Markov Chain Model Research Articles

Livelihood diversification and household welfare among farm households in the Philippines

AbstractDiversification of income sources is one of the most common strategies households employ to minimize household income variability and to ensure a minimum level of income that guarantee their food security. This study examines the impact of livelihood diversification on farm profits among smallholder rice farmers in the Central Luzon Region (CLR) of the Philippines using long‐term farm‐level panel data spanning from 1966 to 2016. We employed a random‐effects ordered probit model to investigate the drivers of livelihood diversification and then used the mixed Markov chain model to analyze the transition of households from less to more diversified livelihoods and its impact on farm profits over time. Our findings reveal substantial diversification among households over time. In particular, the elevation of the farm location is a key driver of rice farm households’ probability to diversify. The estimates show that an increase in the latitude of the farm location increases the probability of rice farmers in the CLR to diversify. We find that 64% of the rice farm households constitute farmers for whom diversification can be a strategy for survival. For medium‐ to high‐profit farm categories, diversification tends to protect farmers against farm profit losses arising from adverse climatic and weather variability.

Beyond bigrams: call sequencing in the common marmoset ( Callithrix jacchus ) vocal system

Over the last two decades, an emerging body of research has demonstrated that non-human animals exhibit the ability to combine context-specific calls into larger sequences. These structures have frequently been compared with language’s syntax, whereby linguistic units are combined to form larger structures, and leveraged to argue that syntax might not be unique to language. Currently, however, the overwhelming majority of examples of call combinations are limited to simple sequences comprising just two calls which differ dramatically from the open-ended hierarchical structuring of the syntax found in language. We revisit this issue by taking a whole-repertoire approach to investigate combinatoriality in common marmosets ( Callithrix jacchus ). We use Markov chain models to quantify the vocal sequences produced by marmosets providing evidence for structures beyond the bigram, including three-call and even combinations of up to eight or nine calls. Our analyses of these longer vocal sequences are suggestive of potential further internal organization, including some amount of recombination, nestedness and non-adjacent dependencies. We argue that data-driven, whole-repertoire analyses are fundamental to uncovering the combinatorial complexity of non-human animals and will further facilitate meaningful comparisons with language’s combinatoriality.

Modeling Emergency Traffic Using a Continuous-Time Markov Chain

This paper aims to propose a novel call for help traffic (SOS) and study its impact over Machine-to-Machine (M2M) and Human-to-Human (H2H) traffic in Internet of Things environments, specifically during disaster events. During such events (e.g., the spread COVID-19), SOS traffic, with its predicted exponential increase, will significantly influence all mobile networks. SOS traffic tends to cause many congestion overload problems that significantly affect the performance of M2M and H2H traffic. In our project, we developed a new Continuous-Time Markov Chain (CTMC) model to analyze and measure radio access performance in terms of massive SOS traffic that influences M2M and H2H traffic. Afterwards, we validate the proposed CTMC model through extensive Monte Carlo simulations. By analyzing the traffic during an emergency case, we can spot a huge impact over the three traffic types of M2M, H2H and SOS traffic. To solve the congestion problems while keeping the SOS traffic without any influence, we propose to grant the SOS traffic the highest priority over the M2M and H2H traffic. However, by implementing this solution in different proposed scenarios, the system becomes able to serve all SOS requests, while only 20% of M2M and H2H traffic could be served in the worst-case scenario. Consequently, we can alleviate the expected shortage of SOS requests during critical events, which might save many humans and rescue them from being isolated.

A Conceptual Model for Health Impact Assessment of Personalized Prevention Policies

Abstract Background Personalized prevention and pharmacogenomics are emerging fields in global health, promoting tailored approaches to disease prevention and individualized drug therapy, based on the individuals’ genetic profile. However, adoption of these technologies in public health is challenged by the possibility of health disparities. Health Impact Assessment (HIA) is a useful tool to address this issue, by examining how policies differentially impact population groups, guiding stakeholders to more equitable and inclusive outcomes. Solid frameworks for assessing health impacts are needed to avoid heterogeneity when accounting for equity outcomes in HIA. This study tested a conceptual model for HIA in personalized medicine, using a case study in pharmacogenetics to map potential impacts and outcomes, with a focus on health equity. Methods A conceptual model for a HIA was developed to map expected impacts and outcomes of a health policy, aimed at enforcing DPYD genotype-guided dosing to prevent fluoropyrimidine toxicity in colorectal cancer patients. The conceptual model was developed based on literature reviews and consultation with stakeholders. Identification of relevant impacts guided selection of indicators and quantitative impact assessment using a Markov chain model. Results Impact pathways were identified using a three-tiered reference framework encompassing individual and population perspectives, organizational factors, and overarching health system considerations. The final diagram outlined key outcomes in four categories: health, organizational, economic and equity. HIA projections using this model were obtained for the DPYD testing policy. Conclusions In the rise of personalized medicine and pharmacogenomics, health inequalities challenges need to be addressed before implementation in healthcare. This study provided a novel HIA conceptual model for personalized prevention policies based on genomic information, with an emphasis on equity. Key messages • HIA is an adequate tool to evaluate potential health inequalities resulting from personalized medicine policies in healthcare. • This study provides a validated framework for HIA of personalized prevention policies grounded in genomic data.

Real‐time reliability evaluation method for IGBT module in MMC based on junction temperature and bond wire failure online monitoring

AbstractThe lack of an MMC reliability evaluation approach that incorporates data on essential components' health monitoring in the already related research prevents reliability from being further improved. Therefore, this article proposes a real‐time reliability evaluation method of the multi‐state IGBT module in MMC through iterative calculation of the Markov chain model. The degradation process of the IGBT module is divided into multiple states based on the monitored bond wire failure, and the failure rate of the IGBT module is updated in real time using the accumulated monitoring data of junction temperature. The proposed method can more accurately reflect the real state of MMC compared with the binary state evaluation method. Besides, a multi‐channel high‐speed data acquisition board is designed to measuring the gate voltage and collector voltage of the IGBT for the health evaluation of IGBT modules in MMC; the typically measured results validate the effectiveness of the data acquisition board.

Predicting the Impact of Distributed Denial of Service (DDoS) Attacks in Long-Term Evolution for Machine (LTE-M) Networks Using a Continuous-Time Markov Chain (CTMC) Model

The way we connect with the physical world has completely changed because of the advancement of the Internet of Things (IoT). However, there are several difficulties associated with this change. A significant advancement has been the emergence of intelligent machines that are able to gather data for analysis and decision-making. In terms of IoT security, we are seeing a sharp increase in hacker activities worldwide. Botnets are more common now in many countries, and such attacks are very difficult to counter. In this context, Distributed Denial of Service (DDoS) attacks pose a significant threat to the availability and integrity of online services. In this paper, we developed a predictive model called Markov Detection and Prediction (MDP) using a Continuous-Time Markov Chain (CTMC) to identify and preemptively mitigate DDoS attacks. The MDP model helps in studying, analyzing, and predicting DDoS attacks in Long-Term Evolution for Machine (LTE-M) networks and IoT environments. The results show that using our MDP model, the system is able to differentiate between Authentic, Suspicious, and Malicious traffic. Additionally, we are able to predict the system behavior when facing different DDoS attacks.

Markov Chain Analysis of Land-use Changes in India and Maharashtra

Dynamics of Agriculture resembles the history of agriculture in India which examines the emphasizing significance to the nation's legacy, culture and civilization. India's agriculture changed after independence from a food-scarce nation to one that exports food this comes true primarily due to the result of scientific advancements. The present study is based on secondary data with the use of Markov Chain analysis to ascertain land-use changes in India and Maharashtra state. Changes in land use brought on by agriculture, urbanization and environmental factors significantly impact resource management and sustainability. We build a Markov Chain model with secondary land utilization data from government reports to measure the transition probabilities between various land use categories over time. The Markov chain model is easy to understand and communicate. It effectively captures transitions between land-use changes over time. The model works with limited data as compared to other complex models, making it accessible for studies. The results of the study emphasize dynamic interactions between various land-use patterns in India and Maharashtra. There we have concluded a stable land-use changing pattern. A trend towards intensive cropping has been observed during this study. This study reveals important trends in land conversion, most notably the growth of urban areas at the expense of forested and agricultural areas. The predictive abilities of model highlight the significance of well-informed policy-making for sustainable land management by offering insights into potential future land use scenarios.

Enhancing Reliability in Multilevel Inverter: A Fault‐Tolerant Approach With Reduced Switch Count

ABSTRACTReliability is a major concern in multilevel inverters (MLI) due to the increased number of power switches and diodes, which increases the chance of switch failures and complicates the inverter design. The primary challenges faced by the current MLI design include higher control complexity, increased parasitic elements, more extensive protection, and lack of multiple fault tolerance. Therefore, developing reduced switch MLI with fault‐tolerant ability is crucial to ensure system availability and prevent adverse outcomes. To address these challenges, this article introduces a highly resilient fault‐tolerant reduced switch count (FTRSC) MLI topology offering fault tolerance with minimal switch count. The proposed topology provides fault tolerance in case of single‐ and multiple‐switch open‐circuit (OC) failures. The inherent fault‐tolerant operation is analyzed for the proposed topology's symmetric and asymmetric configurations. The simulation and experimental results validate the effectiveness of the proposed topology considering normal, faulty, and postfault conditions. Furthermore, the reliability evaluation of the proposed work is carried out using the Markov chain model, resulting in a high total mean time to failure of approximately 117,550 h/failures, along with an efficiency of 96.3%. These findings demonstrate that the proposed circuit has enhanced reliability and extended mean time to failure, affirming its suitability for sensitive industrial applications. Finally, a thorough comparison with recent studies highlights the superiority of the proposed system.

Bayesian Ensemble Kalman Filter for Gaussian Mixture Models

AbstractInverse theory and data assimilation methods are commonly used in earth and environmental science studies to predict unknown variables, such as the physical properties of underground rocks, from a set of measured geophysical data, like geophysical seismic or electromagnetic data. A new Bayesian approach based on the ensemble Kalman filter using Gaussian mixture models is presented to overcome the assumption of Gaussian distribution of the unknown variables commonly used in the data assimilation literature and to generalize the algorithm to inverse problems with multimodal probability distributions. In applications of subsurface characterization, the multimodality of the unknown variables is generally due to the presence of different rock types, also known as geological facies. In the proposed method, the weights of the Gaussian mixture model represent the facies proportions, and they follow a Markov chain model. The proposed Bayesian model generates the unknown model parameters conditioned on measured data using a Markov chain Monte Carlo sampler. The validity of the method is demonstrated on a data assimilation problem where the goal is to estimate the posterior distribution of the unknown rock density from a set of repeated measurements of acoustic wave velocity measured at different times. The proposed method provides accurate estimates with efficient computational times.

Cost-Effectiveness of an Ultrasound-First Strategy in the Diagnostic Evaluation of Noncalcified Lesions Recalled From Screening Digital Breast Tomosynthesis.

BACKGROUND. Ultrasound may be sufficient in the diagnostic evaluation of many noncalcified lesions recalled from screening digital breast tomosynthesis (DBT). In some scenarios, omission of diagnostic mammography can save health care costs. OBJECTIVE. The purpose of this study was to evaluate the cost-effectiveness of a strategy of performing ultrasound first versus diagnostic mammography first in the diagnostic evaluation of noncalcified lesions recalled from screening DBT. METHODS. Decision tree analysis was performed to compare ultrasound first versus diagnostic mammography first in the diagnostic evaluation of DBT-recalled noncalcified lesions from a U.S. health care system perspective with a 40-year horizon. The analysis used probabilities and prevalence information from published single-institution prospective data, additional literature-derived estimates of diagnostic test performance, and Medicare-allowable reimbursement rates. Health states were represented in a Markov chain model. For each strategy, the total cost and effectiveness (expressed in quality-adjusted life-years [QALYs]) were estimated. Cost-effectiveness was assessed through incremental cost-effectiveness ratios (ICERs) and incremental net monetary benefit, with use of a willingness-to-pay (WTP) threshold of US$100,000 per QALY gained. Deterministic sensitivity analyses were performed to estimate the impact of different input parameters, and probabilistic sensitivity analysis with Monte Carlo simulations was conducted to estimate the impact of combined uncertainty across parameters. RESULTS. In the base-case scenario, for diagnostic evaluation of DBT-recalled noncalcified lesions, a strategy of performing ultrasound first versus diagnostic mammography first resulted in more cost savings (total cost, US$17,672 vs US$18,323) and greater cost-effectiveness (QALYs, 23.1309 vs 23.1306) over the 40-year horizon. The ultrasound-first strategy resulted in an ICER of -2,170,000 (expressed as U.S. dollars per QALY) and an incremental net monetary benefit of US$681 versus the diagnostic mammography-first strategy. Therefore, performing ultrasound first was deemed the more cost-effective strategy at the WTP threshold. In deterministic sensitivity analyses, the most important driver of cost-effectiveness was lost utility from delayed diagnosis, followed by the relative sensitivities of ultrasound and diagnostic mammography. In probabilistic sensitivity analysis, ultrasound first was the better strategy in 93.0% of iterations. CONCLUSION. A strategy of performing ultrasound first, with or without diagnostic mammography, is more cost-effective than a traditional strategy of conducting diagnostic mammography first. CLINICAL IMPACT. This cost-effectiveness analysis supports the growing prioritization of ultrasound as the primary method for evaluating DBT-recalled noncalcified lesions.

DINÁMICA Y VULNERABILIDAD ECOLÓGICA DE LA RESERVA ESTATAL SIERRA DEL TENTZO, MÉXICO

<p><strong>Background:</strong> Knowledge and evaluation of ecological changes in Protected Natural Areas is important for their planning and management. The use of tools such as land use change prediction models and satellite image analysis are low-cost and high-precision alternatives that can contribute to this purpose. <strong>Objective:</strong> Estimate the trend of land use change and vulnerability of the ecosystems of the Sierra del Tentzo State Reserve (REST), Mexico. <strong>Methodology:</strong> The REST sites with the highest probability of change in their ecosystems were identified according to phases of the adaptive cycles of the Panarchy theory; the use of Landsat images to classify land use and vegetation types, and the identification of areas of greatest vulnerability with a Markov chain model. <strong>Results:</strong> The trend of change from a state of conservation to a state of growth predominates in the reserve, which indicates a deterioration of the ecosystems with primary vegetation that are being transformed into grasslands and crop areas. While, in the future projection, the areas with greater ecological vulnerability are grouped in a pattern associated with human settlements in population centers. <strong>Implications:</strong> The study was on a regional scale, so changes and vulnerability at the local level were not explored. <strong>Conclusion:</strong> In the Sierra del Tentzo State Reserve there is a process of ecosystem degradation and some areas, particularly those most susceptible to human activities, are more vulnerable to this degradation. These areas should be considered in the reserve management program as priority attention.</p>

Literature Review on Stochastic Modeling of Wet and Dry Spells

With increasing concerns about climate change and its profound implications for water resources, stochastic modeling of wet and dry spells has emerged as a critical domain within hydrology and climatology, providing insight into the temporal patterns of precipitation and drought occurrences. This review of the literature synthesizes recent advances in the modeling of wet and dry spells using stochastic methods, focusing on publications from 2000 to 2024. The review examines various modeling approaches, including Markov chain models, mixed probability models, non-homogeneous models, and time series approaches. Key findings indicate that Markov chain models are effective in simulating the sequential occurrence of wet and dry spells, with higher-order variants addressing issues of overdispersion. Mixed probability models excel at representing heterogeneity and extremes in precipitation data, especially in regions with distinct wet and dry seasons. Non-homogeneous models are valuable for understanding the temporal dynamics and irregularities of dry spells, revealing significant shifts in their frequency and duration over time. These can be complemented by time-series methods to highlight long-term trends and seasonal variations. The review underscores the importance of regional specificity in modeling approaches to accurately capture local climatic and geographical variability. Recommendations for future research include the integration of hybrid models that combine the strengths of various approaches to improve predictive precision, focusing on the impacts of climate change, and conducting cross-regional comparisons to generalize findings and enhance the robustness of the models.

Prediction of Low-Visibility Events by Integrating the Potential of Persistence and Machine Learning for Aviation Services

Fog typically results in reduced atmospheric visibility. Severely limited visibility has a significant impact on transportation, particularly the operations of aircraft. Precise forecasts of low visibility are essential for aviation services, primarily for the efficient planning of airport activities. Despite the utilization of sophisticated numerical weather prediction (NWP) models, the prediction of fog and limited visibility remains challenging. The intricacy of fog prediction is due to limitations in understanding the micro-scale factors that lead to fog genesis, intensification, persistence, and dissipation. This study investigates the occurrence of fog (surface visibility <1000 m) and dense fog (surface visibility < 200 m) throughout the climatological low-visibility months (November to February) to analyze the persistence of low-visibility events and predict them in the specific conditions of the frog prone Indo-Gangetic Plain (IGP) regions. A representative station, Jay Prakash Narayan International (JPNI) Airport in Patna, India, has been considered given the availability of instrumental quality datasets. The analysis investigates the long-term and short-term persistence and prediction of the series using a diverse variety of machine learning (ML) algorithms. To conduct a comprehensive analysis over an extended period, detrended fluctuation analysis (DFA) is employed to determine the similarities between the time series of large-scale fog and dense fog. A Markov chain model is used to look at the binary time series and figure out how long low-visibility events (like fog and dense fog) last in the short term ( 1-5 hours). Ultimately, we analyze a short-term forecast (Nowcast) with a lead time of one to five hours for instances of low visibility (fog or dense fog). This nowcasting is generated utilizing diverse methodologies, including Markov chain models, persistence analysis, and machine learning (ML) methods. Finally, establish that the most favorable and reliable results in this prediction problem are attained by employing a Mixture of Experts model that integrates persistence-based methods and ML algorithms.

A GIRS-based analysis of urban green space losses with land-use changes and its relationship with surface urban heat island in the city of Tabriz

This research investigates the relationship between urbanization, land-use land-cover (LULC) changes, and surface urban heat islands (SUHIs) in Tabriz, Iran. Using Landsat satellite data from 2000 to 2021, the study analyzed changes in urban green space (UGS) and their impact on SUHIs. The maximum likelihood classification algorithm was employed to assess LULC changes, while the perceptron neural network model and cellular automaton Markov chain model were used to simulate and predict future LULC dynamics. Various indices, including the UGS Change Intensity Index (CII), Land Use Dynamic Degree Index (LUDD), and UGS Land Index, were used to quantify urban expansion, green space changes, and per capita green space (PCG) availability. The findings revealed a significant increase in built-up area and a subsequent decline in green space. While green space initially increased, it later decreased, leading to a reduction in both UGS area and PCG. Surprisingly, the analysis of heat islands associated with green space and built-up areas showed that built-up areas had a more pronounced mitigating effect on SUHIs compared to green space. Consequently, the green space heat island effect intensified, while the built-up area heat island effect decreased over time.

Multistage stochastic programming with a random number of stages: Applications in hurricane disaster relief logistics planning

We consider a logistics planning problem of prepositioning relief commodities in preparation for an impending hurricane landfall. We model the problem as a multi-period network flow problem where the objective is to minimize the total expected logistics cost of operating the network to meet the demand for relief commodities. We assume that the hurricane’s attributes evolve over time according to a Markov chain model, and the demand quantity at each demand point is calculated based on the hurricane’s attributes (intensity and location) at the terminal stage, which corresponds to the hurricane’s landfall. We introduce a fully adaptive multi-stage stochastic programming (MSP) model that allows the decision-maker to adapt their logistics decisions over time according to the evolution of the hurricane’s attributes. In addition, we develop a novel extension of the standard MSP model to address the challenge of having a random number of stages in the planning horizon due to the uncertain landfall time of the hurricane. We benchmark the performance of the adaptive decision policy given by the MSP models with alternative decision policies, including a static policy, a rolling-horizon policy, a wait-and-see policy, and a decision-tree-based policy, all based on two-stage stochastic programming models. Our numerical results and sensitivity analyses provide key insights into the value of MSP in the hurricane disaster relief logistics planning problem.

Exploring the dynamics and future projections of land use land cover changes by exploiting geospatial techniques; A case study of the Kabul River Basin

Global land cover change has caused significant environmental degradation and biodiversity loss. It affects ecosystem functions, livelihoods, and climate variation and has drawn substantial attention in recent decades. In the Kabul River Basin (KRB), there are limited studies on the historical Land Use/Land Cover (LULC) pattern, transition, intensity and future perspective. Therefore, this study aims to investigate long-term LULC changes and major drivers of LULC in the KRB over the past thirty years (1990–2020) and then to project the future LULC pattern for the years 2030, 2040 and 2050. Landsat Imageries of (1990–2020) were used as input data by utilizing the Random Forest Classifier algorithm (RF) in the Google Earth Engine (GEE) to classify the LULC. The LULC was then projected for the future, using the Cellular Automata Markov Chain Model (CA-MCM). The results demonstrated drastic LULC changes, controlled primarily by urbanization and agriculture expansion, which expanded from 467 Km2 (0.7 %) to 2312 km2 (3.4 %) and 6528 km2 (9.6 %) to 10812 (15.9 %), between 1990 and 2020. In contrast, bare land decreased from 70606 km2 (82.1 %) to 48212 km2 (70.9 %) between 1990 and 2020. In addition, the study depicts that the expansion in built-up and vegetation areas in the KRB during the study period were at the utilization of bare land. Future LULC predictions indicated that between 2020 and 2050, bare land would trend downward from 48212 km2 (70.9 %) to 46172 km2 (67.9 %), while vegetation and built-up areas would trend upward from 2312 km2 (3.4 %) to 3640 km2 (5.3 %), 10812 km2 (15.9 %) to 11622 km2 (17.1 %), and water bodies and snowcover would slightly vary from 1.2 % to 0.9 % and 7.9 %–9.0 %. In addition, the results of LULC dynamics reveal a significant strong positive correlation between population and built, as well as population and vegetation. Conversely, there is a strong negative correlation between population and bare land. Our results provide precise insights on LULC patterns and trends in the KRB, which could be employed to design a sustainable framework for land use and ecosystem protection.

The Ecological Construction Path of English Vocabulary Teaching Based on Markov Chain in the Perspective of Ecological Linguistics

The new curriculum standards have put forward new requirements for high school English vocabulary teaching, and the English vocabulary eco-classroom under the guidance of ecological linguistics theory can precisely make up for the shortcomings in the traditional vocabulary classroom and meet the challenges of the times. However, most of the existing researches on ecological classroom are combined with macro English subjects, and few of them are about English vocabulary teaching. This study takes the principles of ecological linguistics as the theoretical basis to support the conceptual construction and morphological reliance of the vocabulary ecological classroom, supplemented by modal theory as the process orientation of the four major stages in the teaching process, constructs a new vocabulary ecological classroom model based on Markov chain model, and applies the ecological classroom model to high school English vocabulary teaching to verify its teaching effects. The experimental results show that the Markov chain-based high school English vocabulary teaching under the “ecological linguistics” model can help students’ interest in vocabulary learning and promote their vocabulary learning level, accounting for 15% improvement in the learning effect.

A Model of Alcohol Consumption with Semi-Markov Variable Coefficients

This paper focuses on the in-depth study of a stochastic approximation methodology involving semi-Markov switches in an averaging scheme with a minor parameter. We present a model where perceived objects are impacted by noise variables dependent on the semi-Markov process. The emphasis is on analyzing the convergence and stability of the stochastic approximation process within this context. Theoretical results are presented alongside numeric simulations, demonstrating the practical applications in managing complex stochastic systems. This work opens promising paths for the use of stochastic approximation approaches in the wider field of semi-Markov processes. Recognizing the mutable nature of alcohol consumption and its dependency on a variety of factors, we propose encoding such dynamism within a semi-Markov parameter structure. This model handles the patterns of individual alcohol consumption as a semi-Markov process; the transition probabilities between different states of alcohol consumption are directed by sociodemographic variables that change over time. Our approach, thus, bridges the gap between the realities of ever-changing alcohol consumption trends and static traditional Markov-chain models. By integrating real-world variables into our innovative model, we offer a cutting-edge analytical tool that lays down new paths for understanding and addressing the challenges with alcohol consumption patterns. Further, our insights have the potential to significantly impact the formulation of effective strategies and public health interventions aimed at alcohol-related harm reduction

SpeciateIT and vSpeciateDB: novel, fast, and accurate per sequence 16S rRNA gene taxonomic classification of vaginal microbiota

BackgroundClustering of sequences into operational taxonomic units (OTUs) and denoising methods are a mainstream stopgap to taxonomically classifying large numbers of 16S rRNA gene sequences. Environment-specific reference databases generally yield optimal taxonomic assignment.ResultsWe developed SpeciateIT, a novel taxonomic classification tool which rapidly and accurately classifies individual amplicon sequences (https://github.com/Ravel-Laboratory/speciateIT). We also present vSpeciateDB, a custom reference database for the taxonomic classification of 16S rRNA gene amplicon sequences from vaginal microbiota. We show that SpeciateIT requires minimal computational resources relative to other algorithms and, when combined with vSpeciateDB, affords accurate species level classification in an environment-specific manner.ConclusionsHerein, two resources with new and practical importance are described. The novel classification algorithm, SpeciateIT, is based on 7th order Markov chain models and allows for fast and accurate per-sequence taxonomic assignments (as little as 10 min for 107 sequences). vSpeciateDB, a meticulously tailored reference database, stands as a vital and pragmatic contribution. Its significance lies in the superiority of this environment-specific database to provide more species-resolution over its universal counterparts.

Assessing the impact of land use and land cover change on the Densu Delta wetland using Markov chain modeling and artificial neural networks

This study investigates the dynamics of land use and land cover (LULC) changes in the Densu Delta wetlands, a critical ecosystem in Ghana. Here, satellite images spanning from 1998 to 2023 were used to analyse the spatio-temporal patterns of LULC changes and their implications for water bodies, wetlands, vegetations, bare lands and urban areas in the Densu Delta wetland. Employing advanced techniques such as Markov chain modelling and artificial neural networks (ANNs), the research assesses and predicts LULC alterations. Significantly, the largest loss of LULC is observed in the Densu Delta wetland, where wetlands transition to waterbody cover type (14.02 km²). Model validation for 2023 attests to the accuracy of the model, boasting a correctness percentage of 70% and a kappa value of 0.74. In-depth analyses explore regional variations in the Densu Delta wetlands, revealing distinct patterns in the rates of LULC change before and after 2013. Notably, urbanization emerges as a prominent factor post-2013, with urban areas experiencing remarkable rates of change in the wetland. Transition matrices underscore the intricate interplay of different land cover classes over the years. Simulated LULC predictions for 2033 and 2043 highlight the urban land cover type as having the highest positive change, recording approximately 0.39% for the Densu Delta wetland. The wetland land cover in the Densu Delta wetland exhibit negative changes of about −0.52%. The synthesis of LULC data enhances our understanding of the complex interactions shaping these critical ecosystems. This research offers valuable insights for sustainable environmental conservation, emphasizing the pivotal role of informed urban planning policies. It also unveils potential challenges posed by climate change, advocating for a holistic approach to preserve these vital wetland ecosystems.