Markov Chain Analysis Research Articles

Comprehensive Analysis of Production and Import Dependency of Pulses in India

Background: The multifaceted importance of pulses in India’s agricultural landscape is explored along with its implications particularly the surge in pulse exports to regions such as the Middle East, Southeast Asia and Africa. By highlighting India’s pivotal role as a global pulse producer and exporter, shedding light on its impact on foreign exchange earnings, employment generation, comprehensive analysis and overall economic growth is attempted. Two key objectives, relating to pulse production, import and export trends, the direction of pulse trade from India to major importing countries, through Markov Chain Analysis, market dynamics are uncovered. Methods: The data on pulses production and export in terms of quantity and value in India were collected from secondary sources. Result: Pulse production remained relatively stable with moderate variability, exports experienced significant fluctuations, particularly in pulse quantities and values. Compound Annual Growth Rate (CAGR) analysis confirms consistent production growth, with pulses showing positive CAGR figures. Impact of interaction between area and yield was significant as compared to area and yield independently. Pulse exports saw steady growth in both quantity and value. Further, The Markov chain analysis underscored Pakistan’s stability as a primary export destination, while Bangladesh, the UAE and others exhibited varying degrees of export retention probabilities. Overall, the data reflects a stable domestic production environment and an increasingly dynamic international pulse export market for India.

Progress in Digital Climate Governance in China: Statistical Measurement, Regional Differences, and Dynamic Evolution

The capacity for climate governance is crucial for sustainable advancement, with data elements being a pivotal production factor in contemporary governance. This study examines the trajectory and strategy of digital transformation in climate governance, creating a three-dimensional dataset encapsulating 11 primary and 36 secondary indicators to facilitate the assessment of digital climate governance. Employing spatiotemporal analysis and coupling coordination models, this study evaluates the digitalization levels in climate governance across 30 regions in China, examining how to progress digital integration from governmental and market perspectives. Findings reveal a consistent improvement in China’s regional digital climate governance, bolstering economic and social progress. Nonetheless, regional disparities and developmental lags persist, with convergence analysis indicating a divergence trend in provincial climate governance capabilities. Moreover, kernel density and Markov chain analyses suggest an ongoing evolution in regional digital climate governance efforts, aiming at achieving a higher development plateau. The study emphasizes the dual role of government and market dynamics in boosting digital governance levels, deducing from two-stage regression that effective government-market interplay is vital for elevating governance quality and fostering new productive forces, recommending an integrated governance mechanism for optimal synergy.

LEED certification system for green buildings in China: Examining spatial differences, temporal evolution, and spatial overflow

This paper examines the spread of green building development in China by analyzing the implementation of the LEED certification system across 31 provinces from 2006 to 2022. To investigate spatial differences, spatial-temporal evolution, and spatial spillovers of green building development in China, we employed various statistical methods, including Standard Distance Ellipse, Dagum's Gini Coefficient, Kernel Density Estimation, Markov Chain Analysis, Moran's I Exponential Analysis, and Social Network Analysis. The findings reveal the formation of a green building cluster in a northeast-southwest orientation, at the core of the Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta regions. There is compelling evidence of substantial regional disparities in green building development, primarily driven by inter-regional differences, which has resulted in a significant gap between the eastern and non-eastern regions. This study identifies the presence of “club convergence” and the “Matthew effect” in China's green building landscape, where spatial factors play a crucial role. Furthermore, a distinct and stable spatial clustering is observed, indicating a complex and multi-faceted spatial association network that goes beyond geographical proximity and neighborhood effects. This network reveals diverse positions and roles for provinces and cities. The eastern provinces and cities are found to exert strong control and dominance while also playing a significant siphoning role. On the other hand, the western provinces and cities have been marginalized, experiencing minimal dividend spillover from the development of other provinces.

SMART: Spectral energy distributions Markov chain Analysis with Radiative Transfer models

Abstract In this paper we present the publicly available open-source spectral energy distribution (SED) fitting code SMART (Spectral energy distributions Markov chain Analysis with Radiative Transfer models). Implementing a Bayesian Markov chain Monte Carlo (MCMC) method, SMART fits the ultraviolet to millimetre SEDs of galaxies exclusively with radiative transfer models that currently constitute four types of pre-computed libraries, which describe the starburst, active galactic nucleus (AGN) torus, host galaxy and polar dust components. An important novelty of SMART is that, although it fits SEDs exclusively with radiative transfer models, it takes comparable time to popular energy balance methods to run. Here we describe the key features of SMART and test it by fitting the multi-wavelength SEDs of the 42 local ultraluminous infrared galaxies (ULIRGs) that constitute the HERschel Ultraluminous Infrared Galaxy Survey (HERUS) sample. The Spitzer spectroscopy data of the HERUS ULIRGs are included in the fitting at a spectral resolution, which is matched to that of the radiative transfer models. We also present other results that highlight the performance and versatility of SMART. SMART promises to be a useful tool for studying galaxy evolution in the James Webb Space Telescope (JWST) era. SMART is developed in Python and is available at https://github.com/ch-var/SMART.git.

REDD implementation for greenhouse gas reduction and climate change mitigation in Hyrcanian forests: a case study of the Kojoor Watershed, Northern Iran

Reducing emissions from deforestation and forest degradation (REDD) is a specific strategy for combating deforestation and forest degradation to alleviate the effects of climate change. In this study, the potential greenhouse gas (GHG) emission reduction resulting from the implementation of a REDD project is estimated. Changes in forest cover throughout the years 1985, 1990, 1995, 2000, 2010, 2015, and 2020 were analyzed using time-series Landsat imagery (TM, ETM + , and OLI) and a random forest algorithm. Multilayer perceptron neural networks were used to model the transition potential of the forest cover, which were then predicted via Markov chain analysis. The change detection analysis revealed two discernible patterns in forest cover dynamics. Between 1985 and 2000, a notable decrease in forest cover was seen, whereas from 2000 to 2020, it significantly increased. The results suggested that the absence of REDD implementation would result in the deforestation of approximately 199,569 hectares of forest cover between 2020 and 2050, leading to the release of 1,995,695 tCO2e of emissions into the atmosphere. However, with the implementation of REDD, these emissions would be reduced to 405,512 tCO2e, effectively preventing the release of 1,590,183 tCO2e of emissions into the upper atmosphere. This study demonstrates that the implementation of REDD projects can be an effective strategy for reducing GHG emissions and mitigating climate change in the Hyrcanian forests.

An Application of Markov Chain Analysis to Study the Indian Cocoa Products Export Performance

The paper attempts to quantify the changing structure of Indian cocoa product exports. Data for analysis was considered for a period of 10 years from 2013-14 to 2022-23. The growth rates of exports of Indian cocoa products to Nigeria, Indonesia, USA, UAE, The Netherlands and other countries have shown a positive trend. The Markov chain analysis was attempted through the linear programming method to assess the transition probabilities for the major cocoa markets. The results revealed that Nigeria, Nepal, UAE and other countries were stable markets for Indian cocoa export products. Whereas, Indonesia and The Netherlands were the least stable markets based on the magnitude of transition probabilities. The export share predictions for 2025-26 behaved stagnant while also increasing slightly for major destinations.

Markov Chain analysis of rainfall of Coimbatore

Rainfall is considered one of the most important weather parameters which helps in deciding the time of sowing, pest and disease management and harvesting. Markov chain analysis deals with predicting future values based on past values. In the present study, Markov Chain analysis was used to predict the future probability of monthly rainfall and examine the pattern and distribution of rainfall using daily rainfall data from the year 1982 to 2016 (34 years) in the Coimbatore district. This study mainly analysed the probability of rainfall in the Coimbatore district of Tamil Nadu based on Markov chain process. Based on the National Center for Hydrology and Meteorology, the intensity of rainfall per day was categorized and a day is considered as no rain if rainfall was less than 0.1 mm, low rain if rainfall was between 0.1 mm to 10 mm, moderate rain if rainfall was between 10 mm to 20 mm and heavy rain if rainfall was above 20 mm. By calculating the transition probability matrices and steady-state probability matrices for each month based on the conditional probability of rain on a particular day given that rain on the previous day which is to predict the state of rainfall on the next day. This study reported that the availability of water for crop production is higher during the winter, pre-monsoon, the onset of the southwest monsoon, and at the end of the northeast monsoon. There may be a scarcity of water from August to November for agricultural activities. Based on this study, farmers can plan for a better cropping system in advance to get a better yield.

Production and Export Performance of Fresh and Dried Figs from Afghanistan vis-à-vis India

The study was conducted on the production and export performance of fresh and dried figs from Afghanistan and India. The study was based on secondary data on area, production, productivity, and export of figs from 2000-01 to 2020-21. The data were analyzed using tools such as compound growth rates, coefficients of variation, the Cuddy-Della Valle Instability Index, and Markov Chain Analysis. The results revealed that the growth rates of area, production, productivity, export quantity, and export value of fresh and dried figs from both countries were favourable, with the exception of non-significant growth for the area in Afghanistan and the export quantity in India. The highest fluctuation in both countries was reflected in the exports of the quantity and value of fresh and dried figs. In contrast, instability in terms of area, production, and productivity of figs in Afghanistan was higher than in India. India was recognized as the most stable market for Afghan fresh and dried figs, followed by the UAE. In contrast, the Maldives was found to be the most loyal market for Indian fresh and dried figs, followed by Hong Kong and the USA respectively.

An Analysis of Indian Sugar Exports: Markov Chain Approach

India, the world's leading producer and exporter of sugar, plays a crucial role in the food industry due to sugar's sweet flavour and its multifaceted applications as a sweetener, preservative, texture enhancer, colouring agent, flavouring agent, and bulking agent. In the year 2020- 21 India exported an impressive 70 lakh tonnes of sugar, following 59 lakh tonnes in 2019- 20. India's sugar exports reach as many as 121 countries, with Sudan, Myanmar, Sri Lanka, Somalia, the United Arab Emirates, Pakistan, and Iran being some of the most consistent importers. The present study discusses the above approach envisaging the above analysis of Indian sugar exports to different countries from 2001-02 to 2020-21 employed Markov chain analysis, the results unveiled that Sudan retained the lion's share at 89.4% of India's sugar exports, followed by Myanmar at 57.3%, Somalia at 55.8%, Iran at 48%, the United Arab Emirates at 36.6%, Sri Lanka at 28.2%, Bangladesh at 10.7%, Pakistan at 5.4%, with the remaining 56.2% distributed among various other nations. Notably, India maintained loyal sugar exporters in Sudan, Myanmar, Somalia, Iran, the UAE, and Sri Lanka.

Inter-voivodship migration in Poland in the 2000–2020 period based on Markov chain analysis

The paper presents the scale and directions of inter-voivodship migration in Poland in selected years of the 2000–2020 period. The study focused on permanent residence migration and aimed to identify areas of migration attractiveness and migration catchment voivodships. To study the stochastic nature of these migrations, a Markov chain model was used, in which the states were voivodships. An important aspect of the study involved determining the properties of the transition probability matrix as well as stationary probability in order to characterise the mechanism of inter-voivodship migrations in the years 2000, 2010 and 2020. Data obtained from Statistics Poland were used in the analysis. The transition probability matrix showed that the states were connected and irreducible to each other, while the stationary probability of migration to Dolnos´ al˛skie, Małopolskie, Pomorskie, and Wielkopolskie voivodships increased in 2020 compared to 2000. The analysis of the mechanism of migration in the years 2000, 2010, 2020 indicated that Mazowieckie Voivodship was still the main destination for migrants, with the highest stationary probability reaching 0.18 in 2010.

Food Grain Trade Prospect of India: Markov Chain Analysis

Food Grain Trade Prospect of India: Markov Chain Analysis

Is natural inflation in agreement with CMB data?

Natural inflation is a well-motivated model for the early universe in which an inflaton potential of the pseudo-Nambu-Goldstone form, V(ϕ) = Λ4[1 + cos(ϕ/f)], can naturally drive a cosmic accelerated epoch. This paper investigates the observational viability of the minimally and non-minimally coupled natural inflation scenarios in light of current Cosmic Microwave Background (CMB) observations. We find that a small and negative coupling of the field with gravity can alleviate the well-known observational discrepancies of the minimally coupled model. We perform a Monte Carlo Markov Chain analysis of the Planck 2018 CMB and BICEP/Keck Array B-mode polarization data to estimate how strong the coupling ξ should be to achieve concordance with data. We also briefly discuss the impact of these results on the physical interpretation of the natural inflation scenario.

Comprehensive evaluation and spatial-temporal evolution characteristics of urban resilience in Chengdu-Chongqing Economic Circle

To clarify the connotations and extensions of urban resilience, this study focuses on the Chengdu-Chongqing Economic Circle with 16 cities as research subjects. A comprehensive evaluation index system was constructed to measure the resilience of each city from 2003 to 2020. The spatial-temporal evolution characteristics were analyzed using Kernel density estimation, standard deviation ellipse, and spatial Markov chain analysis, and the spatial Tobit model was introduced to discover the influencing factors. The results indicate the following: ① Urban resilience in the Chengdu-Chongqing Economic Circle displays an upward trend, with the center of gravity moving to the southwest, and the polarization phenomenon intensifying. ② The urban resilience level in a region has certain spatial and geographical dependence, while the probability of urban resilience transfer differs in adjacent cities with different resilience levels. ③ Urban centrality, economic scale, openness level, and financial development promote urban resilience, whereas government scale significantly inhibits it. Finally, this paper proposes countermeasures and suggestions to improve the urban resilience of the Chengdu-Chongqing Economic Circle.

Current State of Advances in Quantification and Modeling of Hydrological Droughts

Hydrological droughts may be referred to as sustained and regionally extensive water shortages as reflected in streamflows that are noticeable and gauged worldwide. Hydrological droughts are largely analyzed using the truncation level approach to represent the desired flow condition such as the median, mean, or any other flow quantile of an annual, monthly, or weekly flow sequence. The quantification of hydrologic droughts is accomplished through indices, such as the standardized streamflow index (SSI) in tandem with the standardized precipitation index (SPI) commonly used in meteorological droughts. The runs of deficits in the SSI sequence below the truncation level are treated as drought episodes, and thus, the theory of runs forms an essential tool for analysis. The parameters of significance from the modeling perspective of hydrological droughts (or tantamount to streamflow droughts in this paper) are the longest duration and the largest magnitude over a desired return period of T-year (or month or week) of the streamflow sequences. It is to be stressed that the magnitude component of the hydrological drought is of paramount importance for the design and operation of water resource storage systems such as reservoirs. The time scales chosen for the hydrologic drought analysis range from daily to annual, but for most applications, a monthly scale is deemed appropriate. For modeling the aforesaid parameters, several methodologies are in vogue, i.e., the empirical fitting of the historical drought sequences through a known probability density function (pdf), extreme number theorem, Markov chain analysis, log-linear, copulas, entropy-based analyses, and machine learning (ML)-based methods such as artificial neural networks (ANN), wavelet transform (WT), support vector machines (SVM), adaptive neuro-fuzzy inference systems (ANFIS), and hybrid methods involving entropy, copulas, and machine learning-based methods. The forecasting of the hydrologic drought is rigorously conducted through machine learning-based methodologies. However, the traditional stochastic methods such as autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), copulas, and entropy-based methods are still popular. New techniques for flow simulation are based on copula and entropy-based concepts and machine learning methodologies such as ANN, WT, SVM, etc. The simulated flows could be used for deriving drought parameters in consonance with traditional Monte Carlo methods of data generation. Efforts are underway to use hydrologic drought models for reservoir sizing across rivers. The ML methods whilst combined in the hybrid form hold promise in drought forecasting for better management of existing water resources during the drought periods. Data mining and pre-processing techniques are expected to play a significant role in hydrologic drought modeling and forecasting in future.

Markov chain‐based analysis and fault tolerance technique for enhancing chain‐based routing inWSNs

SummaryWireless sensor networks (WSNs) are faced with the challenge of energy conservation, which makes efficient routing protocols crucial for prolonging network lifetime. In addition, delay time from sensors to the base station is critical in applications such as military, medical, and security monitoring systems. Chain‐based protocols like PEGASIS, CCBRP, and CCM have been developed to address routing in WSNs, with the aim of reducing energy consumption and delay. However, node failures are inevitable due to energy reduction and node mobility. Therefore, fault tolerance techniques must be integrated into routing protocols. A new fault tolerance method has been proposed to prevent early chain failures in WSNs by formulating network availability and reliability using Markov chain analysis. The results indicate that chain‐based routing protocols with one spare node are more reliable than those with two spare nodes.

Study on the spatiotemporal evolution of coupled and coordinated digital economic resilience and efficiency

The coordinated development of resilience and efficiency in the digital economy guarantees a sustainable and efficient growth model. We used panel data from 30 provinces and regions in China from 2013 to 2021 to evaluate the resilience and efficiency levels of the digital economy. We calculated their coordinated development levels using kernel density estimation, spatial autocorrelation, and Markov chain analysis to explore the spatiotemporal evolution characteristics of their coordinated development. The results indicate that (1) the resilience level of the digital economy is on the rise, while efficiency fluctuations are decreasing. Resilience exhibits an “east high–west low” spatial pattern with the “Hu Huanyong Line” as a boundary, while efficiency shows a “collapse in the central and northern regions” trend. (2) The overall level of coordinated development between resilience and efficiency has decreased; however, the differences in coordination between different regions have reduced, gradually forming a spatial pattern of “high in the east, low in the west, low in the south, and high in the north.” (3) A significant positive spatial correlation exists between the development levels of digital economic resilience and efficiency, which continues to strengthen over time. This finding reveals a spatial pattern of high-level concentration centered around Beijing–Tianjin, Jiangsu–Zhejiang–Shanghai, and low-level concentration centered around the central and western regions. (4) Coordinated development shows a “club convergence” effect, indicating stability in maintaining the existing state.

Beyond [formula omitted]CDM with [formula omitted]CDM: Criticalities and solutions of Padé Cosmography

Recently, cosmography emerged as a valuable tool to effectively describe the vast amount of astrophysical observations without relying on a specific cosmological model. Its model-independent nature ensures a faithful representation of data, free from theoretical biases. Indeed, the commonly assumed fiducial model, the ΛCDM, shows some shortcomings and tensions between data at late and early times that need to be further investigated. In this paper, we explore an extension of the standard cosmological model by adopting the f(z)CDM approach, where f(z) represents the cosmographic series characterizing the evolution of recent universe driven by dark energy. To construct f(z), we take into account the Padé series, since this rational polynomial approximation offers a better convergence at high redshifts than the standard Taylor series expansion. Several orders of such an approximant have been proposed in previous works, here we want to answer the questions: What is the impact of the cosmographic series choice on the parameter constraints? Which series is the best for the analysis? So, we analyze the most promising ones by identifying which order is preferred in terms of stability and goodness of fit. Theoretical predictions of the f(z)CDM model are obtained by the Boltzmann solver code and the posterior distributions of the cosmological and cosmographic parameters are constrained by a Monte Carlo Markov Chains analysis. We consider a joint data set of cosmic microwave background temperature measurements from the Planck collaboration, type Ia supernovae data from the latest Pantheon+ sample, baryonic acoustic oscillations and cosmic chronometers data. In conclusions, we state which series can be used when only late time data are used, while which orders has to be considered in order to achieve the necessary stability when large redshifts are considered.

Parameter synthesis for Markov models: covering the parameter space

Markov chain analysis is a key technique in formal verification. A practical obstacle is that all probabilities in Markov models need to be known. However, system quantities such as failure rates or packet loss ratios, etc. are often not—or only partially—known. This motivates considering parametric models with transitions labeled with functions over parameters. Whereas traditional Markov chain analysis relies on a single, fixed set of probabilities, analysing parametric Markov models focuses on synthesising parameter values that establish a given safety or performance specification φ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varphi $$\\end{document}. Examples are: what component failure rates ensure the probability of a system breakdown to be below 0.00000001?, or which failure rates maximise the performance, for instance the throughput, of the system? This paper presents various analysis algorithms for parametric discrete-time Markov chains and Markov decision processes. We focus on three problems: (a) do all parameter values within a given region satisfy φ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varphi $$\\end{document}?, (b) which regions satisfy φ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varphi $$\\end{document} and which ones do not?, and (c) an approximate version of (b) focusing on covering a large fraction of all possible parameter values. We give a detailed account of the various algorithms, present a software tool realising these techniques, and report on an extensive experimental evaluation on benchmarks that span a wide range of applications.

Growth Dynamics of vegetable crops in Eastern Dry Zone of Karnataka, India: An Economic Analysis

The present study was initiated in the Kolar and Chikkaballapur districts of Karnataka with the overall objective of assessing the growth in production and allocation of the area under vegetables in situations. The study was based on time series data collected from the Directorate of Economics and Statistics, Bangalore. Compound Annual Growth Rate and Triennium averages were worked out to examine the trend in area, production and productivity of major vegetable crops. Markov chain analysis was used to examine the dynamic shift in the area, production, and productivity of major vegetable crops for a decade from 2007-08 to 2017-18. The triennium end average of area major vegetable crops in Kolar district for the period TE 2010-11 was 2480.20 ha reduced to 1424.00 ha in TE 2017-18. The productivity of major vegetable crops is increasing to 15134.30 kg/ha from 8830.10 kg/ha (TE 2000-01). The triennium end average of area major vegetable crops in Chikkaballapur district for the period TE 2010-11 was 1085.20 ha increased to 1311.00 ha in TE 2017-18. The productivity of major vegetable crops is increasing to 14422.30 kg/ha from 12166.40 kg/ha (TE 2000-01). The overall growth in the area and productivity of vegetables in Kolar was -7.37 and 8.14 per cent, respectively whereas the overall growth in the area and productivity of vegetables in Chikkaballapur was 4.80 and 1.98 per cent, respectively. Implies growth in the area, production, and productivity showing a decline turn to negative. Whereas, the triennium end average of area for major vegetable crops for the period TE 2017-18 was 124765.7 ha growth of 3.1 per cent and a productivity average of 2165.7 kg/ha with a growth of -0.3 per cent. The tomato and cabbage were the highest retention capacity of 69 per cent equally in both districts. Beans, Brinjal, and Dry chilli have zero retention capacity in the Chikkaballapur district whereas onion, brinjal, and cabbage have zero retention capacity in the Kolar district. i.e these crops lost their 100 per cent area to the other crops. This will be projected by using Markov chain analysis.

Design and analysis of fault-tolerant sequential logic circuits for safety-critical applications

Safety-critical systems used in applications that demand high levels of dependability, efficiency, and fault-tolerance often use sequential logic circuits in its design and implementation. The safety-critical digital system typically uses latches, flip-flops, and other memory elements, which are prone to the effects of natural faults and single event upsets (SEUs) caused by radiation-induced effects. The faults can lead to subsystem failures due to the continuous advancement in the realization of the small size transistor. To design a reliable digital-based system, it is essential to develop new fault-tolerance approaches that are integrated into the design of sequential logic circuits. This work proposes a novel fault-tolerant approach based on the redundancy of sequential logic circuit, which consists of a variety of design components, D flip-flop storage elements linked to a fault injection unit, a duplicate modular redundancy, and data monitoring units with a switching circuit. The experimental simulation results using a five-state Markov chain analysis model prove that the proposed fault-tolerant system can achieve 0.99999998 for reliability of the fault detection coverage (C) which equal to 0.99999. Finally, we believe that using this new approach of fault-tolerance and redundancy would improve the dependability and reliability of next generation safety-critical applications.