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The external environment in the transitional zone of the ecological barrier is fragile, and economic growth has resulted in a series of land degradation issues, significantly impacting regional economic development and the ecological environment. Therefore, monitoring, assessing, and predicting land use changes are crucial for ecological security and sustainable development. This study developed an integrated model comprising convolutional neural network, cellular automata, and Markov chain to forecast the land use status of western Jilin, located in the transitional zone of the ecological barrier, by the year 2030. Additionally, the study evaluated the role of land use policies in the context of land use changes in western Jilin. The findings demonstrate that the coupled modeling approach exhibits excellent predictive performance for land use prediction in western Jilin, yielding a Kappa coefficient of 93.26%. Policy drivers play a significant role in shaping land use patterns in western Jilin, as evidenced by the declining farmland accompanied by improved land utilization, the sustained high levels of forest aligning with sustainable development strategies, the ongoing restoration of waters and grassland, which are expected to show positive growth by 2030, and the steady growth in built-up areas. This study contributes to understanding the dynamics of land use in the transitional zone of the ecological barrier, thereby promoting sustainable development and ecological resilience in the region. [ABSTRACT FROM AUTHOR]

Bridge deterioration reflects changes in bridge conditions and is caused by many factors. Predicting bridge deterioration is critical for implementing predictive interventions, but developing such credible models that consider multiple influencing factors remains difficult. This study uses Markov Chain (MC) and Recurrent Neural Network (RNN) to establish deterioration prediction models. Then, comparative studies of these two models are conducted on a censored database regarding the mean deterioration progress, deterioration progress of bridges with different deck types, deterioration progress of a single bridge, and the influence of each factor on deterioration progress. The results indicate that RNN-predicted deterioration to a worse state takes less time than MC-predicted deterioration, but the difference is less than 5 years. Deterioration progresses of bridges with different deck types show that PC deck and steel deck are more durable than RC deck and concrete-steel composite deck. Qualitative and quantitative analysis infers eight factors substantially affecting the deterioration progress. Overall, the deterioration progress provided by these two models can be utilized to determine bridge's remaining life and analyze the effects of external factors affecting the deterioration. This information is extremely valuable for assisting in developing and implementing effective management and maintenance strategies. [ABSTRACT FROM AUTHOR]

In this paper, we consider a decode-and-forward (DF) buffer-aided (BA) multi-relay system using double differential (DD) encoding and decoding for the transmission and reception of data packets. The proposed system does not require carrier frequency offset (CFO) information and channel state information (CSI) at any communicating node. A priority-based max link selection protocol has been adopted to select links based on the buffer status and channel quality. The Markov-chain approach is used for developing the state transition matrix, using which the steady state probability of the system is obtained. The outage probability and average bit error rate (ABER) expressions are derived using the steady-state probability. The proposed setup performance is then compared with the conventional coherent BA system. It is established that the considered setup is affected by a signal-to-noise ratio (SNR) penalty of $\approx 3$ dB, which is considerably lower than the well-known 6 dB SNR penalty existing for DD modulation compared to coherent modulation. Also, the proposed setup outcompetes the coherent max-link approach in the presence of a CFO.

In this paper, we introduce the concept of buffer-aided cooperative relaying for a free-space optical (FSO) communication system. The proposed FSO system consists of $K$ decode-and-forward(DF) relays, each equipped with a buffer of size $L$. The proposed system undergoes both path loss and atmospheric turbulence (AT) induced fading, and to evaluate the system performance a weak, as well as strong AT region is considered. The Markov chain (MC) approach is used to derive the state transition matrix of the system, which is then further used to model the evolution of buffer states. Analytical expressions of the outage probability and average bit error rate (ABER) are obtained with the help of state transition matrix. The average packet delay of the system is also evaluated with the help of obtained outage probability. The performance of the considered system is analyzed for different values of $K$ and $L$. The performance is then further compared with the non-buffer-aided (NBA) cooperative relaying for FSO communication systems and it has been observed that the proposed buffer-aided FSO system significantly outperforms the NBA FSO systems for entire range of turbulence. Finally, the impact of $L$ and $K$ on the average packet delay of the system is examined.

The Saaty analytic network process (Saaty-ANP) is a generalization of the analytic hierarchy process. The Markov chain-based ANP (MC-ANP) is another decision-making approach suitable for general structures. Both ANPs use a relative measurement (paired comparisons with ratio scales) to estimate tangible and intangible factors, use a stochastic matrix (SM) to solve feedback problems and obtain the same results under some conditions. The Saaty-ANP does not define the basic concepts, nor does it check the rationality of the structure, which may lead to meaningless solutions and ignore a subclass of feedback decision problems. MC-ANP separates the alternatives from the criteria and defines the attributes, criteria, criterion dominated relations (CDRs) and reasonable constraints of the CDRs by means of digraphs; it also represents CDRs as Markov chain transition diagrams and corresponding (stochastic) adjacency matrices and obtains solutions from a system of linear equations. With the MC-ANP, for the real alternative problems (Class I), the solutions are priorities of the alternatives obtained by the parametric positive left eigenvectors of the SM, and for the nominal alternative problems (Class II), the solutions are priorities of the criteria obtained by the nonnegative right eigenvector of the SM. We analyze the conditions and causes of rank reversal; note that rank reversal does not appear in Class II problems; the study offers a rank reversal ANP example (with feedback) and presents a rank-preserving method for Class I problems. We discuss the contribution of MC-ANP, how to compensate for the defects of Saaty-AHP/ANP, and present issues that need further consideration.

Classifiers based on machine learning are usually trained to distinguish between several known classes. For an electronic intelligence application, however, it is of great importance to recognize if an intercepted signal belongs to an unknown radar emitter. In the machine learning literature, this task is called open-set recognition. This article investigates six approaches in several configurations to recognize unknown emitters. It is based on a hierarchical emission model that understands emissions as a language with an inherent hierarchical structure. We consider two general approaches, which are the “memoryless” Markov chain and the Long Short-Term Memory recurrent neural network, which is especially designed to “remember” the past. The performance is demonstrated with two evaluation metrics in ten scenarios that contain different combinations of known and unknown emitters. An evaluation with corrupted data provides an estimate on the methods’ accuracies under challenging conditions. The results show that unknown emitters that do not use known waveforms are reliably recognized even with corrupted data, while unknown emitters that are more similar to known ones are harder to detect. [ABSTRACT FROM AUTHOR]

In recent years, the rapid expansion of urban spaces has accelerated the mutual evolution of landscape types. Analyzing and simulating spatio-temporal dynamic features of urban landscape can help to reveal its driving mechanisms and facilitate reasonable planning of urban land resources. The purpose of this study was to design a hybrid cellular automata model to simulate dynamic change in urban landscapes. The model consists of four parts: a geospatial partition, a Markov chain (MC), a multi-layer perceptron artificial neural network (MLP-ANN), and cellular automata (CA). This study employed multivariate land use data for the period 2000–2015 to conduct spatial clustering for the Ganjingzi District and to simulate landscape status evolution via a divisional composite cellular automaton model. During the period of 2000–2015, construction land and forest land areas in Ganjingzi District increased by 19.43% and 15.19%, respectively, whereas farmland, garden lands, and other land areas decreased by 43.42%, 52.14%, and 75.97%, respectively. Land use conversion potentials in different sub-regions show different characteristics in space. The overall land-change prediction accuracy for the subarea-composite model is 3% higher than that of the non-partitioned model, and misses are reduced by 3.1%. Therefore, by integrating geospatial zoning and the MLP-ANN hybrid method, the land type conversion rules of different zonings can be obtained, allowing for more effective simulations of future urban land use change. The hybrid cellular automata model developed here will provide a reference for urban planning and policy formulation.

A smart learning system (SLS) is a knowledge-based software acting as an intelligent tutor for teaching and training purposes. A SLS is technically difficult to be built and maintained. In this paper a Markov chain model is introduced on the steps of the construction of a SLS evaluating the difficulties that appear during this process. Examples of SLSs for teaching Mathematics are also presented illustrating the applicability of the model in real situations. [ABSTRACT FROM AUTHOR]

Siem Reap River has played a crucial role in maintaining the Angkor temple complex and livelihood of the people in the basin since the 12th century. Land use in this watershed has changed considerably over the last few decades, which is thought to have had an influence on river. This study was carried out as part of assessing the land use and climate change on hydrology of the upper Siem Reap River. The objective was to reconstruct patterns of annual deforestation from 1988 to 2018 and to explore scenarios of land use 40 and 80 years into the future. A supervised maximum likelihood classification was applied to investigate forest cover change in the last three decades. Multi-layer perceptron neural network-Markov chain (MLPNN-MC) was used to forecast land use and land cover (LULC) change for the years 2058 and 2098. The results show that there has been a significantly decreasing trend in forest cover at the rate 1.22% over the last three decades, and there would be a continuous upward trend of deforestation and downward trend of forest cover in the future. This study emphasizes the impacts of land use change on water supply for the Angkor temple complex (World Heritage Site) and the surrounding population.

To improve the performance of sensorless induction motor (IM) drives, an adaptive speed and flux estimation method based on the multiple-model extended Kalman filter (EKF) with Markov chain for IMs is proposed in this paper. In this algorithm, the multiple model EKF for speed and flux estimation is established, and the transition of the models obeys the Markov chain and the estimation value is obtained by mixing the outputs of different models in different weightings, and the calculation of the weighting is researched. Simultaneously, the transition probability can be continuously self-tuned by the residual sequence, the prior information is modified by the posterior information, and the more accurate transition among the models is obtained. Therefore, the proposed method improves the model adaptability to the actual systems and the environmental variations, and reduces the speed estimation error. The correctness and the effectiveness of the proposed method are verified by the simulation and experimental results. [ABSTRACT FROM PUBLISHER]

This paper presents a predictive energy management strategy for a parallel hybrid electric vehicle (HEV) based on velocity prediction and reinforcement learning (RL). The design procedure starts with modeling the parallel HEV as a systematic control-oriented model and defining a cost function. Fuzzy encoding and nearest neighbor approaches are proposed to achieve velocity prediction, and a finite-state Markov chain is exploited to learn transition probabilities of power demand. To determine the optimal control behaviors and power distribution between two energy sources, a novel RL-based energy management strategy is introduced. For comparison purposes, the two velocity prediction processes are examined by RL using the same realistic driving cycle. The look-ahead energy management strategy is contrasted with shortsighted and dynamic programming based counterparts, and further validated by hardware-in-the-loop test. The results demonstrate that the RL-optimized control is able to significantly reduce fuel consumption and computational time. [ABSTRACT FROM PUBLISHER]

This paper investigates the buffer-aided relay selection problem for a decode-and-forward cooperative wireless network with K relays. We propose a new relay selection scheme that incorporates the status of the relay buffers and the instantaneous strength of the wireless links. Specifically, each link is assigned with a weight related to the buffer status; then, the best relay is selected with the largest weight among all the qualified source–relay and relay–destination links. We derive the closed-form expression for the outage probability and the diversity gain by introducing several Markov chains (MCs) to model the evolution of the buffer status. The analysis shows that the proposed scheme can achieve the optimal diversity gain \mbox{2}K for a small L (i.e., L\ge \mbox3), an improvement in comparison with the existing max-link scheme that achieves the optimal diversity gain only when L is sufficiently large, where $L$ denotes the buffer size of each relay. The provided theoretical and numerical results confirm the performance gain of the proposed relay selection scheme over the existing max-link scheme. [ABSTRACT FROM PUBLISHER]

Call Admission Control (CAC) is one of the key traffic management mechanisms that must be deployed in order to meet the strict requirements for dependability imposed on the services provided by modern wireless networks. In this paper, we develop an executable top-down hierarchical Colored Petri Net (CPN) model for multi-traffic CAC in Orthogonal Frequency Division Multiple Access (OFDMA) system. By theoretic analysis and CPN simulation, it is demonstrated that the CPN model is isomorphic to Markov Chain (MC) assuming that each data stream follows Poisson distribution and the corresponding arrival time interval is an exponential random variable, and it breaks through MC's explicit limitation, which includes MC's memoryless property and proneness to state space explosion in evaluating CAC process. Moreover, we present four CAC schemes based on CPN model taking into account call-level and packet-level Quality of Service (QoS). The simulation results show that CPN offers significant advantages over MC in modeling CAC strategies and evaluating their performance with less computational complexity in addition to its flexibility and adaptability to different scenarios. [ABSTRACT FROM AUTHOR]

Bang-bang phase-locked loops (BBPLLs) are inherently nonlinear systems due to the binary phase detector (BPD). While they are typically used for clock and data recovery, the ongoing trend toward digital loop implementations has resulted in several digital BBPLLs (DBBPLLs) suitable for frequency synthesis. This brief investigates the effect of nonaccumulative reference clock jitter (due to white phase noise) in second-order DBBPLLs, comparing the output jitter with that of first-order DBBPLLs. For small clock jitter, the nonlinear loop behavior is modeled as a 2-D Markov chain, and the output jitter is smaller than but close to that of a first-order loop. For large clock jitter, the BPD nonlinearity is linearized, and the output jitter is larger than that of a first-order loop; it is proportional to the clock jitter and inversely proportional to the square root of the stability factor—the ratio of the proportional-path gain to the integral-path gain of the digital loop filter. [ABSTRACT FROM PUBLISHER]

The IEEE 802.16 wireless metropolitan area network (WMAN) standard is a promising and cost-effective, last-mile wireless technology for the provision of broadband Internet access to end users. In this paper, we present an accurate analytical model that describes the contention-based bandwidth (BW) request scheme of the 802.16 standard, which is also known as WiMAX, for the persistent and nonpersistent request generation cases. We first model the contention procedure with a Markov chain, taking into account the exponential back-off procedure as well as the waiting time for a BW assignment and the possible timeout for lost messages. The accuracy of the model is then evaluated by comparing it with simulation results for a wide range of values of the parameters involved. We use this model to accurately calculate the capacity of the contention slots in delivering BW requests, from which the average access delay is also found. These measures are used to determine a proper configuration tor the efficient operation of the contention-based BW request scheme. The proposed model provides a useful analytical tool for devising adaptive configuration mechanisms for the contention access mode of the 802.16 medium access control (MAC) layer. [ABSTRACT FROM AUTHOR]

Genetic algorithms have proven to be reasonably good optimization algorithms. Despite many successful applications, there is a lack of theoretical insight into why they work so well. In this paper, Vose-Liepius' so called "infinite population model" is used to derive a lower and upper bound for the expected probability of the global optimal solution under proportional selection and uniform crossover. Elitist selection is not assumed. The approach is to aggregate the Markov chain (MC) into subsets of decreasing Hamming distances. The aggregation is based on a proof of equally likelihood in probability of elements in these subsets. The aggregation model is then extended to Nix-Vose's fully realistic "finite population model." This leads to a lower and upper bound expression based on the first passage theory of the MC for the probability of success of the algorithm. The proof of equally likelihood is extended correspondingly to permutations of populations. Numerical simulations reveal that the bounds are useful for small perturbations of the fitness function for all problem sizes in the infinite population model. Due to the computational burden, however, the aggregated finite population model is still restricted to relatively small problem sizes. Finally, an approximate aggregated finite population model that does not require computation of the full mixing matrix is found to give excellent performance. [ABSTRACT FROM AUTHOR]

Rapid population growth is the main driver of the accelerating urban sprawl into agricultural lands in Egypt. This is particularly obvious in governorates where there is no desert backyard (e.g., Gharbia) for urban expansion. This work presents an overview of machine learning-based and state-of-the-art remote sensing products and methodologies to address the issue of random urban expansion, which negatively impacts environmental sustainability. The study aims (1) to investigate the land-use/land-cover (LULC) changes over the past 27 years, and to simulate the future LULC dynamics over Gharbia; and (2) to produce an Urbanization Risk Map in order for the decision-makers to be informed of the districts with priority for sustainable planning. Time-series Landsat images were utilized to analyze the historical LULC change between 1991 and 2018, and to predict the LULC change by 2033 and 2048 based on a logistic regression–Markov chain model. The results show that there is a rapid urbanization trend corresponding to a diminution of the agricultural land. The agricultural sector represented 91.2% of the total land area in 1991, which was reduced to 83.7% in 2018. The built-up area exhibited a similar (but reversed) pattern. The results further reveal that the observed LULC dynamics will continue in a like manner in the future, confirming a remarkable urban sprawl over the agricultural land from 2018 to 2048. The cultivated land changes have a strong negative correlation with the built-up cover changes (the R2 were 0.73 in 1991–2003, and 0.99 in 2003–2018, respectively). Based on the Fuzzy TOPSIS technique, Mahalla Kubra and Tanta are the districts which were most susceptible to the undesirable environmental and socioeconomic impacts of the persistent urbanization. Such an unplanned loss of the fertile agricultural lands of the Nile Delta could negatively influence the production of premium agricultural crops for the local market and export. This study is substantial for the understanding of future trends of LULC changes, and for the proposal of alternative policies to reduce urban sprawl on fertile agricultural lands. [ABSTRACT FROM AUTHOR]