A pavement management system needs to be implemented as a form of road management to ensure that roads function properly. The Markov Chain model describes future pavement conditions to produce optimal highway maintenance. The research was carried out on Suprapto Street, Ahmad Yani Street, and Yos Sudarso Street in Indramayu Regency. Based on road conditions in 2024 by PCI and SDI assessment results, predictions are made by multiplying the initial condition vector by the transition probability matrix. Once the condition of the road is known, the proposed type of maintenance can be determined based on Ministerial Regulation of Public Work No. 13/PRT/M/2011, along with the costs required based on the maintenance cost history. The Markov Chain predictions show that road conditions will deteriorate and there will be an increase in severe damage over the years if no treatment is carried out. Maintenance action patterns vary; sections that experience a high level of damage will receive more serious treatment, and in the following year maintenance will decrease and then increase again according to the level of damage. The pattern of maintenance costs also follows the handling actions taken. Costs in the initial year were the highest, while most costs were spent on Suprapto Street.

The detection efficiency evaluation of sonars is crucial for optimizing task planning and resource scheduling. The existing static evaluation methods based on single indicators face significant challenges. First, static modeling has difficulty coping with complex scenes where the relative situation changes in real time in the task process. Second, a single evaluation dimension cannot characterize the data distribution characteristics of efficiency indicators. In this paper, we propose a multidimensional detection efficiency evaluation method for sonar search paths based on dynamic spatiotemporal interactions. We develop a dynamic multidimensional evaluation framework. It consists of three parts, namely, spatiotemporal discrete modeling, situational dynamic deduction, and probability-based statistical analysis. This framework can achieve dynamic quantitative expression of the sonar detection efficiency. Specifically, by accurately characterizing the spatiotemporal interaction process between the sonars and targets, we overcome the bottleneck in entire-path detection efficiency evaluation. We introduce a Markov chain model to guide the Monte Carlo sampling; it helps to specify the uncertain situations by constructing a high-fidelity target motion trajectory database. To simulate the actual sensor working state, we add observation error to the sensor, which significantly improves the authenticity of the target’s trajectories. For each discrete time point, the minimum mean square error is used to estimate the sonar detection probability and cumulative detection probability. Based on the above models, we construct the multidimensional sonar detection efficiency evaluation indicator system by implementing a confidence analysis, effective detection rate calculation, and a data volatility quantification analysis. We conducted relevant simulation studies by setting the source level parameter of the target base on the sonar equation. In the simulation, we took two actual sonar search paths as examples and conducted an efficiency evaluation based on multidimensional evaluation indicators, and compared the evaluation results corresponding to the two paths. The simulation results show that in the passive and active working modes of sonar, for the detection probability, the box length of path 2 is reduced by 0∼0.2 and 0∼0.5, respectively, compared to path 1 during the time period from T = 11 to T = 15. For the cumulative detection probability, during the time period from T = 15 to T = 20, the box length of path 2 decreased by 0∼0.1 and 0∼0.2, respectively, compared to path 1, and the variance decreased by 0∼0.02 and 0∼0.03, respectively, compared to path 1. The numerical simulation results show that the data distribution corresponding to path 2 is more concentrated and stable, and its search ability is better than path 1, which reflects the advantages of the proposed multidimensional evaluation method.

A Markov chain model for earthquake occurrence analysis in Megathrust 4 (M4), Sumatra, Indonesia

Indonesia's tourism sector experienced a drastic decline due to the pandemic, with the number of foreign tourists falling by 64.64% in 2020, disrupting contributions to the country's GDP and foreign exchange. The lack of application of stochastic models to predict foreign tourist arrivals nationwide is a challenge in policy planning. This research aims to build a Markov Chain-based prediction model to estimate the number of foreign tourists in 2026, overcoming the weaknesses of conventional approaches that are deterministic. The method used is the analysis of the probability of transition between states (Increase/Decrease/Stable) based on historical data of tourist arrivals. The prediction results show that the number of foreign tourists in 2026 reached 18,202,215 people, indicating an optimistic growth trend and potential recovery of the tourism sector. The conclusion of this study confirms that the Markov Chain model is effective for macro projection of tourist fluctuations, so that it can be a reference in the preparation of adaptive and data-based tourism policies.

The connected and automated vehicles (CAV) smoothing mixed traffic flow has gained attention, and a thorough assessment of these control algorithms is necessary. Our previous research proposed the time-varying model predictive control (TV-MPC) strategy, which considers the time-varying driving style of human driven vehicles (HDV), performing better than current baseline models. Due TV-MPC can be applied to any traffic congestion scenario and the dynamic modeling that considers driving style, can be easily transferred to other control algorithms. Thus, TV-MPC enable to represent typical control algorithms in mixed traffic flow. This study investigates the performance of TV-MPC under diverse disturbance characteristics and mixed platoons. Firstly, quantifying mixed traffic flow with different CAV penetration rates and platooning intensities by a Markov chain model. Secondly, by constructing evaluation indicators for micro-level operation of mixed traffic flow, this paper analyzed the impact of TV-MPC on the operation of mixed traffic flow through simulation. The results demonstrate that (1) CAV achieve optimal control at specific positions within mixed traffic flow; (2) higher CAV penetration enhances TV-MPC performance; (3) dispersed CAV distributions improve control effectiveness; and (4) TV-MPC excels in scenarios with significant disturbances.

With the advancement of modern agricultural technology and the expansion of large-scale production, this article aims to solve the difficulties in plant disease and pest control through the application of artificial intelligence and automation technology, and provide accurate disease and pest warning mechanisms. This study first conducted a detailed identification and classification of plant disease and pest warning mechanisms, and established a dynamic model of disease and pests based on the environmental factors and symptoms of affected areas. On this basis, using the isomorphism relationship between generalized stochastic Petri nets and Markov chains, a plant disease and pest diagnosis model based on generalized stochastic Petri nets and an equivalent Markov chain model were constructed. The simulation results show that different combinations of infection rates have a significant impact on the probability of meeting treatment standards, with the combination of moderate and severe infection rates having the greatest impact on the probability of meeting treatment standards, while the impact of mild infection rates is relatively small. By comprehensively analyzing the interaction between mild, moderate, and severe infection rates, the critical zone surface under different disease and pest warning thresholds was obtained. Through actual data verification, the generalized stochastic Petri net model can effectively quantify the dynamic characteristics of disease and pest propagation. Combined with the equivalent analysis of Markov chains, it can provide key thresholds and decision support for disease and pest warning. This method provides a theoretical basis for automated monitoring and precise control of pests and diseases in large-scale agricultural planting, and it has high practical application value.

Abstract The transition from conventional agriculture to regenerative systems represents one of the greatest contemporary challenges in the face of climate, ecological, and food crises. This study proposes a first-order Markov chain model, conditioned by energy indicators, to represent and simulate agroecological transitions in Brazil between 2010 and 2023. The transition matrix was parameterized based on three structural variables: renewable energy consumption, fossil fuel use, and energy depletion relative to gross national product. The results indicate a progressive decline in conventional agriculture and a significant increase in consolidated regenerative agriculture, particularly in contexts with higher shares of renewable sources. The modeling revealed that transitions occur sequentially, moving through intermediate stages and being strongly influenced by the energy structure. The model was statistically validated and demonstrated high sensitivity to decarbonization incentive policies. The proposed approach contributes to sustainable territorial planning by integrating energy and agroecological variables, offering a robust tool to support public policies and ecological transition strategies in rural territories.

The development of technologies for screening proteins that bind to specific tissues in vivo and facilitate delivery of large cargos remains challenging, with most approaches limited to cell culture systems that often yield clinically irrelevant hits. To overcome this limitation, we developed a novel molecular screening platform using an extracellular vesicle (EV) display library. EVs are natural molecular carriers capable of delivering diverse cargos, which can be engineered to enhance specificity and targeting through surface modifications. We constructed an EV-display library presenting monobody repertoires on EV surfaces, with genetic cargo inside the EVs corresponding to the displayed proteins. These libraries were screened for tissue specific delivery through serial passage in mice via sequential intravenous administration in and recovery of tissue-selected EVs and amplification of their encapsulated monobody genes at each passage. Our results demonstrated successful selection of tissue-specific targeting proteins, as revealed by fluorescence and bioluminescence imaging followed by DNA sequencing. To understand the stochastic relationship between displayed proteins and packaged genes, we developed a Markov chain model that quantified selection dynamics and predicted enrichment patterns despite the imperfect correlation between phenotype and genotype. This EV-based monobody screening approach, combined with mathematical modeling, is a significant advancement in targeted drug delivery by leveraging the natural capabilities of EVs with the selection of targeting proteins in a physiologically relevant environment.

Modern energy systems necessitated precise short-term forecasting of power requirements to ensure the efficient operation of Demand Flexibility Services (DFS). This work introduced an innovative hybrid forecasting model that combined KMeans clustering with Fuzzy Time Series (FTS) and Markov Chain methodologies. The suggested technique used KMeans to create adaptive fuzzy intervals from normalised historical data, in contrast to classic fuzzy models that relied on fixed partitions. The intervals facilitated the construction of fuzzy logical connections (FLRs) and a state transition matrix, so allowing dynamic forecasting of DFS power demand. The model was assessed with actual DFS data collected at 30-minute intervals. Forecasts were produced using a defuzzification technique informed by Markov transition probabilities. Experimental findings demonstrated a significant correlation between anticipated and actual values, yielding a Mean Absolute Error (MAE) of 74.60 MW and a Root Mean Square Error (RMSE) of 86.04 MW. The results demonstrated that the model accurately represented temporal demand patterns and maintained robustness across different load levels. The technique shown considerable promise for practical use in smart grid forecasting systems. Its simplicity, interpretability, and flexibility made it an invaluable instrument for real-time energy management and decision-making. Keywords: Demand Flexibility Services (DFS), Fuzzy Time Series (FTS), KMeans Clustering, Markov Chain Forecasting, Short-Term Load Predictions

Modeling Incremental Benefit of Medication Reconciliation on ICU Outcomes.

Longitudinal Analysis of the Transition Between Multimorbidity and Mortality Patterns from a Syndemic Perspective.

Energy transitions under anti-environmentalism via Markov chain modeling

Land use change analysis and prediction of urban growth using multi-layer perceptron neural network Markov chain model in Faridabad- A data-scarce region of Northwestern India

In this study, we conceptualize the demands imposed on emergency supply chains during extraordinary emergency events as “stress” and develop a scenario-based stress evolution (SE) analytical approach in emergency mobilization decision-making. First, we characterize emergency supply chain stress by uncertainty, abruptness, urgency, massiveness of scale, and latency. Leveraging lifecycle theory and aligning it with the event’s natural lifecycle progression, we construct a dual-cycle model—the emergency event-stress dual-cycle curve model—to intuitively conceptualize the SE process. Second, taking China’s emergency medical supply chain as an illustrative example, we employ set theory to achieve a structured representation of emergency supply chain stress evolution (ESCSE). Third, we propose a novel ESCSE modeling methodology based on stochastic Petri nets and establish both an ESCSE model and a corresponding isomorphic Markov chain model. To address parameter uncertainties inherent in the modeling process, the fuzzy theory is integrated for parameter optimization, enabling realistic simulation of emergency supply chain stress evolution dynamics. Finally, the SE of the ibuprofen supply chain in Beijing during the COVID-19 pandemic is presented as a case study to demonstrate the working principle of the model. The results indicate that the ESCSE model effectively simulates the SE process, identifies critical states, and triggers actions. It also reveals the evolution trends of key scenario elements, thereby assisting decision-makers in deploying more targeted mobilization strategies in dynamic and changing environments.

Developing a hydrological forecasting model based on past records is crucial for effective hydropower reservoir management and scheduling. Numerous popular discharge forecasting models have been developed; however, real-time forecasts remain challenging. This study evaluates discharge forecasts using the Markov Chain model, Seasonal Autoregressive Integrated Moving Average (SARIMA), and Multiple Linear Regression (MLR) models for forecasting monthly discharge time series. This study compares the accuracy of the discharge forecast results produced by the Markov Chain, SARIMA, and Multiple Linear Regression using five statistical indicators. Based on the simulation results, the Markov Chain, SARIMA, and MLR have accuracy levels of probability in discharge of 63%, 66%, and 76%, respectively. In comparison to other models, the highest correlation (r) is found in the MLR model (0.76) with MAPE (0.19), followed by SARIMA and Markov Chain. Therefore, the most accurate, precise, and representative water source model alternative for forecasts is the MLR model. The Markov Chain model and the SARIMA model are time series generation models, while the MLR model is a statistical regression model. In addition, this model is to be selected as the basis for modeling in forecasting river flow or optimal management of a reservoir, as well as determining future discharge, especially in monsoon climate regions.

This study applies a Markov Chain model to forecast the behaviour of the Dar Es Salaam Stock Exchange (DSE) market. DSE serves as the only official platform for share trading in Tanzania. The application of the Markov Chain model in forecasting future states relies on the inherent randomness exhibited by the DSE market. This study utilizes the Markov Chain model to forecast future conditions based on the inherent randomness observed in the DSE market. It aims to explore the long-term dynamics of the market, estimate how often specific states are revisited, and calculate the expected time for the market to return to those states. The analysis utilizes secondary data from 1,847 trading days of DSE index values, covering the period from August 25, 2014, to November 18, 2021, sourced from the DSE office. The study reveals that in the long run, regardless of the current state of the DSE market, share prices are most likely to remain stable, with a probability of approximately 91%, while the chances of depreciation and appreciation are relatively low, at around 5% and 4%, respectively. This indicates a relatively steady market environment with minimal and predictable price movements, reflecting low long-term volatility. Such stability may appeal to long-term, risk-averse investors, but it also highlights limited opportunities for short-term speculative gains. The findings underscore the need to enhance market activity and liquidity by encouraging more company listings, increasing public awareness, and improving market infrastructure. These efforts could help develop a more vibrant and responsive stock market, thereby strengthening the DSE’s role in supporting broader economic development.

Vehicular Internet of Things (V-IoT) networks, sustained by a high-density deployment of roadside units and sensor-equipped vehicles, are currently at the edge of next-generation intelligent transportation system evolution. However, offering stable, low-latency, and energy-efficient communication in such heterogeneous and delay-prone environments is challenging due to limited spectral resources and diverse quality of service (QoS) requirements. This paper presents a Priority-Aware Spectrum Management (PASM) scheme for IoT-based vehicular networks. This dynamic spectrum access scheme integrates interweave, underlay, and coexistence modes to optimize spectrum utilization, energy efficiency, and throughput while minimizing blocking and interruption probabilities. The algorithm manages resources efficiently and gives proper attention to each device based on its priority, so all IoT devices, from high to low priority, receive continuous and reliable service. A Continuous-Time Markov Chain (CTMC) model is derived to analyze the proposed algorithm for various network loads. Simulation results indicate improved spectral efficiency, throughput, delay, and overall QoS compliance over conventional access methods. These findings establish that the proposed algorithm is a scalable solution for dynamic V-IoT environments.

In the stock market, the stock price is a random variable that changes over time, and its fluctuation exhibits the characteristics of a random walk. This research focuses on the A-share "Shanghai Kweichow Moutai Co., Ltd." listed on the Shanghai Stock Exchange, utilizing data from 100 trading days. The study begins with confirming the presence of Markov properties within the stock’s price movements, followed by the construction of a Markov model to analyze and forecast its price trends. The analysis reveals that the Markov model provides reasonably accurate results, offering valuable insights into the cyclical patterns of stock prices. This approach aids in understanding the underlying mechanisms that drive price fluctuations and enhances the ability to predict future price movements, which can be beneficial for investors and financial analysts in decision-making processes. Additionally, the model demonstrates the potential of applying Markov processes to financial markets, particularly in predicting trends and making more informed predictions based on historical data.

Markov chains, due to their "memoryless" property, align well with the characteristics of many phenomena in life, making Markov prediction models an important tool for predicting random events in daily life. This paper aims to summarize the rich findings obtained by researchers on Markov prediction models, and to specifically demonstrate the powerful capabilities of Markov prediction models in forecasting stock prices, allowing readers to intuitively experience the differences in prediction accuracy brought about by continuously improved models. Specifically, the paper first introduces the origin and development history of Markov chains. It then briefly covers the relevant knowledge of Markov chains and the basic steps for using them in predictions. Following this, it presents the effects of three progressively advanced Markov chain prediction methods in stock price forecasting. The study finds that when more careful consideration is given to the relationships between variables and more complex and precise algorithms are integrated, the prediction results are more accurate, and the predictability extends over longer periods. Overall, although there are difficulties in data collection and state classification, its advantages in handling stochastic processes make it one of the important choices for practical applications in various fields. With the advancement of technology, these models will become more efficient and accurate, allowing them to be applied in a wider range of fields. Moreover, attempts to combine various algorithms with Markov chain models will be a direction for improving prediction results.

Abstract Understanding land use and land cover (LULC) dynamics in seismically active regions is crucial for risk‐informed urban planning and sustainable post‐disaster recovery. This study investigates the impact of the Mw 6.8 Elazığ earthquake (24 January 2020) on LULC patterns in eastern Turkey by integrating high‐resolution Sentinel‐2 satellite imagery with geographic information systems (GIS), remote sensing (RS), artificial neural networks (ANNs), and Markov chain modelling. The methodology comprises four phases: establishing a pre‐earthquake baseline (2015–2019), assessing post‐earthquake changes (2015–2023), analysing transition probabilities to identify key LULC drivers, and forecasting land‐use scenarios for 2030 and 2050 under seismic and non‐seismic conditions. Results reveal that seismic activity significantly accelerates urban expansion, shifting development towards geologically stable zones. By 2050, artificial surfaces are projected to occupy 54.70% of the region under seismic influence, compared to 48.87% without it. Agricultural land is more preserved in the seismic scenario (26.54%) than in the non‐seismic case (22.68%), while pasture and meadow areas decline sharply to 6.18%, raising concerns for biodiversity and ecosystem services. These findings emphasise the importance of integrating ecological considerations and seismic risk into land‐use planning frameworks. By combining multicriteria decision‐making with machine learning‐based forecasting, the study offers a replicable and scalable model for balancing urban growth, environmental conservation, and resilience. Framed within interdisciplinary insights from disaster resilience theory, urban governance, and spatial risk modelling, this research contributes to the global discourse on sustainable urban transformation in the face of increasing natural hazards.