Search results for: probabilistic tractography

Authors: Vish Putcha, Chandrasekhar Putcha, Siva Hari

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In the current digital era of free trading and pandemic-driven remote work culture, markets worldwide gained momentum for retail investors to trade from anywhere easily. The number of retail traders rose to 24% of the market from 15% at the pre-pandemic level. Most of them are young retail traders with high-risk tolerance compared to the previous generation of retail traders. This trend boosted the growth of subscription-based market predictors and market data vendors. Young traders are betting on these predictors, assuming one of them is correct. However, 90% of retail traders are on the losing end. This paper presents multiple indicators and attempts to derive behavioral patterns from the underlying stocks. The two major indicators that traders and investors follow are technical and fundamental. The famous investor, Warren Buffett, adheres to the “Value Investing” method that is based on a stock’s fundamental Analysis. In this paper, we present multiple indicators from various methods to understand the behavior patterns of stocks. For this research, we picked five stocks with a market capitalization of more than $200M, listed on the exchange for more than 20 years, and from different industry sectors. To study the behavioral pattern over time for these five stocks, a total of 8 indicators are chosen from fundamental, technical, and financial indicators, such as Price to Earning (P/E), Price to Book Value (P/B), Debt to Equity (D/E), Beta, Volatility, Relative Strength Index (RSI), Moving Averages and Dividend yields, followed by detailed mathematical Analysis. This is an interdisciplinary paper between various disciplines of Engineering, Accounting, and Finance. The research takes a new approach to identify clear indicators affecting stocks. Statistical Analysis of the data will be performed in terms of the probabilistic distribution, then follow and then determine the probability of the stock price going over a specific target value. The Chi-square test will be used to determine the validity of the assumed distribution. Preliminary results indicate that this approach is working well. When the complete results are presented in the final paper, they will be beneficial to the community.

Keywords: stock pattern, stock market analysis, stock predictions, trading, investing, fundamental analysis, technical analysis, quantitative trading, financial analysis, behavioral analysis

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Authors: Peter J. Robinson, W. J. Wouter Botzen

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Individuals often over-weight low probabilities and under-weight moderate to high probabilities, however very low probabilities are either significantly over-weighted or neglected. Little is known about factors affecting probability weighting in Prospect Theory related to emotions specific to risk (anticipatory and anticipated emotions), the threshold of concern, as well as personality traits like locus of control. This study provides these insights by examining factors that influence probability weighting in the context of flood insurance demand in an economic experiment. In particular, we focus on determinants of flood probability neglect to provide recommendations for improved risk management. In addition, results obtained using real incentives and no performance-based payments are compared in the experiment with high experimental outcomes. Based on data collected from 1’041 Dutch homeowners, we find that: flood probability neglect is related to anticipated regret, worry and the threshold of concern. Moreover, locus of control and regret affect probabilistic pessimism. Nevertheless, we do not observe strong evidence that incentives influence flood probability neglect nor probability weighting. The results show that low, moderate and high flood probabilities are under-weighted, which is related to framing in the flooding context and the degree of realism respondents attach to high probability property damages. We suggest several policies to overcome psychological factors related to under-weighting flood probabilities to improve flood preparations. These include policies that promote better risk communication to enhance insurance decisions for individuals with a high threshold of concern, and education and information provision to change the behaviour of internal locus of control types as well as people who see insurance as an investment. Multi-year flood insurance may also prevent short-sighted behaviour of people who have a tendency to regret paying for insurance. Moreover, bundling low-probability/high-impact risks with more immediate risks may achieve an overall covered risk which is less likely to be judged as falling below thresholds of concern. These measures could aid the development of a flood insurance market in the Netherlands for which we find to be demand.

Keywords: flood insurance demand, prospect theory, risk perceptions, risk preferences

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Authors: Faizul Azly Abd Dzubir, Muhammad F. Othman

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Conventional structural health monitoring approach for mechanical equipment uses inspection data from Non-Destructive Testing (NDT) during plant shut down window and fitness for service evaluation to estimate the integrity of the equipment that is prone to crack damage. Yet, this forecast is fraught with uncertainty because it is often based on assumptions of future operational parameters, and the prediction is not continuous or online. Advanced Diagnostic and Prognostic Technology (ADaPT) uses Acoustic Emission (AE) technology and a stochastic prognostic model to provide real-time monitoring and prediction of mechanical defects or cracks. The forecast can help the plant authority handle their cracked equipment before it ruptures, causing an unscheduled shutdown of the facility. The ADaPT employs process historical data trending, finite element analysis, fitness for service, and probabilistic statistical analysis to develop a prediction model for crack initiation and growth due to mechanical damage. The prediction model is combined with live equipment operating data for real-time prediction of the remaining life span owing to fracture. ADaPT was devised at a hot combined feed exchanger (HCFE) that had suffered creep crack damage. The ADaPT tool predicts the initiation of a crack at the top weldment area by April 2019. During the shutdown window in April 2019, a crack was discovered and repaired. Furthermore, ADaPT successfully advised the plant owner to run at full capacity and improve output by up to 7% by April 2019. ADaPT was also used on a coke drum that had extensive fatigue cracking. The initial cracks are declared safe with ADaPT, with remaining crack lifetimes extended another five (5) months, just in time for another planned facility downtime to execute repair. The prediction model, when combined with plant information data, allows plant operators to continuously monitor crack propagation caused by mechanical damage for improved maintenance planning and to avoid costly shutdowns to repair immediately.

Keywords: mechanical damage, cracks, continuous monitoring tool, remaining life, acoustic emission, prognostic model

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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: Natalia Uribe Corrales, María Fernanda Benavides Erazo, Santiago Henao Villegas

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Introduction: Transport is a crucial link in the porcine chain because it is considered a stressful event in the animal, due to it is a new environment, which generates new interactions, together with factors such as speed, noise, temperature changes, vibrations, deprivation of food and water. Therefore, inadequate handling at this stage can lead to bruises, musculoskeletal injuries, fatigue, and mortality, resulting in canal seizures and economic losses. Objective: To characterize the transport and driving practices for the mobilization of standing pigs directed to slaughter plants in the Valle de Aburrá, Antioquia, Colombia in 2017. Methods: A descriptive cross-sectional study was carried out with the transporters arriving at the slaughterhouses approved by National Institute for Food and Medicine Surveillance (INVIMA) during 2017 in the Valle de Aburrá. The process of obtaining the samples was made from probabilistic sampling. Variables such as journey time, mechanical technical certificate, training in animal welfare, driving speed, material, and condition of floors and separators, supervision of animals during the trip, load density and mortality were analyzed. It was approved by the ethics committee for the use and care of animals CICUA of CES University, Act number 14 of 2015. Results: 190 trucks were analyzed, finding that 12.4% did not have updated mechanical technical certificate; the transporters experience in pig’s transportation was an average of 9.4 years (d.e.7.5). The 85.8% reported not having received training in animal welfare. Other results were that the average speed was 63.04km/hr (d.e 13.46) and the 62% had floors in good condition; nevertheless, the 48% had bad conditions on separators. On the other hand, the 88% did not supervise their animals during the journey, although the 62.2% had an adequate loading density, in relation to the average mortality was 0.2 deaths/travel (d.e. 0.5). Conclusions: Trainers should be encouraged on issues such as proper maintenance of vehicles, animal welfare, obligatory review of animals during mobilization and speed of driving, as these poorly managed indicators generate stress in animals, increasing generation of injuries as well as possible accidents; also, it is necessary to continue to improve aspects such as aluminum floors and separators that favor easy cleaning and maintenance, as well as the appropriate handling in the density of load that generates animal welfare.

Keywords: animal welfare, driving practices, pigs, truck infrastructure

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Authors: M. De Iuliis, O. Kammouh, G. P. Cimellaro, S. Tesfamariam

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Community Resilience has gained a significant attention due to the recent unexpected natural and man-made disasters. Resilience is the process of maintaining livable conditions in the event of interruptions in normally available services. Estimating the resilience of systems, ranging from individuals to communities, is a formidable task due to the complexity involved in the process. The most challenging parameter involved in the resilience assessment is the 'downtime'. Downtime is the time needed for a system to recover its services following a disaster event. Estimating the exact downtime of a system requires a lot of inputs and resources that are not always obtainable. The uncertainties in the downtime estimation are usually handled using probabilistic methods, which necessitates acquiring large historical data. The estimation process also involves ignorance, imprecision, vagueness, and subjective judgment. In this paper, a fuzzy-based approach to estimate the downtime of building structures following earthquake events is proposed. Fuzzy logic can integrate descriptive (linguistic) knowledge and numerical data into the fuzzy system. This ability allows the use of walk down surveys, which collect data in a linguistic or a numerical form. The use of fuzzy logic permits a fast and economical estimation of parameters that involve uncertainties. The first step of the method is to determine the building’s vulnerability. A rapid visual screening is designed to acquire information about the analyzed building (e.g. year of construction, structural system, site seismicity, etc.). Then, a fuzzy logic is implemented using a hierarchical scheme to determine the building damageability, which is the main ingredient to estimate the downtime. Generally, the downtime can be divided into three main components: downtime due to the actual damage (DT1); downtime caused by rational and irrational delays (DT2); and downtime due to utilities disruption (DT3). In this work, DT1 is computed by relating the building damageability results obtained from the visual screening to some already-defined components repair times available in the literature. DT2 and DT3 are estimated using the REDITM Guidelines. The Downtime of the building is finally obtained by combining the three components. The proposed method also allows identifying the downtime corresponding to each of the three recovery states: re-occupancy; functional recovery; and full recovery. Future work is aimed at improving the current methodology to pass from the downtime to the resilience of buildings. This will provide a simple tool that can be used by the authorities for decision making.

Keywords: resilience, restoration, downtime, community resilience, fuzzy logic, recovery, damage, built environment

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Authors: Paulina Ojeda, Damaris Estrella, Hector Rubio

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Insomnia is a disorder characterized by difficulty falling asleep, staying asleep or both. It negatively affects the life quality of people, it hinders the concentration, attention, memory, motor skills, among other abilities that complicate work or learning. Some studies show that women are more susceptible to insomnia. Medicine curricula usually involve a great deal of theoretical and memory content, especially in the early years of the course. The way to accredit a university course is to demonstrate the level of competence or acquired knowledge. In Mexico the most widely used form of measurement is written exams, with numerical scales results. The prevalence of sleep disorders in university students is usually high, so it is important to know if insomnia has an effect on school performance in men and women. A cross-sectional study was designed that included a probabilistic sample of 118 regular students from the School of Medicine of the Autonomous University of Yucatan, Mexico. All on legally age. The project was authorized by the School of Medicine and all the ethical implications of the case were monitored. Participants completed anonymously the following questionnaires: Pittsburgh Sleep Quality Index, Insomnia Severity Index, AUDIT test, epidemiological and clinical data. Academic performance was assessed by the average number of official grades earned on written exams, as well as the number of approved or non-approved courses. These data were obtained officially through the corresponding school authorities. Students with at least one unapproved course or average less than 70 were considered to be poor performers. With all courses approved and average between 70-79 as regular performance and with an average of 80 or higher as a good performance. Statistical analysis: t-Student, difference of proportions and ANOVA. 65 men with a mean age of 19.15 ± 1.60 years and 53 women of 18.98 ± 1.23 years, were included. 96% of the women and 78.46% of the men sleep in the family home. 16.98% of women and 18.46% of men consume tobacco. Most students consume caffeinated beverages. 3.7% of the women and 10.76% of the men complete criteria of harmful consumption of alcohol. 98.11% of the women and 90.76% of the men are perceived with poor sleep quality. Insomnia was present in 73% of women and 66% of men. Women had higher levels of moderate insomnia (p=0.02) compared to men and only one woman had severe insomnia. 50.94% of the women and 44.61% of the men had poor academic performance. 18.86% of women and 27% of men performed well. Only in the group of women we found a significant association between poor performance with mild (p= 0.0035) and moderate (p=0.031) insomnia. The medical students reported poor sleep quality and insomnia. In women, levels of insomnia were associated with poor academic performance.

Keywords: scholar-average, sex, sleep, university

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Authors: Erica Siqueira, Adriana Bin, Rachel Stefanuto

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The paper aims to identify and to discuss the impact and results of ex-ante, mid-term and ex-post evaluation initiatives in Brazilian Social Enterprises from the point of view of the entrepreneurs and investors, highlighting the processes involved in these activities and their aftereffects. The study was conducted using a descriptive methodology, primarily qualitative. A multiple-case study was used, and, for that, semi-structured interviews were conducted with ten entrepreneurs in the (i) social finance, (ii) education, (iii) health, (iv) citizenship and (v) green tech fields, as well as three representatives of various impact investments, which are (i) venture capital, (ii) loan and (iii) equity interest areas. Convenience (non-probabilistic) sampling was adopted to select both businesses and investors, who voluntarily contributed to the research. The evaluation is still incipient in most of the studied business cases. Some stand out by adopting well-known methodologies like Global Impact Investing Report System (GIIRS), but still, have a lot to improve in several aspects. Most of these enterprises use nonexperimental research conducted by their own employees, which is ordinarily not understood as 'golden standard' to some authors in the area. Nevertheless, from the entrepreneur point of view, it is possible to identify that most of them including those routines in some extent in their day-by-day activities, despite the difficulty they have of the business in general. In turn, the investors do not have overall directions to establish evaluation initiatives in respective enterprises; they are funding. There is a mechanism of trust, and this is, usually, enough to prove the impact for all stakeholders. The work concludes that there is a large gap between what the literature states in regard to what should be the best practices in these businesses and what the enterprises really do. The evaluation initiatives must be included in some extension in all enterprises in order to confirm social impact that they realize. Here it is recommended the development and adoption of more flexible evaluation mechanisms that consider the complexity involved in these businesses’ routines. The reflections of the research also suggest important implications for the field of Social Enterprises, whose practices are far from what the theory preaches. It highlights the risk of the legitimacy of these enterprises that identify themselves as 'social impact', sometimes without the proper proof based on causality data. Consequently, this makes the field of social entrepreneurship fragile and susceptible to questioning, weakening the ecosystem as a whole. In this way, the top priorities of these enterprises must be handled together with the results and impact measurement activities. Likewise, it is recommended to perform further investigations that consider the trade-offs between impact versus profit. In addition, research about gender, the entrepreneur motivation to call themselves as Social Enterprises, and the possible unintended consequences from these businesses also should be investigated.

Keywords: evaluation practices, impact, results, social enterprise, social entrepreneurship ecosystem

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Authors: Neil Bar, Andrew Heweston

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Probability of failure (PF) often appears alongside factor of safety (FS) in design acceptance criteria for rock slope, underground excavation and open pit mine designs. However, the design acceptance criteria generally provide no guidance relating to how PF should be calculated for homogeneous and heterogeneous rock masses, or what qualifies a ‘reasonable’ PF assessment for a given slope design. Observational and kinematic methods were widely used in the 1990s until advances in computing permitted the routine use of numerical modelling. In the 2000s and early 2010s, PF in numerical models was generally calculated using the point estimate method. More recently, some limit equilibrium analysis software offer statistical parameter inputs along with Monte-Carlo or Latin-Hypercube sampling methods to automatically calculate PF. Factors including rock type and density, weathering and alteration, intact rock strength, rock mass quality and shear strength, the location and orientation of geologic structure, shear strength of geologic structure and groundwater pore pressure influence the stability of rock slopes. Significant engineering and geological judgment, interpretation and data interpolation is usually applied in determining these factors and amalgamating them into a geotechnical model which can then be analysed. Most factors are estimated ‘approximately’ or with allowances for some variability rather than ‘exactly’. When it comes to numerical modelling, some of these factors are then treated deterministically (i.e. as exact values), while others have probabilistic inputs based on the user’s discretion and understanding of the problem being analysed. This paper discusses the importance of understanding the key aspects of slope design for homogeneous and heterogeneous rock masses and how they can be translated into reasonable PF assessments where the data permits. A case study from a large open pit gold mine in a complex geological setting in Western Australia is presented to illustrate how PF can be calculated using different methods and obtain markedly different results. Ultimately sound engineering judgement and logic is often required to decipher the true meaning and significance (if any) of some PF results.

Keywords: probability of failure, point estimate method, Monte-Carlo simulations, sensitivity analysis, slope stability

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Authors: Cledinaldo A. Dias, Isabela C. Santos, Marcus V. S. Siqueira

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In the contemporary globalization, the competitiveness generated in the search of new markets aiming at the growth of productivity and, consequently, of profits, implies the redefinition of productive processes and new forms of work organization. As a result of this structuring, unemployment, labor force turnover and the increase in outsourcing and informal work occur. Considering the different relationships and working conditions of outsourced employees, this study aims to identify the most present stressors among outsourced service providers from a Federal Institution of Higher Education in Brazil. To reach this objective, a descriptive exploratory study with a quantitative approach was carried out. The qualitative approach was chosen to provide an in-depth analysis of the occupational conditions of outsourced workers since this method seeks to focus on the social as a world of investigated meanings and the language or speech of each subject as the object of this approach. The survey was conducted in the city of Montes Claros - Minas Gerais (Brazil) and involved eighty workers from companies hired by the institution, including armed security guards, porters, cleaners, drivers, gardeners, and administrative assistants. The choice of professionals obeyed non-probabilistic criteria for convenience or accessibility. Data collection was performed by means of a structured questionnaire composed of sixty questions, in a Likert-type frequency interval scale format, in order to identify potential organizational stressors. The results obtained evidence that the stress factors pointed out by the workers are, in most cases, a determining factor due to the low productive performance at work. Amongst the factors associated with stress, the ones that stood out most were those related to organizational communication failures, the incentive to competition, lack of expectations of professional growth, insecurity and job instability. Based on the results, the need for greater concern and organizational responsibility with the well-being and mental health of the outsourced worker and the recognition of their physical and psychological limitations, and care that goes beyond the functional capacity for the work. Specifically for the preservation of mental health, physical and quality of life, it is concluded that it is necessary for the professional to be inserted in the external world that favors it internally since this set is complemented so that the individual remains in balance and obtain satisfaction in your work.

Keywords: occupational health, outsourced, organizational studies, stressors

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Authors: Adeola Adenikinju, Olusanya E. Olubusoye, Lateef O. Akinpelu, Dilinna L. Nwobi

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Since the enactment of the Fiscal Responsibility Act (2007), the Federal Government of Nigeria (FGN) has made public its fiscal budget and the subsequent implementation report. A critical review of these documents shows significant variations in the five macroeconomic variables which are inputs in each Presidential budget; oil Production target (mbpd), oil price ($), Foreign exchange rate(N/$), and Gross Domestic Product growth rate (%) and inflation rate (%). This results in underperformance of the Federal budget expected output in terms of non-oil and oil revenue aggregates. This paper evaluates first the existing variance between budgeted and actuals, then the relationship and causality between the determinants of Federal fiscal budget assumptions, and finally the determinants of FGN’s Gross Oil Revenue. The paper employed the use of descriptive statistics, the Autoregressive distributed lag (ARDL) model, and a Profit oil probabilistic model to achieve these objectives. This model permits for both the static and dynamic effect(s) of the independent variable(s) on the dependent variable, unlike a static model that accounts for static or fixed effect(s) only. It offers a technique for checking the existence of a long-run relationship between variables, unlike other tests of cointegration, such as the Engle-Granger and Johansen tests, which consider only non-stationary series that are integrated of the same order. Finally, even with small sample size, the ARDL model is known to generate a valid result, for it is the dependent variable and is the explanatory variable. The results showed that there is a long-run relationship between oil revenue as a proxy for budget performance and its determinants; oil price, produced oil quantity, and foreign exchange rate. There is a short-run relationship between oil revenue and its determinants; oil price, produced oil quantity, and foreign exchange rate. There is a long-run relationship between non-oil revenue and its determinants; inflation rate, GDP growth rate, and foreign exchange rate. The grangers’ causality test results show that there is a mono-directional causality between oil revenue and its determinants. The Federal budget assumptions only explain 68% of oil revenue and 62% of non-oil revenue. There is a mono-directional causality between non-oil revenue and its determinants. The Profit oil Model describes production sharing contracts, joint ventures, and modified carrying arrangements as the greatest contributors to FGN’s gross oil revenue. This provides empirical justification for the selected macroeconomic variables used in the Federal budget design and performance evaluation. The research recommends other variables, debt and money supply, be included in the Federal budget design to explain the Federal budget revenue performance further.

Keywords: ARDL, budget performance, oil price, oil quantity, oil revenue

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Authors: Kevin De Castro Cogle, Mirian Kubo, Maria Anastasiadi, Fady Mohareb, Claire Rossi

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Background: The bioaccessibility of bioactive compounds is a critical determinant of the nutritional quality of various food products. Despite its importance, there is a limited number of comprehensive studies aimed at assessing how the composition of a food matrix influences the bioaccessibility of a compound of interest. This knowledge gap has prompted a growing need to investigate the intricate relationship between food matrix formulations and the bioaccessibility of bioactive compounds. One such class of bioactive compounds that has attracted considerable attention is curcuminoids. These naturally occurring phytochemicals, extracted from the roots of Curcuma longa, have gained popularity owing to their purported health benefits and also well known for their poor bioaccessibility Project aim: The primary objective of this research project is to systematically assess the influence of matrix composition on the bioaccessibility of curcuminoids. Additionally, this study aimed to develop a series of predictive models for bioaccessibility, providing valuable insights for optimising the formula for functional foods and provide more descriptive nutritional information to potential consumers. Methods: Food formulations enriched with curcuminoids were subjected to in vitro digestion simulation, and their bioaccessibility was characterized with chromatographic and spectrophotometric techniques. The resulting data served as the foundation for the development of predictive models capable of estimating bioaccessibility based on specific physicochemical properties of the food matrices. Results: One striking finding of this study was the strong correlation observed between the concentration of macronutrients within the food formulations and the bioaccessibility of curcuminoids. In fact, macronutrient content emerged as a very informative explanatory variable of bioaccessibility and was used, alongside other variables, as predictors in a Bayesian hierarchical model that predicted curcuminoid bioaccessibility accurately (optimisation performance of 0.97 R2) for the majority of cross-validated test formulations (LOOCV of 0.92 R2). These preliminary results open the door to further exploration, enabling researchers to investigate a broader spectrum of food matrix types and additional properties that may influence bioaccessibility. Conclusions: This research sheds light on the intricate interplay between food matrix composition and the bioaccessibility of curcuminoids. This study lays a foundation for future investigations, offering a promising avenue for advancing our understanding of bioactive compound bioaccessibility and its implications for the food industry and informed consumer choices.

Keywords: bioactive bioaccessibility, food formulation, food matrix, machine learning, probabilistic modelling

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Authors: Oscar Rubiano, Oscar Ortiz, Natalia Morales, Lida Alfonso, Johana Alvarado, Adriana Gutierrez, Daniel Botero

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Athletes who usually use protein supplements, are those who practice strength and power sports, whose goal is to achieve a large muscle mass. However, it has also been explored in sports or endurance activities such as cycling, and where despite requiring high power, prominent muscle development can impede good competitive performance due to the determinant of body mass for good performance of the athlete body. This research shows, the effect with protein supplements establishes a protein - muscle mass ratio, although in a lesser proportion the relationship between protein types and muscle power. Thus, we intend to explore as a first approximation, the behavior of muscle power in lower limbs after the intake of two protein supplements from different sources. The aim of the study was to describe the behavior of muscle power in lower limbs after the consumption of animal protein (AP) and vegetable protein (VP) in four route cyclists from 18 to 20 years of the Bogota cycling league. The methodological design of this study is quantitative, with a non-probabilistic sampling, based on a pre-experimental model. The jumping power was evaluated before and after the intervention by means of the squat jump test (SJ), Counter movement jump (CMJ) and Abalacov (AB). Cyclists consumed a drink with whey protein and a soy isolate after training four times a week for three months. The amount of protein in each cyclist, was calculated according to body weight (0.5 g / kg of muscle mass). The results show that subjects who consumed PV improved muscle strength and landing strength. In contrast, the power and landing force decreased for subjects who consumed PA. For the group that consumed PV, the increase was positive at 164.26 watts, 135.70 watts and 33.96 watts for the AB, SJ and CMJ jumps respectively. While for PA, the differences of the medians were negative at -32.29 watts, -82.79 watts and -143.86 watts for the AB, SJ and CMJ jumps respectively. The differences of the medians in the AB jump were positive for both the PV (121.61 Newton) and PA (454.34 Newton) cases, however, the difference was greater for PA. For the SJ jump, the difference for the PA cases was 371.52 Newton, while for the PV cases the difference was negative -448.56 Newton, so the difference was greater in the SJ jump for PA. In jump CMJ, the differences of the medians were negative for the cases of PA and PV, being -7.05 for PA and - 958.2 for PV. So the difference was greater for PA. The conclusion of this study shows that serum protein supplementation showed no improvement in muscle power in the lower limbs of the cyclists studied, which could suggest that whey protein does not have a beneficial effect on performance in terms of power, either, showed an impact on body composition. In contrast, supplementation with soy isolate showed positive effects on muscle power, body.

Keywords: animal protein (AP), muscle power, supplements, vegetable protein (VP)

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Authors: Michael Kenomore, Mohamed Hassan, Amjad Shah, Hom Dhakal

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Shale gas reservoirs have been of greater importance compared to shale oil reservoirs since 2009 and with the current nature of the oil market, understanding the technical and economic performance of shale gas reservoirs is of importance. Using the Barnett shale as a case study, an economic model was developed to quantify the effect of finding and development costs and gas prices on the basins in the Barnett shale using net present value as an evaluation parameter. A rate of return of 20% and a payback period of 60 months or less was used as the investment hurdle in the model. The Barnett was split into four basins (Strawn Basin, Ouachita Folded Belt, Forth-worth Syncline and Bend-arch Basin) with analysis conducted on each of the basin to provide a holistic outlook. The dataset consisted of only horizontal wells that started production from 2008 to at most 2015 with 1835 wells coming from the strawn basin, 137 wells from the Ouachita folded belt, 55 wells from the bend-arch basin and 724 wells from the forth-worth syncline. The data was analyzed initially on Microsoft Excel to determine the estimated ultimate recoverable (EUR). The range of EUR from each basin were loaded in the Palisade Risk software and a log normal distribution typical of Barnett shale wells was fitted to the dataset. Monte Carlo simulation was then carried out over a 1000 iterations to obtain a cumulative distribution plot showing the probabilistic distribution of EUR for each basin. From the cumulative distribution plot, the P10, P50 and P90 EUR values for each basin were used in the economic model. Gas production from an individual well with a EUR similar to the calculated EUR was chosen and rescaled to fit the calculated EUR values for each basin at the respective percentiles i.e. P10, P50 and P90. The rescaled production was entered into the economic model to determine the effect of the finding and development cost and gas price on the net present value (10% discount rate/year) as well as also determine the scenario that satisfied the proposed investment hurdle. The finding and development costs used in this paper (assumed to consist only of the drilling and completion costs) were £1 million, £2 million and £4 million while the gas price was varied from $2/MCF-$13/MCF based on Henry Hub spot prices from 2008-2015. One of the major findings in this study was that wells in the bend-arch basin were least economic, higher gas prices are needed in basins containing non-core counties and 90% of the Barnet shale wells were not economic at all finding and development costs irrespective of the gas price in all the basins. This study helps to determine the percentage of wells that are economic at different range of costs and gas prices, determine the basins that are most economic and the wells that satisfy the investment hurdle.

Keywords: shale gas, Barnett shale, unconventional gas, estimated ultimate recoverable

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Authors: Varun Agarwal

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Introduction: With the advent of personalized medicine, histopathological review of whole slide images (WSIs) for cancer diagnosis presents an exceedingly time-consuming, complex task. Specifically, detecting metastatic regions in WSIs of sentinel lymph node biopsies necessitates a full-scanned, holistic evaluation of the image. Thus, digital pathology, low-level image manipulation algorithms, and machine learning provide significant advancements in improving the efficiency and accuracy of WSI analysis. Using Camelyon16 data, this paper proposes a deep learning pipeline to automate and ameliorate breast cancer metastasis localization and WSI classification. Methodology: The model broadly follows five stages -region of interest detection, WSI partitioning into image tiles, convolutional neural network (CNN) image-segment classifications, probabilistic mapping of tumor localizations, and further processing for whole WSI classification. Transfer learning is applied to the task, with the implementation of Inception-ResNetV2 - an effective CNN classifier that uses residual connections to enhance feature representation, adding convolved outputs in the inception unit to the proceeding input data. Moreover, in order to augment the performance of the transfer learning CNN, a stack of convolutional denoising autoencoders (CDAE) is applied to produce embeddings that enrich image representation. Through a saliency-detection algorithm, visual training segments are generated, which are then processed through a denoising autoencoder -primarily consisting of convolutional, leaky rectified linear unit, and batch normalization layers- and subsequently a contrast-normalization function. A spatial pyramid pooling algorithm extracts the key features from the processed image, creating a viable feature map for the CNN that minimizes spatial resolution and noise. Results and Conclusion: The simplified and effective architecture of the fine-tuned transfer learning Inception-ResNetV2 network enhanced with the CDAE stack yields state of the art performance in WSI classification and tumor localization, achieving AUC scores of 0.947 and 0.753, respectively. The convolutional feature retention and compilation with the residual connections to inception units synergized with the input denoising algorithm enable the pipeline to serve as an effective, efficient tool in the histopathological review of WSIs.

Keywords: breast cancer, convolutional neural networks, metastasis mapping, whole slide images

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Authors: S. Chowdhury, A. Corlett

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Large tsunamis strike the British Columbia coast every few hundred years. The Cascadia Subduction Zone, which extends along the Pacific coast from Vancouver Island to Northern California is one of the most seismically active regions in Canada. Significant earthquakes have occurred in this region, including the 1700 Cascade Earthquake with an estimated magnitude of 9.2. Based on geological records, experts have predicted a 'great earthquake' of a similar magnitude within this region may happen any time. This earthquake is expected to generate a large tsunami that could impact the coastal communities on Vancouver Island. Since many of these communities are in remote locations, they are more likely to be vulnerable, as the post-earthquake relief efforts would be impacted by the damage to critical road infrastructures. To assess the coastal vulnerability within these communities, a hydrodynamic model has been developed using MIKE-21 software. We have considered a 500 year probabilistic earthquake design criteria including the subsidence in this model. The bathymetry information was collected from Canadian Hydrographic Services (CHS), and National Oceanic Atmospheric and Administration (NOAA). The arial survey was conducted using a Cessna-172 aircraft for the communities, and then the information was converted to generate a topographic digital elevation map. Both survey information was incorporated into the model, and the domain size of the model was about 1000km x 1300km. This model was calibrated with the tsunami occurred off the west coast of Moresby Island on October 28, 2012. The water levels from the model were compared with two tide gauge stations close to the Vancouver Island and the output from the model indicates the satisfactory result. For this study, the design water level was considered as High Water Level plus the Sea Level Rise for 2100 year. The hourly wind speeds from eight directions were collected from different wind stations and used a 200-year return period wind speed in the model for storm events. The regional model was set for 12 hrs simulation period, which takes more than 16 hrs to complete one simulation using double Xeon-E7 CPU computer plus a K-80 GPU. The boundary information for the local model was generated from the regional model. The local model was developed using a high resolution mesh to estimate the coastal flooding for the communities. It was observed from this study that many communities will be effected by the Cascadia tsunami and the inundation maps were developed for the communities. The infrastructures inside the coastal inundation area were identified. Coastal vulnerability planning and resilient design solutions will be implemented to significantly reduce the risk.

Keywords: tsunami, coastal flooding, coastal vulnerable, earthquake, Vancouver, wave propagation

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Authors: Mohammed El Raey, Moustafa Osman Mohammed

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The spatial-ecological modeling is relating sustainable dispersions with social development. Sustainability with spatial-ecological model gives attention to urban environments in the design review management to comply with Earth’s System. Naturally exchange patterns of ecosystems have consistent and periodic cycles to preserve energy flows and materials in Earth’s System. The probabilistic risk assessment (PRA) technique is utilized to assess the safety of industrial complex. The other analytical approach is the Failure-Safe Mode and Effect Analysis (FMEA) for critical components. The plant safety parameters are identified for engineering topology as employed in assessment safety of industrial ecology. In particular, the most severe accidental release of hazardous gaseous is postulated, analyzed and assessment in industrial region. The IAEA- safety assessment procedure is used to account the duration and rate of discharge of liquid chlorine. The ecological model of plume dispersion width and concentration of chlorine gas in the downwind direction is determined using Gaussian Plume Model in urban and ruler areas and presented with SURFER®. The prediction of accident consequences is traced in risk contour concentration lines. The local greenhouse effect is predicted with relevant conclusions. The spatial-ecological model is also predicted the distribution schemes from the perspective of pollutants that considered multiple factors of multi-criteria analysis. The data extends input–output analysis to evaluate the spillover effect, and conducted Monte Carlo simulations and sensitivity analysis. Their unique structure is balanced within “equilibrium patterns”, such as the biosphere and collective a composite index of many distributed feedback flows. These dynamic structures are related to have their physical and chemical properties and enable a gradual and prolonged incremental pattern. While this spatial model structure argues from ecology, resource savings, static load design, financial and other pragmatic reasons, the outcomes are not decisive in artistic/ architectural perspective. The hypothesis is an attempt to unify analytic and analogical spatial structure for development urban environments using optimization software and applied as an example of integrated industrial structure where the process is based on engineering topology as optimization approach of systems ecology.

Keywords: spatial-ecological modeling, spatial structure orientation impact, composite structure, industrial ecology

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Authors: Nejoud Alostad, Anup Bora, Prashant Dhote

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Kuwait Oil Company (KOC) has been producing from its major reservoirs that are well defined and highly productive and of superior reservoir quality. These reservoirs are maturing and priority is shifting towards difficult reservoir to meet future production requirements. This paper discusses the results of the detailed integrated study for one of the satellite complex field discovered in the early 1960s. Following acquisition of new 3D seismic data in 1998 and re-processing work in the year 2006, an integrated G&G study was undertaken to review Lower Cretaceous prospectivity of this reservoir. Nine wells have been drilled in the area, till date with only three wells showing hydrocarbons in two formations. The average oil density is around 300API (American Petroleum Institute), and average porosity and water saturation of the reservoir is about 23% and 26%, respectively. The area is dissected by a number of NW-SE trending faults. Structurally, the area consists of horsts and grabens bounded by these faults and hence compartmentalized. The Wara/Burgan formation consists of discrete, dirty sands with clean channel sand complexes. There is a dramatic change in Upper Wara distributary channel facies, and reservoir quality of Wara and Burgan section varies with change of facies over the area. So predicting reservoir facies and its quality out of sparse well data is a major challenge for delineating the prospective area. To characterize the reservoir of Wara/Burgan formation, an integrated workflow involving seismic, well, petro-physical, reservoir and production engineering data has been used. Porosity and water saturation models are prepared and analyzed to predict reservoir quality of Wara and Burgan 3rd sand upper reservoirs. Subsequently, boundary conditions are defined for reservoir and non-reservoir facies by integrating facies, porosity and water saturation. Based on the detailed analyses of volumetric parameters, potential volumes of stock-tank oil initially in place (STOIIP) and gas initially in place (GIIP) were documented after running several probablistic sensitivity analysis using Montecalro simulation method. Sensitivity analysis on probabilistic models of reservoir horizons, petro-physical properties, and oil-water contacts and their effect on reserve clearly shows some alteration in the reservoir geometry. All these parameters have significant effect on the oil in place. This study has helped to identify uncertainty and risks of this prospect particularly and company is planning to develop this area with drilling of new wells.

Keywords: original oil-in-place, sensitivity, uncertainty, sandstone, reservoir modeling, Monte-Carlo simulation

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this work is to evaluate the advantages and disadvantages of various machine learning techniques in two key steps of seismic hazard and risk assessment of different types of structures. The first step is the development of ground-motion models, which are used for forecasting ground-motion intensity measures (IM) given source characteristics, source-to-site distance, and local site condition for future events. IMs such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available. Second, it is investigated how machine learning techniques could be beneficial for developing probabilistic seismic demand models (PSDMs), which provide the relationship between the structural demand responses (e.g., component deformations, accelerations, internal forces, etc.) and the ground motion IMs. In the risk framework, such models are used to develop fragility curves estimating exceeding probability of damage for pre-defined limit states, and therefore, control the reliability of the predictions in the risk assessment. In this study, machine learning algorithms like artificial neural network, random forest, and support vector machine are adopted and trained on the demand parameters to derive PSDMs for them. It is observed that such models can provide more accurate estimates of prediction in relatively shorter about of time compared to conventional methods. Moreover, they can be used for sensitivity analysis of fragility curves with respect to many modeling parameters without necessarily requiring more intense numerical response-history analysis.

Keywords: artificial neural network, machine learning, random forest, seismic risk analysis, seismic hazard analysis, support vector machine

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Authors: Esteban Maioli

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Generational studies have an important research tradition in social and human sciences. On the one hand, the speed of social change in the context of globalization imposes the need to research the transformations are identified both the subjectivity of the agents involved and its inclusion in the institutional matrix, specifically employment. Generation Z, (generally considered as the population group whose birth occurs after 1995) have unique psycho-social characteristics. Gen Z is characterized by a different set of values, beliefs, attitudes and ambitions that impact in their concrete action in organizational structures. On the other hand, managers often have to deal with generational differences in the workplace. Organizations have members who belong to different generations; they had never before faced the challenge of having such a diverse group of members. The members of each historical generation are characterized by a different set of values, beliefs, attitudes and ambitions that are manifest in their concrete action in organizational structures. Gen Z it’s the only one who can fully be considered "global," while its members were born in the consolidated context of globalization. Some salient features of the Generation Z can be summarized as follows. They’re the first fully born into a digital world. Social networks and technology are integrated into their lives. They are concerned about the challenges of the modern world (poverty, inequality, climate change, among others). They are self-expressive, more liberal and open to change. They often bore easily, with short attention spans. They do not like routine tasks. They want to achieve a good life-work balance, and they are interested in a flexible work environment, as opposed to traditional work schedule. They are critical thinkers, who come with innovative and creative ideas to help. Research design considered methodological triangulation. Data was collected with two techniques: a self-administered survey with multiple choice questions and attitudinal scales applied over a non-probabilistic sample by reasoned decision. According to the multi-method strategy, also it was conducted in-depth interviews. Organizations constantly face new challenges. One of the biggest ones is to learn to manage a multi-generational scope of work. While Gen Z has not yet been fully incorporated (expected to do so in five years or so), many organizations have already begun to implement a series of changes in its recruitment and development. The main obstacle to retaining young talent is the gap between the expectations of iGen applicants and what companies offer. Members of the iGen expect not only a good salary and job stability but also a clear career plan. Generation Z needs to have immediate feedback on their tasks. However, many organizations have yet to improve both motivation and monitoring practices. It is essential for companies to take a review of organizational practices anchored in the culture of the organization.

Keywords: employment, expectations, generation Z, organizational culture, organizations, psycho-social characteristics

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Authors: Nuno Biga

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Artificial Intelligence (AI) allows machines to interpret information and learn from context analysis, giving them the ability to make predictions adjusted to each specific situation. In addition to learning by performing deterministic and probabilistic calculations, the 'artificial brain' also learns through information and data provided by those who train it, namely its users. The "Assisted-BIGAMES" version of the Accident & Emergency (A&E) simulator introduces the concept of a "Virtual Assistant" (VA) that provides users with useful suggestions, namely to pursue the following operations: a) to relocate workstations in order to shorten travelled distances and minimize the stress of those involved; b) to identify in real time the bottleneck(s) in the operations system so that it is possible to quickly act upon them; c) to identify resources that should be polyvalent so that the system can be more efficient; d) to identify in which specific processes it may be advantageous to establish partnership with other teams; and e) to assess possible solutions based on the suggested KPIs allowing action monitoring to guide the (re)definition of future strategies. This paper is built on the BIGAMES© simulator and presents the conceptual AI model developed in a pilot project. Each Virtual Assisted BIGAME is a management simulator developed by the author that guides operational and strategic decision making, providing users with useful information in the form of management recommendations that make it possible to predict the actual outcome of different alternative management strategic actions. The pilot project developed incorporates results from 12 editions of the BIGAME A&E that took place between 2017 and 2022 at AESE Business School, based on the compilation of data that allows establishing causal relationships between decisions taken and results obtained. The systemic analysis and interpretation of this information is materialised in the Assisted-BIGAMES through a computer application called "BIGAMES Virtual Assistant" that players can use during the Game. Each participant in the Virtual Assisted-BIGAMES permanently asks himself about the decisions he should make during the game in order to win the competition. To this end, the role of the VA of each team consists in guiding the players to be more effective in their decision making through presenting recommendations based on AI methods. It is important to note that the VA's suggestions for action can be accepted or rejected by the managers of each team, and as the participants gain a better understanding of the game, they will more easily dispense with the VA's recommendations and rely more on their own experience, capability, and knowledge to support their own decisions. Preliminary results show that the introduction of the VA provides a faster learning of the decision-making process. The facilitator (Serious Game Controller) is responsible for supporting the players with further analysis and the recommended action may be (or not) aligned with the previous recommendations of the VA. All the information should be jointly analysed and assessed by each player, who are expected to add “Emotional Intelligence”, a component absent from the machine learning process.

Keywords: artificial intelligence (AI), gamification, key performance indicators (KPI), machine learning, management simulators, serious games, virtual assistant

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Authors: A. P. Mikhailovich, V. V. Fomin, E. M. Agapitov, V. E. Rogachev, E. A. Kostousova, E. S. Perekhodova

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Woody vegetation among the upper limit of its habitat is a sensitive indicator of biota reaction to regional climate changes. Quantitative assessment of temporal and spatial changes in the distribution of trees and plant biocenoses calls for the development of new modeling approaches based upon selected data from measurements on the ground level and ultra-resolution aerial photography. Statistical models were developed for the study area located in the Polar Urals. These models allow obtaining probabilistic estimates for placing Siberian Larch trees into one of the three age intervals, namely 1-10, 11-40 and over 40 years, based on the Weilbull distribution of the maximum horizontal crown projection. Authors developed the distribution map for larch trees with crown diameters exceeding twenty centimeters by deciphering aerial photographs made by a UAV from an altitude equal to fifty meters. The total number of larches was equal to 88608, forming the following distribution row across the abovementioned intervals: 16980, 51740, and 19889 trees. The results demonstrate that two processes can be observed in the course of recent decades: first is the intensive forestation of previously barren or lightly wooded fragments of the study area located within the patches of wood, woodlands, and sparse stand, and second, expansion into mountain tundra. The current expansion of the Siberian Larch in the region replaced the depopulation process that occurred in the course of the Little Ice Age from the late 13ᵗʰ to the end of the 20ᵗʰ century. Using data from field measurements of Siberian larch specimen biometric parameters (including height, diameter at root collar and at 1.3 meters, and maximum projection of the crown in two orthogonal directions) and data on tree ages obtained at nine circular test sites, authors developed a model for artificial neural network including two layers with three and two neurons, respectively. The model allows quantitative assessment of a specimen's age based on height and maximum crone projection values. Tree height and crown diameters can be quantitatively assessed using data from aerial photographs and lidar scans. The resulting model can be used to assess the age of all Siberian larch trees. The proposed approach, after validation, can be applied to assessing the age of other tree species growing near the upper tree boundaries in other mountainous regions. This research was collaboratively funded by the Russian Ministry for Science and Education (project No. FEUG-2023-0002) and Russian Science Foundation (project No. 24-24-00235) in the field of data modeling on the basis of artificial neural network.

Keywords: treeline, dynamic, climate, modeling

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Authors: A. Falenski, A. Kaesbohrer, M. Filter

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Introduction: Risk assessments can be performed in various ways and in different degrees of complexity. In order to assess risks associated with imported foods additional information needs to be taken into account compared to a risk assessment on regional products. The present review is an overview on currently available best practise approaches and data sources used for food import risk assessments (IRAs). Methods: A literature review has been performed. PubMed was searched for articles about food IRAs published in the years 2004 to 2014 (English and German texts only, search string “(English [la] OR German [la]) (2004:2014 [dp]) import [ti] risk”). Titles and abstracts were screened for import risks in the context of IRAs. The finally selected publications were analysed according to a predefined questionnaire extracting the following information: risk assessment guidelines followed, modelling methods used, data and software applied, existence of an analysis of uncertainty and variability. IRAs cited in these publications were also included in the analysis. Results: The PubMed search resulted in 49 publications, 17 of which contained information about import risks and risk assessments. Within these 19 cross references were identified to be of interest for the present study. These included original articles, reviews and guidelines. At least one of the guidelines of the World Organisation for Animal Health (OIE) and the Codex Alimentarius Commission were referenced in any of the IRAs, either for import of animals or for imports concerning foods, respectively. Interestingly, also a combination of both was used to assess the risk associated with the import of live animals serving as the source of food. Methods ranged from full quantitative IRAs using probabilistic models and dose-response models to qualitative IRA in which decision trees or severity tables were set up using parameter estimations based on expert opinions. Calculations were done using @Risk, R or Excel. Most heterogeneous was the type of data used, ranging from general information on imported goods (food, live animals) to pathogen prevalence in the country of origin. These data were either publicly available in databases or lists (e.g., OIE WAHID and Handystatus II, FAOSTAT, Eurostat, TRACES), accessible on a national level (e.g., herd information) or only open to a small group of people (flight passenger import data at national airport customs office). In the IRAs, an uncertainty analysis has been mentioned in some cases, but calculations have been performed only in a few cases. Conclusion: The current state-of-the-art in the assessment of risks of imported foods is characterized by a great heterogeneity in relation to general methodology and data used. Often information is gathered on a case-by-case basis and reformatted by hand in order to perform the IRA. This analysis therefore illustrates the need for a flexible, modular framework supporting the connection of existing data sources with data analysis and modelling tools. Such an infrastructure could pave the way to IRA workflows applicable ad-hoc, e.g. in case of a crisis situation.

Keywords: import risk assessment, review, tools, food import

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Authors: Carolina Busco, Daniela Osses

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The development of digital literacy has become a fundamental element that allows for citizen inclusion, access to quality jobs, and a labor market capable of responding to the digital economy. There are no methodological instruments available in Chile to measure the workforce’s digital literacy and improve national policies on this matter. Thus, the objective of this research is to develop a survey to measure digital literacy in a sample of 200 Chilean workers. Dimensions considered in the instrument are sociodemographics, access to infrastructure, digital education, digital skills, and the ability to use e-government services. To achieve the research objective of developing a digital literacy model of indicators and a research instrument for this purpose, along with an exploratory analysis of data using factor analysis, we used an empirical, quantitative-qualitative, exploratory, non-probabilistic, and cross-sectional research design. The research instrument is a survey created to measure variables that make up the conceptual map prepared from the bibliographic review. Before applying the survey, a pilot test was implemented, resulting in several adjustments to the phrasing of some items. A validation test was also applied using six experts, including their observations on the final instrument. The survey contained 49 items that were further divided into three sets of questions: sociodemographic data; a Likert scale of four values ranked according to the level of agreement; iii) multiple choice questions complementing the dimensions. Data collection occurred between January and March 2022. For the factor analysis, we used the answers to 12 items with the Likert scale. KMO showed a value of 0.626, indicating a medium level of correlation, whereas Bartlett’s test yielded a significance value of less than 0.05 and a Cronbach’s Alpha of 0.618. Taking all factor selection criteria into account, we decided to include and analyze four factors that together explain 53.48% of the accumulated variance. We identified the following factors: i) access to infrastructure and opportunities to develop digital skills at the workplace or educational establishment (15.57%), ii) ability to solve everyday problems using digital tools (14.89%), iii) online tools used to stay connected with others (11.94%), and iv) residential Internet access and speed (11%). Quantitative results were discussed within six focus groups using heterogenic selection criteria related to the most relevant variables identified in the statistical analysis: upper-class school students; middle-class university students; Ph.D. professors; low-income working women, elderly individuals, and a group of rural workers. The digital divide and its social and economic correlations are evident in the results of this research. In Chile, the items that explain the acquisition of digital tools focus on access to infrastructure, which ultimately puts the first filter on the development of digital skills. Therefore, as expressed in the literature review, the advance of these skills is radically different when sociodemographic variables are considered. This increases socioeconomic distances and exclusion criteria, putting those who do not have these skills at a disadvantage and forcing them to seek the assistance of others.

Keywords: digital literacy, digital society, workforce digitalization, digital skills

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Authors: Mathuravech Thanaphon, Thephasit Nat

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The Electricity Generating Authority of Thailand (EGAT) is focusing on expanding its pumped storage hydropower (PSH) capacity to increase the reliability of the system during peak demand and allow for greater integration of renewables. To achieve this requirement, Thailand will have to double its current renewable electricity production. To address the challenges of balancing supply and demand in the grid with increasing levels of RE penetration, as well as rising peak demand, EGAT has already been studying the potential for additional PSH capacity for several years to enable an increased share of RE and replace existing fossil fuel-fired generation. In addition, the role that pumped-storage hydropower would play in fulfilling multiple grid functions and renewable integration. The proposed sites for new PSH would help increase the reliability of power generation in Thailand. However, most of the electricity generation will come from RE, chiefly wind and photovoltaic, and significant additional Energy Storage capacity will be needed. In this paper, the impact of integrating the PSH system on the adequacy of renewable rich power generating systems to reduce the thermal power generating units is investigated. The variations of system adequacy indices are analyzed for different PSH-renewables capacities and storage levels. Power Development Plan 2018 rev.1 (PDP2018 rev.1), which is modified by integrating a six-new PSH system and RE planning and development aftermath in 2030, is the very challenge. The system adequacy indices through power generation are obtained using Multi-Objective Genetic Algorithm (MOGA) Optimization. MOGA is a probabilistic heuristic and stochastic algorithm that is able to find the global minima, which have the advantage that the fitness function does not necessarily require the gradient. In this sense, the method is more flexible in solving reliability optimization problems for a composite power system. The optimization with hourly time step takes years of planning horizon much larger than the weekly horizon that usually sets the scheduling studies. The objective function is to be optimized to maximize RE energy generation, minimize energy imbalances, and minimize thermal power generation using MATLAB. The PDP2018 rev.1 was set to be simulated based on its planned capacity stepping into 2030 and 2050. Therefore, the four main scenario analyses are conducted as the target of renewables share: 1) Business-As-Usual (BAU), 2) National Targets (30% RE in 2030), 3) Carbon Neutrality Targets (50% RE in 2050), and 5) 100% RE or full-decarbonization. According to the results, the generating system adequacy is significantly affected by both PSH-RE and Thermal units. When a PSH is integrated, it can provide hourly capacity to the power system as well as better allocate renewable energy generation to reduce thermal generations and improve system reliability. These results show that a significant level of reliability improvement can be obtained by PSH, especially in renewable-rich power systems.

Keywords: pumped storage hydropower, renewable energy integration, system adequacy, power development planning, RE100, multi-objective genetic algorithm

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Authors: Nicole Merkle, Stefan Zander

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Ambient Assisted Living (AAL) describes a technological and methodological stack of (e.g. formal model-theoretic semantics, rule-based reasoning and machine learning), different aspects regarding the behavior, activities and characteristics of humans. Hence, a semantic representation of the user environment and its relevant elements are required in order to allow assistive agents to recognize situations and deduce appropriate actions. Furthermore, the user and his/her characteristics (e.g. physical, cognitive, preferences) need to be represented with a high degree of expressiveness in order to allow software agents a precise evaluation of the users’ context models. The correct interpretation of these context models highly depends on temporal, spatial circumstances as well as individual user preferences. In most AAL approaches, model representations of real world situations represent the current state of a universe of discourse at a given point in time by neglecting transitions between a set of states. However, the AAL domain currently lacks sufficient approaches that contemplate on the dynamic adaptions of context-related representations. Semantic representations of relevant real-world excerpts (e.g. user activities) help cognitive, rule-based agents to reason and make decisions in order to help users in appropriate tasks and situations. Furthermore, rules and reasoning on semantic models are not sufficient for handling uncertainty and fuzzy situations. A certain situation can require different (re-)actions in order to achieve the best results with respect to the user and his/her needs. But what is the best result? To answer this question, we need to consider that every smart agent requires to achieve an objective, but this objective is mostly defined by domain experts who can also fail in their estimation of what is desired by the user and what not. Hence, a smart agent has to be able to learn from context history data and estimate or predict what is most likely in certain contexts. Furthermore, different agents with contrary objectives can cause collisions as their actions influence the user’s context and constituting conditions in unintended or uncontrolled ways. We present an approach for dynamically updating a semantic model with respect to the current user context that allows flexibility of the software agents and enhances their conformance in order to improve the user experience. The presented approach adapts rules by learning sensor evidence and user actions using probabilistic reasoning approaches, based on given expert knowledge. The semantic domain model consists basically of device-, service- and user profile representations. In this paper, we present how this semantic domain model can be used in order to compute the probability of matching rules and actions. We apply this probability estimation to compare the current domain model representation with the computed one in order to adapt the formal semantic representation. Our approach aims at minimizing the likelihood of unintended interferences in order to eliminate conflicts and unpredictable side-effects by updating pre-defined expert knowledge according to the most probable context representation. This enables agents to adapt to dynamic changes in the environment which enhances the provision of adequate assistance and affects positively the user satisfaction.

Keywords: ambient intelligence, machine learning, semantic web, software agents

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Authors: Andrea N. Alarcon, Julian D. Castro, Gloria C. Rojas, Paola A. Vaca, Santiago Ortiz, Gustavo Martinez, Pablo D. Lemoine

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Recently, electoral results across many countries have shown to be inconsistent with rational decision theory, which states that individuals make decisions based on maximizing benefits and reducing risks. An alternative explanation has emerged: Fear and rage-driven vote have been proved to be highly effective for political persuasion and mobilization. This phenomenon has been evident in the 2016 elections in the United States, 2006 elections in Mexico, 1998 elections in Venezuela, and 2004 elections in Bolivia. In Colombia, it has occurred recently in the 2016 plebiscite for peace and 2018 presidential elections. The aim of this study is to explain this phenomenon using cultural cognition theory, referring to the psychological predisposition individuals have to believe that its own and its peer´s behavior is correct and, therefore, beneficial to the entire society. Cultural cognition refers to the tendency of individuals to fit perceived risks, and factual beliefs into group shared values; the Cultural Cognition Worldview Scales (CCWS) measures cultural perceptions through two different dimensions: Individualism-communitarianism and hierarchy-egalitarianism. The former refers to attitudes towards social dominance based on conspicuous and static characteristics (sex, ethnicity or social class), while the latter refers to attitudes towards a social ordering in which it is expected from individuals to guarantee their own wellbeing without society´s or government´s intervention. A probabilistic national sample was obtained from different polls from the consulting and public opinion company Centro Nacional de Consultoría. Sociodemographic data was obtained along with CCWS scores, a subjective measure of left-right ideological placement and vote intention for 2019 Mayor´s elections were also included in the questionnaires. Finally, the question “In your opinion, what is the greatest risk Colombia is facing right now?” was included to identify perceived risk in the population. Preliminary results show that Colombians are highly distributed among hierarchical communitarians and egalitarian individualists (30.9% and 31.7%, respectively), and to a less extent among hierarchical individualists and egalitarian communitarians (19% and 18.4%, respectively). Males tended to be more hierarchical (p < .000) and communitarian (p=.009) than females. ANOVA´s revealed statistically significant differences between groups (quadrants) for the level of schooling, left-right ideological orientation, and stratum (p < .000 for all), and proportion differences revealed statistically significant differences for groups of age (p < .001). Differences and distributions for vote intention and perceived risks are still being processed and results are yet to be analyzed. Results show that Colombians are differentially distributed among quadrants in regard to sociodemographic data and left-right ideological orientation. These preliminary results indicate that this study may shed some light on why Colombians vote the way they do, and future qualitative data will show the fears emerging from the identified values in the CCWS and the relation this has with vote intention.

Keywords: communitarianism, cultural cognition, egalitarianism, hierarchy, individualism, perceived risks

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Authors: Vijaya Dixit Aasheesh Dixit

Abstract:

Shipbuilding industry operates in Engineer Procure Construct (EPC) context. Product mix of a shipyard comprises of various types of ships like bulk carriers, tankers, barges, coast guard vessels, sub-marines etc. Each order is unique based on the type of ship and customized requirements, which are engineered into the product right from design stage. Thus, to execute every new project, a shipyard needs to upgrade its production expertise. As a result, over the long run, holistic learning occurs across different types of projects which contributes to the knowledge base of the shipyard. Simultaneously, in the short term, during execution of a project comprising of multiple sister ships, repetition of similar tasks leads to learning at activity level. This research aims to capture above learnings of a shipyard and incorporate learning curve effect in project scheduling and materials procurement to improve project performance. Extant literature provides support for the existence of such learnings in an organization. In shipbuilding, there are sequences of similar activities which are expected to exhibit learning curve behavior. For example, the nearly identical structural sub-blocks which are successively fabricated, erected, and outfitted with piping and electrical systems. Learning curve representation can model not only a decrease in mean completion time of an activity, but also a decrease in uncertainty of activity duration. Sister ships have similar material requirements. The same supplier base supplies materials for all the sister ships within a project. On one hand, this provides an opportunity to reduce transportation cost by batching the order quantities of multiple ships. On the other hand, it increases the inventory holding cost at shipyard and the risk of obsolescence. Further, due to learning curve effect the production scheduled of each consequent ship gets compressed. Thus, the material requirement schedule of every next ship differs from its previous ship. As more and more ships get constructed, compressed production schedules increase the possibility of batching the orders of sister ships. This work aims at integrating materials management with project scheduling of long duration projects for manufacturing of multiple sister ships. It incorporates the learning curve effect on progressively compressing material requirement schedules and addresses the above trade-off of transportation cost and inventory holding and shortage costs while satisfying budget constraints of various stages of the project. The activity durations and lead time of items are not crisp and are available in the form of probabilistic distribution. A Stochastic Mixed Integer Programming (SMIP) model is formulated which is solved using evolutionary algorithm. Its output provides ordering dates of items and degree of order batching for all types of items. Sensitivity analysis determines the threshold number of sister ships required in a project to leverage the advantage of learning curve effect in materials management decisions. This analysis will help materials managers to gain insights about the scenarios: when and to what degree is it beneficial to treat a multiple ship project as an integrated one by batching the order quantities and when and to what degree to practice distinctive procurement for individual ship.

Keywords: learning curve, materials management, shipbuilding, sister ships

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Authors: Margaret Boone Rappaport, Christopher J. Corbally

Abstract:

The ancient ape ancestral population from which living great ape and human species evolved had demographic features affecting their evolution. The population was large, had great genetic variability, and natural selection was effective at honing adaptations. The emerging populations of chimpanzees and humans were affected more by founder effects and genetic drift because they were smaller. Natural selection did not disappear, but it was not as strong. Consequences of the 'population crash' and the human effective population size are introduced briefly. The history of the ancient apes is written in the genomes of living humans and great apes. The expansion of the brain began before the human line emerged. Coalescence times for some genes are very old – up to several million years, long before Homo sapiens. The mismatch between gene trees and species trees highlights the anthropoid speciation processes, and gives the human genome history a fuzzy, probabilistic quality. However, it suggests traits that might form a foundation for capacities emerging later. A theoretical model is presented in which the genomes of early ape populations provide the substructure for the emergence of religious capacity later on the human line. The model does not search for religion, but its foundations. It suggests a course by which an evolutionary line that began with prosimians eventually produced a human species with biologically based religious capacity. The model of the sequential emergence of religious capacity relies on cognitive science, neuroscience, paleoneurology, primate field studies, cognitive archaeology, genomics, and population genetics. And, it emphasizes five trait types: (1) Documented, positive selection of sensory capabilities on the human line may have favored survival, but also eventually enriched human religious experience. (2) The bonobo model suggests a possible down-regulation of aggression and increase in tolerance while feeding, as well as paedomorphism – but, in a human species that remains cognitively sharp (unlike the bonobo). The two species emerged from the same ancient ape population, so it is logical to search for shared traits. (3) An up-regulation of emotional sensitivity and compassion seems to have occurred on the human line. This finds support in modern genetic studies. (4) The authors’ published model of morality's emergence in Homo erectus encompasses a cognitively based, decision-making capacity that was hypothetically overtaken, in part, by religious capacity. Together, they produced a strong, variable, biocultural capability to support human sociability. (5) The full flowering of human religious capacity came with the parietal expansion and smaller face (klinorhynchy) found only in Homo sapiens. Details from paleoneurology suggest the stage was set for human theologies. Larger parietal lobes allowed humans to imagine inner spaces, processes, and beings, and, with the frontal lobe, led to the first theologies composed of structured and integrated theories of the relationships between humans and the supernatural. The model leads to the evolution of a small population of African hominins that was ready to emerge with religious capacity when the species Homo sapiens evolved two hundred thousand years ago. By 50-60,000 years ago, when human ancestors left Africa, they were fully enabled.

Keywords: genetic drift, genomics, parietal expansion, religious capacity

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Authors: Andreas Rudisch, Ralf Lampert, Andreas Kolbitsch

Abstract:

It is a well-known fact among the engineering community that earthquakes with comparatively low magnitudes can cause serious damage to nonstructural components (NSCs) of buildings, even when the supporting structure performs relatively well. Past research works focused mainly on NSCs of nuclear power plants and industrial plants. Particular attention should also be given to architectural façade elements of old masonry buildings (e.g. ornamental figures, balustrades, vases), which are very vulnerable under seismic excitation. Large numbers of these historical nonstructural components (HiNSCs) can be found in highly frequented historical city centers and in the event of failure, they pose a significant danger to persons. In order to estimate the vulnerability of acceleration sensitive HiNSCs, the peak horizontal floor acceleration (PHFA) is used. The PHFA depends on the dynamic characteristics of the building, the ground excitation, and induced nonlinearities. Consequently, the PHFA can not be generalized as a simple function of height. In the present research work, an extensive case study was conducted to investigate the influence of induced nonlinearity on the PHFA for old masonry buildings. Probabilistic nonlinear FE time-history analyses considering three different hazard levels were performed. A set of eighteen synthetically generated ground motions was used as input to the structure models. An elastoplastic macro-model (multiPlas) for nonlinear homogenized continuum FE-calculation was calibrated to multiple scales and applied, taking specific failure mechanisms of masonry into account. The macro-model was calibrated according to the results of specific laboratory and cyclic in situ shear tests. The nonlinear macro-model is based on the concept of multi-surface rate-independent plasticity. Material damage or crack formation are detected by reducing the initial strength after failure due to shear or tensile stress. As a result, shear forces can only be transmitted to a limited extent by friction when the cracking begins. The tensile strength is reduced to zero. The first goal of the calibration was the consistency of the load-displacement curves between experiment and simulation. The calibrated macro-model matches well with regard to the initial stiffness and the maximum horizontal load. Another goal was the correct reproduction of the observed crack image and the plastic strain activities. Again the macro-model proved to work well in this case and shows very good correlation. The results of the case study show that there is significant scatter in the absolute distribution of the PHFA between the applied ground excitations. An absolute distribution along the normalized building height was determined in the framework of probability theory. It can be observed that the extent of nonlinear behavior varies for the three hazard levels. Due to the detailed scope of the present research work, a robust comparison with code-recommendations and simplified PHFA distributions are possible. The chosen methodology offers a chance to determine the distribution of PHFA along the building height of old masonry structures. This permits a proper hazard assessment of HiNSCs under seismic loads.

Keywords: nonlinear macro-model, nonstructural components, time-history analysis, unreinforced masonry

Procedia PDF Downloads 140