Search results for: predictive equations

Authors: Mahbod Seyednia, Shidvash Vakilipour, Mehran Masdari

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Computations for two-dimensional flow past a stationary and harmonically pitching wind turbine airfoil at a moderate value of Reynolds number (400000) are carried out by progressively increasing the angle of attack for stationary airfoil and at fixed pitching frequencies for rotary one. The incompressible Navier-Stokes equations in conjunction with Unsteady Reynolds Average Navier-Stokes (URANS) equations for turbulence modeling are solved by OpenFOAM package to investigate the aerodynamic phenomena occurred at stationary and pitching conditions on a NACA 6-series wind turbine airfoil. The aim of this study is to enhance the accuracy of numerical simulation in predicting the aerodynamic behavior of an oscillating airfoil in OpenFOAM. Hence, for turbulence modelling, k-ω-SST with low-Reynolds correction is employed to capture the unsteady phenomena occurred in stationary and oscillating motion of the airfoil. Using aerodynamic and pressure coefficients along with flow patterns, the unsteady aerodynamics at pre-, near-, and post-static stall regions are analyzed in harmonically pitching airfoil, and the results are validated with the corresponding experimental data possessed by the authors. The results indicate that implementing the mentioned turbulence model leads to accurate prediction of the angle of static stall for stationary airfoil and flow separation, dynamic stall phenomenon, and reattachment of the flow on the surface of airfoil for pitching one. Due to the geometry of the studied 6-series airfoil, the vortex on the upper surface of the airfoil during upstrokes is formed at the trailing edge. Therefore, the pattern flow obtained by our numerical simulations represents the formation and change of the trailing-edge vortex at near- and post-stall regions where this process determines the dynamic stall phenomenon.

Keywords: CFD, moderate Reynolds number, OpenFOAM, pitching oscillation, unsteady aerodynamics, wind turbine

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Authors: Alok Kumar, Raviraj Patil

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The ability to respond to an incident of a disastrous event in a vulnerable area is very crucial an aspect of emergency management. The ability to constantly predict the likelihood of an event along with its severity in an area and react to those significant events which are likely to have a high impact allows the authorities to respond by allocating resources optimally in a timely manner. It provides for measuring, monitoring, and modeling facilities that integrate underlying systems into one solution to improve operational efficiency, planning, and coordination. We were particularly involved in this innovative incubation work on the current state of research and development in collaboration. technologies & systems for a disaster.

Keywords: predictive analytics, advanced analytics, area flood likelihood model, area flood severity model, level of impact model, mortality score, economic loss score, resource allocation, crew allocation

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Authors: Heiko Weiß, Andreas Meister, Christoph Ament, Nils Dreifke

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Linear actuators are deployed in a wide range of applications. This paper presents the modeling and system identification of a variable excited linear direct drive (LDD). The LDD is designed based on linear hybrid stepper technology exhibiting the characteristic tooth structure of mover and stator. A three-phase topology provides the thrust force caused by alternating strengthening and weakening of the flux of the legs. To achieve best possible synchronous operation, the phases are commutated sinusoidal. Despite the fact that these LDDs provide high dynamics and drive forces, noise emission limits their operation in calm workspaces. To overcome this drawback an additional excitation of the magnetic circuit is introduced to LDD using additional enabling coils instead of permanent magnets. The new degree of freedom can be used to reduce force variations and related noise by varying the excitation flux that is usually generated by permanent magnets. Hence, an identified simulation model is necessary to analyze the effects of this modification. Especially the force variations must be modeled well in order to reduce them sufficiently. The model can be divided into three parts: the current dynamics, the mechanics and the force functions. These subsystems are described with differential equations or nonlinear analytic functions, respectively. Ordinary nonlinear differential equations are derived and transformed into state space representation. Experiments have been carried out on a test rig to identify the system parameters of the complete model. Static and dynamic simulation based optimizations are utilized for identification. The results are verified in time and frequency domain. Finally, the identified model provides a basis for later design of control strategies to reduce existing force variations.

Keywords: force variations, linear direct drive, modeling and system identification, variable excitation flux

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Authors: Mohand Kessal, Rachid Boucetta, Mourad Tikobaini, Mohammed Zamoum

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Leakage problem of two-component fluids flow is modeled for a transient one-dimensional homogeneous bubbly flow and developed by taking into account the effect of a leak located at the middle point of the pipeline. The corresponding three conservation equations are numerically resolved by an improved characteristic method. The obtained results are explained and commented in terms of physical impact on the flow parameters.

Keywords: fluid transients, pipelines leaks, method of characteristics, leakage problem

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Authors: Charles Horace Ampong

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Non-linear models have been found to be useful in modeling the elasticity (measure of degree of responsiveness) of a dependent variable with respect to a set of independent variables ceteris paribus. This constant elasticity principle was applied to the dependent variable (Compressive Strength of Concrete in MPa) which was found to be non-linearly related to the independent variable (Water-Cement ratio in kg/m3) for given Ages of Concrete in days (3, 7, 28) at different levels of admixtures Superplasticizer (in kg/m3), Blast Furnace Slag (in kg/m3) and Fly Ash (in kg/m3). The levels of the admixtures were categorized as: S1=Some Plasticizer added & S0=No Plasticizer added; B1=some Blast Furnace Slag added & B0=No Blast Furnace Slag added; F1=Some Fly Ash added & F0=No Fly Ash added. The number of observations (samples) used for the research was one-hundred and thirty-two (132) in all. For Superplasticizer, it was found that Compressive Strength of Concrete was more elastic with regards to Water-Cement ratio at S1 level than at S0 level for the given ages of concrete 3, 7and 28 days. For Blast Furnace Slag, Compressive Strength with regards to Water-Cement ratio was more elastic at B0 level than at B1 level for concrete ages 3, 7 and 28 days. For Fly Ash, Compressive Strength with regards to Water-Cement ratio was more elastic at B0 level than at B1 level for Ages 3, 7 and 28 days. The research also tested for different combinations of the levels of Superplasticizer, Blast Furnace Slag and Fly Ash. It was found that Compressive Strength elasticity with regards to Water-Cement ratio was lowest (Elasticity=-1.746) with a combination of S0, B0 and F0 for concrete age of 3 days. This was followed by Elasticity of -1.611 with a combination of S0, B0 and F0 for a concrete of age 7 days. Next, the highest was an Elasticity of -1.414 with combination of S0, B0 and F0 for a concrete age of 28 days. Based on preceding outcomes, three (3) non-linear model equations for predicting the output elasticity of Compressive Strength of Concrete (in %) or the value of Compressive Strength of Concrete (in MPa) with regards to Water to Cement was formulated. The model equations were based on the three different ages of concrete namely 3, 7 and 28 days under investigation. The three models showed that higher elasticity translates into higher compressive strength. And the models revealed a trend of increasing concrete strength from 3 to 28 days for a given amount of water to cement ratio. Using the models, an increasing modulus of elasticity from 3 to 28 days was deduced.

Keywords: concrete, compressive strength, elasticity, water-cement

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Authors: Arpit Patil, Atharav Bhagwat, Rajas Bhope, Pramod Bide

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This research integrates IoT and ML to predict heart failure risks, utilizing the Framingham dataset. IoT devices gather real-time physiological data, focusing on heart rate dynamics, while ML, specifically Random Forest, predicts heart failure. Rigorous feature selection enhances accuracy, achieving over 90% prediction rate. This amalgamation marks a transformative step in proactive healthcare, highlighting early detection's critical role in cardiovascular risk mitigation. Challenges persist, necessitating continual refinement for improved predictive capabilities.

Keywords: cardiovascular diseases, internet of things, machine learning, cardiac risk assessment, heart failure prediction, early detection, cardio data analysis

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Authors: Khachen Kongpakwattana, Nathorn Chaiyakunapruk

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Background: Decision-analytic models for Alzheimer’s disease (AD) have been advanced to discrete-event simulation (DES), in which individual-level modelling of disease progression across continuous severity spectra and incorporation of key parameters such as treatment persistence into the model become feasible. This study aimed to apply the DES to perform a cost-effectiveness analysis of treatment for AD in Thailand. Methods: A dataset of Thai patients with AD, representing unique demographic and clinical characteristics, was bootstrapped to generate a baseline cohort of patients. Each patient was cloned and assigned to donepezil, galantamine, rivastigmine, memantine or no treatment. Throughout the simulation period, the model randomly assigned each patient to discrete events including hospital visits, treatment discontinuation and death. Correlated changes in cognitive and behavioral status over time were developed using patient-level data. Treatment effects were obtained from the most recent network meta-analysis. Treatment persistence, mortality and predictive equations for functional status, costs (Thai baht (THB) in 2017) and quality-adjusted life year (QALY) were derived from country-specific real-world data. The time horizon was 10 years, with a discount rate of 3% per annum. Cost-effectiveness was evaluated based on the willingness-to-pay (WTP) threshold of 160,000 THB/QALY gained (4,994 US$/QALY gained) in Thailand. Results: Under a societal perspective, only was the prescription of donepezil to AD patients with all disease-severity levels found to be cost-effective. Compared to untreated patients, although the patients receiving donepezil incurred a discounted additional costs of 2,161 THB, they experienced a discounted gain in QALY of 0.021, resulting in an incremental cost-effectiveness ratio (ICER) of 138,524 THB/QALY (4,062 US$/QALY). Besides, providing early treatment with donepezil to mild AD patients further reduced the ICER to 61,652 THB/QALY (1,808 US$/QALY). However, the dominance of donepezil appeared to wane when delayed treatment was given to a subgroup of moderate and severe AD patients [ICER: 284,388 THB/QALY (8,340 US$/QALY)]. Introduction of a treatment stopping rule when the Mini-Mental State Exam (MMSE) score goes below 10 to a mild AD cohort did not deteriorate the cost-effectiveness of donepezil at the current treatment persistence level. On the other hand, none of the AD medications was cost-effective when being considered under a healthcare perspective. Conclusions: The DES greatly enhances real-world representativeness of decision-analytic models for AD. Under a societal perspective, treatment with donepezil improves patient’s quality of life and is considered cost-effective when used to treat AD patients with all disease-severity levels in Thailand. The optimal treatment benefits are observed when donepezil is prescribed since the early course of AD. With healthcare budget constraints in Thailand, the implementation of donepezil coverage may be most likely possible when being considered starting with mild AD patients, along with the stopping rule introduced.

Keywords: Alzheimer's disease, cost-effectiveness analysis, discrete event simulation, health technology assessment

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Authors: M. Ouassaf, S. Belaidi

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Aromatase is an estrogen biosynthetic enzyme belonging to the cytochrome P450 family, which catalyzes the limiting step in the conversion of androgens to estrogens. As it is relevant for the promotion of tumor cell growth. A set of thirty 1,2,3-triazole derivatives was used in the quantitative structure activity relationship (QSAR) study using regression multiple linear (MLR), We divided the data into two training and testing groups. The results showed a good predictive ability of the MLR model, the models were statistically robust internally (R² = 0.982) and the predictability of the model was tested by several parameters. including external criteria (R²pred = 0.851, CCC = 0.946). The knowledge gained in this study should provide relevant information that contributes to the origins of aromatase inhibitory activity and, therefore, facilitates our ongoing quest for aromatase inhibitors with robust properties.

Keywords: aromatase inhibitors, QSAR, MLR, 1, 2, 3-triazole

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Authors: Oluwafunmibi Omotayo Fasanya, Augustine Kena Adjei

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Effective management of hypertension remains a critical public health challenge, particularly among racially and ethnically diverse populations. This study employs sophisticated statistical models to rigorously investigate the predictors of poor blood pressure (BP) control, with a specific focus on demographic, socioeconomic, and clinical risk factors. Leveraging a large sample of 19,253 adults drawn from the National Health and Nutrition Examination Survey (NHANES) across three distinct time periods (2013-2014, 2015-2016, and 2017-2020), we applied multivariable logistic regression and generalized estimating equations (GEE) to account for the clustered structure of the data and potential within-subject correlations. Our multivariable models identified significant associations between poor BP control and several key predictors, including race/ethnicity, age, gender, body mass index (BMI), prevalent diabetes, and chronic kidney disease (CKD). Non-Hispanic Black individuals consistently exhibited higher odds of poor BP control across all periods (OR = 1.99; 95% CI: 1.69, 2.36 for the overall sample; OR = 2.33; 95% CI: 1.79, 3.02 for 2017-2020). Younger age groups demonstrated substantially lower odds of poor BP control compared to individuals aged 75 and older (OR = 0.15; 95% CI: 0.11, 0.20 for ages 18-44). Men also had a higher likelihood of poor BP control relative to women (OR = 1.55; 95% CI: 1.31, 1.82), while BMI ≥35 kg/m² (OR = 1.76; 95% CI: 1.40, 2.20) and the presence of diabetes (OR = 2.20; 95% CI: 1.80, 2.68) were associated with increased odds of poor BP management. Further analysis using GEE models, accounting for temporal correlations and repeated measures, confirmed the robustness of these findings. Notably, individuals with chronic kidney disease displayed markedly elevated odds of poor BP control (OR = 3.72; 95% CI: 3.09, 4.48), with significant differences across the survey periods. Additionally, higher education levels and better self-reported diet quality were associated with improved BP control. College graduates exhibited a reduced likelihood of poor BP control (OR = 0.64; 95% CI: 0.46, 0.89), particularly in the 2015-2016 period (OR = 0.48; 95% CI: 0.28, 0.84). Similarly, excellent dietary habits were associated with significantly lower odds of poor BP control (OR = 0.64; 95% CI: 0.44, 0.94), underscoring the importance of lifestyle factors in hypertension management. In conclusion, our findings provide compelling evidence of the complex interplay between demographic, clinical, and socioeconomic factors in predicting poor BP control. The application of advanced statistical techniques such as GEE enhances the reliability of these results by addressing the correlated nature of repeated observations. This study highlights the need for targeted interventions that consider racial/ethnic disparities, clinical comorbidities, and lifestyle modifications in improving BP control outcomes.

Keywords: hypertension, blood pressure, NHANES, generalized estimating equations

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Authors: Stéphanie Pellerin, Bérengére Podvin, Luc Pastur

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In aerovehicles context, the flow around an Ahmed body profile is simulated using the velocity-vorticity formulation of the Navier-Stokes equations, associated to a penalization method for solids and Large Eddy Simulation for turbulence. The study focuses both on the ground influence on the flow and on the dissymetry of the wake, observed for a ground clearance greater than 10% of the body height H. Unsteady and mean flows are presented and analyzed. POD study completes the analysis and gives information on the most energetic structures of the flow.

Keywords: Ahmed body, bi-stability, LES, near wake

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Authors: Malika El Kyal, Ahmed Machmoum

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We prove the superlinear convergence of asynchronous multi-splitting methods applied to differential equations. This study is based on the technique of nested sets. It permits to specify kind of the convergence in the asynchronous mode.The main characteristic of an asynchronous mode is that the local algorithm not have to wait at predetermined messages to become available. We allow some processors to communicate more frequently than others, and we allow the communication delays to be substantial and unpredictable. Note that synchronous algorithms in the computer science sense are particular cases of our formulation of asynchronous one.

Keywords: parallel methods, asynchronous mode, multisplitting, ODE

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Authors: Rim Messaoudi, Nogaye-Gueye Gning, François Azelart

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Automatic text classification applies mostly natural language processing (NLP) and other AI-guided techniques to automatically classify text in a faster and more accurate manner. This paper discusses the subject of using predictive maintenance to manage incident tickets inside the sociality. It focuses on proposing a tool that treats and analyses comments and notes written by administrators after resolving an incident ticket. The goal here is to increase the quality of these comments. Additionally, this tool is based on NLP and machine learning techniques to realize the textual analytics of the extracted data. This approach was tested using real data taken from the French National Railways (SNCF) company and was given a high-quality result.

Keywords: machine learning, text classification, NLP techniques, semantic representation

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Authors: Zakiul Fuady, Michaela Spiske

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Inundation events, such as storms and tsunamis can leave onshore sedimentary evidence like sand deposits or large boulders. These deposits store indirect information on the related inundation parameters (e.g., flow velocity, flow depth, wave height). One tool to reveal these parameters are inverse models that use the physical characteristics of the deposits to refer to the magnitude of inundation. This study used boulders of the 2004 Indian Ocean Tsunami from Thailand (Pakarang Cape) and form a historical tsunami event that inundated the outer British Virgin Islands (Anegada). For the largest boulder found in Pakarang Cape with a volume of 26.48 m³ the required tsunami wave height is 0.44 m and storm wave height are 1.75 m (for a bulk density of 1.74 g/cm³. In Pakarang Cape the highest tsunami wave height is 0.45 m and storm wave height are 1.8 m for transporting a 20.07 m³ boulder. On Anegada, the largest boulder with a diameter of 2.7 m is the asingle coral head (species Diploria sp.) with a bulk density of 1.61 g/cm³, and requires a minimum tsunami wave height of 0.31 m and storm wave height of 1.25 m. The highest required tsunami wave height on Anegada is 2.12 m for a boulder with a bulk density of 2.46 g/cm³ (volume 0.0819 m³) and the highest storm wave height is 5.48 m (volume 0.216 m³) from the same bulk density and the coral type is limestone. Generally, the higher the bulk density, volume, and weight of the boulders, the higher the minimum tsunami and storm wave heights required to initiate transport. It requires 4.05 m/s flow velocity by Nott’s equation (2003) and 3.57 m/s by Nandasena et al. (2011) to transport the largest boulder in Pakarang Cape, whereas on Anegada, it requires 3.41 m/s to transport a boulder with diameter 2.7 m for both equations. Thus, boulder equations need to be handled with caution because they make many assumptions and simplifications. Second, the physical boulder parameters, such as density and volume need to be determined carefully to minimize any errors.

Keywords: tsunami wave height, storm wave height, flow velocity, boulders, Anegada, Pakarang Cape

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Authors: Ajayi Olusola Olajide, Alonge Olaide Moses

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Predicting the outcome of soccer matches poses an interesting challenge for which it is realistically impossible to successfully do so for every match. Despite this, there are lots of resources that are being expended on the correct prediction of soccer matches weekly, and all over the world. Soccer Match Result Prediction System Model (SMRPSM) is a system that is proposed whereby the results of matches between two soccer teams are auto-generated, with the added excitement of giving users a chance to test their predictive abilities. Soccer teams from different league football are loaded by the application, with each team’s corresponding manager and other information like team location, team logo and nickname. The user is also allowed to interact with the system by selecting the match to be predicted and viewing of the results of completed matches after registering/logging in.

Keywords: predicting, soccer match, outcome, soccer, matches, result prediction, system, model

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Authors: Bing Yan

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Nanomaterials are widely used in various industrial sectors, biomedicine, and more than 1300 consumer products. Although there is still no standard safety regulation, their potential toxicity is a major concern worldwide. We discovered that nanoparticles target and enter human cells1, perturb cellular signaling pathways2, affect various cell functions3, and cause malfunctions in animals4,5. Because the majority of atoms in nanoparticles are on the surface, chemistry modification on their surface may change their biological properties significantly. We modified nanoparticle surface using nano-combinatorial chemistry library approach6. Novel nanoparticles were discovered to exhibit significantly reduced toxicity6,7, enhance cancer targeting ability8, or re-program cellular signaling machineries7. Using computational chemistry, quantitative nanostructure-activity relationship (QNAR) is established and predictive models have been built to predict biocompatible nanoparticles.

Keywords: nanoparticle, nanotoxicity, nano-bio, nano-combinatorial chemistry, nanoparticle library

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Authors: Nia Mair Fry, Matthew Profit, Chenfeng Li

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This work focuses on the development and implementation of a fully implicit-implicit, coupled mechanical deformation and porous flow, finite element software tool. The fully implicit software accurately predicts classical fundamental analytical solutions such as the Terzaghi consolidation problem. Furthermore, it can capture other analytical solutions less well known in the literature, such as Gibson’s sedimentation rate problem and Coussy’s problems investigating wellbore stability for poroelastic rocks. The mechanical volume strains are transferred to the porous flow governing equation in an implicit framework. This will overcome some of the many current industrial issues, which use explicit solvers for the mechanical governing equations and only implicit solvers on the porous flow side. This can potentially lead to instability and non-convergence issues in the coupled system, plus giving results with an accountable degree of error. The specification of a fully monolithic implicit-implicit coupled porous media code sees the solution of both seepage-mechanical equations in one matrix system, under a unified time-stepping scheme, which makes the problem definition much easier. When using an explicit solver, additional input such as the damping coefficient and mass scaling factor is required, which are circumvented with a fully implicit solution. Further, improved accuracy is achieved as the solution is not dependent on predictor-corrector methods for the pore fluid pressure solution, but at the potential cost of reduced stability. In testing of this fully monolithic porous media code, there is the comparison of the fully implicit coupled scheme against an existing staggered explicit-implicit coupled scheme solution across a range of geotechnical problems. These cases include 1) Biot coefficient calculation, 2) consolidation theory with Terzaghi analytical solution, 3) sedimentation theory with Gibson analytical solution, and 4) Coussy well-bore poroelastic analytical solutions.

Keywords: coupled, implicit, monolithic, porous media

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Authors: Sławomir Lasota, Tomasz Kajdanowicz

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This paper delves into adapting the Tweeting Factory paradigm to achieve universal production optimization under the Industry 5.0 framework. The proposed system creates a dynamic decision-making environment by collecting and analyzing structured telemetry data (”tweets”) from production lines. A hybrid recommendation engine combines rule-based systems with machine learning models to enhance real-time responsiveness and operator engagement. The research evaluates the system’s ability to optimize diverse industrial processes through predictive KPIs and real-time feedback loops. Results indicate significant advancements in eco-efficiency and operator productivity, showcasing the versatility of the Tweeting Factory approach in meeting the demands of human-centric and sustainable production.

Keywords: tweeting factory, production optimization, industry 5.0, recommendation

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Authors: Khairil I. Othman, Mahfuzah Mahayaddin, Zarina Bibi Ibrahim

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We develop and demonstrate a computationally efficient numerical technique to solve first order stiff differential equations. This technique is based on block method whereby three approximate points are calculated. The Cholistani of varied step sizes are presented in divided difference form. Stability regions of the formulae are briefly discussed in this paper. Numerical results show that this block method perform very well compared to existing methods.

Keywords: block method, divided difference, stiff, computational

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Authors: Raja Omman Zafar, Yves Rybarczyk, Johan Borg

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This article proposes a conceptual framework for the application of digital twin technology in home care. The main goal is to bridge the gap between advanced digital twin concepts and their practical implementation in home care. This study uses a literature review and thematic analysis approach to synthesize existing knowledge and proposes a structured framework suitable for homecare applications. The proposed framework integrates key components such as IoT sensors, data-driven models, cloud computing, and user interface design, highlighting the importance of personalized and predictive homecare solutions. This framework can significantly improve the efficiency, accuracy, and reliability of homecare services. It paves the way for the implementation of digital twins in home care, promoting real-time monitoring, early intervention, and better outcomes.

Keywords: digital twin, homecare, older adults, healthcare, IoT, artificial intelligence

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Authors: Faruk Ozdemir, Roy Kalawsky, Peter Hubbard

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Accurate estimation of RUL provides a basis for effective predictive maintenance, reducing unexpected downtime for industrial equipment. However, while models such as the Random Forest have effective predictive capabilities, they are the so-called ‘black box’ models, where interpretability is at a threshold to make critical diagnostic decisions involved in industries related to aviation. The purpose of this work is to present a prognostic framework that embeds Explainable Artificial Intelligence (XAI) techniques in order to provide essential transparency in Machine Learning methods' decision-making mechanisms based on sensor data, with the objective of procuring actionable insights for the aviation industry. Sensor readings have been gathered from critical equipment such as turbofan jet engine and landing gear, and the prediction of the RUL is done by a Random Forest model. It involves steps such as data gathering, feature engineering, model training, and evaluation. These critical components’ datasets are independently trained and evaluated by the models. While suitable predictions are served, their performance metrics are reasonably good; such complex models, however obscure reasoning for the predictions made by them and may even undermine the confidence of the decision-maker or the maintenance teams. This is followed by global explanations using SHAP and local explanations using LIME in the second phase to bridge the gap in reliability within industrial contexts. These tools analyze model decisions, highlighting feature importance and explaining how each input variable affects the output. This dual approach offers a general comprehension of the overall model behavior and detailed insight into specific predictions. The proposed framework, in its third component, incorporates the techniques of causal analysis in the form of Granger causality tests in order to move beyond correlation toward causation. This will not only allow the model to predict failures but also present reasons, from the key sensor features linked to possible failure mechanisms to relevant personnel. The causality between sensor behaviors and equipment failures creates much value for maintenance teams due to better root cause identification and effective preventive measures. This step contributes to the system being more explainable. Surrogate Several simple models, including Decision Trees and Linear Models, can be used in yet another stage to approximately represent the complex Random Forest model. These simpler models act as backups, replicating important jobs of the original model's behavior. If the feature explanations obtained from the surrogate model are cross-validated with the primary model, the insights derived would be more reliable and provide an intuitive sense of how the input variables affect the predictions. We then create an iterative explainable feedback loop, where the knowledge learned from the explainability methods feeds back into the training of the models. This feeds into a cycle of continuous improvement both in model accuracy and interpretability over time. By systematically integrating new findings, the model is expected to adapt to changed conditions and further develop its prognosis capability. These components are then presented to the decision-makers through the development of a fully transparent condition monitoring and alert system. The system provides a holistic tool for maintenance operations by leveraging RUL predictions, feature importance scores, persistent sensor threshold values, and autonomous alert mechanisms. Since the system will provide explanations for the predictions given, along with active alerts, the maintenance personnel can make informed decisions on their end regarding correct interventions to extend the life of the critical machinery.

Keywords: predictive maintenance, explainable artificial intelligence, prognostic, RUL, machine learning, turbofan engines, C-MAPSS dataset

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Authors: Suha K. Shihab, Zahid A. Khan, Aas Mohammad, Arshad Noor Siddiquee

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This paper presents the influence of cutting parameters (cutting speed, feed and depth of cut) on flank wear (VB) in turning of 52100 hard alloy steel using multilayer coated carbide insert under dry condition. Nine experiments were performed based on Taguchi’s L9 orthogonal array. Analysis of variance (ANOVA) was used to determine the effects of the cutting parameters on flank wear. The results of the study revealed that the cutting speed (A) and feed rate (B) are the dominant factors affecting flank wear, while the depth of cut (C) has not a significant effect. The optimal combination of the cutting parameters for flank wear is found to be A1B1C1. The mathematical model for flank wear is found to be statistically significant. The predicted and measured values of flank wear are found to be very close to each other.

Keywords: flank wear, hard turning, Taguchi approach, optimization

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Authors: Haj Najafi Leila, Tehranizadeh Mohsen

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Dependencies between diverse factors involved in probabilistic seismic loss evaluation are recognized to be an imperative issue in acquiring accurate loss estimates. Dependencies among component damage costs could be taken into account considering two partial distinct states of independent or perfectly-dependent for component damage states; however, in our best knowledge, there is no available procedure to take account of loss dependencies in story level. This paper attempts to present a method called "modal cost superposition method" for decoupling story damage costs subjected to earthquake ground motions dealt with closed form differential equations between damage cost and engineering demand parameters which should be solved in complex system considering all stories' cost equations by the means of the introduced "substituted matrixes of mass and stiffness". Costs are treated as probabilistic variables with definite statistic factors of median and standard deviation amounts and a presumed probability distribution. To supplement the proposed procedure and also to display straightforwardness of its application, one benchmark study has been conducted. Acceptable compatibility has been proven for the estimated damage costs evaluated by the new proposed modal and also frequently used stochastic approaches for entire building; however, in story level, insufficiency of employing modification factor for incorporating occurrence probability dependencies between stories has been revealed due to discrepant amounts of dependency between damage costs of different stories. Also, more dependency contribution in occurrence probability of loss could be concluded regarding more compatibility of loss results in higher stories than the lower ones, whereas reduction in incorporation portion of cost modes provides acceptable level of accuracy and gets away from time consuming calculations including some limited number of cost modes in high mode situation.

Keywords: dependency, story-cost, cost modes, engineering demand parameter

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Authors: Masoud Ghaemi, MohammadJafar Hedayati, Faezeh Yousefzadeh, Hoseinali Heydarzadeh

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According to the statistics of the International Committee on Large Dams, internal erosion and piping (scour) are major causes of the destruction of earth-fill dams. If such dams are constructed in narrow valleys, the valley walls will increase the arching of the dam body due to the transfer of vertical and horizontal stresses, so the occurrence of hydraulic fracturing in these embankments is more likely. Roudbar Dam in Lorestan is a clay-core pebble earth-fill dam constructed in a relatively narrow valley in western Iran. Three years after the onset of impoundment, there has been a fall in dam behavior. Evaluation of the dam behavior based on the data recorded on the instruments installed inside the dam body and foundation confirms the occurrence of internal erosion in the lower and adjacent parts of the core on the left support (abutment). The phenomenon of hydraulic fracturing is one of the main causes of the onset of internal erosion in this dam. Accordingly, the main objective of this paper is to evaluate the validity of this hypothesis. To evaluate the validity of this hypothesis, the dam behavior during construction and impoundment has been first simulated with a three-dimensional numerical model. Then, using validated empirical equations, the safety factor of the occurrence of hydraulic fracturing phenomenon upstream of the dam score was calculated. Then, using the artificial neural network, the failure time of the given section was predicted based on the maximum stress trend created. The study results show that steep slopes of valley walls, sudden changes in coefficient, and differences in compressibility properties of dam body materials have caused considerable stress transfer from core to adjacent valley walls, especially at its lower levels. This has resulted in the coefficient of confidence of the occurrence of hydraulic fracturing in each of these areas being close to one in each of the empirical equations used.

Keywords: arching, artificial neural network, FLAC3D, hydraulic fracturing, internal erosion, pore water pressure

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Authors: Sina Dehghani, Zhikang Rong

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Historically, a variety of approaches have been taken to forecast commodity prices due to the significant implications of these values on the global economy. An accurate forecasting tool for a valuable commodity would significantly benefit investors and governmental agencies. Silver, in particular, has grown significantly as a commodity in recent years due to its use in healthcare and technology. This manuscript aims to utilize the Geometric Brownian Motion predictive model to forecast silver commodity prices over multiple 3-year periods. The results of the study indicate that the model has several limitations, particularly its inability to work effectively over longer periods of time, but still was extremely effective over shorter time frames. This study sets a baseline for silver commodity forecasting with GBM, and the model could be further strengthened with refinement.

Keywords: geometric Brownian motion, commodity, risk management, volatility, stochastic behavior, price forecasting

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Authors: Xu Du, Aparna S. Varde

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In this research, we propose to conduct diagnostic and predictive analysis about the key factors and consequences of urban population relocation. To achieve this goal, urban simulation models extract the urban development trends as land use change patterns from a variety of data sources. The results are treated as part of urban big data with other information such as population change and economic conditions. Multiple data mining methods are deployed on this data to analyze nonlinear relationships between parameters. The result determines the driving force of population relocation with respect to urban sprawl and urban sustainability and their related parameters. Experiments so far reveal that data mining methods discover useful knowledge from the multicity urban data. This work sets the stage for developing a comprehensive urban simulation model for catering to specific questions by targeted users. It contributes towards achieving sustainability as a whole.

Keywords: data mining, environmental modeling, sustainability, urban planning

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Authors: Khairil Iskandar Othman, Nadhirah Kamal, Zarina Bibi Ibrahim

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Block methods for solving stiff systems of ordinary differential equations (ODEs) are based on backward differential formulas (BDF) with PE(CE)2 and Newton method. In this paper, we introduce Modified Newton as a new strategy to get more efficient result. The derivation of BBDF using modified block Newton method is presented. This new block method with predictor-corrector gives more accurate result when compared to the existing BBDF.

Keywords: modified Newton, stiff, BBDF, Jacobian matrix

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Authors: Saltanat Zhilkibayeva, Muratulla Utenov, Nurzhan Utenov

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The distributed inertia forces of complex nature appear in links of rod mechanisms within the motion process. Such loads raise a number of problems, as the problems of destruction caused by a large force of inertia; elastic deformation of the mechanism can be considerable, that can bring the mechanism out of action. In this work, a new analytical approach for the definition of internal forces in links of robotic systems and mechanisms with statically indeterminate and determinate structures taking into account the distributed inertial and concentrated forces is proposed. The relations between the intensity of distributed inertia forces and link weight with geometrical, physical and kinematic characteristics are determined in this work. The distribution laws of inertia forces and dead weight make it possible at each position of links to deduce the laws of distribution of internal forces along the axis of the link, in which loads are found at any point of the link. The approximation matrixes of forces of an element under the action of distributed inertia loads with the trapezoidal intensity are defined. The obtained approximation matrixes establish the dependence between the force vector in any cross-section of the element and the force vector in calculated cross-sections, as well as allow defining the physical characteristics of the element, i.e., compliance matrix of discrete elements. Hence, the compliance matrixes of an element under the action of distributed inertial loads of trapezoidal shape along the axis of the element are determined. The internal loads of each continual link are unambiguously determined by a set of internal loads in its separate cross-sections and by the approximation matrixes. Therefore, the task is reduced to the calculation of internal forces in a final number of cross-sections of elements. Consequently, it leads to a discrete model of elastic calculation of links of rod mechanisms. The discrete model of the elements of mechanisms and robotic systems and their discrete model as a whole are constructed. The dynamic equilibrium equations for the discrete model of the elements are also received in this work as well as the equilibrium equations of the pin and rigid joints expressed through required parameters of internal forces. Obtained systems of dynamic equilibrium equations are sufficient for the definition of internal forces in links of mechanisms, which structure is statically definable. For determination of internal forces of statically indeterminate mechanisms (in the way of determination of internal forces), it is necessary to build a compliance matrix for the entire discrete model of the rod mechanism, that is reached in this work. As a result by means of developed technique the programs in the MAPLE18 system are made and animations of the motion of the fourth class mechanisms of statically determinate and statically indeterminate structures with construction on links the intensity of cross and axial distributed inertial loads, the bending moments, cross and axial forces, depending on kinematic characteristics of links are obtained.

Keywords: distributed inertial forces, internal forces, statically determinate mechanisms, statically indeterminate mechanisms

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Authors: Nasrin Akter, Prem Chhetri, Shams Rahman

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The present study tested Ajzen’s Theory of Planned Behavior (TPB) model to explain the formation of business customers’ intention to use 3PL services in Bangladesh. The findings show that the TPB model has a good fit to the data. Based on theoretical support and suggested modification indices, a refined TPB model was developed afterwards which provides a better predictive power for intention. Consistent with the theory, the results of a structural equation analysis revealed that the intention to use 3PL services is predicted by attitude and subjective norms but not by perceived behavioral control. Further investigation indicated that the paths between (attitude and intention) and (subjective norms and intention) did not statistically differ between 3PL user and non-user. Findings of this research provide an evidence base to formulate business strategies to increase the use of 3PL services in Bangladesh to enhance productivity and to gain economic efficiency.

Keywords: Bangladesh, intention, third-party logistics, Theory of Planned Behavior

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Authors: Ryan B. Simpson, Margaret A. Waskow, Aishwarya Venkat, Elena N. Naumova

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The Centers for Disease Control and Prevention (CDC) estimate that 31 known foodborne pathogens cause 9.4 million cases of these illnesses annually in US. Over 90% of these illnesses are associated with exposure to Campylobacter, Cryptosporidium, Cyclospora, Listeria, Salmonella, Shigella, Shiga-Toxin Producing E.Coli (STEC), Vibrio, and Yersinia. Contaminated products contain parasites typically causing an intestinal illness manifested by diarrhea, stomach cramping, nausea, weight loss, fatigue and may result in deaths in fragile populations. Since 1998, the National Outbreak Reporting System (NORS) has allowed for routine collection of suspected and laboratory-confirmed cases of food poisoning. While retrospective analyses have revealed common pathogen-specific seasonal patterns, little is known concerning the stability of those patterns over time and whether they can be used for preventative forecasting. The objective of this study is to construct a calendar of foodborne outbreaks of nine infections based on the peak timing of outbreak incidence in the US from 1996 to 2017. Reported cases were abstracted from FoodNet for Salmonella (135115), Campylobacter (121099), Shigella (48520), Cryptosporidium (21701), STEC (18022), Yersinia (3602), Vibrio (3000), Listeria (2543), and Cyclospora (758). Monthly counts were compiled for each agent, seasonal peak timing and peak intensity were estimated, and the stability of seasonal peaks and synchronization of infections was examined. Negative Binomial harmonic regression models with the delta-method were applied to derive confidence intervals for the peak timing for each year and overall study period estimates. Preliminary results indicate that five infections continue to lead as major causes of outbreaks, exhibiting steady upward trends with annual increases in cases ranging from 2.71% (95%CI: [2.38, 3.05]) in Campylobacter, 4.78% (95%CI: [4.14, 5.41]) in Salmonella, 7.09% (95%CI: [6.38, 7.82]) in E.Coli, 7.71% (95%CI: [6.94, 8.49]) in Cryptosporidium, and 8.67% (95%CI: [7.55, 9.80]) in Vibrio. Strong synchronization of summer outbreaks were observed, caused by Campylobacter, Vibrio, E.Coli and Salmonella, peaking at 7.57 ± 0.33, 7.84 ± 0.47, 7.85 ± 0.37, and 7.82 ± 0.14 calendar months, respectively, with the serial cross-correlation ranging 0.81-0.88 (p < 0.001). Over 21 years, Listeria and Cryptosporidium peaks (8.43 ± 0.77 and 8.52 ± 0.45 months, respectively) have a tendency to arrive 1-2 weeks earlier, while Vibrio peaks (7.8 ± 0.47) delay by 2-3 weeks. These findings will be incorporated in the forecast models to predict common paths of the spread, long-term trends, and the synchronization of outbreaks across etiological agents. The predictive modeling of foodborne outbreaks should consider long-term changes in seasonal timing, spatiotemporal trends, and sources of contamination.

Keywords: foodborne outbreak, national outbreak reporting system, predictive modeling, seasonality

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Authors: Sezer Kefeli, Sertaç Arslan

Abstract:

In this paper, supercavitationphenomena, and parameters are explained, and hydrodynamic design approaches are investigated for supercavitating torpedoes. In addition, drag force calculation methods ofsupercavitatingvehicles are obtained. Basically, conventional heavyweight torpedoes reach up to ~50 knots by classic hydrodynamic techniques, on the other hand super cavitating torpedoes may reach up to ~200 knots, theoretically. However, in order to reachhigh speeds, hydrodynamic viscous forces have to be reduced or eliminated completely. This necessity is revived the supercavitation phenomena that is implemented to conventional torpedoes. Supercavitation is a type of cavitation, after all, it is more stable and continuous than other cavitation types. The general principle of supercavitation is to separate the underwater vehicle from water phase by surrounding the vehicle with cavitation bubbles. This situation allows the torpedo to operate at high speeds through the water being fully developed cavitation. Conventional torpedoes are entitled as supercavitating torpedoes when the torpedo moves in a cavity envelope due to cavitator in the nose section and solid fuel rocket engine in the rear section. There are two types of supercavitation phase, these are natural and artificial cavitation phases. In this study, natural cavitation is investigated on the disk cavitators based on numerical methods. Once the supercavitation characteristics and drag reduction of natural cavitationare studied on CFD platform, results are verified with the empirical equations. As supercavitation parameters cavitation number (), pressure distribution along axial axes, drag coefficient (C_?) and drag force (D), cavity wall velocity (U_?) and dimensionless cavity shape parameters, which are cavity length (L_?/d_?), cavity diameter(d_ₘ/d_?) and cavity fineness ratio (〖L_?/d〗_ₘ) are investigated and compared with empirical results. This paper has the characteristics of feasibility study to carry out numerical solutions of the supercavitation phenomena comparing with empirical equations.

Keywords: CFD, cavity envelope, high speed underwater vehicles, supercavitating flows, supercavitation, drag reduction, supercavitation parameters

Procedia PDF Downloads 173