Search results for: working models

Authors: Syed Atif Ali, Farzan Yahya

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This research paper covers the issue of risk management impact on the company performance. Degree of financial leverage (DFL), degree of operating leverage (DOL) and the working capital ratio (WCR) are taken as independent variables which are the representative of risk and the earning price per share (EPS), return on assets (ROA), return on equity (ROE), Sales and Net profits which are the representative of performance. Last 10 years (2004-2013) of Cement sector of Pakistan data is chosen as sample for analyze their relations by multiple regression technique. Through analyses, it is found that WCR impact adequately on the company performance because if company has enough liquidity than it perform its operations smoothly and enhance its performance very well. DFL should be control moderately because enough DFL leads performance of company downward. On the other hand, the DOL should be less because it causes the less profitability for a company from its operations.

Keywords: degree of financial leverage (DFL), degree of operating leverage (DOL), working capital ratio (WCR), earning per share (EPS), return on equity (ROE), return on assets (ROA)

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Authors: Gulshan Sachdeva, Vaibhav Jain, S. S. Kachhwaha

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Cascade refrigeration systems employ series of single stage vapor compression units which are thermally coupled with evaporator/condenser cascades. Different refrigerants are used in each of the circuit depending on the optimum characteristics shown by the refrigerant for a particular application. In the present research study, a steady state thermodynamic model is developed which simulates the working of an actual cascade system. The model provides COP and all other system parameters like total compressor work, temperature, pressure, enthalpy and entropy at different state points. The working fluid in Low Temperature Circuit (LTC) is CO2 (R744) while ammonia (R717), propane (R290), propylene (R1270), R404A and R12 are the refrigerants in High Temperature Circuit (HTC). The performance curves of ammonia, propane, propylene, and R404A are compared with R12 to find its nearest substitute. Results show that ammonia is the best substitute of R12.

Keywords: cascade system, refrigerants, thermodynamic model, production engineering

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Authors: Melodi Mbuyi Mata, Blessing Olamide Taiwo, Afolabi Ayodele David

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The purpose of this research is to create empirical models for assessing the profitability of granite aggregate production in Akure, Ondo state aggregate quarries. In addition, an artificial neural network (ANN) model and multivariate predicting models for granite profitability were developed in the study. A formal survey questionnaire was used to collect data for the study. The data extracted from the case study mine for this study includes granite marketing operations, royalty, production costs, and mine production information. The following methods were used to achieve the goal of this study: descriptive statistics, MATLAB 2017, and SPSS16.0 software in analyzing and modeling the data collected from granite traders in the study areas. The ANN and Multi Variant Regression models' prediction accuracy was compared using a coefficient of determination (R²), Root mean square error (RMSE), and mean square error (MSE). Due to the high prediction error, the model evaluation indices revealed that the ANN model was suitable for predicting generated profit in a typical quarry. More quarries in Nigeria's southwest region and other geopolitical zones should be considered to improve ANN prediction accuracy.

Keywords: national development, granite, profitability assessment, ANN models

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Authors: A. Khaleel, S. Gao

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Both steady and unsteady turbulent mixed convection heat transfer in a 3D lid-driven enclosure, which has constant heat flux on the middle of bottom wall and with isothermal moving sidewalls, is reported in this paper for working fluid with Prandtl number Pr = 0.71. The other walls are adiabatic and stationary. The dimensionless parameters used in this research are Reynolds number, Re = 5000, 10000 and 15000, and Richardson number, Ri = 1 and 10. The simulations have been done by using different turbulent methods such as RANS, URANS, and LES. The effects of using different k- models such as standard, RNG and Realizable k- model are investigated. Interesting behaviours of the thermal and flow fields with changing the Re or Ri numbers are observed. Isotherm and turbulent kinetic energy distributions and variation of local Nusselt number at the hot bottom wall are studied as well. The local Nusselt number is found increasing with increasing either Re or Ri number. In addition, the turbulent kinetic energy is discernibly affected by increasing Re number. Moreover, the LES results have shown a good ability of this method in predicting more detailed flow structures in the cavity.

Keywords: mixed convection, lid-driven cavity, turbulent flow, RANS model, large Eddy simulation

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Authors: Mohd Raish Ansari, Tauheed Alam Khan

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This research paper is a comparative study of the belt truss in the Regular RC frame structure at the different positions of the floor. The method used in this research is the response spectrum method with the help of the ETABS Software, there are six models in this paper with belt truss. The Indian standard code used in this work are IS 456:2000, IS 800:2007, IS 875 part-1, IS 875 part-1, and IS 1893 Part-1:2016. The cross-section of the belt truss is the I-section, a grade of steel that is made up of Mild Steel. The basic model in this research paper is the same, only position of the belt truss is going to change, and the dimension of the belt truss is remain constant for all models. The plan area of all models is 24.5 meters x 28 meters, and the model has G+20, where the height of the ground floor is 3.5 meters, and all floor height is 3.0 meters remains constant. This comparative research work selected some important seismic parameters to check the stability of all models, the parameters are base shear, fundamental period, storey overturning moment, and maximum storey displacement.

Keywords: belt truss, RC frames structure, ETABS, response spectrum analysis, special moment resisting frame

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Authors: Ishapathik Das

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The purpose of this article is to find a method of comparing designs for ordinal regression models using quantile dispersion graphs in the presence of linear predictor misspecification. The true relationship between response variable and the corresponding control variables are usually unknown. Experimenter assumes certain form of the linear predictor of the ordinal regression models. The assumed form of the linear predictor may not be correct always. Thus, the maximum likelihood estimates (MLE) of the unknown parameters of the model may be biased due to misspecification of the linear predictor. In this article, the uncertainty in the linear predictor is represented by an unknown function. An algorithm is provided to estimate the unknown function at the design points where observations are available. The unknown function is estimated at all points in the design region using multivariate parametric kriging. The comparison of the designs are based on a scalar valued function of the mean squared error of prediction (MSEP) matrix, which incorporates both variance and bias of the prediction caused by the misspecification in the linear predictor. The designs are compared using quantile dispersion graphs approach. The graphs also visually depict the robustness of the designs on the changes in the parameter values. Numerical examples are presented to illustrate the proposed methodology.

Keywords: model misspecification, multivariate kriging, multivariate logistic link, ordinal response models, quantile dispersion graphs

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Authors: Hemin Yuan, Yun Wang, Xiangchun Wang

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Gas hydrate is drawing attention due to the fact that it has an enormous amount all over the world, which is almost twice the conventional hydrocarbon reserves, making it a potential alternative source of energy. It is widely distributed in permafrost and continental ocean shelves, and many countries have launched national programs for investigating the gas hydrate. Gas hydrate is mainly explored through seismic methods, which include bottom simulating reflectors (BSR), amplitude blanking, and polarity reverse. These seismic methods are effective at finding the gas hydrate formations but usually contain large uncertainties when applying to invert the micro-scale petrophysical properties of the formations due to lack of constraints. Rock physics modeling links the micro-scale structures of the rocks to the macro-scale elastic properties and can work as effective constraints for the seismic methods. A number of rock physics models have been proposed for gas hydrate modeling, which addresses different mechanisms and applications. However, these models are generally not well classified, and it is confusing to determine the appropriate model for a specific study. Moreover, since the modeling usually involves multiple models and steps, it is difficult to determine the source of uncertainties. To solve these problems, we summarize the developed models/methods and make four classifications of the models according to the hydrate micro-scale morphology in sediments, the purpose of reservoir characterization, the stage of gas hydrate generation, and the lithology type of hosting sediments. Some sub-categories may overlap each other, but they have different priorities. Besides, we also analyze the priorities of different models, bring up the shortcomings, and explain the appropriate application scenarios. Moreover, by comparing the models, we summarize a general workflow of the modeling procedure, which includes rock matrix forming, dry rock frame generating, pore fluids mixing, and final fluid substitution in the rock frame. These procedures have been widely used in various gas hydrate modeling and have been confirmed to be effective. We also analyze the potential sources of uncertainties in each modeling step, which enables us to clearly recognize the potential uncertainties in the modeling. In the end, we explicate the general problems of the current models, including the influences of pressure and temperature, pore geometry, hydrate morphology, and rock structure change during gas hydrate dissociation and re-generation. We also point out that attenuation is also severely affected by gas hydrate in sediments and may work as an indicator to map gas hydrate concentration. Our work classifies rock physics models of gas hydrate into different categories, generalizes the modeling workflow, analyzes the modeling uncertainties and potential problems, which can facilitate the rock physics characterization of gas hydrate bearding sediments and provide hints for future studies.

Keywords: gas hydrate, rock physics model, modeling classification, hydrate morphology

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Authors: Kari M. Solomon

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Companies with a culture accepting of change management and performance excellence are better suited to determine their sustainability performance and impacts. A mature corporate culture supportive of performance excellence is better positioned to integrate sustainability management tools into their standard business strategy. Companies use various sustainability management tools and reporting standards to communicate levels of sustainability performance to their stakeholders, more often focusing on shareholders and investors. A research gap remains in understanding how companies adapt business excellence models to define corporate sustainability performance. A content analysis of medium-sized enterprises using corporate sustainability reports and business excellence models reveals the challenges and opportunities of reporting sustainability performance in the context of organizational excellence. The outcomes of this content analysis contribute knowledge on the resources needed for companies to build sustainability performance management systems integral to existing management systems. The findings of this research inform academic research areas of corporate sustainability performance, the business community contributing to sustainable development initiatives, and integrating sustainable development issues into business excellence models. There are potential research links between sustainability performance management and the alignment of the United Nations Sustainable Development Goals (UN SDGs) when organizations promote a culture of performance or business excellence.

Keywords: business excellence, corporate sustainability, performance excellence, sustainability performance

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Authors: Natina Yaduma, Allan Williams, Sangwon Park, Andrew Lockwood

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This paper analyses flexible working, and the employment of migrants, as determinants of productivity in hotels. Controlling for the institutional environment, by focussing on a single firm, it analyses data on actual hours worked and outputs, on a weekly basis, over an 8 year period. The unusually disaggregated data allows the paper to examine not only inter-establishment, but also intra-establishment (departmental) variations in productivity, and to compare financial versus physical measures. The findings emphasise the complexity of productivity findings, sometimes contrasting evidence for establishments versus departments, and the positive but scale and measure-specific contributions of both the employment of migrants and flexible working, especially the utilisation of zero hours contracts.

Keywords: labour productivity, physical productivity, financial productivity, numerical flexibility, functional flexibility, migrant employment, cero-contract employment

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Authors: Royia Soliman, Salma ElAnsary, Akram Amin Abdellatif, Florian Holzapfel

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Model-Based Safety Analysis (MBSA) is an approach in which the system and safety engineers share a common system model created using a model-based development process. The model can also be extended by the failure modes of the system components. There are two famous approaches for the addition of fault behaviors to system models. The first one is to enclose the failure into the system design directly. The second approach is to develop a fault model separately from the system model, thus combining both independent models for safety analysis. This paper introduces a hybrid approach of MBSA. The approach tries to use informal abstracted models to investigate failure behaviors. The approach will combine various concepts such as directed graph traversal, event lists and Constraint Satisfaction Problems (CSP). The approach is implemented using an Object Oriented programming language. The components are abstracted to its failure logic and relationships of connected components. The implemented approach is tested on various flight control systems, including electrical and multi-domain examples. The various tests are analyzed, and a comparison to different approaches is represented.

Keywords: flight control systems, model based safety analysis, safety assessment analysis, system modelling

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Authors: Zoran Barac, Maja Martinovic

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Higher Education Institutions (HEIs) are a special kind of organization, with its unique purpose and combination of actors. From the societal point of view, they are central institutions in the society that are involved in the activities of education, research, and innovation. At the same time, their societal function derives complex relationships between involved actors, ranging from students, faculty and administration, business community and corporate partners, government agencies, to the general public. HEIs are also particularly interesting as objects of governance research because of their unique public purpose and combination of stakeholders. Furthermore, they are the special type of institutions from an organizational viewpoint. HEIs are often described as “loosely coupled systems” or “organized anarchies“ that implies the challenging nature of their governance models. Governance models of HEIs describe roles, constellations, and modes of interaction of the involved actors in the process of strategic direction and holistic control of institutions, taking into account each particular context. Many governance models of the HEIs are primarily based on the balance of power among the involved actors. Besides the actors’ power and influence, leadership style and environmental contingency could impact the governance model of an HEI. Analyzing them through the frameworks of institutional and contingency theories, HEI governance models originate as outcomes of their institutional and contingency adaptation. HEIs tend to fit to institutional context comprised of formal and informal institutional rules. By fitting to institutional context, HEIs are converging to each other in terms of their structures, policies, and practices. On the other hand, contingency framework implies that there is no governance model that is suitable for all situations. Consequently, the contingency approach begins with identifying contingency variables that might impact a particular governance model. In order to be effective, the governance model should fit to contingency variables. While the institutional context creates converging forces on HEI governance actors and approaches, contingency variables are the causes of divergence of actors’ behavior and governance models. Finally, an HEI governance model is a balanced adaptation of the HEIs to the institutional context and contingency variables. It also encompasses roles, constellations, and modes of interaction of involved actors influenced by institutional and contingency pressures. Actors’ adaptation to the institutional context brings benefits of legitimacy and resources. On the other hand, the adaptation of the actors’ to the contingency variables brings high performance and effectiveness. HEI governance models outlined and analyzed in this paper are collegial, bureaucratic, entrepreneurial, network, professional, political, anarchical, cybernetic, trustee, stakeholder, and amalgam models.

Keywords: governance, governance models, higher education institutions, institutional context, situational context

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Authors: Sadeep Sasidharan, T. B. Isha

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Modern day electric vehicles require compact high torque/power density motors for electric propulsion. This necessitates proper thermal management of the electric motors. The main focus of this paper is to compare the steady state thermal analysis of a conventional 20 kW 8/6 Switched Reluctance Motor (SRM) with that of an Induction Motor and Inset Permanent Magnet (IPM) motor of the same rating. The goal is to develop a proper thermal model of the three types of models for Finite Element Thermal Analysis. JMAG software is used for the development and simulation of the thermal models. The results show that the induction motor is subjected to more heating when used for electric vehicle application constantly, compared to the SRM and IPM.

Keywords: electric vehicles, induction motor, inset permanent magnet motor, loss models, switched reluctance motor, thermal analysis

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Authors: András Szekrényes

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Delamination is one of the major failure modes in laminated composite structures. Delamination tips are mostly captured by spatial numerical models in order to predict crack growth. This paper presents some mechanical models of delaminated composite shells based on shallow shell theories. The mechanical fields are based on a third-order displacement field in terms of the through-thickness coordinate of the laminated shell. The undelaminated and delaminated parts are captured by separate models and the continuity and boundary conditions are also formulated in a general way providing a large size boundary value problem. The system of differential equations is solved by the state space method for an elliptic delaminated shell having simply supported edges. The comparison of the proposed and a numerical model indicates that the primary indicator of the model is the deflection, the secondary is the widthwise distribution of the energy release rate. The model is promising and suitable to determine accurately the J-integral distribution along the delamination front. Based on the proposed model it is also possible to develop finite elements which are able to replace the computationally expensive spatial models of delaminated structures.

Keywords: J-integral, levy method, third-order shell theory, state space solution

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Authors: Betül sönmez, Fatma Azizoğlu, S. Bilge Hapçıoğlu, Aytolan Yıldırım

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Background: Social innovativeness contains the procurement of a sustainable benefit for a number of problems from working conditions to education, social development, health, and from environmental control to climate change, as well as the development of new social productions and services. Objectives: This study was conducted to determine the correlation between the social innovation tendency of nursing and medical faculty students and value types. Methods and participants: The population of this correlational study consisted of third-year students studying at a medical faculty and a nursing faculty in a public university in Istanbul. Ethics committee approval and permission from the school administrations were obtained in order to conduct the study and voluntary participation of the students in the study was ensured. 524 questionnaires were obtained with a total return rate of 57.1% (65.0% in nurse student and 52.1% in physic students). The data of the study were collected by using the Portrait Values Questionnaire and a questionnaire containing the Social Innovativeness Scale. Results: The effect of the subscale scores of Portrait Values Questionnaire on the total score of Social Innovativeness Scale was 26.6%. In the model where a significance was determined (F=37.566; p<0.01), the highest effect was observed in the subscale of universalism. The effect of subscale scores obtained from the Portrait Values Questionnaire, as well as age, gender and number of siblings was 25% on the Social Innovativeness in nursing students and 30.8% in medical faculty students. In both models where a significance was determined (p<0.01), the nursing students had the values of power, universalism and kindness, whereas the medical faculty students had the values of self-direction, stimulation, hedonism and universalism showed the highest effect in both models. Conclusions: Universalism is the value with the highest effect upon the social innovativeness in both groups, which is an expected result by the nature of professions. The effect of the values of independent thinking and self-direction, as well as openness to change involving quest for innovation (stimulation), which are observed in medical faculty students, also supports the literature of innovative behavior. These results are thought to guide educators and administrators in terms of developing socially innovative behaviors.

Keywords: social innovativeness, portrait values questionnaire, nursing students, medical faculty students

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Authors: M. M. Wagh, V. V. Kulkarni

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The rising economic activities, growing population and improving living standards of world have led to a steady growth in its appetite for quality and quantity of energy services. As the economy expands the electricity demand is going to grow further, increasing the challenges of the more generation and stresses on the utility grids. Appropriate energy model will help in proper utilization of the locally available renewable energy sources such as solar, wind, biomass, small hydro etc. to integrate in the available grid, reducing the investments in energy infrastructure. Further to these new technologies like smart grids, decentralized energy planning, energy management practices, energy efficiency are emerging. In this paper, the attempt has been made to study and review the recent energy planning models, energy forecasting models, and renewable energy integration models. In addition, various modeling techniques and tools are reviewed and discussed.

Keywords: energy modeling, integration of renewable energy, energy modeling tools, energy modeling techniques

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Authors: Sam Khozama, Ali M. Mayya

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Breast cancer is one of the most common types in women. Early prediction of breast cancer helps physicians detect cancer in its early stages. Big cancer data needs a very powerful tool to analyze and extract predictions. Machine learning and deep learning are two of the most efficient tools for predicting cancer based on textual data. In this study, we developed a fusion model of two machine learning and deep learning models. To obtain the final prediction, Long-Short Term Memory (LSTM) and ensemble learning with hyper parameters optimization are used, and score-level fusion is used. Experiments are done on the Breast Cancer Surveillance Consortium (BCSC) dataset after balancing and grouping the class categories. Five different training scenarios are used, and the tests show that the designed fusion model improved the performance by 3.3% compared to the individual models.

Keywords: machine learning, deep learning, cancer prediction, breast cancer, LSTM, fusion

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Authors: Zhilin Wang, Zhihao Zheng, Linxin Liu

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This study proposes a low-code model-building approach that aims to simplify the development and deployment of artificial intelligence (AI) models. With an intuitive way to drag and drop and connect components, users can easily build complex models and integrate multiple algorithms for training. After the training is completed, the system automatically generates a callable model service API. This method not only lowers the technical threshold of AI development and improves development efficiency but also enhances the flexibility of algorithm integration and simplifies the deployment process of models. The core strength of this method lies in its ease of use and efficiency. Users do not need to have a deep programming background and can complete the design and implementation of complex models with a simple drag-and-drop operation. This feature greatly expands the scope of AI technology, allowing more non-technical people to participate in the development of AI models. At the same time, the method performs well in algorithm integration, supporting many different types of algorithms to work together, which further improves the performance and applicability of the model. In the experimental part, we performed several performance tests on the method. The results show that compared with traditional model construction methods, this method can make more efficient use, save computing resources, and greatly shorten the model training time. In addition, the system-generated model service interface has been optimized for high availability and scalability, which can adapt to the needs of different application scenarios.

Keywords: low-code, model building, artificial intelligence, algorithm integration, model deployment

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Authors: Watheq Mansour, Waleed Bin Owais, Mohammad Basheer Kotit, Khaled Khan

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Efficient query processing is one of the utmost requisites in any business environment to satisfy consumer needs. This paper investigates the various types of indexing models, viz. primary, secondary, and multi-level. The investigation is done under the ambit of various types of queries to which each indexing model performs with efficacy. This study also discusses the inherent advantages and disadvantages of each indexing model and how indexing models can be chosen based on a particular environment. This paper also draws parallels between various indexing models and provides recommendations that would help a Database administrator to zero-in on a particular indexing model attributed to the needs and requirements of the production environment. In addition, to satisfy industry and consumer needs attributed to the colossal data generation nowadays, this study has proposed two novel indexing techniques that can be used to index highly unstructured and structured Big Data with efficacy. The study also briefly discusses some best practices that the industry should follow in order to choose an indexing model that is apposite to their prerequisites and requirements.

Keywords: indexing, hashing, latent semantic indexing, B-tree

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Authors: Rafid Saeed Abdulrazak Alshkaki

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In this paper, zero-one inflated negative binomial distribution is considered, along with some of its structural properties, then its parameters were estimated using the method of moments. It is found that the method of moments to estimate the parameters of the zero-one inflated negative binomial models is not a proper method and may give incorrect conclusions.

Keywords: zero one inflated models, negative binomial distribution, moments estimator, non negative integer sampling

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Authors: Salam Saudagar, Ankit Kamboj, Niraj Mohan, Satgounda Patil, Nilesh Powar

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Task Assignment and Scheduling is a challenging Operations Research problem when there is a limited number of resources and comparatively higher number of tasks. The Cost Management team at Cummins needs to assign tasks based on a deadline and must prioritize some of the tasks as per business requirements. Moreover, there is a constraint on the resources that assignment of tasks should be done based on an individual skill level, that may vary for different tasks. Another constraint is for scheduling the tasks that should be evenly distributed in terms of number of working hours, which adds further complexity to this problem. The proposed greedy approach to solve assignment and scheduling problem first assigns the task based on management priority and then by the closest deadline. This is followed by an iterative selection of an available resource with the least allocated total working hours for a task, i.e. finding the local optimal choice for each task with the goal of determining the global optimum. The greedy approach task allocation is compared with a variant of Hungarian Algorithm, and it is observed that the proposed approach gives an equal allocation of working hours among the resources. The comparative study of the proposed approach is also done with manual task allocation and it is noted that the visibility of the task timeline has increased from 2 months to 6 months. An interactive dashboard app is created for the greedy assignment and scheduling approach and the tasks with more than 2 months horizon that were waiting in a queue without a delivery date initially are now analyzed effectively by the business with expected timelines for completion.

Keywords: assignment, deadline, greedy approach, Hungarian algorithm, operations research, scheduling

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Authors: Gholam Reza Mirzaei

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This research aimed to investigate the effectiveness of acceptance and commitment therapy (ACT) in reducing corona disease anxiety in the staff working at Shahid Beheshti Hospital of Shiraz. The current research was a quasi-experimental study having pre-test and post-test with two experimental and control groups. The statistical population of the research included all the staff of Shahid Beheshti Hospital of Shiraz in 2021. From among the statistical population, 30 participants (N =15 in the experimental group and N =15 in the control group) were selected by available sampling. The materials used in the study comprised the Cognitive Emotion Regulation Questionnaire (CERQ) and Corona Disease Anxiety Scale (CDAS). Following data collection, the participants’ scores were analyzed using SPSS 20 at both descriptive (mean and standard deviation) and inferential (analysis of covariance) levels. The results of the analysis of covariance (ANCOVA) showed that acceptance and commitment therapy (ACT) is effective in reducing Corona disease anxiety (mental and physical symptoms) in the staff working at Shahid Beheshti Hospital of Shiraz. The effectiveness of acceptance and commitment therapy (ACT) on reducing mental symptoms was 25.5% and on physical symptoms was 13.8%. The mean scores of the experimental group in the sub-scales of Corona disease anxiety (mental and physical symptoms) in the post-test were lower than the mean scores of the control group.

Keywords: acceptance and commitment therapy, corona disease anxiety, hospital staff, Shiraz

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Authors: Khadidja Bellifa, Sidi Mohamed Mekelleche

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Phenols and especially halogenated phenols represent a substantial part of the chemicals produced worldwide and are known as aquatic pollutants. Quantitative structure–toxicity relationship (QSTR) models are useful for understanding how chemical structure relates to the toxicity of chemicals. In the present study, the acute toxicities of 45 halogenated phenols to Tetrahymena Pyriformis are estimated using no cost semi-empirical quantum chemistry methods. QSTR models were established using the multiple linear regression technique and the predictive ability of the models was evaluated by the internal cross-validation, the Y-randomization and the external validation. Their structural chemical domain has been defined by the leverage approach. The results show that the best model is obtained with the AM1 method (R²= 0.91, R²CV= 0.90, SD= 0.20 for the training set and R²= 0.96, SD= 0.11 for the test set). Moreover, all the Tropsha’ criteria for a predictive QSTR model are verified.

Keywords: halogenated phenols, toxicity mechanism, hydrophobicity, electrophilicity index, quantitative stucture-toxicity relationships

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Authors: Guang Zou, Kian Banisoleiman, Arturo González

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Crack initiation and propagation threatens structural integrity of welded joints and normally inspections are assigned based on crack propagation models. However, the approach based on crack propagation models may not be applicable for some high-quality welded joints, because the initial flaws in them may be so small that it may take long time for the flaws to develop into a detectable size. This raises a concern regarding the inspection planning of high-quality welded joins, as there is no generally acceptable approach for modeling the whole fatigue process that includes the crack initiation period. In order to address the issue, this paper reviews treatment methods for crack initiation period and initial crack size in crack propagation models applied to inspection planning. Generally, there are four approaches, by: 1) Neglecting the crack initiation period and fitting a probabilistic distribution for initial crack size based on statistical data; 2) Extrapolating the crack propagation stage to a very small fictitious initial crack size, so that the whole fatigue process can be modeled by crack propagation models; 3) Assuming a fixed detectable initial crack size and fitting a probabilistic distribution for crack initiation time based on specimen tests; and, 4) Modeling the crack initiation and propagation stage separately using small crack growth theories and Paris law or similar models. The conclusion is that in view of trade-off between accuracy and computation efforts, calibration of a small fictitious initial crack size to S-N curves is the most efficient approach.

Keywords: crack initiation, fatigue reliability, inspection planning, welded joints

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Authors: Emad A. Mohammed

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Utilizing the capabilities of Data Mining and Artificial Intelligence in the prediction of the minimum miscibility pressure (MMP) required for multi-contact miscible (MCM) displacement of reservoir petroleum by hydrocarbon gas flooding using Fuzzy Logic models and Artificial Neural Network models will help a lot in giving accurate results. The factors affecting the (MMP) as it is proved from the literature and from the dataset are as follows: XC2-6: Intermediate composition in the oil-containing C2-6, CO2 and H2S, in mole %, XC1: Amount of methane in the oil (%),T: Temperature (°C), MwC7+: Molecular weight of C7+ (g/mol), YC2+: Mole percent of C2+ composition in injected gas (%), MwC2+: Molecular weight of C2+ in injected gas. Fuzzy Logic and Neural Networks have been used widely in prediction and classification, with relatively high accuracy, in different fields of study. It is well known that the Fuzzy Inference system can handle uncertainty within the inputs such as in our case. The results of this work showed that our proposed models perform better with higher performance indices than other emprical correlations.

Keywords: MMP, gas flooding, artificial intelligence, correlation

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Authors: Mili Dutta, Lokender Prashad

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Introduction: Population ageing is a phenomenon that can be observed around the globe. The health-related quality of life (HRQOL) is a measurement of health status of an individual, and it describes the effect of physical and mental health disorders on the well-being of a person. The present study is aimed to describe the influence of socio-economic characteristics of elderly on their health-related quality of life in India. Methods: EQ-5D instrument and population-based EQ-5D index score has been measured to access the HRQOL among elderly. Present study utilized the Study on Global Ageing and Adult Health (SAGE) data which was conducted in 2007 in India. Multiple Logistic Regression model and Multivariate Linear Regression model has been employed. Result: In the present study, it was found that the female are more likely to have problems in mobility (OR=1.41, 95% Cl: 1.14 to 1.74), self-care (OR=1.26, 95% Cl: 1.01 to 1.56) and pain or discomfort (OR=1.50, 95% Cl: 1.16 to 1.94). Elderly residing in rural area are more likely to have problems in pain/discomfort (OR=1.28, 95% Cl: 1.01 to 1.62). More older and non-working elderly are more likely whereas higher educated and highest wealth quintile elderly are less likely to have problems in all the dimensions of EQ-5D viz. mobility, self-care, usual activity, pain/discomfort and anxiety/depression. The present study has also shown that oldest old people, residing in rural area and currently not working elderly are more likely to report low EQ-5D index score whereas elderly with high education level and high wealth quintile are more likely to report high EQ-5D index score than their counterparts. Conclusion: The present study has found EQ-5D instrument as the valid measure for assessing the HRQOL of elderly in India. The study indicates socio-economic characteristics of elderly such as female, more older people, residing in rural area, non-educated, poor and currently non-working as the major risk groups of having poor HRQOL in India. Findings of the study will be helpful for the programmes and policy makers, researchers, academician and social workers who are working in the field of ageing.

Keywords: ageing, HRQOL, India, EQ-5D, SAGE, socio-economic characteristics

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Authors: Zhengrong Wu, Haibo Yang

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In the context of escalating global climate change and environmental degradation, the complexity and frequency of natural disasters are continually increasing. Confronted with an abundance of information regarding natural disasters, traditional knowledge graph construction methods, which heavily rely on grammatical rules and prior knowledge, demonstrate suboptimal performance in processing complex, multi-source disaster information. This study, drawing upon past natural disaster reports, disaster-related literature in both English and Chinese, and data from various disaster monitoring stations, constructs question-answer templates based on large language models. Utilizing the P-Tune method, the ChatGLM2-6B model is fine-tuned, leading to the development of a disaster knowledge graph based on large language models. This serves as a knowledge database support for disaster emergency response.

Keywords: large language model, knowledge graph, disaster, deep learning

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Authors: Steven Latré, Frederik Desplentere, Ilya Straumit, Stepan V. Lomov

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A method is proposed in order to create a three-dimensional finite element model representing fibre reinforced insulation materials for the simulation software Siemens NX. VoxTex software, a tool for quantification of µCT images of fibrous materials, is used for the transformation of microtomography images of random fibre reinforced composites into finite element models. An automatic tool was developed to execute the import of the models to the thermal solver module of Siemens NX. The paper describes the numerical tools used for the image quantification and the transformation and illustrates them on several thermal simulations of fibre reinforced insulation blankets filled with low thermal conductive fillers. The calculation of thermal conductivity is validated by comparison with the experimental data.

Keywords: analysis, modelling, thermal, voxel

Procedia PDF Downloads 287

Authors: Anusha P. Wijesundara, Dulap I. Rathnayake, Nihal D. Perera

Abstract:

Bathymetric information is fundamental importance to coastal and marine planning and management, nautical navigation, and scientific studies of marine environments. Satellite-derived bathymetry data provide detailed information in areas where conventional sounding data is lacking and conventional surveys are inaccessible. The two empirical approaches of log-linear bathymetric inversion model and non-linear bathymetric inversion model are applied for deriving bathymetry from high-resolution multispectral satellite imagery. This study compares these two approaches by means of geographical error analysis for the site Kankesanturai using WorldView-2 satellite imagery. Based on the Levenberg-Marquardt method calibrated the parameters of non-linear inversion model and the multiple-linear regression model was applied to calibrate the log-linear inversion model. In order to calibrate both models, Single Beam Echo Sounding (SBES) data in this study area were used as reference points. Residuals were calculated as the difference between the derived depth values and the validation echo sounder bathymetry data and the geographical distribution of model residuals was mapped. The spatial autocorrelation was calculated by comparing the performance of the bathymetric models and the results showing the geographic errors for both models. A spatial error model was constructed from the initial bathymetry estimates and the estimates of autocorrelation. This spatial error model is used to generate more reliable estimates of bathymetry by quantifying autocorrelation of model error and incorporating this into an improved regression model. Log-linear model (R²=0.846) performs better than the non- linear model (R²=0.692). Finally, the spatial error models improved bathymetric estimates derived from linear and non-linear models up to R²=0.854 and R²=0.704 respectively. The Root Mean Square Error (RMSE) was calculated for all reference points in various depth ranges. The magnitude of the prediction error increases with depth for both the log-linear and the non-linear inversion models. Overall RMSE for log-linear and the non-linear inversion models were ±1.532 m and ±2.089 m, respectively.

Keywords: log-linear model, multi spectral, residuals, spatial error model

Procedia PDF Downloads 297

Authors: Norhayati Mohd Rasip, Abd. Samad Hasan Basari , Nuzulha Khilwani Ibrahim, Burairah Hussin

Abstract:

The allocation of working schedule especially for shift environment is hard to fulfill its fairness among them. In the case of nurse scheduling, to set up the working time table for them is time consuming and complicated, which consider many factors including rules, regulation and human factor. The scenario is more complicated since most nurses are women which have personnel constraints and maternity leave factors. The undesirable schedule can affect the nurse productivity, social life and the absenteeism can significantly as well affect patient's life. This paper aimed to enhance the scheduling process by utilizing the particle swarm optimization in order to solve nurse scheduling problem. The result shows that the generated multiple initial schedule is fulfilled the requirements and produces the lowest cost of constraint violation.

Keywords: nurse scheduling, particle swarm optimisation, nurse rostering, hard and soft constraint

Procedia PDF Downloads 373

Authors: Ibrahim Can, Fatih Tosunoğlu

Abstract:

The flow duration curve (FDC) is an informative method that represents the flow regime’s properties for a river basin. Therefore, the FDC is widely used for water resource projects such as hydropower, water supply, irrigation and water quality management. The primary purpose of this study is to obtain synthetic daily flow duration curves for Çoruh Basin, Turkey. For this aim, we firstly developed univariate auto-regressive moving average (ARMA) models for daily flows of 9 stations located in Çoruh basin and then these models were used to generate 100 synthetic flow series each having same size as historical series. Secondly, flow duration curves of each synthetic series were drawn and the flow values exceeded 10, 50 and 95 % of the time and 95% confidence limit of these flows were calculated. As a result, flood, mean and low flows potential of Çoruh basin will comprehensively be represented.

Keywords: ARMA models, Çoruh basin, flow duration curve, Turkey

Procedia PDF Downloads 404