Search results for: mixed-integer linear programming

Authors: Bimali Sanjeevani Weerakoon, Toshiaki Osuga, Takehisa Konishi

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Magnetic Resonance Imaging Contrast Agents (MRI-CM) are significant in the clinical and biological imaging as they have the ability to alter the normal tissue contrast, thereby affecting the signal intensity to enhance the visibility and detectability of images. Superparamagnetic Iron Oxide (SPIO) nanoparticles, coated with dextran or carboxydextran are currently available for clinical MR imaging of the liver. Most SPIO contrast agents are T2 shortening agents and Resovist (Ferucarbotran) is one of a clinically tested, organ-specific, SPIO agent which has a low molecular carboxydextran coating. The enhancement effect of Resovist depends on its relaxivity which in turn depends on factors like magnetic field strength, concentrations, nanoparticle properties, pH and temperature. Therefore, this study was conducted to investigate the impact of field strength and different contrast concentrations on enhancement effects of Resovist. The study explored the MRI signal intensity of Resovist in the physiological range of plasma from T2-weighted spin echo sequence at three magnetic field strengths: 0.47 T (r1=15, r2=101), 1.5 T (r1=7.4, r2=95), and 3 T (r1=3.3, r2=160) and the range of contrast concentrations by a mathematical simulation. Relaxivities of r1 and r2 (L mmol-1 Sec-1) were obtained from a previous study and the selected concentrations were 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 2.0, and 3.0 mmol/L. T2-weighted images were simulated using TR/TE ratio as 2000 ms /100 ms. According to the reference literature, with increasing magnetic field strengths, the r1 relaxivity tends to decrease while the r2 did not show any systematic relationship with the selected field strengths. In parallel, this study results revealed that the signal intensity of Resovist at lower concentrations tends to increase than the higher concentrations. The highest reported signal intensity was observed in the low field strength of 0.47 T. The maximum signal intensities for 0.47 T, 1.5 T and 3 T were found at the concentration levels of 0.05, 0.06 and 0.05 mmol/L, respectively. Furthermore, it was revealed that, the concentrations higher than the above, the signal intensity was decreased exponentially. An inverse relationship can be found between the field strength and T2 relaxation time, whereas, the field strength was increased, T2 relaxation time was decreased accordingly. However, resulted T2 relaxation time was not significantly different between 0.47 T and 1.5 T in this study. Moreover, a linear correlation of transverse relaxation rates (1/T2, s–1) with the concentrations of Resovist can be observed. According to these results, it can conclude that the concentration of SPIO nanoparticle contrast agents and the field strengths of MRI are two important parameters which can affect the signal intensity of T2-weighted SE sequence. Therefore, when MR imaging those two parameters should be considered prudently.

Keywords: Concentration, resovist, field strength, relaxivity, signal intensity

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Authors: Soheila Sadeghi

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Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

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Authors: Fagner O. Serrano, Danielle R. Bouchard, Todd A. Duhame

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Walking cadence (steps/min) is an effective way to prescribe exercise so an individual can reach a moderate intensity, which is recommended to optimize health benefits. To our knowledge, there is no study on the required walking cadence to reach a moderate intensity for people that present chronic conditions or risk factors for chronic conditions such as Cardiovascular Diseases (CVD). The objectives of this study were: 1- to identify the walking cadence needed for people at risk of CVD to a reach moderate intensity, and 2- to develop and test an equation using clinical variables to help professionals working with individuals at risk of CVD to estimate the walking cadence needed to reach moderate intensity. Ninety-one people presenting a minimum of two risk factors for CVD completed a medically supervised graded exercise test to assess maximum oxygen consumption at the first visit. The last visit consisted of recording walking cadence using a foot pod Garmin FR-60 and a Polar heart rate monitor, aiming to get participants to reach 40% of their maximal oxygen consumption using a portable metabolic cart on an indoor flat surface. The equation to predict the walking cadence needed to reach moderate intensity in this sample was developed as follows: The sample was randomly split in half and the equation was developed with one half of the participants, and validated using the other half. Body mass index, height, stride length, leg height, body weight, fitness level (VO2max), and self-selected cadence (over 200 meters) were measured using objective measured. Mean walking cadence to reach moderate intensity for people age 64.3 ± 10.3 years old at risk of CVD was 115.8  10.3 steps per minute. Body mass index, height, body weight, fitness level, and self-selected cadence were associated with walking cadence at moderate intensity when evaluated in bivariate analyses (r ranging from 0.22 to 0.52; all P values ≤0.05). Using linear regression analysis including all clinical variables associated in the bivariate analyses, body weight was the significant predictor of walking cadence for reaching a moderate intensity (ß=0.24; P=.018) explaining 13% of walking cadence to reach moderate intensity. The regression model created was Y = 134.4-0.24 X body weight (kg).Our findings suggest that people presenting two or more risk factors for CVD are reaching moderate intensity while walking at a cadence above the one officially recommended (116 steps per minute vs. 100 steps per minute) for healthy adults.

Keywords: cardiovascular disease, moderate intensity, older adults, walking cadence

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Authors: Oladele V. Adeniyi, B. Longo-Mbenza, Daniel T. Goon

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Background: Lipids are often low or in the normal ranges and controversial in the atherogenesis among Black Africans. The effect of the severity of obesity on some traditional and novel cardiovascular disease risk factors is unclear before and after a diet and exercise maintenance programme among obese black South Africans with type 2 diabetes mellitus (T2DM). Therefore, this study aimed to identify the risk factors to discriminate obesity classes among patients with T2DM before and after a diet and exercise programme. Methods: This interventional cohort of Black South Africans with T2DM was followed by a very – low calorie diet and exercise programme in Mthatha, between August and November 2013. Gender, age, and the levels of body mass index (BMI), blood pressure, monthly income, daily frequency of meals, blood random plasma glucose (RPG), serum creatinine, total cholesterol (TC), triglycerides (TG), LDL –C, HDL – C, Non-HDL, ratios of TC/HDL, TG/HDL, and LDL/HDL were recorded. Univariate analysis (ANOVA) and multivariate discriminant analysis were performed to separate obesity classes: normal weight (BMI = 18.5 – 24.9 kg/m2), overweight (BMI = 25 – 29.9 kg/m2), obesity Class 1 (BMI = 30 – 34.9 kg/m2), obesity Class 2 (BMI = 35 – 39.9 kg/m2), and obesity Class 3 (BMI ≥ 40 kg/m2). Results: At the baseline (1st Month September), all 327 patients were overweight/obese: 19.6% overweight, 42.8% obese class 1, 22.3% obese class 2, and 15.3% obese class 3. In discriminant analysis, only systolic blood pressure (SBP with positive association) and LDL/HDL ratio (negative association) significantly separated increasing obesity classes. At the post – evaluation (3rd Month November), out of all 327 patients, 19.9%, 19.3%, 37.6%, 15%, and 8.3% had normal weight, overweight, obesity class 1, obesity class 2, and obesity class 3, respectively. There was a significant negative association between serum creatinine and increase in BMI. In discriminant analysis, only age (positive association), SBP (U – shaped relationship), monthly income (inverted U – shaped association), daily frequency of meals (positive association), and LDL/HDL ratio (positive association) classified significantly increasing obesity classes. Conclusion: There is an epidemic of diabesity (Obesity + T2DM) in this Black South Africans with some weight loss. Further studies are needed to understand positive or negative linear correlations and paradoxical curvilinear correlations between these markers and increase in BMI among black South African T2DM patients.

Keywords: atherogenic dyslipidaemia, dietary interventions, obesity, south africans

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Authors: Rui Teixeira, Alan O’Connor, Maria Nogal

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The statistical theory of extreme events is progressively a topic of growing interest in all the fields of science and engineering. The changes currently experienced by the world, economic and environmental, emphasized the importance of dealing with extreme occurrences with improved accuracy. When it comes to the design of offshore structures, particularly offshore wind turbines, the importance of efficiently characterizing extreme events is of major relevance. Extreme events are commonly characterized by extreme values theory. As an alternative, the accurate modeling of the tails of statistical distributions and the characterization of the low occurrence events can be achieved with the application of the Peak-Over-Threshold (POT) methodology. The POT methodology allows for a more refined fit of the statistical distribution by truncating the data with a minimum value of a predefined threshold u. For mathematically approximating the tail of the empirical statistical distribution the Generalised Pareto is widely used. Although, in the case of the exceedances of significant wave data (H_s) the 2 parameters Weibull and the Exponential distribution, which is a specific case of the Generalised Pareto distribution, are frequently used as an alternative. The Generalized Pareto, despite the existence of practical cases where it is applied, is not completely recognized as the adequate solution to model exceedances over a certain threshold u. References that set the Generalised Pareto distribution as a secondary solution in the case of significant wave data can be identified in the literature. In this framework, the current study intends to tackle the discussion of the application of statistical models to characterize exceedances of wave data. Comparison of the application of the Generalised Pareto, the 2 parameters Weibull and the Exponential distribution are presented for different values of the threshold u. Real wave data obtained in four buoys along the Irish coast was used in the comparative analysis. Results show that the application of the statistical distributions to characterize significant wave data needs to be addressed carefully and in each particular case one of the statistical models mentioned fits better the data than the others. Depending on the value of the threshold u different results are obtained. Other variables of the fit, as the number of points and the estimation of the model parameters, are analyzed and the respective conclusions were drawn. Some guidelines on the application of the POT method are presented. Modeling the tail of the distributions shows to be, for the present case, a highly non-linear task and, due to its growing importance, should be addressed carefully for an efficient estimation of very low occurrence events.

Keywords: extreme events, offshore structures, peak-over-threshold, significant wave data

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Authors: Boris Barbolyas, Kristina Buckova, Tomas Volensky, Cyril Belavy, Ladislav Dedik

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Background: Based on statokinezigram, the human balance control is often studied. Approach to human postural reaction analysis is based on a combination of stabilometry output signal with retroreflective marker data signal processing, analysis, and understanding, in this study. The study shows another original application of Method of Developed Statokinesigram Trajectory (MDST), too. Methods: In this study, the participants maintained quiet bipedal standing for 10 s on stabilometry platform. Consequently, bilateral vibration stimuli to Achilles tendons in 20 s interval was applied. Vibration stimuli caused that human postural system took the new pseudo-steady state. Vibration frequencies were 20, 60 and 80 Hz. Participant's body segments - head, shoulders, hips, knees, ankles and little fingers were marked by 12 retroreflective markers. Markers positions were scanned by six cameras system BTS SMART DX. Registration of their postural reaction lasted 60 s. Sampling frequency was 100 Hz. For measured data processing were used Method of Developed Statokinesigram Trajectory. Regression analysis of developed statokinesigram trajectory (DST) data and retroreflective marker developed trajectory (DMT) data were used to find out which marker trajectories most correlate with stabilometry platform output signals. Scaling coefficients (λ) between DST and DMT by linear regression analysis were evaluated, too. Results: Scaling coefficients for marker trajectories were identified for all body segments. Head markers trajectories reached maximal value and ankle markers trajectories had a minimal value of scaling coefficient. Hips, knees and ankles markers were approximately symmetrical in the meaning of scaling coefficient. Notable differences of scaling coefficient were detected in head and shoulders markers trajectories which were not symmetrical. The model of postural system behavior was identified by MDST. Conclusion: Value of scaling factor identifies which body segment is predisposed to postural instability. Hypothetically, if statokinesigram represents overall human postural system response to vibration stimuli, then markers data represented particular postural responses. It can be assumed that cumulative sum of particular marker postural responses is equal to statokinesigram.

Keywords: center of pressure (CoP), method of developed statokinesigram trajectory (MDST), model of postural system behavior, retroreflective marker data

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Authors: Saifalarab A. Mohmmed, Morgana E. Vianna, Jonathan C. Knowles

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The issue of apical periodontitis has received considerable critical attention. Bacteria is integrated into communities, attached to surfaces and consequently form biofilm. The biofilm structure provides bacteria with a series protection skills against, antimicrobial agents and enhances pathogenicity (e.g. apical periodontitis). Sodium hypochlorite (NaOCl) has become the irrigant of choice for elimination of bacteria from the root canal system based on its antimicrobial findings. The aim of the study was to investigate the effect of different agitation techniques on the efficacy of 2.5% NaOCl to eliminate the biofilm from the surface of the lateral canal using the residual biofilm, and removal rate of biofilm as outcome measures. The effect of canal complexity (lateral canal) on the efficacy of the irrigation procedure was also assessed. Forty root canal models (n = 10 per group) were manufactured using 3D printing and resin materials. Each model consisted of two halves of an 18 mm length root canal with apical size 30 and taper 0.06, and a lateral canal of 3 mm length, 0.3 mm diameter located at 3 mm from the apical terminus. E. faecalis biofilms were grown on the apical 3 mm and lateral canal of the models for 10 days in Brain Heart Infusion broth. Biofilms were stained using crystal violet for visualisation. The model halves were reassembled, attached to an apparatus and tested under a fluorescence microscope. Syringe and needle irrigation protocol was performed using 9 mL of 2.5% NaOCl irrigant for 60 seconds. The irrigant was either left stagnant in the canal or activated for 30 seconds using manual (gutta-percha), sonic and ultrasonic methods. Images were then captured every second using an external camera. The percentages of residual biofilm were measured using image analysis software. The data were analysed using generalised linear mixed models. The greatest removal was associated with the ultrasonic group (66.76%) followed by sonic (45.49%), manual (43.97%), and passive irrigation group (control) (38.67%) respectively. No marked reduction in the efficiency of NaOCl to remove biofilm was found between the simple and complex anatomy models (p = 0.098). The removal efficacy of NaOCl on the biofilm was limited to the 1 mm level of the lateral canal. The agitation of NaOCl results in better penetration of the irrigant into the lateral canals. Ultrasonic agitation of NaOCl improved the removal of bacterial biofilm.

Keywords: 3D printing, biofilm, root canal irrigation, sodium hypochlorite

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Authors: Bilel Chalghaf, Mathieu Varin

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Forest characterization in Quebec, Canada, is usually assessed based on photo-interpretation at the stand level. For species identification, this often results in a lack of precision. Very high spatial resolution imagery, such as DigitalGlobe, and Light Detection and Ranging (LiDAR), have the potential to overcome the limitations of aerial imagery. To date, few studies have used that data to map a large number of species at the tree level using machine learning techniques. The main objective of this study is to map 11 individual high tree species ( > 17m) at the tree level using an object-based approach in the broadleaf forest of Kenauk Nature, Quebec. For the individual tree crown segmentation, three canopy-height models (CHMs) from LiDAR data were assessed: 1) the original, 2) a filtered, and 3) a corrected model. The corrected CHM gave the best accuracy and was then coupled with imagery to refine tree species crown identification. When compared with photo-interpretation, 90% of the objects represented a single species. For modeling, 313 variables were derived from 16-band WorldView-3 imagery and LiDAR data, using radiance, reflectance, pixel, and object-based calculation techniques. Variable selection procedures were employed to reduce their number from 313 to 16, using only 11 bands to aid reproducibility. For classification, a global approach using all 11 species was compared to a semi-hierarchical hybrid classification approach at two levels: (1) tree type (broadleaf/conifer) and (2) individual broadleaf (five) and conifer (six) species. Five different model techniques were used: (1) support vector machine (SVM), (2) classification and regression tree (CART), (3) random forest (RF), (4) k-nearest neighbors (k-NN), and (5) linear discriminant analysis (LDA). Each model was tuned separately for all approaches and levels. For the global approach, the best model was the SVM using eight variables (overall accuracy (OA): 80%, Kappa: 0.77). With the semi-hierarchical hybrid approach, at the tree type level, the best model was the k-NN using six variables (OA: 100% and Kappa: 1.00). At the level of identifying broadleaf and conifer species, the best model was the SVM, with OA of 80% and 97% and Kappa values of 0.74 and 0.97, respectively, using seven variables for both models. This paper demonstrates that a hybrid classification approach gives better results and that using 16-band WorldView-3 with LiDAR data leads to more precise predictions for tree segmentation and classification, especially when the number of tree species is large.

Keywords: tree species, object-based, classification, multispectral, machine learning, WorldView-3, LiDAR

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Authors: Rosely Sichieri, Bruna K. Hassan, Michele Sgambato, Barbara S. N. Souza, Rosangela A. Pereira, Edna M. Yokoo, Diana B. Cunha

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Obesity is increasing at a fast rate in low and middle-income countries where few school-based obesity interventions have been conducted. Results of obesity prevention studies are still inconclusive mainly due to underestimation of sample size in cluster-randomized trials and overestimation of changes in body mass index (BMI). The pooled analysis in the present study overcomes these design problems by analyzing 4,448 students (mean age 11.7 years) from three randomized behavioral school-based interventions, conducted in public schools of the metropolitan area of Rio de Janeiro, Brazil. The three studies focused on encouraging students to change their drinking and eating habits over one school year, with monthly 1-h sessions in the classroom. Folders explaining the intervention program and suggesting the participation of the family, such as reducing the purchase of sodas were sent home. Classroom activities were delivered by research assistants in the first two interventions and by the regular teachers in the third one, except for culinary class aimed at developing cooking skills to increase healthy eating choices. The first intervention was conducted in 2005 with 1,140 fourth graders from 22 public schools; the second, with 644 fifth graders from 20 public schools in 2010; and the last one, with 2,743 fifth and sixth graders from 18 public schools in 2016. The result was a non-significant change in BMI after one school year of positive changes in dietary behaviors associated with obesity. Pooled intention-to-treat analysis using linear mixed models was used for the overall and subgroup analysis by BMI status, sex, and race. The estimated mean BMI changes were from 18.93 to 19.22 in the control group and from 18.89 to 19.19 in the intervention group; with a p-value of change over time of 0.94. Control and intervention groups were balanced at baseline. Subgroup analyses were statistically and clinically non-significant, except for the non-overweight/obese group with a 0.05 reduction of BMI comparing the intervention with control. In conclusion, this large pooled analysis showed a very small effect on BMI only in the normal weight students. The results are in line with many of the school-based initiatives that have been promising in relation to modifying behaviors associated with obesity but of no impact on excessive weight gain. Changes in BMI may require great changes in energy balance that are hard to achieve in primary prevention at school level.

Keywords: adolescents, obesity prevention, randomized controlled trials, school-based study

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Authors: Haris Setiawan, Phongsakorn Chuammitri, Korawan Sringarm, Montira Intanon, Anucha Sathanawongs

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Capacitation is a prerequisite to achieving sperm competency to penetrate the oocyte naturally occurring in vivo throughout the female reproductive tract and entangling secretory fluid and epithelial cells. One of the crucial compounds in the oviductal fluid which promotes capacitation is albumin, secreted in major concentrations. However, the difficulties in the collection and the inconsistency of the oviductal fluid composition throughout the estrous cycle have replaced its function with serum-based albumins such as bovine serum albumin (BSA). BSA has been primarily involved and evidenced for their stabilizing effect to maintain the acrosome intact during the capacitation process, modulate hyperactivation, and elevate the number of sperm bound to zona pellucida. Contrary to its benefits, the use of blood-derived products in the culture system is not sustainable and increases the risk of disease transmissions, such as Creutzfeldt-Jakob disease (CJD) and bovine spongiform encephalopathy (BSE). Moreover, it has been asserted that this substance is an aeroallergen that produces allergies and respiratory problems. In an effort to identify an alternative sustainable and non-toxic albumin source, the present work evaluated sperm reactions to a capacitation medium containing albumin derived from the flesh of the snakehead fish (Channa striata). Before examining the ability of this non-serum albumin to promote capacitation in bovine sperm, the presence of albumin was detected using bromocresol purple (BCP) at the level of 25% from snakehead fish extract. Following the SDS-PAGE and densitometric analysis, two major bands at 40 kDa and 47 kDa consisting of 57% and 16% of total protein loaded were detected as the potential albumin-related bands. Significant differences were observed in all kinematic parameters upon incubation in the capacitation medium. Moreover, consistently higher values were shown for the kinematic parameters related to hyperactivation, such as amplitude lateral head (ALH), velocity curve linear (VCL), and linearity (LIN) when sperm were treated with 3 mg/mL of snakehead fish albumin among other treatments. Likewise, substantial differences of higher acrosome intact presented in sperm upon incubation with various concentrations of snakehead fish albumin for 90 minutes, indicating that this level of snakehead fish albumin can be used to replace the bovine serum albumin. However, further study is highly required to purify the albumin from snakehead fish extract for more reliable findings.

Keywords: capacitation promoter, snakehead fish, non-serum albumin, bovine sperm

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Authors: Rachel Cherner

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Introduction: The current study investigates several user characteristics that may predict the adoption of digital mental health supports. The extent to which individual characteristics predict preferences for functional elements of computer-mediated social support (CMSS) platforms and mental health (MH) apps is relatively unstudied. Aims: The present study seeks to illuminate the relationship between broad user characteristics and perceived attraction to CMSS platforms and MH apps. Methods: Participants (n=353) were recruited using convenience sampling methods (i.e., digital flyers, email distribution, and online survey forums). The sample was 68% male, and 32% female, with a mean age of 29. Participant racial and ethnic breakdown was 75% White, 7%, 5% Asian, and 5% Black or African American. Participants were asked to complete a 25-minute self-report questionnaire that included empirically validated measures assessing a battery of characteristics (i.e., subjective levels of anxiety/depression via PHQ-9 (Patient Health Questionnaire 9-item) and GAD-7 (Generalized Anxiety Disorder 7-item); attachment style via MAQ (Measure of Attachment Qualities); personality types via TIPI (The 10-Item Personality Inventory); growth mindset and mental health-seeking attitudes via GM (Growth Mindset Scale) and MHSAS (Mental Help Seeking Attitudes Scale)) and subsequent attitudes toward CMSS platforms and MH apps. Results: A stepwise linear regression was used to test if user characteristics significantly predicted attitudes towards key features of CMSS platforms and MH apps. The overall regression was statistically significant (R² =.20, F(1,344)=14.49, p<.000). Conclusion: This original study examines the clinical and sociocultural factors influencing decisions to use CMSS platforms and MH apps. Findings provide valuable insight for increasing adoption and engagement with digital mental health support. Fostering a growth mindset may be a method of increasing participant/patient engagement. In addition, CMSS platforms and MH apps may empower under-resourced and minority groups to gain basic access to mental health support. We do not assume this final model contains the best predictors of use; this is merely a preliminary step toward understanding the psychology and attitudes of CMSS platform/MH app users.

Keywords: computer-mediated social support platforms, digital mental health, growth mindset, health-seeking attitudes, mental health apps, user characteristics

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Authors: I. Ahumada, L. Ascar, C. Pedraza, J. Montecino

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It has been found that the addition of biosolids to soil is beneficial to soil health, enriching soil with essential nutrient elements. Although this sludge has properties that allow for the improvement of the physical features and productivity of agricultural and forest soils and the recovery of degraded soils, they also contain trace elements, organic trace and pathogens that can cause damage to the environment. The application of these biosolids to land without the total reclamation and the treated wastewater can transfer these compounds into terrestrial and aquatic environments, giving rise to potential accumulation in plants. The general aim of this study was to evaluate the bioavailability of bisphenol A (BPA), and 17 α-ethynyl estradiol (EE2) in a soil-biosolid system using wheat (Triticum aestivum) plant assays and a predictive extraction method using a solution of hydroxypropyl-β-cyclodextrin (HPCD) to determine if it is a reliable surrogate for this bioassay. Two soils were obtained from the central region of Chile (Lo Prado and Chicauma). Biosolids were obtained from a regional wastewater treatment plant. The soils were amended with biosolids at 90 Mg ha-1. Soils treated with biosolids, spiked with 10 mgkg-1 of the EE2 and 15 mgkg-1 and 30 mgkg-1of BPA were also included. The BPA, and EE2 concentration were determined in biosolids, soils and plant samples through ultrasound assisted extraction, solid phase extraction (SPE) and gas chromatography coupled to mass spectrometry determination (GC/MS). The bioavailable fraction found of each one of soils cultivated with wheat plants was compared with results obtained through a cyclodextrin biosimulator method. The total concentration found in biosolid from a treatment plant was 0.150 ± 0.064 mgkg-1 and 12.8±2.9 mgkg-1 of EE2 and BPA respectively. BPA and EE2 bioavailability is affected by the organic matter content and the physical and chemical properties of the soil. The bioavailability response of both compounds in the two soils varied with the EE2 and BPA concentration. It was observed in the case of EE2, the bioavailability in wheat plant crops contained higher concentrations in the roots than in the shoots. The concentration of EE2 increased with increasing biosolids rate. On the other hand, for BPA, a higher concentration was found in the shoot than the roots of the plants. The predictive capability the HPCD extraction was assessed using a simple linear correlation test, for both compounds in wheat plants. The correlation coefficients for the EE2 obtained from the HPCD extraction with those obtained from the wheat plants were r= 0.99 and p-value ≤ 0.05. On the other hand, in the case of BPA a correlation was not found. Therefore, the methodology was validated with respect to wheat plants bioassays, only in the EE2 case. Acknowledgments: The authors thank FONDECYT 1150502.

Keywords: emerging compounds, bioavailability, biosolids, endocrine disruptors

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Authors: Giuliana Scuderi, Patrick Teuffel

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The proven vulnerability of the existing social housing building heritage to natural or induced earthquakes requires the development of new design concepts and conceptual method to preserve materials and object, at the same time providing new performances. An integrate intervention between civil engineering, building physics and architecture can convert the social housing districts from a critical part of the city to a strategic resource of revitalization. Referring to bio-mimicry principles the present research proposes a taxonomy with the exoskeleton of the insect, an external, light and resistant armour whose role is to protect the internal organs from external potentially dangerous inputs. In the same way, a “building exoskeleton”, acting from the outside of the building as an enclosing cage, can restore, protect and support the existing building, assuming a complex set of roles, from the structural to the thermal, from the aesthetical to the functional. This study evaluates the structural efficiency of shape memory alloys devices (SMADs) connecting the “building exoskeleton” with the existing structure to rehabilitate, in order to prevent the out-of-plane collapse of walls and for the passive dissipation of the seismic energy, with a calibrated operability in relation to the intensity of the horizontal loads. The two case studies of a masonry structure and of a masonry structure with concrete frame are considered, and for each case, a theoretical social housing building is exposed to earthquake forces, to evaluate its structural response with or without SMADs. The two typologies are modelled with the finite element program SAP2000, and they are respectively defined through a “frame model” and a “diagonal strut model”. In the same software two types of SMADs, called the 00-10 SMAD and the 05-10 SMAD are defined, and non-linear static and dynamic analyses, namely push over analysis and time history analysis, are performed to evaluate the seismic response of the building. The effectiveness of the devices in limiting the control joint displacements resulted higher in one direction, leading to the consideration of a possible calibrated use of the devices in the different walls of the building. The results show also a higher efficiency of the 00-10 SMADs in controlling the interstory drift, but at the same time the necessity to improve the hysteretic behaviour, to maximise the passive dissipation of the seismic energy.

Keywords: adaptive structure, biomimetic design, building exoskeleton, social housing, structural envelope, structural retrofitting

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Authors: Frans J. Smit, Wisdom A. Munzeiwa, Hermanus C. M. Vosloo, Lyn-Marie Birkholtz, Richard K. Haynes

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Malaria and antimicrobial infections remain significant global health concerns, necessitating the continuous search for novel therapeutic approaches. This abstract presents an overview of the potential use of triazenes as effective agents against malaria and various antimicrobial pathogens. Triazenes are a class of compounds characterized by a linear arrangement of three nitrogen atoms, rendering them structurally distinct from their cyclic counterparts. This study investigates the efficacy of triazenes against malaria and explores their antimicrobial activity. Preliminary results revealed significant antimalarial activity of the triazenes, as evidenced by in vitro screening against P. falciparum, the causative agent of malaria. Furthermore, the compounds exhibited broad-spectrum antimicrobial activity, indicating their potential as effective antimicrobial agents. These compounds have shown inhibitory effects on various essential enzymes and processes involved in parasite survival, replication, and transmission. The mechanism of action of triazenes against malaria involves interactions with critical molecular targets, such as enzymes involved in the parasite's metabolic pathways and proteins responsible for host cell invasion. The antimicrobial activity of the triazenes against bacteria and fungi was investigated through disc diffusion screening. The antimicrobial efficacy of triazenes has been observed against both Gram-positive and Gram-negative bacteria, as well as multidrug-resistant strains, making them potential candidates for combating drug-resistant infections. Furthermore, triazenes possess favourable physicochemical properties, such as good stability, solubility, and low toxicity, which are essential for drug development. The structural versatility of triazenes allows for the modification of their chemical composition to enhance their potency, selectivity, and pharmacokinetic properties. These modifications can be tailored to target specific pathogens, increasing the potential for personalized treatment strategies. In conclusion, this study highlights the potential of triazenes as promising candidates for the development of novel antimalarial and antimicrobial therapeutics. Further investigations are necessary to determine the structure-activity relationships and optimize the pharmacological properties of these compounds. The results warrant additional research, including MIC studies, to further explore the antimicrobial activity of the triazenes. Ultimately, these findings contribute to the development of more effective strategies for combating malaria and microbial infections.

Keywords: malaria, anti-microbials, triazene, resistance

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Authors: Sayantan Saha, Sambit Supriya Dash, Vinayak Malhotra

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Efficient propulsive systems development is an area of major interest and concern in aerospace industry. Combustion forms the most reliable and basic form of propulsion for ground and space applications. The generation of large amount of energy from a small volume relates mostly to the flaming combustion. This study deals with instabilities associated with flaming combustion. Combustion is always accompanied by acoustics be it external or internal. Chemical propulsion oriented rockets and space systems are well known to encounter acoustic instabilities. Acoustic brings in changes in inter-energy conversion and alter the reaction rates. The modified heat fluxes, owing to wall temperature, reaction rates, and non-linear heat transfer are observed. The thermoacoustic instabilities significantly result in reduced combustion efficiency leading to uncontrolled liquid rocket engine performance, serious hazards to systems, assisted testing facilities, enormous loss of resources and every year a substantial amount of money is spent to prevent them. Present work attempts to fundamentally understand the mechanisms governing the thermoacoustic combustion in liquid rocket engine using a simplified experimental setup comprising a butane cylinder and an impinging acoustic source. Rocket engine produces sound pressure level in excess of 153 Db. The RL-10 engine generates noise of 180 Db at its base. Systematic studies are carried out for varying fuel flow rates, acoustic levels and observations are made on the flames. The work is expected to yield a good physical insight into the development of acoustic devices that when coupled with the present propulsive devices could effectively enhance combustion efficiency leading to better and safer missions. The results would be utilized to develop impinging acoustic devices that impinge sound on the combustion chambers leading to stable combustion thus, improving specific fuel consumption, specific impulse, reducing emissions, enhanced performance and fire safety. The results can be effectively applied to terrestrial and space application.

Keywords: combustion instability, fire safety, improved performance, liquid rocket engines, thermoacoustics

Procedia PDF Downloads 145

Authors: Benjamin Mugisha Bugingo

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Healthcare staffhas been considered as asignificant pillar to the health care system. However, the contest of human resources for health in terms of the turnover of health workers in Uganda has been more distinct in the latest years. The objective of the paper, therefore, were to investigate the influence Role Human resource management functions in on employeeperformance of professional health workers in public district hospitals in Kampala. The study objectives were: to establish the effect of performance management function, financialincentives, non-financial incentives, participation, and involvement in the decision-making on the employee performance of professional health workers in public district hospitals in Kampala. The study was devised in the social exchange theory and the equity theory. This study adopted a descriptive research design using quantitative approaches. The study used a cross-sectional research design with a mixed-methods approach. With a population of 402 individuals, the study considered a sample of 252 respondents, including doctors, nurses, midwives, pharmacists, and dentists from 3 district hospitals. The study instruments entailed a questionnaire as a quantitative data collection tool and interviews and focus group discussions as qualitative data gathering tools. To analyze quantitative data, descriptive statistics were used to assess the perceived status of Human resource management functions and the magnitude of intentions to stay, and inferential statistics were used to show the effect of predictors on the outcome variable by plotting a multiple linear regression. Qualitative data were analyzed in themes and reported in narrative and verbatim quotes and were used to complement descriptive findings for a better understanding of the magnitude of the study variables. The findings of this study showed a significant and positive effect of performance management function, financialincentives, non-financial incentives, and participation and involvement in decision-making on employee performance of professional health workers in public district hospitals in Kampala. This study is expected to be a major contributor for the improvement of the health system in the country and other similar settings as it has provided the insights for strategic orientation in the area of human resources for health, especially for enhanced employee performance in relation with the integrated human resource management approach

Keywords: human resource functions, employee performance, employee wellness, profecial workers

Procedia PDF Downloads 100

Authors: Chyong-Ru Liu, Wen-Shiung Huang, Wan-Ching Tang, Shan-Pei Chen

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Two of the biggest challenges companies involved in sports and exercise information services face are how to strengthen participation in virtual sports/exercise communities and how to increase the ongoing participatoriness of those communities. In the past, relatively little research has explored mechanisms for strengthening alignment practice and community response from the perspective of self-construal, and as such this study seeks to explore the self-construal of virtual sports/exercise communities, the role it plays in the emotional commitment of forming communities, and the factor that can strengthen alignment practice. Moreover, which factor of the emotional commitment of forming virtual communities have the effect of strengthening interference in the process of transforming customer citizenship behaviors? This study collected 625 responses from the two leading websites in terms of fan numbers in the provision of information on road race and marathon events in Taiwan, with model testing conducted through linear structural equation modelling and the bootstrapping technique to test the proposed hypotheses. The results proved independent construal had a stronger positive direct effect on affective commitment to fellow customers than did interdependent construal, and the influences of affective commitment to fellow customers in enhancing customer citizenship behavior. Public self-consciousness moderates the relationships among independent self-construal and interdependent self-construal on effective commitment to fellow customers. Perceived playfulness moderates the relationships between effective commitment to fellow customers and customer citizenship behavior. The findings of this study provide significant insights for the researchers and related organizations. From the theoretical perspective, this is empirical research that investigated the self-construal theory and responses (i.e., affective commitment to fellow customers, customer citizenship behavior) in virtual sports/exercise communities. We further explore how to govern virtual sports/exercise community participants’ heterogeneity through public self-consciousness mechanism to align participants’ affective commitment. Moreover, perceived playfulness has the effect of strengthening effective commitment to fellow customers with customer citizenship behaviors. The results of this study can provide a foundation for the construction of future theories and can be provided to related organizations for reference in their planning of virtual communities.

Keywords: self-construal theory, public self-consciousness, affective commitment, customer citizenship behavior

Procedia PDF Downloads 107

Authors: Hamed Kalhori, Lin Ye

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In this study, an inverse method was developed to reconstruct the magnitude and duration of impact forces exerted to a rectangular carbon fibre-epoxy composite honeycomb sandwich panel. The dynamic signals captured by Piezoelectric (PZT) sensors installed on the panel remotely from the impact locations were utilized to reconstruct the impact force generated by an instrumented hammer through an extended deconvolution approach. Two discretized forms of convolution integral are considered; the traditional one with an explicit transfer function and the modified one without an explicit transfer function. Deconvolution, usually applied to reconstruct the time history (e.g. magnitude) of a stochastic force at a defined location, is extended to identify both the location and magnitude of the impact force among a number of potential impact locations. It is assumed that a number of impact forces are simultaneously exerted to all potential locations, but the magnitude of all forces except one is zero, implicating that the impact occurs only at one location. The extended deconvolution is then applied to determine the magnitude as well as location (among the potential ones), incorporating the linear superposition of responses resulted from impact at each potential location. The problem can be categorized into under-determined (the number of sensors is less than that of impact locations), even-determined (the number of sensors equals that of impact locations), or over-determined (the number of sensors is greater than that of impact locations) cases. For an under-determined case, it comprises three potential impact locations and one PZT sensor for the rectangular carbon fibre-epoxy composite honeycomb sandwich panel. Assessments are conducted to evaluate the factors affecting the precision of the reconstructed force. Truncated Singular Value Decomposition (TSVD) and the Tikhonov regularization are independently chosen to regularize the problem to find the most suitable method for this system. The selection of optimal value of the regularization parameter is investigated through L-curve and Generalized Cross Validation (GCV) methods. In addition, the effect of different width of signal windows on the reconstructed force is examined. It is observed that the impact force generated by the instrumented impact hammer is sensitive to the impact locations of the structure, having a shape from a simple half-sine to a complicated one. The accuracy of the reconstructed impact force is evaluated using the correlation co-efficient between the reconstructed force and the actual one. Based on this criterion, it is concluded that the forces reconstructed by using the extended deconvolution without an explicit transfer function together with Tikhonov regularization match well with the actual forces in terms of magnitude and duration.

Keywords: honeycomb composite panel, deconvolution, impact localization, force reconstruction

Procedia PDF Downloads 536

Authors: Arindam Chaudhuri

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Classification of data has been actively used for most effective and efficient means of conveying knowledge and information to users. The prima face has always been upon techniques for extracting useful knowledge from data such that returns are maximized. With emergence of huge datasets the existing classification techniques often fail to produce desirable results. The challenge lies in analyzing and understanding characteristics of massive data sets by retrieving useful geometric and statistical patterns. We propose a supervised parallel fuzzy rough support vector machine (PFRSVM) for data classification in cloud environment. The classification is performed by PFRSVM using hyperbolic tangent kernel. The fuzzy rough set model takes care of sensitiveness of noisy samples and handles impreciseness in training samples bringing robustness to results. The membership function is function of center and radius of each class in feature space and is represented with kernel. It plays an important role towards sampling the decision surface. The success of PFRSVM is governed by choosing appropriate parameter values. The training samples are either linear or nonlinear separable. The different input points make unique contributions to decision surface. The algorithm is parallelized with a view to reduce training times. The system is built on support vector machine library using Hadoop implementation of MapReduce. The algorithm is tested on large data sets to check its feasibility and convergence. The performance of classifier is also assessed in terms of number of support vectors. The challenges encountered towards implementing big data classification in machine learning frameworks are also discussed. The experiments are done on the cloud environment available at University of Technology and Management, India. The results are illustrated for Gaussian RBF and Bayesian kernels. The effect of variability in prediction and generalization of PFRSVM is examined with respect to values of parameter C. It effectively resolves outliers’ effects, imbalance and overlapping class problems, normalizes to unseen data and relaxes dependency between features and labels. The average classification accuracy for PFRSVM is better than other classifiers for both Gaussian RBF and Bayesian kernels. The experimental results on both synthetic and real data sets clearly demonstrate the superiority of the proposed technique.

Keywords: FRSVM, Hadoop, MapReduce, PFRSVM

Procedia PDF Downloads 491

Authors: B. Petrović, M. Petrović, L. Rutonjski, I. Djan, V. Ivanović

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Objective: Cardiac pacemakers are very sensitive to radiotherapy treatment from two sources: electromagnetic influence from the medical linear accelerator producing ionizing radiation- influencing electronics within the pacemaker, and the absorption of dose to the device. On the other hand, patients with cardiac pacemakers at the place of a tumor are rather rare, and single clinic hardly has experience with the management of such patients. The widely accepted international guidelines for management of radiation oncology patients recommend that these patients should be closely monitored and examined before, during and after radiotherapy treatment by cardiologist, and their device and condition followed up. The number of patients having both cancer and pacemaker, is growing every year, as both cancer incidence, as well as cardiac diseases incidence, are inevitably growing figures. Materials and methods: Female patient, age 69, was diagnozed with valvular cardiomyopathy and got implanted a pacemaker in 2005 and prosthetic mitral valve in 1993 (cancer was diagnosed in 2012). She was stable cardiologically and came to radiation therapy department with the diagnosis of right breast cancer, with the tumor in upper lateral quadrant of the right breast. Since she had all lymph nodes positive (28 in total), she had to have irradiated the supraclavicular region, as well as the breast with the tumor bed. She previously received chemotherapy, approved by the cardiologist. The patient was estimated to be with the high risk as device was within the field of irradiation, and the patient had high dependence on her pacemaker. The radiation therapy plan was conducted as 3D conformal therapy. The delineated target was breast with supraclavicular region, where the pacemaker was actually placed, with the addition of a pacemaker as organ at risk, to estimate the dose to the device and its components as recommended, and the breast. The targets received both 50 Gy in 25 fractions (where 20% of a pacemaker received 50 Gy, and 60% of a device received 40 Gy). The electrode to the heart received between 1 Gy and 50 Gy. Verification of dose planned and delivered was performed. Results: Evaluation of the patient status according to the guidelines and especially evaluation of all associated risks to the patient during treatment was done. Patient was irradiated by prescribed dose and followed up for the whole year, with no symptoms of failure of the pacemaker device during, or after treatment in follow up period. The functionality of a device was estimated to be unchanged, according to the parameters (electrode impedance and battery energy). Conclusion: Patient was closely monitored according to published guidelines during irradiation and afterwards. Pacemaker irradiated with the full dose did not show any signs of failure despite recommendations data, but in correlation with other published data.

Keywords: cardiac pacemaker, breast cancer, radiotherapy treatment planning, complications of treatment

Procedia PDF Downloads 439

Authors: Narendra Akhadkar, Silvestre Cano, Christophe Gourru

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Injection-molded parts are widely used in power system protection products. One of the biggest challenges in an injection molding process is shrinkage and warpage of the molded parts. All these geometrical variations may have an adverse effect on the quality of the product, functionality, cost, and time-to-market. The situation becomes more challenging in the case of intricate shapes and in mass production using multi-cavity tools. To control the effects of shrinkage and warpage, it is very important to correctly find out the input parameters that could affect the product performance. With the advances in the computer-aided engineering (CAE), different tools are available to simulate the injection molding process. For our case study, we used the MoldFlow insight tool. Our aim is to predict the spread of the functional dimensions and geometrical variations on the part due to variations in the input parameters such as material viscosity, packing pressure, mold temperature, melt temperature, and injection speed. The input parameters may vary during batch production or due to variations in the machine process settings. To perform the accurate product assembly variation simulation, the first step is to perform an individual part variation simulation to render realistic tolerance ranges. In this article, we present a method to simulate part variations coming from the input parameters variation during batch production. The method is based on computer simulations and experimental validation using the full factorial design of experiments (DoE). The robustness of the simulation model is verified through input parameter wise sensitivity analysis study performed using simulations and experiments; all the results show a very good correlation in the material flow direction. There exists a non-linear interaction between material and the input process variables. It is observed that the parameters such as packing pressure, material, and mold temperature play an important role in spread on functional dimensions and geometrical variations. This method will allow us in the future to develop accurate/realistic virtual prototypes based on trusted simulated process variation and, therefore, increase the product quality and potentially decrease the time to market.

Keywords: correlation, molding process, tolerance, sensitivity analysis, variation simulation

Procedia PDF Downloads 179

Authors: Natalia Bustos Sierra, Katrien Tersago

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It is currently proven that both extremes of temperature are associated with increased mortality and that air pollution is associated with temperature. This relationship is complex, and in countries with important seasonal variations in weather such as Belgium, some effects can appear as non-significant when the analysis is done over the entire year. We, therefore, analyzed the effect of short-term outdoor air pollution exposure on cardiovascular mortality during the warmer and colder months separately. We used daily cardiovascular deaths from acute cardiovascular diagnostics according to the International Classification of Diseases, 10th Revision (ICD-10: I20-I24, I44-I49, I50, I60-I66) during the period 2008-2013. The environmental data were population-weighted concentrations of particulates with an aerodynamic diameter less than 10 µm (PM₁₀) and less than 2.5 µm (PM₂.₅) (daily average), nitrogen dioxide (NO₂) (daily maximum of the hourly average) and ozone (O₃) (daily maximum of the 8-hour running mean). A Generalized linear model was applied adjusting for the confounding effect of season, temperature, dew point temperature, the day of the week, public holidays and the incidence of influenza-like illness (ILI) per 100,000 inhabitants. The relative risks (RR) were calculated for an increase of one interquartile range (IQR) of the air pollutant (μg/m³). These were presented for the four hottest months (June, July, August, September) and coldest months (November, December, January, February) in Belgium. We applied both individual lag model and unconstrained distributed lag model methods. The cumulative effect of a four-day exposure (day of exposure and three consecutive days) was calculated from the unconstrained distributed lag model. The IQR for PM₁₀, PM₂.₅, NO₂, and O₃ were respectively 8.2, 6.9, 12.9 and 25.5 µg/m³ during warm months and 18.8, 17.6, 18.4 and 27.8 µg/m³ during cold months. The association with CV mortality was statistically significant for the four pollutants during warm months and only for NO₂ during cold months. During the warm months, the cumulative effect of an IQR increase of ozone for the age groups 25-64, 65-84 and 85+ was 1.066 (95%CI: 1.002-1.135), 1.041 (1.008-1.075) and 1.036 (1.013-1.058) respectively. The cumulative effect of an IQR increase of NO₂ for the age group 65-84 was 1.066 (1.020-1.114) during warm months and 1.096 (1.030-1.166) during cold months. The cumulative effect of an IQR increase of PM₁₀ during warm months reached 1.046 (1.011-1.082) and 1.038 (1.015-1.063) for the age groups 65-84 and 85+ respectively. Similar results were observed for PM₂.₅. The short-term effect of air pollution on cardiovascular mortality is greater during warm months for lower pollutant concentrations compared to cold months. Spending more time outside during warm months increases population exposure to air pollution and can, therefore, be a confounding factor for this association. Age can also affect the length of time spent outdoors and the type of physical activity exercised. This study supports the deleterious effect of air pollution on cardiovascular mortality (CV) which varies according to season and age groups in Belgium. Public health measures should, therefore, be adapted to seasonality.

Keywords: air pollution, cardiovascular, mortality, season

Procedia PDF Downloads 166

Authors: Gulfam Haider, sana danish

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Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification, and the choice of optimizers profoundly affects their performance. The study of optimizers and their adaptations remains a topic of significant importance in machine learning research. While numerous studies have explored and advocated for various optimizers, the efficacy of these optimization techniques is still subject to scrutiny. This work aims to address the challenges surrounding the effectiveness of optimizers by conducting a comprehensive analysis and evaluation. The primary focus of this investigation lies in examining the performance of different optimizers when employed in conjunction with the popular activation function, Rectified Linear Unit (ReLU). By incorporating ReLU, known for its favorable properties in prior research, the aim is to bolster the effectiveness of the optimizers under scrutiny. Specifically, we evaluate the adjustment of these optimizers with both the original Softmax activation function and the modified ReLU activation function, carefully assessing their impact on overall performance. To achieve this, a series of experiments are conducted using a well-established benchmark dataset for image classification tasks, namely the Canadian Institute for Advanced Research dataset (CIFAR-10). The selected optimizers for investigation encompass a range of prominent algorithms, including Adam, Root Mean Squared Propagation (RMSprop), Adaptive Learning Rate Method (Adadelta), Adaptive Gradient Algorithm (Adagrad), and Stochastic Gradient Descent (SGD). The performance analysis encompasses a comprehensive evaluation of the classification accuracy, convergence speed, and robustness of the CNN models trained with each optimizer. Through rigorous experimentation and meticulous assessment, we discern the strengths and weaknesses of the different optimization techniques, providing valuable insights into their suitability for image classification tasks. By conducting this in-depth study, we contribute to the existing body of knowledge surrounding optimizers in CNNs, shedding light on their performance characteristics for image classification. The findings gleaned from this research serve to guide researchers and practitioners in making informed decisions when selecting optimizers and activation functions, thus advancing the state-of-the-art in the field of image classification with convolutional neural networks.

Keywords: deep neural network, optimizers, RMsprop, ReLU, stochastic gradient descent

Procedia PDF Downloads 127

Authors: Takuya Suzuki, Atsushi Shirai, Takashi Seki

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Acupuncture therapy is one of the treatments in traditional Chinese medicine. Recently, some reports have shown the effectiveness of acupuncture. However, its full acceptance has been hindered by the lack of understanding on mechanism of the therapy. Acupuncture applied to Zusanli (ST-36) enhances blood flow volume in superior mesenteric artery (SMA), yielding peripheral vascular resistance – regulated blood flow of SMA dominated by the parasympathetic system and inhibition of sympathetic system. In this study, a lumped-parameter approximation model of blood flow in the systemic arteries was developed. This model was extremely simple, consisting of the aorta, carotid arteries, arteries of the four limbs and SMA, and their peripheral vascular resistances. Here, the individual artery was simplified to a tapered tube and the resistances were modelled by a linear resistance. We numerically investigated contribution of the peripheral vascular resistance of SMA to the systemic blood distribution using this model. In addition to the upstream end of the model, which correlates with the left ventricle, two types of boundary condition were applied; mean left ventricular pressure which correlates with blood pressure (BP) and mean cardiac output which corresponds to cardiac index (CI). We examined it to reproduce the experimentally obtained hemodynamic change, in terms of the ratio of the aforementioned hemodynamic parameters from their initial values before the acupuncture, by regulating the peripheral vascular resistances and the upstream boundary condition. First, only the peripheral vascular resistance of SMA was changed to show contribution of the resistance to the change in blood flow volume in SMA, expecting reproduction of the experimentally obtained change. It was found, however, this was not enough to reproduce the experimental result. Then, we also changed the resistances of the other arteries together with the value given at upstream boundary. Here, the resistances of the other arteries were changed simultaneously in the same amount. Consequently, we successfully reproduced the hemodynamic change to find that regulation of the upstream boundary condition to the value experimentally obtained after the stimulation is necessary for the reproduction, though statistically significant changes in BP and CI were not observed in the experiment. It is generally known that sympathetic and parasympathetic tones take part in regulation of whole the systemic circulation including the cardiac function. The present result indicates that stimulation to ST-36 could induce vasodilation of peripheral circulation of SMA and vasoconstriction of that of other arteries. In addition, it implies that experimentally obtained small changes in BP and CI induced by the acupuncture may be involved in the therapeutic response.

Keywords: acupuncture, hemodynamics, lumped-parameter approximation, modeling, systemic vascular resistance

Procedia PDF Downloads 224

Authors: T. Schmitt, J. Rosin, C. Butenweg

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Seismic design of buried pipeline systems for energy and water supply is not only important for plant and operational safety, but in particular for the maintenance of supply infrastructure after an earthquake. Past earthquakes have shown the vulnerability of pipeline systems. After the Kobe earthquake in Japan in 1995 for instance, in some regions the water supply was interrupted for almost two months. The present paper shows special issues of the seismic wave impacts on buried pipelines, describes calculation methods, proposes approaches and gives calculation examples. Buried pipelines are exposed to different effects of seismic impacts. This paper regards the effects of transient displacement differences and resulting tensions within the pipeline due to the wave propagation of the earthquake. Other effects are permanent displacements due to fault rupture displacements at the surface, soil liquefaction, landslides and seismic soil compaction. The presented model can also be used to calculate fault rupture induced displacements. Based on a three-dimensional Finite Element Model parameter studies are performed to show the influence of several parameters such as incoming wave angle, wave velocity, soil depth and selected displacement time histories. In the computer model, the interaction between the pipeline and the surrounding soil is modeled with non-linear soil springs. A propagating wave is simulated affecting the pipeline punctually independently in time and space. The resulting stresses mainly are caused by displacement differences of neighboring pipeline segments and by soil-structure interaction. The calculation examples focus on pipeline bends as the most critical parts. Special attention is given to the calculation of long-distance heat pipeline systems. Here, in regular distances expansion bends are arranged to ensure movements of the pipeline due to high temperature. Such expansion bends are usually designed with small bending radii, which in the event of an earthquake lead to high bending stresses at the cross-section of the pipeline. Therefore, Karman's elasticity factors, as well as the stress intensity factors for curved pipe sections, must be taken into account. The seismic verification of the pipeline for wave propagation in the soil can be achieved by observing normative strain criteria. Finally, an interpretation of the results and recommendations are given taking into account the most critical parameters.

Keywords: buried pipeline, earthquake, seismic impact, transient displacement

Procedia PDF Downloads 188

Authors: Yanping Liao, Zenan Wu, Ruigang Zhao

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Frequency diverse array (FDA) beamforming is a technology developed in recent years, and its antenna pattern has a unique angle-distance-dependent characteristic. However, the beam is always required to have strong concentration, high resolution and low sidelobe level to form the point-to-point interference in the concentrated set. In order to eliminate the angle-distance coupling of the traditional FDA and to make the beam energy more concentrated, this paper adopts a multi-carrier FDA structure based on proposed power exponential frequency offset to improve the array structure and frequency offset of the traditional FDA. The simulation results show that the beam pattern of the array can form a dot-shape beam with more concentrated energy, and its resolution and sidelobe level performance are improved. However, the covariance matrix of the signal in the traditional adaptive beamforming algorithm is estimated by the finite-time snapshot data. When the number of snapshots is limited, the algorithm has an underestimation problem, which leads to the estimation error of the covariance matrix to cause beam distortion, so that the output pattern cannot form a dot-shape beam. And it also has main lobe deviation and high sidelobe level problems in the case of limited snapshot. Aiming at these problems, an adaptive beamforming technique based on exponential correction for multi-carrier FDA is proposed to improve beamforming robustness. The steps are as follows: first, the beamforming of the multi-carrier FDA is formed under linear constrained minimum variance (LCMV) criteria. Then the eigenvalue decomposition of the covariance matrix is ​​performed to obtain the diagonal matrix composed of the interference subspace, the noise subspace and the corresponding eigenvalues. Finally, the correction index is introduced to exponentially correct the small eigenvalues ​​of the noise subspace, improve the divergence of small eigenvalues ​​in the noise subspace, and improve the performance of beamforming. The theoretical analysis and simulation results show that the proposed algorithm can make the multi-carrier FDA form a dot-shape beam at limited snapshots, reduce the sidelobe level, improve the robustness of beamforming, and have better performance.

Keywords: adaptive beamforming, correction index, limited snapshot, multi-carrier frequency diverse array, robust

Procedia PDF Downloads 130

Authors: Ahmed S. Fayed, Umima M. Mansour

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Three polyvinyl chloride membrane sensors were developed for the electrochemical evaluation of ferrous, manganese and zinc ions. The sensors were used for assaying metal ions in cervical mucus (CM) of Egyptian river buffalo-cows (Bubalus bubalis) as their levels vary dependent on cyclical hormone variation during different phases of estrus cycle. The presented sensors are based on using ionophores, β-cyclodextrin (β-CD), hydroxypropyl β-cyclodextrin (HP-β-CD) and sulfocalix-4-arene (SCAL) for sensors 1, 2 and 3 for Fe²⁺, Mn²⁺ and Zn²⁺, respectively. Dioctyl phthalate (DOP) was used as the plasticizer in a polymeric matrix of polyvinylchloride (PVC). For increasing the selectivity and sensitivity of the sensors, each sensor was enriched with a suitable complexing agent, which enhanced the sensor’s response. For sensor 1, β-CD was mixed with bathophenanthroline; for sensor 2, porphyrin was incorporated with HP-β-CD; while for sensor 3, oxine was the used complexing agent with SCAL. Linear responses of 10^-7-10^-2 M with cationic slopes of 53.46, 45.01 and 50.96 over pH range 4-8 were obtained using coated graphite sensors for ferrous, manganese and zinc ionic solutions, respectively. The three sensors were validated, according to the IUPAC guidelines. The obtained results by the presented potentiometric procedures were statistically analyzed and compared with those obtained by atomic absorption spectrophotometric method (AAS). No significant differences for either accuracy or precision were observed between the two techniques. Successful application for the determination of the three studied cations in CM, for the purpose to determine the proper time for artificial insemination (AI) was achieved. The results were compared with those obtained upon analyzing the samples by AAS. Proper detection of estrus and correct time of AI was necessary to maximize the production of buffaloes. In this experiment, 30 multi-parous buffalo-cows were in second to third lactation and weighting 415-530 kg, and were synchronized with OVSynch protocol. Samples were taken in three times around ovulation, on day 8 of OVSynch protocol, on day 9 (20 h before AI) and on day 10 (1 h before AI). Beside analysis of trace elements (Fe²⁺, Mn²⁺ and Zn²⁺) in CM using the three sensors, the samples were analyzed for the three cations and also Cu²⁺ by AAS in the CM samples and blood samples. The results obtained were correlated with hormonal analysis of serum samples and ultrasonography for the purpose of determining of the optimum time of AI. The results showed significant differences and powerful correlation with Zn²⁺ composition of CM during heat phase and the ovulation time, indicating that the parameter could be used as a tool to decide optimal time of AI in buffalo-cows.

Keywords: PVC Sensors, buffalo-cows, cyclodextrins, atomic absorption spectrophotometry, artificial insemination, OVSynch protocol

Procedia PDF Downloads 219

Authors: Siti Nur Syazni Mohd Zuki, Tan Ling Ling, Nina Suhaity Azmi, Chong Kwok Feng, Lee Yook Heng

Abstract:

Nitrite (NO2-) ion is used prevalently as a preservative in processed meat. Elevated levels of nitrite also found in edible bird’s nests (EBNs). Consumption of NO2- ion at levels above the health-based risk may cause cancer in humans. Spectrophotometric Griess test is the simplest established standard method for NO2- ion detection, however, it requires careful control of pH of each reaction step and susceptible to strong oxidants and dyeing interferences. Other traditional methods rely on the use of laboratory-scale instruments such as GC-MS, HPLC and ion chromatography, which cannot give real-time response. Therefore, it is of significant need for devices capable of measuring nitrite concentration in-situ, rapidly and without reagents, sample pretreatment or extraction step. Herein, we constructed a microspheres-based microbial optode for visual quantitation of NO2- ion. Raoutella planticola, the bacterium expressing NAD(P)H nitrite reductase (NiR) enzyme has been successfully extracted by microbial technique from EBN collected from local birdhouse. The whole cells and the lipophilic Nile Blue chromoionophore were physically absorbed on the photocurable poly(n-butyl acrylate-N-acryloxysuccinimide) [poly (nBA-NAS)] microspheres, whilst the reduced coenzyme NAD(P)H was covalently immobilized on the succinimide-functionalized acrylic microspheres to produce a reagentless biosensing system. Upon the NiR enzyme catalyzes the oxidation of NAD(P)H to NAD(P)+, NO2- ion is reduced to ammonium hydroxide, and that a colour change from blue to pink of the immobilized Nile Blue chromoionophore is perceived as a result of deprotonation reaction increasing the local pH in the microspheres membrane. The microspheres-based optosensor was optimized with a reflectance spectrophotometer at 639 nm and pH 8. The resulting microbial bio-optode membrane could quantify NO2- ion at 0.1 ppm and had a linear response up to 400 ppm. Due to the large surface area to mass ratio of the acrylic microspheres, it allows efficient solid state diffusional mass transfer of the substrate to the bio-recognition phase, and achieve the steady state response as fast as 5 min. The proposed optical microbial biosensor requires no sample pre-treatment step and possesses high stability as the whole cell biocatalyst provides protection to the enzymes from interfering substances, hence it is suitable for measurements in contaminated samples.

Keywords: acrylic microspheres, microbial bio-optode, nitrite ion, reflectometric

Procedia PDF Downloads 448

Authors: Priscila A. Bernardes, Marcos E. Buzanskas, Luciana C. A. Regitano, Ricardo V. Ventura, Danisio P. Munari

Abstract:

The best linear unbiased predictor (BLUP) is a method commonly used in genetic evaluations of breeding programs. However, this approach can lead to higher inbreeding coefficients in the population due to the intensive use of few bulls with higher genetic potential, usually presenting some degree of relatedness. High levels of inbreeding are associated to low genetic viability, fertility, and performance for some economically important traits and therefore, should be constantly monitored. Unreliable pedigree data can also lead to misleading results. Genomic information (i.e., single nucleotide polymorphism – SNP) is a useful tool to estimate the inbreeding coefficient. Runs of homozygosity have been used to evaluate homozygous segments inherited due to direct or collateral inbreeding and allows inferring population selection history. This study aimed to evaluate runs of homozygosity (ROH) and inbreeding in a population of Nelore beef cattle. A total of 814 animals were genotyped with the Illumina BovineHD BeadChip and the quality control was carried out excluding SNPs located in non-autosomal regions, with unknown position, with a p-value in the Hardy-Weinberg equilibrium lower than 10⁻⁵, call rate lower than 0.98 and samples with the call rate lower than 0.90. After the quality control, 809 animals and 509,107 SNPs remained for analyses. For the ROH analysis, PLINK software was used considering segments with at least 50 SNPs with a minimum length of 1Mb in each animal. The inbreeding coefficient was calculated using the ratio between the sum of all ROH sizes and the size of the whole genome (2,548,724kb). A total of 25.711 ROH were observed, presenting mean, median, minimum, and maximum length of 3.34Mb, 2Mb, 1Mb, and 80.8Mb, respectively. The number of SNPs present in ROH segments varied from 50 to 14.954. The longest ROH length was observed in one animal, which presented a length of 634Mb (24.88% of the genome). Four bulls were among the 10 animals with the longest extension of ROH, presenting 11% of ROH with length higher than 10Mb. Segments longer than 10Mb indicate recent inbreeding. Therefore, the results indicate an intensive use of few sires in the studied data. The distribution of ROH along the chromosomes showed that chromosomes 5 and 6 presented a large number of segments when compared to other chromosomes. The mean, median, minimum, and maximum inbreeding coefficients were 5.84%, 5.40%, 0.00%, and 24.88%, respectively. Although the mean inbreeding was considered low, the ROH indicates a recent and intensive use of few sires, which should be avoided for the genetic progress of breed.

Keywords: autozygosity, Bos taurus indicus, genomic information, single nucleotide polymorphism

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Authors: Mustafa Alhamdi

Abstract:

Industrial application to classify gamma rays and neutron events is investigated in this study using deep machine learning. The identification using a convolutional neural network and recursive neural network showed a significant improvement in predication accuracy in a variety of applications. The ability to identify the isotope type and activity from spectral information depends on feature extraction methods, followed by classification. The features extracted from the spectrum profiles try to find patterns and relationships to present the actual spectrum energy in low dimensional space. Increasing the level of separation between classes in feature space improves the possibility to enhance classification accuracy. The nonlinear nature to extract features by neural network contains a variety of transformation and mathematical optimization, while principal component analysis depends on linear transformations to extract features and subsequently improve the classification accuracy. In this paper, the isotope spectrum information has been preprocessed by finding the frequencies components relative to time and using them as a training dataset. Fourier transform implementation to extract frequencies component has been optimized by a suitable windowing function. Training and validation samples of different isotope profiles interacted with CdTe crystal have been simulated using Geant4. The readout electronic noise has been simulated by optimizing the mean and variance of normal distribution. Ensemble learning by combing voting of many models managed to improve the classification accuracy of neural networks. The ability to discriminate gamma and neutron events in a single predication approach using deep machine learning has shown high accuracy using deep learning. The paper findings show the ability to improve the classification accuracy by applying the spectrogram preprocessing stage to the gamma and neutron spectrums of different isotopes. Tuning deep machine learning models by hyperparameter optimization of neural network models enhanced the separation in the latent space and provided the ability to extend the number of detected isotopes in the training database. Ensemble learning contributed significantly to improve the final prediction.

Keywords: machine learning, nuclear physics, Monte Carlo simulation, noise estimation, feature extraction, classification

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