Search results for: daily probability model

Authors: O. Benabdeljelil, A. Karioun, S. Amami, R. Rouger, M. Hamidine

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A model for preventing the risks related to climate conditions in the agricultural sector is presented. It will determine the yearly optimum premium to be paid by a producer in order to reach his required turnover. The model is based on both climatic stability and 'soft' responses of usually grown species to average climate variations at the same place and inside a safety ball which can be determined from past meteorological data. This allows the use of linear regression expression for dependence of production result in terms of driving meteorological parameters, the main ones of which are daily average sunlight, rainfall and temperature. By simple best parameter fit from the expert table drawn with professionals, optimal representation of yearly production is determined from records of previous years, and yearly payback is evaluated from minimum yearly produced turnover. The model also requires accurate pricing of commodity at N+1. Therefore, a pricing model is developed using 3 state variables, namely the spot price, the difference between the mean-term and the long-term forward price, and the long-term structure of the model. The use of historical data enables to calibrate the parameters of state variables, and allows the pricing of commodity. Application to beet sugar underlines pricer precision. Indeed, the percentage of accuracy between computed result and real world is 99,5%. Optimal premium is then deduced and gives the producer a useful bound for negotiating an offer by insurance companies to effectively protect its harvest. The application to beet production in French Oise department illustrates the reliability of present model with as low as 6% difference between predicted and real data. The model can be adapted to almost any agricultural field by changing state parameters and calibrating their associated coefficients.

Keywords: agriculture, production model, optimal price, meteorological factors, 3-factor model, parameter calibration, forward price

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Authors: Jay L. Fu

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Patients with Alzheimer's disease progressively lose their memory and thinking skills and, eventually, the ability to carry out simple daily tasks. The disease is irreversible, but early detection and treatment can slow down the disease progression. In this research, publicly available MRI data and demographic data from 373 MRI imaging sessions were utilized to build models to predict dementia. Various machine learning models, including logistic regression, k-nearest neighbor, support vector machine, random forest, and neural network, were developed. Data were divided into training and testing sets, where training sets were used to build the predictive model, and testing sets were used to assess the accuracy of prediction. Key risk factors were identified, and various models were compared to come forward with the best prediction model. Among these models, the random forest model appeared to be the best model with an accuracy of 90.34%. MMSE, nWBV, and gender were the three most important contributing factors to the detection of Alzheimer’s. Among all the models used, the percent in which at least 4 of the 5 models shared the same diagnosis for a testing input was 90.42%. These machine learning models allow early detection of Alzheimer’s with good accuracy, which ultimately leads to early treatment of these patients.

Keywords: Alzheimer's disease, clinical diagnosis, magnetic resonance imaging, machine learning prediction

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Authors: Bo-wen Zhu, Bin Wu, Feng Chen

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It has become a common measure among governments to support green development of enterprises through Green Credit policies. In China, the Central Bank of China and other authorities even put forward corresponding assessment requirements for proportion of green credit in commercial banks. Policy changes might raise concerns about commercial banks turning a blind eye to greenwashing behavior by enterprises. The lack of effective regulation may lead to a diffusion of such behavior, and eventually result in the phenomenon of “bad money driving out good money”, which could dampen the incentive effect of Green Credit policies. This paper employs a complex network model based on an evolutionary game analysis framework involving enterprises, banks, and regulatory authorities to investigate inhibitory effect of the Green Credit regulation on enterprises’ greenwashing behavior, banks’ opportunistic and collusive behaviors. The findings are as follows: (1) Banking opportunism rises with Green Credit evaluation criteria and requirements for the proportion of credit balance. Restrictive regulation against violating banks is necessary as there is an increasing trend of banks adopting opportunistic strategy. (2) Raising penalties and probability of regulatory inspections can effectively suppress banks’ opportunistic behavior, however, it cannot entirely eradicate the opportunistic behavior on the bank side. (3) Although maintaining a certain inspection probability can inhibit enterprises from adopting greenwashing behavior, enterprises choose a catering production strategy instead. (4) One-time rewards from local government have limited effects on the equilibrium state and diffusion trend of bank regulatory decision-making.

Keywords: green credit, greenwashing behavior, regulation, diffusion effect

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Authors: Taufik Hidayat, Yudha Nurhantari, Bambang U. D. Rianto

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Introduction: The examination of the Short Tandem Repeats (STR) locus on nuclear DNA is very useful in solving the paternity cases. The purpose of this study is to know the description of paternity cases and paternity index/probability of paternity analysis based on Indonesian allele frequency at Sardjito Hospital Yogyakarta. Method: This was an observational study with cross-sectional analytic method. Population and sample were all cases of disputed paternity from January 2011 to June 2015 that fulfill the inclusion and exclusion criteria and were examined at Forensic Medicine Unit of Sardjito Hospital, Medical Faculty of Gadjah Mada University. The paternity index was calculated with EasyDNA Program by Fung (2013). Analysis of the study was conducted by comparing the results through unpaired categorical test using Kolmogorov-Smirnov test. This study was designed with 95% confidence interval (CI) with α = 5% and significance level is p < 0,05. Results: From 42 disputed paternity cases we obtained trio paternity cases were 32 cases (76.2%) and duo without a mother was 10 cases (23.8%). The majority of the fathers' estimated ages were 21-30 years (33.3%) and the mother's age was 31-40 years (38.1%). The majority of the ages of children examined for paternity were under 12 months (47.6%). The majority of ethnic clients are Javanese. Conclusion of inclusion was 57.1%, and exclusion was 42.9%. The Kolmogorov-Smirnov test obtained p-value = 0.673. Conclusion: There is no significant difference between paternity index/probability of paternity based on Indonesian allele frequency between trio and duo of paternity.

Keywords: disputed paternity, paternity index, probability of paternity, short tandem

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Authors: Yousef S. Al-Othman

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The growth and expansion of the industrial facilities comes proportional to the market increasing demand of products and services. Furthermore, raw material producers such as oil companies usually undergo massive revamping projects to maintain a synchronized supply. These revamping projects are usually delivered through challenging construction projects held and associated with daily site risks related to the construction process. Henceforth, a case study related to these risks and corresponding on-spot corrective measurements has been made on a certain number of construction project contractors at Kuwait Oil Company (KOC) to derive the benefits and overall effectiveness of the on-spot corrective measurements during the construction phase of a project, and how would the same help in avoiding major incidents, ensuring a smooth, cost effective and on time delivery of the project. Findings of this case study shall have an added value to the overall risk management process by minimizing the daily site risks that may affect the project lead time, resulting in an undisturbed on-site construction process.

Keywords: oil and gas, risk management, construction projects, project lead time

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Authors: Ceren Vardar Acar, Mine Caglar

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In finance, the price of a volatile asset can be modeled using fractional Brownian motion (fBm) with Hurst parameter H>1/2. The Black-Scholes model for the values of returns of an asset using fBm is given as, 〖Y_t=Y_0 e^((r+μ)t+σB)〗_t^H, 0≤t≤T where Y_0 is the initial value, r is constant interest rate, μ is constant drift and σ is constant diffusion coefficient of fBm, which is denoted by B_t^H where t≥0. Black-Scholes model can be constructed with some Markov processes such as Brownian motion. The advantage of modeling with fBm to Markov processes is its capability of exposing the dependence between returns. The real life data for a volatile asset display long-range dependence property. For this reason, using fBm is a more realistic model compared to Markov processes. Investors would be interested in any kind of information on the risk in order to manage it or hedge it. The maximum possible loss is one way to measure highest possible risk. Therefore, it is an important variable for investors. In our study, we give some theoretical bounds on the distribution of maximum possible loss of fBm. We provide both asymptotical and strong estimates for the tail probability of maximum loss of standard fBm and fBm with drift and diffusion coefficients. In the investment point of view, these results explain, how large values of possible loss behave and its bounds.

Keywords: maximum drawdown, maximum loss, fractional brownian motion, large deviation, Gaussian process

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Authors: Li-hsing Shih, Wei-Jen Hsu

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Recently, the sale of electrical vehicles (EVs) has increased dramatically due to maturing technology development and decreasing cost. Governments of many countries have made regulations and policies in favor of EVs due to their long-term commitment to net zero carbon emissions. However, due to uncertain factors such as the future price of EVs, forecasting the future market share of EVs is a challenging subject for both the auto industry and local government. This study tries to forecast the market share of EVs using conjoint models and Monte Carlo simulation. The research is conducted in three phases. (1) A conjoint model is established to represent the customer preference structure on purchasing vehicles while five product attributes of both EV and internal combustion engine vehicles (ICEV) are selected. A questionnaire survey is conducted to collect responses from Taiwanese consumers and estimate the part-worth utility functions of all respondents. The resulting part-worth utility functions can be used to estimate the market share, assuming each respondent will purchase the product with the highest total utility. For example, attribute values of an ICEV and a competing EV are given respectively, two total utilities of the two vehicles of a respondent are calculated and then knowing his/her choice. Once the choices of all respondents are known, an estimate of market share can be obtained. (2) Among the attributes, future price is the key attribute that dominates consumers’ choice. This study adopts the assumption of a learning curve to predict the future price of EVs. Based on the learning curve method and past price data of EVs, a regression model is established and the probability distribution function of the price of EVs in 2030 is obtained. (3) Since the future price is a random variable from the results of phase 2, a Monte Carlo simulation is then conducted to simulate the choices of all respondents by using their part-worth utility functions. For instance, using one thousand generated future prices of an EV together with other forecasted attribute values of the EV and an ICEV, one thousand market shares can be obtained with a Monte Carlo simulation. The resulting probability distribution of the market share of EVs provides more information than a fixed number forecast, reflecting the uncertain nature of the future development of EVs. The research results can help the auto industry and local government make more appropriate decisions and future action plans.

Keywords: conjoint model, electrical vehicle, learning curve, Monte Carlo simulation

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Authors: Ren Moses, Emmanuel Kidando, Eren Ozguven, Yassir Abdelrazig

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Although older drivers (defined as those of age 65 and above) are less involved with speeding, alcohol use as well as night driving, they are more vulnerable to severe crashes. The major contributing factors for severe crashes include frailty and medical complications. Several studies have evaluated the contributing factors on severity of crashes. However, few studies have established the impact of travel time reliability (TTR) on road safety. In particular, the impact of TTR on senior adults who face several challenges including hearing difficulties, decreasing of the processing skills and cognitive problems in driving is not well established. Therefore, this study focuses on determining possible impacts of TTR on the traffic safety with focus on elderly drivers. Historical travel speed data from freeway links in the study area were used to calculate travel time and the associated TTR metrics that is, planning time index, the buffer index, the standard deviation of the travel time and the probability of congestion. Four-year information on crashes occurring on these freeway links was acquired. The binary logit model estimated using the Markov Chain Monte Carlo (MCMC) sampling technique was used to evaluate variables that could be influencing elderly crash severity. Preliminary results of the analysis suggest that TTR is statistically significant in affecting the severity of a crash involving an elderly driver. The result suggests that one unit increase in the probability of congestion reduces the likelihood of the elderly severe crash by nearly 22%. These findings will enhance the understanding of TTR and its impact on the elderly crash severity.

Keywords: highway safety, travel time reliability, elderly drivers, traffic modeling

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Authors: B. Ramakrishna, S. Sivamurthy Reddy

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The various types of aggregates such as granite, dolerite, Quartzite, dolomitic limestone, limestone and river gravel were used to produce the concrete with 28-day target compressive strength of 35, 60, and 80 Mpa. The compressive strength of concrete, as well as aggregates, was measured to study the effect of rupture probability of aggregate on the fracture surface of the concrete. Also, the petrographic studies were carried out to study the texture, type of minerals present and their relative proportions in various types of aggregates. The concrete of various grades produced with the same aggregate has shown a rise in RPCA with strength. However, the above relationship has ceased to exist in the concretes of the same grade, made of different types of aggregates. The carbonate aggregates namely Limestone and Dolomitic limestone have produced concrete with higher RPCA irrespective of the strength of concrete. The mode of origin, texture and mineralogical composition of aggregates have a significant impact on their pulse velocity and thereby the pulse velocity of concrete.

Keywords: RPCA, DL, G, LS, RG

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Authors: Arafat Husain

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The lubricants are one of the most used resource in today’s world. Lot of the superpowers are dependent on the lubricant resource for their country to function. To see that the lubricants are not adulterated we need to develop some efficient ways and to see which fluid has been added to the lubricant. So to observe the these malpractices in the lubricant we need to develop a method. We take a elastic ball and through it at probability circle in the submerged in the lubricant at a fixed force and see the distance of pitching and the point of fall. Then we the ratio of distance of falling to the distance of pitching and if the measured ratio is greater than one the fluid is less viscous and if the ratio is lesser than the lubricant is viscous. We will check the falling point of pure lubricant at fixed force and every pure lubricant would have a fixed falling point. After that we would adulterate the lubricant and note the falling point and if the falling point is less than the standard value then adulterate is solid and if the adulterate is liquid the falling point will be more than the standard value. Hence the comparison with the standard falling point will give the efficiency of the lubricant.

Keywords: falling point of lubricant, falling point ratios, probability circle, octane number

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Authors: Maged Assem Soliman Mossallam

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The conversion of solar thruster model to an upper stage hydrogen rocket is considered. Solar thruster categorization limits its capabilities to low and moderate thrust system with high specific impulse. The current study proposes a different concept for such systems by increasing the thrust which enables using as an upper stage rocket and for future launching purposes. A computational model for the thruster is discussed for solar thruster subsystems. The first module depends on ray tracing technique to determine the intercepted solar power by the hydrogen combustion chamber. The cavity receiver is modeled using finite volume technique. The final module imports the heated hydrogen properties to the nozzle using quasi one dimensional simulation. The probability of shock waves formulation inside the nozzle is almost diminished as the outlet pressure in space environment tends to zero. The computational model relates the high thrust hydrogen rocket conversion to the design parameters and operating conditions of the thruster. Three different designs for solar thruster systems are discussed. The first design is a low thrust high specific impulse design that produces about 10 Newton of thrust .The second one output thrust is about 250 Newton and the third design produces about 1000 Newton.

Keywords: space propulsion, hydrogen rocket, thrust, specific impulse

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Authors: Robert Vanderburg, Nicholas Gibson

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Objectives: There has been an increased focus in education students’ mathematics skills in the last two years. Universities have, specifically, had problems teaching students struggling with mathematics. This paper focuses on the ability of a digital platform to significantly improve mathematics skills for struggling students. Methods: 32 students who demonstrated low scores on a mathematics test were selected to take part in a one-month tutorial program using a digital mathematics portal. Students were provided feedback for questions posted on the portal and a fortnightly tutorial session. Results: A pre-test post-test design was analyzed using a one-way analysis of variance (ANOVA). The analysis suggested that students improved skills in algebra, geometry, statistics, probability, ratios, fractions, and probability. Conclusion: Distance university students can improve their mathematics skills using a digital platform.

Keywords: digital education, distance education, higher education, mathematics education

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Authors: Deepa Das, Susmita Das

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Cognitive Radio (CR) is an emerging technology to combat the spectrum scarcity issues. This is achieved by consistently sensing the spectrum, and detecting the under-utilized frequency bands without causing undue interference to the primary user (PU). In soft decision fusion (SDF) based cooperative spectrum sensing, various evolutionary algorithms have been discussed, which optimize the weight coefficient vector for maximizing the detection performance. In this paper, we propose the hybrid invasive weed optimization and particle swarm optimization (IWO/PSO) algorithm as a fast and global optimization method, which improves the detection probability with a lesser sensing time. Then, the efficiency of this algorithm is compared with the standard invasive weed optimization (IWO), particle swarm optimization (PSO), genetic algorithm (GA) and other conventional SDF based methods on the basis of convergence and detection probability.

Keywords: cognitive radio, spectrum sensing, soft decision fusion, GA, PSO, IWO, hybrid IWO/PSO

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Authors: Sadaf Nawaz, Adnan Ahmed Khan, Asad Mahmood, Chaudhary Farrukh Javed

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Classically, an energy detector is implemented in time domain (TD). However, frequency domain (FD) based energy detector has demonstrated an improved performance. This paper presents a comparison between the two approaches as to analyze their pros and cons. A detailed performance analysis of the classical TD energy-detector and the periodogram based detector is performed. Exact and approximate mathematical expressions for probability of false alarm (Pf) and probability of detection (Pd) are derived for both approaches. The derived expressions naturally lead to an analytical as well as intuitive reasoning for the improved performance of (Pf) and (Pd) in different scenarios. Our analysis suggests the dependence improvement on buffer sizes. Pf is improved in FD, whereas Pd is enhanced in TD based energy detectors. Finally, Monte Carlo simulations results demonstrate the analysis reached by the derived expressions.

Keywords: cognitive radio, energy detector, periodogram, spectrum sensing

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Authors: Ishay Wolf

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In this study, the financial position of pension actors in the market during the pension system transition toward a more funded capitalized scheme is explored, mainly via an option benefit model. This is enabled by not considering the economy as a single earning cohort. We analytically demonstrate a socio-economic anomaly in the funded pension system, which is in favor of high earning cohorts on at the expense of low earning cohorts. This anomaly is realized by a lack of insurance and exposure to financial and systemic risks. Furthermore, the anomaly might lead to pension re-reform back to unfunded scheme, mostly due to political pressure. We find that a minimum pension guarantee is a rebalance mechanism to this anomaly, which increases the probability to of the sustainable pension scheme. Specifically, we argue that implementing the guarantee with an intra-generational, risk-sharing mechanism is the most efficient way to reduce the effect of this abnormality. Moreover, we exhibit the convergence process toward implementing minimum pension guarantee in many countries which have capitalized their pension systems during the last three decades, particularly among Latin America and CEE countries.

Keywords: benefits, pension scheme, put option, social security

Procedia PDF Downloads 122

Authors: Helen Pluut, Remus Ilies, Nikos Dimotakis, Maral Darouei

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This study aims to examine recovery from work as an important daily activity with implications for workplace behavior. Building on affective events theory and the stressor-detachment model as frameworks, this paper proposes and tests a comprehensive within-individual model that uncovers the role of recovery processes at home in linking workplace demands (e.g., workload) and stressors (e.g., workplace incivility) to next-day organizational citizenship behaviors (OCBs). Our sample consisted of 126 full-time employees in a large Midwestern University. For a period of 16 working days, these employees were asked to fill out 3 electronic surveys while at work. The first survey (sent out in the morning) measured self-reported sleep quality, recovery experiences the previous day at home, and momentary effect. The second survey (sent out close to the end of the workday) measured job demands and stressors as well as OCBs, while the third survey in the evening assessed job strain. Data were analyzed using Hierarchical Linear Modeling (HLM). Results indicated that job demands and stressors at work made it difficult to unwind properly at home and have a good night’s sleep, which had repercussions for next day’s morning effect, which, in turn, influenced OCBs. It can be concluded that processes of recovery are vital to an individual’s daily effective functioning and behavior at work, but recovery may become impaired after a hard day’s work. Thus, our study sheds light on the potentially persistent nature of strain experienced as a result of work and points to the importance of recovery processes to enable individuals to avoid such cross-day spillover. Our paper will discuss this implication for theory and practice as well as potential directions for future research.

Keywords: affect, job demands, organizational citizenship behavior, recovery, strain

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Authors: Kim Anema, Matthias Max, Chris Zevenbergen

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For any flood risk manager the 'safety paradox' has to be a familiar concept: low probability leads to a sense of safety, which leads to more investments in the area, which leads to higher potential consequences: keeping the aggregated risk (probability*consequences) at the same level. Therefore, it is important to mitigate potential consequences apart from probability. However, when the (perceived) probability is so low that there is no recognizable trend for society to adapt to, addressing the potential consequences will always be the lagging point on the agenda. Preparedness programs fail because of lack of interest and urgency, policy makers are distracted by their day to day business and there's always a more urgent issue to spend the taxpayer's money on. The leading question in this study was how to address the social consequences of flooding in a context of (perceived) low probability. Disruptions of everyday urban life, large or small, can be caused by a variety of (un)expected things - of which flooding is only one possibility. Variability like this is typically addressed with resilience - and we used the concept of Community Resilience as the framework for this study. Drawing on face to face interviews, an extensive questionnaire and publicly available statistical data we explored the 'whole society response' to two recent urban flood events; the Brisbane Floods (AUS) in 2011 and the Dresden Floods (GE) in 2013. In Brisbane, we studied how the societal impacts of the floods were counteracted by both authorities and the public, and in Dresden we were able to validate our findings. A large part of the reactions, both public as institutional, to these two urban flood events were not fuelled by preparedness or proper planning. Instead, more important success factors in counteracting social impacts like demographic changes in neighborhoods and (non-)economic losses were dynamics like community action, flexibility and creativity from authorities, leadership, informal connections and a shared narrative. These proved to be the determining factors for the quality and speed of recovery in both cities. The resilience of the community in Brisbane was good, due to (i) the approachability of (local) authorities, (ii) a big group of ‘secondary victims’ and (iii) clear leadership. All three of these elements were amplified by the use of social media and/ or web 2.0 by both the communities and the authorities involved. The numerous contacts and social connections made through the web were fast, need driven and, in their own way, orderly. Similarly in Dresden large groups of 'unprepared', ad hoc organized citizens managed to work together with authorities in a way that was effective and speeded up recovery. The concept of community resilience is better fitted than 'social adaptation' to deal with the potential consequences of an (im)probable flood. Community resilience is built on capacities and dynamics that are part of everyday life and which can be invested in pre-event to minimize the social impact of urban flooding. Investing in these might even have beneficial trade-offs in other policy fields.

Keywords: community resilience, disaster response, social consequences, preparedness

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Authors: Phontip Kanlahasuth

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This paper presents the environmental safety and occupational health risk assessment of rocket static test by assessing risk level from probability and severity and then appropriately applying the risk control measures. Before the environmental safety and occupational health measures are applied, the serious hazards level is 31%, medium level is 24% and low level is 45%. Once risk control measures are practically implemented, the serious hazard level can be diminished, medium level is 38%, low level is 45% and eliminated level is 17%. It is clearly shown that the environmental safety and occupational health measures can significantly reduce the risk level.

Keywords: rocket static test, hazard, risk, risk assessment, risk analysis, environment, safety, occupational health, acceptable risk, probability, severity, risk level

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Authors: Jia-Shiun Chen, Quoc-Viet Huynh

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This work develops a flexible-body dynamic model of an all-terrain vehicle (ATV), capable of recovering dynamic stresses while the ATV travels on random bumpy roads. The fatigue life of components is forecasted as well. While considering the interaction between dynamic forces and structure deformation, the proposed model achieves a highly accurate structure stress prediction and fatigue life prediction. During the simulation, stress time history of the ATV structure is retrieved for life prediction. Finally, the hot sports of the ATV frame are located, and the frame life for combined road conditions is forecasted, i.e. 25833.6 hr. If the usage of vehicle is eight hours daily, the total vehicle frame life is 8.847 years. Moreover, the reaction force and deformation due to the dynamic motion can be described more accurately by using flexible body dynamics than by using rigid-body dynamics. Based on recommendations made in the product design stage before mass production, the proposed model can significantly lower development and testing costs.

Keywords: flexible-body dynamics, veicle, dynamics, fatigue, durability

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Authors: Obuzor Eze Obuzor, Ekpoke Okurube Sliver

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The study was carried out at Debee farms at Ahoada West Local Government area, Rivers State, Nigeria. To evaluate the impact of two different cost-effective available feed composition on growth performance of weaned piglets. Thirty weaned uncontrolled cross bred (Large white x pietrain) piglets of average initial weight of 3.04 Kg weaned at 30days were assigned to three dietary treatments, comprising three replicates of 10 weaned piglets each, piglets were kept at 7 °C in different pens with dimensions of 4.50 × 4.50 m. The design of the experiment was completely randomized design, data from the study were subjected to one-way analysis of variance (ANOVA) and significant means were separated using Duncan's Multiple Range Test using Statistical Analysis System (SAS) software for windows (2 0 0 3), statistical significance was assessed at P < 0.05 (95% confidence interval) while survival rate was calculated using simple percentage. A standard diet was prepared to meet the nutrient requirements of weaned piglets at (20.8% crude protein). The three diets were fed to the animals in concrete feeding trough, control diet (C) had soybean meal while first treatment had spent grain (T1) and the second treatment had wheat offal (T2) respectively. The experiment was partitioned into four weeks periods (days 1-7, 8-14, 15-21 and 22-28). Feed and water were given unrestrictedly throughout the period of the experiment. The feed intake and weights of the pigs were recorded on weekly basis. Feed conversion ratio and daily weight gain were calculated and the study lasted for four weeks. There was no significant (P>0.05) effect of diet on survival rate, final body weight, average daily weight gain, daily feed intake and feed conversion ratio. The overall performance showed that treatment one (T1) had survival rate (93%), improved daily weight gain (36.21 g), average daily feed intake (120.14 g) and had the best feed conversion ratio (0.29) similar high mean value with the control while treatment two (T2) had lowest and negative response to all parameters. It could be concluded that feed formulated with spent grain is cheaper than control (soybean meal) and also improved the growth performance of weaned piglets.

Keywords: piglets, weaning, feed conversions ratio, daily weight gain

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Authors: Gul Nihal Gugul, Merih Aydinalp-Koksal

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In this study, hourly energy consumption model of a single detached house in Ankara, Turkey is developed using ESP-r building energy simulation software. Natural gas is used for space heating, cooking, and domestic water heating in this two story 4500 square feet four-bedroom home. Hourly electricity consumption of the home is monitored by an automated meter reading system, and daily natural gas consumption is recorded by the owners during 2013. Climate data of the region and building envelope data are used to develop the model. The heating energy consumption of the house that is estimated by the ESP-r model is then compared with the actual heating demand to determine the performance of the model. Scenarios are applied to the model to determine the amount of reduction in the total energy consumption of the house. The scenarios are using photovoltaic panels to generate electricity, ground source heat pumps for space heating and solar panels for domestic hot water generation. Alternative scenarios such as improving wall and roof insulations and window glazing are also applied. These scenarios are evaluated based on annual energy, associated CO2 emissions, and fuel expenditure savings. The pay-back periods for each scenario are also calculated to determine best alternative energy source or technology option for this home to reduce annual energy use and CO2 emission.

Keywords: ESP-r, building energy simulation, residential energy saving, CO2 reduction

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Authors: Melese Tadesse Morebo

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Pastoral and agro-pastoral communities in East Africa, particularly in Ethiopia, are vulnerable to climate-related risks. The aim of this study is to characterize the annual, seasonal, and monthly rainfall and temperature of the middle and lower awash areas of Ethiopia. Start of season (SOS), end of season (EOS), length of growing season (LGS), number of rainy days, and probability of dry spell occurrences were analyzed using INSTAT Plus (v3.7) software. Daily rainfall and temperature data for 33 years (1990–2022) from six stations were analyzed. The result of the study revealed that the annual rainfall in the study area as a whole showed an increasing trend, but its trend was statistically non-significant. During the study period, the Kiremt rainfall at Amibara station showed statistically significant increasing trends. The trend analysis of SOS, EOS, and LGS shows up and down trends at all stations. The mean lengths of growing seasons in the study area ranged from 20 to 61 days during the study period. In the study area, the annual mean maximum temperature ranged between 34.1°C and 38.3°C over the last three decades. All stations within the research area during the study period, the annual minimum temperature exhibited a substantial impact.

Keywords: annual rainfall, LGS, minimum temperature, Mann-Kendall test

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Authors: Ravinder Bahl, Jamini Sharma

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The paper demonstrates analysis of the cervical cancer based on a probabilistic model. It involves technique for classification and prediction by recognizing typical and diagnostically most important test features relating to cervical cancer. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases. The combination of the conventional statistical and machine learning tools is applied for the analysis. Experimental study with real data demonstrates the feasibility and potential of the proposed approach for the said cause.

Keywords: cervical cancer, recurrence, no recurrence, probabilistic, classification, prediction, machine learning

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Authors: Mohammad Rahim Rahnama, Amir Ali Kharazmi, Safiye Rokni

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The tourism industry will be the most important unlimited, sustainable source of income after the oil and automotive industries by 2020 and not only countries, but cities are striving to apprehend its various facets. In line with this objective, the present descriptive-analytical study, through survey and using a questionnaire, seeks to evaluate the probability of tourists’ return and their recommendation to their countrymen to travel to Mashhad, Iran. The population under study is a sample of 384 foreign tourists who, in 2016, arrived at Mashhad, the second metropolis in Iran and its biggest religious city. The Kaplan-Meier estimator was used to analyze the data. Twenty-six percent of the tourists are female and 74% are male. On average, each tourist has had 3.02 trips abroad and 2.1 trips to Mashhad. Tourists from 14 different countries have arrived at Mashhad. Kuwait (15.9%), Armenia (15.6%), and Iraq (10.9%) were the countries where most tourists originated. Seventy-six percent of the tourists traveled with family and 90% of the tourists arrived at Mashhad via airplane. Major purposes of tourists’ trip include pilgrimage (27.9%), treatment (22.1%) followed by pilgrimage and treatment combined (35.4%). Major issues for tourists, in the order of priority, include quality of goods and services (30.2%), shopping (18%), and inhabitants’ treatment of foreigners (15.9%). Main tourist attractions, in addition to the Holy Shrine of Imam Reza, include Torqabeh and Shandiz (Torqabeh 40.9% and Shandiz 29.9%), Neyshabour (18.2%) followed by Kalat, 4.4%. The average willingness to return among tourists is 3.13, which is higher than the mean 3, indicating satisfaction with the stay in Mashhad. Similarly, the average for tourists’ recommending to their countrymen to visit Mashhad is 3.42, which is also an indicator of tourists’ satisfaction with their presence in Mashhad. According to the findings of the Kaplan-Meier estimator, an increase in the number of tourists’ trips to Mashhad, and an increase in the number of tourists’ foreign trips, reduces the probability of recommending a trip to Mashhad by tourists. Similarly, willingness to return is higher among those who stayed at a relatives’ home compared with other patterns of residence (hotels, self-catering accommodation, and pilgrim houses). Therefore, addressing the issues raised by tourists is essential for their return and their recommendation to others to travel to Mashhad.

Keywords: international tourist, probability of return, satisfaction, Mashhad

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Authors: Xiaohan Bookman, Xiaoyan Zhu

Abstract:

The outputs of non-linear feed-forward neural network are positive, which could be treated as probability when they are normalized to one. If we take Entropy-Based Principle into consideration, the outputs for each sample could be represented as the distribution of this sample for different clusters. Entropy-Based Principle is the principle with which we could estimate the unknown distribution under some limited conditions. As this paper defines two processes in Feed-Forward Neural Network, our limited condition is the abstracted features of samples which are worked out in the abstraction process. And the final outputs are the probability distribution for different clusters in the clustering process. As Entropy-Based Principle is considered into the feed-forward neural network, a clustering method is born. We have conducted some experiments on six open UCI data sets, comparing with a few baselines and applied purity as the measurement. The results illustrate that our method outperforms all the other baselines that are most popular clustering methods.

Keywords: feed-forward neural network, clustering, max-entropy principle, probabilistic models

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Authors: Mechlouch Ridha Fethi

Abstract:

This paper presents a recent mathematical model in political sciences concerning democracy. The model is represented by a logarithmic equation linking the Relative Index of Democracy (RID) to Participation Ratio (PR). Firstly the meanings of the different parameters of the model were presented; and the variation curve of the RID according to PR with different critical areas was discussed. Secondly, the model was applied to a virtual group where we show that the model can be applied depending on the gender. Thirdly, it was observed that the model can be extended to different language models of democracy and that little use to assess the state of democracy for some International organizations like UNO.

Keywords: democracy, mathematic, modelization, quantification

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Authors: B. Kriheli, E. Levner, T. C. E. Cheng, C. T. Ng

Abstract:

During the last decades, research interest in planning, scheduling, and control of emergency response operations, especially people rescue and evacuation from the dangerous zone of marine accidents, has increased dramatically. Until the survivors (called ‘targets’) are found and saved, it may cause loss or damage whose extent depends on the location of the targets and the search duration. The problem is to efficiently search for and detect/rescue the targets as soon as possible with the help of intelligent mobile robots so as to maximize the number of saved people and/or minimize the search cost under restrictions on the amount of saved people within the allowable response time. We consider a special situation when the autonomous mobile robots (AMR), e.g., unmanned aerial vehicles and remote-controlled robo-ships have no operator on board as they are guided and completely controlled by on-board sensors and computer programs. We construct a mathematical model for the search process in an uncertain environment and provide a new fast algorithm for scheduling the activities of the autonomous robots during the search-and rescue missions after an accident at sea. We presume that in the unknown environments, the AMR’s search-and-rescue activity is subject to two types of error: (i) a 'false-negative' detection error where a target object is not discovered (‘overlooked') by the AMR’s sensors in spite that the AMR is in a close neighborhood of the latter and (ii) a 'false-positive' detection error, also known as ‘a false alarm’, in which a clean place or area is wrongly classified by the AMR’s sensors as a correct target. As the general resource-constrained discrete search problem is NP-hard, we restrict our study to finding local-optimal strategies. A specificity of the considered operational research problem in comparison with the traditional Kadane-De Groot-Stone search models is that in our model the probability of the successful search outcome depends not only on cost/time/probability parameters assigned to each individual location but, as well, on parameters characterizing the entire history of (unsuccessful) search before selecting any next location. We provide a fast approximation algorithm for finding the AMR route adopting a greedy search strategy in which, in each step, the on-board computer computes a current search effectiveness value for each location in the zone and sequentially searches for a location with the highest search effectiveness value. Extensive experiments with random and real-life data provide strong evidence in favor of the suggested operations research model and corresponding algorithm.

Keywords: disaster management, intelligent robots, scheduling algorithm, search-and-rescue at sea

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Authors: H. S. Kang

Abstract:

Coupled electrical and optical model for conversion of electrical energy into coherent optical energy for transmitter-receiver link by solid state device is presented. Probability distribution for travelling laser beam switching time intervals and the number of switchings in the time interval is obtained. Selector function mapping is employed to regulate optical data transmission speed. It is established that regulated laser transmission from PhotoActive Laser transmitter follows principal of invariance. This considerably simplifies design of PhotoActive Laser Transmission networks.

Keywords: computational mathematics, finite difference Markov chain methods, sequence spaces, singularly perturbed differential equations

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Authors: Abd El Hady N. Ebraheim

Abstract:

This paper introduces a new generalization of the two parameter Weibull distribution. To this end, the quadratic rank transmutation map has been used. This new distribution is named exponentiated transmuted Weibull (ETW) distribution. The ETW distribution has the advantage of being capable of modeling various shapes of aging and failure criteria. Furthermore, eleven lifetime distributions such as the Weibull, exponentiated Weibull, Rayleigh and exponential distributions, among others follow as special cases. The properties of the new model are discussed and the maximum likelihood estimation is used to estimate the parameters. Explicit expressions are derived for the quantiles. The moments of the distribution are derived, and the order statistics are examined.

Keywords: exponentiated, inversion method, maximum likelihood estimation, transmutation map

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Authors: Gurbir Kaur, Surinder Pal Singh

Abstract:

The present study is a part of the research work on the effect of limestone powder (LP), silica fume (SF) and metakaolin (MK), on the flexural fatigue performance of steel fibre reinforced concrete (SFRC). Corrugated rectangular steel fibres of size 0.6x2.0x35 mm at a constant volume fraction of 1.0% have been incorporated in all mix combinations as the reinforcing material. Three mix combinations were prepared by replacing 30% of ordinary Portland cement (OPC) by weight with these cement additives in binary and ternary fashion to demonstrate their contribution. An experimental programme was conducted to obtain the fatigue lives of all mix combinations at various stress levels. The fatigue life data have been analysed as an attempt to determine the relationship between stress level ‘S’, number of cycles to failure ‘N’ and probability of failure ‘Pf’ for all mix combinations. The experimental coefficients of the fatigue equation have also been obtained from the fatigue data to represent the S-N-Pf curves analytically.

Keywords: cement additives, fatigue life, probability of failure, steel fibre reinforced concrete

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