Multi State Research Articles

Reliability Modeling for Sparsely Connected Homogeneous Multistate Consecutive-k-Out-of-n: GSystems

Compared to binary-state system models, multistate (MS) system models are more practical and useful when describing complex systems in applications, such as communication systems, oil pipeline systems, and grid or cloud computing infrastructures. This paper models a generalized sparsely connected MS consecutive- ${k}$ -out-of- ${n}$ : ${G}$ system. The proposed model is regarded as a natural extension of the consecutive- ${k}$ -out-of- ${n}$ system. Moreover, the proposed system model could cover three special MS submodels, i.e., a decreasing MS: ${G}$ system model, an increasing MS: ${G}$ system model, and a nonmonotonic MS: ${G}$ system model. Three corresponding real-world examples are given to illustrate the applications of the model. Meanwhile, by utilizing the well-known finite Markov chain imbedding method, the explicit system reliability and state distribution for the three kinds of submodels are presented, respectively. Finally, the developed indexes and analysis method are demonstrated by numerical examples.

A fractional-order chaotic system with hidden attractor and self-excited attractor and its DSP implementation

• A new 3 dimensional fractional order chaotic system with hidden attractor and self excited attractor is proposed. • The dynamical behaviors of FMACS self excited attractors and hidden attractors are explored separately with order q , and some comparisons are made by using LEs and bifurcation model. • The coexistence phenomenon of attractors in FMACS with hidden attractors is studied. It is interesting that attractors with different shapes appear in different orders. • FMACS is implemented on the DSP board. • A non linear term is replaced by e x , FMACS has a multi state transition and eventually quickly degenerates into a periodic state. The definition of fractional calculus is introduced into a 3D multi-attribute chaotic system in this paper. The fractional multi-attribute chaotic system (FMACS) numerical solution is obtained based on the Adomian decomposition method (ADM). The balance points and dynamical behaviors of self-excited and hidden attractors in FMACS are compared and analyzed through the Lyapunov spectrum, bifurcation model, and complexity. It is worth noting that some hidden coexistence attractors with different shapes are affected by the order. Besides, a novel chaotic system without equilibrium points is constructed, in which the nonlinear function term in FMACS is replaced with a rare nonlinear function e x . Meanwhile, its degradation phenomenon and state transition phenomenon are analyzed in detail. Finally, the digital circuit of the system is realized on the DSP board. The research result shows that FMACS has richer dynamical behaviors and higher complexity. This research provides a theoretical basis and guidance for the application of fractional chaotic systems.

Behavioral Based Insider Threat Detection Using Deep Learning

The most detrimental cyber attacks are usually not originated by malicious outsiders or malware but from trusted insiders. The main advantage insider attackers have over external elements is their ability to bypass security checks and remain undiscovered, this may cause serious damage to the organizational assets. This paper focuses on insider threat detection through behavioral analysis of users. User behavior is categorized as normal or malicious based on user activity. A series of events and activities are analyzed for feature selection to efficiently detect adversarial behavior. Selected feature vectors are used for model training during the implementation phase. A deep learning based approach is proposed that detects insiders with greater accuracy and low false positive rate. A rich event / user role based feature set containing Logon/Logoff events, User_role, Functional_unit etc are used for detection. The dataset used is the CMU CERT synthetic insider threat dataset r4.2. Performance of our proposed algorithm has been compared to other well-known techniques i.e. long short term Memory- convolutional neural network, random forest, long short term memory- recurrent neural network, one class support vector machine, Markov chain model, multi state long short term memory & convolutional neural network, gated recurrent unit & skipgram. The comparison proved that our novel approach produces relatively good accuracy(90.60%), precision(97%) and F1 Score (94%).

Flexible Multi State Switch of Distribution Network Based on Flux Controlled Adjustable Reactor

Flexible Multi-states Switch based on power electronics technology can realize the flexible interconnection between feeders, elastically control feeder power flow and solve the serious unbalance of feeder power flow in the distribution network. To solve the problem, this paper proposes that using flux-controlled variable reactor as the topology of flexible multi-states switch in distribution network. Firstly, flux-controlled variable reactor’s principle will be introduced; Then, we analyze the principle of feeder power flow’s control using variable reactor; After that, the control strategy for the flexible multi-states switch with different access methods is proposed; At last, a MATLAB simulation model and the prototype experiment platform are built. The results verified the power flow control function of flexible multi-states switch and effectiveness of proposed controlled strategy.

Sorghum biomass production in the continental United States and its potential impacts on soil organic carbon and nitrous oxide emissions

AbstractNational scale projections of bioenergy crop yields and their environmental impacts are essential to identify appropriate locations to place bioenergy crops and ensure sustainable land use strategies. In this study, we used the process‐based Daily Century (DAYCENT) model with site‐specific environmental data to simulate sorghum (Sorghum bicolor L. Moench) biomass yield, soil organic carbon (SOC) change, and nitrous oxide emissions across cultivated lands in the continental United States. The simulated rainfed dry biomass productivity ranged from 0.8 to 19.2 Mg ha−1 year−1, with a spatiotemporal average of Mg ha−1 year−1, and a coefficient of variation of 35%. The average SOC sequestration and direct nitrous oxide emission rates were simulated as Mg CO2e ha−1 year−1 and Mg CO2e ha−1 year−1, respectively. Compared to field‐observed biomass yield data at multiple locations, model predictions of biomass productivity showed a root mean square error (RMSE) of 5.6 Mg ha−1 year−1. In comparison to the multi State (n = 21) NASS database, our results showed RMSE of 5.5 Mg ha−1 year−1. Model projections of baseline SOC showed RMSE of 1.9 kg/m2 in comparison to a recently available continental SOC stock dataset. The model‐predicted N2O emissions are close to 1.25% of N input. Our results suggest 10.2 million ha of cultivated lands in the Southern and Lower Midwestern United States will produce >10 Mg ha−1 year−1 with net carbon sequestration under rainfed conditions. Cultivated lands in Upper Midwestern states including Iowa, Minnesota, Montana, Michigan, and North Dakota showed lower sorghum biomass productivity (average: 6.9 Mg ha−1 year−1) with net sequestration (average: 0.13 Mg CO2e ha−1 year−1). Our national‐scale spatially explicit results are critical inputs for robust life cycle assessment of bioenergy production systems and land use‐based climate change mitigation strategies.

Irrigation Potential Utilization Estimation using Time-Series Satellite Data

In India, significant irrigation potential has been created to increase the agricultural crop intensity and its production. However, these irrigation systems, built with huge financial investments, exhibit significant gap in potential created and utilized. The contributing factors are multi fold and State & Central Governments initiated many programs to bridge the gap in irrigation potential created & utilized under command areas. Objective assessment of actual irrigation utilization in command areas is prerequisite for planning appropriate interventions to bridge the gap. Conventional field-based data collection mechanism is tedious and do not provide spatial patterns of irrigation utilization. Satellite data with multi-spectral and temporal dimensionality provides opportunities to estimate irrigation utilization and thus to derive its spatial patterns quantifying the gap between created and utilized. In this study, the irrigation potential utilized, as against the potential created was analyzed for Krishna basin using multi-temporal satellite data. Seasonal and annual crop map was derived from times series Advanced Wide Field Sensor (AWiFS) data through unsupervised classification. The crop area identified from the remote sensed information was separated into irrigated and rainfed classes based on temporal profiles of Normalized Differential Vegetation Index (NDVI) with in a crop season. Irrigation Potential Utilized (IPU) was determined as the gross irrigated area. https://doi.org/10.23953/cloud.ijarsg.475

System reliability for a multi-state distribution network with multiple terminals under stocks

To achieve the most efficiency in supply chain management, the capability of distribution networks is a key point for the entire supply chain. Stocks are critical for enhancing the efficiency of satisfying the demand of retailers in the distribution network. A configuration of a distribution network is consisted of routes and nodes. Each route connects a pair of nodes and each node is denoted as a supplier, a distribution center, or a retailer. For each route, it has a carrier whose available capacity for demand transmission is multi state. Hence, a distribution network is also regarded as a multi state network and such a network is named as a multi state distribution network (MDN) in here. The propose of this paper is to evaluate the system reliability which is defined as the probability that the MDN can meet all retailers’ demand under stocks. In practical, all retailers’ demand should be satisfied by stocks in the distribution centers (DCs) firstly. Therefore flow assignment in MDN model is mainly clarified by the relationship between the demand of retailers, stocks on DCs, and suppliers. The concept of minimal capacity vectors (MCVs) is then proposed and an algorithm is developed to obtaining MCVs for evaluating system reliability.

Possibilistic-probabilistic self-scheduling of PEVAggregator for participation in spinning reserve market considering uncertain DRPs

In today’s electrical power markets, due to the presence of versatile uncertain parameters such as increasing penetration rate of plug-in electric vehicles, larger portion of renewable energy resources in power production, and demand side behaviors, decision making under uncertainty is a non-separable requirement. In this context, lacks of historical data and in some cases data privacy make dealing with extracting the accurate probability distribution functions a challenging task. As a solution, the current paper applies Z-number possibilistic-probabilistic method, based on fuzzy logic principles, to deal with uncertainties in a plug-in electric aggregators-scheduling problem (PEVAgg-SSDRP)without relying on probability distribution functions of uncertain parameters with objective function of benefit maximization through optimal bidding to spinning reserve market. In the proposed model, aggregators participate in the spinning reserve market and the load reduction amount during peak hours is obtained. The uncertain parameters are PEV aggregation and demand response program’s participation where the former modeled by a systematic method based on multi state Markov and to model the later, due to lack of historical data, Z-number method is utilized. In addition, because of easy implementation and fast convergence, gravitational search algorithm is used to solve the proposed mixed integer non-linear problem.

Impact of DNA/RNA self–alignment in a strong magnetic field on the interpretation of indirect spin–spin interactions using NMR line shape analysis of a multi–state DNA/RNA ligand binding mechanism in gum cancer cells

Impact of DNA/RNA self–alignment in a strong magnetic field on the interpretation of indirect spin–spin interactions using NMR line shape analysis of a multi–state DNA/RNA ligand binding mechanism in gum cancer cells

Nature of the 11Bu and 21Ag Excited States of Butadiene and the Goldilocks Principle of Basis Set Diffuseness.

Butadiene, being the simplest conjugated organic molecule, has been studied extensively by experiments and various high-level theoretical methods. Previous studies conclude that the complete active space (CAS) self-consistent field (SCF) method was unable to obtain the correct 11Bu and 21Ag state ordering and that it introduces artificial valence-Rydberg mixing into the 11Bu state because of the lack of external correlation. Basis sets and initial guesses specifically constructed for this problem were able to improve the vertical excitation energy of the 11Bu state but did not resolve the controversy of the nature of the 11Bu state. In the present work, we demonstrate that, using standard intermediately diffuse basis sets such as jul-cc-pVTZ and ma-TZVP, CASSCF is able to obtain the 11Bu and 21Ag states of trans-butadiene with much improved vertical excitation energy and reasonable wave function characteristics, and it provides a good reference wave function (capable of yielding quantitative excitation energies for excited states) for three methods that treat electron correlation in different ways, namely, multiconfiguration pair-density functional theory (MC-PDFT), CAS second-order perturbation theory (PT2), and multistate (MS) CASPT2. We demonstrate that a combined analysis of the orbital second moments, state second moments, and MC-PDFT energy components is a very useful approach in determining excited-state characteristics and assigning states, and we show that basis sets without diffuse functions or with very diffuse basis functions are not stable or accurate in predicting the excited states of butadiene. We show that the 21Ag state is valence-like and has an atypical double/single excitation character and the 11Bu state has a certain degree of Rydberg character that is not artificial, settling the decades of controversy of the characters of these states.

Multi state Markov model with no classification errors with application to vertical transmission

Multi state Markov model with no classification errors with application to vertical transmission

Grid Connection Control of DFIG for Variable Speed Wind Turbines under Turbulent Conditions

Doubly-fed induction generators (DFIGs) are widely used in wind energy conversion systems. The dynamic features of DFIGs make it important to focus on designing high-performance control schemes. However, the dynamic characteristics of such generators depend on nonlinear parameters, such as stator flux, stator current, and rotor current, which increase overall system complexity. Therefore, robust controllers must be implemented with the ability to support the dynamic frequencies of wind energy to ensure system stability. Conventional vector control configurations that use proportional-integral controllers have various drawbacks, such as parameter tuning difficulties, mediocre dynamic performance, and reduced robustness. In this study, we focused on improving DFIG synchronization to the grid by applying Multi State Feedback (MSF) current controllers with a feedforward component to smooth the connection to the grid as well as to improve the steady-state and transient characteristics of the controller. MSF controllers are proposed to replace the proportional-integral controllers on both the rotor and grid sides. The proposed controller is designed using a multivariable system and feedforward control for input reference and incorporating disturbances into the control equations for fast synchronization and transient responses. To demonstrate the advantages of this controller, experimental studies are presented for both the transient and steady states.

A method for modelling operational risk with fuzzy cognitive maps and Bayesian belief networks

A main concern of risk management in financial institutions is measurement of operational risk and its value at risk as a requirement of Basel II accord. Besides risk quantification, identifying causal mechanism leading to operational loss is necessary to plan risk mitigation activities. Bayesian belief networks (BBN) is a causal modelling method able to achieve both goals simultaneously. Eliciting BBN causal model and its parameters from expert knowledge is an alternative to data driven models in case of data scarcity. However, there is still a problem with parameter extraction for complex models with a number of multi parent and multi state nodes. In this paper, we proposed a method combining fuzzy cognitive maps (FCM) and BBN in order to improve BBN capability in modelling operational risks. In the first phase, a causal model is constructed by applying FCM and then a new migration method is proposed to translate FCM parameters to BBN ones. A case study of an Iranian private bank is then given to examine and validate the proposed method.

Stress–strength reliability analysis of multi-state system based on generalized survival signature

The stress–strength model has been widely used in reliability design of system. In the traditional stress–strength reliability theory, the system and each component are assumed to be only in one of two possible states being either working or failed, and the notion of stress–strength reliability is the probability that the strength is larger than the stress. In this paper, we study the stress–strength reliability of multi-state system based on generalized survival signature. It is supposed that the state of multi-state system is defined by using the ratio between strength and stress random variables. The definitions of generalized survival signature for a certain class of multi state systems with multi-state components in both discrete and continuous cases are given. In addition, the expressions of stress–strength reliability in both discrete and continuous situations are derived. In the case of continuous multi-state system, it is assumed that the random strength and stress are both from the Weibull distributions with different scale parameters, and the two different continuous kernel functions are Pareto and generalized half logistic distribution functions, respectively. Based on the assumptions, the stress–strength reliability is estimated by using both classical and Bayesian statistical theories. The uniformly minimum variance unbiased estimator and maximum likelihood estimator for the stress–strength reliability of the continuous multi-state system are derived. Under the squared error loss function, the exact expression of Bayes estimator for the stress–strength reliability of the continuous multi-state system is developed by using Gauss hypergeometric function. Finally, the Monte Carlo simulations are performed to compare the performances of the proposed stress–strength reliability estimators, and a real data set is also analyzed for an illustration of the findings. • The definitions of the generalized survival signature (GSS) are given. • We study the stress–strength reliability (SSR) of multi-state system based on GSS. • The multi-state system in both discrete and continuous cases are considered. • Classical and Bayesian statistical theories are used to estimate the SSR.

Multi State Farmer Dealer Assistant

A Web venture with Android App to enable agriculturists to guarantee more noteworthy benefit through direct rancher to provider and agriculturist to agriculturist correspondence over various states. This administration supports business correspondence and acquires straightforwardness the framework. This creative site takes into account great agriculturist, retailer and provider correspondence. It enables ranchers to login and refresh the items, cost, and stock with convey to particular merchants. At the point when merchants give arrange rancher get message notice. Additionally, giving Tracking office to the merchants. Furthermore, at whatever point merchant/Farmer distributes a notice or offer, the individual ranchers get advised by means of SMS message. The ranchers may likewise present their grievances and grumblings to particular merchants or experts utilizing their agriculturist login on a different protestations page and specialists will gain admittance to that page frequently utilizing their login id and passwords. Watchwords: Multi State, Farmer, Supplier, Admin, Sell-Buy, Booking.

Geometrical analysis and control optimization of a predator-prey model with multi state-dependent impulse

In this paper, a predator-prey model with Holling type-I functional response and multi state impulsive feedback control is established, where the intensity of pesticide spraying and the release amount of natural enemies are linearly dependent on the given threshold in the second impulse. Firstly, the existence of order-1 periodic solution of the system is investigated by successor functions and Bendixson theorem of impulsive differential equations, then the stability of periodic solutions is proved by the analogue of the Poincaré criterion. Furthermore, in order to reduce the actual total cost and obtain the best economic benefit, the optimal economic threshold is obtained, which provides the optimal strategy for the practical application. Finally, numerical simulations for specific examples are carried out to illustrate the feasibility of the above conclusions.

A fuzzy expectation maximization based method for estimating the parameters of a multi-state degradation model from imprecise maintenance outcomes

Multi-State (MS) reliability models are used in practice to describe the evolution of degradation in industrial components and systems. To estimate the MS model parameters, we propose a method based on the Fuzzy Expectation-Maximization (FEM) algorithm, which integrates the evidence of the field inspection outcomes with information taken from the maintenance operators about the transition times from one state to another. Possibility distributions are used to describe the imprecision in the expert statements. A procedure for estimating the Remaining Useful Life (RUL) based on the MS model and conditional on such imprecise evidence is, then, developed. The proposed method is applied to a case study concerning the degradation of pipe welds in the coolant system of a Nuclear Power Plant (NPP). The obtained results show that the combination of field data with expert knowledge can allow reducing the uncertainty in degradation estimation and RUL prediction.

Excitation Energies of Canonical Nucleobases Computed by Multiconfigurational Perturbation Theories.

In this computational work, we assessed the performance of abinitio multireference (MR) methods for the calculation of vertical excitation energies of five nucleobases: adenine, guanine, cytosine, thymine and uracil. In total, we have studied 38 singlet and 30 triplet excited states. Where possible we used the multireference configuration interaction (MRCI) method as a reference for various flavors of multireference perturbation theory to second order. In particular, we have benchmarked CASPT2, NEVPT2 and XMCQDPT2. For CASPT2, we have analyzed the single-state, multistate (MS) and extended MS variants. In addition, we have assessed the effect of the ionization potential electron affinity (IPEA) shift. For NEVPT2, we have used the partially and the strongly contracted variants. Further, we have tested the commonly used RI-CC2, RI-ADC2 and EOM-CCSD methods. Generally, we observe the following trends for singlet excited states: NEVPT2 is the closest MR method to MRCISD+Q, closely followed by CASPT2 with the default IPEA shift. The same trend is observed for triplet states, although NEVPT2 and CASPT2-IPEA are getting closer. Interestingly, the n, π* singlet excited states were described more accurately than π, π* excited states, while for triplet states the trend is inverted except for NEVPT2. This work is an important benchmark for future photochemical investigations.

Aiming to Reduce Admissions and Also Re-Admissions in Pediatric Sickle Cell Disease: A Single Institutional Experience with Six Interventions

BACKGROUND AND OBJECTIVESSickle Cell disease is an inherited blood disorder of the hemoglobin beta-globin protein in the red blood cells which results in significant risk for hospitalization. Since over 100,000 people in the USA have Sickle Cell Disease and recurrent hospitalizations occur; this constitutes a significant disease burden that are not only costly, but also contribute to loss of income, learned helplessness, additional immense morbidities and psychosocial stressors.Readmission within 30 days after hospitalization for sickle cell crises was developed at the National Association of Children's Hospitals (NACHRI) as a measurable benchmark to improve hospital quality. The NACHRI, multi state study of sickle cell disease care utilization found an overall readmission rate of 33.4% with lower rates in children (12.8% in children age1-9 and 23.4% for patients ages 10-17).At our institution, we implemented several interventions to reduce the readmission rate for sickle cell pain crises and will share results and insights.INTERVENTIONSThe following interventions were employed: a) services of a sickle cell coordinator to co-ordinate clinic visits and patient care b) provide additional standardized teaching on sickle cell care via direct interactions with caregivers, provision of educational materials and organization of a sickle cell awareness conference c) verbal discussions to improve understanding and compliance with a written sickle cell action plan, d) obtaining more frequent parental approval for hydroxyurea therapy, e) increasing tracking immunization rates (influenza), and f) 24hr/72hr discharge telephone call.The sickle cell team comprising of physicians, nurse practitioners, care coordinator, nurses and social worker actively participated in implementing these steps, and majority of the interventions took place in the outpatient setting.RESULTSWe reviewed sickle cell admissions and readmissions in 2014 and 2015 in patient's age 0-26 years. Mean age of patients was16.8year (N= 14 Standard deviation = 8.04).Pre-intervention in 2014 there were 91 admissions with 14/91 re-admissions (15.4%). After beginning implementation of interventions in 2015 there were not only 59% fewer admissions (63), but also 58% fewer readmissions (6 re admit/63 discharges = 9.5%). Thus our interventions affected both admission rate and readmission rates.CONCLUSIONSickle cell admissions and subsequent readmissions continue to be a major challenge in successful management of young people with sickle cell disease. Our interventions may help abate some of the hospitalizations and re-admissions as shown by an approximately 40% reduction rate. Although there is undoubtedly much room for additional progress in this area, we have made a good start towards improving our sickle cell population's health. DisclosuresNo relevant conflicts of interest to declare.

Automated Forecasting Approach Minimizing Prediction Errors of CPU Availability in Distributed Computing Systems

Forecasting CPU availab ility in volunteer computing systems using a single prediction algorith m is insufficient due to the d iversity of the world -wide distributed resources. In this paper, we draw-up the main guidelines to develop an appropriate CPU availability prediction system fo r such computing infrastructures. To reduce solution time and to enhance precision, we use simp le pred iction techniques, precisely vector autoregressive models and a tendency-based technique. We propose a predictor construction process which automatically checks assumptions of vector autoregressive models in time series. Three different past analyses are performed. For a given volunteer resource, the proposed prediction system selects the appropriate predictor using the mult i-state based prediction technique. Then, it uses the selected predictor to forecast CPU availability indicators. We evaluated our predict ion system using real traces of more than 226000 hosts of Seti@ho me. We found that the proposed prediction system improves the prediction accuracy by around 24%. Index Terms—CPU availab ility prediction, predict ion system, mult ivariate time series, mult i-state based prediction, volunteer computing system.