oil in the short run. Overall, the results support arguments that there are relationships among environmental quality, energy use and economic output but the associations can to be differed by the sources of energy in the case of Israel over of period 1980-2010.

Keywords: CO2 emissions, energy consumption, GDP, Israel, time series analysis

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Authors: Jonathan Ham, Christopher Koch

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There are consistently observed associations between certain odors and colors, and there is an association between the ability to imagine vivid visual objects and imagine vivid odors. However, little has been done to investigate how the associations between odors and visual information effect visual processes. This study seeks to understand the relationship between odor imaging, color associations, and visual attention by utilizing a Stroop-task based on common odor-color associations. This Stroop-task was designed using three fruits with distinct odors that are associated with the color of the fruit: lime with green, strawberry with red, and lemon with yellow. Each possible word-color combination was presented in the experimental trials. When the word matched the associated color (lime written in green) it was considered congruent; if it did not, it was considered incongruent (lime written in red or yellow). In experiment I (n = 34) participants were asked to both imagine the odor of the fruit on the screen and identify which fruit it was, and each word-color combination was presented 20 times (a total of 180 trials, with 60 congruent and 120 incongruent instances). Response time and error rate of the participant responses were recorded. There was no significant difference in either measure between the congruent and incongruent trials. In experiment II participants (n = 18) followed the identical procedure as in the previous experiment with the addition of an odorant in the room. The odorant (orange) was not the fruit or color used in the experimental trials. With a fruit-based odorant in the room, the response times (measured in milliseconds) between congruent and incongruent trials were significantly different, with incongruent trials (M = 755.919, SD = 239.854) having significantly longer response times than congruent trials (M = 690.626, SD = 198.822), t (1, 17) = 4.154, p < 0.01. This suggests that odor imagery does affect visual attention to colors, and the ability to inhibit odor-color associations; however, odor imagery is difficult and appears to be facilitated in the presence of a related odorant.

Keywords: odor-color associations, odor imagery, visual attention, inhibition

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Authors: Giorgio Bertolazzi, Panayiotis Benos, Michele Tumminello, Claudia Coronnello

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MicroRNAs are small non-coding RNAs that post-transcriptionally regulate the expression levels of messenger RNAs. MicroRNA regulation activity depends on the recognition of binding sites located on mRNA molecules. ComiR (Combinatorial miRNA targeting) is a user friendly web tool realized to predict the targets of a set of microRNAs, starting from their expression profile. ComiR incorporates miRNA expression in a thermodynamic binding model, and it associates each gene with the probability of being a target of a set of miRNAs. ComiR algorithms were trained with the information regarding binding sites in the 3’UTR region, by using a reliable dataset containing the targets of endogenously expressed microRNA in D. melanogaster S2 cells. This dataset was obtained by comparing the results from two different experimental approaches, i.e., inhibition, and immunoprecipitation of the AGO1 protein; this protein is a component of the microRNA induced silencing complex. In this work, we tested whether including coding region binding sites in the ComiR algorithm improves the performance of the tool in predicting microRNA targets. We focused the analysis on the D. melanogaster species and updated the ComiR underlying database with the currently available releases of mRNA and microRNA sequences. As a result, we find that the ComiR algorithm trained with the information related to the coding regions is more efficient in predicting the microRNA targets, with respect to the algorithm trained with 3’utr information. On the other hand, we show that 3’utr based predictions can be seen as complementary to the coding region based predictions, which suggests that both predictions, from 3'UTR and coding regions, should be considered in a comprehensive analysis. Furthermore, we observed that the lists of targets obtained by analyzing data from one experimental approach only, that is, inhibition or immunoprecipitation of AGO1, are not reliable enough to test the performance of our microRNA target prediction algorithm. Further analysis will be conducted to investigate the effectiveness of the tool with data from other species, provided that validated datasets, as obtained from the comparison of RISC proteins inhibition and immunoprecipitation experiments, will be available for the same samples. Finally, we propose to upgrade the existing ComiR web-tool by including the coding region based trained model, available together with the 3’UTR based one.

Keywords: AGO1, coding region, Drosophila melanogaster, microRNA target prediction

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Authors: Khaled Barkaoui

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Research on second-language (L2) learning tends to focus on comparing students with different levels of proficiency at one point in time. However, to understand L2 development, we need more longitudinal research. In this study, we adopt a longitudinal approach to examine changes in three indicators of L2 ability, complexity, accuracy, and fluency (CAF), as reflected in the writing of L2 learners when writing on different tasks before and after a period L2 instruction. Each of 85 Chinese learners of English at three levels of English language proficiency responded to two writing tasks (independent and integrated) before and after nine months of English-language study in China. Each essay (N= 276) was analyzed in terms of numerous CAF indices using both computer coding and human rating: number of words written, number of errors per 100 words, ratings of error severity, global syntactic complexity (MLS), complexity by coordination (T/S), complexity by subordination (C/T), clausal complexity (MLC), phrasal complexity (NP density), syntactic variety, lexical density, lexical variation, lexical sophistication, and lexical bundles. Results were then compared statistically across tasks, L2 proficiency levels, and time. Overall, task type had significant effects on fluency and some syntactic complexity indices (complexity by coordination, structural variety, clausal complexity, phrase complexity) and lexical density, sophistication, and bundles, but not accuracy. L2 proficiency had significant effects on fluency, accuracy, and lexical variation, but not syntactic complexity. Finally, fluency, frequency of errors, but not accuracy ratings, syntactic complexity indices (clausal complexity, global complexity, complexity by subordination, phrase complexity, structural variety) and lexical complexity (lexical density, variation, and sophistication) exhibited significant changes after instruction, particularly for the independent task. We discuss the findings and their implications for assessment, instruction, and research on CAF in the context of L2 writing.

Keywords: second language writing, Fluency, accuracy, complexity, longitudinal

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Authors: Mathew Wakefield, Matthew Mitchell, Lisa Wise, Christopher McCarthy

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The properties of memory representations in artificial neural networks have cognitive implications. Distributed representations that encode instances as a pattern of activity across layers of nodes afford memory compression and enforce the selection of a single point in instance space. These encoding schemes also appear to distort the representational space, as well as trading off the ability to validate that input information is within the bounds of past experience. In contrast, a localist representation which encodes some meaningful information into individual nodes in a network layer affords less memory compression while retaining the integrity of the representational space. This allows the validity of an input to be determined. The validity (or familiarity) of input along with the capacity of localist representation for multiple instance selections affords a memory sampling approach that dynamically balances the bias-variance trade-off. When the input is familiar, bias may be high by referring only to the most similar instances in memory. When the input is less familiar, variance can be increased by referring to more instances that capture a broader range of features. Using this approach in a localist instance memory network, an experiment demonstrates a relationship between representational conflict, generalization performance, and memorization demand. Relatively small sampling ranges produce the best performance on a classic machine learning dataset of visual objects. Combining memory validity with conflict detection produces a reliable confidence judgement that can separate responses with high and low error rates. Confidence can also be used to signal the need for supervisory input. Using this judgement, the need for supervised learning as well as memory encoding can be substantially reduced with only a trivial detriment to classification performance.

Keywords: artificial neural networks, representation, memory, conflict monitoring, confidence

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Authors: Musa Akdere, Gunnar Seide, Thomas Gries

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Carbon fibres are fibrous materials with a carbon atom amount of more than 90%. They combine excellent mechanicals properties with a very low density. Thus carbon fibre reinforced plastics (CFRP) are very often used in lightweight design and construction. The precursor material is usually polyacrylonitrile (PAN) based and wet-spun. During the production of carbon fibre, the precursor has to be stabilized thermally to withstand the high temperatures of up to 1500 °C which occur during carbonization. Even though carbon fibre has been used since the late 1970s in aerospace application, there is still no general method available to find the optimal production parameters and the trial-and-error approach is most often the only resolution. To have a much better insight into the process the chemical reactions during stabilization have to be analyzed particularly. Therefore, a model of the chemical reactions (cyclization, dehydration, and oxidation) based on the research of Dunham and Edie has been developed. With the presented model, it is possible to perform a complete simulation of the fibre undergoing all zones of stabilization. The fiber bundle is modeled as several circular fibers with a layer of air in-between. Two thermal mechanisms are considered to be the most important: the exothermic reactions inside the fiber and the convective heat transfer between the fiber and the air. The exothermic reactions inside the fibers are modeled as a heat source. Differential scanning calorimetry measurements have been performed to estimate the amount of heat of the reactions. To shorten the required time of a simulation, the number of fibers is decreased by similitude theory. Experiments were conducted to validate the simulation results of the fibre temperature during stabilization. The experiments for the validation were conducted on a pilot scale stabilization oven. To measure the fibre bundle temperature, a new measuring method is developed. The comparison of the results shows that the developed simulation model gives good approximations for the temperature profile of the fibre bundle during the stabilization process.

Keywords: carbon fibre, coupled simulation, exothermic reactions, fibre-air-interface

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Authors: Ogunlana Olarewaju Fatai, Kelani Fatai Adeshina

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The interplay of corruption and institutional quality determines how effective and efficient an economy progresses. An efficient institutional quality is a key requirement for economic stability. Institutional quality in most cases has been used interchangeably with Governance and these have given room for proxies that legitimized Governance as measures for institutional quality. A poorly-tailored institutional quality has a penalizing effect on corruption and economic growth, while defective institutional quality breeds corruption. Corruption is a hydra-headed phenomenon as it manifests in different forms. The most celebrated definition of corruption is given as “the use or abuse of public office for private benefits or gains”. It also denotes an arrangement between two mutual parties in the determination and allocation of state resources for pecuniary benefits to circumvent state efficiency. This study employed Barro (1990) type augmented model to analyze the nexus among corruption, institutional quality and economic growth in Nigeria using annual time series data, which spanned the period 1996-2019. Within the analytical framework of Johansen Cointegration technique, Error Correction Mechanism (ECM) and Granger Causality tests, findings revealed a long-run relationship between economic growth, corruption and selected measures of institutional quality. The long run results suggested that all the measures of institutional quality except voice & accountability and regulatory quality are positively disposed to economic growth. Moreover, the short-run estimation indicated a reconciliation of the divergent views on corruption which pointed at “sand the wheel” and “grease the wheel” of growth. In addition, regulatory quality and the rule of law indicated a negative influence on economic growth in Nigeria. Government effectiveness and voice & accountability, however, indicated a positive influence on economic growth. The Granger causality test results suggested a one-way causality between GDP and Corruption and also between corruption and institutional quality. Policy implications from this study pointed at checking corruption and streamlining institutional quality framework for better and sustained economic development.

Keywords: institutional quality, corruption, economic growth, public policy

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Authors: Sohyoung Won, Heebal Kim, Dajeong Lim

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Genomic prediction is an effective way to measure the abilities of livestock for breeding based on genomic estimated breeding values, statistically predicted values from genotype data using best linear unbiased prediction (BLUP). Using haplotypes, clusters of linked single nucleotide polymorphisms (SNPs), as markers instead of individual SNPs can improve the reliability of genomic prediction since the probability of a quantitative trait loci to be in strong linkage disequilibrium (LD) with markers is higher. To efficiently use haplotypes in genomic prediction, finding optimal ways to define haplotypes is needed. In this study, 770K SNP chip data was collected from Hanwoo (Korean cattle) population consisted of 2506 cattle. Haplotypes were first defined in three different ways using 770K SNP chip data: haplotypes were defined based on 1) length of haplotypes (bp), 2) the number of SNPs, and 3) k-medoids clustering by LD. To compare the methods in parallel, haplotypes defined by all methods were set to have comparable sizes; in each method, haplotypes defined to have an average number of 5, 10, 20 or 50 SNPs were tested respectively. A modified GBLUP method using haplotype alleles as predictor variables was implemented for testing the prediction reliability of each haplotype set. Also, conventional genomic BLUP (GBLUP) method, which uses individual SNPs were tested to evaluate the performance of the haplotype sets on genomic prediction. Carcass weight was used as the phenotype for testing. As a result, using haplotypes defined by all three methods showed increased reliability compared to conventional GBLUP. There were not many differences in the reliability between different haplotype defining methods. The reliability of genomic prediction was highest when the average number of SNPs per haplotype was 20 in all three methods, implying that haplotypes including around 20 SNPs can be optimal to use as markers for genomic prediction. When the number of alleles generated by each haplotype defining methods was compared, clustering by LD generated the least number of alleles. Using haplotype alleles for genomic prediction showed better performance, suggesting improved accuracy in genomic selection. The number of predictor variables was decreased when the LD-based method was used while all three haplotype defining methods showed similar performances. This suggests that defining haplotypes based on LD can reduce computational costs and allows efficient prediction. Finding optimal ways to define haplotypes and using the haplotype alleles as markers can provide improved performance and efficiency in genomic prediction.

Keywords: best linear unbiased predictor, genomic prediction, haplotype, linkage disequilibrium

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Authors: Amritee Bora, B. S. Mipun

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Land degradation is often used to describe the land environmental phenomena that reduce land’s original productivity both qualitatively and quantitatively. The study of land degradation vulnerability primarily deals with “Environmentally Sensitive Areas” (ESA) and the amount of topsoil loss due to erosion. In many studies, it is observed that the assessment of the existing status of land degradation is used to represent the vulnerability. Moreover, it is also noticed that in most studies, the primary emphasis of land degradation vulnerability is to assess its sensitivity to soil erosion only. However, the concept of land degradation vulnerability can have different objectives depending upon the perspective of the study. It shows the extent to which changes in land use land cover can imprint their effect on the land. In other words, it represents the susceptibility of a piece of land to degrade its productive quality permanently or in the long run. It is also important to mention that the vulnerability of land degradation is not a single factor outcome. It is a probability assessment to evaluate the status of land degradation and needs to consider both biophysical and human induce parameters. To avoid the complexity of the previous models in this regard, the present study has emphasized on to generate a simplified model to assess the land degradation vulnerability in terms of its current human population pressure, land use practices, and existing biophysical conditions. It is a “Mixed-Method” termed as the land degradation vulnerability index (LDVi). It was originally inspired by the MEDALUS model (Mediterranean Desertification and Land Use), 1999, and Farazadeh’s 2007 revised version of it. It has followed the guidelines of Space Application Center, Ahmedabad / Indian Space Research Organization for land degradation vulnerability. The model integrates the climatic index (Ci), vegetation index (Vi), erosion index (Ei), land utilization index (Li), population pressure index (Pi), and cover management index (CMi) by giving equal weightage to each parameter. The final result shows that the very high vulnerable zone primarily indicates three (3) prominent circumstances; land under continuous population pressure, high concentration of human settlement, and high amount of topsoil loss due to surface runoff within the study sites. As all the parameters of the model are amalgamated with equal weightage further with the help of regression analysis, the LDVi model also provides a strong grasp of each parameter and how far they are competent to trigger the land degradation process.

Keywords: population pressure, land utilization, soil erosion, land degradation vulnerability

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Authors: Vanessa A. Segovia, Sonia E. Ruiz

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The use of energy dissipation systems for seismic applications has increased worldwide, thus it is necessary to develop practical and modern criteria for their optimal design. Here, a direct displacement-based seismic design approach for frame buildings with hysteretic energy dissipation systems (HEDS) is applied. The building is constituted by two individual structural systems consisting of: 1) A main elastic structural frame designed for service loads and 2) A secondary system, corresponding to the HEDS, that controls the effects of lateral loads. The procedure implies to control two design parameters: A) The stiffness ratio (α=K_frame/K_(total system)), and B) The strength ratio (γ= V_damper / V_(total system)). The proposed damage-controlled approach contributes to the design of a more sustainable and resilient building because the structural damage is concentrated on the HEDS. The reduction of the design displacement spectrum is done by means of a damping factor (recently published) for elastic structural systems with HEDS, located in Mexico City. Two limit states are verified: Serviceability and near collapse. Instead of the traditional trial-error approach, a procedure that allows the designer to establish the preliminary sizes of the structural elements of both systems is proposed. The design methodology is applied to an 8-story steel building with buckling restrained braces, located in soft soil of Mexico City. With the aim of choosing the optimal design parameters, a parametric study is developed considering different values of α and γ. The simplified methodology is for preliminary sizing, design, and evaluation of the effectiveness of HEDS, and it constitutes a modern and practical tool that enables the structural designer to select the best design parameters.

Keywords: damage-controlled buildings, direct displacement-based seismic design, optimal hysteretic energy dissipation systems, hysteretic dampers

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Authors: Idit Kalisher, Israel Luski

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One result of the growing dynamicity of labor markets in recent decades is a wider scope of employment mobility – i.e., transitions between employers, either within or between careers. Employment mobility decisions are primarily affected by the current employment status of the worker, which is reflected in wage and tenure. Using 34,328 observations from the National Longitudinal Survey of Youth 1979 (NLS79), which were derived from the USA population between 1990 and 2012, this paper aims to investigate the effects of wage and tenure over employment mobility choices, and additionally to examine the effects of other personal characteristics, individual labor market characteristics and macroeconomic factors. The estimation strategy was designed to address two challenges that arise from the combination of the model and the data: (a) endogeneity of the wage and the tenure in the choice equation; and (b) unobserved heterogeneity, as the data of this research is longitudinal. To address (a), estimation was performed using two-stage limited dependent variable procedure (2SLDV); and to address (b), the second stage was estimated using femlogit – an implementation of the multinomial logit model with fixed effects. Among workers who have experienced at least one turnover, the wage was found to have a main effect on career turnover likelihood of all workers, whereas the wage effect on job turnover likelihood was found to be dependent on individual characteristics. The wage was found to negatively affect the turnover likelihood and the effect was found to vary across wage level: high-wage workers were more affected compared to low-wage workers. Tenure was found to have a main positive effect on both turnover types’ likelihoods, though the effect was moderated by the wage. The findings also reveal that as their wage increases, women are more likely to turnover than men, and academically educated workers are more likely to turnover within careers. Minorities were found to be as likely as Caucasians to turnover post wage-increase, but less likely to turnover with each additional tenure year. The wage and the tenure effects were found to vary also between careers. The difference in attitude towards money, labor market opportunities and risk aversion could explain these findings. Additionally, the likelihood of a turnover was found to be affected by previous unemployment spells, age, and other labor market and personal characteristics. The results of this research could assist policymakers as well as business owners and employers. The former may be able to encourage women and older workers’ employment by considering the effects of gender and age on the probability of a turnover, and the latter may be able to assess their employees’ likelihood of a turnover by considering the effects of their personal characteristics.

Keywords: employment mobility, endogeneity, femlogit, turnover

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Authors: C. Romero, R. Ortega, P. Sánchez-Camacho, P. Aguilar, V. Segur, J. Ruiz, G. Gutiérrez

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Introduction: Breast cancer is a leading cause of death in CLM. (2.8% of all deaths in women and 13,8% of deaths from tumors in womens). It is the most tumor incidence in CLM region with 26.1% from all tumours, except nonmelanoma skin (Cancer Incidence in Five Continents, Volume X, IARC). Cancer registries are a good information source to estimate cancer incidence, however the data are usually available with a lag which makes difficult their use for health managers. By contrast, mortality and survival statistics have less delay. In order to serve for resource planning and responding to this problem, a method is presented to estimate the incidence of mortality and survival data. Objectives: To estimate the incidence of breast cancer by age group in CLM in the period 1991-2013. Comparing the data obtained from the model with current incidence data. Sources: Annual number of women by single ages (National Statistics Institute). Annual number of deaths by all causes and breast cancer. (Mortality Registry CLM). The Breast cancer relative survival probability. (EUROCARE, Spanish registries data). Methods: A Weibull Parametric survival model from EUROCARE data is obtained. From the model of survival, the population and population data, Mortality and Incidence Analysis MODel (MIAMOD) regression model is obtained to estimate the incidence of cancer by age (1991-2013). Results: The resulting model is: Ix,t = Logit [const + age1*x + age2*x2 + coh1*(t – x) + coh2*(t-x)2] Where: Ix,t is the incidence at age x in the period (year) t; the value of the parameter estimates is: const (constant term in the model) = -7.03; age1 = 3.31; age2 = -1.10; coh1 = 0.61 and coh2 = -0.12. It is estimated that in 1991 were diagnosed in CLM 662 cases of breast cancer (81.51 per 100,000 women). An estimated 1,152 cases (112.41 per 100,000 women) were diagnosed in 2013, representing an increase of 40.7% in gross incidence rate (1.9% per year). The annual average increases in incidence by age were: 2.07% in women aged 25-44 years, 1.01% (45-54 years), 1.11% (55-64 years) and 1.24% (65-74 years). Cancer registries in Spain that send data to IARC declared 2003-2007 the average annual incidence rate of 98.6 cases per 100,000 women. Our model can obtain an incidence of 100.7 cases per 100,000 women. Conclusions: A sharp and steady increase in the incidence of breast cancer in the period 1991-2013 is observed. The increase was seen in all age groups considered, although it seems more pronounced in young women (25-44 years). With this method you can get a good estimation of the incidence.

Keywords: breast cancer, incidence, cancer registries, castilla-la mancha

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Authors: Aliaa M. Issa, Mahmoud N. ElRouby, Sahar A. S. Ahmad, Mahmoud M. El-Merzabani

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Sider honey is a type of honey produced by bees feeding on the nectar of Sider tree, Ziziphus spina-christi (L) Desf . Honey is an effective agent for preventing, inhibiting and treating the growth of human and animal cancer cell lines in vitro and in vivo. The aim of the present study was to evaluate the impact of different dilutions from crude Sider honey and different duration times of exposure on the growth of six tumor cell lines (human cervical cancer cell line, HeLa; human hepatocellular carcinoma cell line, HepG-2; human larynx carcinoma cell line, Hep-2; brain tumor cell line, U251) as well as one animal cancerous cell line (Ehrlich ascites carcinoma cells line, EAC) and one normal cell line, Homo sapiens, human, (WISH) CCL-25. Different concentrations and treatment durations with Sider honey were tested on the growth of several cancer cell lines types. Histopathological changes in the tumor masses, animal survival, apoptosis and necrosis of the used cancer cell lines (using flow cytometry) were evaluated. Sider honey was administers either to the tumor mass itself by intratumoral injection or via drinking water. One-way ANOVA test was used for the analysis of (the means + standard error) of the optical density obtained from the Elisa reader and flow cytometry. The study revealed that different concentrations of Sider honey affected the growth patterns of all the studied cancer cell lines as well as their histopathological changes, and it depended on the cell line nature and the concentration of honey used. It is obvious that the relative animal survival percentage (bearing Ehrlich ascites carcinoma, EAC cells) was proportionally increased with the increase in the used honey concentrations. The study of apoptosis and necrosis using the flow cytometry technique emphasized the viability results. In conclusion, Sider honey was effective as antitumor agent, in the used concentrations.

Keywords: antitumor, honey, sider, tumor cell lines

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Authors: Komal Rathore, Aydin Sunol, Gita Iranipour, Luke Mulford

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Advanced wastewater treatment plants have complex biological kinetics, time variant influent flow rates and long processing times. Due to these factors, the modeling and operational control of advanced wastewater treatment plants become complicated. However, development of a robust model for advanced wastewater treatment plants has become necessary in order to increase the efficiency of the plants, reduce energy costs and meet the discharge limits set by the government. A dynamic model was designed using the Envirosim (Canada) platform software called BioWin for several wastewater treatment plants in Hillsborough County, Florida. Proper control strategies for various parameters such as mixed liquor suspended solids, recycle activated sludge and waste activated sludge were developed for models to match the plant performance. The models were tuned using both the influent and effluent data from the plant and their laboratories. The plant SCADA was used to predict the influent wastewater rates and concentration profiles as a function of time. The kinetic parameters were tuned based on sensitivity analysis and trial and error methods. The dynamic models were validated by using experimental data for influent and effluent parameters. The dissolved oxygen measurements were taken to validate the model by coupling them with Computational Fluid Dynamics (CFD) models. The Biowin models were able to exactly mimic the plant performance and predict effluent behavior for extended periods. The models are useful for plant engineers and operators as they can take decisions beforehand by predicting the plant performance with the use of BioWin models. One of the important findings from the model was the effects of recycle and wastage ratios on the mixed liquor suspended solids. The model was also useful in determining the significant kinetic parameters for biological wastewater treatment systems.

Keywords: BioWin, kinetic modeling, flowsheet simulation, dynamic modeling

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Authors: S. R. Dehghani, G. F. Naterer, Y. S. Muzychka

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Wave-impact sea spray creates many droplets which form a spray cloud traveling over marine objects same as marine vessels and offshore structures. In cold climates such as Arctic reigns, sea spray icing, which is ice accretion on cold substrates, is strongly dependent on the wave-impact sea spray. The rate of cooling of droplets affects the process of icing that can yield to dry or wet ice accretion. Trajectories of droplets determine the potential places for ice accretion. Combining two models of trajectories and heat transfer for droplets can predict the risk of ice accretion reasonably. The majority of the cooling of droplets is because of droplet evaporations. In this study, a combined model using trajectory and heat transfer evaluate the situation of a cloud of spray from the generation to impingement. The model uses some known geometry and initial information from the previous case studies. The 3D model is solved numerically using a standard numerical scheme. Droplets are generated in various size ranges from 7 mm to 0.07 mm which is a suggested range for sea spray icing. The initial temperature of droplets is considered to be the sea water temperature. Wind velocities are assumed same as that of the field observations. Evaluations are conducted using some important heading angles and wind velocities. The characteristic of size-velocity dependence is used to establish a relation between initial sizes and velocities of droplets. Time intervals are chosen properly to maintain a stable and fast numerical solution. A statistical process is conducted to evaluate the probability of expected occurrences. The medium size droplets can reach the highest heights. Very small and very large droplets are limited to lower heights. Results show that higher initial velocities create the most expanded cloud of spray. Wind velocities affect the extent of the spray cloud. The rate of droplet cooling at the start of spray formation is higher than the rest of the process. This is because of higher relative velocities and also higher temperature differences. The amount of water delivery and overall temperature for some sample surfaces over a marine vessel are calculated. Comparing results and some field observations show that the model works accurately. This model is suggested as a primary model for ice accretion on marine vessels.

Keywords: evaporation, sea spray, marine icing, numerical solution, trajectory

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Authors: Richard O’Riordan, Saritha Unnikrishnan

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Autonomous Vehicles (AVs) are incorporating an increasing number of ADAS features, including automated lane-keeping systems. In recent years, many research papers into lane detection algorithms have been published, varying from computer vision techniques to deep learning methods. The transition from lower levels of autonomy defined in the SAE framework and the progression to higher autonomy levels requires increasingly complex models and algorithms that must be highly reliable in their operation and functionality capacities. Furthermore, these algorithms have no room for error when operating at high levels of autonomy. Although the current research details existing computer vision and deep learning algorithms and their methodologies and individual results, the research also details challenges faced by the algorithms and the resources needed to operate, along with shortcomings experienced during their detection of lanes in certain weather and lighting conditions. This paper will explore these shortcomings and attempt to implement a lane detection algorithm that could be used to achieve improvements in AV lane detection systems. This paper uses a pre-trained LaneNet model to detect lane or non-lane pixels using binary segmentation as the base detection method using an existing dataset BDD100k followed by a custom dataset generated locally. The selected roads will be modern well-laid roads with up-to-date infrastructure and lane markings, while the second road network will be an older road with infrastructure and lane markings reflecting the road network's age. The performance of the proposed method will be evaluated on the custom dataset to compare its performance to the BDD100k dataset. In summary, this paper will use Transfer Learning to provide a fast and robust lane detection algorithm that can handle various road conditions and provide accurate lane detection.

Keywords: ADAS, autonomous vehicles, deep learning, LaneNet, lane detection

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Authors: Getaneh Berie Tarekegn

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A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Mirza Waseem Abbas, Syed Danish Raza

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For many countries agriculture is still the major force driving the economy and a critically important socioeconomic sector, despite exceptional industrial development across the globe. In countries like Pakistan, this sector is considered the backbone of the economy, and most of the economic decision making revolves around agricultural outputs and data. Timely and accurate facts and figures about this vital sector hold immense significance and have serious implications for the long-term development of the economy. Therefore, any significant improvements in the statistics and other forms of data regarding agriculture sector are considered important by all policymakers. This is especially true for decision making for the betterment of crops and the agriculture sector in general. Provincial and federal agricultural departments collect data for all cash and non-cash crops and the sector, in general, every year. Traditional data collection for such a large sector i.e. agriculture, being time-consuming, prone to human error and labor-intensive, is slowly but gradually being replaced by remote sensing techniques. For this study, remotely sensed data were used for change detection (machine learning, supervised & unsupervised classification) to assess the increase or decrease in area under agriculture over the last fifteen years due to urbanization. Detailed Landsat Images for the selected agricultural districts were acquired for the year 2000 and compared to images of the same area acquired for the year 2016. Observed differences validated through detailed analysis of the areas show that there was a considerable decrease in vegetation during the last fifteen years in four major agricultural districts of the Punjab province due to urbanization (housing societies).

Keywords: change detection, area estimation, machine learning, urbanization, remote sensing

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Authors: Justin P. Pool, Haruyo Yoshida

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This research focuses on the effects of creating an English as a foreign language curriculum that combines online learning and classroom teaching in a complementary manner. Through pre- and post-test results, teacher observation, and learner reflection, it will be shown that learners can benefit from online programs focusing on receptive skills if combined with a communicative classroom environment that encourages learners to develop their productive skills. Much research has lamented the fact that many modern mobile assisted language learning apps do not take advantage of the affordances of modern technology by focusing only on receptive skills rather than inviting learners to interact with one another and develop communities of practice. This research takes into account the realities of the state of such apps and focuses on how to best create a curriculum that complements apps which focus on receptive skills. The research involved 15 adult learners working for a business in Japan simultaneously engaging in 1) a commercial online English language learning application that focused on reading, listening, grammar, and vocabulary and 2) a 15-week class focused on communicative language teaching, presentation skills, and mitigation of error aversion tendencies. Participants of the study experienced large gains on a standardized test, increased motivation and willingness to communicate, and asserted that they felt more confident regarding English communication. Moreover, learners continued to study independently at higher rates after the study than they had before the onset of the program. This paper will include the details of the program, reveal the improvement in test scores, share learner reflections, and critically view current evaluation models for mobile assisted language learning applications.

Keywords: adult learners, communicative language teaching, mobile assisted language learning, motivation

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Authors: Mohamed Abdelkareem

Abstract:

Remote sensing data contributed on predicting the prospective areas of water resources. Integration of microwave and multispectral data along with climatic, hydrologic, and geological data has been used here. In this article, Sentinel-2, Landsat-8 Operational Land Imager (OLI), Shuttle Radar Topography Mission (SRTM), Tropical Rainfall Measuring Mission (TRMM), and Advanced Land Observing Satellite (ALOS) Phased Array Type L‐band Synthetic Aperture Radar (PALSAR) data were utilized to identify the geological, hydrologic and structural features of Wadi Asyuti which represents a defunct tributary of the Nile basin, in the eastern Sahara. The image transformation of Sentinel-2 and Landsat-8 data allowed characterizing the different varieties of rock units. Integration of microwave remotely-sensed data and GIS techniques provided information on physical characteristics of catchments and rainfall zones that are of a crucial role for mapping groundwater prospective zones. A fused Landsat-8 OLI and ALOS/PALSAR data improved the structural elements that difficult to reveal using optical data. Lineament extraction and interpretation indicated that the area is clearly shaped by the NE-SW graben that is cut by NW-SE trend. Such structures allowed the accumulation of thick sediments in the downstream area. Processing of recent OLI data acquired on March 15, 2014, verified the flood potential maps and offered the opportunity to extract the extent of the flooding zone of the recent flash flood event (March 9, 2014), as well as revealed infiltration characteristics. Several layers including geology, slope, topography, drainage density, lineament density, soil characteristics, rainfall, and morphometric characteristics were combined after assigning a weight for each using a GIS-based knowledge-driven approach. The results revealed that the predicted groundwater potential zones (GPZs) can be arranged into six distinctive groups, depending on their probability for groundwater, namely very low, low, moderate, high very, high, and excellent. Field and well data validated the delineated zones.

Keywords: GIS, remote sensing, groundwater, Egypt

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Authors: Osama Moustafa Zayed

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Aim of the work: To evaluate the effectiveness of venous glucose, levels of serum lactate and base deficit in polytraumatized patients as simple parameters to predict the mortality in these patients. Compared to the predictive value of Trauma and injury severity (TRISS) and Acute Physiology And Chronic Health Evaluation IV (APACHE IV). Introduction: Trauma is a serious global health problem, accounting for approximately one in 10 deaths worldwide. Trauma accounts for 5 million deaths per year. Prediction of mortality in trauma patients is an important part of trauma care. Several trauma scores have been devised to predict injury severity and risk of mortality. The trauma and injury severity score (TRISS) was most common used. Regardless of the accuracy of trauma scores, is based on an anatomical description of every injury and cannot be assigned to the patients until a full diagnostic procedure has been performed. So we hypothesized that alterations in admission glucose, lactate levels and base deficit would be an early and easy rapid predictor of mortality. Patient and Method: a comparative cross-sectional study. 282 Polytraumatized patients attended to the Emergency Department(ED) of the Suez Canal university Hospital constituted. The period from 1/1/2012 to 1/4/2013 was included. Results: We found that the best cut off value of TRISS probability of survival score for prediction of mortality among poly-traumatized patients is = 90, with 77% sensitivity and 89% specificity using area under the ROC curve (0.89) at (95%CI). APACHE IV demonstrated 67% sensitivity and 95% specificity at 95% CI at cut off point 99. The best cutoff value of Random Blood Sugar (RBS) for prediction of mortality was>140 mg/dl, with 89%, sensitivity, 49% specificity. The best cut off value of base deficit for prediction of mortality was less than -5.6 with 64% sensitivity, 93% specificity. The best cutoff point of lactate for prediction of mortality was > 2.6 mmol/L with 92%, sensitivity, 42% specificity. Conclusion: According to our results from all evaluated predictors of mortality (laboratory and scores) and mortality based on the estimated cutoff values using ROC curves analysis, the highest risk of mortality was found using a cutoff value of 90 in TRISS score while with laboratory parameters the highest risk of mortality was with serum lactate > 2.6 . Although that all of the three parameter are accurate in predicting mortality in poly-traumatized patients and near with each other, as in serum lactate the area under the curve 0.82, in BD 0.79 and 0.77 in RBS.

Keywords: APACHE IV, emergency department, polytraumatized patients, serum lactate

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Authors: Christian Skowronski, Andrew Shanholtzer, Brent Yelton, Muayad Almahariq, Daniel J. Krauss

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Prostate cancer has the third highest incidence rate and is the second leading cause of cancer death for men in the United States. Of the diagnostic tools available for intermediate-risk prostate cancer, magnetic resonance imaging (MRI) provides superior soft tissue delineation serving as a valuable tool for both diagnosis and treatment planning. Currently, there is minimal data regarding the practical utility of MRI for evaluation of intermediate-risk prostate cancer. As such, the National Comprehensive Cancer Network’s guidelines indicate MRI as optional in intermediate-risk prostate cancer evaluation. This project aims to elucidate whether MRI affects radiation treatment decisions for intermediate-risk prostate cancer. This was a retrospective study evaluating 210 patients with intermediate-risk prostate cancer, treated with definitive radiotherapy at our institution between 2019-2020. NCCN risk stratification criteria were used to define intermediate-risk prostate cancer. Patients were divided into two groups: those with pretreatment prostate MRI, and those without pretreatment prostate MRI. We compared the use of external beam radiotherapy, brachytherapy alone, brachytherapy boost, and androgen depravation therapy between the two groups. Inverse probability of treatment weighting was used to match the two groups for age, comorbidity index, American Urologic Association symptoms index, pretreatment PSA, grade group, and percent core involvement on prostate biopsy. Wilcoxon Rank Sum and Chi-squared tests were used to compare continuous and categorical variables. Of the patients who met the study’s eligibility criteria, 133 had a prostate MRI and 77 did not. Following propensity matching, there were no differences between baseline characteristics between the two groups. There were no statistically significant differences in treatments pursued between the two groups: 42% vs 47% were treated with brachytherapy alone, 40% vs 42% were treated with external beam radiotherapy alone, 18% vs 12% were treated with external beam radiotherapy with a brachytherapy boost, and 24% vs 17% received androgen deprivation therapy in the non-MRI and MRI groups, respectively. This analysis suggests that pretreatment MRI does not significantly impact radiation therapy or androgen deprivation therapy decisions in patients with intermediate-risk prostate cancer. Obtaining a pretreatment prostate MRI should be used judiciously and pursued only to answer a specific question, for which the answer is likely to impact treatment decision. Further follow up is needed to correlate MRI findings with their impacts on specific oncologic outcomes.

Keywords: magnetic resonance imaging, prostate cancer, definitive radiotherapy, gleason score 7

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Authors: Gulnara Galeeva, Yulia Kasperskaya

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The study deals with the relevant problem related to the formation of the efficient investment portfolio of an enterprise. The structure of the investment portfolio is connected to the degree of influence of intangible assets on the enterprise’s income. This determines the importance of research on the content of intangible assets. However, intangible assets studies do not take into consideration how the enterprise state can affect the content and the importance of intangible assets for the enterprise`s income. This affects accurateness of the calculations. In order to study this problem, the research was divided into several stages. In the first stage, intangible assets were classified based on their synergies as the underlying intangibles and the additional intangibles. In the second stage, this classification was applied. It showed that the lifecycle model and the theory of abrupt development of the enterprise, that are taken into account while designing investment projects, constitute limit cases of a more general theory of bifurcations. The research identified that the qualitative content of intangible assets significant depends on how close the enterprise is to being in crisis. In the third stage, the author developed and applied the Wide Pairwise Comparison Matrix method. This allowed to establish that using the ratio of the standard deviation to the mean value of the elements of the vector of priority of intangible assets makes it possible to estimate the probability of a full-blown crisis of the enterprise. The author has identified a criterion, which allows making fundamental decisions on investment feasibility. The study also developed an additional rapid method of assessing the enterprise overall status based on using the questionnaire survey with its Director. The questionnaire consists only of two questions. The research specifically focused on the fundamental role of stochastic resonance in the emergence of bifurcation (crisis) in the economic development of the enterprise. The synergetic approach made it possible to describe the mechanism of the crisis start in details and also to identify a range of universal ways of overcoming the crisis. It was outlined that the structure of intangible assets transforms into a more organized state with the strengthened synchronization of all processes as a result of the impact of the sporadic (white) noise. Obtained results offer managers and business owners a simple and an affordable method of investment portfolio optimization, which takes into account how close the enterprise is to a state of a full-blown crisis.

Keywords: analytic hierarchy process, bifurcation, investment portfolio, intangible assets, wide matrix

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Authors: Dedjo Yao Simon, Takahiro Saito, Norikazu Inuzuka, Ikuo Sugiyama

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Abidjan city (Republic of Ivory Coast) is an emerging megacity and an urban coastal area where the number of floods reported is on a rapid increase due to climate change and unplanned urbanization. However, comprehensive disaster mitigation plans, policies, and financial resources are still lacking as the population ignores the extent and location of the flood zones; making them unprepared to mitigate the damages. Considering the existing condition, this paper aims to discuss an approach for flood risk reduction in Cocody Commune through value capture strategy and flood risk communication. Using geospatial techniques and hydrological simulation, we start our study by delineating flood zones and depths under several return periods in the study area. Then, through a questionnaire a field survey is conducted in order to validate the flood maps, to estimate the flood risk and to collect some sample of the opinion of residents on how the flood risk information disclosure could affect the values of property located inside and outside the flood zones. The results indicate that the study area is highly vulnerable to 5-year floods and more, which can cause serious harm to human lives and to properties as demonstrated by the extent of the 5-year flood of 2014. Also, it is revealed there is a high probability that the values of property located within flood zones could decline, and the values of surrounding property in the safe area could increase when risk information disclosure commences. However in order to raise public awareness of flood disaster and to prevent future housing promotion in high-risk prospective areas, flood risk information should be disseminated through the establishment of an early warning system. In order to reduce the effect of risk information disclosure and to protect the values of property within the high-risk zone, we propose that property tax increments in flood free zones should be captured and be utilized for infrastructure development and to maintain the early warning system that will benefit people living in flood prone areas. Through this case study, it is shown that combination of value capture strategy and risk communication could be an effective tool to educate citizen and to invest in flood risk reduction in emerging countries.

Keywords: Cocody-Abidjan, flood, geospatial techniques, risk communication, value capture

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Authors: Shriya Chawla, Vinayak Malhotra

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Space exploration has noted an exponential rise in the past two decades. The world has started probing the alternatives for efficient and resourceful sustenance along with utilization of advanced technology viz., satellites on earth. Space propulsion forms the core of space exploration. Of all the issues encountered, space debris has increasingly threatened the space exploration and propulsion. The efforts have resulted in the presence of disastrous space debris fragments orbiting the earth at speeds up to several kilometres per hour. Debris are well known as a potential damage to the future missions with immense loss of resources, mankind, and huge amount of money is invested in active research on them. Appreciable work had been done in the past relating to active space debris-removal technologies such as harpoon, net, drag sail. The primary emphasis is laid on confined removal. In recently, remove debris spacecraft was used for servicing and capturing cargo ships. Airbus designed and planned the debris-catching net experiment, aboard the spacecraft. The spacecraft represents largest payload deployed from the space station. However, the magnitude of the issue suggests that active space debris-removal technologies, such as harpoons and nets, still would not be enough. Thus, necessitating the need for better and operative space debris removal system. Techniques based on diverting the path of debris or the spacecraft to avert damage have turned out minimal usage owing to limited predictions. Present work focuses on an active hybrid space debris removal system. The work is motivated by the need to have safer and efficient space missions. The specific objectives of the work are 1) to thoroughly analyse the existing and conventional debris removal techniques, their working, effectiveness and limitations under varying conditions, 2) to understand the role of key controlling parameters in coupled operation of debris capturing and removal. The system represents the utilization of the latest autonomous technology available with an adaptable structural design for operations under varying conditions. The design covers advantages of most of the existing technologies while removing the disadvantages. The system is likely to enhance the probability of effective space debris removal. At present, systematic theoretical study is being carried out to thoroughly observe the effects of pseudo-random debris occurrences and to originate an optimal design with much better features and control.

Keywords: space exploration, debris removal, space crafts, space accidents

Procedia PDF Downloads 154

Authors: Kazumasa Kawasaki, Isamu Tsuji, Hiroshi Gunbara

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A face gear drive is actually composed of a spur or helical pinion that is in mesh with a face gear and transfers power and motion between intersecting or skew axes. Due to the peculiarity of the face gear drive in shunt and confluence drive, it shows potential advantages in the application in the helicopter transmission. The advantages of such applications are the possibility of the split of the torque that appears to be significant where a pinion drives two face gears to provide an accurate division of power and motion. This mechanism greatly reduces the weight and cost compared to conventional design. Therefore, this has been led to revived interest and the face gear drive has been utilized in substitution for bevel and hypoid gears in limited cases. The face gear drive with a spur or a helical pinion is newly designed in order to determine an effective meshing area under the design parameters and specific design dimensions. The face gear has two unique dimensions which control the face width of the tooth, and the outside and inside diameters of the face gear. On the other hand, it is necessary to modify the tooth surfaces of face gear drive in order to avoid the influences of alignment errors on the tooth contact patterns in practical use. In this case, the pinion tooth surfaces are usually modified in the conventional method. However, it is hard to control the tooth contact pattern intentionally and adjust the position of the pinion axis in meshing of the gear pair. Therefore, a method of the modification of the tooth surfaces of the face gear is proposed. Moreover, based on tooth contact analysis, the tooth contact pattern and transmission errors of the designed face gear drive are analyzed, and the influences of alignment errors on the tooth contact patterns and transmission errors are investigated. These results showed that the tooth contact patterns and transmission errors were controllable and the face gear drive which is insensitive to alignment errors can be obtained.

Keywords: alignment error, face gear, gear design, helicopter transmission, tooth contact analysis

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Authors: Jeewantha Ranawaka, Gunawardane Kapila, Wijethunaga Mudiyanselage B. G. Jayathilake

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Introduction: Sweeping of the membranes is a fairly simple technique that may positively influence the shift from maintenance of pregnancy to the beginning of labor. Objective: To assess the effectiveness and acceptability of twice versus once-only membrane sweeping in uncomplicated primi gravid at 40 weeks of gestational age in a tertiary care hospital in Sri Lanka. Methods: A randomized controlled clinical trial was done in Ward 05 of Teaching Hospital, Kandy. The participants were primi-gravida with a singleton live fetus who was at 40 weeks of gestation with intact fetal membranes and with a Modified Bishop’s score <5. After randomization both groups received membrane sweeping at 40 weeks of gestation and the experimental group received membrane sweeping after 48 hours (40+2 days). The modified Bishop Score was assessed at 40+5 days. In two groups who did not go into natural labor at 40+5 days were managed according to the ward policy of cervical ripening and with labor induction at 40+5 days. Two different methods were used to assess discomfort and pain. Patient acceptability was assessed using recommendation to another patient and acceptance during next pregnancy. Perinatal, maternal and labour outcomes were assessed. Results: A change of the Bishops score was 67.3% (n= 31 of 46) in experimental group whereas in control group it was 57.5% (n= 38 of 66). (p = 0.21, OR-1.52, CI = 0.6 -3.34). Mean (SD) of Modified Bishop score was 6.36 (1.94) in experimental group and 6.03 (.84) in control group (p = 0.354). The probability of having the spontaneous onset of labour in experimental group was 61.6% (n=74 of 120) whereas in control group it was 45% (n= 54 of 120) (p=0.01, OR-1.966, CI = 1.17 – 3.28 NNT = 5.99). Recommending the method to another among experimental group was 75% (n= 90 of 120) whereas in control group it was 79.2% (n= 95 of 120) (p= 0.443). Accepting membrane Sweeping for subsequent pregnancy among experimental was 72.5% (n=87 of 120) whereas in control group was 72.5% (n=87 of 120) (p= 1.00) Need of formal induction of labour at 40+ 5 days in experimental group was 38.4% (n=46 of 120) whereas in control group was 61.6% (n=66 of 120) (p=0.01, OR=0.5, CI= 0.3 – 0.8, NNT=6). Neonatal outcome, labour outcome such as Cesarean -section rate, need for augmentation and maternal complications such as fever, Premature rupture of membrane, bleeding were comparable in two groups. Conclusions and Recommendations: It can be concluded that twice sweeping of membrane was effective to reduce the need of formal induction of labour and increase the chances of having spontaneous onset of labour (SOL) at 40+5 days without increasing maternal or fetal morbidity. Acceptability of twice sweeping is not different from sweeping once. Hence we recommend consideration of multiple membranes sweeping as first line for women at 40 weeks of gestation.

Keywords: acceptability, induction, labour, membrane sweeping

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Authors: Min L. Stewart, Patrick Johnston

Abstract:

Predictive coding has been proposed as a general explanatory framework for understanding the neural mechanisms of perception. As such, an underweighting of perceptual priors has been hypothesised to underpin a range of differences in inferential and sensory processing in autism spectrum disorders. However, empirical evidence to support this has not been well established. The present study uses an electroencephalography paradigm involving changes of facial identity and person category (actors etc.) to explore how levels of autistic traits (AT) affect predictive coding at multiple stages in the visual processing hierarchy. The study uses a rapid serial presentation of faces, with hierarchically structured sequences involving both periodic and aperiodic repetitions of different stimulus attributes (i.e., person identity and person category) in order to induce contextual expectations relating to these attributes. It investigates two main predictions: (1) significantly larger and late neural responses to change of expected visual sequences in high-relative to low-AT, and (2) significantly reduced neural responses to violations of contextually induced expectation in high- relative to low-AT. Preliminary frequency analysis data comparing high and low-AT show greater and later event-related-potentials (ERPs) in occipitotemporal areas and prefrontal areas in high-AT than in low-AT for periodic changes of facial identity and person category but smaller ERPs over the same areas in response to aperiodic changes of identity and category. The research advances our understanding of how abnormalities in predictive coding might underpin aberrant perceptual experience in autism spectrum. This is the first stage of a research project that will inform clinical practitioners in developing better diagnostic tests and interventions for people with autism.

Keywords: hierarchical visual processing, face processing, perceptual hierarchy, prediction error, predictive coding

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Authors: Marius Gavrilescu, Sabina-Adriana Floria, Florin Leon, Silvia Curteanu, Costel Anton

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Natural sciences provide a wide range of experimental data whose related problems require study and modeling beyond the capabilities of conventional methodologies. Such problems have solution spaces whose complexity and high dimensionality require correspondingly complex regression methods for proper characterization. In this context, we propose an optimization method which consists in a hybrid dual optimizer setup: a global optimizer based on a modified variant of the popular Imperialist Competitive Algorithm (ICA), and a local optimizer based on a gradient descent approach. The ICA is modified such that intermediate solution populations are more quickly and efficiently pruned of low-fitness individuals by appropriately altering the assimilation, revolution and competition phases, which, combined with an initialization strategy based on low-discrepancy sampling, allows for a more effective exploration of the corresponding solution space. Subsequently, gradient-based optimization is used locally to seek the optimal solution in the neighborhoods of the solutions found through the modified ICA. We use this combined approach to find the optimal configuration and weights of a fully-connected neural network, resulting in regression models used to characterize the process of obtained bricks using silicon-based materials. Installations in the raw ceramics industry, i.e., bricks, are characterized by significant energy consumption and large quantities of emissions. Thus, the purpose of our approach is to determine by simulation the working conditions, including the manufacturing mix recipe with the addition of different materials, to minimize the emissions represented by CO and CH4. Our approach determines regression models which perform significantly better than those found using the traditional ICA for the aforementioned problem, resulting in better convergence and a substantially lower error.

Keywords: optimization, biologically inspired algorithm, regression models, bricks, emissions

Procedia PDF Downloads 76