Search results for: working models

Authors: Mythri G., Manjunath ML, Girish Babu M., Shireen Swaliha Quadri

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Background: The increase in diabetes among the elderly is of concern because in addition to the wide range of traditional diabetes complications, evidence has been growing that diabetes is associated with increased risk of cognitive decline. Aims and Objectives: To find out if there is any association between blood sugar levels and short-term and working memory status in patients of type 2 diabetes. Materials and Methods: The study was carried out in 200 individuals aged between 40-65 years consisting of 100 diagnosed cases of Type 2 Diabetes Mellitus and 100 non-diabetics from OPD of Mc Gann Hospital, Shivamogga. Rye’s Auditory Verbal Learning Test, Verbal Fluency Test and Visual Reproduction Test, Working Digit Span Test and Validation Span Test were used to assess short-term and working memory. Fasting and Post Prandial blood sugar levels were estimated. Statistical analysis was done using SPSS 21. Results: Memory test scores of type 2 diabetics were significantly reduced (p < 0.001) when compared to the memory scores of age and gender matched non-diabetics. Fasting blood sugar levels were found to have a negative correlation with memory scores for all 5 tests: AVLT (r=-0.837), VFT (r=-0.888), VRT(r=-0.787), WDST (r=-0.795) and VST (r=-0.943). Post- Prandial blood sugar levels were found to have a negative correlation with memory scores for all 5 tests: AVLT (r=-0.922), VFT (r=-0.848), VRT(r=-0.707),WDST (r=-0.729) and VST (r=-0.880) Memory scores in all 5 tests were found to be negatively correlated with the FBS and PPBS levels in diabetic patients (p < 0.001). Conclusion: The decreased memory status in diabetic patients may be due to many factors like hyperglycemia, vascular disease, insulin resistance, amyloid deposition and also some of the factor combine to produce additive effects like, type of diabetes, co-morbidities, age of onset, duration of the disease and type of therapy. These observed effects of blood sugar levels of diabetics on memory status are of potential clinical importance because even mild cognitive impairment could interfere with todays’ activities.

Keywords: diabetes, cognition, diabetes, HRV, respiratory medicine

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Authors: Agus Masrukhin

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This research aims to find and analyze the model of character building in the Police Headquarters in Bali with a case study of Muslim members in improving the quality of the morality of its members. The formation of patterns of thinking, behavior, mentality, and police officers noble character, later can be used as a solution to reduce the hedonistic nature of the challenges in the era of globalization. The benefit of this study is expected to be a positive recommendation to find a constructive character building models of police officers in the Republic of Indonesia, especially Bali Police. For the long term, the discovery of the character building models can be developed for the entire police force in Indonesia. The type of research that would apply in this study researchers mix the qualitative research methods based on the narrative between the subject and the concrete experience of field research and quantitative research methods with 92 respondents from the police regional police Bali. This research used a descriptive analysis and SWOT analysis then it is presented in the FGD (focus group discussion). The results of this research indicate that the variable modeling the leadership of the police and variable police offices culture have significant influence on the implementation of spiritual development.

Keywords: positive constructive, hedonistic, character models, morality

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Authors: Zhuoneng Li, Zeeshan A. Rana, Karl W. Jenkins

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In industry, to validate a case, often a multitude of simulation are required and in order to demonstrate confidence in the process where users tend to use a coarser mesh. Therefore, it is imperative to establish the coarsest mesh that could be used while keeping reasonable simulation accuracy. To date, the two most reliable, affordable and broadly used advanced simulations are the hybrid RANS (Reynolds Averaged Navier Stokes)/LES (Large Eddy Simulation) and wall modelled LES. The potentials in these two simulations will still be developed in the next decades mainly because the unaffordable computational cost of a DNS (Direct Numerical Simulation). In the wall modelled LES, the turbulence model is applied as a sub-grid scale model in the most inner layer near the wall. The RANS turbulence models cover the entire boundary layer region in a hybrid RANS/LES (Detached Eddy Simulation) and its variants, therefore, the RANS still has a very important role in the state of art simulations. This research focuses on the turbulence model mesh sensitivity analysis where various turbulence models such as the S-A (Spalart-Allmaras), SSG (Speziale-Sarkar-Gatski), K-Omega transitional SST (Shear Stress Transport), K-kl-Omega, γ-Reθ transitional model, v2f are evaluated within the OpenFOAM. The simulations are conducted on a fully developed turbulent flow over a flat plate where the skin friction coefficient as well as velocity profiles are obtained to compare against experimental values and DNS results. A concrete conclusion is made to clarify the mesh sensitivity for different turbulence models.

Keywords: mesh sensitivity, turbulence models, OpenFOAM, RANS

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Authors: Niya Chen, Jennifer Chan

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In the financial market, risk management helps to minimize potential loss and maximize profit. There are two ways to assess risks; the first way is to calculate the risk directly based on the volatility. The most common risk measurements are Value at Risk (VaR), sharp ratio, and beta. Alternatively, we could look at the quantile of the return to assess the risk. Popular return models such as GARCH and stochastic volatility (SV) focus on modeling the mean of the return distribution via capturing the volatility dynamics; however, the quantile/expectile method will give us an idea of the distribution with the extreme return value. It will allow us to forecast VaR using return which is direct information. The advantage of using these non-parametric methods is that it is not bounded by the distribution assumptions from the parametric method. But the difference between them is that expectile uses a second-order loss function while quantile regression uses a first-order loss function. We consider several quantile functions, different volatility measures, and estimates from some volatility models. To estimate the expectile of the model, we use Realized Conditional Autoregressive Expectile (CARE) model with the bayesian method to achieve this. We would like to see if our proposed models outperform existing models in cryptocurrency, and we will test it by using Bitcoin mainly as well as Ethereum.

Keywords: expectile, CARE Model, CARR Model, quantile, cryptocurrency, Value at Risk

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Authors: Beriache Abderazik

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It is clear that the psychological effects of noise and physiological eloquent on the workers, what will inevitably affect the performance of both productivity and efficiency in all its aspects, industrial noise became among the most prominent modern professional problems, That require study and analysis in order to arrive at solutions and ways that you can reduce the effects of industrial noise. These factors, in addition to other reasons, made us try in this research to know the real impact of industrial noise on the professional satisfaction of workers. In light of this title we have identified the following general problem: - Is the professional satisfaction factor varies depending on the noise level in the work environment? For the purpose of ascertaining the veracity of the assumptions, we have a comparative study between two samples of equal workers, the first sample is working under the influence of industrial noise severe about (100 Db), and the second sample is working under the influence of industrial noise is low (about 63 Db), and applied them test the professional satisfaction. The results support the hypotheses and confirm all sincerity.

Keywords: industrial noise, job satisfaction, the psychological effects of noise, work environment

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Authors: Alireza Ansariyar, Safieh Laaly

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Over the last decades, the vehicle industry has shown increased interest in integrating autonomous, connected, and electrical technologies in vehicle design with the primary hope of improving mobility and road safety while reducing transportation’s environmental impact. Using the State of Maryland (M.D.) in the United States as a pilot study, this research investigates CAVs’ fuel consumption and air pollutants (C.O., PM, and NOx) and utilizes meaningful linear regression models to predict CAV’s environmental effects. Maryland transportation network was simulated in VISUM software, and data on a set of variables were collected through a comprehensive survey. The number of pollutants and fuel consumption were obtained for the time interval 2010 to 2021 from the macro simulation. Eventually, four linear regression models were proposed to predict the amount of C.O., NOx, PM pollutants, and fuel consumption in the future. The results highlighted that CAVs’ pollutants and fuel consumption have a significant correlation with the income, age, and race of the CAV customers. Furthermore, the reliability of four statistical models was compared with the reliability of macro simulation model outputs in the year 2030. The error of three pollutants and fuel consumption was obtained at less than 9% by statistical models in SPSS. This study is expected to assist researchers and policymakers with planning decisions to reduce CAV environmental impacts in M.D.

Keywords: connected and autonomous vehicles, statistical model, environmental effects, pollutants and fuel consumption, VISUM, linear regression models

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Authors: Evgeniya Ivanova

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Nowadays, in the literature, there is still no fixed definition of knowledge management that often remains only as an academic discipline. The current market situation is changing very quickly, the need of new technologies is high, and knowledge management is the area that ensures that the know-how has not been lost during market development and adoption. The study examines how knowledge management is being leveraged and practiced in the management consultancy companies and provides not only the tips and best practices of applied knowledge management approaches but also the validation matrix for its successful or unsuccessful implementation. Different knowledge management approaches are explored on the basis of their practical implementation, including related challenges, knowledge sharing process, and barriers that are typical for consulting firms mostly driven by the agile working culture. The relevance of proposed topic is confirmed by the finding that corporate working culture and the exponentially developing technologies have a direct impact on the success of practical implementation of knowledge management.

Keywords: knowledge management, knowledge management in practice, consulting firm, knowledge management success

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Authors: Carl van Walraven, Meltem Tuna

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Background: Network meta-analysis (NMA) quantifies the relative efficacy of 3 or more interventions from studies containing a subgroup of interventions. This study applied the analytical approach of NMA to quantify the relative accuracy of prediction models with distinct inclusion criteria that are evaluated on a common population (‘concurrent external validation’). Methods: We simulated binary events in 5000 patients using a known risk function. We biased the risk function and modified its precision by pre-specified amounts to create 15 prediction models with varying accuracy and distinct patient applicability. Prediction model accuracy was measured using the Scaled Brier Score (SBS). Overall prediction model accuracy was measured using fixed-effects methods that accounted for model applicability patterns. Prediction model accuracy was summarized as the Network Relative Model Accuracy (NeRMA) Score which ranges from -∞ through 0 (accuracy of random guessing) to 1 (accuracy of most accurate model in concurrent external validation). Results: The unbiased prediction model had the highest SBS. The NeRMA score correctly ranked all simulated prediction models by the extent of bias from the known risk function. A SAS macro and R-function was created to implement the NeRMA Score. Conclusions: The NeRMA Score makes it possible to quantify the accuracy of binomial prediction models having distinct inclusion criteria in a concurrent external validation.

Keywords: prediction model accuracy, scaled brier score, fixed effects methods, concurrent external validation

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Authors: Wei Zhang, Lifen Zhao

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The present study aims to translate and validate the Career Motivation Scale among Chinese university and vocational college teachers. Exploratory factor analysis supported a three-factor structure that was consistent with the original structure of career motivation: career insight, career identity, and career resilience. Confirmatory factor analysis showed that a second-order three-factor model with correlated measurement errors best fit the data. Configural, metric, and scalar invariance models were tested, demonstrating that the Chinese version of the Career Motivation Scale did not differ across groups of school type, educational level, and working years in current institutions. The concurrent validity of the Chinese Career Motivation Scale was confirmed by its significant correlations with work engagement, career adaptability, career satisfaction, job crafting, and intention to quit. The results of the study indicated that the Chinese Career Motivation Scale was a valid and reliable measure of career motivation among university and vocational college teachers in China.

Keywords: career motivation scale, Chinese University, vocational college teachers, measurement invariance, validation

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Authors: Praveen S. Muthukumarana, Achala C. Aponso

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A large percentage of global crop harvest is lost due to crop diseases. Timely identification and treatment of crop diseases is difficult in many developing nations due to insufficient trained professionals in the field of agriculture. Many crop diseases can be accurately diagnosed by visual symptoms. In the past decade, deep learning has been successfully utilized in domains such as healthcare but adoption in agriculture for plant disease detection is rare. The literature shows that models trained with popular datasets such as PlantVillage does not generalize well on real world images. This paper attempts to find out how to make plant disease identification models that generalize well with real world images.

Keywords: agriculture, convolutional neural network, deep learning, plant disease classification, plant disease detection, plant disease diagnosis

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Authors: Konstantinos Perifanos, Eirini Florou, Dionysis Goutsos

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This paper presents and benchmarks a number of end-to-end Deep Learning based models for metaphor detection in Greek. We combine Convolutional Neural Networks and Recurrent Neural Networks with representation learning to bear on the metaphor detection problem for the Greek language. The models presented achieve exceptional accuracy scores, significantly improving the previous state-of-the-art results, which had already achieved accuracy 0.82. Furthermore, no special preprocessing, feature engineering or linguistic knowledge is used in this work. The methods presented achieve accuracy of 0.92 and F-score 0.92 with Convolutional Neural Networks (CNNs) and bidirectional Long Short Term Memory networks (LSTMs). Comparable results of 0.91 accuracy and 0.91 F-score are also achieved with bidirectional Gated Recurrent Units (GRUs) and Convolutional Recurrent Neural Nets (CRNNs). The models are trained and evaluated only on the basis of training tuples, the related sentences and their labels. The outcome is a state-of-the-art collection of metaphor detection models, trained on limited labelled resources, which can be extended to other languages and similar tasks.

Keywords: metaphor detection, deep learning, representation learning, embeddings

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Authors: Nandy Putra, Atrialdipa Duanovsah, Kristofer Haliansyah

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The aim of this research is to design a LHP with low thermal resistance and low condenser temperature. A Self-designed cascade LHP was tested by using biomaterial, sintered copper powder, and aluminum screen mesh as the wick. Using pure water as the working fluid for the first level of the LHP and 96% alcohol as the working fluid for the second level of LHP, the experiments were run with 10W, 20W, and 30W heat input. Experimental result shows that the usage of biomaterial as wick could reduce more temperature at evaporator than by using sintered copper powder and screen mesh up to 22.63% and 37.41% respectively. The lowest thermal resistance occurred during the usage of biomaterial as wick of heat pipe, which is 2.06 ^oC/W. The usage of cascade system could be applied to LHP to reduce the temperature at condenser and reduced thermal resistance up to 17.6%.

Keywords: biomaterial, cascade loop heat pipe, screen mesh, sintered Cu

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Authors: Mekwa Eme, Anya chukwuma

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The act of evaluating a particular site physically and socially in order to create a good design solution that will address the physical and interior environment of the location is known as architectural site analysis. This essay will describe site analysis as a useful design component. According to the introduction and supporting research, site evaluation and analysis are crucial to good design in terms of topography, orientation, site size, accessibility, rainfall, wind direction, and times of sunrise and sunset. Methodology: Both quantitative and qualitative analyses are used in this paper. The primary and secondary types of data collection are as follows. This information was gathered via the case study approach, already published literature, journals, the internet, a local poll, oral interviews, inquiries, and in-person interviews. The purpose of this is to clarify the benefits of site analysis for the design process and its implications for the working or building stage. Results: Each site's criteria are unique in terms of things like soil, plants, trees, accessibility, topography, and security. This will make it easier for the architect and environmentalist to decide on the idea, shape, and supporting structures of the design. It is crucial because before any design work is done, the nature of the target location will be determined through site visits and research. The location, contours, site features, and accessibility are just a few of the topics included in this site study. In order for students and working architects to understand the nature of the site they will be working on, site analysis is a key component of architectural education. The building's orientation, the site's circulation, and the sustainability of the site may all be determined with thorough research of the site's features.

Keywords: analysis, climate, statistics, design

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Authors: Lidija R. Jevrić, Sanja O. Podunavac-Kuzmanović, Strahinja Z. Kovačević

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This study considers the selection of the most suitable in silico molecular descriptors that could be used for s-triazine pesticides characterization. Suitable descriptors among topological, geometrical and physicochemical are used for quantitative structure-retention relationships (QSRR) model establishment. Established models were obtained using linear regression (LR) and multiple linear regression (MLR) analysis. In this paper, MLR models were established avoiding multicollinearity among the selected molecular descriptors. Statistical quality of established models was evaluated by standard and cross-validation statistical parameters. For detection of similarity or dissimilarity among investigated s-triazine pesticides and their classification, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used and gave similar grouping. This study is financially supported by COST action TD1305.

Keywords: chemometrics, classification analysis, molecular descriptors, pesticides, regression analysis

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Authors: Claudineia Brazil, Luciane Salvi, Margareth Haubrich

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The research aims to understand the determinants of Quality of Life at Work (QWL) and the main indicators that influence the productivity of employees working in construction. The methodology is based on the qualitative theoretical approach, in which information is collected in works that have already been carried out, providing a more detailed compression of the research from the point of view of other authors. In this research, pioneering models for assessing Quality of Life at Work (QWL) were investigated, seeking to find the best quality of life indicators in the work environment. The elements investigated in the research were classified into three main groups: Organizational, Environmental and Behavioral. In order to obtain the results, the information obtained through bibliographic research was compared and it was possible to conclude that the focus on the quality of life at work influences the individual and collective productivity of employees, causing the company to be positively impacted. This advocates the need for strategic actions in the area of people management, which will meet these needs. Therefore, it is hoped that this study can contribute to the more effective management of human resources in organizations, reflecting on increased productivity.

Keywords: construction, management, productivity, quality of life at work

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Authors: Selvakumar Ulaganathan, Ivo Couckuyt, Francesco Ferranti, Tom Dhaene, Eric Laermans

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Variable-fidelity surrogate modelling offers an efficient way to approximate function data available in multiple degrees of accuracy each with varying computational cost. In this paper, a Kriging-based variable-fidelity surrogate modelling approach is introduced to approximate such deterministic data. Initially, individual Kriging surrogate models, which are enhanced with gradient data of different degrees of accuracy, are constructed. Then these Gradient enhanced Kriging surrogate models are strategically coupled using a recursive CoKriging formulation to provide an accurate surrogate model for the highest fidelity data. While, intuitively, gradient data is useful to enhance the accuracy of surrogate models, the primary motivation behind this work is to investigate if it is also worthwhile incorporating gradient data of varying degrees of accuracy.

Keywords: Kriging, CoKriging, Surrogate modelling, Variable- fidelity modelling, Gradients

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Authors: Donglan Zha, Jiansong Si

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Because of its flexibility, CES attracts much interest in economic growth and programming models, and the macroeconomics or micro-macro models. This paper focuses on the development, estimating methods of CES production function considering energy as an input. We leave for future research work of relaxing the assumption of constant returns to scale, the introduction of potential input factors, and the generalization method of the optimal nested form of multi-factor production functions.

Keywords: bias of technical change, CES production function, elasticity of substitution, energy input

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Authors: Puttharapong Sakulwaropas, Uraiwan Jaroengeratikun

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Casualty insurance business, the optimal premium pricing and adequate cost for an insurance company are important in risk management. Normally, the insurance pure premium can be determined by multiplying the claim frequency with the claim cost. The aim of this research was to study in the application of generalized linear models to select the risk factor for model of claim frequency and claim cost for estimating a pure premium. In this study, the data set was the claim of comprehensive motor insurance, which was provided by one of the insurance company in Thailand. The results of this study found that the risk factors significantly related to pure premium at the 0.05 level consisted of no claim bonus (NCB) and used of the car (Car code).

Keywords: generalized linear models, risk factor, pure premium, regression model

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Authors: Edlira Kalemi, Sule Yildirim-Yayilgan

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Online Social Networks (OSNs) are nowadays being used widely and intensively for crime investigation and prevention activities. As they provide a lot of information they are used by the law enforcement and intelligence. An extensive review on existing solutions and models for collecting intelligence from this source of information and making use of it for solving crimes has been presented in this article. The main focus is on smart solutions and models where ontologies have been used as the main approach for representing criminal domain knowledge. A framework for a prototype ontology named SC-Ont will be described. This defines terms of the criminal domain ontology and the relations between them. The terms and the relations are extracted during both this review and the discussions carried out with domain experts. The development of SC-Ont is still ongoing work, where in this paper, we report mainly on the motivation for using smart ontology models and the possible benefits of using them for solving crimes.

Keywords: criminal digital evidence, social media, ontologies, reasoning

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Authors: Hassan Jebreen

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These models of a conservative pollutant in groundwater do not include representation of processes in soils and in the unsaturated zone, or biogeochemical processes in groundwater, These demonstration models can be used as the basis for more detailed simulations of the impacts of pollution sources at a local scale, but such studies should address processes related to specific pollutant species, and should consider local hydrogeology in more detail, particularly in relation to possible impacts on shallow systems which are likely to respond more quickly to changes in pollutant inputs. The results have demonstrated the interaction between groundwater flow fields and pollution sources in abstraction areas, and help to emphasise that wadi development is one of the key elements of water resources planning. The quality of groundwater in the Hebron area indicates a gradual increase in chloride and nitrate with time. Since the aquifers in Hebron districts are highly vulnerable due to their karstic nature, continued disposal of untreated domestic and industrial wastewater into the wadi will lead to unacceptably poor water quality in drinking water, which may ultimately require expensive treatment if significant health problems are to be avoided. Improvements are required in wastewater treatment at the municipal and domestic levels, the latter requiring increased public awareness of the issues, as well as improved understanding of the hydrogeological behaviour of the aquifers.

Keywords: groundwater, models, pollutants, wadis, hebron

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Authors: Said Benkaciali, Mourad Haddadi, Abdallah Khellaf, Kacem Gairaa, Mawloud Guermoui

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In this study, many experiments were carried out to assess the influence of the input parameters on the performance of multilayer perceptron which is one the configuration of the artificial neural networks. To estimate the daily global solar radiation on the horizontal surface, we have developed some models by using seven combinations of twelve meteorological and geographical input parameters collected from a radiometric station installed at Ghardaïa city (southern of Algeria). For selecting of best combination which provides a good accuracy, six statistical formulas (or statistical indicators) have been evaluated, such as the root mean square errors, mean absolute errors, correlation coefficient, and determination coefficient. We noted that multilayer perceptron techniques have the best performance, except when the sunshine duration parameter is not included in the input variables. The maximum of determination coefficient and correlation coefficient are equal to 98.20 and 99.11%. On the other hand, some empirical models were developed to compare their performances with those of multilayer perceptron neural networks. Results obtained show that the neural networks techniques give the best performance compared to the empirical models.

Keywords: empirical models, multilayer perceptron neural network, solar radiation, statistical formulas

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Authors: Leonard Maaya, Michel Meulders, Martina Vandebroek

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Discrete Choice Experiments (DCE) are used to investigate how product attributes affect decision-makers’ choices. In DCEs, choice situations consisting of several alternatives are presented from which choice-makers select the preferred alternative. Standard multinomial logit models based on random utility theory can be used to estimate the utilities for the attributes. The overarching principle in these models is that respondents understand and use all the attributes when making choices. However, studies suggest that respondents sometimes ignore some attributes (commonly referred to as Attribute Non-Attendance/ANA). The choice modeling literature presents ANA as a static process, i.e., respondents’ ANA behavior does not change throughout the experiment. However, respondents may ignore attributes due to changing factors like availability of information on attributes, learning/fatigue in experiments, etc. We develop a dynamic mixture latent Markov model to model changes in ANA when information on attributes is provided. The model is illustrated on e-consumers’ webshop choices. The results indicate that the dynamic ANA model describes the behavioral changes better than modeling the impact of information using changes in parameters. Further, we find that providing information on attributes leads to an increase in the attendance probabilities for the investigated attributes.

Keywords: choice models, discrete choice experiments, dynamic models, e-commerce, statistical modeling

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Authors: M. A. Khanday, Aasma Rafiq, Khalid Nazir

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This paper is an attempt to establish the mathematical models to understand the distribution of drug administration in the human body through oral and intravenous routes. Three models were formulated based on diffusion process using Fick’s principle and the law of mass action. The rate constants governing the law of mass action were used on the basis of the drug efficacy at different interfaces. The Laplace transform and eigenvalue methods were used to obtain the solution of the ordinary differential equations concerning the rate of change of concentration in different compartments viz. blood and tissue medium. The drug concentration in the different compartments has been computed using numerical parameters. The results illustrate the variation of drug concentration with respect to time using MATLAB software. It has been observed from the results that the drug concentration decreases in the first compartment and gradually increases in other subsequent compartments.

Keywords: Laplace transform, diffusion, eigenvalue method, mathematical model

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Authors: Anu Soikkeli

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Early 20th century functionalism aimed at generalising living and rationalising construction, thus laying the foundation for the standardisation of construction components and products. From the 1930s onwards, all measurement and quality instructions for building products, different types of building components, descriptions of working methods complying with advisable building practises, planning, measurement and calculation guidelines, terminology, etc. were called standards. Standardisation was regarded as a necessary prerequisite for the mass production of housing. This article examines the early stages of standardisation in Finland in the 1940s and 1950s, as reflected on the working history of an individual architect, Erkki Koiso-Kanttila (1914-2006). In 1950 Koiso-Kanttila was appointed the Head of Design of the Finnish Association of Architects’ Building Standards Committee, a position which he held until 1958. His main responsibilities were the development of the RT Building Information File and compiling of the files.

Keywords: architecture, post WWII period, reconstruction, standardisation

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Authors: Mario Isaza-Ruget, Claudia C. Colmenares-Mejia, Nancy Yomayusa, Camilo A. González, Andres Cely, Jossie Murcia

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Quantification of risks associated with complications development from chronic kidney disease (CKD) through accurate survival models can help with patient management. A retrospective cohort that included patients diagnosed with CKD from a primary care program and followed up between 2013 and 2018 was carried out. Time-dependent and static covariates associated with demographic, clinical, and laboratory factors were included. Deep Learning (DL) survival analyzes were developed for three CKD outcomes: CKD stage progression, >25% decrease in Estimated Glomerular Filtration Rate (eGFR), and Renal Replacement Therapy (RRT). Models were evaluated and compared with Random Survival Forest (RSF) based on concordance index (C-index) metric. 2.143 patients were included. Two models were developed for each outcome, Deep Neural Network (DNN) model reported C-index=0.9867 for CKD stage progression; C-index=0.9905 for reduction in eGFR; C-index=0.9867 for RRT. Regarding the RSF model, C-index=0.6650 was reached for CKD stage progression; decreased eGFR C-index=0.6759; RRT C-index=0.8926. DNN models applied in survival analysis context with considerations of longitudinal covariates at the start of follow-up can predict renal stage progression, a significant decrease in eGFR and RRT. The success of these survival models lies in the appropriate definition of survival times and the analysis of covariates, especially those that vary over time.

Keywords: artificial intelligence, chronic kidney disease, deep neural networks, survival analysis

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Authors: Fernando Miguel Campos, Carlos Ferreira, João Pestana, Pedro Campos, Nils Ehrenberg, Wojciech Hydzik

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The current trend of organizations offering their workers open-office spaces and co-working offices has been primed for stimulating teamwork and collaboration. However, this is not always valid as these kinds of spaces bring other types of challenges that compromise workers productivity and creativity. We present an approach for improving creativity and productivity at the workspace by redesigning an office chair that incorporates subtle technological elements that help users focus, relax and being more productive and creative. This sheds light on how we can better design interactive furniture for such popular contexts, as we develop this new chair through a multidisciplinary approach using ergonomics, interior design, interaction design, hardware and software engineering and psychology.

Keywords: creativity, co-working, ergonomics, human-computer interaction, interaction, interactive furniture, productivity

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Authors: Ka C. Lam, Oluwafunmibi S. Idowu

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Uncertainty in cost is a major factor affecting performance of construction projects. To our knowledge, several conceptual cost models have been developed with varying degrees of accuracy. Incorporating conceptual quantities into conceptual cost models could improve the accuracy of early predesign cost estimates. Hence, the development of quantity models for estimating conceptual quantities of framed reinforced concrete structures using supervised machine learning is the aim of the current research. Using measured quantities of structural elements and design variables such as live loads and soil bearing pressures, response and predictor variables were defined and used for constructing conceptual quantities models. Twenty-four models were developed for comparison using a combination of non-parametric support vector regression, linear regression, and bootstrap resampling techniques. R programming language was used for data analysis and model implementation. Gross soil bearing pressure and gross floor loading were discovered to have a major influence on the quantities of concrete and reinforcement used for foundations. Building footprint and gross floor loading had a similar influence on beams and slabs. Future research could explore the modelling of other conceptual quantities for walls, finishes, and services using machine learning techniques. Estimation of conceptual quantities would assist construction planners in early resource planning and enable detailed performance evaluation of early cost predictions.

Keywords: bootstrapping, conceptual quantities, modelling, reinforced concrete, support vector regression

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Authors: H. A. Jawan

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This review describes the models of environmental-related crack propagation of aluminum alloys (7xxx) during the last few decades. Acknowledge on effects of different factors on the susceptibility to SCC permits to propose valuable mechanisms on crack advancement. The reliable mechanism of cracking give a possibility to propose the optimum chemical composition and thermal treatment conditions resulting in microstructure the most suitable for real environmental condition and stress state.

Keywords: microstructure, environmental, propagation, mechanism

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Authors: Samvel H. Sargsyan

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Together with the study of electron-optical properties of nanostructures and proceeding from experiment-based data, the study of the mechanical properties of nanostructures has become quite actual. For the study of the mechanical properties of fullerene, carbon nanotubes, graphene and other nanostructures one of the crucial issues is the construction of their adequate mathematical models. Among all mathematical models of graphene or carbon nano-tubes, this so-called discrete-continuous model is specifically important. It substitutes the interactions between atoms by elastic beams or springs. The present paper demonstrates the construction of the discrete-continual beam model for carbon nanotubes or graphene, where the micropolar beam model based on the theory of moment elasticity is accepted. With the account of the energy balance principle, the elastic moment constants for the beam model, expressed by the physical and geometrical parameters of carbon nanotube or graphene, are determined. By switching from discrete-continual beam model to the continual, the models of micropolar elastic cylindrical shell and micropolar elastic plate are confirmed as continual models for carbon nanotube and graphene respectively.

Keywords: carbon nanotube, discrete-continual, elastic, graphene, micropolar, plate, shell

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Authors: Salah Alrabeei, Mohammad Yousuf

Abstract:

The goal of option pricing theory is to help the investors to manage their money, enhance returns and control their financial future by theoretically valuing their options. Modeling option pricing by Black-School models with jumps guarantees to consider the market movement. However, only numerical methods can solve this model. Furthermore, not all the numerical methods are efficient to solve these models because they have nonsmoothing payoffs or discontinuous derivatives at the exercise price. In this paper, the exponential time differencing (ETD) method is applied for solving partial integrodifferential equations arising in pricing European options under Merton’s and Kou’s jump-diffusion models. Fast Fourier Transform (FFT) algorithm is used as a matrix-vector multiplication solver, which reduces the complexity from O(M2) into O(M logM). A partial fraction form of Pad`e schemes is used to overcome the complexity of inverting polynomial of matrices. These two tools guarantee to get efficient and accurate numerical solutions. We construct a parallel and easy to implement a version of the numerical scheme. Numerical experiments are given to show how fast and accurate is our scheme.

Keywords: Integral differential equations, , L-stable methods, pricing European options, Jump–diffusion model

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