Search results for: transverse flux PM linear machine

Authors: Bahar Salavati Vie Le Sage, Ismaïl Ben Hariz, Flavien Viguier, Sirine Noura Kahil, Audrey Jacquin, Maxime Convert

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The ballast layer plays a key role in railroad maintenance and the geometry of the track structure. Ballast also holds the track in place as the trains roll over it. Track ballast is packed between the sleepers and on the sides of railway tracks. An imbalance in ballast volume on the tracks can lead to safety issues as well as a quick degradation of the overall quality of the railway segment. If there is a lack of ballast in the track bed during the summer, there is a risk that the rails will expand and buckle slightly due to the high temperatures. Furthermore, the knowledge of the ballast quantities that will be excavated during renewal works is important for efficient ballast management. The volume of excavated ballast per meter of track can be calculated based on excavation depth, excavation width, volume of track skeleton (sleeper and rail) and sleeper spacing. Since 2012, SNCF has been collecting 3D points cloud data covering its entire railway network by using 3D laser scanning technology (LiDAR). This vast amount of data represents a modelization of the entire railway infrastructure, allowing to conduct various simulations for maintenance purposes. This paper aims to present an automated method for ballast volume estimation based on the processing of LiDAR data. The estimation of abnormal volumes in ballast on the tracks is performed by analyzing the cross-section of the track. Further, since the amount of ballast required varies depending on the track configuration, the knowledge of the ballast profile is required. Prior to track rehabilitation, excess ballast is often present in the ballast shoulders. Based on 3D laser scans, a Digital Terrain Model (DTM) was generated and automatic extraction of the ballast profiles from this data is carried out. The surplus in ballast is then estimated by performing a comparison between this ballast profile obtained empirically, and a geometric modelization of the theoretical ballast profile thresholds as dictated by maintenance standards. Ideally, this excess should be removed prior to renewal works and recycled to optimize the output of the ballast renewal machine. Based on these parameters, an application has been developed to allow the automatic measurement of ballast profiles. We evaluated the method on a 108 kilometers segment of railroad LiDAR scans, and the results show that the proposed algorithm detects ballast surplus that amounts to values close to the total quantities of spoil ballast excavated.

Keywords: ballast, railroad, LiDAR , cloud point, track ballast, 3D point

Procedia PDF Downloads 109

Authors: Spencer Liu, William Chan, Marlena Komatz, Tommy Ramos, Mark Trentalange, Faye Rim, Dae Kim, Mary Kelly, Samuel Schuessler, Roberta Stack, Justas Lauzadis, Kathryn DelPizzo, Seth Waldman, Alexandra Sideris

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Background & Significance: The increasing prevalence of cannabis use in the United States has important safety considerations in the perioperative setting, as chronic or heavy preoperative cannabis use may increase the risk of intraoperative complications, postoperative nausea and vomiting (PONV), increased postoperative pain levels, and acute side effects associated with cannabis use cessation. In this retrospective chart review study, we sought to determine the prevalence of self-reported cannabis use in the past 5-years at a single institution in New York City. We hypothesized that there is an increasing prevalence of preoperative self-reported cannabis use among adult and adolescent patients undergoing orthopedic surgery. Methods: After IRB approval for this retrospective study, surgical cases performed on patients 12 years of age and older at the hospital’s main campus and two ambulatory surgery centers between January 1st, 2018, and December 31st, 2023, with preoperatively self-disclosed cannabis use entered in the social history intake form were identified using the tool SlicerDicer in Epic. Case and patient characteristics were extracted, and trends in utilization over time were assessed by the Cochran-Armitage trend test. Results: Overall, the prevalence of self-reported cannabis use increased from 6.6% in 2018 to 10.6% in 2023. By age group, the prevalence of self-reported cannabis use among adolescents remained consistently low (2018: 2.6%, 2023: 2.6%) but increased with significant evidence for a linear trend (p < 0.05) within every adult age group. Among adults, patients who were 18-24 years old (2018: 18%, 2023: 20.5%) and 25-34 years old (2018: 15.9%, 2023: 24.2%) had the highest prevalences of disclosure, whereas patients who were 75 years of age or older had the lowest prevalence of disclosure (2018: 1.9%, 2023: 4.6%). Patients who were 25-34 years old had the highest percent difference in disclosure rates of 8.3%, which corresponded to a 52.2% increase from 2018 to 2023. The adult age group with the highest percent change was patients who were 75 years of age or older, with a difference of 2.7%, which corresponded to a 142.1% increase from 2018 to 2023. Conclusions: These trends in preoperative self-reported cannabis use among patients undergoing orthopedic surgery have important implications for perioperative care and clinical outcomes. Efforts are underway to refine and standardize cannabis use data capture at our institution.

Keywords: orthopedic surgery, cannabis, postoperative pain, postoperative nausea

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Authors: Joseph Mwiti Marangu, Joseph Karanja Thiong'o, Jackson Muthengia Wachira

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The mechanical performance of hydrated cements mortars mainly depends on its compressive strength and setting time. These properties are crucial in the construction industry. Pozzolana based cements are mostly characterized by low 28 day compressive strength and long setting times. These are some of the major impediments to their production and diverse uses despite numerous technological and environmental benefits associated with them. The study investigated the effects of potential chemical activators on calcined clay- Portland cement blends with an aim to achieve high early compressive strength and shorter setting times in cement mortar. In addition, standard consistency, soundness and insoluble residue of all cement categories was determined. The test cement was made by blending calcined clays with Ordinary Portland Cement (OPC) at replacement levels from 35 to 50 percent by mass of the OPC to make test cement labeled PCC for the purposes of this study. Mortar prisms measuring 40mmx40mmx160mm were prepared and cured in accordance with KS EAS 148-3:2000 standard. Solutions of Na2SO4, NaOH, Na2SiO3 and Na2CO3 containing 0.5- 2.5M were separately added during casting. Compressive strength was determined at 2rd, 7th, 28th and 90th day of curing. For comparison purposes, commercial Portland Pozzolana cement (PPC) and Ordinary Portland Cement (OPC) were also investigated without activators under similar conditions. X-Ray Florescence (XRF) was used for chemical analysis while X-Ray Diffraction (XRD) and Fourier Transform Infrared Spectroscopy (FTIR) were used for mineralogical analysis of the test samples. The results indicated that addition of activators significantly increased the 2nd and 7th day compressive strength but minimal increase on the 28th and 90th day compressive strength. A relatively linear relationship was observed between compressive strength and concentration of activator solutions up to 28th of curing. Addition of the said activators significantly reduced both initial and final setting time. Standard consistency and soundness varied with increased amount of clay in the test cement and concentration of activators. Amount of insoluble residues increased with increased replacement of OPC with calcined clays. Mineralogical studies showed that N-A-S-H is formed in addition to C-S-H. In conclusion, the concentration of 2 molar for all activator solutions produced the optimum compressive strength and greatly reduced the setting times for all cement mortars.

Keywords: activators, admixture, cement, clay, pozzolana

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Authors: Leonard Tiong, Sonit Singh, Kevin Ho Shon, Sarah Lewis

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Investigation into the use of artificial intelligence in radiology continues to develop at a rapid rate. During the coronavirus pandemic, the combination of an exponential increase in chest x-rays and unpredictable staff shortages resulted in a huge strain on the department's workload. There is a World Health Organisation estimate that two-thirds of the global population does not have access to diagnostic radiology. Therefore, there could be demand for a program that could detect acute changes in imaging compatible with infection to assist with screening. We generated a conventional neural network and tested its efficacy in recognizing changes compatible with coronavirus infection. Following ethics approval, a deidentified set of 77 normal and 77 abnormal chest x-rays in patients with confirmed coronavirus infection were used to generate an algorithm that could train, validate and then test itself. DICOM and PNG image formats were selected due to their lossless file format. The model was trained with 100 images (50 positive, 50 negative), validated against 28 samples (14 positive, 14 negative), and tested against 26 samples (13 positive, 13 negative). The initial training of the model involved training a conventional neural network in what constituted a normal study and changes on the x-rays compatible with coronavirus infection. The weightings were then modified, and the model was executed again. The training samples were in batch sizes of 8 and underwent 25 epochs of training. The results trended towards an 85.71% true positive/true negative detection rate and an area under the curve trending towards 0.95, indicating approximately 95% accuracy in detecting changes on chest X-rays compatible with coronavirus infection. Study limitations include access to only a small dataset and no specificity in the diagnosis. Following a discussion with our programmer, there are areas where modifications in the weighting of the algorithm can be made in order to improve the detection rates. Given the high detection rate of the program, and the potential ease of implementation, this would be effective in assisting staff that is not trained in radiology in detecting otherwise subtle changes that might not be appreciated on imaging. Limitations include the lack of a differential diagnosis and application of the appropriate clinical history, although this may be less of a problem in day-to-day clinical practice. It is nonetheless our belief that implementing this program and widening its scope to detecting multiple pathologies such as lung masses will greatly assist both the radiology department and our colleagues in increasing workflow and detection rate.

Keywords: artificial intelligence, COVID, neural network, machine learning

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Authors: Claudia García-García, Catalina B. García-García, Román Salmerón-Gómez

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Much economic-environmental research includes the analysis of possible interactions by using Moderated Regression Analysis (MRA), which is a specific application of multiple linear regression analysis. This methodology allows analyzing how the effect of one of the independent variables is moderated by a second independent variable by adding a cross-product term between them as an additional explanatory variable. Due to the very specification of the methodology, the moderated factor is often highly correlated with the constitutive terms. Thus, great multicollinearity problems arise. The appearance of strong multicollinearity in a model has important consequences. Inflated variances of the estimators may appear, there is a tendency to consider non-significant regressors that they probably are together with a very high coefficient of determination, incorrect signs of our coefficients may appear and also the high sensibility of the results to small changes in the dataset. Finally, the high relationship among explanatory variables implies difficulties in fixing the individual effects of each one on the model under study. These consequences shifted to the moderated analysis may imply that it is not worth including an interaction term that may be distorting the model. Thus, it is important to manage the problem with some methodology that allows for obtaining reliable results. After a review of those works that applied the MRA among the ten top journals of the field, it is clear that multicollinearity is mostly disregarded. Less than 15% of the reviewed works take into account potential multicollinearity problems. To overcome the issue, this work studies the possible application of recent methodologies to MRA. Particularly, the raised regression is analyzed. This methodology mitigates collinearity from a geometrical point of view: the collinearity problem arises because the variables under study are very close geometrically, so by separating both variables, the problem can be mitigated. Raise regression maintains the available information and modifies the problematic variables instead of deleting variables, for example. Furthermore, the global characteristics of the initial model are also maintained (sum of squared residuals, estimated variance, coefficient of determination, global significance test and prediction). The proposal is implemented to data from countries of the European Union during the last year available regarding greenhouse gas emissions, per capita GDP and a dummy variable that represents the topography of the country. The use of a dummy variable as the moderator is a special variant of MRA, sometimes called “subgroup regression analysis.” The main conclusion of this work is that applying new techniques to the field can improve in a substantial way the results of the analysis. Particularly, the use of raised regression mitigates great multicollinearity problems, so the researcher is able to rely on the interaction term when interpreting the results of a particular study.

Keywords: multicollinearity, MRA, interaction, raise

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Authors: Kyung Jin Kim, Jiwon Kwak, Jae-Hoon Lee, Soo Suk Lee

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MicroRNAs (miRNA) are a class of endogenous, single-stranded, small, and non-protein coding RNA molecules typically 20-25 nucleotides long. They are thought to regulate the expression of other genes in a broad range by binding to 3’- untranslated regions (3’-UTRs) of specific mRNAs. The detection of miRNAs is very important for understanding of the function of these molecules and in the diagnosis of variety of human diseases. However, detection of miRNAs is very challenging because of their short length and high sequence similarities within miRNA families. So, a simple-to-use, low-cost, and highly sensitive method for the detection of miRNAs is desirable. In this study, we demonstrate a novel bi-directional extension (BDE) assay. In the first step, a specific linear RT primer is hybridized to 6-10 base pairs from the 3’-end of a target miRNA molecule and then reverse transcribed to generate a cDNA strand. After reverse transcription, the cDNA was hybridized to the 3’-end which is BDE sequence; it played role as the PCR template. The PCR template was amplified in an SYBR green-based quantitative real-time PCR. To prove the concept, we used human brain total RNA. It could be detected quantitatively in the range of seven orders of magnitude with excellent linearity and reproducibility. To evaluate the performance of BDE assay, we contrasted sensitivity and specificity of the BDE assay against a commercially available poly (A) tailing method using miRNAs for let-7e extracted from A549 human epithelial lung cancer cells. The BDE assay displayed good performance compared with a poly (A) tailing method in terms of specificity and sensitivity; the CT values differed by 2.5 and the melting curve showed a sharper than poly (A) tailing methods. We have demonstrated an innovative, cost-effective BDE assay that allows improved sensitivity and specificity in detection of miRNAs. Dynamic range of the SYBR green-based RT-qPCR for miR-145 could be represented quantitatively over a range of 7 orders of magnitude from 0.1 pg to 1.0 μg of human brain total RNA. Finally, the BDE assay for detection of miRNA species such as let-7e shows good performance compared with a poly (A) tailing method in terms of specificity and sensitivity. Thus BDE proves a simple, low cost, and highly sensitive assay for various miRNAs and should provide significant contributions in research on miRNA biology and application of disease diagnostics with miRNAs as targets.

Keywords: bi-directional extension (BDE), microRNA (miRNA), poly (A) tailing assay, reverse transcription, RT-qPCR

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Authors: Vinayak Malhotra, Samanyu Raina, Ajinkya Vajurkar

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Enhancing the rocket efficiency by controlling the external factors in solid rockets motors has been an active area of research for most of the terrestrial and extra-terrestrial system operations. Appreciable work has been done, but the complexity of the problem has prevented thorough understanding due to heterogenous heat and mass transfer. On record, severe issues have surfaced amounting to irreplaceable loss of mankind, instruments, facilities, and huge amount of money being invested every year. The coupled effect of an external heat source and external heat sink is an aspect yet to be articulated in combustion. Better understanding of this coupled phenomenon will induce higher safety standards, efficient missions, reduced hazard risks, with better designing, validation, and testing. The experiment will help in understanding the coupled effect of an external heat sink and heat source on the burning process, contributing in better combustion and fire safety, which are very important for efficient and safer rocket flights and space missions. Safety is the most prevalent issue in rockets, which assisted by poor combustion efficiency, emphasizes research efforts to evolve superior rockets. This signifies real, engineering, scientific, practical, systems and applications. One potential application is Solid Rocket Motors (S.R.M). The study may help in: (i) Understanding the effect on efficiency of core engines due to the primary boosters if considered as source, (ii) Choosing suitable heat sink materials for space missions so as to vary the efficiency of the solid rocket depending on the mission, (iii) Giving an idea about how the preheating of the successive stage due to previous stage acting as a source may affect the mission. The present work governs the temperature (resultant) and thus the heat transfer which is expected to be non-linear because of heterogeneous heat and mass transfer. The study will deepen the understanding of controlled inter-energy conversions and the coupled effect of external source/sink(s) surrounding the burning fuel eventually leading to better combustion thus, better propulsion. The work is motivated by the need to have enhanced fire safety and better rocket efficiency. The specific objective of the work is to understand the coupled effect of external heat source and sink on propellant burning and to investigate the role of key controlling parameters. Results as of now indicate that there exists a singularity in the coupled effect. The dominance of the external heat sink and heat source decides the relative rocket flight in Solid Rocket Motors (S.R.M).

Keywords: coupled effect, heat transfer, sink, solid rocket motors, source

Procedia PDF Downloads 223

Authors: Luis Guilherme Resende Santos, Ido Nevat, Leslie Norford

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The Urban Heat Island (UHI) is characterized by increased air temperatures in urban areas compared to undeveloped rural surrounding environments. With urbanization and densification, the intensity of UHI increases, bringing negative impacts on livability, health and economy. In order to reduce those effects, it is required to take into consideration design factors when planning future developments. Given design constraints such as population size and availability of area for development, non-trivial decisions regarding the buildings’ dimensions and their spatial distribution are required. We develop a framework for optimization of urban design in order to jointly minimize UHI intensity and buildings’ energy consumption. First, the design constraints are defined according to spatial and population limits in order to establish realistic boundaries that would be applicable in real life decisions. Second, the tools Urban Weather Generator (UWG) and EnergyPlus are used to generate outputs of UHI intensity and total buildings’ energy consumption, respectively. Those outputs are changed based on a set of variable inputs related to urban morphology aspects, such as building height, urban canyon width and population density. Lastly, an optimization problem is cast where the utility function quantifies the performance of each design candidate (e.g. minimizing a linear combination of UHI and energy consumption), and a set of constraints to be met is set. Solving this optimization problem is difficult, since there is no simple analytic form which represents the UWG and EnergyPlus models. We therefore cannot use any direct optimization techniques, but instead, develop an indirect “black box” optimization algorithm. To this end we develop a solution that is based on stochastic optimization method, known as the Cross Entropy method (CEM). The CEM translates the deterministic optimization problem into an associated stochastic optimization problem which is simple to solve analytically. We illustrate our model on a typical residential area in Singapore. Due to fast growth in population and built area and land availability generated by land reclamation, urban planning decisions are of the most importance for the country. Furthermore, the hot and humid climate in the country raises the concern for the impact of UHI. The problem presented is highly relevant to early urban design stages and the objective of such framework is to guide decision makers and assist them to include and evaluate urban microclimate and energy aspects in the process of urban planning.

Keywords: building energy consumption, stochastic optimization, urban design, urban heat island, urban weather generator

Procedia PDF Downloads 131

Authors: A. Bentley, A. Prost, N. Daruwalla, D. Osrin

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Background: This study aims to investigate the relationship between experiences of violence against women in the family, and levels of self-esteem in women residing in informal settlement (slum) areas of Mumbai, India. The authors hypothesise that violence against women in Indian households extends beyond that of intimate partner violence (IPV), to include other members of the family and that experiences of violence are associated with lower levels of self-esteem. Methods: Experiences of violence were assessed through a cross-sectional survey of 598 women, including questions about specific acts of emotional, economic, physical and sexual violence across different time points, and the main perpetrator of each. Self-esteem was assessed using the Rosenberg self-esteem questionnaire. A global score for self-esteem was calculated and the relationship between violence in the past year and Rosenberg self-esteem score was assessed using multivariable linear regression models, adjusted for years of education completed, and clustering using robust standard errors. Results: 482 (81%) women consented to interview. On average, they were 28.5 years old, had completed 6 years of education and had been married 9.5 years. 88% were Muslim and 46% lived in joint families. 44% of women had experienced at least one act of violence in their lifetime (33% emotional, 22% economic, 24% physical, 12% sexual). Of the women who experienced violence after marriage, 70% cited a perpetrator other than the husband for at least one of the acts. 5% had low self-esteem (Rosenberg score < 15). For women who experienced emotional violence in the past year, the Rosenberg score was 2.6 points lower (p < 0.001). It was 1.2 points lower (p = 0.03) for women who experienced economic violence. For physical or sexual violence in the past year, no statistically significant relationship with Rosenberg score was seen. However, for a one-unit increase in the number of different acts of each type of violence experienced in the past year, a decrease in Rosenberg score was seen (-0.62 for emotional, -0.76 for economic, -0.53 for physical and -0.47 for sexual; p < 0.05 for all). Discussion: The high prevalence of violence experiences across the lifetime was likely due to the detailed assessment of violence and the inclusion of perpetrators within the family other than the husband. Experiences of emotional or economic violence in the past year were associated with lower Rosenberg scores and therefore lower self-esteem, but no relationship was seen between experiences of physical or sexual violence and Rosenberg score overall. For all types of violence in the past year, a greater number of different acts were associated with a decrease in Rosenberg score. Emotional violence showed the strongest relationship with self-esteem, but for all types of violence the more complex the pattern of perpetration with different methods used, the lower the levels of self-esteem. Due to the cross-sectional nature of the study causal directionality cannot be attributed. Further work to investigate the relationship between severity of violence and self-esteem and whether self-esteem mediates relationships between violence and poorer mental health would be beneficial.

Keywords: family violence, India, informal settlements, Rosenberg self-esteem scale, self-esteem, violence against women

Procedia PDF Downloads 126

Authors: Anne C. Richer-De-Forges, Dominique Arrouays, Songchao Chen, Mercedes Roman Dobarco

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Most of the digital soil mapping (DSM) products rely on machine learning (ML) prediction models and/or the use or pedotransfer functions (PTF) in which calibration data come from soil analyses performed in labs. However, many other observations (often qualitative, nominal, or ordinal) could be used as proxies of lab measurements or as input data for ML of PTF predictions. DSM and ML are briefly described with some examples taken from the literature. Then, we explore the potential of an ordinal qualitative variable, i.e., the hand-feel soil texture (HFST) estimating the mineral particle distribution (PSD): % of clay (0-2µm), silt (2-50µm) and sand (50-2000µm) in 15 classes. The PSD can also be measured by lab measurements (LAST) to determine the exact proportion of these particle-sizes. However, due to cost constraints, HFST are much more numerous and spatially dense than LAST. Soil texture (ST) is a very important soil parameter to map as it is controlling many of the soil properties and functions. Therefore, comes an essential question: is it possible to use HFST as a proxy of LAST for calibration and/or validation of DSM predictions of ST? To answer this question, the first step is to compare HFST with LAST on a representative set where both information are available. This comparison was made on ca 17,400 samples representative of a French region (34,000 km2). The accuracy of HFST was assessed, and each HFST class was characterized by a probability distribution function (PDF) of its LAST values. This enables to randomly replace HFST observations by LAST values while respecting the PDF previously calculated and results in a very large increase of observations available for the calibration or validation of PTF and ML predictions. Some preliminary results are shown. First, the comparison between HFST classes and LAST analyses showed that accuracies could be considered very good when compared to other studies. The causes of some inconsistencies were explored and most of them were well explained by other soil characteristics. Then we show some examples applying these relationships and the increase of data to several issues related to DSM. The first issue is: do the PDF functions that were established enable to use HSFT class observations to improve the LAST soil texture prediction? For this objective, we replaced all HFST for topsoil by values from the PDF 100 time replicates). Results were promising for the PTF we tested (a PTF predicting soil water holding capacity). For the question related to the ML prediction of LAST soil texture on the region, we did the same kind of replacement, but we implemented a 10-fold cross-validation using points where we had LAST values. We obtained only preliminary results but they were rather promising. Then we show another example illustrating the potential of using HFST as validation data. As in numerous countries, the HFST observations are very numerous; these promising results pave the way to an important improvement of DSM products in all the countries of the world.

Keywords: digital soil mapping, improvement of digital soil mapping predictions, potential of using hand-feel soil texture, soil texture prediction

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Authors: Junior Akunzi

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In the matter of patient treatment planning quality assurance in 3D conformational therapy (3D-CRT) and volumetric arc therapy (VMAT or RapidArc), the independent monitor unit verification calculation (MUVC) is an indispensable part of the process. Concerning 3D-CRT treatment planning, the MUVC can be performed manually applying the standard ESTRO formalism. However, due to the complex shape and the amount of beams in advanced treatment planning technic such as RapidArc, the manual independent MUVC is inadequate. Therefore, commercially available software such as RadCalc can be used to perform the MUVC in complex treatment planning been. Indeed, RadCalc (version 6.3 LifeLine Inc.) uses a simplified Clarkson algorithm to compute the dose contribution for individual RapidArc fields to the isocenter. The purpose of this project is the validation of RadCalc in 3D-CRT and RapidArc for treatment planning dosimetry quality assurance at Antoine Lacassagne center (Nice, France). Firstly, the interfaces between RadCalc and our treatment planning systems (TPS) Isogray (version 4.2) and Eclipse (version13.6) were checked for data transfer accuracy. Secondly, we created test plans in both Isogray and Eclipse featuring open fields, wedges fields, and irregular MLC fields. These test plans were transferred from TPSs according to the radiotherapy protocol of DICOM RT to RadCalc and the linac via Mosaiq (version 2.5). Measurements were performed in water phantom using a PTW cylindrical semiflex ionisation chamber (0.3 cm³, 31010) and compared with the TPSs and RadCalc calculation. Finally, 30 3D-CRT plans and 40 RapidArc plans created with patients CT scan were recalculated using the CT scan of a solid PMMA water equivalent phantom for 3D-CRT and the Octavius II phantom (PTW) CT scan for RapidArc. Next, we measure the doses delivered into these phantoms for each plan with a 0.3 cm³ PTW 31010 cylindrical semiflex ionisation chamber (3D-CRT) and 0.015 cm³ PTW PinPoint ionisation chamber (Rapidarc). For our test plans, good agreements were found between calculation (RadCalc and TPSs) and measurement (mean: 1.3%; standard deviation: ± 0.8%). Regarding the patient plans, the measured doses were compared to the calculation in RadCalc and in our TPSs. Moreover, RadCalc calculations were compared to Isogray and Eclispse ones. Agreements better than (2.8%; ± 1.2%) were found between RadCalc and TPSs. As for the comparison between calculation and measurement the agreement for all of our plans was better than (2.3%; ± 1.1%). The independent MU verification calculation software RadCal has been validated for clinical use and for both 3D-CRT and RapidArc techniques. The perspective of this project includes the validation of RadCal for the Tomotherapy machine installed at centre Antoine Lacassagne.

Keywords: 3D conformational radiotherapy, intensity modulated radiotherapy, monitor unit calculation, dosimetry quality assurance

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Authors: Elia Favarelli, Enrico Testi, Andrea Giorgetti

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Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by density-based time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., mean value, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one class classifier (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, a new anomaly detector strategy is proposed, namely one class classifier neural network two (OCCNN2), which exploit the classification capability of standard classifiers in an anomaly detection problem, finding the standard class (the boundary of the features space in normal operating conditions) through a two-step approach: coarse and fine boundary estimation. The coarse estimation uses classics OCC techniques, while the fine estimation is performed through a feedforward neural network (NN) trained that exploits the boundaries estimated in the coarse step. The detection algorithms vare then compared with known methods based on principal component analysis (PCA), kernel principal component analysis (KPCA), and auto-associative neural network (ANN). In many cases, the proposed solution increases the performance with respect to the standard OCC algorithms in terms of F1 score and accuracy. In particular, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 96% with the proposed method.

Keywords: anomaly detection, frequencies selection, modal analysis, neural network, sensor network, structural health monitoring, vibration measurement

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Authors: Dijiang Lyu, Shaoying Li, Zhangzhi Tan, Zhifeng Wu, Feng Gao

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Nowadays, China is experiencing rapidly urban rail transit expansions in the world. The purpose of this study is to finely model factors influencing transit ridership at multi-time dimensions within transit stations’ pedestrian catchment area (PCA) in Guangzhou, China. This study was based on multi-sources spatial data, including smart card data, high spatial resolution images, points of interest (POIs), real-estate online data and building height data. Eight multiple linear regression models using backward stepwise method and Geographic Information System (GIS) were created at station-level. According to Chinese code for classification of urban land use and planning standards of development land, residential land-use were divided into three categories: first-level (e.g. villa), second-level (e.g. community) and third-level (e.g. urban villages). Finally, it concluded that: (1) four factors (CBD dummy, number of feeder bus route, number of entrance or exit and the years of station operation) were proved to be positively correlated with transit ridership, but the area of green land-use and water land-use negative correlated instead. (2) The area of education land-use, the second-level and third-level residential land-use were found to be highly connected to the average value of morning peak boarding and evening peak alighting ridership. But the area of commercial land-use and the average height of buildings, were significantly positive associated with the average value of morning peak alighting and evening peak boarding ridership. (3) The area of the second-level residential land-use was rarely correlated with ridership in other regression models. Because private car ownership is still large in Guangzhou now, and some residents living in the community around the stations go to work by transit at peak time, but others are much more willing to drive their own car at non-peak time. The area of the third-level residential land-use, like urban villages, was highly positive correlated with ridership in all models, indicating that residents who live in the third-level residential land-use are the main passenger source of the Guangzhou Metro. (4) The diversity of land-use was found to have a significant impact on the passenger flow on the weekend, but was non-related to weekday. The findings can be useful for station planning, management and policymaking.

Keywords: fine-scale modeling, Guangzhou city, multi-time dimensions, multi-sources spatial data, transit ridership

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Authors: Mequanent Wale Mekonen, Edoardo Otranto, Angela Alibrandi

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Diabetic kidney disease is one of the main microvascular complications caused by diabetes. Several clinical and biochemical variables are reported to be associated with diabetic kidney disease in people with type 2 diabetes. However, their interrelations could distort the effect estimation of these variables for the disease's progression. The objective of the study is to determine how the biochemical and clinical variables in people with type 2 diabetes are interrelated with each other and their effects on kidney disease progression through advanced statistical methods. First, principal component analysis was used to explore how the biochemical and clinical variables intercorrelate with each other, which helped us reduce a set of correlated biochemical variables to a smaller number of uncorrelated variables. Then, ordered logit regression models (cumulative, stage, and adjacent) were employed to assess the effect of biochemical and clinical variables on the order-level response variable (progression of kidney function) by considering the proportionality assumption for more robust effect estimation. This retrospective cross-sectional study retrieved data from a type 2 diabetic cohort in a polyclinic hospital at the University of Messina, Italy. The principal component analysis yielded three uncorrelated components. These are principal component 1, with negative loading of glycosylated haemoglobin, glycemia, and creatinine; principal component 2, with negative loading of total cholesterol and low-density lipoprotein; and principal component 3, with negative loading of high-density lipoprotein and a positive load of triglycerides. The ordered logit models (cumulative, stage, and adjacent) showed that the first component (glycosylated haemoglobin, glycemia, and creatinine) had a significant effect on the progression of kidney disease. For instance, the cumulative odds model indicated that the first principal component (linear combination of glycosylated haemoglobin, glycemia, and creatinine) had a strong and significant effect on the progression of kidney disease, with an effect or odds ratio of 0.423 (P value = 0.000). However, this effect was inconsistent across levels of kidney disease because the first principal component did not meet the proportionality assumption. To address the proportionality problem and provide robust effect estimates, alternative ordered logit models, such as the partial cumulative odds model, the partial adjacent category model, and the partial continuation ratio model, were used. These models suggested that clinical variables such as age, sex, body mass index, medication (metformin), and biochemical variables such as glycosylated haemoglobin, glycemia, and creatinine have a significant effect on the progression of kidney disease.

Keywords: diabetic kidney disease, ordered logit model, principal component analysis, type 2 diabetes

Procedia PDF Downloads 39

Authors: M. Aboalazayem, Essam A. Gouda, Ahmed M. Moussa, Amr E. Flifl

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Field data collecting is considered one of the most difficult work due to the difficulty of accessing large zones such as large lakes. Also, it is well known that the cost of obtaining field data is very expensive. Remotely monitoring of lake water quality (WQ) provides an economically feasible approach comparing to field data collection. Researchers have shown that lake WQ can be properly monitored via Remote sensing (RS) analyses. Using satellite images as a method of WQ detection provides a realistic technique to measure quality parameters across huge areas. Landsat (LS) data provides full free access to often occurring and repeating satellite photos. This enables researchers to undertake large-scale temporal comparisons of parameters related to lake WQ. Satellite measurements have been extensively utilized to develop algorithms for predicting critical water quality parameters (WQPs). The goal of this paper is to use RS to derive WQ indicators in Aswan High Dam Reservoir (AHDR), which is considered Egypt's primary and strategic reservoir of freshwater. This study focuses on using Landsat8 (L-8) band surface reflectance (SR) observations to predict water-quality characteristics which are limited to Turbidity (TUR), total suspended solids (TSS), and chlorophyll-a (Chl-a). ArcGIS pro is used to retrieve L-8 SR data for the study region. Multiple linear regression analysis was used to derive new correlations between observed optical water-quality indicators in April and L-8 SR which were atmospherically corrected by values of various bands, band ratios, and or combinations. Field measurements taken in the month of May were used to validate WQP obtained from SR data of L-8 Operational Land Imager (OLI) satellite. The findings demonstrate a strong correlation between indicators of WQ and L-8 .For TUR, the best validation correlation with OLI SR bands blue, green, and red, were derived with high values of Coefficient of correlation (R2) and Root Mean Square Error (RMSE) equal 0.96 and 3.1 NTU, respectively. For TSS, Two equations were strongly correlated and verified with band ratios and combinations. A logarithm of the ratio of blue and green SR was determined to be the best performing model with values of R2 and RMSE equal to 0.9861 and 1.84 mg/l, respectively. For Chl-a, eight methods were presented for calculating its value within the study area. A mix of blue, red, shortwave infrared 1(SWR1) and panchromatic SR yielded the greatest validation results with values of R2 and RMSE equal 0.98 and 1.4 mg/l, respectively.

Keywords: remote sensing, landsat 8, nasser lake, water quality

Procedia PDF Downloads 92

Authors: Mahshid Arabi

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In recent years, remarkable advancements in the fields of artificial intelligence and machine learning have led to the development of facial recognition technologies. These technologies are now employed in a wide range of applications, including security, surveillance, healthcare, and education. In the field of education, the identification of university entrance exam candidates has been one of the fundamental challenges. Traditional methods such as using ID cards and handwritten signatures are not only inefficient and prone to fraud but also susceptible to errors. In this context, utilizing advanced technologies like facial recognition can be an effective and efficient solution to increase the accuracy and reliability of identity verification in entrance exams. This article examines the use of FaceMatch software for recognizing the faces of university entrance exam candidates in Iran. The main objective of this research is to evaluate the efficiency and accuracy of FaceMatch software in identifying university entrance exam candidates to prevent fraud and ensure the authenticity of individuals' identities. Additionally, this research investigates the advantages and challenges of using this technology in Iran's educational systems. This research was conducted using an experimental method and random sampling. In this study, 1000 university entrance exam candidates in Iran were selected as samples. The facial images of these candidates were processed and analyzed using FaceMatch software. The software's accuracy and efficiency were evaluated using various metrics, including accuracy rate, error rate, and processing time. The research results indicated that FaceMatch software could accurately identify candidates with a precision of 98.5%. The software's error rate was less than 1.5%, demonstrating its high efficiency in facial recognition. Additionally, the average processing time for each candidate's image was less than 2 seconds, indicating the software's high efficiency. Statistical evaluation of the results using precise statistical tests, including analysis of variance (ANOVA) and t-test, showed that the observed differences were significant, and the software's accuracy in identity verification is high. The findings of this research suggest that FaceMatch software can be effectively used as a tool for identifying university entrance exam candidates in Iran. This technology not only enhances security and prevents fraud but also simplifies and streamlines the exam administration process. However, challenges such as preserving candidates' privacy and the costs of implementation must also be considered. The use of facial recognition technology with FaceMatch software in Iran's educational systems can be an effective solution for preventing fraud and ensuring the authenticity of university entrance exam candidates' identities. Given the promising results of this research, it is recommended that this technology be more widely implemented and utilized in the country's educational systems.

Keywords: facial recognition, FaceMatch software, Iran, university entrance exam

Procedia PDF Downloads 46

Authors: Mai Abdul Latif, Yuntian Feng

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Applied Element Method (AEM) is a method that was developed to aid in the analysis of the collapse of structures. Current available methods cannot deal with structural collapse accurately; however, AEM can simulate the behavior of a structure from an initial state of no loading until collapse of the structure. The elements in AEM are connected with sets of normal and shear springs along the edges of the elements, that represent the stresses and strains of the element in that region. The elements are rigid, and the material properties are introduced through the spring stiffness. Nonlinear dynamic analysis has been widely modelled using the finite element method for analysis of progressive collapse of structures; however, difficulties in the analysis were found at the presence of excessively deformed elements with cracking or crushing, as well as having a high computational cost, and difficulties on choosing the appropriate material models for analysis. The Applied Element method is developed and coded to significantly improve the accuracy and also reduce the computational costs of the method. The scheme works for both linear elastic, and nonlinear cases, including elasto-plastic materials. This paper will focus on elastic and elasto-plastic material behaviour, where the number of springs required for an accurate analysis is tested. A steel cantilever beam is used as the structural element for the analysis. The first modification of the method is based on the Gaussian Quadrature to distribute the springs. Usually, the springs are equally distributed along the face of the element, but it was found that using Gaussian springs, only up to 2 springs were required for perfectly elastic cases, while with equal springs at least 5 springs were required. The method runs on a Newton-Raphson iteration scheme, and quadratic convergence was obtained. The second modification is based on adapting the number of springs required depending on the elasticity of the material. After the first Newton Raphson iteration, Von Mises stress conditions were used to calculate the stresses in the springs, and the springs are classified as elastic or plastic. Then transition springs, springs located exactly between the elastic and plastic region, are interpolated between regions to strictly identify the elastic and plastic regions in the cross section. Since a rectangular cross-section was analyzed, there were two plastic regions (top and bottom), and one elastic region (middle). The results of the present study show that elasto-plastic cases require only 2 springs for the elastic region, and 2 springs for the plastic region. This showed to improve the computational cost, reducing the minimum number of springs in elasto-plastic cases to only 6 springs. All the work is done using MATLAB and the results will be compared to models of structural elements using the finite element method in ANSYS.

Keywords: applied element method, elasto-plastic, Gaussian springs, nonlinear

Procedia PDF Downloads 225

Authors: Triveni Gogoi, Rima Chatterjee

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Shear wave velocity (Vs) estimation is an important approach in the seismic exploration and characterization of a hydrocarbon reservoir. There are varying methods for prediction of S-wave velocity, if recorded S-wave log is not available. But all the available methods for Vs prediction are empirical mathematical models. Shear wave velocity can be estimated using P-wave velocity by applying Castagna’s equation, which is the most common approach. The constants used in Castagna’s equation vary for different lithologies and geological set-ups. In this study, multiple regression analysis has been used for estimation of S-wave velocity. The EMERGE module from Hampson-Russel software has been used here for generation of S-wave log. Both single attribute and multi attributes analysis have been carried out for generation of synthetic S-wave log in Upper Assam basin. Upper Assam basin situated in North Eastern India is one of the most important petroleum provinces of India. The present study was carried out using four wells of the study area. Out of these wells, S-wave velocity was available for three wells. The main objective of the present study is a prediction of shear wave velocities for wells where S-wave velocity information is not available. The three wells having S-wave velocity were first used to test the reliability of the method and the generated S-wave log was compared with actual S-wave log. Single attribute analysis has been carried out for these three wells within the depth range 1700-2100m, which corresponds to Barail group of Oligocene age. The Barail Group is the main target zone in this study, which is the primary producing reservoir of the basin. A system generated list of attributes with varying degrees of correlation appeared and the attribute with the highest correlation was concerned for the single attribute analysis. Crossplot between the attributes shows the variation of points from line of best fit. The final result of the analysis was compared with the available S-wave log, which shows a good visual fit with a correlation of 72%. Next multi-attribute analysis has been carried out for the same data using all the wells within the same analysis window. A high correlation of 85% has been observed between the output log from the analysis and the recorded S-wave. The almost perfect fit between the synthetic S-wave and the recorded S-wave log validates the reliability of the method. For further authentication, the generated S-wave data from the wells have been tied to the seismic and correlated them. Synthetic share wave log has been generated for the well M2 where S-wave is not available and it shows a good correlation with the seismic. Neutron porosity, density, AI and P-wave velocity are proved to be the most significant variables in this statistical method for S-wave generation. Multilinear regression method thus can be considered as a reliable technique for generation of shear wave velocity log in this study.

Keywords: Castagna's equation, multi linear regression, multi attribute analysis, shear wave logs

Procedia PDF Downloads 229

Authors: Rezaul K. Sk., Mohammad Ashiq, Avinash K. Srivastava

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The use of radiation shielding technology ranging from EMI to high energy gamma rays in various areas such as devices, medical science, defense, nuclear power plants, medical diagnostics etc. is increasing all over the world. However, exposure to different radiations such as X-ray, gamma ray, neutrons and EMI above the permissible limits is harmful to living beings, the environment and sensitive laboratory equipment. In order to solve this problem, there is a need to develop effective radiation shielding materials. Conventionally, lead and lead-based materials are used in making shielding materials, as lead is cheap, dense and provides very effective shielding to radiation. However, the problem associated with the use of lead is its toxic nature and carcinogenic. So, to overcome these drawbacks, there is a great need for lead-free radiation shielding materials and that should also be economically sustainable. Therefore, it is necessary to look for the synthesis of radiation-shielding glass by using other heavy metal oxides (HMO) instead of lead. The lead-free BaO-doped TeZnCaB glass systems have been synthesized by the traditional melt-quenching method. X-ray diffraction analysis confirmed the glassy nature of the synthesized samples. The densities of the developed glass samples were increased by doping the BaO concentration, ranging from 4.292 to 4.725 g/cm3. The vibrational and bending modes of the BaO-doped glass samples were analyzed by Raman spectroscopy, and FTIR (Fourier-transform infrared spectroscopy) was performed to study the functional group present in the samples. UV-visible characterization revealed the significance of optical parameters such as Urbach’s energy, refractive index and optical energy band gap. The indirect and direct energy band gaps were decreased with the BaO concentration whereas the refractive index was increased. X-ray attenuation measurements were performed to determine the radiation shielding parameters such as linear attenuation coefficient (LAC), mass attenuation coefficient (MAC), half value layer (HVL), tenth value layer (TVL), mean free path (MFP), attenuation factor (Att%) and lead equivalent thickness of the lead-free BaO-doped TeZnCaB glass system. It was observed that the radiation shielding characteristics were enhanced with the addition of BaO content in the TeZnCaB glass samples. The glass samples with higher contents of BaO have the best attenuation performance. So, it could be concluded that the addition of BaO into TeZnCaB glass samples is a significant technique to improve the radiation shielding performance of the glass samples. The best lead equivalent thickness was 2.626 mm, and these glasses could be good materials for medical diagnostics applications.

Keywords: heavy metal oxides, lead-free, melt-quenching method, x-ray attenuation

Procedia PDF Downloads 31

Authors: Tessa Sellis, Marike A. Wierda, Elke Tichelman, Mirjam T. Van Lohuizen, Marjolein Berger, François Schellevis, Claudi Bockting, Lilian Peters, Huib Burger

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Introduction: Most women develop a healthy bond with their children, however, adequate mother-to-infant bonding cannot be taken for granted. Mother-to-infant bonding refers to the feelings and emotions experienced by the mother towards her child. It is an ongoing process that starts during pregnancy and develops during the first year postpartum and likely throughout early childhood. The prevalence of inadequate bonding ranges from 7 to 11% in the first weeks postpartum. An impaired mother-to-infant bond can cause long-term complications for both mother and child. Very little research has been conducted on the direct relationship between the personality of the mother and mother-to-infant bonding. This study explores the associations between maternal personality and postnatal mother-to-infant bonding. The main hypothesis is that there is a relationship between neuroticism and mother-to-infant bonding. Methods: Data for this study were used from the Pregnancy Anxiety and Depression Study (2010-2014), which examined symptoms of and risk factors for anxiety or depression during pregnancy and the first year postpartum of 6220 pregnant women who received primary, secondary or tertiary care in the Netherlands. The study was expanded in 2015 to investigate postnatal mother-to-infant bonding. For the current research 3836 participants were included. During the first trimester of gestation, baseline characteristics, as well as personality, were measured through online questionnaires. Personality was measured by the NEO Five Factor Inventory (NEO-FFI), which covers the big five of personality (neuroticism, extraversion, openness, altruism and conscientiousness). Mother-to-infant bonding was measured postpartum by the Postpartum Bonding Questionnaire (PBQ). Univariate linear regression analysis was performed to estimate the associations. Results: 5% of the PBQ-respondents reported impaired bonding. A statistically significant association was found between neuroticism and mother-to-infant bonding (p < .001): mothers scoring higher on neuroticism, reported a lower score on mother-to-infant bonding. In addition, a positive correlation was found between the personality traits extraversion (b: -.081), openness (b: -.014), altruism (b: -.067), conscientiousness (b: -.060) and mother-to-infant bonding. Discussion: This study is one of the first to demonstrate a direct association between the personality of the mother and mother-to-infant bonding. A statistically significant relationship has been found between neuroticism and mother-to-infant bonding, however, the percentage of variance predictable by a personality dimension is very small. This study has examined one part of the multi-factorial topic of mother-to-infant bonding and offers more insight into the rarely investigated and complex matter of mother-to-infant bonding. For midwives, it is important recognize the risks for impaired bonding and subsequently improve policy for women at risk.

Keywords: mother-to-infant bonding, personality, postpartum, pregnancy

Procedia PDF Downloads 364

Authors: Medine Demir, Volker John

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Fluid equations in a rotating frame of reference have a broad class of important applications in meteorology and oceanography, especially in the large-scale flows considered in ocean and atmosphere, as well as many physical and industrial applications. The Coriolis and the centripetal forces, resulting from the rotation of the earth, play a crucial role in such systems. For such applications it may be required to solve the system in complex three-dimensional geometries. In recent years, the Navier--Stokes equations in a rotating frame have been investigated in a number of papers using the classical inf-sup stable mixed methods, like Taylor-Hood pairs, to contribute to the analysis and the accurate and efficient numerical simulation. Numerical analysis reveals that these classical methods introduce a pressure-dependent contribution in the velocity error bounds that is proportional to some inverse power of the viscosity. Hence, these methods are optimally convergent but small velocity errors might not be achieved for complicated pressures and small viscosity coefficients. Several approaches have been proposed for improving the pressure-robustness of pairs of finite element spaces. In this contribution, a pressure-robust space discretization of the incompressible Navier--Stokes equations in a rotating frame of reference is considered. The discretization employs divergence-free, $H^1$-conforming mixed finite element methods like Scott--Vogelius pairs. However, this approach might come with a modification of the meshes, like the use of barycentric-refined grids in case of Scott--Vogelius pairs. However, this strategy requires the finite element code to have control on the mesh generator which is not realistic in many engineering applications and might also be in conflict with the solver for the linear system. An error estimate for the velocity is derived that tracks the dependency of the error bound on the coefficients of the problem, in particular on the angular velocity. Numerical examples illustrate the theoretical results. The idea of pressure-robust method could be cast on different types of flow problems which would be considered as future studies. As another future research direction, to avoid a modification of the mesh, one may use a very simple parameter-dependent modification of the Scott-Vogelius element, the pressure-wired Stokes element, such that the inf-sup constant is independent of nearly-singular vertices.

Keywords: navier-stokes equations in a rotating frame of refence, coriolis force, pressure-robust error estimate, scott-vogelius pairs of finite element spaces

Procedia PDF Downloads 64

Authors: Z. Razi, S. M. J. Mortazavi, N. Shokrpour, Z. Shayan, F. Amiri

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Long-term exposure to low doses of ionizing radiation occurs in radiation health care workplaces. Although doses in health professions are generally very low, there are still matters of concern. The radiation safety program promotes occupational radiation safety through accurate and reliable monitoring of radiation workers in order to effectively manage radiation protection. To achieve this goal, it has become mandatory to implement health examination periodically. As a result, based on the hematological alterations, working populations with a common occupational radiation history are screened. This paper calls into question the effectiveness of blood component analysis as a screening program which is mandatory for medical radiation workers in some countries. This study details the distribution and trends of changes in blood components, including white blood cells (WBCs), red blood cells (RBCs) and platelets as well as received cumulative doses from occupational radiation exposure. This study was conducted among 199 participants and 100 control subjects at the medical imaging departments at the central hospital of Shiraz University of Medical Sciences during the years 2006–2010. Descriptive and analytical statistics, considering the P-value<0.05 as statistically significance was used for data analysis. The results of this study show that there is no significant difference between the radiation workers and controls regarding WBCs and platelet count during 4 years. Also, we have found no statistically significant difference between the two groups with respect to RBCs. Besides, no statistically significant difference was observed with respect to RBCs with regards to gender, which has been analyzed separately because of the lower reference range for normal RBCs levels in women compared to men and. Moreover, the findings confirm that in a separate evaluation between WBCs count and the personnel’s working experience and their annual exposure dose, results showed no linear correlation between the three variables. Since the hematological findings were within the range of control levels, it can be concluded that the radiation dosage (which was not more than 7.58 mSv in this study) had been too small to stimulate any quantifiable change in medical radiation worker’s blood count. Thus, use of more accurate method for screening program based on the working profile of the radiation workers and their accumulated dose is suggested. In addition, complexity of radiation-induced functions and the influence of various factors on blood count alteration should be taken into account.

Keywords: blood cell count, mandatory testing, occupational exposure, radiation

Procedia PDF Downloads 461

Authors: Haoming Ma, Guo Yu, Peiru Zhou

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Background: Previous studies show that dysglycemia, mostly hyperglycemia, hypoglycemia and glycemic variability(GV), are associated with excess mortality in critically ill patients, especially those without diabetes. Glycemic variability is an increasingly important measure of glucose control in the intensive care unit (ICU) due to this association. However, there is limited data pertaining to the relationship between different clinical factors and glycemic variability and clinical outcomes categorized by their DM status. This retrospective study of 958 intensive care unit(ICU) patients was conducted to investigate the relationship between GV and outcome in critically ill patients and further to determine the significant factors that contribute to the glycemic variability. Aim: We hypothesize that the factors contributing to mortality and the glycemic variability are different from critically ill patients with or without diabetes. And the primary aim of this study was to determine which dysglycemia (hyperglycemia\hypoglycemia\glycemic variability) is independently associated with an increase in mortality among critically ill patients in different groups (DM/Non-DM). Secondary objectives were to further investigate any factors affecting the glycemic variability in two groups. Method: A total of 958 diabetic and non-diabetic patients with severe diseases in the ICU were selected for this retrospective analysis. The glycemic variability was defined as the coefficient of variation (CV) of blood glucose. The main outcome was death during hospitalization. The secondary outcome was GV. The logistic regression model was used to identify factors associated with mortality. The relationships between GV and other variables were investigated using linear regression analysis. Results: Information on age, APACHE II score, GV, gender, in-ICU treatment and nutrition was available for 958 subjects. Predictors remaining in the final logistic regression model for mortality were significantly different in DM/Non-DM groups. Glycemic variability was associated with an increase in mortality in both DM(odds ratio 1.05; 95%CI:1.03-1.08,p＜0.001) or Non-DM group(odds ratio 1.07; 95%CI:1.03-1.11,p=0.002). For critically ill patients without diabetes, factors associated with glycemic variability included APACHE II score(regression coefficient, 95%CI:0.29,0.22-0.36,p＜0.001), Mean BG(0.73,0.46-1.01,p＜0.001), total parenteral nutrition(2.87,1.57-4.17,p＜0.001), serum albumin(-0.18,-0.271 to -0.082,p＜0.001), insulin treatment(2.18,0.81-3.55,p=0.002) and duration of ventilation(0.006,0.002-1.010,p=0.003).However, for diabetes patients, APACHE II score(0.203,0.096-0.310,p＜0.001), mean BG(0.503,0.138-0.869,p=0.007) and duration of diabetes(0.167,0.033-0.301,p=0.015) remained as independent risk factors of GV. Conclusion: We found that the relation between dysglycemia and mortality is different in the diabetes and non-diabetes groups. And we confirm that GV was associated with excess mortality in DM or Non-DM patients. Furthermore, APACHE II score, Mean BG, total parenteral nutrition, serum albumin, insulin treatment and duration of ventilation were significantly associated with an increase in GV in Non-DM patients. While APACHE II score, mean BG and duration of diabetes (years) remained as independent risk factors of increased GV in DM patients. These findings provide important context for further prospective trials investigating the effect of different clinical factors in critically ill patients with or without diabetes.

Keywords: diabetes, glycemic variability, predictors, severe disease

Procedia PDF Downloads 189

Authors: Jalil ur Rehman, Ramesh C. Tailor, Muhammad Isa Khan, Jahnzeeb Ashraf, Muhammad Afzal, Geofferry S. Ibbott

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Purpose: The purpose of this analysis was to investigate dose distribution of different techniques (3D-CRT, IMRT and VMAT) of head and neck cancer using 3-dimensional dosimeter called PRESAGETM Dosimeter. Materials and Methods: Computer tomography (CT) scans of radiological physics center (RPC) head and neck anthropomorphic phantom with both RPC standard insert and PRESAGETM insert were acquired separated with Philipp’s CT scanner and both CT scans were exported via DICOM to the Pinnacle version 9.4 treatment planning system (TPS). Each plan was delivered twice to the RPC phantom first containing the RPC standard insert having TLD and film dosimeters and then again containing the Presage insert having 3-D dosimeter (PRESAGETM) by using a Varian True Beam linear accelerator. After irradiation, the standard insert including point dose measurements (TLD) and planar Gafchromic® EBT film measurement were read using RPC standard procedure. The 3D dose distribution from PRESAGETM was read out with the Duke Midsized optical scanner dedicated to RPC (DMOS-RPC). Dose volume histogram (DVH), mean and maximal doses for organs at risk were calculated and compared among each head and neck technique. The prescription dose was same for all head and neck radiotherapy techniques which was 6.60 Gy/friction. Beam profile comparison and gamma analysis were used to quantify agreements among film measurement, PRESAGETM measurement and calculated dose distribution. Quality assurances of all plans were performed by using ArcCHECK method. Results: VMAT delivered the lowest mean and maximum doses to organ at risk (spinal cord, parotid) than IMRT and 3DCRT. Such dose distribution was verified by absolute dose distribution using thermoluminescent dosimeter (TLD) system. The central axial, sagittal and coronal planes were evaluated using 2D gamma map criteria(± 5%/3 mm) and results were 99.82% (axial), 99.78% (sagital), 98.38% (coronal) for VMAT plan and found the agreement between PRESAGE and pinnacle was better than IMRT and 3D-CRT plan excludes a 7 mm rim at the edge of the dosimeter. Profile showed good agreement for all plans between film, PRESAGE and pinnacle and 3D gamma was performed for PTV and OARs, VMAT and 3DCRT endow with better agreement than IMRT. Conclusion: VMAT delivered lowered mean and maximal doses to organs at risk and better PTV coverage during head and neck radiotherapy. TLD, EBT film and PRESAGETM dosimeters suggest that VMAT was better for the treatment of head and neck cancer than IMRT and 3D-CRT.

Keywords: RPC, 3DCRT, IMRT, VMAT, EBT2 film, TLD, PRESAGETM

Procedia PDF Downloads 395

Authors: Wisdom Robert Duruji

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The outbreak of SARS-CoV-2 serotype infection was recorded in January 2020 in Wuhan City, Hubei Province, China. This study examines the concepts of the COVID-19 pandemic and the implications of vaccines for health security in Nigeria and Diasporas. It challenges the widely accepted assumption that the first case of coronavirus infection in Nigeria was recorded on February 27th, 2020, in Lagos. The study utilizes a range of research methods to achieve its objectives. These include the double-layered culture technique, literature review, website knowledge, Google search, news media information, academic journals, fieldwork, and on-site observations. These diverse methods allow for a comprehensive analysis of the concepts and the implications being studied. The study finds that coronavirus infection can be asymptomatic; it may be the antigenicity of the leukocytes (white blood cells), which produce immunogenic hapten or interferons (α, β and γ) that fight infectious parasites, was an immune response that prevented severe virulence in healthy individuals; the reason healthy patients of coronavirus infection in Nigeria naturally recovered after two to three weeks of on-set of infection and test negative. However, the fatality data from the Nigerian Centre for Disease Control (NCDC) is incorrect in this study’s finding; it perused that the fatalities were primarily due to underlying ailments, hunger, and malnutrition in debilitated, comorbid, or compromised patients. This study concluded that the kits and Polymerase Chain Reaction (PCR) machine currently used by the Nigerian Centre for Disease Control (NCDC) in testing and confirming COVID-19 in Nigeria is not ideal; it is programmed and negates separating the strain to its specific serotypes amongst its genera coronavirus, and family Coronaviridae; and might have confirmed patients with the symptoms of febrile caused by cough, catarrh, typhoid and malaria parasites as Covid-19 positive. Therefore, it is recommended that the coronavirus species infected in Nigeria are opportunistic parasites that thrive in human immuno-suppressed conditions like the herpesvirus; it cannot be eradicated by vaccines; the only virucides are interferons, immunoglobulins, and probably synthetic antiviral guanosine drugs like copegus or ribavirin. The findings emphasized that COVID-19 is not the primary pandemic disease in Nigeria; the lockdown was a mirage and not necessary; but rather, pandemic diseases in Nigeria are corruption, nepotism, hunger, and malnutrition caused by ineptitude in governance, religious dichotomy, and ethnic conflicts.

Keywords: coronavirus, corruption, Covid-19 pandemic, lock-down, Nigeria, vaccine

Procedia PDF Downloads 67

Authors: W. Meron Mebrahtu, R. Absi

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Velocity distribution in turbulent open-channel flows is organized in a complex manner. This is due to the large spatial and temporal variability of fluid motion resulting from the free-surface turbulent flow condition. This phenomenon is complicated further due to the complex geometry of channels and the presence of solids transported. Thus, several efforts were made to understand the phenomenon and obtain accurate mathematical models that are suitable for engineering applications. However, predictions are inaccurate because oversimplified assumptions are involved in modeling this complex phenomenon. Therefore, the aim of this work is to study velocity distribution profiles and obtain simple, more accurate, and predictive mathematical models. Particular focus will be made on the acceptable simplification of the general transport equations and an accurate representation of eddy viscosity. Wide rectangular open-channel seems suitable to begin the study; other assumptions are smooth-wall, and sediment-free flow under steady and uniform flow conditions. These assumptions will allow examining the effect of the bottom wall and the free surface only, which is a necessary step before dealing with more complex flow scenarios. For this flow condition, two ordinary differential equations are obtained for velocity profiles; from the Reynolds-averaged Navier-Stokes (RANS) equation and equilibrium consideration between turbulent kinetic energy (TKE) production and dissipation. Then different analytic models for eddy viscosity, TKE, and mixing length were assessed. Computation results for velocity profiles were compared to experimental data for different flow conditions and the well-known linear, log, and log-wake laws. Results show that the model based on the RANS equation provides more accurate velocity profiles. In the viscous sublayer and buffer layer, the method based on Prandtl’s eddy viscosity model and Van Driest mixing length give a more precise result. For the log layer and outer region, a mixing length equation derived from Von Karman’s similarity hypothesis provides the best agreement with measured data except near the free surface where an additional correction based on a damping function for eddy viscosity is used. This method allows more accurate velocity profiles with the same value of the damping coefficient that is valid under different flow conditions. This work continues with investigating narrow channels, complex geometries, and the effect of solids transported in sewers.

Keywords: accuracy, eddy viscosity, sewers, velocity profile

Procedia PDF Downloads 112

Authors: Eliane G. Tótoli, Hérida Regina N. Salgado

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Daptomycin is an important antimicrobial agent used in clinical practice nowadays, since it is very active against some Gram-positive bacteria that are particularly challenges for the medicine, such as methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococci (VRE). The importance of environmental preservation has receiving special attention since last years. Considering the evident need to protect the natural environment and the introduction of strict quality requirements regarding analytical procedures used in pharmaceutical analysis, the industries must seek environmentally friendly alternatives in relation to the analytical methods and other processes that they follow in their routine. In view of these factors, green analytical chemistry is prevalent and encouraged nowadays. In this context, infrared spectroscopy stands out. This is a method that does not use organic solvents and, although it is formally accepted for the identification of individual compounds, also allows the quantitation of substances. Considering that there are few green analytical methods described in literature for the analysis of daptomycin, the aim of this work was the development and validation of a green analytical method for the quantification of this drug in lyophilized powder for injectable solution, by Fourier-transform infrared spectroscopy (FT-IR). Method: Translucent potassium bromide pellets containing predetermined amounts of the drug were prepared and subjected to spectrophotometric analysis in the mid-infrared region. After obtaining the infrared spectrum and with the assistance of the IR Solution software, quantitative analysis was carried out in the spectral region between 1575 and 1700 cm-1, related to a carbonyl band of the daptomycin molecule, and this band had its height analyzed in terms of absorbance. The method was validated according to ICH guidelines regarding linearity, precision (repeatability and intermediate precision), accuracy and robustness. Results and discussion: The method showed to be linear (r = 0.9999), precise (RSD% < 2.0), accurate and robust, over a concentration range from 0.2 to 0.6 mg/pellet. In addition, this technique does not use organic solvents, which is one great advantage over the most common analytical methods. This fact contributes to minimize the generation of organic solvent waste by the industry and thereby reduces the impact of its activities on the environment. Conclusion: The validated method proved to be adequate to quantify daptomycin in lyophilized powder for injectable solution and can be used for its routine analysis in quality control. In addition, the proposed method is environmentally friendly, which is in line with the global trend.

Keywords: daptomycin, Fourier-transform infrared spectroscopy, green analytical chemistry, quality control, spectrometry in IR region

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Authors: Md. Rafiul Biswas, Uzair Shah, Mohammad Alkayal, Zubair Shah, Othman Althawadi, Kamila Swart

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Background: Social media platforms play a significant role in the mediated consumption of sport, especially so for sport mega-event. The characteristics of Twitter data (e.g., user mentions, retweets, likes, #hashtag) accumulate the users in one ground and spread information widely and quickly. Analysis of Twitter data can reflect the public attitudes, behavior, and sentiment toward a specific event on a larger scale than traditional surveys. Qatar is going to be the first Arab country to host the mega sports event FIFA World Cup 2022 (Q22). Qatar has hosted the FIFA Arab Cup 2021 (FAC21) to serve as a preparation for the mega-event. Objectives: This study investigates public sentiments and experiences about FAC21 and provides an insight to enhance the public experiences for the upcoming Q22. Method: FCA21-related tweets were downloaded using Twitter Academic research API between 01 October 2021 to 18 February 2022. Tweets were divided into three different periods: before T1 (01 Oct 2021 to 29 Nov 2021), during T2 (30 Nov 2021 -18 Dec 2021), and after the FAC21 T3 (19 Dec 2021-18 Feb 2022). The collected tweets were preprocessed in several steps to prepare for analysis; (1) removed duplicate and retweets, (2) removed emojis, punctuation, and stop words (3) normalized tweets using word lemmatization. Then, rule-based classification was applied to remove irrelevant tweets. Next, the twitter-XLM-roBERTa-base model from Huggingface was applied to identify the sentiment in the tweets. Further, state-of-the-art BertTopic modeling will be applied to identify trending topics over different periods. Results: We downloaded 8,669,875 Tweets posted by 2728220 unique users in different languages. Of those, 819,813 unique English tweets were selected in this study. After splitting into three periods, 541630, 138876, and 139307 were from T1, T2, and T3, respectively. Most of the sentiments were neutral, around 60% in different periods. However, the rate of negative sentiment (23%) was high compared to positive sentiment (18%). The analysis indicates negative concerns about FAC21. Therefore, we will apply BerTopic to identify public concerns. This study will permit the investigation of people’s expectations before FAC21 (e.g., stadium, transportation, accommodation, visa, tickets, travel, and other facilities) and ascertain whether these were met. Moreover, it will highlight public expectations and concerns. The findings of this study can assist the event organizers in enhancing implementation plans for Q22. Furthermore, this study can support policymakers with aligning strategies and plans to leverage outstanding outcomes.

Keywords: FIFA Arab Cup, FIFA, Twitter, machine learning

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Authors: Sanjeet Patra, Soham Roychowdhury

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In recent times, functionally graded dielectric elastomer (FGDE) has gained significant attention within the realm of soft actuation due to its dual capacity to exert highly localized stresses while maintaining its compliant characteristics on application of electro-mechanical loading. Nevertheless, the full potential of dielectric elastomer (DE) has not been fully explored due to their susceptibility to instabilities when subjected to electro-mechanical loads. As a result, study and analysis of such instabilities becomes crucial for the design and realization of dielectric actuators. Prismatic bifurcation is a type of instability that has been recognized in a DE tube. Though several studies have reported on the analysis for prismatic bifurcation in an isotropic DE tube, there is an insufficiency in studies related to prismatic bifurcation of FGDE tubes. Therefore, this paper aims to determine the onset of prismatic bifurcations on an incompressible FGDE tube when subjected to electrical loading across the thickness of the tube and internal pressurization. The analysis has been conducted by imposing two axial boundary conditions on the tube, specifically axially free ends and axially clamped ends. Additionally, the rigidity modulus of the tube has been linearly graded in the direction of thickness where the inner surface of the tube has a lower stiffness than the outer surface. The static equilibrium equations for deformation of the axisymmetric tube are derived and solved using numerical technique. The condition for prismatic bifurcation of the axisymmetric static equilibrium solutions has been obtained by using the linearized incremental constitutive equations. Two modes of bifurcations, corresponding to two different non-circular cross-sectional geometries, have been explored in this study. The outcomes reveal that the FGDE tubes experiences prismatic bifurcation before the Hessian criterion of failure is satisfied. It is observed that the lower mode of bifurcation can be triggered at a lower critical voltage as compared to the higher mode of bifurcation. Furthermore, the tubes with larger stiffness gradient require higher critical voltages for triggering the bifurcation. Moreover, with the increase in stiffness gradient, a linear variation of the critical voltage is observed with the thickness of the tube. It has been found that on applying internal pressure to a tube with low thickness, the tube becomes less susceptible to bifurcations. A thicker tube with axially free end is found to be more stable than the axially clamped end tube at higher mode of bifurcation.

Keywords: critical voltage, functionally graded dielectric elastomer, linearized incremental approach, modulus of rigidity, prismatic bifurcation

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Authors: Mir Mohammad Badrul Hasan, Anwar Abdkader, Chokri Cherif

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With the increasing demand and use of carbon fibre reinforced composites (CFRC), disposal of the carbon fibres (CF) and end of life composite parts is gaining tremendous importance on the issue especially of sustainability. Furthermore, a number of processes (e. g. pyrolysis, solvolysis, etc.) are available currently to obtain recycled CF (rCF) from end-of-life CFRC. Since the CF waste or rCF are neither allowed to be thermally degraded nor landfilled (EU Directive 1999/31/EC), profitable recycling and re-use concepts are urgently necessary. Currently, the market for materials based on rCF mainly consists of random mats (nonwoven) made from short fibres. The strengths of composites that can be achieved from injection-molded components and from nonwovens are between 200-404 MPa and are characterized by low performance and suitable for non-structural applications such as in aircraft and vehicle interiors. On the contrary, spinning rCF to yarn constructions offers good potential for higher CFRC material properties due to high fibre orientation and compaction of rCF. However, no investigation is reported till yet on the direct comparison of the mechanical properties of thermoplastic CFRC manufactured from virgin CF filament yarn and spun yarns from staple rCF. There is a lack of understanding on the level of performance of the composites that can be achieved from hybrid yarns consisting of rCF and PA6 fibres. In this drop back, extensive research works are being carried out at the Textile Machinery and High-Performance Material Technology (ITM) on the development of new thermoplastic CFRC from hybrid yarns consisting of rCF. For this purpose, a process chain is developed at the ITM starting from fibre preparation to hybrid yarns manufacturing consisting of staple rCF by mixing with thermoplastic fibres. The objective is to apply such hybrid yarns for the manufacturing of load bearing textile reinforced thermoplastic CFRCs. In this paper, the development of innovative multi-component core-sheath hybrid yarn structures consisting of staple rCF and polyamide 6 (PA 6) on a DREF-3000 friction spinning machine is reported. Furthermore, Unidirectional (UD) CFRCs are manufactured from the developed hybrid yarns, and the mechanical properties of the composites such as tensile and flexural properties are analyzed. The results show that the UD composite manufactured from the developed hybrid yarns consisting of staple rCF possesses approximately 80% of the tensile strength and E-module to those produced from virgin CF filament yarn. The results show a huge potential of the DREF-3000 friction spinning process to develop composites from rCF for high-performance applications.

Keywords: recycled carbon fibres, hybrid yarn, friction spinning, thermoplastic composite

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