Search results for: ruin probabilities

Authors: Sabyasachi Mukhopadhyay, Joseph Ogutu, Hans-Peter Piepho

Abstract:

We aim to model dynamics of wildlife or pastoral livestock population for understanding of their population change and hence for wildlife conservation and promoting human welfare. The study is motivated by an age-sex structured population counts in different regions of Serengeti-Mara during the period 1989-2003. Developing reliable and realistic models for population dynamics of large herbivore population can be a very complex and challenging exercise. However, the Bayesian statistical domain offers some flexible computational methods that enable the development and efficient implementation of complex population dynamics models. In this work, we have used a novel Bayesian state-space model to analyse the dynamics of topi and hartebeest populations in the Serengeti-Mara Ecosystem of East Africa. The state-space model involves survival probabilities of the animals which further depend on various factors like monthly rainfall, size of habitat, etc. that cause recent declines in numbers of the herbivore populations and potentially threaten their future population viability in the ecosystem. Our study shows that seasonal rainfall is the most important factors shaping the population size of animals and indicates the age-class which most severely affected by any change in weather conditions.

Keywords: bayesian state-space model, Markov Chain Monte Carlo, population dynamics, conservation

Procedia PDF Downloads 173

Authors: Lichung Jen, Yi Chun Liu, Kuan-Wei Lee

Abstract:

Fruitful data has been accumulated in database nowadays and is commonly used as support for decision-making. In the healthcare industry, hospital, for instance, ordering pharmacy inventory is one of the key decision. With large drug inventory, the current cost increases and its expiration dates might lead to future issue, such as drug disposal and recycle. In contrast, underestimating demand of the pharmacy inventory, particularly standing drugs, affects the medical treatment and possibly hospital reputation. Prescription behaviour of hospital physicians is one of the critical factor influencing this decision, particularly irregular prescription behaviour. If a drug’s usage amount in the month is irregular and less than the regular usage, it may cause the trend of subsequent stockpiling. On the contrary, if a drug has been prescribed often than expected, it may result in insufficient inventory. We proposed a hierarchical Bayesian mixture model with two components to identify physicians’ regular/irregular prescription patterns with probabilities. Heterogeneity of hospital is considered in our proposed hierarchical Bayes model. The result suggested that modeling the prescription patterns of physician is beneficial for estimating the order quantity of medication and pharmacy inventory management of the hospital. Managerial implication and future research are discussed.

Keywords: hierarchical Bayesian model, poission mixture model, medicines prescription behavior, irregular behavior

Procedia PDF Downloads 101

Authors: Steven D. P. Moore

Abstract:

An electrical discharge in the air induces an electromagnetic (EM) wave capable of wireless transfer, reception, and conversion back into electrical discharge at a distant location. Following Norton’s ground wave principles, EM wave radiation (EMR) runs parallel to the Earth’s surface. Energy in an EMR wave can move through the air and be focused to create a spark at a distant location, focused by a receiver to generate a local electrical discharge. This local discharge can be amplified and stored but also has the propensity to initiate another EMR wave. In addition to typical EM waves, lightning is also associated with atmospheric events, trans-ionospheric pulse pairs, the most powerful natural EMR signal on the planet. With each lightning strike, regardless of global position, it generates naturally occurring pulse-pairs that are emitted towards space within a narrow cone. An EMR wave can self-propagate, travel at the speed of light, and, if polarized, contain vector properties. If this reflective pulse could be directed by design through structures that have increased probabilities for lighting strikes, it could theoretically travel near the surface of the Earth at light speed towards a selected receiver for local transformation into electrical energy. Through research, there are several influencing parameters that could be modified to model, test, and increase the potential for adopting this technology towards the goal of developing a global grid that utilizes natural sources of energy.

Keywords: electricity, sparkgap, wireless, electromagnetic

Procedia PDF Downloads 164

Authors: Ilaria Lucrezia Amerise

Abstract:

The statistical modelling of precipitation data for a given portion of territory is fundamental for the monitoring of climatic conditions and for Hydrogeological Management Plans (HMP). This modelling is rendered particularly complex by the changes taking place in the frequency and intensity of precipitation, presumably to be attributed to the global climate change. This paper applies the Wakeby distribution (with 5 parameters) as a theoretical reference model. The number and the quality of the parameters indicate that this distribution may be the appropriate choice for the interpolations of the hydrological variables and, moreover, the Wakeby is particularly suitable for describing phenomena producing heavy tails. The proposed estimation methods for determining the value of the Wakeby parameters are the same as those used for density functions with heavy tails. The commonly used procedure is the classic method of moments weighed with probabilities (probability weighted moments, PWM) although this has often shown difficulty of convergence, or rather, convergence to a configuration of inappropriate parameters. In this paper, we analyze the problem of the likelihood estimation of a random variable expressed through its quantile function. The method of maximum likelihood, in this case, is more demanding than in the situations of more usual estimation. The reasons for this lie, in the sampling and asymptotic properties of the estimators of maximum likelihood which improve the estimates obtained with indications of their variability and, therefore, their accuracy and reliability. These features are highly appreciated in contexts where poor decisions, attributable to an inefficient or incomplete information base, can cause serious damages.

Keywords: generalized extreme values, likelihood estimation, precipitation data, Wakeby distribution

Procedia PDF Downloads 112

Authors: S. Benkraouda, Z. Djelloul-Khedda, B. Yagoubi

Abstract:

we present a method for early detection of forest fires from a thermal infrared satellite image, using the image matrix of the probability of belonging. The principle of the method is to compare a theoretical mathematical model to an experimental model. We considered that each line of the image matrix, as an embodiment of a non-stationary random process. Since the distribution of pixels in the satellite image is statistically dependent, we divided these lines into small stationary and ergodic intervals to characterize the image by an adequate mathematical model. A standard deviation was chosen to generate random variables, so each interval behaves naturally like white Gaussian noise. The latter has been selected as the mathematical model that represents a set of very majority pixels, which we can be considered as the image background. Before modeling the image, we made a few pretreatments, then the parameters of the theoretical Gaussian model were extracted from the modeled image, these settings will be used to calculate the probability of each interval of the modeled image to belong to the theoretical Gaussian model. The high intensities pixels are regarded as foreign elements to it, so they will have a low probability, and the pixels that belong to the background image will have a high probability. Finally, we did present the reverse of the matrix of probabilities of these intervals for a better fire detection.

Keywords: forest fire, forest fire detection, satellite image, normal distribution, theoretical gaussian model, thermal infrared matrix image

Procedia PDF Downloads 113

Authors: Murat Oral, Seda Bostancı, Sadık Ata, Kevser Dincer

Abstract:

When evaluating the aesthetics of cities, an analysis of the urban form development depending on design properties with a variety of factors is performed together with a study of the effects of this appearance on human beings. Different methods are used while making an aesthetical evaluation related to a city. Entropy, in its preliminary meaning, is the mathematical representation of thermodynamic results. Measuring the entropy is related to the distribution of positional figures of a message or information from the probabilities standpoint. In this study, analysis of evaluation the urban skylines by the entropy approach was modelled with Rule-Based Mamdani-Type Fuzzy (RBMTF) modelling technique. Input-output parameters were described by RBMTF if-then rules. Numerical parameters of input and output variables were fuzzificated as linguistic variables: Very Very Low (L1), Very Low (L2), Low (L3), Negative Medium (L4), Medium (L5), Positive Medium (L6), High (L7), Very High (L8) and Very Very High (L9) linguistic classes. The comparison between application data and RBMTF is done by using absolute fraction of variance (R2). The actual values and RBMTF results indicated that RBMTF can be successfully used for the analysis of evaluation the urban skylines by the entropy approach. As a result, RBMTF model has shown satisfying relation with experimental results, which suggests an alternative method to evaluation of the urban skylines by the entropy approach.

Keywords: urban skylines, entropy, rule-based Mamdani type, fuzzy logic

Procedia PDF Downloads 264

Authors: Salah Hassab Elnaby, Omnia Hamdy, Aziza Ahmed Hassan, Salwa Abdelkawi, Ibrahim Abdelhalim

Abstract:

Laser corneal reshaping has been proposed as a successful treatment of many refraction disorders. However, some physical and chemical demonstrations of the laser effect upon interaction with the corneal tissue are still not fully explained. Therefore, different computational and mathematical models have been implemented to predict the depth of the ablated channel and calculate the ablation threshold and the local temperature rise. In the current paper, we present a modified model that aims to answer some of the open questions about the ablation threshold, the ablation rate, and the physical and chemical mechanisms of that action. The proposed model consists of three parts. The first part deals with possible photochemical reactions between the incident photons and various components of the cornea (collagen, water, etc.). Such photochemical reactions may end by photo-ablation or just the electronic excitation of molecules. Then a chemical reaction is responsible for the ablation threshold. Finally, another chemical reaction produces fragments that can be cleared out. The model takes into account all processes at the same time with different probabilities. Moreover, the effect of applying different laser wavelengths that have been studied before, namely the common excimer laser (193-nm) and the solid state lasers (213-nm & 266-nm), has been investigated. Despite the success and ubiquity of the ArF laser, the presented results reveal that a carefully designed 213-nm laser gives the same results with lower operational drawbacks. Moreover, the use of mode locked laser could also decrease the risk of heat generation and diffusion.

Keywords: UV lasers, mathematical model, corneal ablation, photochemical ablation

Procedia PDF Downloads 52

Authors: Reza Mohammadzadeh

Abstract:

The major challenges in geotechnical engineering in underground spaces arise from uncertainties and different probabilities. The collection, collation, and collaboration of existing data to incorporate them in analysis and design for given prospect evaluation would be a reliable, practical problem solving method under uncertainty. Machine learning (ML) is a subfield of artificial intelligence in statistical science which applies different techniques (e.g., Regression, neural networks, support vector machines, decision trees, random forests, genetic programming, etc.) on data to automatically learn and improve from them without being explicitly programmed and make decisions and predictions. In this paper, a conceptual database schema of geotechnical risks in underground coal mining based on a cloud system architecture has been designed. A new approach of risk assessment using a three-dimensional risk matrix supported by the level of knowledge (LoK) has been proposed in this model. Subsequently, the model workflow methodology stages have been described. In order to train data and LoK models deployment, an ML platform has been implemented. IBM Watson Studio, as a leading data science tool and data-driven cloud integration ML platform, is employed in this study. As a Use case, a data set of geotechnical hazards and risk assessment in underground coal mining were prepared to demonstrate the performance of the model, and accordingly, the results have been outlined.

Keywords: data model, geotechnical risks, machine learning, underground coal mining

Procedia PDF Downloads 245

Authors: Petr Gurný

Abstract:

One of the most important tasks in the risk management is the correct determination of probability of default (PD) of particular financial subjects. In this paper a possibility of determination of financial institution’s PD according to the credit-scoring models is discussed. The paper is divided into the two parts. The first part is devoted to the estimation of the three different models (based on the linear discriminant analysis, logit regression and probit regression) from the sample of almost three hundred US commercial banks. Afterwards these models are compared and verified on the control sample with the view to choose the best one. The second part of the paper is aimed at the application of the chosen model on the portfolio of three key Czech banks to estimate their present financial stability. However, it is not less important to be able to estimate the evolution of PD in the future. For this reason, the second task in this paper is to estimate the probability distribution of the future PD for the Czech banks. So, there are sampled randomly the values of particular indicators and estimated the PDs’ distribution, while it’s assumed that the indicators are distributed according to the multidimensional subordinated Lévy model (Variance Gamma model and Normal Inverse Gaussian model, particularly). Although the obtained results show that all banks are relatively healthy, there is still high chance that “a financial crisis” will occur, at least in terms of probability. This is indicated by estimation of the various quantiles in the estimated distributions. Finally, it should be noted that the applicability of the estimated model (with respect to the used data) is limited to the recessionary phase of the financial market.

Keywords: credit-scoring models, multidimensional subordinated Lévy model, probability of default

Procedia PDF Downloads 425

Authors: Majid Mathlouthi, Fethi Lebdi

Abstract:

Throughout the world, the extent and magnitude of droughts have economic, social and environmental consequences. Today climate change has become more and more felt; most likely they increase the frequency and duration of droughts. An analysis by event of dry event, from series of observations of the daily rainfall is carried out. A daily precipitation threshold value has been set. A catchment localized in Northern Tunisia where the average rainfall is about 600 mm has been studied. Rainfall events are defined as an uninterrupted series of rainfall days understanding at least a day having received a precipitation superior or equal to a fixed threshold. The dry events are constituted of a series of dry days framed by two successive rainfall events. A rainfall event is a vector of coordinates the duration, the rainfall depth per event and the duration of the dry event. The depth and duration are found to be correlated. So we use conditional probabilities to analyse the depth per event. The negative binomial distribution fits well the dry event. The duration of the rainfall event follows a geometric distribution. The length of the climatically cycle adjusts to the Incomplete Gamma. Results of this analysis was used to study of the effects of climate change on water resources and crops and to calibrate precipitation models with little rainfall records. In response to long droughts in the basin, the drought management system is based on three phases during each of the three phases; different measurements are applied and executed. The first is before drought, preparedness and early warning; the second is drought management, mitigation in the event of drought; and the last subsequent drought, when the drought is over.

Keywords: dry spell, precipitation threshold, climate vulnerability, adaptation measures

Procedia PDF Downloads 54

Authors: Lukumba Phiri, Simon Tembo, Kumbuso Joshua Nyoni

Abstract:

In Zambia recent initiatives by various power operators like ZESCO, CEC, and consumers like the mines to upgrade power systems into smart grids target an even tighter integration with information technologies to enable the integration of renewable energy sources, local and bulk generation, and demand response. Thus, for the reliable operation of smart grids, its information infrastructure must be secure and reliable in the face of both failures and cyberattacks. Due to the nature of the systems, ICS/SCADA cybersecurity and governance face additional challenges compared to the corporate networks, and critical systems may be left exposed. There exist control frameworks internationally such as the NIST framework, however, there are generic and do not meet the domain-specific needs of the SCADA systems. Zambia is also lagging in cybersecurity awareness and adoption, therefore there is a concern about securing ICS controlling key infrastructure critical to the Zambian economy as there are few known facts about the true posture. In this paper, we introduce a stochastic Edged-based Anomaly Detection for SCADA systems (SEADS) framework for threat modeling and risk assessment. SEADS enables the calculation of steady-steady probabilities that are further applied to establish metrics like system availability, maintainability, and reliability.

Keywords: anomaly, availability, detection, edge, maintainability, reliability, stochastic

Procedia PDF Downloads 76

Authors: K. K. Lee, H. W. Han, H. L. Kang, T. A. Kim, S. H. Han

Abstract:

For the robust optimization of the manufacturing product design, there are design objectives that must be achieved, such as a minimization of the mean and standard deviation in objective functions within the required sensitivity constraints. The authors utilized the sensitivity of objective functions and constraints with respect to the effective design variables to reduce the computational burden associated with the evaluation of the probabilities. The individual mean and sensitivity values could be estimated easily by using the 9 level orthogonal array based response surface models optimized by the stepwise regression. The present study evaluates a proposed procedure from the robust optimization of rubber domes that are commonly used for keyboard switching, by using the 9 level orthogonal array and stepwise regression along with a desirability function. In addition, a new robust optimization process, i.e., the I2GEO (Identify, Integrate, Generate, Explore and Optimize), was proposed on the basis of the robust optimization in rubber domes. The optimized results from the response surface models and the estimated results by using the finite element analysis were consistent within a small margin of error. The standard deviation of objective function is decreasing 54.17% with suggested sensitivity based robust optimization. (Business for Cooperative R&D between Industry, Academy, and Research Institute funded Korea Small and Medium Business Administration in 2017, S2455569)

Keywords: objective function, orthogonal array, response surface model, robust optimization, stepwise regression

Procedia PDF Downloads 261

Authors: Rudzani Lusunzi, Frans Waanders, Elvis Fosso-Kankeu, Robert Khashane Netshitungulwana

Abstract:

The Sabie Goldfield has a history of gold mining dating back more than a century. Acid mine drainage (AMD) from the Nestor mine tailings storage facility (MTSF) poses a serious threat to the nearby ecosystem, specifically the Sabie River system. This study aims at developing mitigation measures for the AMD emanating from the Nestor MTSF using materials from the Glynns Lydenburg MTSF. The Nestor MTSF (NM) and the Glynns Lydenburg MTSF (GM) each provided about 20 kg of bulk composite samples. Using samples from the Nestor MTSF and the Glynns Lydenburg MTSF, two mixtures were created. MIX-A is a mixture that contains 25% weight percent (GM) and 75% weight percent (NM). MIX-B is the name given to the second mixture, which contains 50% AN and 50% AG. The same static test, i.e., acid–base accounting (ABA), net acid generation (NAG), and acid buffering characteristics curve (ABCC) was used to estimate the acid-generating probabilities of samples NM and GM for MIX-A and MIX-B. Furthermore, the mineralogy of the Nestor MTSF samples consists of the primary acid-producing mineral pyrite as well as the secondary minerals ferricopiapite and jarosite, which are common in acidic conditions. The Glynns Lydenburg MTSF samples, on the other hand, contain primary acid-neutralizing minerals calcite and dolomite. Based on the assessment conducted, materials from the Glynns Lydenburg are capable of neutralizing AMD from Nestor MTSF. Therefore, the alkaline tailings materials from the Glynns Lydenburg MTSF can be used to rehabilitate the acidic Nestor MTSF.

Keywords: Nestor Mine, acid mine drainage, mitigation, Sabie River system

Procedia PDF Downloads 53

Authors: Roman Graf, Sergiu Gordea, Heather M. Ryan

Abstract:

This paper presents an approach for the classification of an unstructured format description for identification of file formats. The main contribution of this work is the employment of data mining techniques to support file format selection with just the unstructured text description that comprises the most important format features for a particular organisation. Subsequently, the file format indentification method employs file format classifier and associated configurations to support digital preservation experts with an estimation of required file format. Our goal is to make use of a format specification knowledge base aggregated from a different Web sources in order to select file format for a particular institution. Using the naive Bayes method, the decision support system recommends to an expert, the file format for his institution. The proposed methods facilitate the selection of file format and the quality of a digital preservation process. The presented approach is meant to facilitate decision making for the preservation of digital content in libraries and archives using domain expert knowledge and specifications of file formats. To facilitate decision-making, the aggregated information about the file formats is presented as a file format vocabulary that comprises most common terms that are characteristic for all researched formats. The goal is to suggest a particular file format based on this vocabulary for analysis by an expert. The sample file format calculation and the calculation results including probabilities are presented in the evaluation section.

Keywords: data mining, digital libraries, digital preservation, file format

Procedia PDF Downloads 472

Authors: Todd Zhou, Mikhail Yurochkin

Abstract:

Out-of-distribution (OOD) detection is receiving increasing amounts of attention in the machine learning research community, boosted by recent technologies, such as autonomous driving and image processing. This newly-burgeoning field has called for the need for more effective and efficient methods for out-of-distribution generalization methods. Without accessing the label information, deploying machine learning models to out-of-distribution domains becomes extremely challenging since it is impossible to evaluate model performance on unseen domains. To tackle this out-of-distribution detection difficulty, we designed a model selection pipeline algorithm and developed a model selection framework with different impurity and randomness measurements to evaluate and choose the best-performing models for out-of-distribution data. By exploring different randomness scores based on predicted probabilities, we adopted the out-of-distribution entropy and developed a custom-designed score, ”CombinedScore,” as the evaluation criterion. This proposed score was created by adding labeled source information into the judging space of the uncertainty entropy score using harmonic mean. Furthermore, the prediction bias was explored through the equality of opportunity violation measurement. We also improved machine learning model performance through model calibration. The effectiveness of the framework with the proposed evaluation criteria was validated on the Folktables American Community Survey (ACS) datasets.

Keywords: model selection, domain generalization, model fairness, randomness measurements, bias index

Procedia PDF Downloads 101

Authors: Belén Vinaixa Kinnear, Arturo Hidalgo López, Bernardo Elembo Wilasi, Pablo Fernández Pérez, Cecilia Hernández Fuentealba

Abstract:

The expanded use of risk assessment in legislative and corporate decision-making has increased the role of expert judgement in giving data for security-related decision-making. Expert judgements are required in most steps of risk assessment: danger recognizable proof, hazard estimation, risk evaluation, and examination of choices. This paper presents a fault tree analysis (FTA), which implies a probabilistic failure analysis applied to leakage of oil in a subsea production system. In standard FTA, the failure probabilities of items of a framework are treated as exact values while evaluating the failure probability of the top event. There is continuously insufficiency of data for calculating the failure estimation of components within the drilling industry. Therefore, fuzzy hypothesis can be used as a solution to solve the issue. The aim of this paper is to examine the leaks from the Zafiro West subsea oil facility by using fuzzy fault tree analysis (FFTA). As a result, the research has given theoretical and practical contributions to maritime safety and environmental protection. It has been also an effective strategy used traditionally in identifying hazards in nuclear installations and power industries.

Keywords: expert judgment, probability assessment, fault tree analysis, risk analysis, oil pipelines, subsea production system, drilling, quantitative risk analysis, leakage failure, top event, off-shore industry

Procedia PDF Downloads 160

Authors: Mohsen Farhadloo, Majid Farhadloo

Abstract:

Aspect-level opinion mining that aims at discovering aspects (aspect identification) and their corresponding ratings (sentiment identification) from customer reviews have increasingly attracted attention of researchers and practitioners as it provides valuable insights about products/services from customer's points of view. Instead of addressing aspect identification and sentiment identification in two separate steps, it is possible to simultaneously identify both aspects and sentiments. In recent years many graphical models based on Latent Dirichlet Allocation (LDA) have been proposed to solve both aspect and sentiment identifications in a single step. Although LDA models have been effective tools for the statistical analysis of document collections, they also have shortcomings in addressing some unique characteristics of opinion mining. Our goal in this paper is to address one of the limitations of topic models to date; that is, they fail to directly model the associations among topics. Indeed in many text corpora, it is natural to expect that subsets of the latent topics have higher probabilities. We propose a probabilistic graphical model called focus-LDA, to better capture the associations among topics when applied to aspect-level opinion mining. Our experiments on real-life data sets demonstrate the improved effectiveness of the focus-LDA model in terms of the accuracy of the predictive distributions over held out documents. Furthermore, we demonstrate qualitatively that the focus-LDA topic model provides a natural way of visualizing and exploring unstructured collection of textual data.

Keywords: aspect-level opinion mining, document modeling, Latent Dirichlet Allocation, LDA, sentiment analysis

Procedia PDF Downloads 77

Authors: Yen-Luan Chen

Abstract:

Maintenance has a great impact on the capacity of production and on the quality of the products, and therefore, it deserves continuous improvement. Maintenance procedure done before a failure is called preventive maintenance (PM). Sequential PM, which specifies that a system should be maintained at a sequence of intervals with unequal lengths, is one of the commonly used PM policies. This article proposes a generalized sequential PM policy for a system subject to shocks with imperfect maintenance and random working time. The shocks arrive according to a non-homogeneous Poisson process (NHPP) with varied intensity function in each maintenance interval. As a shock occurs, the system suffers two types of failures with number-dependent probabilities: type-I (minor) failure, which is rectified by a minimal repair, and type-II (catastrophic) failure, which is removed by a corrective maintenance (CM). The imperfect maintenance is carried out to improve the system failure characteristic due to the altered shock process. The sequential preventive maintenance-last (PML) policy is defined as that the system is maintained before any CM occurs at a planned time Ti or at the completion of a working time in the i-th maintenance interval, whichever occurs last. At the N-th maintenance, the system is replaced rather than maintained. This article first takes up the sequential PML policy with random working time and imperfect maintenance in reliability engineering. The optimal preventive maintenance schedule that minimizes the mean cost rate of a replacement cycle is derived analytically and determined in terms of its existence and uniqueness. The proposed models provide a general framework for analyzing the maintenance policies in reliability theory.

Keywords: optimization, preventive maintenance, random working time, minimal repair, replacement, reliability

Procedia PDF Downloads 243

Authors: Galina N. Parshina, Olga N. Shemshura, Ulzhan S. Mukiyanova, Gulnur M. Beisetbayeva

Abstract:

From a medical point of view some species from the family Lamiaceae Lindl. attract the attention of scientists. Many plant species from this family are used in science and medicine. Some researchers believe that the medicinal properties of these plants are caused by the action on the organism of the individual components (camphor, menthol, thymol, eugenol, phenols, flavonoids, alcohols, and their derivatives) or the entire complex of essential oils. Biologically active substances (BAS), isolated from these medicinal plants can be an effective supplement in the complex treatment of infectious diseases. The substances of the phenolic group such as flavonoids and phenolic acids; and also alkaloids included in the component composition of the plants from the family Lamiaceae Lindl. present the scientific and practical interest for future investigations of their biological activity and development of medicinal products. The research objects are the species from the family Lamiaceae Lindl., cultivated in the North-Kazakhstan region. In this abstract, we present the results of the investigation of polyphenolic complex (flavonoids and phenolic acids) and alkaloids in aqueous and ethanol extracts. Investigation of the qualitative composition of flavonoids in the aqueous extracts showed that the species Monarda Diana contains flavone, Dracocephalum moldavica contains rutin, Ocimum basilicum (purple form) contains both ruin and quercetin. Biochemical analysis revealed that the ethanol extract of Monarda Diana has phenolic acids, similar to protocatechuic and benzoic acids by their chromatographic characteristics. But the aqueous extract contains four phenolic acids, one of which is an analogue of gentisic acid; and the other three are not identified yet. The phenolic acids such as benzoic and gentisic acids identified in ethanol extracts of species Ocimum basilicum (purple form) and Satureja hortensis, correspondingly. But the same phenolic acids did not appear in aqueous extracts. The phenolic acids were not determined neither in the ethanol or aqueous extracts of species Dracocephalum moldavica. The biochemical analysis did not reveal the content of alkaloids in aqueous extracts of investigated plants. However, the alkaloids in the amount of 5-13 components were identified in the ethanolic extracts of plants by the qualitative reactions. The results of analysis with reagent of Dragendorff showed that next amounts of alkaloids were identified in extracts of Monarda Diana (6-7), Satureja hortensis (6), Ocimum basilicum (7-9) and Dracocephalum moldavica (5-6). The reactions with reagent of Van-Urca showed that next amounts of alkaloids were identified in extracts of Monarda Diana (9-12), Satureja hortensis (9-10), two alkaloids of them with Rf6=0,39 and Rf6=0,31 similar to roquefortine), Ocimum basilicum (11) and Dracocephalum moldavica (13, two of them with Rf5=0,34 and Rf5=0,33 by their chromatographic characteristics similar to epikostaklavin).

Keywords: biologically active substances, Lamiaceae, component composition, medicinal plant

Procedia PDF Downloads 474

Authors: Chin-Chih Chang

Abstract:

Many manufacturing systems suffer failures due to complex degradation processes and various environment conditions such as random shocks. Consider an operating system is subject to random shocks and works at random times for successive jobs. When successive jobs often result in production losses and performance deterioration, it would be better to do maintenance or replacement at a planned time. A preventive replacement (PR) policy is presented to replace the system before a failure occurs at a continuous time T. In such a policy, the failure characteristics of the system are designed as follows. Each job would cause a random amount of additive damage to the system, and the system fails when the cumulative damage has exceeded a failure threshold. Suppose that the deteriorating system suffers one of the two types of shocks with age-dependent probabilities: type-I (minor) shock is rectified by a minimal repair, or type-II (catastrophic) shock causes the system to fail. A corrective replacement (CR) is performed immediately when the system fails. In summary, a generalized maintenance model to scheduling replacement plan for an operating system is presented below. PR is carried out at time T, whereas CR is carried out when any type-II shock occurs and the total damage exceeded a failure level. The main objective is to determine the optimal continuous schedule time of preventive replacement through minimizing the mean cost rate function. The existence and uniqueness of optimal replacement policy are derived analytically. It can be seen that the present model is a generalization of the previous models, and the policy with preventive replacement outperforms the one without preventive replacement.

Keywords: preventive replacement, working time, cumulative damage model, minimal repair, imperfect maintenance, optimization

Procedia PDF Downloads 332

Authors: Sudhir K. Gupta

Abstract:

Introduction: When a forensic doctor determines that a suspicious death is a suicide, homicide, or accident, the decision virtually becomes incontestable by the investigating police officer, and it becomes an issue whether the medical opinion was created with necessary checks and balances on the other probabilities of the case. It is suggested that the opinion of Forensic Medical experts is conventional, mutable, and shifting from one expert to another. The determination of suicide, accident, or homicide is mandatorily required, which is the Gold Standard for conducting death investigations. Forensic investigations serve many audiences, but the court is by far the most critical. The likely questions on direct and cross-examination determine how forensic doctors gather and handle evidence and what conclusions they reach. Methodology: The author interacted with the investigative authority, and a crime scene visit was also done along with the perusal of the Postmortem report, subsequent opinion, and crime scene photographs and statements of the witness and accused. Further analysis of all relevant scientific documents and opinions of other forensic doctors, forensic scientists, and ballistic experts involved in these cases was done to arrive at an opinion with scientific justification. Findings: The opinions arrived at by the author and how they helped the judiciary in delivering justice in these cases have been discussed in this article. This can help the readers to understand the process involved in formulating a credible forensic medical expert opinion for investigators and the judiciary. Conclusion: A criminal case might be won or lost over doubt cast on the chain of custody. Medically trained forensic doctors, therefore, learn to practice their profession in legally appropriate ways, and opinions must be based on medical justifications with credible references.

Keywords: forensic doctor, professional credibility, investigation, expert opinion

Procedia PDF Downloads 46

Authors: Lavanya Annadi, Fahimeh Jafari

Abstract:

Transportation brings immense benefits to society, but it also has its costs. Costs include such as the cost of infrastructure, personnel and equipment, but also the loss of life and property in traffic accidents on the road, delays in travel due to traffic congestion and various indirect costs in terms of air transport. More research has been done to identify the various factors that affect road accidents, such as road infrastructure, traffic, sociodemographic characteristics, land use, and the environment. The aim of this research is to predict the probabilistic crash prediction of vehicles using machine learning due to natural and structural reasons by excluding spontaneous reasons like overspeeding etc., in the United States. These factors range from weather factors, like weather conditions, precipitation, visibility, wind speed, wind direction, temperature, pressure, and humidity to human made structures like road structure factors like bump, roundabout, no exit, turning loop, give away, etc. Probabilities are dissected into ten different classes. All the predictions are based on multiclass classification techniques, which are supervised learning. This study considers all crashes that happened in all states collected by the US government. To calculate the probability, multinomial expected value was used and assigned a classification label as the crash probability. We applied three different classification models, including multiclass Logistic Regression, Random Forest and XGBoost. The numerical results show that XGBoost achieved a 75.2% accuracy rate which indicates the part that is being played by natural and structural reasons for the crash. The paper has provided in-deep insights through exploratory data analysis.

Keywords: road safety, crash prediction, exploratory analysis, machine learning

Procedia PDF Downloads 79

Authors: Joanna Maj

Abstract:

Body machine interfaces (BMIs) afford users a non-invasive way coordinate movement. Vibrotactile stimulation has been incorporated into BMIs to allow feedback in real-time and guide movement control to benefit patients with cognitive deficits, such as stroke survivors. To advance research in this area, we examined vibrational discrimination thresholds at four body locations to determine suitable application sites for future multi-channel BMIs using vibration cues to guide movement planning and control. Twelve healthy adults had a pair of small vibrators (tactors) affixed to the skin at each location: forearm, shoulders, torso, and knee. A "standard" stimulus (186 Hz; 750 ms) and "probe" stimuli (11 levels ranging from 100 Hz to 235 Hz; 750 ms) were delivered. Probe and test stimulus pairs could occur sequentially or simultaneously (timing). Participants verbally indicated which stimulus felt more intense. Stimulus order was counterbalanced across tactors and body locations. Probabilities that probe stimuli felt more intense than the standard stimulus were computed and fit with a cumulative Gaussian function; the discrimination threshold was defined as one standard deviation of the underlying distribution. Threshold magnitudes depended on stimulus timing and location. Discrimination thresholds were better for stimuli applied sequentially vs. simultaneously at the torso as well as the knee. Thresholds were small (better) and relatively insensitive to timing differences for vibrations applied at the shoulder. BMI applications requiring multiple channels of simultaneous vibrotactile stimulation should therefore consider the shoulder as a deployment site for a vibrotactile BMI interface.

Keywords: electromyography, electromyogram, neuromuscular disorders, biomedical instrumentation, controls engineering

Procedia PDF Downloads 37

Authors: O. V. Vyazovska

Abstract:

Behavioral efficiency is one of the main principles to be successful in nature. Accuracy of visual discrimination is determined by the attention, learning experience, and memory. In the experimental condition, pigeons’ responses to visual stimuli presented on the screen of the monitor are behaviorally manifested by pecking or not pecking the stimulus, by the number of pecking, reaction time, etc. The higher the probability of rewarding is, the more likely pigeons will respond to the stimulus. We trained 8 pigeons (Columba livia) on a stagewise go/no-go visual discrimination task.16 visual stimuli were created from all possible combinations of four binary dimensions: brightness (dark/bright), size (large/small), line orientation (vertical/horizontal), and shape (circle/square). In the first stage, we presented S+ and 4 S-stimuli: the first that differed in all 4-dimensional values from S+, the second with brightness dimension sharing with S+, the third sharing brightness and orientation with S+, the fourth sharing brightness, orientation and size. Then all 16 stimuli were added. Pigeons rejected correctly 6-8 of 11 new added S-stimuli at the beginning of the second stage. The results revealed that pigeons’ behavior at the beginning of the second stage was controlled by probabilities of rewarding for 4 dimensions learned in the first stage. More or fewer mistakes with dimension discrimination at the beginning of the second stage depended on the number S- stimuli sharing the dimension with S+ in the first stage. A significant inverse correlation between the number of S- stimuli sharing dimension values with S+ in the first stage and the dimensional learning rate at the beginning of the second stage was found. Pigeons were more confident in discrimination of shape and size dimensions. They made mistakes at the beginning of the second stage, which were not associated with these dimensions. Thus, the received results help elucidate the principles of dimensional stimulus control during learning compound multidimensional visual stimuli.

Keywords: visual go/no go discrimination, selective attention, dimensional stimulus control, pigeon

Procedia PDF Downloads 110

Authors: Sally Heyeon Hwang

Abstract:

Within decision theory, there are normative principles that dictate how one should act in addition to empirical theories of actual behavior. As a normative guide to one’s actual behavior, evidential or causal decision-theoretic equations allow one to identify outcomes with maximal utility values. The choice that each person makes, however, will, of course, differ according to varying assignments of weight and probability values. Regarding these different choices, it remains a subject of considerable philosophical controversy whether individual subjects have the capacity to exercise free will with respect to the assignment of probabilities, or whether instead the assignment is in some way constrained. A version of this question is given a precise form in Richard Jeffrey’s assumption that free will is necessary for Newcomb’s paradox to count as a decision problem. This paper will argue, against Jeffrey, that decision theory does not require the assumption of libertarian freedom. One of the hallmarks of decision-making is its application across a wide variety of contexts; the implications of a background assumption of free will is similarly varied. One constant across the contexts of decision is that there are always at least two levels of choice for a given agent, depending on the degree of prior constraint. Within the context of Newcomb’s problem, when the predictor is attempting to guess the choice the agent will make, he or she is analyzing the determined aspects of the agent such as past characteristics, experiences, and knowledge. On the other hand, as David Lewis’ backtracking argument concerning the relationship between past and present events brings to light, there are similarly varied ways in which the past can actually be dependent on the present. One implication of this argument is that even in deterministic settings, an agent can have more free will than it may seem. This paper will thus argue against the view that a stable background assumption of free will or determinism in decision theory is necessary, arguing instead for a compatibilist decision theory yielding a novel treatment of Newcomb’s problem.

Keywords: decision theory, compatibilism, free will, Newcomb’s problem

Procedia PDF Downloads 297

Authors: Pius Marthin, Nihal Ata Tutkun

Abstract:

Survival analysis has become one of the paramount procedures in the modeling of time-to-event data. When we encounter complex survival problems, the traditional approach remains limited in accounting for the complex correlational structure between the covariates and the outcome due to the strong assumptions that limit the inference and prediction ability of the resulting models. Several studies exist on the deep learning approach to survival modeling; moreover, the application for the case of complex survival problems still needs to be improved. In addition, the existing models need to address the data structure's complexity fully and are subject to noise and redundant information. In this study, we design a deep learning technique (CmpXRnnSurv_AE) that obliterates the limitations imposed by traditional approaches and addresses the above issues to jointly predict the risk-specific probabilities and survival function for recurrent events with competing risks. We introduce the component termed Risks Information Weights (RIW) as an attention mechanism to compute the weighted cumulative incidence function (WCIF) and an external auto-encoder (ExternalAE) as a feature selector to extract complex characteristics among the set of covariates responsible for the cause-specific events. We train our model using synthetic and real data sets and employ the appropriate metrics for complex survival models for evaluation. As benchmarks, we selected both traditional and machine learning models and our model demonstrates better performance across all datasets.

Keywords: cumulative incidence function (CIF), risk information weight (RIW), autoencoders (AE), survival analysis, recurrent events with competing risks, recurrent neural networks (RNN), long short-term memory (LSTM), self-attention, multilayers perceptrons (MLPs)

Procedia PDF Downloads 56

Authors: Taleb L., Benarbia M., Boutira F. M., Allam H., Boukerche A.

Abstract:

Introduction and purpose of the study: Meningiomas are the most common non-glial intracranial tumors in adults, accounting for approximately 30% of all central nervous system tumors. The aim of our study is to determine the epidemiological, clinical, therapeutic, and evolutionary characteristics of a cohort of patients with intracranial meningioma treated with radiotherapy at the Emir Abdelkader Cancer Center in Oran. Material and methods: This is a retrospective study of 44 patients during the period from 2014 to 2020. The overall survival and relapse-free survival curves were calculated using the Kaplan-Meier method. Results and statistical analysis: The median age of the patients was 49 years [21-76 years] with a clear female predominance (sex ratio=2.4). The average diagnostic delay was seven months [2 to 24 months], the circumstances of the discovery of which were dominated by headaches in 54.5% of cases (n=24), visual disturbances in 40.9% (n=18), and motor disorders in 15.9% (n=7). The seat of the tumor was essentially at the level of the base of the skull in 52.3% of patients (n=23), including 29.5% (n=13) at the level of the cavernous sinus, 27.3% (n=12) at the parasagittal level and 20.5% (n=9) at the convexity. The diagnosis was confirmed surgically in 36 patients (81.8%) whose anatomopathological study returned in favor of grades I, II, and III in respectively 40.9%, 29.5%, and 11.4% of the cases. Radiotherapy was indicated postoperatively in 45.5% of patients (n=20), exclusive in 27.3% (n=12) and after tumor recurrence in 27.3% of cases (n=18). The irradiation doses delivered were as follows: 50 Gy (20.5%), 54 Gy (65.9%), and 60 Gy (13.6%). With a median follow-up of 69 months, the probabilities of relapse-free survival and overall survival at three years are 93.2% and 95.4%, respectively, whereas they are 71.2% and 80.7% at five years. Conclusion: Meningiomas are common primary brain tumors. Most often benign but can also progress aggressively. Their treatment is essentially surgical, but radiotherapy retains its place in specific situations, allowing good tumor control and overall survival.

Keywords: diagnosis, meningioma, surgery, radiotherapy, survival

Procedia PDF Downloads 66

Authors: G. Stamatopoulou, M. Symeonaki, C. Michalopoulou

Abstract:

In this work, we study the intergenerational social mobility in Greece, in order to provide up-to-date evidence on the changes in the mobility patterns throughout the years. An analysis for both men and women aged between 25-64 years old is carried out. Three main research objectives are addressed. First, we aim to examine the relationship between the socio-economic status of parents and their children. Secondly, we investigate the evolution of the mobility patterns between different birth cohorts. Finally, the role of education is explored in shaping the mobility patterns. For the analysis, we draw data on both parental and individuals' social outcomes from different national databases. The social class of origins and destination is measured according to the European Socio-Economic Classification (ESeC), while the respondents' educational attainment is coded into categories based on the International Standard Classification of Education (ISCED). Applying the Markov transition probability theory, and a range of measures and models, this work focuses on the magnitude and the direction of the movements that take place in the Greek labour market, as well as the level of social fluidity. Three-way mobility tables are presented, where the transition probabilities between the classes of destination and origins are calculated for different cohorts. Additionally, a range of absolute and relative mobility rates, as well as distance measures, are presented. The study covers a large time span beginning in 1940 until 1995, shedding light on the effects of the national institutional processes on the social movements of individuals. Given the evidence on the mobility patterns of the most recent birth cohorts, we also investigate the possible effects of the 2008 economic crisis.

Keywords: cohort analysis, education, Greece, intergenerational mobility, social class

Procedia PDF Downloads 84

Authors: Ana Nemsadze

Abstract:

Contemporary era and society are called postindustrial era and postindustrial society and/or informational era and informational society. Today science intends to define concept of information and comprehend informations role and function in contemporary society. Organization of social environment and governance of public processes on the base of information and tools of communication are main characteristics of informational era. This was defined by technological changes which were accomplished in culture in the second half of twentieth century. Today Georgia as an independent state needs to create an informational discourse of the country and therefore it is very important to study political and social cases which accomplished in the country after collapse of the Soviet Union because they start to define the present and the future of the country. The purpose of this study is to analyze political cases of the latest history of Georgia in terms of culture and information, concretely to elucidate which political cases transformed social life of post Soviet Georgia most of all who accomplished these political cases which visual and verbal messages was each political case spread with. The research is conducted on the base of interview. Participants of the interview are people of various specializations. Their professional activity is related to reflections on culture and theme of visual communication. They are philosophers sociologists a journalist media researcher a politologist a painter. The participants of the interview enumerated political cases and characterized them separately. Every expert thinks that declaration of independence of Georgia is the most important fact among all facts which were implemented in Georgia after collapse of the Soviet Union. The research revealed important social and political cases. Most of the cases are related to independence and territorial integrity of the state. Presidents of Georgia Zviad Gamsakhurdia Eduard Shevardnadze Mikheil Saakashvili Catholocos-Patriarch of All Georgia, the Archbishop of Mtskheta Tbilisi and Metropolitan bishop of Bichvinta and Tskhum Abkhazia Ilia II, businessman Bidzina Ivanishvili assumed dominating roles in cases. Verbal narrative of the cases accomplished during Zviad Gamsakhurdia presidential term expresses national freedom and visual part of cases of the same period expresses ruin of social-political structure. Verbal narrative of the cases accomplished during Eduard Sevardnadze presidential term expresses Free State and stability and reestablishment of Georgias political function in international relations and visual part of cases of the same period describes the most important moment of his presidential term and Eduard Shevardnadzes face appears too. Verbal narrative of the cases accomplished during Mikheil Saakashvilis presidential term expresses social renewal and visual part of cases of the same period describes August war and Mikheil Saakashvilis face appears too. The results of the study also reveal other details of visual verbal narrative of political and social cases of post Soviet Georgia. This gives a chance to start further reflection.

Keywords: culture, narrative, post soviet, visual communication

Procedia PDF Downloads 280

Authors: Titanilla Komenda

Abstract:

Human-machine-collaboration defines a direct interaction between humans and machines to fulfil specific tasks. Those so-called collaborative machines are used without fencing and interact with humans in predefined workspaces. Even though, human-machine-collaboration enables a flexible adaption to variable degrees of freedom, industrial applications are rarely found. The reasons for this are not technical progress but rather limitations in planning processes ensuring safety for operators. Until now, humans and machines were mainly considered separately in the planning process, focusing on ergonomics and system performance respectively. Within human-machine-collaboration, those aspects must not be seen in isolation from each other but rather need to be analysed in interaction. Furthermore, a simulation model is needed that can validate the system performance and ensure the safety for the operator at any given time. Following on from this, a holistic simulation model is presented, enabling a simulative representation of collaborative tasks – including both, humans and machines. The presented model does not only include a geometry and a motion model of interacting humans and machines but also a numerical behaviour model of humans as well as a Boole’s probabilistic sensor model. With this, error scenarios can be simulated by validating system behaviour in unplanned situations. As these models can be defined on the basis of Failure Mode and Effects Analysis as well as probabilities of errors, the implementation in a collaborative model is discussed and evaluated regarding limitations and simulation times. The functionality of the model is shown on industrial applications by comparing simulation results with video data. The analysis shows the impact of considering human factors in the planning process in contrast to only meeting system performance. In this sense, an optimisation function is presented that meets the trade-off between human and machine factors and aids in a successful and safe realisation of collaborative scenarios.

Keywords: human-machine-system, human-robot-collaboration, safety, simulation

Procedia PDF Downloads 336