Search results for: generalized uncertainty principle

Authors: Ulrike Dowie, Ralph Grothmann

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Rational planning decisions are based upon forecasts. Precise forecasting has, therefore, a central role in business. The prediction of customer demand is a prime example. This paper introduces recurrent neural networks to model customer demand and combines the forecast with uncertainty measures to derive decision support of the demand planning department. It identifies and describes the keys to the successful implementation of an AI-based solution: bringing together data with business knowledge, AI methods, and user experience, and applying agile software development practices.

Keywords: agile software development, AI project success factors, deep learning, demand forecasting, forecast uncertainty, neural networks, supply chain management

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Authors: Mohadeseh Shojaei Shahrokhabadi, Ding-Geng (Din) Chen

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Positive continuous outcomes with a substantial number of zero values and incomplete longitudinal follow-up are quite common in medical cost data. To jointly model semi-continuous longitudinal cost data and survival data and to provide marginalized covariate effect estimates, a marginalized two-part joint model (MTJM) has been developed for outcome variables with lognormal distributions. In this paper, we propose MTJM models for outcome variables from a generalized gamma (GG) family of distributions. The GG distribution constitutes a general family that includes approximately all of the most frequently used distributions like the Gamma, Exponential, Weibull, and Log Normal. In the proposed MTJM-GG model, the conditional mean from a conventional two-part model with a three-parameter GG distribution is parameterized to provide the marginal interpretation for regression coefficients. In addition, MTJM-gamma and MTJM-Weibull are developed as special cases of MTJM-GG. To illustrate the applicability of the MTJM-GG, we applied the model to a set of real electronic health record data recently collected in Iran, and we provided SAS code for application. The simulation results showed that when the outcome distribution is unknown or misspecified, which is usually the case in real data sets, the MTJM-GG consistently outperforms other models. The GG family of distribution facilitates estimating a model with improved fit over the MTJM-gamma, standard Weibull, or Log-Normal distributions.

Keywords: marginalized two-part model, zero-inflated, right-skewed, semi-continuous, generalized gamma

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Authors: Chhaya Gangwal, R. N. Bhaumik, Shishir Kumar

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Handling uncertainty and impreciseness of knowledge appears to be a challenging task in Information Systems. Intuitionistic fuzzy (IF) and rough set theory enhances databases by allowing it for the management of uncertainty and impreciseness. This paper presents a new efficient query optimization technique for the multi-valued or imprecise IF rough database. The usefulness of this technique was illustrated on malaria knowledge from the rural tribal communities of Tripura where most of the information is multi-valued and imprecise. Then, the querying about knowledge on malaria is executed into SQL server to make the implementation of IF rough data querying simpler.

Keywords: intuitionistic fuzzy set, rough set, relational database, IF rough relational database

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Authors: A. L. Agarwal, D. A. Mahajan

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Construction industry tumbled along with other industry/sectors during recent economic crash. Construction business could not regain thereafter and still pass through slowdown phase, resulted many real estate as well as infrastructure projects not completed on schedule and within budget. There are many theories, tools, techniques with software packages available in the market to analyze construction schedule. This study focuses on the construction project schedule and uncertainties associated with construction activities. The infrastructure construction project has been considered for the analysis of uncertainty on project activities affecting project duration and analysis is done using @RISK software. Different simulation results arising from three probability distribution functions are compiled to benefit construction project managers to plan more realistic schedule of various construction activities as well as project completion to document in the contract and avoid compensations or claims arising out of missing the planned schedule.

Keywords: construction project, distributions, project schedule, uncertainty

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Authors: Paiboon Chuwatthanakij

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In reference to the legal state in the Thai legal system, most people understand the minor principles of the legal state form, which are the principles that can be explained and understood easily and the results can be seen clearly, especially in the legitimacy of administrative acts. Therefore, there is no awareness of justice, which is the fundamental value of Thai law. The legitimacy of administrative acts requires the administration to adhere to the constitution and legislative laws in enforcement of the laws. If it appears that the administrative acts are illegitimate, the administrative court, as the court of justice, will revoke those acts as if they had never been set in the legal system, this will affect people’s trust as they are unaware as to whether the administrative acts that appoint their lives are legitimate or not. Regarding the revocation of administrative orders by the administrative court as if those orders had never existed, the common individual surely cannot be expected to comprehend the security of their juristic position. Therefore, the legal state does not require a revocation of the government’s acts to terminate its legal results merely because those acts are illegitimate, but there should be considerations and realizations regarding the “The Principle of the Protection of Legitimate Expectation,” which is a minor principle in the legal state’s content that focuses on supporting and protecting legitimate expectations of the juristic position of an individual and maintaining justice, which is the fundamental value of Thai law

Keywords: legal state, rule of law, protection of legitimate, adjudication

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Authors: Tarul Garg, P. N. Agrawal

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We propose to define the q-analogue of the α-Bernstein Kantorovich operators and then introduce the q-bivariate generalization of these operators to study the approximation of functions of two variables. We obtain the rate of convergence of these bivariate operators by means of the total modulus of continuity, partial modulus of continuity and the Peetre’s K-functional for continuous functions. Further, in order to study the approximation of functions of two variables in a space bigger than the space of continuous functions, i.e. Bögel space; the GBS (Generalized Boolean Sum) of the q-bivariate operators is considered and degree of approximation is discussed for the Bögel continuous and Bögel differentiable functions with the aid of the Lipschitz class and the mixed modulus of smoothness.

Keywords: Bögel continuous, Bögel differentiable, generalized Boolean sum, K-functional, mixed modulus of smoothness

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Authors: A. Benzian

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The generalization of relativistic theory of gravity based essentially on the principle of equivalence stipulates that for all bodies, the grave mass is equal to the inert mass which leads us to believe that gravitation is not a property of the bodies themselves, but of space, and the conclusion that the gravitational field must curved space-time what allows the abandonment of Minkowski space (because Minkowski space-time being nonetheless null curvature) to adopt Riemannian geometry as a mathematical framework in order to determine the curvature. Therefore the work presented in this paper begins with the evolution of the concept of gravity then tensor field which manifests by Riemannian geometry to formulate the general equation of the gravitational field.

Keywords: inertia, principle of equivalence, tensors, Riemannian geometry

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Authors: Vedat Senol, Gursoy Turan, Anders Helmersson, Vortechz Andersson

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In developing a mathematical model of a real structure, the simulation results of the model may not match the real structural response. This is a general problem that arises during dynamic motion of the structure, which may be modeled by means of parameter variations in the stiffness, damping, and mass matrices. These changes in parameters need to be estimated, and the mathematical model is updated to obtain higher control performances and robustness. In this study, a linear fractional transformation (LFT) is utilized for uncertainty modeling. Further, a general approach to the design of an H∞ control of a magneto-rheological damper (MRD) for vibration reduction in a building with mass, damping, and stiffness uncertainties is presented.

Keywords: uncertainty modeling, structural control, MR Damper, H∞, robust control

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Authors: M. Hadi Bordbar, Matthew England, Alex Sen Gupta, Agus Santoso, Andrea Taschetto, Thomas Martin, Wonsun Park, Mojib Latif

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Climate models generally simulate long-term reductions in the Pacific Walker Circulation with increasing atmospheric greenhouse gases. However, over two recent decades (1992-2011) there was a strong intensification of the Pacific Trade Winds that is linked with a slowdown in global surface warming. Using large ensembles of multiple climate models forced by increasing atmospheric greenhouse gas concentrations and starting from different ocean and/or atmospheric initial conditions, we reveal very diverse 20-year trends in the tropical Pacific climate associated with a considerable uncertainty in the globally averaged surface air temperature (SAT) in each model ensemble. This result suggests low confidence in our ability to accurately predict SAT trends over 20-year timescale only from external forcing. We show, however, that the uncertainty can be reduced when the initial oceanic state is adequately known and well represented in the model. Our analyses suggest that internal variability in the Pacific trade winds can mask the anthropogenic signal over a 20-year time frame, and drive transitions between periods of accelerated global warming and temporary slowdown periods.

Keywords: trade winds, walker circulation, hiatus in the global surface warming, internal climate variability

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Authors: Mauludi Ariesto Pamungkas, Wafa Maftuhin

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Gallium with three outer electrons commonly are used as dopants of silicon to make it P type and N type semiconductor respectively. Silicene, one-atom-thick silicon layer is one of emerging two dimension materials after the success of graphene. The effects of Gallium doping on electronic structure of silicine are investigated by using first principle calculation based on Density Functional Theory (DFT) calculation and norm conserving pseudopotential method implemented in ABINIT code. Bandstructure of Pristine silicene is similar to that of graphene. Effect of Ga doping on bandstructure of silicene depend on the position of Ga adatom on silicene

Keywords: silicene, effects of Gallium doping, Density Functional Theory (DFT), graphene

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Authors: Danni Cong, Meiping Wu, Hua Mu, Xiaofeng He, Junxiang Lian, Juliang Cao, Shaokun Cai, Hao Qin

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Gravity field is of great significance in geoscience, national economy and national security, and gravitational gradient measurement has been extensively studied due to its higher accuracy than gravity measurement. Gravity gradient sensor, being one of core devices of the gravity gradient instrument, plays a key role in measuring accuracy. Therefore, this paper starts from analyzing the working principle of the gravity gradient sensor by Newton’s law, and then considers the relative motion between inertial and non-inertial systems to build a relatively adequate mathematical model, laying a foundation for the measurement error calibration, measurement accuracy improvement.

Keywords: gravity gradient, gravity gradient sensor, accelerometer, single-axis rotation modulation

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Authors: Saheed A. Gbadegeshin

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Commercialization method is a means to make inventions available at the market for final consumption. It is described as an important tool for keeping business enterprises sustainable and improving national economic growth. Thus, there are several scholarly publications on it, either presenting or testing different methods for commercialization. However, young entrepreneurs, technologists and scientists would like to know the best method to commercialize their innovations. Then, this question arises: What is the best commercialization method? To answer the question, a systematic literature review was conducted, and practitioners were interviewed. The literary results revealed that there are many methods but new methods are needed to improve commercialization especially during these times of economic crisis and political uncertainty. Similarly, the empirical results showed there are several methods, but the best method is the one that reduces costs, reduces the risks associated with uncertainty, and improves customer participation and acceptability. Therefore, it was concluded that new commercialization method is essential for today's high technologies and a method was presented.

Keywords: commercialization method, technology, knowledge, intellectual property, innovation, invention

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

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Soils are at the crossing of many issues such as food and water security, sustainable energy, climate change mitigation and adaptation, biodiversity protection, human health and well-being. They deliver many ecosystem services that are essential to life on Earth. Therefore, there is a growing demand for soil information on a national and global scale. Unfortunately, many countries do not have detailed soil maps, and, when existing, these maps are generally based on more or less complex and often non-harmonized soil classifications. An estimate of their uncertainty is also often missing. Thus, there are not easy to understand and often not properly used by end-users. Therefore, there is an urgent need to provide end-users with spatially exhaustive grids of essential soil properties, together with an estimate of their uncertainty. One way to achieve this is digital soil mapping (DSM). The concept of DSM relies on the hypothesis that soils and their properties are not randomly distributed, but that they depend on the main soil-forming factors that are climate, organisms, relief, parent material, time (age), and position in space. All these forming factors can be approximated using several exhaustive spatial products such as climatic grids, remote sensing products or vegetation maps, digital elevation models, geological or lithological maps, spatial coordinates of soil information, etc. Thus, DSM generally relies on models calibrated with existing observed soil data (point observations or maps) and so-called “ancillary co-variates” that come from other available spatial products. Then the model is generalized on grids where soil parameters are unknown in order to predict them, and the prediction performances are validated using various methods. With the growing demand for soil information at a national and global scale and the increase of available spatial co-variates national and continental DSM initiatives are continuously increasing. This short review illustrates the main national and continental advances in Europe, the diversity of the approaches and the databases that are used, the validation techniques and the main scientific and other issues. Examples from several countries illustrate the variety of products that were delivered during the last ten years. The scientific production on this topic is continuously increasing and new models and approaches are developed at an incredible speed. Most of the digital soil mapping (DSM) products rely mainly on machine learning (ML) prediction models and/or the use or pedotransfer functions (PTF) in which calibration data come from soil analyses performed in labs or for existing conventional maps. However, some scientific issues remain to be solved and also political and legal ones related, for instance, to data sharing and to different laws in different countries. Other issues related to communication to end-users and education, especially on the use of uncertainty. Overall, the progress is very important and the willingness of institutes and countries to join their efforts is increasing. Harmonization issues are still remaining, mainly due to differences in classifications or in laboratory standards between countries. However numerous initiatives are ongoing at the EU level and also at the global level. All these progress are scientifically stimulating and also promissing to provide tools to improve and monitor soil quality in countries, EU and at the global level.

Keywords: digital soil mapping, global soil mapping, national and European initiatives, global soil mapping products, mini-review

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Authors: Haifeng Shi, Kun Song, Yili Zhao

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In China, with the extensive promotion of the ‘aging in place’ pension policy as the background, most of the elders will choose to remain in their current homes and communities, finding out of preference or necessity that they will need to remodel their homes to fit their changing needs. This generation elder born in the 1960s to 1970s almost live in the same form of housing-condominium built from 1982 to 2012. Based on the survey of existing multi-family housing, especially in Tianjin, it is found that the current ‘double two-thirds principles’ is becoming the threshold for modification to existing house, particularly in the project of elevators addition to existing condominium (built from 1982 to 2016 without elevators below 6 floors according to the previous building code). Firstly, this article concludes the local policies of elevator addition nationwide, most of which has determined the importance and necessity of the community-based self-organization principle in the operation of the elevator addition. Secondly, by comparing the three existing community management systems (owners' congress, property management system and community committee) in instances, find that the community-based ‘two-thirds’ principle is not conducive to implement for multi-owned property renovation in the community or common accessibility modification in the building. However, analysis the property and other community management related laws, pointing out the shortcomings of the existing community-based ‘two-thirds’ decision-making norms. The analyzation showed that the unit-based and ‘100% principle’ method is more capable of common accessibility in the condominium in China. Differing from existing laws, the unit-based principle will be effective for the process of decision-making and ‘100% principle’ will protect closely profit-related householders for condominium modification in the multi-owned area. These three aspects of the analysis suggest that the establishment of the unit-based self-organization mechanism is a preferred and inevitable method to solve the problem of elevators addition to the existing condominium in China.

Keywords: aging in place, condominium, modification, multi own

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Authors: Isaac Mugume

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Since the beginning of the 21st century, climate change has been an issue due to the reported rise in global temperature and changes in the frequency as well as severity of extreme weather and climatic events. The changing climate has been attributed to rising concentrations of greenhouse gases, including environmental changes such as ecosystems and land-uses. Climatic projections have been carried out under the auspices of the intergovernmental panel on climate change where a couple of models have been run to inform us about the likelihood of future climates. Since one of the major forcings informing the changing climate is emission of greenhouse gases, different scenarios have been proposed and future climates for different periods presented. The global climate models project different areas to experience different impacts. While regional modeling is being carried out for high impact studies, bias correction is less documented. Yet, the regional climate models suffer bias which introduces uncertainty. This is addressed in this study by bias correcting the regional models. This study uses the Weather Research and Forecasting model under different representative concentration pathways and correcting the products of these models using observed climatic data. This study notes that bias correction (e.g., the running-mean bias correction; the best easy systematic estimator method; the simple linear regression method, nearest neighborhood, weighted mean) improves the climatic projection skill and therefore reduce the uncertainty inherent in the climatic projections.

Keywords: bias correction, climatic projections, numerical models, representative concentration pathways

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Authors: Vanessa Morgado, Ricardo Bettencourt da Silva, Carla Palma

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The environmental pollution by microplastics is well recognized. Microplastics were already detected in various matrices from distinct environmental compartments worldwide, some from remote areas. Various methodologies and techniques have been used to determine microplastic in such matrices, for instance, sediment samples from the ocean bottom. In order to determine microplastics in a sediment matrix, the sample is typically sieved through a 5 mm mesh, digested to remove the organic matter, and density separated to isolate microplastics from the denser part of the sediment. The physical analysis of microplastic consists of visual analysis under a stereomicroscope to determine particle size, colour, and shape. The chemical analysis is performed by an infrared spectrometer coupled to a microscope (micro-FTIR), allowing to the identification of the chemical composition of microplastic, i.e., the type of polymer. Creating legislation and policies to control and manage (micro)plastic pollution is essential to protect the environment, namely the coastal areas. The regulation is defined from the known relevance and trends of the pollution type. This work discusses the assessment of contamination trends of a 700 km² oceanic area affected by contamination heterogeneity, sampling representativeness, and the uncertainty of the analysis of collected samples. The methodology developed consists of objectively identifying meaningful variations of microplastic contamination by the Monte Carlo simulation of all uncertainty sources. This work allowed us to unequivocally conclude that the contamination level of the studied area did not vary significantly between two consecutive years (2018 and 2019) and that PET microplastics are the major type of polymer. The comparison of contamination levels was performed for a 99% confidence level. The developed know-how is crucial for the objective and binding determination of microplastic contamination in relevant environmental compartments.

Keywords: measurement uncertainty, micro-ATR-FTIR, microplastics, ocean contamination, sampling uncertainty

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Authors: Abbes Lounis, Kahina Louadj, Mohamed Aidene

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This paper presents an optimal control problem applied to a robot; the problem is to determine a command which makes it possible to reach a final state from a given initial state in record time. The approach followed to solve this optimization problem with constraints on the control starts by presenting the equations of motion of the dynamic system then by applying Pontryagin's maximum principle (PMP) to determine the optimal control, and Picard's successive approximation method combined with the shooting method to solve the resulting differential system.

Keywords: robotics, Pontryagin's Maximum Principle, PMP, Picard's method, shooting method, non-linear differential systems

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Authors: Septimia Sarbu

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The entropy rate of a stochastic process is a fundamental concept in information theory. It provides a limit to the amount of information that can be transmitted reliably over a communication channel, as stated by Shannon's coding theorems. Recently, researchers have focused on developing new measures of information that generalize Shannon's classical theory. The aim is to design more efficient information encoding and transmission schemes. This paper continues the study of generalized entropy rates, by deriving a closed-form solution to the Sharma-Mittal entropy rate for Gaussian processes. Using the squeeze theorem, we solve the limit in the definition of the entropy rate, for different values of alpha and beta, which are the parameters of the Sharma-Mittal entropy. In the end, we compare it with Shannon and Rényi's entropy rates for Gaussian processes.

Keywords: generalized entropies, Sharma-Mittal entropy rate, Gaussian processes, eigenvalues of the covariance matrix, squeeze theorem

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Authors: Monish Sengupta, Benjamin Heydecker, Daniel Woodland

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New train control systems rely mainly on Automatic Train Protection (ATP) and Automatic Train Operation (ATO) dynamically to control the speed and hence performance. The ATP and the ATO form the vital element within the CBTC (Communication Based Train Control) and within the ERTMS (European Rail Traffic Management System) system architectures. Reliable and accurate measurement of train location, speed and acceleration are vital to the operation of train control systems. In the past, all CBTC and ERTMS system have deployed a balise or equivalent to correct the uncertainty element of the train location. Typically a CBTC train is allowed to miss only one balise on the track, after which the Automatic Train Protection (ATP) system applies emergency brake to halt the service. This is because the location uncertainty, which grows within the train control system, cannot tolerate missing more than one balise. Balises contribute a significant amount towards wayside maintenance and studies have shown that balises on the track also forms a constraint for future track layout change and change in speed profile.This paper investigates the causes of the location uncertainty that is currently experienced and considers whether it is possible to identify an effective filter to ascertain, in conjunction with appropriate sensors, more accurate speed, distance and location for a CBTC driven train without the need of any external balises. An appropriate sensor fusion algorithm and intelligent sensor selection methodology will be deployed to ascertain the railway location and speed measurement at its highest precision. Similar techniques are already in use in aviation, satellite, submarine and other navigation systems. Developing a model for the speed control and the use of Kalman filter is a key element in this research. This paper will summarize the research undertaken and its significant findings, highlighting the potential for introducing alternative approaches to train positioning that would enable removal of all trackside location correction balises, leading to huge reduction in maintenances and more flexibility in future track design.

Keywords: ERTMS, CBTC, ATP, ATO

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Authors: Nikk Leavitt, Peter Kellett, Cheryl Currie, Richard Larouche

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Background: Masculinity has been associated with poor mental health outcomes in adult men and is colloquially referred to as toxic. Masculinity is traditionally measured using the Male Role Norms Inventory, which examines behaviors that may be common in men but that are themselves associated with poor mental health regardless of gender (e.g., aggressiveness). The purpose of this study was to examine if masculinity is associated with generalized anxiety among men using this inventory vs. a man’s personal definition of it. Method: An online survey collected data from 1,200 men aged 18-65 across Canada in July 2022. Masculinity was measured using: 1) the Male Role Norms Inventory Short Form and 2) by asking men to self-define what being masculine means. Men were then asked to rate the extent they perceived themselves to be masculine on a scale of 1 to 10 based on their definition of the construct. Generalized anxiety disorder was measured using the GAD-7. Multiple linear regression was used to examine associations between each masculinity score and anxiety score, adjusting for confounders. Results: The masculinity score measured using the inventory was positively associated with increased anxiety scores among men (β = 0.02, p < 0.01). Masculinity subscales most strongly correlated with higher anxiety were restrictive emotionality (β = 0.29, p < 0.01) and dominance (β = 0.30, p < 0.01). When traditional masculinity was replaced by a man’s self-rated masculinity score in the model, the reverse association was found, with increasing masculinity resulting in a significantly reduced anxiety score (β = -0.13, p = 0.04). Discussion: These findings highlight the need to revisit the ways in which masculinity is defined and operationalized in research to better understand its impacts on men’s mental health. The findings also highlight the importance of allowing participants to self-define gender-based constructs, given they are fluid and socially constructed.

Keywords: masculinity, generalized anxiety disorder, race, intersectionality

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Authors: Riku Hayashida, Tomoaki Hashimoto

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This paper provides a robust stabilization method for rotational motion of underwater robots against parameter uncertainties. Underwater robots are expected to be used for various work assignments. The large variety of applications of underwater robots motivates researchers to develop control systems and technologies for underwater robots. Several control methods have been proposed so far for the stabilization of nominal system model of underwater robots with no parameter uncertainty. Parameter uncertainties are considered to be obstacles in implementation of the such nominal control methods for underwater robots. The objective of this study is to establish a robust stabilization method for rotational motion of underwater robots against parameter uncertainties. The effectiveness of the proposed method is verified by numerical simulations.

Keywords: robust control, stabilization method, underwater robot, parameter uncertainty

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Authors: Ezgi Nevruz, Kasirga Yildirak, Ashis SenGupta

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Comparing or ranking risks is the main motivating factor behind the human trait of making choices. Cumulative prospect theory (CPT) is a preference theory approach that evaluates perception and bias in decision making under risk and uncertainty. We aim to investigate the aggregate claims of different risk classes in terms of their comparability and amenability to ordering when the impact of risk perception is considered. For this aim, we prioritize the aggregate claims taken as actuarial risks by using various stochastic ordering relations. In order to prioritize actuarial risks, we use stochastic relations such as stochastic dominance and stop-loss dominance that are proposed in the frame of partial order theory. We take into account the dependency of the individual claims exposed to similar environmental risks. At first, we modify the zero-utility premium principle in order to obtain a solution for the stop-loss premium under CPT. Then, we propose a stochastic stop-loss dominance of the aggregate claims and find a relation between the stop-loss dominance and the first-order stochastic dominance under the dependence assumption by using properties of the familiar as well as some emerging multivariate claim distributions.

Keywords: cumulative prospect theory, partial order theory, risk perception, stochastic dominance, stop-loss dominance

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Authors: Devinder Singh, Rajneesh Kumar, Arvind Kumar

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The present investigation deals with generalized model of the equations for a homogeneous isotropic microstretch thermoelastic half space with mass diffusion medium. Theories of generalized thermoelasticity Lord-Shulman (LS) Green-Lindsay (GL) and Coupled Theory (CT) theories are applied to investigate the problem. The stresses in the considered medium have been studied due to normal force and tangential force. The normal mode analysis technique is used to calculate the normal stress, shear stress, couple stresses and microstress. A numerical computation has been performed on the resulting quantity. The computed numerical results are shown graphically.

Keywords: microstretch, thermoelastic, normal mode analysis, normal and tangential force, microstress force

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Authors: Kai Zhang, Xi Jiang

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Effect of fuel variabilities on premixed combustion of bio-syngas mixtures is of great importance in bio-syngas utilisation. The uncertainties of concentrations of fuel constituents such as H2, CO and CH4 may lead to unpredictable combustion performances, combustion instabilities and hot spots which may deteriorate and damage the combustion hardware. Numerical modelling and simulations can assist in understanding the behaviour of bio-syngas combustion with pre-defined species concentrations, while the evaluation of variabilities of concentrations is expensive. To be more specific, questions such as ‘what is the burning velocity of bio-syngas at specific equivalence ratio?’ have been answered either experimentally or numerically, while questions such as ‘what is the likelihood of burning velocity when precise concentrations of bio-syngas compositions are unknown, but the concentration ranges are pre-described?’ have not yet been answered. Uncertainty quantification (UQ) methods can be used to tackle such questions and assess the effects of fuel compositions. An efficient probabilistic UQ method based on Polynomial Chaos Expansion (PCE) techniques is employed in this study. The method relies on representing random variables (combustion performances) with orthogonal polynomials such as Legendre or Gaussian polynomials. The constructed PCE via Galerkin Projection provides easy access to global sensitivities such as main, joint and total Sobol indices. In this study, impacts of fuel compositions on combustion (adiabatic flame temperature and laminar flame speed) of bio-syngas fuel mixtures are presented invoking this PCE technique at several equivalence ratios. High-pressure effects on bio-syngas combustion instability are obtained using detailed chemical mechanism - the San Diego Mechanism. Guidance on reducing combustion instability from upstream biomass gasification process is provided by quantifying the significant contributions of composition variations to variance of physicochemical properties of bio-syngas combustion. It was found that flame speed is very sensitive to hydrogen variability in bio-syngas, and reducing hydrogen uncertainty from upstream biomass gasification processes can greatly reduce bio-syngas combustion instability. Variation of methane concentration, although thought to be important, has limited impacts on laminar flame instabilities especially for lean combustion. Further studies on the UQ of percentage concentration of hydrogen in bio-syngas can be conducted to guide the safer use of bio-syngas.

Keywords: bio-syngas combustion, clean energy utilisation, fuel variability, PCE, targeted uncertainty reduction, uncertainty quantification

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Authors: Timothy Kayode Samson, Adedoyin Isola Lawal

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The past five years have shown a sharp increase in public interest in the crypto market, with its market capitalization growing from $100 billion in June 2017 to $2158.42 billion on April 5, 2022. Despite the outrageous nature of the volatility of cryptocurrencies, the use of skewed error innovation distributions in modelling the volatility behaviour of these digital currencies has not been given much research attention. Hence, this study models the volatility of 5 largest cryptocurrencies by market capitalization (Bitcoin, Ethereum, Tether, Binance coin, and USD Coin) using four variants of GARCH models (GJR-GARCH, sGARCH, EGARCH, and APARCH) estimated using three skewed error innovation distributions (skewed normal, skewed student- t and skewed generalized error innovation distributions). Daily closing prices of these currencies were obtained from Yahoo Finance website. Finding reveals that the Binance coin reported higher mean returns compared to other digital currencies, while the skewness indicates that the Binance coin, Tether, and USD coin increased more than they decreased in values within the period of study. For both Bitcoin and Ethereum, negative skewness was obtained, meaning that within the period of study, the returns of these currencies decreased more than they increased in value. Returns from these cryptocurrencies were found to be stationary but not normality distributed with evidence of the ARCH effect. The skewness parameters in all best forecasting models were all significant (p<.05), justifying of use of skewed error innovation distributions with a fatter tail than normal, Student-t, and generalized error innovation distributions. For Binance coin, EGARCH-sstd outperformed other volatility models, while for Bitcoin, Ethereum, Tether, and USD coin, the best forecasting models were EGARCH-sstd, APARCH-sstd, EGARCH-sged, and GJR-GARCH-sstd, respectively. This suggests the superiority of skewed Student t- distribution and skewed generalized error distribution over the skewed normal distribution.

Keywords: skewed generalized error distribution, skewed normal distribution, skewed student t- distribution, APARCH, EGARCH, sGARCH, GJR-GARCH

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Authors: Mohammad Abdullah-Al-Wadud

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In the current era of automation and artificial intelligence, different systems have been increasingly keeping on depending on decision-making capabilities of machines. Such systems/applications may range from simple classifiers to sophisticated surveillance systems based on traditional sensors and related equipment which are becoming more common in the internet of things (IoT) paradigm. However, the available data for such problems are usually imprecise and incomplete, which leads to uncertainty in decisions made based on traditional probability-based classifiers. This requires a robust fusion framework to combine the available information sources with some degree of certainty. The theory of evidence can provide with such a method for combining evidence from different (may be unreliable) sources/observers. This talk will address the employment of the Dempster-Shafer Theory of evidence in some practical applications.

Keywords: decision making, dempster-shafer theory, evidence fusion, incomplete data, uncertainty

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Authors: Muhammad Nouman Qureshi, Muhammad Hanif

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Adaptive cluster sampling (ACS) is specifically developed for the estimation of highly clumped populations and applied to a wide range of situations like animals of rare and endangered species, uneven minerals, HIV patients and drug users. In this paper, we proposed a generalized semi-exponential estimator with two auxiliary variables under the framework of ACS design. The expressions of approximate bias and mean square error (MSE) of the proposed estimator are derived. Theoretical comparisons of the proposed estimator have been made with existing estimators. A numerical study is conducted on real and artificial populations to demonstrate and compare the efficiencies of the proposed estimator. The results indicate that the proposed generalized semi-exponential estimator performed considerably better than all the adaptive and non-adaptive estimators considered in this paper.

Keywords: auxiliary information, adaptive cluster sampling, clustered populations, Hansen-Hurwitz estimation

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Authors: Riadh Zorgati, Thomas Triboulet

Abstract:

In quite diverse application areas such as astronomy, medical imaging, geophysics or nondestructive evaluation, many problems related to calibration, fitting or estimation of a large number of input parameters of a model from a small amount of output noisy data, can be cast as inverse problems. Due to noisy data corruption, insufficient data and model errors, most inverse problems are ill-posed in a Hadamard sense, i.e. existence, uniqueness and stability of the solution are not guaranteed. A wide class of inverse problems in physics relates to the Fredholm equation of the first kind. The ill-posedness of such inverse problem results, after discretization, in a very ill-conditioned linear system of equations, the condition number of the associated matrix can typically range from 109 to 1018. This condition number plays the role of an amplifier of uncertainties on data during inversion and then, renders the inverse problem difficult to handle numerically. Similar problems appear in other areas such as numerical optimization when using interior points algorithms for solving linear programs leads to face ill-conditioned systems of linear equations. Devising efficient solution approaches for such system of equations is therefore of great practical interest. Efficient iterative algorithms are proposed for solving a system of linear equations. The approach is based on a preconditioning of the initial matrix of the system with an approximation of a generalized inverse leading to a stochastic preconditioned matrix. This approach, valid for non-negative matrices, is first extended to hermitian, semi-definite positive matrices and then generalized to any complex rectangular matrices. The main results obtained are as follows: 1) We are able to build a generalized inverse of any complex rectangular matrix which satisfies the convergence condition requested in iterative algorithms for solving a system of linear equations. This completes the (short) list of generalized inverse having this property, after Kaczmarz and Cimmino matrices. Theoretical results on both the characterization of the type of generalized inverse obtained and the convergence are derived. 2) Thanks to its properties, this matrix can be efficiently used in different solving schemes as Richardson-Tanabe or preconditioned conjugate gradients. 3) By using Lp norms, we propose generalized Kaczmarz’s type matrices. We also show how Cimmino's matrix can be considered as a particular case consisting in choosing the Euclidian norm in an asymmetrical structure. 4) Regarding numerical results obtained on some pathological well-known test-cases (Hilbert, Nakasaka, …), some of the proposed algorithms are empirically shown to be more efficient on ill-conditioned problems and more robust to error propagation than the known classical techniques we have tested (Gauss, Moore-Penrose inverse, minimum residue, conjugate gradients, Kaczmarz, Cimmino). We end on a very early prospective application of our approach based on stochastic matrices aiming at computing some parameters (such as the extreme values, the mean, the variance, …) of the solution of a linear system prior to its resolution. Such an approach, if it were to be efficient, would be a source of information on the solution of a system of linear equations.

Keywords: conditioning, generalized inverse, linear system, norms, stochastic matrix

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Authors: Desiree Llaguno Cerezo, Elizabeth Valdes-Miranda Fernandez

Abstract:

Women have a critical role in sustaining the economy and in the development of trade. However, such a role has long been invisible due to orthodox conceptions that have ignored the gender variable in commercial analyses. Today, it is generally accepted that neither the economy nor business are gender-neutral and that the performance of these activities often impact negatively the lives of women. Women’s participation in trade, on equal terms as men, in any of the various possible roles -producer, wage earner, consumer, merchant, taxpayer- will not only favour the lives of women but also the performance of the economies in which they participate. Transparency, as a principle of the multilateral trading system, can play a significant role as a strategy for the empowerment of women.

Keywords: trade, human rights, gender equality, transparency, WTO, women workers, women's economic empowerment

Procedia PDF Downloads 124

Authors: Geir Kirkebøen

Abstract:

Most patients with serious diagnoses want to know their prognosis, in particular their expected survival time. As part of the informed consent process, physicians are legally obligated to communicate such information to patients. However, there is no established (evidence based) ‘best practice’ for how to do this. The two questions explored in this study are: How do physicians communicate expected survival time to patients, and how should it be done? We explored the first, descriptive question in a study with Norwegian oncologists as participants. The study had a scenario and a survey part. In the scenario part, the doctors should imagine that a patient, recently diagnosed with a serious cancer diagnosis, has asked them: ‘How long can I expect to live with such a diagnosis? I want an honest answer from you!’ The doctors should assume that the diagnosis is certain, and that from an extensive recent study they had optimal statistical knowledge, described in detail as a right-skewed survival curve, about how long such patients with this kind of diagnosis could be expected to live. The main finding was that very few of the oncologists would explain to the patient the variation in survival time as described by the survival curve. The majority would not give the patient an answer at all. Of those who gave an answer, the typical answer was that survival time varies a lot, that it is hard to say in a specific case, that we will come back to it later etc. The survey part of the study clearly indicates that the main reason why the oncologists would not deliver the mortality prognosis was discomfort with its uncertainty. The scenario part of the study confirmed this finding. The majority of the oncologists explicitly used the uncertainty, the variation in survival time, as a reason to not give the patient an answer. Many studies show that patients want realistic information about their mortality prognosis, and that they should be given hope. The question then is how to communicate the uncertainty of the prognosis in a realistic and optimistic – hopeful – way. Based on psychological research, our hypothesis is that the best way to do this is by explicitly describing the variation in survival time, the (usually) right skewed survival curve of the prognosis, and emphasize to the patient the (small) possibility of being a ‘lucky outlier’. We tested this hypothesis in two scenario studies with lay people as participants. The data clearly show that people prefer to receive expected survival time as a median value together with explicit information about the survival curve’s right skewedness (e.g., concrete examples of ‘positive outliers’), and that communicating expected survival time this way not only provides people with hope, but also gives them a more realistic understanding compared with the typical way expected survival time is communicated. Our data indicate that it is not the existence of the uncertainty regarding the mortality prognosis that is the problem for patients, but how this uncertainty is, or is not, communicated and explained.

Keywords: cancer patients, decision psychology, doctor-patient communication, mortality prognosis

Procedia PDF Downloads 298