Search results for: STOCHASTIC

Authors: Massimiliano Frezza, Sergio Bianchi, Augusto Pianese

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Introduced by Shannon in 1948 in the field of information theory as the average rate at which information is produced by a stochastic set of data, the concept of entropy has gained much attention as a measure of uncertainty and unpredictability associated with a dynamical system, eventually depicted by a stochastic process. In particular, the Shannon entropy measures the degree of order/disorder of a given signal and provides useful information about the underlying dynamical process. It has found widespread application in a variety of fields, such as, for example, cryptography, statistical physics and finance. In this regard, many contributions have employed different measures of entropy in an attempt to characterize the financial time series in terms of market efficiency, market crashes and/or financial crises. The Shannon entropy has also been considered as a measure of the risk of a portfolio or as a tool in asset pricing. This work investigates the theoretical link between the Shannon entropy and the multifractional Brownian motion (mBm), stochastic process which recently is the focus of a renewed interest in finance as a driving model of stochastic volatility. In particular, after exploring the current state of research in this area and highlighting some of the key results and open questions that remain, we show a well-defined relationship between the Shannon (log)entropy and the memory function H(t) of the mBm. In details, we allow both the length of time series and time scale to change over analysis to study how the relation modify itself. On the one hand, applications are developed after generating surrogates of mBm trajectories based on different memory functions; on the other hand, an empirical analysis of several international stock indexes, which confirms the previous results, concludes the work.

Keywords: Shannon entropy, multifractional Brownian motion, Hurst–Holder exponent, stock indexes

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Authors: Loïc Warscotte, Jehan Boreux

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The scientific literature of changepoint detection is vast. Today, a lot of methods are available to detect abrupt changes or slight drift in a signal, based on CUSUM or EWMA charts, for example. However, these methods rely on strong assumptions, such as the stationarity of the stochastic underlying process, or even the independence and Gaussian distributed noise at each time. Recently, the breakthrough research on locally stationary processes widens the class of studied stochastic processes with almost no assumptions on the signals and the nature of the changepoint. Despite the accurate description of the mathematical aspects, this methodology quickly suffers from impractical time and space complexity concerning the signals with high-rate data collection, if the characteristics of the process are completely unknown. In this paper, we then addressed the problem of making this theory usable to our purpose, which is monitoring a high-speed weigh-in-motion system (HS-WIM) towards direct enforcement without supervision. To this end, we first compute bounded approximations of the initial detection theory. Secondly, these approximating bounds are empirically validated by generating many independent long-run stochastic processes. The abrupt changes and the drift are both tested. Finally, this relaxed methodology is tested on real signals coming from a HS-WIM device in Belgium, collected over several months.

Keywords: changepoint, weigh-in-motion, process, non-parametric

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Authors: Karim Hamidi Machekposhti, Hossein Sedghi

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This study was designed to find the best stochastic model (using of time series analysis) for annual extreme streamflow (peak and maximum streamflow) of Karkheh River at Iran. The Auto-regressive Integrated Moving Average (ARIMA) model used to simulate these series and forecast those in future. For the analysis, annual extreme streamflow data of Jelogir Majin station (above of Karkheh dam reservoir) for the years 1958–2005 were used. A visual inspection of the time plot gives a little increasing trend; therefore, series is not stationary. The stationarity observed in Auto-Correlation Function (ACF) and Partial Auto-Correlation Function (PACF) plots of annual extreme streamflow was removed using first order differencing (d=1) in order to the development of the ARIMA model. Interestingly, the ARIMA(4,1,1) model developed was found to be most suitable for simulating annual extreme streamflow for Karkheh River. The model was found to be appropriate to forecast ten years of annual extreme streamflow and assist decision makers to establish priorities for water demand. The Statistical Analysis System (SAS) and Statistical Package for the Social Sciences (SPSS) codes were used to determinate of the best model for this series.

Keywords: stochastic models, ARIMA, extreme streamflow, Karkheh river

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Authors: Salinee Thumronglaohapun

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The proper number and appropriate locations of service centers can save cost, raise revenue and gain more satisfaction from customers. Establishing service centers is high-cost and difficult to relocate. In long-term planning periods, several factors may affect the service. One of the most critical factors is uncertain demand of customers. The opened service centers need to be capable of serving customers and making a profit although the demand in each period is changed. In this work, the capacitated location-allocation problem with stochastic demand is considered. A mathematical model is formulated to determine suitable locations of service centers and their allocation to maximize total profit for multiple planning periods. Two heuristic methods, a local search and genetic algorithm, are used to solve this problem. For the local search, five different chances to choose each type of moves are applied. For the genetic algorithm, three different replacement strategies are considered. The results of applying each method to solve numerical examples are compared. Both methods reach to the same best found solution in most examples but the genetic algorithm provides better solutions in some cases.

Keywords: location-allocation problem, stochastic demand, local search, genetic algorithm

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Authors: Mehdi Jalalpour, Mazdak Tootkaboni

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We present a computationally efficient method for reliability-based topology optimization under material properties uncertainty, which is assumed to be lognormally distributed and correlated within the domain. Computational efficiency is achieved through estimating the response statistics with stochastic perturbation of second order, using these statistics to fit an appropriate distribution that follows the empirical distribution of the response, and employing an efficient gradient-based optimizer. The proposed algorithm is utilized for design of new structures and the changes in the optimized topology is discussed for various levels of target reliability and correlation strength. Predictions were verified thorough comparison with results obtained using Monte Carlo simulation.

Keywords: material uncertainty, stochastic perturbation, structural reliability, topology optimization

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Authors: Mehmet Savsar, Majid Aldaihani

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Flexible Manufacturing Systems (FMS) are used to produce a variety of parts on the same equipment. Therefore, their utilization is higher than traditional machining systems. Higher utilization, on the other hand, results in more frequent equipment failures and additional need for maintenance. Therefore, it is necessary to carefully analyze operational characteristics and productivity of FMS or Flexible Manufacturing Cells (FMC), which are smaller configuration of FMS, before installation or during their operation. Appropriate models should be developed to determine production rates based on operational conditions, including equipment reliability, availability, and repair capacity. In this paper, a stochastic model is developed for an automated FMC system, which consists of two machines served by two robots and a single repairman. The model is used to determine system productivity and equipment utilization under different operational conditions, including random machine failures, random repairs, and limited repair capacity. The results are compared to previous study results for FMC system with sufficient repair capacity assigned to each machine. The results show that the model will be useful for design engineers and operational managers to analyze performance of manufacturing systems at the design or operational stages.

Keywords: flexible manufacturing, FMS, FMC, stochastic modeling, production rate, reliability, availability

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Authors: Hyunsun Lee, Yi Zhu

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Wireless ad-hoc network is a decentralized type of temporary machine-to-machine connection that is spontaneous or impromptu so that it does not rely on any fixed infrastructure and centralized administration. As unmanned aerial vehicles (UAVs), also called drones, have recently become more accessible and widely utilized in military and civilian domains such as surveillance, search and detection missions, traffic monitoring, remote filming, product delivery, to name a few. The communication between these UAVs become possible and materialized through Flying Ad-hoc Networks (FANETs). However, due to the high mobility of UAVs that may cause different types of transmission interference, it is vital to design robust routing protocols for FANETs. In this talk, the multicast routing method based on a modified stochastic branching process is proposed. The stochastic branching process is often used to describe an early stage of an infectious disease outbreak, and the reproductive number in the process is used to classify the outbreak into a major or minor outbreak. The reproductive number to regulate the local transmission rate is adapted and modified for flying ad-hoc network communication. The performance of the proposed routing method is compared with other well-known methods such as flooding method and gossip method based on three measures; average reachability, average node usage and average branching factor. The proposed routing method achieves average reachability very closer to flooding method, average node usage closer to gossip method, and outstanding average branching factor among methods. It can be concluded that the proposed multicast routing scheme is more efficient than well-known routing schemes such as flooding and gossip while it maintains high performance.

Keywords: Flying Ad-hoc Networks, Multicast Routing, Stochastic Branching Process, Unmanned Aerial Vehicles

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Authors: N. Massoum, B. Bouazza

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In this paper, we have developed a numerical simulation model to describe the electrical properties of GaInP MESFET with submicron gate (Lg = 0.5 µm). This model takes into account the three-dimensional (3D) distribution of the load in the short channel and the law effect of mobility as a function of electric field. Simulation software based on a stochastic method such as Monte Carlo has been established. The results are discussed and compared with those of the experiment. The result suggests experimentally that, in a very small gate length in our devices (smaller than 40 nm), short-channel tunneling explains the degradation of transistor performance, which was previously enhanced by velocity overshoot.

Keywords: Monte Carlo simulation, transient electron transport, MESFET device, simulation software

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Authors: Ho Yuan-Hong, Huang Chiung-Ju

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This study conducts simulation analyses to find the optimal debt ceiling of Taiwan, while factoring in welfare maximization under a dynamic stochastic general equilibrium framework. The simulation is based on Taiwan's 2001 to 2011 economic data and shows that welfare is maximized at a "debt"⁄"GDP" ratio of 0.2, increases in the "debt"⁄"GDP " ratio leads to increases in both tax and interest rates and decreases in the consumption ratio and working hours. The study results indicate that the optimal debt ceiling of Taiwan is 20% of GDP, where if the "debt"⁄"GDP" ratio is greater than 40%, the welfare will be negative and result in welfare loss.

Keywords: debt sustainability, optimal debt ceiling, dynamic stochastic general equilibrium, welfare maximization

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Authors: D. Calogine, O. Chau, S. Dotti, O. Ramiarinjanahary, P. Rasoavonjy, F. Tovondahiniriko

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Mafate is a natural circus in the north-western part of Reunion Island, without an electrical grid and road network. A micro-grid concept is being experimented in this area, composed of a photovoltaic production combined with electrochemical batteries, in order to meet the local population for self-consumption of electricity demands. This work develops a discrete model as well as a stochastic model in order to reach an optimal equilibrium between production and consumptions for a cluster of houses. The management of the energy power leads to a large linearized programming system, where the time interval of interest is 24 hours The experimental data are solar production, storage energy, and the parameters of the different electrical devices and batteries. The unknown variables to evaluate are the consumptions of the various electrical services, the energy drawn from and stored in the batteries, and the inhabitants’ planning wishes. The objective is to fit the solar production to the electrical consumption of the inhabitants, with an optimal use of the energies in the batteries by satisfying as widely as possible the users' planning requirements. In the discrete model, the different parameters and solutions of the linear programming system are deterministic scalars. Whereas in the stochastic approach, the data parameters and the linear programming solutions become random variables, then the distributions of which could be imposed or established by estimation from samples of real observations or from samples of optimal discrete equilibrium solutions.

Keywords: photovoltaic production, power consumption, battery storage resources, random variables, stochastic modeling, estimations of probability distributions, mixed integer linear programming, smart micro-grid, self-consumption of electricity.

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Authors: Faramarz Khosravi, Gokhan Izbirak

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A weighted statistical stochastic based Analytical Hierarchy Process (AHP) model for modeling the potential barriers and enablers of sustainability for measuring and assessing the sustainability level is proposed. For context-dependent potential barriers and enablers, the proposed model takes the basis of the properties of the variables describing the sustainability functions and was developed into a realistic analytical model for the sustainable behavior of an organization. This thus serves as a means for measuring the sustainability of the organization. The main focus of this paper was the application of the AHP tool in a statistically-based model for measuring sustainability. Hence a strong weighted stochastic AHP based procedure was achieved. A case study scenario of a widely reported major Canadian electric utility was adopted to demonstrate the applicability of the developed model and comparatively examined its results with those of an equal-weighted model method. Variations in the sustainability of a company, as fluctuations, were figured out during the time. In the results obtained, sustainability index for successive years changed form 73.12%, 79.02%, 74.31%, 76.65%, 80.49%, 79.81%, 79.83% to more exact values 73.32%, 77.72%, 76.76%, 79.41%, 81.93%, 79.72%, and 80,45% according to priorities of factors that have found by expert views, respectively. By obtaining relatively necessary informative measurement indicators, the model can practically and effectively evaluate the sustainability extent of any organization and also to determine fluctuations in the organization over time.

Keywords: AHP, sustainability fluctuation, environmental indicators, performance measurement

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Authors: Nurul Bin Ibrahim

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Advancements in neural networks have offered valuable insights into Fluid Dynamics, notably in addressing turbulence-related challenges. In this research, we introduce multiple applications of models of neural networks, namely Feed-Forward and Recurrent Neural Networks, to explore the relationship between jet formations and stratified turbulence within stochastically excited Boussinesq systems. Using machine learning tools like TensorFlow and PyTorch, the study has created models that effectively mimic and show the underlying features of the complex patterns of jet formation and stratified turbulence. These models do more than just help us understand these patterns; they also offer a faster way to solve problems in stochastic systems, improving upon traditional numerical techniques to solve stochastic differential equations such as the Euler-Maruyama method. In addition, the research includes a thorough comparison with the Statistical State Dynamics (SSD) approach, which is a well-established method for studying chaotic systems. This comparison helps evaluate how well neural networks can help us understand the complex relationship between jet formations and stratified turbulence. The results of this study underscore the potential of neural networks in computational physics and fluid dynamics, opening up new possibilities for more efficient and accurate simulations in these fields.

Keywords: neural networks, machine learning, computational fluid dynamics, stochastic systems, simulation, stratified turbulence

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Authors: Dávid Kovács, Bálint Csanády, Dániel Boros, Iván Ivkovic, Lóránt Nagy, Dalma Tóth-Lakits, László Márkus, András Lukács

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The modeling practice of financial instruments has seen significant change over the last decade due to the recognition of time-dependent and stochastically changing correlations among the market prices or the prices and market characteristics. To represent this phenomenon, the Stochastic Correlation Process (SCP) has come to the fore in the joint modeling of prices, offering a more nuanced description of their interdependence. This approach has allowed for the attainment of realistic tail dependencies, highlighting that prices tend to synchronize more during intense or volatile trading periods, resulting in stronger correlations. Evidence in statistical literature suggests that, similarly to the volatility, the SCP of certain stock prices follows rough paths, which can be described using fractional differential equations. However, estimating parameters for these equations often involves complex and computation-intensive algorithms, creating a necessity for alternative solutions. In this regard, the Fractional Ornstein-Uhlenbeck (fOU) process from the family of fractional processes offers a promising path. We can effectively describe the rough SCP by utilizing certain transformations of the fOU. We employed neural networks to understand the behavior of these processes. We had to develop a fast algorithm to generate a valid and suitably large sample from the appropriate process to train the network. With an extensive training set, the neural network can estimate the process parameters accurately and efficiently. Although the initial focus was the fOU, the resulting model displayed broader applicability, thus paving the way for further investigation of other processes in the realm of financial mathematics. The utility of SCP extends beyond its immediate application. It also serves as a springboard for a deeper exploration of fractional processes and for extending existing models that use ordinary Wiener processes to fractional scenarios. In essence, deploying both SCP and fractional processes in financial models provides new, more accurate ways to depict market dynamics.

Keywords: fractional Ornstein-Uhlenbeck process, fractional stochastic processes, Heston model, neural networks, stochastic correlation, stochastic differential equations, stochastic volatility

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Authors: Sania Maghsodloo, Sirus Mohammadi

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Electric Vehicle (EV) technology is expected to take a major share in the light-vehicle market in the coming decades. Transportation electrification has become an important issue in recent decades and the large scale deployment of EVs has yet to be achieved. The smart coordination of EV demand addresses an improvement in the flexibility of power systems and reduces the costs of power system investment. The uncertainty in EV drivers’ behaviour is one of the main problems to solve to obtain an optimal integration of EVs into power systems Charging of EVs will put an extra burden on the distribution grid and in some cases adjustments will need to be made. The stochastic process of the driving pattern is done to make the outcome of the project more realistic. Based on the stochastic data, the optimization of charging plans is made.

Keywords: electric vehicles (PEVs), smart grid, Monticello, distribution system

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Authors: Nicholas Burica, Nina Selak

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The ADHCAT (Automated Distribution Hosting Capacity Assessment Tool) was designed to run Hosting Capacity Analysis on the ComEd system via a stochastic DER (Distributed Energy Resource) placement on multiple power flow simulations against a set of violation criteria. The violation criteria in the initial version of the tool captured a limited amount of issues that individual departments design against for DER interconnections. Enhancements were made to the tool to further align with individual department violation and operation criteria, as well as the addition of new modules for use for future load profile analysis. A reporting engine was created for future analytical use based on the simulations and observations in the tool.

Keywords: distributed energy resources, hosting capacity, interconnect, voltage optimization

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Authors: Nikolaos Papadimas, Joanna Bennett, Amir Sakhaei, Timothy Dodwell

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Composite modeling of consolidation processes is playing an important role in the process and part design by indicating the formation of possible unwanted prior to expensive experimental iterative trial and development programs. Composite materials in their uncured state display complex constitutive behavior, which has received much academic interest, and this with different models proposed. Errors from modeling and statistical which arise from this fitting will propagate through any simulation in which the material model is used. A general hyperelastic polynomial representation was proposed, which can be readily implemented in various nonlinear finite element packages. In our case, FEniCS was chosen. The coefficients are assumed uncertain, and therefore the distribution of parameters learned using Markov Chain Monte Carlo (MCMC) methods. In engineering, the approach often followed is to select a single set of model parameters, which on average, best fits a set of experiments. There are good statistical reasons why this is not a rigorous approach to take. To overcome these challenges, A hierarchical Bayesian framework was proposed in which population distribution of model parameters is inferred from an ensemble of experiments tests. The resulting sampled distribution of hyperparameters is approximated using Maximum Entropy methods so that the distribution of samples can be readily sampled when embedded within a stochastic finite element simulation. The methodology is validated and demonstrated on a set of consolidation experiments of AS4/8852 with various stacking sequences. The resulting distributions are then applied to stochastic finite element simulations of the consolidation of curved parts, leading to a distribution of possible model outputs. With this, the paper, as far as the authors are aware, represents the first stochastic finite element implementation in composite process modelling.

Keywords: data-driven , material consolidation, stochastic finite elements, surrogate models

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Authors: H. D. Ibrahim, H. C. Chinwenyi, T. Danjuma

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An option is defined as a financial contract that provides the holder the right but not the obligation to buy or sell a specified quantity of an underlying asset in the future at a fixed price (called a strike price) on or before the expiration date of the option. This paper examined two approaches for derivation of Partial Differential Equation (PDE) options price valuation formula for the Heston stochastic volatility model. We obtained various PDE option price valuation formulas using the riskless portfolio method and the application of Feynman-Kac theorem respectively. From the results obtained, we see that the two derived PDEs for Heston model are distinct and non-unique. This establishes the fact of incompleteness in the model for option price valuation.

Keywords: Black-Scholes partial differential equations, Ito process, option price valuation, partial differential equations

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Authors: Ketseas Dimitris

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In this work, we investigate the effect of noise on a classical two-degree-of-freedom pitch-plunge aeroelastic system. The inlet velocity of the flow is modelled as a stochastically varying parameter by the Ornstein-Uhlenbeck (OU) stochastic process. The system is a 2D airfoil, and the elastic problem is simulated using linear springs. We study the manifestation of Limit Cycle Oscillations (LCO) that correspond to the varying fluid velocity under the dynamic stall regime. We aim to delve into the unexplored facets of the classical pitch-plunge aeroelastic system, seeking a comprehensive understanding of how parametric noise influences the occurrence of LCO and expands the boundaries of its known behavior.

Keywords: aerodynamics, aeroelasticity, computational fluid mechanics, stall flutter, stochastical processes, limit cycle oscillation

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Authors: Lukumba Phiri, Simon Tembo, Kumbuso Joshua Nyoni

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

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

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Authors: Ali Ben Abbes, ImedRiadh Farah, Vincent Barra

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Remotely sensed data are a significant source for monitoring and updating databases for land use/cover. Nowadays, changes detection of urban area has been a subject of intensive researches. Timely and accurate data on spatio-temporal changes of urban areas are therefore required. The data extracted from multi-temporal satellite images are usually non-stationary. In fact, the changes evolve in time and space. This paper is an attempt to propose a methodology for changes detection in urban area by combining a non-stationary decomposition method and stochastic modeling. We consider as input of our methodology a sequence of satellite images I₁, I₂, … I_n at different periods (t = 1, 2, ..., n). Firstly, a preprocessing of multi-temporal satellite images is applied. (e.g. radiometric, atmospheric and geometric). The systematic study of global urban expansion in our methodology can be approached in two ways: The first considers the urban area as one same object as opposed to non-urban areas (e.g. vegetation, bare soil and water). The objective is to extract the urban mask. The second one aims to obtain a more knowledge of urban area, distinguishing different types of tissue within the urban area. In order to validate our approach, we used a database of Tres Cantos-Madrid in Spain, which is derived from Landsat for a period (from January 2004 to July 2013) by collecting two frames per year at a spatial resolution of 25 meters. The obtained results show the effectiveness of our method.

Keywords: multi-temporal satellite image, urban growth, non-stationary, stochastic model

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Authors: Sina Dehghani, Zhikang Rong

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Historically, a variety of approaches have been taken to forecast commodity prices due to the significant implications of these values on the global economy. An accurate forecasting tool for a valuable commodity would significantly benefit investors and governmental agencies. Silver, in particular, has grown significantly as a commodity in recent years due to its use in healthcare and technology. This manuscript aims to utilize the Geometric Brownian Motion predictive model to forecast silver commodity prices over multiple 3-year periods. The results of the study indicate that the model has several limitations, particularly its inability to work effectively over longer periods of time, but still was extremely effective over shorter time frames. This study sets a baseline for silver commodity forecasting with GBM, and the model could be further strengthened with refinement.

Keywords: geometric Brownian motion, commodity, risk management, volatility, stochastic behavior, price forecasting

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Authors: Ali Vaezi, Jyotirmoy Dalal, Manish Verma

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The railroad is one of the primary transportation modes for hazardous materials (hazmat) shipments in North America. Installing an emergency response network capable of providing a commensurate response is one of the primary levers to contain (or mitigate) the adverse consequences from rail hazmat incidents. To this end, we propose a two-stage stochastic program to determine the location of and equipment packages to be stockpiled at each response facility. The raw input data collected from publicly available reports were processed, fed into the proposed optimization program, and then tested on a realistic railroad network in Ontario (Canada). From the resulting analyses, we conclude that the decisions based only on empirical datasets would undermine the effectiveness of the resulting network; coverage can be improved by redistributing equipment in the network, purchasing equipment with higher containment capacity, and making use of a disutility multiplier factor.

Keywords: hazmat, rail network, stochastic programming, emergency response

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Authors: Mansour H. Alkmim, Adriano T. Fabro, Marcus V. G. De Morais

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In this paper, a global direct search optimization algorithm to reduce vibration of a tuned liquid column damper (TLCD), a class of passive structural control device, is presented. The objective is to find optimized parameters for the TLCD under stochastic load from different wind power spectral density. A verification is made considering the analytical solution of an undamped primary system under white noise excitation. Finally, a numerical example considering a simplified wind turbine model is given to illustrate the efficacy of the TLCD. Results from the random vibration analysis are shown for four types of random excitation wind model where the response PSDs obtained showed good vibration attenuation.

Keywords: generalized pattern search, parameter optimization, random vibration analysis, vibration suppression

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Authors: Tefera Kebede Leyu

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The purpose of this study was to estimate the impact of LIVES project participation on the level of technical efficiency of farm households in three regions of Ethiopia. We used household-level data gathered by IRLI between February and April 2014 for the year 2013(retroactive). Data on 1,905 (754 intervention and 1, 151 control groups) sample households were analyzed using STATA software package version 14. Efforts were made to combine stochastic frontier modeling with impact evaluation methodology using the Heckman (1979) two-stage model to deal with possible selectivity bias arising from unobservable characteristics in the stochastic frontier model. Results indicate that farmers in the two groups are not efficient and operate below their potential frontiers i.e., there is a potential to increase crop productivity through efficiency improvements in both groups. In addition, the empirical results revealed selection bias in both groups of farmers confirming the justification for the use of selection bias corrected stochastic frontier model. It was also found that intervention farmers achieved higher technical efficiency scores than the control group of farmers. Furthermore, the selectivity bias-corrected model showed a different technical efficiency score for the intervention farmers while it more or less remained the same for that of control group farmers. However, the control group of farmers shows a higher dispersion as measured by the coefficient of variation compared to the intervention counterparts. Among the explanatory variables, the study found that farmer’s age (proxy to farm experience), land certification, frequency of visit to improved seed center, farmer’s education and row planting are important contributing factors for participation decisions and hence technical efficiency of farmers in the study areas. We recommend that policies targeting the design of development intervention programs in the agricultural sector focus more on providing farmers with on-farm visits by extension workers, provision of credit services, establishment of farmers’ training centers and adoption of modern farm technologies. Finally, we recommend further research to deal with this kind of methodological framework using a panel data set to test whether technical efficiency starts to increase or decrease with the length of time that farmers participate in development programs.

Keywords: impact evaluation, efficiency analysis and selection bias, stochastic frontier model, Heckman-two step

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Authors: Azizul Baten, Kamrul Hossain, Abdullah-Al-Zabir

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The objective of this study is to develop an appropriate stochastic frontier secondary schools efficiency model by ICT Intervention and to examine the impact of ICT challenges on secondary schools efficiency in the Sylhet division in Bangladesh using stochastic frontier analysis. The Translog stochastic frontier model was found an appropriate than the Cobb-Douglas model in secondary schools efficiency by ICT Intervention. Based on the results of the Cobb-Douglas model, it is found that the coefficient of the number of teachers, the number of students, and teaching ability had a positive effect on increasing the level of efficiency. It indicated that these are related to technical efficiency. In the case of inefficiency effects for both Cobb-Douglas and Translog models, the coefficient of the ICT lab decreased secondary school inefficiency, but the online class in school was found to increase the level of inefficiency. The coefficients of teacher’s preference for ICT tools like multimedia projectors played a contributor role in decreasing the secondary school inefficiency in the Sylhet division of Bangladesh. The interaction effects of the number of teachers and the classrooms, and the number of students and the number of classrooms, the number of students and teaching ability, and the classrooms and teaching ability of the teachers were recorded with the positive values and these have a positive impact on increasing the secondary school efficiency. The overall mean efficiency of urban secondary schools was found at 84.66% for the Translog model, while it was 83.63% for the Cobb-Douglas model. The overall mean efficiency of rural secondary schools was found at 80.98% for the Translog model, while it was 81.24% for the Cobb-Douglas model. So, the urban secondary schools performed better than the rural secondary schools in the Sylhet division. It is observed from the results of the Tobit model that the teacher-student ratio had a positive influence on secondary school efficiency. The teaching experiences of those who have 1 to 5 years and 10 years above, MPO type school, conventional teaching method have had a negative and significant influence on secondary school efficiency. The estimated value of σ-square (0.0625) was different from Zero, indicating a good fit. The value of γ (0.9872) was recorded as positive and it can be interpreted as follows: 98.72 percent of random variation around in secondary school outcomes due to inefficiency.

Keywords: efficiency, secondary schools, ICT, stochastic frontier analysis

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Authors: Nozar Kishi, Babak Kamrani, Filmon Habte

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Natural hazards such as earthquakes and tropical storms, are very frequent and highly destructive in Japan. Japan experiences, every year on average, more than 10 tropical cyclones that come within damaging reach, and earthquakes of moment magnitude 6 or greater. We have developed stochastic catastrophe models to address the risk associated with the entire suite of damaging events in Japan, for use by insurance, reinsurance, NGOs and governmental institutions. KCC’s (Karen Clark and Company) catastrophe models are procedures constituted of four modular segments: 1) stochastic events sets that would represent the statistics of the past events, hazard attenuation functions that could model the local intensity, vulnerability functions that would address the repair need for local buildings exposed to the hazard, and financial module addressing policy conditions that could estimates the losses incurring as result of. The events module is comprised of events (faults or tracks) with different intensities with corresponding probabilities. They are based on the same statistics as observed through the historical catalog. The hazard module delivers the hazard intensity (ground motion or wind speed) at location of each building. The vulnerability module provides library of damage functions that would relate the hazard intensity to repair need as percentage of the replacement value. The financial module reports the expected loss, given the payoff policies and regulations. We have divided Japan into regions with similar typhoon climatology, and earthquake micro-zones, within each the characteristics of events are similar enough for stochastic modeling. For each region, then, a set of stochastic events is developed that results in events with intensities corresponding to annual occurrence probabilities that are of interest to financial communities; such as 0.01, 0.004, etc. The intensities, corresponding to these probabilities (called CE, Characteristics Events) are selected through a superstratified sampling approach that is based on the primary uncertainty. Region specific hazard intensity attenuation functions followed by vulnerability models leads to estimation of repair costs. Extensive economic exposure model addresses all local construction and occupancy types, such as post-linter Shinand Okabe wood, as well as concrete confined in steel, SRC (Steel-Reinforced Concrete), high-rise.

Keywords: typhoon, earthquake, Japan, catastrophe modelling, stochastic modeling, stratified sampling, loss model, ERM

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Authors: Bohli Afef

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The fifth-generation (5G) wireless systems is featured by extreme densities of cell stations to overcome the higher future demand. Hence, interference management is a crucial challenge in 5G ultra-dense cellular networks. In contrast to the classical inter-cell interference coordination approach, which is no longer fit for the high density of cell-tiers, this paper proposes a novel virtual coordination based on the dynamic common cognitive monitor channel protocol to deal with the inter-cell interference issue. A tractable and flexible model for the coverage probability of a typical user is developed through the use of the stochastic geometry model. The analyses of the performance of the suggested protocol are illustrated both analytically and numerically in terms of coverage probability.

Keywords: ultra dense heterogeneous networks, dynamic common channel protocol, cognitive radio, stochastic geometry, coverage probability

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Authors: Yaping Zhao

Abstract:

In the present study, the exact solutions for the steady response of quasi-linear systems under non-white wide-band random excitation are considered by means of the stochastic averaging method. The non linearity of the systems contains the power-law damping and the cross-product term of the power-law damping and displacement. The drift and diffusion coefficients of the Fokker-Planck-Kolmogorov (FPK) equation after averaging are obtained by a succinct approach. After solving the averaged FPK equation, the joint probability density function and the marginal probability density function in steady state are attained. In the process of resolving, the eigenvalue problem of ordinary differential equation is handled by integral equation method. Some new results are acquired and the novel method to deal with the problems in nonlinear random vibration is proposed.

Keywords: random vibration, stochastic averaging method, FPK equation, transition probability density

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Authors: Zohreh Mohammadi, Jan Kaspar, Peter Lohmander, Robert Marusak, Harald Vacik, Ljusk Ola Eriksson

Abstract:

Storms are important damaging agents in European forest ecosystems. In the latest decades, significant economic losses in European forestry occurred due to storms. This study investigates the problem of optimal harvest planning when forest stands risk to be felled by storms. One of the most applicable mathematical methods which are being used to optimize forest management is stochastic dynamic programming (SDP). This method belongs to the adaptive optimization class. Sequential decisions, such as harvest decisions, can be optimized based on sequential information about events that cannot be perfectly predicted, such as the future storms and the future states of wind protection from other forest stands. In this paper, stochastic dynamic programming is used to maximize the expected present value of the profits from an area consisting of several forest stands. The region of analysis is the Czech Republic. The harvest decisions, in a particular time period, should be simultaneously taken in all neighbor stands. The reason is that different stands protect each other from possible winds. The optimal harvest age of a particular stand is a function of wind speed and different wind protection effects. The optimal harvest age often decreases with wind speed, but it cannot be determined for one stand at a time. When we consider a particular stand, this stand also protects other stands. Furthermore, the particular stand is protected by neighbor stands. In some forest stands, it may even be rational to increase the harvest age under the influence of stronger winds, in order to protect more valuable stands in the neighborhood. It is important to integrate wind risk in forestry decision-making.

Keywords: Czech republic, forest stands, stochastic dynamic programming, wind risk

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Authors: Amit Ghosh, Chanchal Kundu

Abstract:

Recently, the notion of past cumulative inaccuracy (CPI) measure has been proposed in the literature as a generalization of cumulative past entropy (CPE) in univariate as well as bivariate setup. In this paper, we introduce the notion of CPI of order α (alpha) and study the proposed measure for conditionally specified models of two components failed at different time instants called generalized conditional CPI (GCCPI). We provide some bounds using usual stochastic order and investigate several properties of GCCPI. The effect of monotone transformation on this proposed measure has also been examined. Furthermore, we characterize some bivariate distributions under the assumption of conditional proportional reversed hazard rate model. Moreover, the role of GCCPI in reliability modeling has also been investigated for a real-life problem.

Keywords: cumulative past inaccuracy, marginal and conditional past lifetimes, conditional proportional reversed hazard rate model, usual stochastic order

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