Search results for: predicting judgements

Authors: Michael McTighe

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Background: The COVID-19 pandemic caused the cancellation of many large-scale in-person sporting events, which led to an increase in the availability of virtual ultra-marathons. This study intended to assess how participation in virtual long distances races relates to levels of physical activity for an extended period of time. Moreover, traditional ultra-marathons are known for being not only physically demanding, but also mentally and emotionally challenging. A second component of this study was to assess how psychological contructs related to emotion regulation and mental toughness predict overall performance in the sport. Method: 83 virtual runners participating in a four-month 1000-kilometer race with the option to exceed 1000 kilometers completed a questionnaire exploring demographics, their performance, and experience in the virtual race. Participants also completed the Difficulties in Emotions Regulation Scale (DERS) and the Sports Mental Toughness Questionnaire (SMTQ). Logistics regressions assessed these constructs’ utility in predicting completion of the 1000-kilometer distance in the time allotted. Multiple regression was employed to predict the total distance traversed during the fourmonth race beyond 1000-kilometers. Result: Neither mental toughness nor emotional regulation was a significant predictor of completing the virtual race’s basic 1000-kilometer finish. However, both variables included together were marginally significant predictors of total miles traversed over the entire event beyond 1000 K (p = .051). Additionally, participation in the event promoted an increase in healthy activity with participants running and walking significantly more in the four months during the event than the four months leading up to it. Discussion: This research intended to explore how psychological constructs relate to performance in a virtual type of endurance event, and how involvement in these types of events related to levels of activity. Higher levels of mental toughness and lower levels in difficulties in emotion regulation were associated with greater performance, and participation in the event promoted an increase in athletic involvement. Future psychological skill training aimed at improving emotion regulation and mental toughness may be used to enhance athletic performance in these sports, and future investigations into these events could explore how general participation may influence these constructs over time. Finally, these results suggest that participation in this logistically accessible, and affordable type of sport can promote greater involvement in healthy activities related to running and walking.

Keywords: virtual races, emotion regulation, mental toughness, ultra-marathon, predictors in performance

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Authors: Barbora Svechova, Monika Licbinska

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Geochemical modeling of mineralogical changes of various materials in contact with an aqueous solution is an important tool for predicting the processes and development of given materials at the site. The modeling focused on the mutual interaction of groundwater at the contact with the rock mass and its subsequent influence on concrete structures. The studied locality is located in Slovakia in the area of the Liptov Basin, which is a significant inter-mountain lowland, which is bordered on the north and south by the core mountains belt of the Tatras, where in the center the crystalline rises to the surface accompanied by Mesozoic cover. Groundwater in the area is bound to structures with complicated geological structures. From the hydrogeological point of view, it is an environment with a crack-fracture character. The area is characterized by a shallow surface circulation of groundwater without a significant collector structure, and from a chemical point of view, groundwater in the area has been classified as calcium bicarbonate with a high content of CO2 and SO4 ions. According to the European standard EN 206-1, these are waters with medium aggression towards the concrete. Three rock samples were taken from the area. Based on petrographic and mineralogical research, they were evaluated as calcareous shale, micritic limestone and crystalline shale. These three rock samples were placed in demineralized water for one month and the change in the chemical composition of the water was monitored. During the solution-rock interaction there was an increase in the concentrations of all major ions, except nitrates. There was an increase in concentration after a week, but at the end of the experiment, the concentration was lower than the initial value. Another experiment was the interaction of groundwater from the studied locality with a concrete structure. The concrete sample was also left in the water for 1 month. The results of the experiment confirmed the assumption of a reduction in the concentrations of calcium and bicarbonate ions in water due to the precipitation of amorphous forms of CaCO3 on the surface of the sample.Vice versa, it was surprising to increase the concentration of sulphates, sodium, iron and aluminum due to the leaching of concrete. Chemical analyzes from these experiments were performed in the PHREEQc program, which calculated the probability of the formation of amorphous forms of minerals. From the results of chemical analyses and hydrochemical modeling of water collected in situ and water from experiments, it was found: groundwater at the site is unsaturated and shows moderate aggression towards reinforced concrete structures according to EN 206-1a, which will affect the homogeneity and integrity of concrete structures; from the rocks in the given area, Ca, Na, Fe, HCO3 and SO4. Unsaturated waters will dissolve everything as soon as they come into contact with the solid matrix. The speed of this process then depends on the physicochemical parameters of the environment (T, ORP, p, n, water retention time in the environment, etc.).

Keywords: geochemical modeling, concrete , dissolution , PHREEQc

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Authors: Ravija Gunawardana, Banuka Athuraliya

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Machine learning has emerged as a powerful tool for disease diagnosis and prediction. The use of machine learning algorithms has the potential to improve the accuracy of disease prediction, thereby enabling medical professionals to provide more effective and personalized treatments. This study focuses on developing a machine-learning model for disease prediction using symptoms and X-ray images. The importance of this study lies in its potential to assist medical professionals in accurately diagnosing diseases, thereby improving patient outcomes. Respiratory diseases are a significant cause of morbidity and mortality worldwide, and chest X-rays are commonly used in the diagnosis of these diseases. However, accurately interpreting X-ray images requires significant expertise and can be time-consuming, making it difficult to diagnose respiratory diseases in a timely manner. By incorporating machine learning algorithms, we can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The study utilized the Mask R-CNN algorithm, which is a state-of-the-art method for object detection and segmentation in images, to process chest X-ray images. The model was trained and tested on a large dataset of patient information, which included both symptom data and X-ray images. The performance of the model was evaluated using a range of metrics, including accuracy, precision, recall, and F1-score. The results showed that the model achieved an accuracy rate of over 90%, indicating that it was able to accurately detect and segment regions of interest in the X-ray images. In addition to X-ray images, the study also incorporated symptoms as input data for disease prediction. The study used three different classifiers, namely Random Forest, K-Nearest Neighbor and Support Vector Machine, to predict diseases based on symptoms. These classifiers were trained and tested using the same dataset of patient information as the X-ray model. The results showed promising accuracy rates for predicting diseases using symptoms, with the ensemble learning techniques significantly improving the accuracy of disease prediction. The study's findings indicate that the use of machine learning algorithms can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The model developed in this study has the potential to assist medical professionals in diagnosing respiratory diseases more accurately and efficiently. However, it is important to note that the accuracy of the model can be affected by several factors, including the quality of the X-ray images, the size of the dataset used for training, and the complexity of the disease being diagnosed. In conclusion, the study demonstrated the potential of machine learning algorithms for disease prediction using symptoms and X-ray images. The use of these algorithms can improve the accuracy of disease diagnosis, ultimately leading to better patient care. Further research is needed to validate the model's accuracy and effectiveness in a clinical setting and to expand its application to other diseases.

Keywords: K-nearest neighbor, mask R-CNN, random forest, support vector machine

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Authors: Melese Wondatir

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Flooding ranks among the most destructive natural disasters, impacting millions of individuals globally and resulting in substantial economic, social, and environmental repercussions. This study's objective was to create a comprehensive model that assesses the Nile River basin's susceptibility to flood damage and improves existing flood risk management strategies. Authorities responsible for enacting policies and implementing measures may benefit from this research to acquire essential information about the flood, including its scope and susceptible areas. The identification of severe flood damage locations and efficient mitigation techniques were made possible by the use of geospatial data. Slope, elevation, distance from the river, drainage density, topographic witness index, rainfall intensity, distance from road, NDVI, soil type, and land use type were all used throughout the study to determine the vulnerability of flood damage. Ranking elements according to their significance in predicting flood damage risk was done using the Analytic Hierarchy Process (AHP) and geospatial approaches. The analysis finds that the most important parameters determining the region's vulnerability are distance from the river, topographic witness index, rainfall, and elevation, respectively. The consistency ratio (CR) value obtained in this case is 0.000866 (<0.1), which signifies the acceptance of the derived weights. Furthermore, 10.84m2, 83331.14m2, 476987.15m2, 24247.29m2, and 15.83m2 of the region show varying degrees of vulnerability to flooding—very low, low, medium, high, and very high, respectively. Due to their close proximity to the river, the northern-western regions of the Nile River basin—especially those that are close to Sudanese cities like Khartoum—are more vulnerable to flood damage, according to the research findings. Furthermore, the AUC ROC curve demonstrates that the categorized vulnerability map achieves an accuracy rate of 91.0% based on 117 sample points. By putting into practice strategies to address the topographic witness index, rainfall patterns, elevation fluctuations, and distance from the river, vulnerable settlements in the area can be protected, and the impact of future flood occurrences can be greatly reduced. Furthermore, the research findings highlight the urgent requirement for infrastructure development and effective flood management strategies in the northern and western regions of the Nile River basin, particularly in proximity to major towns such as Khartoum. Overall, the study recommends prioritizing high-risk locations and developing a complete flood risk management plan based on the vulnerability map.

Keywords: analytic hierarchy process, Blue Nile Basin, geospatial techniques, flood vulnerability, multi-criteria decision making

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Authors: Ning Chang, Zelong Yuan, Yunpeng Wang, Jianchun Wang

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Large eddy simulation (LES) has been extensively used in the investigation of turbulence. LES calculates the grid-resolved large-scale motions and leaves small scales modeled by sublfilterscale (SFS) models. Among the existing SFS models, the deconvolution model has been used successfully in the LES of the engineering flows and geophysical flows. Despite the wide application of deconvolution models, the effects of subfilter scale dynamics and filter anisotropy on the accuracy of SFS modeling have not been investigated in depth. The results of LES are highly sensitive to the selection of filters and the anisotropy of the grid, which has been overlooked in previous research. In the current study, two critical aspects of LES are investigated. Firstly, we analyze the influence of sub-filter scale (SFS) dynamics on the accuracy of direct deconvolution models (DDM) at varying filter-to-grid ratios (FGR) in isotropic turbulence. An array of invertible filters are employed, encompassing Gaussian, Helmholtz I and II, Butterworth, Chebyshev I and II, Cauchy, Pao, and rapidly decaying filters. The significance of FGR becomes evident, as it acts as a pivotal factor in error control for precise SFS stress prediction. When FGR is set to 1, the DDM models cannot accurately reconstruct the SFS stress due to the insufficient resolution of SFS dynamics. Notably, prediction capabilities are enhanced at an FGR of 2, resulting in accurate SFS stress reconstruction, except for cases involving Helmholtz I and II filters. A remarkable precision close to 100% is achieved at an FGR of 4 for all DDM models. Additionally, the further exploration extends to the filter anisotropy to address its impact on the SFS dynamics and LES accuracy. By employing dynamic Smagorinsky model (DSM), dynamic mixed model (DMM), and direct deconvolution model (DDM) with the anisotropic filter, aspect ratios (AR) ranging from 1 to 16 in LES filters are evaluated. The findings highlight the DDM's proficiency in accurately predicting SFS stresses under highly anisotropic filtering conditions. High correlation coefficients exceeding 90% are observed in the a priori study for the DDM's reconstructed SFS stresses, surpassing those of the DSM and DMM models. However, these correlations tend to decrease as lter anisotropy increases. In the a posteriori studies, the DDM model consistently outperforms the DSM and DMM models across various turbulence statistics, encompassing velocity spectra, probability density functions related to vorticity, SFS energy flux, velocity increments, strain-rate tensors, and SFS stress. It is observed that as filter anisotropy intensify, the results of DSM and DMM become worse, while the DDM continues to deliver satisfactory results across all filter-anisotropy scenarios. The findings emphasize the DDM framework's potential as a valuable tool for advancing the development of sophisticated SFS models for LES of turbulence.

Keywords: deconvolution model, large eddy simulation, subfilter scale modeling, turbulence

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Authors: Khalid A. Salman, Shereen Wagih, Tariq Munshi, Musaed Almalki, Safwan Zatari, Zahid Khan

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Aim: To measure exposure levels to family members and caregivers of Saudi patients treated with low dose I131 therapy, and household radiation exposure rate to predict different factors that can affect radiation exposure. Patients and methods: All adult self dependent patients with hyperthyroidism or cancer thyroid referred for low dose radioactive I131 therapy on outpatient basis are included. Radiation protection procedures are given to the participant and family members in details. TLD’s were dispensed to each participant in sufficient quantity for his/her family members living in the household. TLD’s are collected at fifth days post-dispense from patients who agreed to have a home visit during which the household is inspected and level of radiation contamination of surfaces was measured. Results: Thirty-two patients were enrolled in the current study, with a mean age of 43.1± 17.1 years Out of them 25 patients (78%) are females. I131 therapy was given in twenty patients (63%) for cancer thyroid of and for toxic goiter in the remaining twelve patients (37%), with an overall mean I131 dose of 24.1 ± 7.5mCi that is relatively higher in the former. The overall number of household family members and helpers of patients are 139, out of them77 are females (55.4%) & 62 are males (44.6%) with a mean age of 29.8± 17.6. The mean period of contact with the patient is 7.6 ±5.6hours. The cumulative radiation exposure shows that radiation exposure to all family members is below the exposure constraint (1mSv), with a range of 109 to 503uSv, and a mean value of 220.9±91 uSv. Numerical data shows a little higher exposure rate for family members of those who receive higher dose of I131 (patients with thyroid cancer) and household members who spent longer time with the patient, yet, the difference is statistically insignificant (P>0.05). Besides, no significant correlation was found between the degree of cumulative exposure of the family members to their gender, age, socioeconomic standard, educational level and residential factors. In the 21 home visits all data from bedrooms, reception areas and kitchens are below hazardous limits (0.5uSv/h) apart from bathrooms that give a slightly higher reading of 0.57±0.39 uSv/h in those with cancer thyroid who receive a higher radiation dose. A statistically significant difference was found between radiation exposure rate in bathrooms used by the patient versus those used by family members only, with a mean value of exposure rate of 0.701±0.21 uSv/h and 0.17±0.82 uSv/h respectively, with a p-value of 0.018 (<0.05). Conclusion: Family members of patients treated with low dose I131 on outpatient basis have a good compliance to radiation protection instruction if given properly with a cumulative radiation exposure rate evidently beyond the radiation exposure constraints of 1 mSv. Given I131 dose, hours spent with the patient, age, gender, socioeconomic standard, educational level and residential factors have no significant correlation with the cumulative radiation exposure. The patient bathroom exhibits more radiation exposure rate, needing more strict instructions for patient bathroom use and health hygiene.

Keywords: family members, radiation exposure, radioactive iodine therapy, radiation safety

Procedia PDF Downloads 254

Authors: Alireza Fallahfard, Ludwig Vinches, Stephane Halle

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In the workplace, the exposure level of airborne contaminants should be evaluated due to health and safety issues. It can be done by numerical models or experimental measurements, but the numerical approach can be useful when it is challenging to perform experiments. One of the simplest models is the well-mixed room (WMR) model, which has shown its usefulness to predict inhalation exposure in many situations. However, since the WMR is limited to gases and vapors, it cannot be used to predict exposure to aerosols. The main objective is to modify the WMR model to expand its application to exposure scenarios involving aerosols. To reach this objective, the standard WMR model has been modified to consider the deposition of particles by gravitational settling and Brownian and turbulent deposition. Three deposition models were implemented in the model. The time-dependent concentrations of airborne particles predicted by the model were compared to experimental results conducted in a 0.512 m3 chamber. Polystyrene particles of 1, 2, and 3 µm in aerodynamic diameter were generated with a nebulizer under two air changes per hour (ACH). The well-mixed condition and chamber ACH were determined by the tracer gas decay method. The mean friction velocity on the chamber surfaces as one of the input variables for the deposition models was determined by computational fluid dynamics (CFD) simulation. For the experimental procedure, the particles were generated until reaching the steady-state condition (emission period). Then generation stopped, and concentration measurements continued until reaching the background concentration (decay period). The results of the tracer gas decay tests revealed that the ACHs of the chamber were: 1.4 and 3.0, and the well-mixed condition was achieved. The CFD results showed the average mean friction velocity and their standard deviations for the lowest and highest ACH were (8.87 ± 0.36) ×10-2 m/s and (8.88 ± 0.38) ×10-2 m/s, respectively. The numerical results indicated the difference between the predicted deposition rates by the three deposition models was less than 2%. The experimental and numerical aerosol concentrations were compared in the emission period and decay period. In both periods, the prediction accuracy of the modified model improved in comparison with the classic WMR model. However, there is still a difference between the actual value and the predicted value. In the emission period, the modified WMR results closely follow the experimental data. However, the model significantly overestimates the experimental results during the decay period. This finding is mainly due to an underestimation of the deposition rate in the model and uncertainty related to measurement devices and particle size distribution. Comparing the experimental and numerical deposition rates revealed that the actual particle deposition rate is significant, but the deposition mechanisms considered in the model were ten times lower than the experimental value. Thus, particle deposition was significant and will affect the airborne concentration in occupational settings, and it should be considered in the airborne exposure prediction model. The role of other removal mechanisms should be investigated.

Keywords: aerosol, CFD, exposure assessment, occupational settings, well-mixed room model, zonal model

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Authors: Elakneswaran Yogarajah, Toyoharu Nawa, Eiji Owaki

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Cement-based materials have been using in various reinforced concrete structural components as well as in nuclear waste repositories. The sulfate attack has been an environmental issue for cement-based materials exposed to sulfate bearing groundwater or soils, and it plays an important role in the durability of concrete structures. The reaction between penetrating sulfate ions and cement hydrates can result in swelling, spalling and cracking of cement matrix in concrete. These processes induce a reduction of mechanical properties and a decrease of service life of an affected structure. It has been identified that the precipitation of secondary sulfate bearing phases such as ettringite, gypsum, and thaumasite can cause the damage. Furthermore, crystallization of soluble salts such as sodium sulfate crystals induces degradation due to formation and phase changes. Crystallization of mirabilite (Na₂SO₄:10H₂O) and thenardite (Na₂SO₄) or their phase changes (mirabilite to thenardite or vice versa) due to temperature or sodium sulfate concentration do not involve any chemical interaction with cement hydrates. Over the past couple of decades, an intensive work has been carried out on sulfate attack in cement-based materials. However, there are several uncertainties still exist regarding the mechanism for the damage of concrete in sulfate environments. In this study, modelling work has been conducted to investigate the chemical degradation of cementitious materials in various sulfate environments. Both internal and external sulfate attack are considered for the simulation. In the internal sulfate attack, hydrate assemblage and pore solution chemistry of co-hydrating Portland cement (PC) and slag mixing with sodium sulfate solution are calculated to determine the degradation of the PC and slag-blended cementitious materials. Pitzer interactions coefficients were used to calculate the activity coefficients of solution chemistry at high ionic strength. The deterioration mechanism of co-hydrating cementitious materials with 25% of Na₂SO₄ by weight is the formation of mirabilite crystals and ettringite. Their formation strongly depends on sodium sulfate concentration and temperature. For the external sulfate attack, the deterioration of various types of cementitious materials under external sulfate ingress is simulated through reactive transport model. The reactive transport model is verified with experimental data in terms of phase assemblage of various cementitious materials with spatial distribution for different sulfate solution. Finally, the reactive transport model is used to predict the long-term performance of cementitious materials exposed to 10% of Na₂SO₄ for 1000 years. The dissolution of cement hydrates and secondary formation of sulfate-bearing products mainly ettringite are the dominant degradation mechanisms, but not the sodium sulfate crystallization.

Keywords: thermodynamic calculations, reactive transport, radioactive waste disposal, PHREEQC

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Authors: Faidon Kyriakou, William Dempster, David Nash

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Abdominal Aortic Aneurysm (AAA) is a major life-threatening pathology for which modern approaches reduce the need for open surgery through the use of stenting. The success of stenting though is sometimes jeopardized by the final position of the stent graft inside the human artery which may result in migration, endoleaks or blood flow occlusion. Herein, a finite element (FE) model of the commercial medical device AnacondaTM (Vascutek, Terumo) has been developed and validated in order to create a numerical tool able to provide useful clinical insight before the surgical procedure takes place. The AnacondaTM device consists of a series of NiTi rings sewn onto woven polyester fabric, a structure that despite its column stiffness is flexible enough to be used in very tortuous geometries. For the purposes of this study, a FE model of the device was built in Abaqus® (version 6.13-2) with the combination of beam, shell and surface elements; the choice of these building blocks was made to keep the computational cost to a minimum. The validation of the numerical model was performed by comparing the deployed position of a full stent graft device inside a constructed AAA with a duplicate set-up in Abaqus®. Specifically, an AAA geometry was built in CAD software and included regions of both high and low tortuosity. Subsequently, the CAD model was 3D printed into a transparent aneurysm, and a stent was deployed in the lab following the steps of the clinical procedure. Images on the frontal and sagittal planes of the experiment allowed the comparison with the results of the numerical model. By overlapping the experimental and computational images, the mean and maximum distances between the rings of the two models were measured in the longitudinal, and the transverse direction and, a 5mm upper bound was set as a limit commonly used by clinicians when working with simulations. The two models showed very good agreement of their spatial positioning, especially in the less tortuous regions. As a result, and despite the inherent uncertainties of a surgical procedure, the FE model allows confidence that the final position of the stent graft, when deployed in vivo, can also be predicted with significant accuracy. Moreover, the numerical model run in just a few hours, an encouraging result for applications in the clinical routine. In conclusion, the efficient modelling of a complicated structure which combines thin scaffolding and fabric has been demonstrated to be feasible. Furthermore, the prediction capabilities of the location of each stent ring, as well as the global shape of the graft, has been shown. This can allow surgeons to better plan their procedures and medical device manufacturers to optimize their designs. The current model can further be used as a starting point for patient specific CFD analysis.

Keywords: AAA, efficiency, finite element analysis, stent deployment

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Authors: Alexandru-Ion Marinescu

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There exist a plethora of methods in the scientific literature which tackle the well-established task of credit score evaluation. In its most abstract form, a credit scoring algorithm takes as input several credit applicant properties, such as age, marital status, employment status, loan duration, etc. and must output a binary response variable (i.e. “GOOD” or “BAD”) stating whether the client is susceptible to payment return delays. Data imbalance is a common occurrence among financial institution databases, with the majority being classified as “GOOD” clients (clients that respect the loan return calendar) alongside a small percentage of “BAD” clients. But it is the “BAD” clients we are interested in since accurately predicting their behavior is crucial in preventing unwanted loss for loan providers. We add to this whole context the constraint that the algorithm must yield an actual, tractable mathematical formula, which is friendlier towards financial analysts. To this end, we have turned to genetic algorithms and genetic programming, aiming to evolve actual mathematical expressions using specially tailored mutation and crossover operators. As far as data representation is concerned, we employ a very flexible mechanism – LINQ expression trees, readily available in the C# programming language, enabling us to construct executable pieces of code at runtime. As the title implies, they model trees, with intermediate nodes being operators (addition, subtraction, multiplication, division) or mathematical functions (sin, cos, abs, round, etc.) and leaf nodes storing either constants or variables. There is a one-to-one correspondence between the client properties and the formula variables. The mutation and crossover operators work on a flattened version of the tree, obtained via a pre-order traversal. A consequence of our chosen technique is that we can identify and discard client properties which do not take part in the final score evaluation, effectively acting as a dimensionality reduction scheme. We compare ourselves with state of the art approaches, such as support vector machines, Bayesian networks, and extreme learning machines, to name a few. The data sets we benchmark against amount to a total of 8, of which we mention the well-known Australian credit and German credit data sets, and the performance indicators are the following: percentage correctly classified, area under curve, partial Gini index, H-measure, Brier score and Kolmogorov-Smirnov statistic, respectively. Finally, we obtain encouraging results, which, although placing us in the lower half of the hierarchy, drive us to further refine the algorithm.

Keywords: expression trees, financial credit scoring, genetic algorithm, genetic programming, symbolic evolution

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Authors: Ali Mohammed Ali Lmbash

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The global refugee crisis points to the vital need for sustainable and resistant solutions to different kinds of problems for displaced persons all over the world. Among the myriads of sustainable concerns, however, there are diverse considerations including energy consumption, waste management, water access, and resiliency of structures. Our research aims to develop distinct ideas for sustainable architecture given the exigent problems in disaster-threatened areas starting with the Hatay Refugee camp in Turkey where the majority of the camp dwellers are Syrian refugees. Commencing community-based participatory research which focuses on the socio-environmental issues of displaced populations, this study will apply two approaches with a specific focus on the Hatay region. The initial experiment uses Richter's predictive model and simulations to forecast earthquake outcomes in refugee campers. The result could be useful in implementing architectural design tactics that enhance structural reliability and ensure the security and safety of shelters through earthquakes. In the second experiment a model is generated which helps us in predicting the quality of the existing water sources and since we understand how greatly water is vital for the well-being of humans, we do it. This research aims to enable camp administrators to employ forward-looking practices while managing water resources and thus minimizing health risks as well as building resilience of the refugees in the Hatay area. On the other side, this research assesses other sustainability problems of Hatay Refugee Camp as well. As energy consumption becomes the major issue, housing developers are required to consider energy-efficient designs as well as feasible integration of renewable energy technologies to minimize the environmental impact and improve the long-term sustainability of housing projects. Waste management is given special attention in this case by imposing recycling initiatives and waste reduction measures to reduce the pace of environmental degradation in the camp's land area. As well, study gives an insight into the social and economic reality of the camp, investigating the contribution of initiatives such as urban agriculture or vocational training to the enhancement of livelihood and community empowerment. In a similar fashion, this study combines the latest research with practical experience in order to contribute to the continuing discussion on sustainable architecture during disaster relief, providing recommendations and info that can be adapted on every scale worldwide. Through collaborative efforts and a dedicated sustainability approach, we can jointly get to the root of the cause and work towards a far more robust and equitable society.

Keywords: smart architecture, Hatay Camp, sustainability, machine learning.

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Authors: Keda Li, Hong Hu

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Auxetic materials, as an emerging artificial designed metamaterial has attracted growing attention due to their promising negative Poisson’s ratio behaviors and tunable properties. The conventional auxetic lattice structures for which the deformation process is governed by a bending-dominated mechanism have faced the limitation of poor mechanical performance for many potential engineering applications. Recently, both load-bearing and energy absorption capabilities have become a crucial consideration in auxetic structure design. This study reports the finite element analysis of a class of hexagonal double-arrowhead auxetic structures with enhanced stiffness and energy absorption performance. The structure design was developed by extending the traditional double-arrowhead honeycomb to a hexagon frame, the stretching-dominated deformation mechanism was determined according to Maxwell’s stability criterion. The finite element (FE) models of 2D lattice structures established with stainless steel material were analyzed in ABAQUS/Standard for predicting in-plane structural deformation mechanism, failure process, and compressive elastic properties. Based on the computational simulation, the parametric analysis was studied to investigate the effect of the structural parameters on Poisson’s ratio and mechanical properties. The geometrical optimization was then implemented to achieve the optimal Poisson’s ratio for the maximum specific energy absorption. In addition, the optimized 2D lattice structure was correspondingly converted into a 3D geometry configuration by using the orthogonally splicing method. The numerical results of 2D and 3D structures under compressive quasi-static loading conditions were compared separately with the traditional double-arrowhead re-entrant honeycomb in terms of specific Young's moduli, Poisson's ratios, and specified energy absorption. As a result, the energy absorption capability and stiffness are significantly reinforced with a wide range of Poisson’s ratio compared to traditional double-arrowhead re-entrant honeycomb. The auxetic behaviors, energy absorption capability, and yield strength of the proposed structure are adjustable with different combinations of joint angle, struts thickness, and the length-width ratio of the representative unit cell. The numerical prediction in this study suggests the proposed concept of hexagonal double-arrowhead structure could be a suitable candidate for the energy absorption applications with a constant request of load-bearing capacity. For future research, experimental analysis is required for the validation of the numerical simulation.

Keywords: auxetic, energy absorption capacity, finite element analysis, negative Poisson's ratio, re-entrant hexagonal honeycomb

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Authors: K. H. Reeta, Bhavana Prasher, Mitali Mukerji, Dhwani Dholakia, Sangeeta Khanna, Archana Vats, Shivam Pandey, Sandeep Seth, Subir Kumar Maulik

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Introduction Research has demonstrated a connection between coronary artery disease (CAD) and genetics. We did a deep literature mining using both bioinformatics and manual efforts to identify the susceptible polymorphisms in coronary artery disease. Further, the study sought to validate these findings in an Asian population. Methodology In first phase, we used an automated pipeline which organizes and presents structured information on SNPs, Population and Diseases. The information was obtained by applying Natural Language Processing (NLP) techniques to approximately 28 million PubMed abstracts. To accomplish this, we utilized Python scripts to extract and curate disease-related data, filter out false positives, and categorize them into 24 hierarchical groups using named Entity Recognition (NER) algorithms. From the extensive research conducted, a total of 466 unique PubMed Identifiers (PMIDs) and 694 Single Nucleotide Polymorphisms (SNPs) related to coronary artery disease (CAD) were identified. To refine the selection process, a thorough manual examination of all the studies was carried out. Specifically, SNPs that demonstrated susceptibility to CAD and exhibited a positive Odds Ratio (OR) were selected, and a final pool of 324 SNPs was compiled. The next phase involved validating the identified SNPs in DNA samples of 96 CAD patients and 37 healthy controls from Indian population using Global Screening Array. ResultsThe results exhibited out of 324, only 108 SNPs were expressed, further 4 SNPs showed significant difference of minor allele frequency in cases and controls. These were rs187238 of IL-18 gene, rs731236 of VDR gene, rs11556218 of IL16 gene and rs5882 of CETP gene. Prior researches have reported association of these SNPs with various pathways like endothelial damage, susceptibility of vitamin D receptor (VDR) polymorphisms, and reduction of HDL-cholesterol levels, ultimately leading to the development of CAD. Among these, only rs731236 had been studied in Indian population and that too in diabetes and vitamin D deficiency. For the first time, these SNPs were reported to be associated with CAD in Indian population. Conclusion: This pool of 324 SNP s is a unique kind of resource that can help to uncover risk associations in CAD. Here, we validated in Indian population. Further, validation in different populations may offer valuable insights and contribute to the development of a screening tool and may help in enabling the implementation of primary prevention strategies targeted at the vulnerable population.

Keywords: coronary artery disease, single nucleotide polymorphism, susceptible SNP, bioinformatics

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Authors: Amin Salighehdar, Ziwen Ye, Mingzhe Liu, Ionut Florescu, Alan F. Blumberg

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Storm surge is an abnormal water level caused by a storm. Accurate prediction of a storm surge is a challenging problem. Researchers developed various ensemble modeling techniques to combine several individual forecasts to produce an overall presumably better forecast. There exist some simple ensemble modeling techniques in literature. For instance, Model Output Statistics (MOS), and running mean-bias removal are widely used techniques in storm surge prediction domain. However, these methods have some drawbacks. For instance, MOS is based on multiple linear regression and it needs a long period of training data. To overcome the shortcomings of these simple methods, researchers propose some advanced methods. For instance, ENSURF (Ensemble SURge Forecast) is a multi-model application for sea level forecast. This application creates a better forecast of sea level using a combination of several instances of the Bayesian Model Averaging (BMA). An ensemble dressing method is based on identifying best member forecast and using it for prediction. Our contribution in this paper can be summarized as follows. First, we investigate whether the ensemble models perform better than any single forecast. Therefore, we need to identify the single best forecast. We present a methodology based on a simple Bayesian selection method to select the best single forecast. Second, we present several new and simple ways to construct ensemble models. We use correlation and standard deviation as weights in combining different forecast models. Third, we use these ensembles and compare with several existing models in literature to forecast storm surge level. We then investigate whether developing a complex ensemble model is indeed needed. To achieve this goal, we use a simple average (one of the simplest and widely used ensemble model) as benchmark. Predicting the peak level of Surge during a storm as well as the precise time at which this peak level takes place is crucial, thus we develop a statistical platform to compare the performance of various ensemble methods. This statistical analysis is based on root mean square error of the ensemble forecast during the testing period and on the magnitude and timing of the forecasted peak surge compared to the actual time and peak. In this work, we analyze four hurricanes: hurricanes Irene and Lee in 2011, hurricane Sandy in 2012, and hurricane Joaquin in 2015. Since hurricane Irene developed at the end of August 2011 and hurricane Lee started just after Irene at the beginning of September 2011, in this study we consider them as a single contiguous hurricane event. The data set used for this study is generated by the New York Harbor Observing and Prediction System (NYHOPS). We find that even the simplest possible way of creating an ensemble produces results superior to any single forecast. We also show that the ensemble models we propose generally have better performance compared to the simple average ensemble technique.

Keywords: Bayesian learning, ensemble model, statistical analysis, storm surge prediction

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Authors: Dalia Hanna, Alexander Ferworn

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This research provides detailed proposed training modules for the public safety teams and, specifically, SAR teams responsible for search and rescue operations related to finding lost persons with dementia. Finding a lost person alive is the goal of this training. Time matters if a lost person is to be found alive. Finding lost people living with dementia is quite challenging, as they are unaware they are lost and will not seek help. Even a small contribution to SAR operations could contribute to saving a life. SAR operations will always require expert professional and human volunteers. However, we can reduce their time, save lives, and reduce costs by providing practical training that is based on real-life scenarios. The content for the proposed training is based on the research work done by the researcher in this area. This research has demonstrated that, based on utilizing drones, the algorithmic approach could support a successful search outcome. Understanding the behavior of the lost person, learning where they may be found, predicting their survivability, and automating the search are all contributions of this work, founded in theory and demonstrated in practice. In crisis management, human behavior constitutes a vital aspect in responding to the crisis; the speed and efficiency of the response often get affected by the difficulty of the context of the operation. Therefore, training in this area plays a significant role in preparing the crisis manager to manage the emotional aspects that lead to decision-making in these critical situations. Since it is crucial to gain high-level strategic choices and the ability to apply crisis management procedures, simulation exercises become central in training crisis managers to gain the needed skills to respond critically to these events. The training will enhance the responders’ ability to make decisions and anticipate possible consequences of their actions through flexible and revolutionary reasoning in responding to the crisis efficiently and quickly. As adult learners, search and rescue teams will be approaching training and learning by taking responsibility of the learning process, appreciate flexible learning and as contributors to the teaching and learning happening during that training. These are all characteristics of adult learning theories. The learner self-reflects, gathers information, collaborates with others and is self-directed. One of the learning strategies associated with adult learning is effective elaboration. It helps learners to remember information in the long term and use it in situations where it might be appropriate. It is also a strategy that can be taught easily and used with learners of different ages. Designers must design reflective activities to improve the student’s intrapersonal awareness.

Keywords: training, OER, dementia, drones, search and rescue, adult learning, UDL, instructional design

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Authors: Ran Goldblatt, Nicholas Jones, Jennifer Mannix, Brad Bottoms

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Accurate, complete, and up-to-date geospatial information is the foundation of successful disaster management. When the 2010 Haiti Earthquake struck, accurate and timely information on the distribution of critical infrastructure was essential for the disaster response community for effective search and rescue operations. Existing geospatial datasets such as Google Maps did not have comprehensive coverage of these features. In the days following the earthquake, many organizations released high-resolution satellite imagery, catalyzing a worldwide effort to map Haiti and support the recovery operations. Of these organizations, OpenStreetMap (OSM), a collaborative project to create a free editable map of the world, used the imagery to support volunteers to digitize roads, buildings, and other features, creating the most detailed map of Haiti in existence in just a few weeks. However, large portions of the island are still not fully covered by OSM. There is an increasing need for a tool to automatically identify which areas in Haiti, as well as in other countries vulnerable to disasters, that are not fully mapped. The objective of this project is to leverage different types of remote sensing measurements, together with machine learning approaches, in order to identify geographical areas where OSM coverage of building footprints is incomplete. Several remote sensing measures and derived products were assessed as potential predictors of OSM building footprints coverage, including: intensity of light emitted at night (based on VIIRS measurements), spectral indices derived from Sentinel-2 satellite (normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), soil-adjusted vegetation index (SAVI), urban index (UI)), surface texture (based on Sentinel-1 SAR measurements)), elevation and slope. Additional remote sensing derived products, such as Hansen Global Forest Change, DLR`s Global Urban Footprint (GUF), and World Settlement Footprint (WSF), were also evaluated as predictors, as well as OSM street and road network (including junctions). Using a supervised classification with a random forest classifier resulted in the prediction of 89% of the variation of OSM building footprint area in a given cell. These predictions allowed for the identification of cells that are predicted to be covered but are actually not mapped yet. With these results, this methodology could be adapted to any location to assist with preparing for future disastrous events and assure that essential geospatial information is available to support the response and recovery efforts during and following major disasters.

Keywords: disaster management, Haiti, machine learning, OpenStreetMap, remote sensing

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Authors: Elaine Lima Da Fonseca, Eliomar Pereira Da Silva Filho

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The modeling of areas susceptible to soil loss by hydro erosive processes consists of a simplified instrument of reality with the purpose of predicting future behaviors from the observation and interaction of a set of geoenvironmental factors. The models of potential areas for soil loss will be obtained through binary logistic regression, artificial neural networks, and support vector machines. The choice of the municipality of Colorado do Oeste in the south of the western Amazon is due to soil degradation due to anthropogenic activities, such as agriculture, road construction, overgrazing, deforestation, and environmental and socioeconomic configurations. Initially, a soil erosion inventory map constructed through various field investigations will be designed, including the use of remotely piloted aircraft, orbital imagery, and the PLANAFLORO/RO database. 100 sampling units with the presence of erosion will be selected based on the assumptions indicated in the literature, and, to complement the dichotomous analysis, 100 units with no erosion will be randomly designated. The next step will be the selection of the predictive parameters that exert, jointly, directly, or indirectly, some influence on the mechanism of occurrence of soil erosion events. The chosen predictors are altitude, declivity, aspect or orientation of the slope, curvature of the slope, composite topographic index, flow power index, lineament density, normalized difference vegetation index, drainage density, lithology, soil type, erosivity, and ground surface temperature. After evaluating the relative contribution of each predictor variable, the erosion susceptibility model will be applied to the municipality of Colorado do Oeste - Rondônia through the SPSS Statistic 26 software. Evaluation of the model will occur through the determination of the values of the R² of Cox & Snell and the R² of Nagelkerke, Hosmer and Lemeshow Test, Log Likelihood Value, and Wald Test, in addition to analysis of the Confounding Matrix, ROC Curve and Accumulated Gain according to the model specification. The validation of the synthesis map resulting from both models of the potential risk of soil erosion will occur by means of Kappa indices, accuracy, and sensitivity, as well as by field verification of the classes of susceptibility to erosion using drone photogrammetry. Thus, it is expected to obtain the mapping of the following classes of susceptibility to erosion very low, low, moderate, very high, and high, which may constitute a screening tool to identify areas where more detailed investigations need to be carried out, applying more efficient social resources.

Keywords: modeling, susceptibility to erosion, artificial intelligence, Amazon

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Authors: Mohammad Ehsani, Iman Zarei, Soudabeh Moazemigoudarzi

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The aim of this study is to determine Pro-Environmental Behavioral Intention of Mountain Hikers to the Theory of Planned Behavior. According to many researchers nature-based recreation activities play a significant role in the tourism industry and have provided myriad opportunities for the protection of natural areas. It is essential to investigate individuals' behavior during such activities to avoid further damage to precious and dwindling natural resources. This study develops a robust model that provides a comprehensive understanding of the formation of pro-environmental behavioral intentions among climbers of Mount Damavand National Park in Iran. To this end, we combined the theory of planned behavior (TPB), value-belief-norm theory (VBN), and a hierarchical model of leisure constraints to predict individuals’ pro-environmental hiking behavior during outdoor recreation. It was used structural equation modeling to test the theoretical framework. A sample of 787 climbers was analyzed. Among the theory of planned behavior variables, perceived behavioral control showed the strongest association with behavioral intention (β = .57). This relationship indicates that if people feel they can have fewer negative impacts on national resources while hiking, it will result in more environmentally acceptable behavior. Subjective norms had a moderate positive impact on behavioral intention, indicating the importance of other people on the individual's behavior. Attitude had a small positive effect on intention. Ecological worldview positively influenced attitude and personal belief. Personal belief (awareness of consequences and ascribed responsibility) showed a positive association with TPB variables. Although the data showed a high average score in awareness of consequences (mean = 4.219 out of 5), evidence from Damavand Mount shows that there are many environmental issues that need addressing (e.g., vast amounts of garbage). National park managers need to make sure that their solutions result in awareness about proenvironmental behavior (PEB). Findings showed that negative relationship between constraints and all TPB predictors. Providing proper restrooms and parking spaces in campgrounds, strategies controlling limiting capacity and solutions for removing waste from high altitudes are helpful to decrease the negative impact of structural constraints. In order to address intrapersonal constraints, managers should provide opportunities to interest individuals in environmental activities, such as environmental celebrations or making documentaries about environmental issues. Moreover, promoting a culture of environmental protection in the Damavand Mount area would reduce interpersonal constraints. Overall, the proposed model improved the explanatory power of the TPB by predicting 64.7% of intention compared to the original TPB that accounted for 63.8% of the variance in intention.

Keywords: theory of planned behavior, pro-environmental behavior, national park, constraints

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Authors: Adi Sharabi, Dafna Marom-Golan

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Parents’ involvement in the care of their children with Autism Spectrum Disorder (ASD) and its beneficial effect on the children’s developmental and educational outcomes is well documented. At the same time, parents of children with ASD tend to experience greater psychological distress than parents of children with other developmental disabilities or with typical development. Positive social support is an important resource used by parents to reduce their psychological distress. The goal of the current research was to examine the contribution of formal and informal social support in explaining mothers’ and fathers’ involvement with their young children with ASD. The sample consisted of 107 parents who live in Israel (61 mothers and 46 fathers) of children aged between 2 and 7, all diagnosed with ASD and attending special kindergartens or special day care for children with ASD. Parental involvement and social support perception were assessed. Initial analysis focused on the relations between involvement, support, and demographic variables. In addition, analysis of variance (ANOVA) was conducted to test differences between mothers and fathers. Two hierarchical multiple regression analyses were performed to examine the predicted factors in the involvement model while controlling for group (mothers/fathers). Results indicate that mothers reported significantly higher levels of parenting involvement than fathers. Mothers reported higher levels of general involvement and all sub-types of involvement. For example, mothers reported that they were more interested in and have higher levels of attendance in their child’s educational program. They were also more collaborative in their child’s educational therapeutic program, and socialized with other parents of children from their child’s kindergarten than fathers. Mothers’ involvement was found to be related to their informal support (non-formal relatives). Findings also reveal significant differences between mothers and fathers on the formal support subscale measure of specializes services. Fathers, more than mothers, reported more specializes services support such as social workers or professional therapists. Separate hierarchical multiple regression analyses revealed a unique gender difference in the factors that explained parental involvement. Specifically, informal support only had a unique positive contribution in explaining mothers’, but not fathers’ involvement. This study highlights the central role of mothers in maintaining constant contact with the educational system and the professionals who help care for their child with ASD. At the same time, this research emphasizes the crucial role of both mothers and fathers in their child's development and well-being at every development stage, particularly in early development. Further, different kinds of social support seem to relate to the different kinds of parental involvement. It is in the best interest of educators and family therapists who work with families with children with ASD to support the cohesiveness of the family and the collaboration of the parents by understanding and respecting the way each member addresses the responsibilities of parenting a child with ASD, and her or his need for different types of social support.

Keywords: parental differences, parental involvement, social support, specialized support services

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Authors: Suzanne Zazycki, Bamidele Osamika, Heather Craska, Kaelyn Conaway, Reena Murphy, Stephanie Spence

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Invasive Plant Species (IPS) are an important component of effective preservation and conservation of natural lands management. IPS are non-native plants which can aggressively encroach upon native species and pose a significant threat to the ecology, public health, and social welfare of a community. The presence of IPS in U.S. nature preserves has caused economic costs, which has estimated to exceed $26 billion a year. While different methods have been identified to control IPS, few methods have been recognized for early detection of IPS. This study examined identified methods for early detection of IPS in Hueston Woods State Nature Preserve. Mixed methods research design was adopted in this four-phased study. The first phase entailed data gathering, the phase described the characteristics and qualities of IPS and the importance of early detection (ED). The second phase explored ED methods, Geographic Information Systems (GIS) and Citizen Science were discovered as ED methods for IPS. The third phase of the study involved the creation of hotspot maps to identify likely areas for IPS growth. While the fourth phase involved testing and evaluating mobile applications that can support the efforts of citizen scientists in IPS detection. Literature reviews were conducted on IPS and ED methods, and four regional experts from ODNR and Miami University were interviewed. A questionnaire was used to gather information about ED methods used across the state. The findings revealed that geospatial methods, including Unmanned Aerial Vehicles (UAVs), Multispectral Satellites (MSS), and Normalized Difference Vegetation Index (NDVI), are not feasible for early detection of IPS, as they require GIS expertise, are still an emerging technology, and are not suitable for every habitat for the ED of IPS. Therefore, Other ED methods options were explored, which include predicting areas where IPS will grow, which can be done through monitoring areas that are like the species’ native habitat. Through literature review and interviews, IPS are known to grow in frequently disturbed areas such as along trails, shorelines, and streambanks. The research team called these areas “hotspots” and created maps of these hotspots specifically for HW NP to support and narrow the efforts of citizen scientists and staff in the ED of IPS. The results further showed that utilizing citizen scientists in the ED of IPS is feasible, especially through single day events or passive monitoring challenges. The study concluded that the creation of hotspot maps to direct the efforts of citizen scientists are effective for the early detection of IPS. Several recommendations were made, among which is the creation of hotspot maps to narrow the ED efforts as citizen scientists continues to work in the preserves and utilize citizen science volunteers to identify and record emerging IPS.

Keywords: early detection, hueston woods state nature preserve, invasive plant species, hotspots

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Authors: A. F. Jaber, E. Dean, M. Liu, J. He, D. Sabata, J. Radel

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Background: Stroke is a serious health care concern and a major cause of disability in the United States. This condition impacts the individual’s functional ability to perform daily activities. Predicting functional performance of people with stroke assists health care professionals in optimizing the delivery of health services to the affected individuals. The purpose of this study was to identify significant predictors of Motor FIM and of Cognitive FIM subscores among individuals with stroke after discharge from inpatient rehabilitation (typically 4-6 weeks after stroke onset). A second purpose is to explore the relation among personal characteristics, health status, and functional performance of daily activities within 2 weeks of stroke onset. Methods: This study used a retrospective chart review to conduct a secondary analysis of data obtained from the Healthcare Enterprise Repository for Ontological Narration (HERON) database. The HERON database integrates de-identified clinical data from seven different regional sources including hospital electronic medical record systems of the University of Kansas Health System. The initial HERON data extract encompassed 1192 records and the final sample consisted of 207 participants who were mostly white (74%) males (55%) with a diagnosis of ischemic stroke (77%). The outcome measures collected from HERON included performance scores on the National Institute of Health Stroke Scale (NIHSS), the Glasgow Coma Scale (GCS), and the Functional Independence Measure (FIM). The data analysis plan included descriptive statistics, Pearson correlation analysis, and Stepwise regression analysis. Results: significant predictors of discharge Motor FIM subscores included age, baseline Motor FIM subscores, discharge NIHSS scores, and comorbid electrolyte disorder (R2 = 0.57, p <0.026). Significant predictors of discharge Cognitive FIM subscores were age, baseline cognitive FIM subscores, client cooperative behavior, comorbid obesity, and the total number of comorbidities (R2 = 0.67, p <0.020). Functional performance on admission was significantly associated with age (p < 0.01), stroke severity (p < 0.01), and length of hospital stay (p < 0.05). Conclusions: our findings show that younger age, good motor and cognitive abilities on admission, mild stroke severity, fewer comorbidities, and positive client attitude all predict favorable functional outcomes after inpatient stroke rehabilitation. This study provides health care professionals with evidence to evaluate predictors of favorable functional outcomes early at stroke rehabilitation, to tailor individualized interventions based on their client’s anticipated prognosis, and to educate clients about the benefits of making lifestyle changes to improve their anticipated rate of functional recovery.

Keywords: functional performance, predictors, stroke, recovery

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Authors: Parul Jindal, Shubhi Singh, Priya Ramakrishnan, Shailender Raghuvanshi

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Background: Knowledge of the influence of the age of the child on laryngeal dimensions is essential for all practitioners who are dealing with paediatric airway. Choosing the correct endotracheal tube (ETT) size is a crucial step in pediatric patients because a large-sized tube may cause complications like post-extubation stridor and subglottic stenosis. On the other hand with a smaller tube, there will be increased gas flow resistance, aspiration risk, poor ventilation, inaccurate monitoring of end-tidal gases and reintubation may also be required with a different size of the tracheal tube. Recent advancement in ultrasonography (USG) techniques should now allow for accurate and descriptive evaluation of pediatric airway. Aims and objectives: This study was planned to determine the accuracy of Ultrasonography (USG) to assess the appropriate ETT size and compare it with physical indices based formulae. Methods: After obtaining approval from Institute’s Ethical and Research committee, and parental written and informed consent, the study was conducted on 100 subjects of either sex between 12-60 months of age, undergoing various elective surgeries under general anesthesia requiring endotracheal intubation. The same experienced radiologist performed ultrasonography. The transverse diameter was measured at the level of cricoids cartilage by USG. After USG, general anesthesia was administered using standard techniques followed by the institute. An experienced anesthesiologist performed the endotracheal intubations with uncuffed endotracheal tube (Portex Tracheal Tube Smiths Medical India Pvt. Ltd.) with Murphy’s eye. He was unaware of the finding of the ultrasonography. The tracheal tube was considered best fit if air leak was satisfactory at 15-20 cm H₂O of airway pressure. The obtained values were compared with the values of endotracheal tube size calculated by ultrasonography, various age, height, weight-based formulas and diameter of right and left little finger. The correlation of the size of the endotracheal tube by different modalities was done and Pearson's correlation coefficient was obtained. The comparison of the mean size of the endotracheal tube by ultrasonography and by traditional formula was done by the Friedman’s test and Wilcoxon sign-rank test. Results: The predicted tube size was equal to best fit and best determined by ultrasonography (100%) followed by comparison to left little finger (98%) and right little finger (97%) and age-based formula (95%) followed by multivariate formula (83%) and body length (81%) formula. According to Pearson`s correlation, there was a moderate correlation of best fit endotracheal tube with endotracheal tube size by age-based formula (r=0.743), body length based formula (r=0.683), right little finger based formula (r=0.587), left little finger based formula (r=0.587) and multivariate formula (r=0.741). There was a strong correlation with ultrasonography (r=0.943). Ultrasonography was the most sensitive (100%) method of prediction followed by comparison to left (98%) and right (97%) little finger and age-based formula (95%), the multivariate formula had an even lesser sensitivity (83%) whereas body length based formula was least sensitive with a sensitivity of 78%. Conclusion: USG is a reliable method of estimation of subglottic diameter and for prediction of ETT size in children.

Keywords: endotracheal intubation, pediatric airway, subglottic diameter, traditional formulas, ultrasonography

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Authors: Sakshi Piplani, Ajit Kumar

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Valproic acid (VPA) is the first synthetic therapeutic agent used to treat epileptic disorders, which account for affecting nearly 1% world population. Teratogenicity caused by VPA has prompted the search for next generation drug with better efficacy and lower side effects. Recent studies have posed HDAC8 as direct target of VPA that causes the teratogenic effect in foetus. We have employed molecular dynamics (MD) and docking simulations to understand the binding mode of VPA and their analogues onto HDAC8. A total of twenty 3D-structures of human HDAC8 isoforms were selected using BLAST-P search against PDB. Multiple sequence alignment was carried out using ClustalW and PDB-3F07 having least missing and mutated regions was selected for study. The missing residues of loop region were constructed using MODELLER and energy was minimized. A set of 216 structural analogues (>90% identity) of VPA were obtained from Pubchem and ZINC database and their energy was optimized with Chemsketch software using 3-D CHARMM-type force field. Four major neurotransmitters (GABAt, SSADH, α-KGDH, GAD) involved in anticonvulsant activity were docked with VPA and its analogues. Out of 216 analogues, 75 were selected on the basis of lower binding energy and inhibition constant as compared to VPA, thus predicted to have anti-convulsant activity. Selected hHDAC8 structure was then subjected to MD Simulation using licenced version YASARA with AMBER99SB force field. The structure was solvated in rectangular box of TIP3P. The simulation was carried out with periodic boundary conditions and electrostatic interactions and treated with Particle mesh Ewald algorithm. pH of system was set to 7.4, temperature 323K and pressure 1atm respectively. Simulation snapshots were stored every 25ps. The MD simulation was carried out for 20ns and pdb file of HDAC8 structure was saved every 2ns. The structures were analysed using castP and UCSF Chimera and most stabilized structure (20ns) was used for docking study. Molecular docking of 75 selected VPA-analogues with PDB-3F07 was performed using AUTODOCK4.2.6. Lamarckian Genetic Algorithm was used to generate conformations of docked ligand and structure. The docking study revealed that VPA and its analogues have more affinity towards ‘hydrophobic active site channel’, due to its hydrophobic properties and allows VPA and their analogues to take part in van der Waal interactions with TYR24, HIS42, VAL41, TYR20, SER138, TRP137 while TRP137 and SER138 showed hydrogen bonding interaction with VPA-analogues. 14 analogues showed better binding affinity than VPA. ADMET SAR server was used to predict the ADMET properties of selected VPA analogues for predicting their druggability. On the basis of ADMET screening, 09 molecules were selected and are being used for in-vivo evaluation using Danio rerio model.

Keywords: HDAC8, docking, molecular dynamics simulation, valproic acid

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Authors: Mehrnaz Mostafavi

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The assessment and categorization of incidental lung nodules present a considerable challenge in healthcare, often necessitating resource-intensive multiple computed tomography (CT) scans for growth confirmation. This research addresses this issue by introducing a distinct computational approach leveraging radiomics and deep-learning methods. However, understanding local services is essential before implementing these advancements. With diverse tracking methods in place, there is a need for efficient and accurate identification approaches, especially in the context of managing lung nodules alongside pre-existing cancer scenarios. This study explores the integration of text-based algorithms in medical data curation, indicating their efficacy in conjunction with machine learning and deep-learning models for identifying lung nodules. Combining medical images with text data has demonstrated superior data retrieval compared to using each modality independently. While deep learning and text analysis show potential in detecting previously missed nodules, challenges persist, such as increased false positives. The presented research introduces a Structured-Query-Language (SQL) algorithm designed for identifying pulmonary nodules in a tertiary cancer center, externally validated at another hospital. Leveraging natural language processing (NLP) and machine learning, the algorithm categorizes lung nodule reports based on sentence features, aiming to facilitate research and assess clinical pathways. The hypothesis posits that the algorithm can accurately identify lung nodule CT scans and predict concerning nodule features using machine-learning classifiers. Through a retrospective observational study spanning a decade, CT scan reports were collected, and an algorithm was developed to extract and classify data. Results underscore the complexity of lung nodule cohorts in cancer centers, emphasizing the importance of careful evaluation before assuming a metastatic origin. The SQL and NLP algorithms demonstrated high accuracy in identifying lung nodule sentences, indicating potential for local service evaluation and research dataset creation. Machine-learning models exhibited strong accuracy in predicting concerning changes in lung nodule scan reports. While limitations include variability in disease group attribution, the potential for correlation rather than causality in clinical findings, and the need for further external validation, the algorithm's accuracy and potential to support clinical decision-making and healthcare automation represent a significant stride in lung nodule management and research.

Keywords: lung cancer diagnosis, structured-query-language (SQL), natural language processing (NLP), machine learning, CT scans

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Authors: A. S. Ibrahim Mamane, S. Giljean, M.-J. Pac, G. L’Hostis

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Fiber reinforced composite laminates are particularly subject to residual stresses due to their heterogeneity and the complex chemical, mechanical and thermal mechanisms that occur during their processing. Residual stresses are now well known to cause damage accumulation, shape instability, and behavior disturbance in composite parts. Many works exist in the literature on techniques for minimizing residual stresses in thermosetting and thermoplastic composites mainly. To study in-depth the influence of processing mechanisms on the formation of residual stresses and to minimize them by establishing a reliable correlation, it is essential to be able to measure very precisely the profile of residual stresses in the composite. Residual stresses are important data to consider when sizing composite parts and predicting their behavior. The incremental hole drilling is very effective in measuring the gradient of residual stresses in composite laminates. This method is semi-destructive and consists of drilling incrementally a hole through the thickness of the material and measuring relaxation strains around the hole for each increment using three strain gauges. These strains are then converted into residual stresses using a matrix of coefficients. These coefficients, called calibration coefficients, depending on the diameter of the hole and the dimensions of the gauges used. The reliability of the incremental hole drilling depends on the accuracy with which the calibration coefficients are determined. These coefficients are calculated using a finite element model. The samples’ features and the experimental conditions must be considered in the simulation. Any mismatch can lead to inadequate calibration coefficients, thus introducing errors on residual stresses. Several calibration coefficient correction methods exist for isotropic material, but there is a lack of information on this subject concerning composite laminates. In this work, a Python program was developed to automatically generate the adequate finite element model. This model allowed us to perform a parametric study to assess the influence of experimental errors on the calibration coefficients. The results highlighted the sensitivity of the calibration coefficients to the considered errors and gave an order of magnitude of the precisions required on the experimental device to have reliable measurements. On the basis of these results, improvements were proposed on the experimental device. Furthermore, a numerical method was proposed to correct the calibration coefficients for different types of materials, including thick composite parts for which the analytical approach is too complex. This method consists of taking into account the experimental errors in the simulation. Accurate measurement of the experimental errors (such as eccentricity of the hole, angular deviation of the gauges from their theoretical position, or errors on increment depth) is therefore necessary. The aim is to determine more precisely the residual stresses and to expand the validity domain of the incremental hole drilling technique.

Keywords: fiber reinforced composites, finite element simulation, incremental hole drilling method, numerical correction of the calibration coefficients, residual stresses

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Authors: Muhammmad Riandhy Anindika Yudhy, Harry Patria, Ramadhani Santoso

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Loss of containment is the primary hazard that process safety management is concerned within the oil and gas industry. Escalation to more serious consequences all begins with the loss of containment, starting with oil and gas release from leakage or spillage from primary containment resulting in pool fire, jet fire and even explosion when reacted with various ignition sources in the operations. Therefore, the heart of process safety management is avoiding loss of containment and mitigating its impact through the implementation of safeguards. The most effective safeguard for the case is an early detection system to alert Operations to take action prior to a potential case of loss of containment. The detection system value increases when applied to a long surface pipeline that is naturally difficult to monitor at all times and is exposed to multiple causes of loss of containment, from natural corrosion to illegal tapping. Based on prior researches and studies, detecting loss of containment accurately in the surface pipeline is difficult. The trade-off between cost-effectiveness and high accuracy has been the main issue when selecting the traditional detection method. The current best-performing method, Real-Time Transient Model (RTTM), requires analysis of closely positioned pressure, flow and temperature (PVT) points in the pipeline to be accurate. Having multiple adjacent PVT sensors along the pipeline is expensive, hence generally not a viable alternative from an economic standpoint.A conceptual approach to combine mathematical modeling using computational fluid dynamics and a supervised machine learning model has shown promising results to predict leakage in the pipeline. Mathematical modeling is used to generate simulation data where this data is used to train the leak detection and localization models. Mathematical models and simulation software have also been shown to provide comparable results with experimental data with very high levels of accuracy. While the supervised machine learning model requires a large training dataset for the development of accurate models, mathematical modeling has been shown to be able to generate the required datasets to justify the application of data analytics for the development of model-based leak detection systems for petroleum pipelines. This paper presents a review of key leak detection strategies for oil and gas pipelines, with a specific focus on crude oil applications, and presents the opportunities for the use of data analytics tools and mathematical modeling for the development of robust real-time leak detection and localization system for surface pipelines. A case study is also presented.

Keywords: pipeline, leakage, detection, AI

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Authors: Navid Kaboudi, Ali Shayanfar

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Introduction: Feeding infants with safe milk from the beginning of their life is an important issue. Drugs which are used by mothers can affect the composition of milk in a way that is not only unsuitable, but also toxic for infants. Consuming permeable drugs during that sensitive period by mother could lead to serious side effects to the infant. Due to the ethical restrictions of drug testing on humans, especially women, during their lactation period, computational approaches based on structural parameters could be useful. The aim of this study is to develop mechanistic models to predict the M/P ratio of drugs during breastfeeding period based on their structural descriptors. Methods: Two hundred and nine different chemicals with their M/P ratio were used in this study. All drugs were categorized into two groups based on their M/P value as Malone classification: 1: Drugs with M/P>1, which are considered as high risk 2: Drugs with M/P>1, which are considered as low risk Thirty eight chemical descriptors were calculated by ACD/labs 6.00 and Data warrior software in order to assess the penetration during breastfeeding period. Later on, four specific models based on the number of hydrogen bond acceptors, polar surface area, total surface area, and number of acidic oxygen were established for the prediction. The mentioned descriptors can predict the penetration with an acceptable accuracy. For the remaining compounds (N= 147, 158, 160, and 174 for models 1 to 4, respectively) of each model binary regression with SPSS 21 was done in order to give us a model to predict the penetration ratio of compounds. Only structural descriptors with p-value<0.1 remained in the final model. Results and discussion: Four different models based on the number of hydrogen bond acceptors, polar surface area, and total surface area were obtained in order to predict the penetration of drugs into human milk during breastfeeding period About 3-4% of milk consists of lipids, and the amount of lipid after parturition increases. Lipid soluble drugs diffuse alongside with fats from plasma to mammary glands. lipophilicity plays a vital role in predicting the penetration class of drugs during lactation period. It was shown in the logistic regression models that compounds with number of hydrogen bond acceptors, PSA and TSA above 5, 90 and 25 respectively, are less permeable to milk because they are less soluble in the amount of fats in milk. The pH of milk is acidic and due to that, basic compounds tend to be concentrated in milk than plasma while acidic compounds may consist lower concentrations in milk than plasma. Conclusion: In this study, we developed four regression-based models to predict the penetration class of drugs during the lactation period. The obtained models can lead to a higher speed in drug development process, saving energy, and costs. Milk/plasma ratio assessment of drugs requires multiple steps of animal testing, which has its own ethical issues. QSAR modeling could help scientist to reduce the amount of animal testing, and our models are also eligible to do that.

Keywords: logistic regression, breastfeeding, descriptors, penetration

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Authors: Mathias U. Bonet, Pericles Pilidis, Georgios Doulgeris

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A gas turbine-powered cruise Liner is scheduled to transport pilgrim passengers from Lagos-Nigeria to the Islamic port city of Jeddah in Saudi Arabia. Since the gas turbine is an air breathing machine, changes in the density and/or mass flow at the compressor inlet due to an encounter with variations in weather conditions induce negative effects on the performance of the power plant during the voyage. In practice, all deviations from the reference atmospheric conditions of 15 ^oC and 1.103 bar tend to affect the power output and other thermodynamic parameters of the gas turbine cycle. Therefore, this paper seeks to evaluate how a simple cycle marine gas turbine power plant would react under a variety of scenarios that may be encountered during a voyage as the ship sails across the Atlantic Ocean and the Mediterranean Sea before arriving at its designated port of discharge. It is also an assessment that focuses on the effect of varying aerodynamic and hydrodynamic conditions which deteriorate the efficient operation of the propulsion system due to an increase in resistance that results from some projected levels of the ship hull fouling. The investigated passenger ship is designed to run at a service speed of 22 knots and cover a distance of 5787 nautical miles. The performance evaluation consists of three separate voyages that cover a variety of weather conditions in winter, spring and summer seasons. Real-time daily temperatures and the sea states for the selected transit route were obtained and used to simulate the voyage under the aforementioned operating conditions. Changes in engine firing temperature, power output as well as the total fuel consumed per voyage including other performance variables were separately predicted under both calm and adverse weather conditions. The collated data were obtained online from the UK Meteorological Office as well as the UK Hydrographic Office websites, while adopting the Beaufort scale for determining the magnitude of sea waves resulting from rough weather situations. The simulation of the gas turbine performance and voyage analysis was effected through the use of an integrated Cranfield-University-developed computer code known as ‘Turbomatch’ and ‘Poseidon’. It is a project that is aimed at developing a method for predicting the off design behavior of the marine gas turbine when installed and operated as the main prime mover for both propulsion and powering of all other auxiliary services onboard a passenger cruise liner. Furthermore, it is a techno-economic and environmental assessment that seeks to enable the forecast of the marine gas turbine part and full load performance as it relates to the fuel requirement for a complete voyage.

Keywords: cruise ship, gas turbine, hull fouling, performance, propulsion, weather

Procedia PDF Downloads 148

Authors: Charlotte Entwistle, Ryan Boyd

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Recent developments in information retrieval techniques and natural language processing have allowed for greater exploration of psychological and social processes. Linguistic analysis methods for understanding behaviour have provided useful insights within the field of mental health. One area within mental health that has received little attention though, is borderline personality disorder (BPD). BPD is a common mental health disorder characterised by instability of interpersonal relationships, self-image and affect. It also manifests through maladaptive behaviours, such as impulsivity and self-harm. Examination of language patterns associated with BPD could allow for a greater understanding of the disorder and its links to maladaptive thoughts and behaviours. Language analysis methods could also be used in a predictive way, such as by identifying indicators of BPD or predicting maladaptive thoughts, emotions and behaviours. Additionally, associations that are uncovered between language and maladaptive thoughts and behaviours could then be applied at a more general level. This study explores linguistic characteristics of BPD, and their links to maladaptive thoughts and behaviours, through the analysis of social media data. Data were collected from a large corpus of posts from the publicly available social media platform Reddit, namely, from the ‘r/BPD’ subreddit whereby people identify as having BPD. Data were collected using the Python Reddit API Wrapper and included all users which had posted within the BPD subreddit. All posts were manually inspected to ensure that they were not posted by someone who clearly did not have BPD, such as people posting about a loved one with BPD. These users were then tracked across all other subreddits of which they had posted in and data from these subreddits were also collected. Additionally, data were collected from a random control group of Reddit users. Disorder-relevant behaviours, such as self-harming or aggression-related behaviours, outlined within Reddit posts were coded to by expert raters. All posts and comments were aggregated by user and split by subreddit. Language data were then analysed using the Linguistic Inquiry and Word Count (LIWC) 2015 software. LIWC is a text analysis program that identifies and categorises words based on linguistic and paralinguistic dimensions, psychological constructs and personal concern categories. Statistical analyses of linguistic features could then be conducted. Findings revealed distinct linguistic features associated with BPD, based on Reddit posts, which differentiated these users from a control group. Language patterns were also found to be associated with the occurrence of maladaptive thoughts and behaviours. Thus, this study demonstrates that there are indeed linguistic markers of BPD present on social media. It also implies that language could be predictive of maladaptive thoughts and behaviours associated with BPD. These findings are of importance as they suggest potential for clinical interventions to be provided based on the language of people with BPD to try to reduce the likelihood of maladaptive thoughts and behaviours occurring. For example, by social media tracking or engaging people with BPD in expressive writing therapy. Overall, this study has provided a greater understanding of the disorder and how it manifests through language and behaviour.

Keywords: behaviour analysis, borderline personality disorder, natural language processing, social media data

Procedia PDF Downloads 310

Authors: Venencia Albert, Arulselvi Subramanian, Hara Prasad Pati, Asok K. Mukhophadhyay

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Acute traumatic coagulopathy in an endogenous dysregulation of the intrinsic coagulation system in response to the injury, associated with three-fold risk of poor outcome, and is more amenable to corrective interventions, subsequent to early identification and management. Multiple definitions for stratification of the patients' risk for early acute coagulopathy have been proposed, with considerable variations in the defining criteria, including several trauma-scoring systems based on prehospital data. We aimed to develop a clinically relevant definition for acute coagulopathy of trauma based on conventional coagulation assays and to assess its efficacy in comparison to recently established prehospital prediction models. Methodology: Retrospective data of all trauma patients (n = 490) presented to our level I trauma center, in 2014, was extracted. Receiver operating characteristic curve analysis was done to establish cut-offs for conventional coagulation assays for identification of patients with acute traumatic coagulopathy was done. Prospectively data of (n = 100) adult trauma patients was collected and cohort was stratified by the established definition and classified as "coagulopathic" or "non-coagulopathic" and correlated with the Prediction of acute coagulopathy of trauma score and Trauma-Induced Coagulopathy Clinical Score for identifying trauma coagulopathy and subsequent risk for mortality. Results: Data of 490 trauma patients (average age 31.85±9.04; 86.7% males) was extracted. 53.3% had head injury, 26.6% had fractures, 7.5% had chest and abdominal injury. Acute traumatic coagulopathy was defined as international normalized ratio ≥ 1.19; prothrombin time ≥ 15.5 s; activated partial thromboplastin time ≥ 29 s. Of the 100 adult trauma patients (average age 36.5±14.2; 94% males), 63% had early coagulopathy based on our conventional coagulation assay definition. Overall prediction of acute coagulopathy of trauma score was 118.7±58.5 and trauma-induced coagulopathy clinical score was 3(0-8). Both the scores were higher in coagulopathic than non-coagulopathic patients (prediction of acute coagulopathy of trauma score 123.2±8.3 vs. 110.9±6.8, p-value = 0.31; trauma-induced coagulopathy clinical score 4(3-8) vs. 3(0-8), p-value = 0.89), but not statistically significant. Overall mortality was 41%. Mortality rate was significantly higher in coagulopathic than non-coagulopathic patients (75.5% vs. 54.2%, p-value = 0.04). High prediction of acute coagulopathy of trauma score also significantly associated with mortality (134.2±9.95 vs. 107.8±6.82, p-value = 0.02), whereas trauma-induced coagulopathy clinical score did not vary be survivors and non-survivors. Conclusion: Early coagulopathy was seen in 63% of trauma patients, which was significantly associated with mortality. Acute traumatic coagulopathy defined by conventional coagulation assays (international normalized ratio ≥ 1.19; prothrombin time ≥ 15.5 s; activated partial thromboplastin time ≥ 29 s) demonstrated good ability to identify coagulopathy and subsequent mortality, in comparison to the prehospital parameter-based scoring systems. Prediction of acute coagulopathy of trauma score may be more suited for predicting mortality rather than early coagulopathy. In emergency trauma situations, where immediate corrective measures need to be taken, complex multivariable scoring algorithms may cause delay, whereas coagulation parameters and conventional coagulation tests will give highly specific results.

Keywords: trauma, coagulopathy, prediction, model

Procedia PDF Downloads 159