Search Results (19)

Forests are crucial for human well-being and the health of our planet, particularly due to their role in carbon storage and climate mitigation. Mediterranean forests, in particular, are a vital natural resource for the region. They help absorb anthropogenic carbon emissions, reduce erosion, and provide essential habitats for various species, which in turn increases genetic diversity and species richness. This study combines Random Forest and Markov chain models to propose a highly accurate method for predicting land use. This approach offers substantial scientific support for sustainable land management policies. The methods used demonstrated excellent classification performance over time, allowing for an examination of the evolution of Mediterranean forests in the Aspromonte region. This study also provides a foundation for estimating carbon stored above and below ground using remote sensing images. The model achieved an impressive accuracy of 98.88%, making it a reliable tool for predicting the dynamics of Mediterranean forests. The results of this study have significant implications for urban planning and climate change mitigation efforts. Full article

We investigate the construction of a partial absorbing continuous-time Markov chain (CTMC) using a heuristic algorithm aimed at approximate transient analysis. The accuracy of transient state probabilities is indicated by the probability of absorbing state(s) at the specified time moment. A key challenge is the construction of a partial CTMC that minimizes the probability of reaching the absorbing state(s). The generation of all possible partial CTMCs is too computationally demanding, in general. Thus, we turn to investigation of heuristic algorithms that chose to include one state at a time based on limited information (i.e., the partial chain that is already constructed) and without any assumptions about the structure of the underlying CTMC. We consider three groups of such algorithms: naive, based on state characterization by the shortest path (obtained by Dijkstra method) and based on exact/approximate state probabilities. After introducing the algorithms, we discuss the problem of optimal partial CTMC construction and provide several examples. Then we compare the algorithm performance by constructing the partial CTMCs for two models: car sharing system and a randomly generated CTMC. Our obtained numerical results suggest that heuristic algorithms using state characterization via the shortest path offer a balance between accuracy and computational effort. Full article

The use of individual calving pens in modern farming is widely recognized as a good practice for promoting good animal welfare during parturition. However, determining the optimal time to move a pregnant cow to a calving pen can be a management challenge. Moving cows too early may result in prolonged occupancy of the pen, while moving them too late may increase the risk of calving complications and production-related diseases. In this paper, a simple random walk type Markov Chain Model to predict the optimal time for moving periparturient cows to individual calving pens was proposed. Behavior changes such as lying time, standing time, and rumination time were analyzed using a video monitoring system, and we formulated these changes as the states of a Markov Chain with an absorbing barrier. The model showed that the first time entering an absorbing state was the optimal time for a pregnant cow to be moved to a calving pen. The proposed method was validated through a series of experiments in a real-life dairy farm, showing promising results with high accuracy. Full article

Poor mental health affects nearly one billion people worldwide and can end in suicide if not treated. Unfortunately, stigma and a lack of mental healthcare providers are barriers to receiving needed care. We developed a Markov chain model to determine whether decreasing stigma or increasing available resources improves mental health outcomes. We mapped potential steps in the mental health care continuum with two discrete outcomes: getting better or committing suicide. Using a Markov chain model, we calculated probabilities of each outcome based on projected increases in seeking help or availability of professional resources. Modeling for a 12% increase in awareness of mental health concerns yielded a 0.39% reduction in suicide. A 12% increase in access to professional help yielded a 0.47% reduction in suicide rate. Our results show that expanding access to professional services has a higher impact on reducing suicide rates than creating awareness. Any intervention towards awareness or access positively impacts reducing suicide rates. However, increased access results in a higher reduction in suicide rates. We have made progress in increasing awareness. Awareness campaigns help to increase recognition of mental health needs. However, focusing efforts on increasing access to care may have a higher impact on reducing suicide rates. Full article

Abnormal behavioral changes in the regular daily mobility routine of a pregnant dairy cow can be an indicator or early sign to recognize when a calving event is imminent. Image processing technology and statistical approaches can be effectively used to achieve a more accurate result in predicting the time of calving. We hypothesize that data collected using a 360-degree camera to monitor cows before and during calving can be used to establish the daily activities of individual pregnant cows and to detect changes in their routine. In this study, we develop an augmented Markov chain model to predict calving time and better understand associated behavior. The objective of this study is to determine the feasibility of this calving time prediction system by adapting a simple Markov model for use on a typical dairy cow dataset. This augmented absorbing Markov chain model is based on a behavior embedded transient Markov chain model for characterizing cow behavior patterns during the 48 h before calving and to predict the expected time of calving. In developing the model, we started with an embedded four-state Markov chain model, and then augmented that model by adding calving as both a transient state, and an absorbing state. Then, using this model, we derive (1) the probability of calving at 2 h intervals after a reference point, and (2) the expected time of calving, using their motions between the different transient states. Finally, we present some experimental results for the performance of this model on the dairy farm compared with other machine learning techniques, showing that the proposed method is promising. Full article

The focus of the present paper is the development of a resilience framework suitable to be applied in assessing the safety of ship LNG (Liquefied Natural Gas) bunkering process. Ship propulsion considering LNG as a possible fuel (with dual fuel marine engines installed on board) has favored important discussions about the LNG supply chain and delivery on board to the ship power plant. Within this context, a resilience methodological approach is outlined, including a case study application, to demonstrate its actual effectiveness. With specific reference to the operative steps for LNG bunkering operations in the maritime field, a dynamic model based on Bayesian inference and MCMC simulations can be built, involving the probability of operational perturbations, together with their updates based on the hard (failures) and soft (process variables deviations) evidence emerging during LNG bunkering operations. The approach developed in this work, based on advanced Markov Models and variational fitting algorithms, has proven to be a useful and flexible tool to study, analyze and verify how much the perturbations of systems and subsystems can be absorbed without leading to failure. Full article

This study improves an approach for Markov chain-based photovoltaic-coupled energy storage model in order to serve a more reliable and sustainable power supply system. In this paper, two Markov chain models are proposed: Embedded Markov and Absorbing Markov chain. The equilibrium probabilities of the Embedded Markov chain completely characterize the system behavior at a certain point in time. Thus, the model can be used to calculate important measurements to evaluate the system such as the average availability or the probability when the battery is fully discharged. Also, Absorbing Markov chain is employed to calculate the expected duration until the system fails to serve the load demand, as well as the failure probability once a new battery is installed in the system. The results show that the optimal condition for satisfying the availability of 3 nines (0.999), with an average load usage of 1209.94 kWh, is the energy storage system capacity of 25 MW, and the number of photovoltaic modules is 67,510, which is considered for installation and operation cost. Also, when the initial state of charge is set to 80% or higher, the available time is stable for more than 20,000 h. Full article

The effectiveness of depth information in saliency detection has been fully proved. However, it is still worth exploring how to utilize the depth information more efficiently. Erroneous depth information may cause detection failure, while non-salient objects may be closer to the camera which also leads to erroneously emphasis on non-salient regions. Moreover, most of the existing RGB-D saliency detection models have poor robustness when the salient object touches the image boundaries. To mitigate these problems, we propose a multi-stage saliency detection model with the bilateral absorbing Markov chain guided by depth information. The proposed model progressively extracts the saliency cues with three level (low-, mid-, and high-level) stages. First, we generate low-level saliency cues by explicitly combining color and depth information. Then, we design a bilateral absorbing Markov chain to calculate mid-level saliency maps. In mid-level, to suppress boundary touch problem, we present the background seed screening mechanism (BSSM) for improving the construction of the two-layer sparse graph and better selecting background-based absorbing nodes. Furthermore, the cross-modal multi-graph learning model (CMLM) is designed to fully explore the intrinsic complementary relationship between color and depth information. Finally, to obtain a more highlighted and homogeneous saliency map in high-level, we structure a depth-guided optimization module which combines cellular automata and suppression-enhancement function pair. This optimization module refines the saliency map in color space and depth space, respectively. Comprehensive experiments on three challenging benchmark datasets demonstrate the effectiveness of our proposed method both qualitatively and quantitatively. Full article

American universities use a procedure based on a rolling six-year graduation rate to calculate statistics regarding their students’ final educational outcomes (graduating or not graduating). As an alternative to the six-year graduation rate method, many studies have applied absorbing Markov chains for estimating graduation rates. In both cases, a frequentist approach is used. For the standard six-year graduation rate method, the frequentist approach corresponds to counting the number of students who finished their program within six years and dividing by the number of students who entered that year. In the case of absorbing Markov chains, the frequentist approach is used to compute the underlying transition matrix, which is then used to estimate the graduation rate. In this paper, we apply a sensitivity analysis to compare the performance of the standard six-year graduation rate method with that of absorbing Markov chains. Through the analysis, we highlight significant limitations with regards to the estimation accuracy of both approaches when applied to small sample sizes or cohorts at a university. Additionally, we note that the Absorbing Markov chain method introduces a significant bias, which leads to an underestimation of the true graduation rate. To overcome both these challenges, we propose and evaluate the use of a regularly updating multi-level absorbing Markov chain (RUML-AMC) in which the transition matrix is updated year to year. We empirically demonstrate that the proposed RUML-AMC approach nearly eliminates estimation bias while reducing the estimation variation by more than 40%, especially for populations with small sample sizes. Full article

Some of the most devastating natural events on Earth, such as earthquakes and tropical cyclones, are prone to trigger other natural events, critical infrastructure failures, and socioeconomic disruptions. Man-made disasters may have similar effects, although to a lesser degree. We investigate the space of possible interactions between 19 types of loss-generating events, first by encoding possible one-to-one interactions into an adjacency matrix $\mathit{A}$, and second by calculating the interaction matrix $\mathit{M}$ of emergent chains-of-events. We first present the impact of 24 topologies of $\mathit{A}$ on $\mathit{M}$ to illustrate the non-trivial patterns of cascading processes, in terms of the space of possibilities covered and of interaction amplification by feedback loops. We then encode $\mathit{A}$ from 29 historical cases of cascading disasters and compute the matching matrix $\mathit{M}$. We observe, subject to data incompleteness, emergent cascading behaviors in the technological and socioeconomic systems, across all possible triggers (natural or man-made); disease is also a systematic emergent phenomenon. We find interactions being mostly amplified via two events: network failure and business interruption, the two events with the highest in-degree and betweenness centralities. This analysis demonstrates how cascading disasters grow in and cross over natural, technological, and socioeconomic systems. Full article

Many games in which chance plays a role can be simulated as a random walk over a graph of possible configurations of board pieces, cards, dice or coins. The end of the game generally consists of the appearance of a predefined winning pattern; for random walks, this corresponds to an absorbing trap. The strategy of a player consist of betting on a given sequence, i.e., in placing a trap on the graph. In two-players games, the competition between strategies corresponds to the capabilities of the corresponding traps in capturing the random walks originated by the aleatory components of the game. The concept of dominance transitivity of strategies implies an advantage for the first player, who can choose the strategy that, at least statistically, wins. However, in some games, the second player is statistically advantaged, so these games are denoted “intransitive”. In an intransitive game, the second player can choose a location for his/her trap which captures more random walks than that of the first one. The transitivity concept can, therefore, be extended to generic random walks and in general to Markov chains. We analyze random walks on several kinds of networks (rings, scale-free, hierarchical and city-inspired) with many variations: traps can be partially absorbing, the walkers can be biased and the initial distribution can be arbitrary. We found that the transitivity concept can be quite useful for characterizing the combined properties of a graph and that of the walkers. Full article

The expansion of global production networks has raised many important questions about the interdependence among countries and how future changes in the world economy are likely to affect the countries’ positioning in global value chains. We are approaching the structure and lengths of value chains from a completely different perspective than has been available so far. By assigning a random endogenous variable to a network linkage representing the number of intermediate sales/purchases before absorption (final use or value added), the discrete-time absorbing Markov chains proposed here shed new light on the world input/output networks. The variance of this variable can help assess the risk when shaping the chain length and optimize the level of production. Contrary to what might be expected simply on the basis of comparative advantage, the results reveal that both the input and output chains exhibit the same quasi-stationary product distribution. Put differently, the expected proportion of time spent in a state before absorption is invariant to changes of the network type. Finally, the several global metrics proposed here, including the probability distribution of global value added/final output, provide guidance for policy makers when estimating the resilience of world trading system and forecasting the macroeconomic developments. Full article

This paper models and evaluates three FSA-based (Frame Slotted ALOHA) MAC (Medium Access Control) protocols, namely, FSA-ACK (FSA with ACKnowledgements), FSA-FBP (FSA with FeedBack Packets) and DFSA (Dynamic FSA). The protocols are modeled using an AMC (Absorbing Markov Chain), which allows to derive analytic expressions for the average packet delay, as well as the energy consumption of both the network coordinator and the end-devices. The results, based on computer simulations, show that the analytic model is accurate and outline the benefits of DFSA. In terms of delay, DFSA provides a reduction of 17% (FSA-FBP) and 32% (FSA-ACK), whereas in terms of energy consumption DFSA provides savings of 23% (FSA-FBP) and 28% (FSA-ACK) for the coordinator and savings of 50% (FSA-FBP) and 24% (FSA-ACK) for end-devices. Finally, the paper provides insights on how to configure each FSA variant depending on the network parameters, i.e., depending on the number of end-devices, to minimize delay and energy expenditure. This is specially interesting for massive data collection in IoT (Internet-of-Things) scenarios, which typically rely on FSA-based protocols and where the operation has to be optimized to support a large number of devices with stringent energy consumption requirements. Full article

The misestimation of rating transition probabilities may lead banks to lend money incoherently with borrowers’ default trajectory, causing both a deterioration in asset quality and higher system distress. Applying a Mover-Stayer model to determine the migration risk of small and medium enterprises, we find that banks are over-estimating their credit risk resulting in excessive regulatory capital. This has important macroeconomic implications due to the fact that holding a large capital buffer is costly for banks and this in turn influences their ability to lend in the wider economy. This conclusion is particularly true during economic downturns with the consequence of exacerbating the cyclicality in risk capital that therefore acts to aggravate economic conditions further. We also explain part of the misevaluation of borrowers and the actual relevant weight of non-performing loans within banking portfolios: some of the prudential requirements, at least as regards EMS credit portfolios, cannot be considered effective as envisaged by the regulators who developed the “new” regulation in response to the most recent crisis. The Mover-Stayers approach helps to reduce calculation inaccuracy when analyzing the historical movements of borrowers’ ratings and consequently, improves the efficacy of the resource allocation process and banking industry stability. Full article

In recent years, surveys of personal travel behavior have been conducted around the world and these surveys have been used for understanding the characteristics of people flow. However, it is impossible to acquire people and traffic flows for the modern era (1868–1945). In modern era Japan, some traffic surveys were conducted, and that records still persist. The purpose of this study was to estimate origin-destination (OD) flows in old Tokyo in 1925 based on the historical traffic census record. In this study, OD flows were estimated using an absorbing Markov chain model, which is a simple model based on traffic generation and transition probabilities. Transition probabilities in unobserved nodes were estimated using genetic algorithms (GA). The result of OD distributions is clearly different in the eastern part of Tokyo City, the Shitamachi area, from the western part, the Yamanote area. The traffic was very busy in Shitamachi, an area which included terminal stations and a central business district. In Yamanote, major traffic generation and absorption points were distributed along the main streets to the Shinjuku or Shibuya areas. These results are affected by the distribution of main roads and the locations of residences or workplaces of car owners. Full article