Search results for: mixed effects models

Authors: Baba John, Abdullahi Mohammed

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The aim of this research is to compare the analysis of biodegradation on polythene and plastics buried in fadama soil amended with Organic and Inorganic fertilizer. Physico- chemical properties of the samples were determined. Bacteria and Fungi implicated in the biodegradation were identified and enumerated. Physico- chemical properties before the analysis indicated pH range of the samples from 4.28 – 5.80 , While the percentage Organic carbon and Organic matter was highest in cow dung samples with 3.89% and 6.69% respectively. The total Nitrogen percentage was recorded to be highest in Chicken dropping (0.68), while the availability of Phosphorus (P), Sodium (Na), Pottasium (K), and Magnessium (mg) was recorded to be highest in F – soil (Control), with values to be 37ppm, 1.63 Cmolkg-1, 0.35 Cmolkg-1 and 1.18 Cmolkg-1 respectively, except for calcium which was recorded to be highest in Cow dung (5.80 Cmolkg-1). However, physico – chemical properties of the samples after analysis indicated pH range of 4.6 – 5.80, Percentage Organic carbon and Organic matter was highest in Fadama soil mixed with fertilizer, having 0.7% and 1.2% respectively. Total Percentage Nitrogen content was found to be highest (0.56) in Fadama soil mixed with poultry dropping. Availability of Sodium (Na), Pottasium (K), and Calcium (Ca) was recorded to be highest in Fadama Soil mixed with Cow dung with values to be 0.64 Cmolkg-1, 2.07 Cmolkg-1 and 3.36 Cmolkg-1 respectively. The percentage weight loss of polythene and plastic bags after nine months in fadama soil mixed with poultry dropping was 11.9% for polythene and 6.0% for plastics. Weight loss in fadama soil mixed with cow dung was 18.1% for polythene and 4.7% for plastics. Weight loss of polythene and plastic in fadama soil mixed with fertilizer (NPK) was 7.4% for polythene and 3.3% for plastics. While, the percentage weight loss of polythene and plastics after nine months of burial in fadama soil (control) was 3.5% and 0.0% respectively. The bacteria species isolated from Fadama soil, organic and inorganic fertilizers before amendments include: S. aureus, Micrococcus sp, Streptococcus. pyogenes, Psuedomonas aeruginosa Bacillus subtilis and Bacillus cereus. The fungi species include: Aspergillus niger, Aspergillus fumigatus, Aspergillus flavus, Fusarium sp, Mucor sp Penicillium sp and Candida sp. The bacteria species isolated and characterized after nine months of seeding include: S. aureus, Micrococcus sp, S. pyogenes, P. aeruginosa and B. subtilis. The fungi species are: A. niger A. flavus, A. fumigatus, Mucor sp, Penicillium sp and Fusarium sp. The result of this study indicated that plastic materials can be degraded in the fadama soil irrespective of whether the soil is amended or not. The Period of composting also has a significant impact on the rate at which polythene and plastics are degraded.

Keywords: Fadama, fertilizer, plastic and polythene, biodegradation

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Authors: Pattra Pukdeekiat, Siriluk Ruangrungrote

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This work was originated to precisely determine direct solar radiation by using atmospheric physics models since the accurate prediction of solar radiation is necessary and useful for solar energy applications including atmospheric research. The possible models and techniques for a calculation of regional direct solar radiation were challenging and compulsory for the case of unavailable instrumental measurement. The investigation was mathematically governed by six astronomical parameters i.e. declination (δ), hour angle (ω), solar time, solar zenith angle (θz), extraterrestrial radiation (Iso) and eccentricity (E0) along with two atmospheric parameters i.e. air mass (mr) and dew point temperature at Bangna meteorological station (13.67° N, 100.61° E) in Bangkok, Thailand. Analyses of five models of solar radiation determination with the assumption of clear sky were applied accompanied by three statistical tests: Mean Bias Difference (MBD), Root Mean Square Difference (RMSD) and Coefficient of determination (R2) in order to validate the accuracy of obtainable results. The calculated direct solar radiation was in a range of 491-505 Watt/m2 with relative percentage error 8.41% for winter and 532-540 Watt/m2 with relative percentage error 4.89% for summer 2014. Additionally, dataset of seven continuous days, representing both seasons were considered with the MBD, RMSD and R2 of -0.08, 0.25, 0.86 and -0.14, 0.35, 3.29, respectively, which belong to Kumar model for winter and CSR model for summer. In summary, the determination of direct solar radiation based on atmospheric models and empirical equations could advantageously provide immediate and reliable values of the solar components for any site in the region without a constraint of actual measurement.

Keywords: atmospheric physics models, astronomical parameters, atmospheric parameters, clear sky condition

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Authors: Makram Ben Jeddou

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The ABC classification is widely used by managers for inventory control. The classical ABC classification is based on the Pareto principle and according to the criterion of the annual use value only. Single criterion classification is often insufficient for a closely inventory control. Multi-criteria inventory classification models have been proposed by researchers in order to take into account other important criteria. From these models, we will consider the ZF model in order to make a sensitive analysis on the composite score calculated for each item. In fact, this score based on a normalized average between a good and a bad optimized index can affect the ABC items classification. We will then focus on the weights assigned to each index and propose a classification compromise.

Keywords: ABC classification, multi criteria inventory classification models, ZF-model

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Authors: Vinayak Bassi, Rajpreet Singh

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Over the years, with the emergence of sophisticated computers and algorithms, finance has been quantified using computational prowess. Asset valuation has been one of the key components of quantitative finance. In fact, it has become one of the embryonic steps in determining risk related to a portfolio, the main goal of quantitative finance. This study comprises a drawing comparison between valuation output generated by two stochastic dynamic models, namely Black-Scholes and Dupire’s bi-dimensionality model. Both of these models are formulated for computing the valuation function for a portfolio of European options using Monte Carlo simulation methods. Although Monte Carlo algorithms have a slower convergence rate than calculus-based simulation techniques (like FDM), they work quite effectively over high-dimensional dynamic models. A fidelity gap is analyzed between the static (historical) and stochastic inputs for a sample portfolio of underlying assets. In order to enhance the performance efficiency of the model, the study emphasized the use of variable reduction methods and customizing random number generators to implement parallelization. An attempt has been made to further implement the Dupire’s model on a GPU to achieve higher computational performance. Furthermore, ideas have been discussed around the performance enhancement and bottleneck identification related to the implementation of options-pricing models on GPUs.

Keywords: monte carlo, stochastic models, computational finance, parallel programming, scientific computing

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Authors: Nasim Eslamirad, Mahdi Rasoulinezhad, Francesco De Luca, Sadok Ben Yahia, Kimmo Sakari Lylykangas, Francesco Pilla

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With the ongoing urbanization, cities face increasing environmental challenges impacting human well-being. To tackle these issues, data-driven approaches in urban analysis have gained prominence, leveraging urban data to promote sustainability. Integrating Machine Learning techniques enables researchers to analyze and predict complex environmental phenomena like Urban Heat Island occurrences in urban areas. This paper demonstrates the implementation of data-driven approach and interpretable Machine Learning algorithms with interpretability techniques to conduct comprehensive data analyses for sustainable urban design. The developed framework and algorithms are demonstrated for Tallinn, Estonia to develop sustainable urban strategies to mitigate urban heat waves. Geospatial data, preprocessed and labeled with UHI levels, are used to train various ML models, with Logistic Regression emerging as the best-performing model based on evaluation metrics to derive a mathematical equation representing the area with UHI or without UHI effects, providing insights into UHI occurrences based on buildings and urban features. The derived formula highlights the importance of building volume, height, area, and shape length to create an urban environment with UHI impact. The data-driven approach and derived equation inform mitigation strategies and sustainable urban development in Tallinn and offer valuable guidance for other locations with varying climates.

Keywords: data-driven approach, machine learning transparent models, interpretable machine learning models, urban heat island effect

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Authors: Kritika Kulhari, Aparna Sahu

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Test anxiety results from concerns about failure in examinations or evaluative situations. Attentional biases are known to pronounce the symptomatic expression of test anxiety. In recent times, the inattentional blindness (IB) paradigm has shown promise as an attention bias modification treatment (ABMT) for anxiety by overcoming practice and expectancy effects which preexisting paradigms fail to counter. The IB paradigm assesses the inability of an individual to attend to a stimulus that appears suddenly while indulging in a perceptual discrimination task. The present study incorporated an IB task with three critical items (book, face, and triangle) appearing randomly in the perceptual discrimination task. Attentional biases were assessed as detection and identification of the critical item. The sample (N = 50) consisted of low test anxiety (LTA) and high test anxiety (HTA) groups based on the reactions to tests scale scores. Test threat manipulation was done with pre- and post-test assessment of test anxiety using the State Test Anxiety Inventory. A mixed factorial design with gender, test anxiety, presence or absence of test threat, and critical items was conducted to assess their effects on attentional biases. Results showed only a significant main effect for test anxiety on detection with higher accuracy of detection of the critical item for the LTA group. The study presents promising results in the realm of ABMT for test anxiety.

Keywords: attentional bias, attentional bias modification treatment, inattentional blindness, test anxiety

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Authors: Imene Skhakhfa, Lahbaci Ouerdachi

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To ensure the overall coherence of simulated results, it is necessary to develop a robust validation process. In many applications, it is no longer content to calibrate and validate the model only in relation to the hydro graph measured at the outlet, but we try to better simulate the functioning of the watershed in space. Therefore the timing also performs compared to other variables such as water level measurements in intermediate stations or groundwater levels. As part of this work, we limit ourselves to modeling flood of short duration for which the process of evapotranspiration is negligible. The main parameters to identify the models are related to the method of unit hydro graph (HU). Three different models were tested: SNYDER, CLARK and SCS. These models differ in their mathematical structure and parameters to be calibrated while hydrological data are the same, the initial water content and precipitation. The models are compared on the basis of their performance in terms six objective criteria, three global criteria and three criteria representing volume, peak flow, and the mean square error. The first type of criteria gives more weight to strong events whereas the second considers all events to be of equal weight. The results show that the calibrated parameter values are dependent and also highlight the problems associated with the simulation of low flow events and intermittent precipitation.

Keywords: model calibration, intensity, runoff, hydrograph

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Authors: Wilfred Elliam

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Background: Pain is a prevalent and debilitating symptom among breast cancer patients, impacting their quality of life and overall well-being. The experience of pain in this population is multifaceted, influenced by a combination of disease-related factors, treatment side effects, and individual characteristics. Despite advancements in cancer treatment and pain management, many breast cancer patients continue to suffer from chronic pain, which can persist long after the completion of treatment. Understanding the progression of pain in breast cancer patients over time and identifying its correlates is crucial for effective pain management and supportive care strategies. The purpose of this research is to understand the patterns and progression of pain experienced by breast cancer survivors over time. Methods: Data were collected from breast cancer patients at Hartford Hospital at four time points: baseline, 3, 6 and 12 weeks. Key variables measured include pain, body mass index (BMI), fatigue, musculoskeletal pain, sleep disturbance, and demographic variables (age, employment status, cancer stage, and ethnicity). Binomial generalized linear mixed models were used to examine changes in pain and symptoms over time. Results: A total of 100 breast cancer patients aged  18 years old were included in the analysis. We found that the effect of time on pain (p = 0.024), musculoskeletal pain (p= <0.001), fatigue (p= <0.001), and sleep disturbance (p-value = 0.013) were statistically significant with pain progression in breast cancer patients. Patients using aromatase inhibitors have worse fatigue (<0.05) and musculoskeletal pain (<0.001) compared to patients with Tamoxifen. Patients who are obese (<0.001) and overweight (<0.001) are more likely to report pain compared to patients with normal weight. Conclusion: This study revealed the complex interplay between various factors such as time, pain, sleep disturbance in breast cancer patient. Specifically, pain, musculoskeletal pain, sleep disturbance, fatigue exhibited significant changes across the measured time points, indicating a dynamic pain progression in these patients. The findings provide a foundation for future research and targeted interventions aimed at improving pain in breast cancer patient outcomes.

Keywords: breast cancer, chronic pain, pain management, quality of life

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Authors: Katarzyna Wyka, Dana Sylvan, JoAnn Difede

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Background and significance: The following disaster, population-based screening programs are routinely established to assess physical and psychological consequences of exposure. These data sets are highly skewed as only a small percentage of trauma-exposed individuals develop health issues. Commonly used statistical methodology in post-disaster mental health generally involves population-averaged models. Such models aim to capture the overall response to the disaster and its aftermath; however, they may not be sensitive enough to accommodate population heterogeneity in symptomatology, such as post-traumatic stress or depressive symptoms. Methods: We use an archival longitudinal data set from Weill-Cornell 9/11 Mental Health Screening Program established following the World Trade Center (WTC) terrorist attacks in New York in 2001. Participants are rescue and recovery workers who participated in the site cleanup and restoration (n=2960). The main outcome is the post-traumatic stress symptoms (PTSD) severity score assessed via clinician interviews (CAPS). For a detailed understanding of response to the disaster and its aftermath, we are adapting quantile regression methodology with particular focus on predictors of extreme distress and resilience to trauma. Results: The response variable was defined as the quantile of the CAPS score for each individual under two different scenarios specifying the unconditional quantiles based on: 1) clinically meaningful CAPS cutoff values and 2) CAPS distribution in the population. We present graphical summaries of the differential effects. For instance, we found that the effect of the WTC exposures, namely seeing bodies and feeling that life was in danger during rescue/recovery work was associated with very high PTSD symptoms. A similar effect was apparent in individuals with prior psychiatric history. Differential effects were also present for age and education level of the individuals. Conclusion: We evaluate the utility of quantile regression in disaster research in contrast to the commonly used population-averaged models. We focused on assessing the distribution of risk factors for post-traumatic stress symptoms across quantiles. This innovative approach provides a comprehensive understanding of the relationship between dependent and independent variables and could be used for developing tailored training programs and response plans for different vulnerability groups.

Keywords: disaster workers, post traumatic stress, PTSD, quantile regression

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Authors: Abdunaser Abduelmula, Maria Luisa M. Bastos, José A. Gonçalves

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The modeling of the earth's surface and evaluation of urban environment, with 3D models, is an important research topic. New stereo capabilities of high-resolution optical satellites images, such as the tri-stereo mode of Pleiades, combined with new image matching algorithms, are now available and can be applied in urban area analysis. In addition, photogrammetry software packages gained new, more efficient matching algorithms, such as SGM, as well as improved filters to deal with shadow areas, can achieve denser and more precise results. This paper describes a comparison between 3D data extracted from tri-stereo and dual stereo satellite images, combined with pixel based matching and Wallis filter. The aim was to improve the accuracy of 3D models especially in urban areas, in order to assess if satellite images are appropriate for a rapid evaluation of urban environments. The results showed that 3D models achieved by Pleiades tri-stereo outperformed, both in terms of accuracy and detail, the result obtained from a Geo-eye pair. The assessment was made with reference digital surface models derived from high-resolution aerial photography. This could mean that tri-stereo images can be successfully used for the proposed urban change analyses.

Keywords: 3D models, environment, matching, pleiades

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Authors: Maruf Ariyo Raheem, Patrick Oseloka Ezepue

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This research aimed at determining most appropriate heteroskedastic model to predicting volatility of 10 major Nigerian banks: Access, United Bank for Africa (UBA), Guaranty Trust, Skye, Diamond, Fidelity, Sterling, Union, ETI and Zenith banks using daily closing stock prices of each of the banks from 2004 to 2014. The models employed include ARCH (1), GARCH (1, 1), EGARCH (1, 1) and TARCH (1, 1). The results show that all the banks returns are highly leptokurtic, significantly skewed and thus non-normal across the four periods except for Fidelity bank during financial crises; findings similar to those of other global markets. There is also strong evidence for the presence of heteroscedasticity, and that volatility persistence during crisis is higher than before the crisis across the 10 banks, with that of UBA taking the lead, about 11 times higher during the crisis. Findings further revealed that Asymmetric GARCH models became dominant especially during financial crises and post crises when the second reforms were introduced into the banking industry by the Central Bank of Nigeria (CBN). Generally, one could say that Nigerian banks returns are volatility persistent during and after the crises, and characterised by leverage effects of negative and positive shocks during these periods

Keywords: global financial crisis, leverage effect, persistence, volatility clustering

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Authors: Gonzalo García-Reyes

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Conformastat spherically symmetric exact solutions of Einstein's field equations representing matter distributions made of fluid both perfect and anisotropic from given solutions of Poisson's equation of Newtonian gravity are investigated. The approach is used in the construction of new relativistic models of thick spherical shells and three-component models of galaxies (bulge, disk, and dark matter halo), writing, in this case, the metric in cylindrical coordinates. In addition, the circular motion of test particles (rotation curves) along geodesics on the equatorial plane of matter configurations and the stability of the orbits against radial perturbations are studied. The models constructed satisfy all the energy conditions.

Keywords: general relativity, exact solutions, spherical symmetry, galaxy, kinematics and dynamics, dark matter

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Authors: Subhan Ahmad, Pradeep Bhargava, Ajay Chourasia

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Shear failure in reinforced concrete beams without shear reinforcement leads to loss of property and life since a very little or no warning occurs before failure as in case of flexural failure. Shear strength of reinforced concrete beams decreases as its depth increases. This phenomenon is generally called as the size effect. In this paper, a comparative analysis is performed to estimate the performance of shear strength models in capturing the size effect of reinforced concrete beams made with conventional concrete, self-compacting concrete, and recycled aggregate concrete. Four shear strength models that account for the size effect in shear are selected from the literature and applied on the datasets of slender reinforced concrete beams. Beams prepared with conventional concrete, self-compacting concrete, and recycled aggregate concrete are considered for the analysis. Results showed that all the four models captured the size effect in shear effectively and produced conservative estimates of the shear strength for beams made with normal strength conventional concrete. These models yielded unconservative estimates for high strength conventional concrete beams with larger effective depths ( > 450 mm). Model of Bazant and Kim (1984) captured the size effect precisely and produced conservative estimates of shear strength of self-compacting concrete beams at all the effective depths. Also, shear strength models considered in this study produced unconservative estimates of shear strength for recycled aggregate concrete beams at all effective depths.

Keywords: reinforced concrete beams; shear strength; prediction models; size effect

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Authors: Zhang Dongqing

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Concern for students' mental health and the application of intelligent advanced technologies are driving changes in teaching models. The traditional teacher-centered classroom is beginning to transform into a student-centered smart interactive learning environment. Nowadays, smart classrooms are compatible with constructivist learning. This theory emphasizes the role of teachers in the teaching process as helpers and facilitators of knowledge construction, and students learn by interacting with them. The spatial design of classrooms is closely related to the teaching model and should also be developed in the direction of smart classroom design. The goal is to explore the impact of smart classroom layout on student-centered teaching environment and teacher-student interaction under the guidance of constructivist learning theory, by combining the design process and feedback analysis of the smart transformation project on the campus of Tongji University in Shanghai. During the research process, the theoretical basis of constructivist learning was consolidated through literature research and case analysis. The integration and visual field analysis of the traditional and transformed indoor floor plans were conducted using space syntax tools. Finally, questionnaire surveys and interviews were used to collect data. The main conclusions are as followed: flexible spatial layouts can promote students' learning effects and mental health; the interactivity of smart classroom layouts is different and needs to be combined with different teaching models; the public areas of teaching buildings can also improve the interactive learning atmosphere by adding discussion space. This article provides a data-based research basis for improving students' learning effects and mental health, and provides a reference for future smart classroom design.

Keywords: spatial layout, smart classroom, space syntax, renovation, educational environment

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Authors: Hamidreza Ghazvinian, Khosro Ghazvinian, Touba Khodaiean

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The importance of evaporation estimation in water resources and agricultural studies is undeniable. Pan evaporation are used as an indicator to determine the evaporation of lakes and reservoirs around the world due to the ease of interpreting its data. In this research, intelligent models were investigated in estimating pan evaporation on a daily basis. Shahroud and Mayamey were considered as the studied cities. These two cities are located in Semnan province in Iran. The mentioned cities have dry weather conditions that are susceptible to high evaporation potential. Meteorological data of 11 years of synoptic stations of Shahrood and Mayamey cities were used. The intelligent models used in this study are Artificial Neural Network (ANN), Least Squares Support Vector Machine (LSSVM), and M5 tree models. Meteorological parameters of minimum and maximum air temperature (Tmax, Tmin), wind speed (WS), sunshine hours (SH), air pressure (PA), relative humidity (RH) as selected input data and evaporation data from pan (EP) to The output data was considered. 70% of data is used at the education level, and 30 % of the data is used at the test level. Models used with explanation coefficient evaluation (R2) Root of Mean Squares Error (RMSE) and Mean Absolute Error (MAE). The results for the two Shahroud and Mayamey stations showed that the above three models' operations are rather appropriate.

Keywords: pan evaporation, intelligent methods, shahroud, mayamey

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Authors: Wendu Pang, Yaxin Luo, Junhong Li, Yu Zhao, Danni Cheng, Yufang Rao, Minzi Mao, Ke Qiu, Yijun Dong, Fei Chen, Jun Liu, Jian Zou, Haiyang Wang, Wei Xu, Jianjun Ren

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We aimed to compare the prognostic prediction value of positive lymph node number (PLNN) to the American Joint Committee on Cancer (AJCC) tumor, lymph node, and metastasis (TNM) staging system for patients with hypopharyngeal squamous cell carcinoma (HPSCC). A total of 826 patients with HPSCC from the Surveillance, Epidemiology, and End Results database (2004–2015) were identified and split into two independent cohorts: training (n=461) and validation (n=365). Univariate and multivariate Cox regression analyses were used to evaluate the prognostic effects of PLNN in patients with HPSCC. We further applied six Cox regression models to compare the survival predictive values of the PLNN and AJCC TNM staging system. PLNN showed a significant association with overall survival (OS) and cancer-specific survival (CSS) (P < 0.001) in both univariate and multivariable analyses, and was divided into three groups (PLNN 0, PLNN 1-5, and PLNN>5). In the training cohort, multivariate analysis revealed that the increased PLNN of HPSCC gave rise to significantly poor OS and CSS after adjusting for age, sex, tumor size, and cancer stage; this trend was also verified by the validation cohort. Additionally, the survival model incorporating a composite of PLNN and TNM classification (C-index, 0.705, 0.734) performed better than the PLNN and AJCC TNM models. PLNN can serve as a powerful survival predictor for patients with HPSCC and is a surrogate supplement for cancer staging systems.

Keywords: hypopharyngeal squamous cell carcinoma, positive lymph nodes number, prognosis, prediction models, survival predictive values

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Authors: Chloe Shu-Hua Yeh

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Effects of videogame play on players cognitive abilities is a growing research field in the recent decades, however, little is known about how ‘out-of-school’ use of videogame influences creativity. This interdisciplinary research explores the cognitive and emotional effects of two different types of online videogames (an action videogame and a non-action videogame) on subsequent creativity performances using a within-participant design study with 36 participants. Results showed that after playing the action game participants performed higher originality, elaboration and flexibility than after playing the causal game. The results explored effects of emotional states elicited during playing the games suggesting that arousal may be a significant emotional factor which influence subsequent creativity performance. The cognitive and emotional effects of videogame were discussed followed with implications for emotion-creativity-videogame play research, game designers, educational practitioners and parents.

Keywords: attentional breadth, creativity, emotion, videogame play

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Authors: Awol Seid Ebrie

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HIV results as an incurable disease, AIDS. After a person is infected with virus, the virus gradually destroys all the infection fighting cells called CD4 cells and makes the individual susceptible to opportunistic infections which cause severe or fatal health problems. Several studies show that the CD4 cells count is the most determinant indicator of the effectiveness of the treatment or progression of the disease. The objective of this paper is to investigate the progression of the disease over time among patient under HAART treatment. Two main approaches of the generalized multilevel ordinal models; namely the proportional odds model and the nonproportional odds model have been applied to the HAART data. Also, the multilevel part of both models includes random intercepts and random coefficients. In general, four models are explored in the analysis and then the models are compared using the deviance information criteria. Of these models, the random coefficients nonproportional odds model is selected as the best model for the HAART data used as it has the smallest DIC value. The selected model shows that the progression of the disease increases as the time under the treatment increases. In addition, it reveals that gender, baseline clinical stage and functional status of the patient have a significant association with the progression of the disease.

Keywords: nonproportional odds model, proportional odds model, random coefficients model, random intercepts model

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Authors: Ying Han, Yuanxiang Chen, Yongtao Huang, Jia Fu, Kaile Li, Shangjing Lin, Jianguo Yu

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In long-haul and high-speed optical transmission system, the orthogonal frequency division multiplexing (OFDM) signal suffers various linear and non-linear impairments. In recent years, researchers have proposed compensation schemes for specific impairment, and the effects are remarkable. However, different impairment compensation algorithms have caused an increase in transmission delay. With the widespread application of deep neural networks (DNN) in communication, multi-impairment compensation based on DNN will be a promising scheme. In this paper, we propose and apply DNN to compensate multi-impairment of 16-QAM coherent optical OFDM signal, thereby improving the performance of the transmission system. The trained DNN models are applied in the offline digital signal processing (DSP) module of the transmission system. The models can optimize the constellation mapping signals at the transmitter and compensate multi-impairment of the OFDM decoded signal at the receiver. Furthermore, the models reduce the peak to average power ratio (PAPR) of the transmitted OFDM signal and the bit error rate (BER) of the received signal. We verify the effectiveness of the proposed scheme for 16-QAM Coherent Optical OFDM signal and demonstrate and analyze transmission performance in different transmission scenarios. The experimental results show that the PAPR and BER of the transmission system are significantly reduced after using the trained DNN. It shows that the DNN with specific loss function and network structure can optimize the transmitted signal and learn the channel feature and compensate for multi-impairment in fiber transmission effectively.

Keywords: coherent optical OFDM, deep neural network, multi-impairment compensation, optical transmission

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Authors: Morteza Khashehchi, Kamel Hooman

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Thermal effects on the dynamics and stability of the flow past a circular cylinder operating in the mixed convection regime is studied experimentally for Reynolds number (ReD) between 1000 and 4000, and different cylinder wall temperatures (Tw) between 25 and 75°C by means of Particle Image Velocimetry (PIV). The experiments were conducted in a horizontal wind tunnel with the heated cylinder placed horizontally. With such assumptions, the direction of the thermally induced buoyancy force acting on the fluid surrounding the heated cylinder would be perpendicular to the flow direction. In each experiment, to acquire 3000 PIV image pairs, the temperature and Reynolds number of the approach flow were held constant. By adjusting different temperatures in different Reynolds numbers, the corresponding Richardson number (RiD = Gr/Re^2) was varied between 0:0 (unheated) and 10, resulting in a change in the heat transfer process from forced convection to mixed convection. With increasing temperature of the wall cylinder, significant modifications of the wake flow pattern and wake vortex shedding process were clearly revealed. For cylinder at low wall temperature, the size of the wake and the vortex shedding process are found to be quite similar to those of an unheated cylinder. With high wall temperature, however, the high temperature gradient in the wake shear layer creates a type of vorticity with opposite sign to that of the shear layer vorticity. This temperature gradient vorticity weakens the strength of the shear layer vorticity, causing delay in reaching the recreation point. In addition to the wake characteristics, the shedding frequency for the heated cylinder is determined for all aforementioned cases. It is found that, as the cylinder wall is heated, the organization of the vortex shedding is altered and the relative position of the first detached vortices with respect to the second one is changed. This movement of the first detached vortex toward the second one increases the frequency of the shedding process. It is also found that the wake closure length decreases with increasing the Richardson number.

Keywords: heated cylinder, PIV, wake, Reynolds number

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Authors: Abhishek Saha, Serene Tay, Gerard Pijcke

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The availability of high-resolution topography and rainfall information in urban areas has made it necessary to revise modeling approaches used for simulating flood risk assessments. Lidar derived elevation models that have 1m or lower resolutions are becoming widely accessible. The classical approaches of 1D-2D flow models where channel flow is simulated and coupled with a coarse resolution 2D overland flow models may not fully utilize the information provided by high-resolution data. In this context, a study was undertaken to compare three different modeling approaches to simulate flooding in an urban area. The first model used is the base model used is Sobek, which uses 1D model formulation together with hydrologic boundary conditions and couples with an overland flow model in 2D. The second model uses a full 2D model for the entire area with shallow water equations at the resolution of the digital elevation model (DEM). These models are compared against another shallow water equation solver in 2D, which uses a subgrid method for grid refinement. These models are simulated for different horizontal resolutions of DEM varying between 1m to 5m. The results show a significant difference in inundation extents and water levels for different DEMs. They are also sensitive to the different numerical models with the same physical parameters, such as friction. The study shows the importance of having reliable field observations of inundation extents and levels before a choice of model and data can be made for spatial flood risk assessments.

Keywords: flooding, DEM, shallow water equations, subgrid

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Authors: Akshay Patil

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Objective: Mediation analysis identifies causal pathways by testing the relationships between the NCR, the OS, and an intermediate variable that mediates the relationship between the Nimotuzumab-cisplatin-radiation (NCR) and OS. Introduction: In randomized controlled trials, the primary interest is in the mechanisms by which an intervention exerts its effects on the outcomes. Clinicians are often interested in how the intervention works (or why it does not work) through hypothesized causal mechanisms. In this work, we highlight the value of understanding causal mechanisms in randomized trial by applying causal mediation analysis in a randomized trial in oncology. Methods: Data was obtained from a phase III randomized trial (Subgroup of HPVNOCP). NCR is reported to significantly improve the OS of patients locally advanced head and neck cancer patients undergoing definitive chemoradiation. Here, based on trial data, the mediating effect of NCR on patient overall survival was systematically quantified through progression-free survival(PFS), disease free survival (DFS), Loco-regional failure (LRF), and the disease control rate (DCR), Overall response rate (ORR). Effects of potential mediators on the HR for OS with NCR versus cisplatin-radiation (CR) were analyzed by Cox regression models. Statistical analyses were performed using R software Version 3.6.3 (The R Foundation for Statistical Computing) Results: Effects of potential mediator PFS was an association between NCR treatment and OS, with an indirect-effect (IE) 0.76(0.62 – 0.95), which mediated 60.69% of the treatment effect. Taking into account baseline confounders, the overall adjusted hazard ratio of death was 0.64 (95% CI: 0.43 – 0.96; P=0.03). The DFS was also a significant mediator and had an IE 0.77 (95% CI; 0.62-0.93), 58% mediated). Smaller mediation effects (maximum 27%) were observed for LRF with IE 0.88(0.74 – 1.06). Both DCR and ORR mediated 10% and 15%, respectively, of the effect of NCR vs. CR on the OS with IE 0.65 (95% CI; 0.81 – 1.08) and 0.94(95% CI; 0.79 – 1.04). Conclusion: Our findings suggest that PFS and DFS were the most important mediators of the OS with nimotuzumab to weekly cisplatin-radiation in HPVNOCP.

Keywords: mediation analysis, cancer data, survival, NCR, HPV negative oropharyngeal

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Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas

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To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.

Keywords: building energy prediction, data mining, demand response, electricity market

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Authors: Sandeep Kaur, Jenny Vuong, Marcel Julliard, Sean O'Donoghue

Abstract:

Time-series data are useful for modelling as they can enable model-evaluation. However, when reconstructing models from phosphoproteomic data, often non-exact methods are utilised, as the knowledge regarding the network structure, such as, which kinases and phosphatases lead to the observed phosphorylation state, is incomplete. Thus, such reactions are often hypothesised, which gives rise to uncertainty. Here, we propose a framework, implemented via a web-based tool (as an extension to Minardo), which given time-series phosphoproteomic datasets, can generate κ models. The incompleteness and uncertainty in the generated model and reactions are clearly presented to the user via the visual method. Furthermore, we demonstrate, via a toy EGF signalling model, the use of algorithmic verification to verify κ models. Manually formulated requirements were evaluated with regards to the model, leading to the highlighting of the nodes causing unsatisfiability (i.e. error causing nodes). We aim to integrate such methods into our web-based tool and demonstrate how the identified erroneous nodes can be presented to the user via the visual method. Thus, in this research we present a framework, to enable a user to explore phosphorylation proteomic time-series data in the context of models. The observer can visualise which reactions in the model are highly uncertain, and which nodes cause incorrect simulation outputs. A tool such as this enables an end-user to determine the empirical analysis to perform, to reduce uncertainty in the presented model - thus enabling a better understanding of the underlying system.

Keywords: κ-models, model verification, time-series phosphoproteomic datasets, uncertainty and error visualisation

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Authors: Yungtai Lo

Abstract:

The current study follows an educational intervention on bottle-weaning to simultaneously evaluate the effect of the bottle-weaning intervention on reducing bottle use, daily milk intake from bottles, and daily energy intake in toddlers aged 11 to 13 months. A shared parameter model and a random effects model are used to jointly model bottle use, daily milk intake from bottles, and daily energy intake. We show in the two joint models that the bottle-weaning intervention promotes bottleweaning, and reduces daily milk intake from bottles in toddlers not off bottles and daily energy intake. We also show that the odds of drinking from a bottle were positively associated with the amount of milk intake from bottles and increased daily milk intake from bottles was associated with increased daily energy intake. The effect of bottle use on daily energy intake is through its effect on increasing daily milk intake from bottles that in turn increases daily energy intake.

Keywords: two-part model, semi-continuous variable, joint model, gamma regression, shared parameter model, random effects model

Procedia PDF Downloads 287

Authors: Thiago E. Goto, Carla C. Lopes, Helena B. Nader, Anielle C. A. Silva, Noelio O. Dantas, José R. Siqueira Jr., Luciano Caseli

Abstract:

Quantum dots (QD) are semiconductor nanocrystals that can be employed in biological research as a tool for fluorescence imagings, having the potential to expand in vivo and in vitro analysis as cancerous cell biomarkers. Particularly, cadmium selenide (CdSe) magic-sized quantum dots (MSQDs) exhibit stable luminescence that is feasible for biological applications, especially for imaging of tumor cells. For these facts, it is interesting to know the mechanisms of action of how such QDs mark biological cells. For that, simplified models are a suitable strategy. Among these models, Langmuir films of lipids formed at the air-water interface seem to be adequate since they can mimic half a membrane. They are monomolecular films formed at liquid-gas interfaces that can spontaneously form when organic solutions of amphiphilic compounds are spread on the liquid-gas interface. After solvent evaporation, the monomolecular film is formed, and a variety of techniques, including tensiometric, spectroscopic and optic can be applied. When the monolayer is formed by membrane lipids at the air-water interface, a model for half a membrane can be inferred where the aqueous subphase serve as a model for external or internal compartment of the cell. These films can be transferred to solid supports forming the so-called Langmuir-Blodgett (LB) films, and an ampler variety of techniques can be additionally used to characterize the film, allowing for the formation of devices and sensors. With these ideas in mind, the objective of this work was to investigate the specific interactions of CdSe MSQDs with tumorigenic and non-tumorigenic cells using Langmuir monolayers and LB films of lipids and specific cell extracts as membrane models for diagnosis of cancerous cells. Surface pressure-area isotherms and polarization modulation reflection-absorption spectroscopy (PM-IRRAS) showed an intrinsic interaction between the quantum dots, inserted in the aqueous subphase, and Langmuir monolayers, constructed either of selected lipids or of non-tumorigenic and tumorigenic cells extracts. The quantum dots expanded the monolayers and changed the PM-IRRAS spectra for the lipid monolayers. The mixed films were then compressed to high surface pressures and transferred from the floating monolayer to solid supports by using the LB technique. Images of the films were then obtained with atomic force microscopy (AFM) and confocal microscopy, which provided information about the morphology of the films. Similarities and differences between films with different composition representing cell membranes, with or without CdSe MSQDs, was analyzed. The results indicated that the interaction of quantum dots with the bioinspired films is modulated by the lipid composition. The properties of the normal cell monolayer were not significantly altered, whereas for the tumorigenic cell monolayer models, the films presented significant alteration. The images therefore exhibited a stronger effect of CdSe MSQDs on the models representing cancerous cells. As important implication of these findings, one may envisage for new bioinspired surfaces based on molecular recognition for biomedical applications.

Keywords: biomembrane, langmuir monolayers, quantum dots, surfaces

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Authors: Abd Elrahman Elgamala, N. Darwish, I. Bondouk, Sh. Hamada

Abstract:

Available experimental angular distributions for ⁶Li elastically scattered from ¹⁶O nucleus in the energy range 13.0–50.0 MeV are investigated and reanalyzed using optical model of the conventional phenomenological potential and also using double folding optical model of different interaction models: DDM3Y1, CDM3Y1, CDM3Y2, and CDM3Y3. All the involved models of interaction are of M3Y Paris except DDM3Y1 which is of M3Y Reid and the main difference between them lies in the different values for the parameters of the incorporated density distribution function F(ρ). We have extracted the renormalization factor N_R for ⁶Li+¹⁶O nuclear system in the energy range 13.0–50.0 MeV using the aforementioned interaction models.

Keywords: elastic scattering, optical model, folding potential, density distribution

Procedia PDF Downloads 141

Authors: D.Mendes, J. Henriques, P. Carvalho, T. Rocha, S. Paredes, R. Cabiddu, R. Trimer, R. Mendes, A. Borghi-Silva, L. Kaminsky, E. Ashley, R. Arena, J. Myers

Abstract:

The continued exploration of clinically relevant predictive models continues to be an important pursuit. Cardiorespiratory fitness (CRF) portends clinical vital information and as such its accurate prediction is of high importance. Therefore, the aim of the current study was to develop a data-driven model, based on computational intelligence techniques and, in particular, clustering approaches, to predict CRF. Two prediction models were implemented and compared: 1) the traditional Wasserman/Hansen Equations; and 2) an interpretable clustering approach. Data used for this analysis were from the 'FRIEND - Fitness Registry and the Importance of Exercise: The National Data Base'; in the present study a subset of 10690 apparently healthy individuals were utilized. The accuracy of the models was performed through the computation of sensitivity, specificity, and geometric mean values. The results show the superiority of the clustering approach in the accurate estimation of CRF (i.e., maximal oxygen consumption).

Keywords: cardiorespiratory fitness, data-driven models, knowledge extraction, machine learning

Procedia PDF Downloads 286

Authors: Ilma Robo, Saimir Heta, Emela Dalloshi, Nevila Alliu, Vera Ostreni

Abstract:

The dental whitening process, beyond the fact that it is a mini-invasive dental treatment, has effects on the dental structure, or on the pulp of the tooth, where it is applied. The electronic search was performed using keywords to find articles published within the last 10 years about side effects, assessed as such, of minimally invasive dental bleaching treatment. Methodology: In selected articles, the other aim of the study was to evaluate the side effects of bleaching based on the percentage and type of solution used, where the latter was evaluated on the basic solution used for bleaching. Results: The side effects of bleaching are evaluated in selected articles depending on the method of bleaching application, which means it is carried out with recommended solutions, or with mixtures of alternative solutions or substances based on Internet information. Short conclusion: The dental bleaching process has side effects which have not yet been definitively evaluated, experimentally in large samples of individuals or animals (mice or cattle) to arrive at accurate numerical conclusions. The trend of publications about this topic is increasing in recent years, as long as the trend for aesthetic facial treatments, including dental ones, is increasing.

Keywords: teeth whitening, side effects, permanent teeth, formed dental apex

Procedia PDF Downloads 63

Authors: M. Sasajima, M. Watanabe, T. Yamaguchi Y. Kurosawa, Y. Koike

Abstract:

In very narrow pathways, the speed of sound propagation and the phase of sound waves change due to the air viscosity. We have developed a new Finite Element Method (FEM) that includes the effects of air viscosity for modeling a narrow sound pathway. This method is developed as an extension of the existing FEM for porous sound-absorbing materials. The numerical calculation results for several three-dimensional slit models using the proposed FEM are validated against existing calculation methods.

Keywords: simulation, FEM, air viscosity, slit

Procedia PDF Downloads 369