Stochastic Patterns Research Articles

Using Stochastic Point Pattern Analysis to Track Regional Orientations of Magmatism During the Transition to Cenozoic Extension and Rio Grande Rifting, Southern Rocky Mountains

AbstractThe southern Rocky Mountains in Colorado and northern New Mexico hosted intracontinental magmatism that developed during a tectonic transition from shortening (Laramide orogeny, ca. 75 to 40 Ma) through extension and rifting. We present a novel approach that uses stochastic weighted bootstrap simulations of a large set of new and historical geochronology data to better understand how regional anisotropies responsible for focusing magma emplacement evolved through time. This technique can detect subtle trends in directional distributions, including multi‐modal orientations, and can be filtered from regional to local scales. Our results indicate that magmatism followed first the northeast trend of the Colorado mineral belt between 75 and 40 Ma and deviated afterward. These deviations vary depending on the scale of the analysis. At the smallest scale we evaluated (<75 km), the orientation of magmatism from 45 to 30 Ma rotated counter‐clockwise before aligning with the north‐south trend of the modern Rio Grande rift. Larger, regional‐scale analyses indicate magma centers between 40 to 35 Ma and 25 to 20 Ma were dominantly oriented southwest‐northeast, whereas magmatism between 35 and 25 Ma had north‐south orientation. The large areal footprint of magmatism and shifting regional patterns suggest that ancient zones of weakness in the North American lithosphere accommodated magma flow at different moments in time, rather than controlled by a retreating interface of the Farallon and North American plates.

Stochastic Processes Shape Bacterial Community Diversity Patterns along Plant Niche Gradients

The ecological niche gradient is an important determinant of microbial community structure. In this paper, we studied variation in rhizosphere bacterial diversity and community composition along an ecological niche gradient. We used the high-throughput sequencing of 16S rRNA genes to study changes in the rhizosphere soil microbial communities of six grass and four shrub species during the secondary succession of abandoned farmland on the Loess Plateau of China. A structural equation model (SEM) was employed to disentangle the relative contribution of ecological niche and soil properties to bacterial diversity and community composition. Proteobacteria, Acidobacteria, and Actinobacteria were the dominant phyla of rhizosphere bacteria in all samples. During the dynamics of the plant niche from low to high, bacterial community composition transitioned from Actinobacteria + Acidobacteria to Proteobacteria + Bacteroidetes higher abundance. Moreover, the bacterial diversity and species richness changed with an increasing niche gradient, showing a clear differentiation in the rhizosphere bacterial community of grassland and shrubland. Further, diversity and species richness decreased from the middle niche of B. ischaemum to the poles, indicating that the succession process had not yet reached the climax community stage. Community assembly analysis suggested that the stochastic process gradually strengthened along the increasing ecological niche gradient, especially the drift effect. Furthermore, SEM analysis showed that the ecological niche had significant negative effects on soil properties and bacterial richness, while the effects on bacterial diversity and the stochastic processes of community assembly were weakened and insignificant. Altogether, our findings suggest that the complex interaction of the ecological niche with bacterial diversity and composition was determined by soil properties. Further, bacterial diversity was not necessarily higher with increasing ecological niche gradients.

Determinants of microbial colonization on microplastics through wastewater treatment processes: The role of polymer type and sequential treatment

This study examines the microbial colonization characteristics of microplastics (MPs) in wastewater treatment plants (WWTPs), focusing on polymer types (High-Density Polyethylene (HDPE) and Polyethylene Terephthalate (PET)) and various stages of wastewater treatments. Through individual and sequential deployment approaches, the research aimed to identify the determinants of bacterial colonization on MPs, whether they were introduced at each stage of treatment individually or in sequence from primary to tertiary stages. The study revealed that the stage of wastewater treatment profoundly influenced bacterial colonization on the polymer types MPs, with bacterial attachment being largely niche-specific. HDPE showed increased sensitivity to wastewater composition, leading to selective biofilm formation. For instance, in HDPE, Firmicutes accounted for 25.1 ± 0.04 % during primary treatment, while Alphaproteobacteria increased significantly in the tertiary treatment to 19.8 ± 0.1 %. Conversely, PET exhibited a stochastic pattern of bacterial colonization due to differences in surface hydrophilicity. Additionally, in sequential deployments, a notable shift towards stochastic bacterial attachment on MPs, particularly with HDPE was observed. The Shannon diversity values for MP biofilms were consistently higher than those for wastewater across all stages, with PET showing an increase in diversity in sequential deployments (Shannon diversity: 5.01 ± 0.03 for tertiary stage). These findings highlight the critical role of MPs as carriers of diverse bacteria, emphasizing the necessity for strategies to mitigate their impact in WWTPs. This study presents a significant advancement in our understanding of the interactions between MPs and microbial populations in WWTP environments.

A transparent layered soil technique to investigate geosynthetic-reinforced piled-supported embankments

Geosynthetic reinforced piled supported embankments have been widely used in recent years as a solution to embankments over soft soils. Geosynthetic-reinforced load transfer platforms (LTPs) have been used at the bottom of embankments to improve the distribution of stresses over piles. However, mechanisms governing this technique still demand to be fully understood. This paper employs a transparent soils technique to investigate the mechanisms governing geosynthetic-reinforced LTPs. Transparent small-scale 1-g laboratory models were constructed using two different transparent materials composing a layered transparent system. Fused quartz grains were used to simulate the granular fill material of the LTP, while Laponite RD® was employed to replicate the soft clay. A seeding particles plane was installed inside the model generating a stochastic pattern for images capturing. Distribution of displacements and strains were then interpreted using the Digital Image Correlation (DIC) technology. Based on the displacements and strains responses of the small-scale model, this paper discusses the mechanisms acting in unreinforced and geosynthetic-reinforced LTP (single layer). The presence of geosynthetic reinforcement reduced, in general, the displacements and deformations of the models.

Spatial game with multiple interaction patterns in constrained interaction environment: A computational method based on opponent’s ability

In previous studies of spatial game, it is usually assumed that all individuals in a population play the game in the same interaction pattern. However, in real life, a variety of interaction patterns often emerge spontaneously, especially in a constrained interaction environment. Furthermore, some scenarios reflect the fact that individuals are generally more inclined to choose the capable partners for interactions. For these reasons, we propose a computational method on evolutionary game with multiple interaction patterns based on opponent’s ability and investigate the cooperative dynamics of spatial prisoner’s dilemma. For simplicity, in this work, we just consider the case of two interaction patterns, which are conditional interaction and stochastic interaction. For the individual who adopt the conditional interaction pattern, if the opponent is strong in ability, it will be perfectly willing to interact with opponent, while if the opponent is weak in ability, it will not do that. For the individual who adopt the stochastic interaction pattern, the willingness of interaction with other party varies with the opponent’s abilities, the more powerful the opponent, the stronger the willingness of interaction. Notably, for paired two individuals, regardless of the combined configuration of interaction patterns, the final interaction intensity between them depends on the result of information fusion of their unilateral interaction willingness. Experimental results show that the mechanism with multiple interaction patterns can effectively propagate cooperative behavior in networked population, and the moderate estimation level can lead to the flourish of cooperation. Besides, we find that the more the straightforward individuals (who adopt the conditional interaction pattern) in the population, the more prosperous the propagation of cooperative behavior, while the more the euphemistic individuals (who adopt the stochastic interaction pattern), the easier the emergence of selfish behavior. Considering such a real environment with multiple interaction patterns can provide a brand new perspective for further study on the dynamics of evolutionary game in structured population.

Characterizing optimal LPWAN access delay in massive multi-RAT smart grid deployments

Massive machine-type communications services penetrating the market, such as smart grids, differ from conventional services characterized by stochastic arrival patterns. Requiring permanent end device (ED) connectivity, control centers poll EDs over regular time intervals, leading to batch message arrivals that affect the mean access delay at the air interface. In addition, the smart grid requires high reliability and network availability that can be achieved by utilizing multiple radio access technologies in EDs. Radio access technologies (RATs), i.e., narrowband internet of things (NB-IoT) and long-term evolution machine type communication (LTE-M), have been identified as promising solutions for these use-cases. This work first reports results of an extensive performance evaluation measurement campaign showing that LTE-M can be considered a preferred option and NB-IoT as a possible backup solution for smart grids. We also show that none of the selected technologies can fully meet the requirements of smart grid use cases. We then develop a theoretical model and corresponding association algorithm for balancing traffic load between two low-power wide area network (LPWAN) technologies to minimize the mean access delay. Our results demonstrate that the optimized usage of multi-RAT EDs considerably increases the number of supported EDs operating in polling-based mode. For 500EDs utilizing a single LTE-M technology, the mean access delay is over two seconds — contradicting the minimum requirements of smart grid applications. On the other hand, multi-RAT EDs running the developed algorithm increase the service capacity by up to six times (up to 3000EDs) while still satisfying the two-second latency budget.

Complex Materials with Stochastic Structural Patterns: Spiky Colloids with Enhanced Charge Storage Capacity.

Self-assembled materials with complex nanoscale and mesoscale architectureattract considerable attention in energy and sustainability technologies. Their high performance can be attributed to high surface area, quantum effects, and hierarchical organization. Delineation of these contributions is, however, difficult because complex materials display stochastic structural patterns combining both order and disorder, which is difficult to be consistently reproduced yet being important for materials' functionality. Their compositional variability make systematic studies even harder. Here, a model system of FeSe2 "hedgehog" particles (HPs) was selected to gain insight into the mechanisms of charge storage n complex nanostructured materials common for batteries and supercapacitors. Specifically, HPs represent self-assembled biomimetic nanomaterials with a medium level of complexity; they display an organizational pattern of spiky colloids with considerable disorder yet non-random; this patternt is consistently reproduced from particle to particle. . It was found that HPs can accommodate ≈70× greater charge density than spheroidal nano- and microparticles. Besides expanded surface area, the enhanced charge storage capacity was enabled by improved hole transport and reversible atomic conformations of FeSe2 layers in the blade-like spikes associated with the rotatory motion of the Se atoms around Fe center. The dispersibility of HPs also enables their easy integration into energy storage devices. HPs quadruple stored electrochemical energy and double the storage modulus of structural supercapacitors.

What drives the joint demand for ride-hailing and carsharing services? Understanding consumers’ behaviors, attitudes, & concerns

Driven by the emerging collaborative consumption trends, new shared ownership-based business models provide more flexible and accessible on-demand mobility options. This study simultaneously analyzes factors correlated with the consumers’ use of two interrelated disruptive on-demand mobility services, including ride-hailing (RH) and carsharing (CS). A comprehensive behavioral framework is presented to explicitly address important methodological concerns regarding the complex stochastic dependence between the use of CS and RH programs and the underlying behavioral heterogeneity in latent factors influencing the use of the two shared mobility options. Using unique data from the California Vehicle Survey, a rigorous elliptical and Archimedean copula-based finite mixture bivariate ordered probit (BOP) modeling methodology is used to understand behavioral, attitudes, and concern-related correlates of households’ participation levels in CS and RH programs. Characterized by a clayton-copula based finite mixture BOP model, participation levels in CS and RH programs exhibited complex (both synergistic and competing) non-linear stochastic dependence patterns. With a stronger left tail dependence, majority of the households having lower levels of participation in RH also had lower participation levels in CS programs. Contrarily, of the households with higher levels of participation in RH programs, the majority had lower levels of participation in CS programs (revealing weaker right tail dependence). Taste heterogeneity was observed in the unobserved determinants of CS use. Results show that current users of CS and/or RH programs tend to be those with greater awareness, owners of plug-in electric vehicles, those making greater transit trips, workers with greater commute distances, and those lacking free parking at their residences. Compared to ride-hailing, the negative effect of more frequent drivers in a household on CS use was less pronounced. Analysis of marginal and joint marginal effects provided deeper insights into the (interactive) effects of other behavioral and socio-demographic correlates. Treatment effects were simulated and discussed to further demonstrate the policy implications of our results. The new empirical insights provide a more granular understanding of the use patterns of on-demand shared mobility services and can inform more context-sensitive travel forecasts for planning and programming purposes.

Random Furrowing for a Stochastic Unit Commitment Solution

The Unit Commitment Problem involves the inherent difficulty of obtaining optimal combinatorial power generation schedules over a future short term period. The formulation of the generalized Unit Commitment Schedule formulation involves the specific combination of generation units at several de-rated capacities during each hour of the planning horizon, the load demand profile, load indeterminateness and several other operating constraints. This largely deterministic schedule continues to find favor with several plant operators, keeping in mind the close operating time-periods involved. However, the deterministic nature of the load profile is sought to be phased out by a stochastic pattern that is realistic and mirrors real-life situations, owing to modern trends in Demand side management. This shift is in tune with the ongoing power restructuring activities of electricity power reforms. The stochastic profile is obtained by a suitably tuned 2-parameter Weibull distribution that uses appropriate shape and scale parameters. The resulting band of generated load profiles are used to evaluate net power and penal costs associated with a set of pervasive randomized probability indices. The exact UCS comprises of a specific unit absolute state corresponding to a certain time period within the planning horizon. Subsequently, regression analysis is applied to establish the correlation between the absolute states and the cumulative randomized load demand against the intervals within the planning horizon. This method is analogous to random furrowing of probabilistic demand profile.

Stochastic Turing patterns of trichomes in Arabidopsis leaves

Biological patterns that emerge during the morphogenesis of multicellular organisms can display high precision at large scales, while at cellular scales, cells exhibit large fluctuations stemming from cell-cell differences in molecular copy numbers also called demographic noise. We study the conflicting interplay between high precision and demographic noise in trichome patterns on the epidermis of wild-type Arabidopsis thaliana leaves, as a two-dimensional model system. We carry out a statistical characterization of these patterns and show that their power spectra display fat tails-a signature compatible with noise-driven stochastic Turing patterns-which are absent in power spectra of patterns driven by deterministic instabilities. We then present a theoretical model that includes demographic noise stemming from birth-death processes of genetic regulators which we study analytically and by stochastic simulations. The model captures the observed experimental features of trichome patterns.

Patch formation driven by stochastic effects of interaction between viruses and defective interfering particles.

Defective interfering particles (DIPs) are virus-like particles that occur naturally during virus infections. These particles are defective, lacking essential genetic materials for replication, but they can interact with the wild-type virus and potentially be used as therapeutic agents. However, the effect of DIPs on infection spread is still unclear due to complicated stochastic effects and nonlinear spatial dynamics. In this work, we develop a model with a new hybrid method to study the spatial-temporal dynamics of viruses and DIPs co-infections within hosts. We present two different scenarios of virus production and compare the results from deterministic and stochastic models to demonstrate how the stochastic effect is involved in the spatial dynamics of virus transmission. We compare the spread features of the virus in simulations and experiments, including the formation and the speed of virus spread and the emergence of stochastic patchy patterns of virus distribution. Our simulations simultaneously capture observed spatial spread features in the experimental data, including the spread rate of the virus and its patchiness. The results demonstrate that DIPs can slow down the growth of virus particles and make the spread of the virus more patchy.

Optimal continuous control of refrigerator for electricity cost minimization—Hierarchical reinforcement learning approach

A refrigerator is a commonly used household appliance; however, limited research has focused on optimizing temperature control policy with the consideration of Time-of-Use (ToU) electricity price. This paper introduces a novel framework that utilizes hierarchical reinforcement learning (HRL) to control the intensity of refrigerator motors. The objective is to achieve both temperature regulation and cost savings under ToU and stochastic usage patterns. The problem is tackled by two HRL agents. The high-level agent is responsible for determining temperature reference based on ToU pricing, while the low-level agent adjusts the motor intensity to meet this temperature reference. To tackle non-stationarity in HRL, the high-level agent employs hindsight action transition and reward function approximation, while the low-level agent employs hindsight goal transition. Through the experimental evaluation, we found that the proposed method outperforms the conventional control methods and standard reinforcement learning approaches. It achieves the lowest total costs, resulting in a significant cost reduction of 5%–24%.

Holistically Nested Edge Detection and particle filtering for subtle vibration extraction

Extracting and analyzing subtle motion via optical sensing techniques is becoming a more important research area within the vibration community. Computer vision approaches and machine learning architecture have been adopted to contest the complexity concerning hand instrumentation of sensors. Pre-treatment using a stochastic pattern for optical analysis or traditional wired sensing is not always permitted for large structures. In this work, Holistically Nested Edge Detection (HED) is utilized to highlight high-spatial-frequency content features in images for structural dynamic analysis. Following the accentuation of edge features, phase-based motion magnification (PMM) is used to amplify motion that is not visible to the naked eye. This permits the band-passing of higher order resonant frequencies for structural dynamic evaluation. Implementation of two-dimensional particle filtering (PF) is utilized to track the accentuated edge features in the presence of ghosting artifacts that appear post magnification. Within this work, compared to traditional edge-thresholding, the combination of HED and PF (HED-PF) is more proficient to handle noise and ultimately improves magnified image quality and modal parameter extraction. Uncertainties related to using PF for vibration measurement are analytically presented to provide guidance on the behavior of the algorithm. To demonstrate the effectiveness of HED-PF, experimental modal testing takes place on both a tier-structure and cantilever beam to demonstrate the approaches’ capability in handling varying sorts of excitation. Finally, introduction of background occlusions and non-ideal lighting scenarios are used to test the limitations of the approach. Ultimately, this will highlight HED-PF’s robust tracking capabilities compared to the current state of the art.

Temporal complexity in photoplethysmography and its influence on blood pressure.

Objective: The temporal complexity of photoplethysmography (PPG) provides valuable information about blood pressure (BP). In this study, we aim to interpret the stochastic PPG patterns with a model-based simulation, which may help optimize the BP estimation algorithms. Methods: The classic four-element Windkessel model is adapted in this study to incorporate BP-dependent compliance profiles. Simulations are performed to generate PPG responses to pulse and continuous stimuli at various timescales, aiming to mimic sudden or gradual hemodynamic changes observed in real-life scenarios. To quantify the temporal complexity of PPG, we utilize the Higuchi fractal dimension (HFD) and autocorrelation function (ACF). These measures provide insights into the intricate temporal patterns exhibited by PPG. To validate the simulation results, continuous recordings of BP, PPG, and stroke volume from 40 healthy subjects were used. Results: Pulse simulations showed that central vascular compliance variation during a cardiac cycle, peripheral resistance, and cardiac output (CO) collectively contributed to the time delay, amplitude overshoot, and phase shift of PPG responses. Continuous simulations showed that the PPG complexity could be generated by random stimuli, which were subsequently influenced by the autocorrelation patterns of the stimuli. Importantly, the relationship between complexity and hemodynamics as predicted by our model aligned well with the experimental analysis. HFD and ACF had significant contributions to BP, displaying stability even in the presence of high CO fluctuations. In contrast, morphological features exhibited reduced contribution in unstable hemodynamic conditions. Conclusion: Temporal complexity patterns are essential to single-site PPG-based BP estimation. Understanding the physiological implications of these patterns can aid in the development of algorithms with clear interpretability and optimal structures.

Forecasting the Fuel Consumption and Price for a Future Pandemic Outbreak: A Case Study in the USA under COVID-19

The COVID-19 epidemic and the measures adopted to contain it have had a significant impact on energy patterns throughout the world. The pandemic and movement restrictions led to unpredictable fluctuations in power systems demand and the fuel price for a delayed period. Monkeypox, another viral disease, appeared during the post-COVID period. It is assumed that the outbreak of monkeypox is unlikely due to the implication of preventive measures experienced from COVID-19. At the same time, the probability of an epidemic cannot be blindly overlooked. This paper aims to examine and analyze historical data to look at how much petroleum fuel was used for generating power and how the price of petroleum fuel changed over seven years, from January 2016 to August 2022. This period covers the time before the COVID-19 pandemic, during the pandemic, and after the pandemic. Several time-series forecasting models, including all four benchmark methods (Mean, Naive, Drift, and Snaive), Seasonal and Trend decomposition using Loess (STL), Exponential Smoothing (ETS), and Autoregressive Integrated Moving Average (ARIMA) methods have been applied for both fuel consumption and price prediction. The best forecasting method for fuel price and consumption has been identified among these methods. The best forecasting method for fuel consumption observed is ETS based on the RMSE value, which is 799.59, and the ARIMA method for fuel price, with RMSE 4.67. The paper also utilizes the ARIMAX model by incorporating multiple exogenous variables, such as monthly mean temperature, mean fuel price, and mileage of vehicles traveling during a certain period of pandemic lock-down. It will assist in capturing the non-smooth and stochastic pattern of fuel consumption and price due to the pandemic by separating the seasonal influence and, thus, provide a prediction of the consumption pattern in the event of any future pandemic. The novelty of the article will assist in exploring the potential energy demand in terms of cost and consumption of fuel during any pandemic period, considering the associated abnormalities.

Behavioral investigation of stochastic lateral wandering patterns in mixed traffic flow

The vehicles’ lateral movement exhibits inherent stochastic behavior, and when considered collectively, the traffic flow demonstrates certain lateral wandering pattern which serves a fundamental input for pavement design and sustainability analysis. Naturalistic driving trajectories show that the vehicles on neighboring lanes tend to interact laterally, which leads to correlated wandering on a multi-lane road. Compared with human-driven vehicles (HDV), autonomous vehicles (AVs) have a more centered wandering pattern, which accelerates the pavement fatigue. As the market penetration rate of the AVs changes, the wandering pattern of the mixed AVs flow would evolve accordingly, which further influence pavement performances. The evolutionary wandering pattern of mixed AVs flow which considers the interactions between neighboring lanes’ traffic flow is not fully studied yet. This research fills this gap with a stochastic behavioral lateral movement model that bridges the microscopic behavior and lateral wandering pattern. Firstly, a lateral movement model is developed, which assumes that lateral dynamics are influenced by lane markings, road boundaries, intra-lane interaction, and noise. The model is expressed in a stochastic differential form. The solution of the model which gives the lateral wandering is expressed by the Euler-Maruyama scheme, Fokker-Planck equation and Markov chain. Then the model is employed to investigate the wandering patterns under different scenarios. The results show that the model's outputs are consistent with current research results and empirical observations. This research provides additional insights regarding lateral wandering under mixed autonomous vehicle flow, and offers a theoretical tool for future road pavement design.

Exploring the Relationship between Stochastic Events and Patterns of International Tourism in Kenya

Purpose: The purpose of this research was to investigate the fluctuations in international tourist arrivals in Kenya and identify the underlying factors contributing to these trends. The study aimed to gain a deeper understanding of the impact of stochastic events on tourist arrivals.
 Methodology: The research focused on international tourist arrivals in Kenya across seven geographical areas. It utilized annual time series data from 1980 to 2020 and examined various variables, including tourism arrivals, terrorism, political instability, conferences, and natural disasters. Descriptive statistics, the Kruskal-Wallis test, Lepage change point detection, and a Vector Autoregressive Model (VAR) with Granger causality tests were employed to analyze the data.
 Findings: The findings revealed significant fluctuations in preferred destinations, source markets, purpose of visits, and length of stay among international tourists in Kenya over the study period. Major stochastic events were observed to coincide with significant changes in net arrivals, source markets, or destinations. Notably, terrorism, conferences, and the combined impact of all factors had a significant influence on net arrivals. Political instability, pandemics, and natural disasters were also found to affect international tourism arrivals.
 Recommendations: Based on the results, policymakers are advised to prioritize safety and security measures to mitigate the adverse effects of pandemics and political instability on the tourism industry in Kenya. Additionally, the study recommends the development of targeted marketing strategies to attract resilient source markets. Furthermore, promoting sustainable tourism practices is essential to mitigate the long-term impact of negative events on the industry. These recommendations aim to enhance the resilience and growth of the tourism sector in Kenya.

Deep Learning-Assisted Citation Recommendation System Using Multi- cell RNN Approach

A process of retrieving relevant or suitable papers that are mostly related to the research topic while carrying research knowing the behavior and patterns of citation for mapping the disciplines and subjects. This is used increasingly for judging usually takes a huge amount of effort and time. The citation recommendation is used frequently for solving this issue on suggesting the list of candidate papers automatically which may match the references of user. On applying Deep learning (DL) models in the citation recommendation system, the performance enhancement is greatly visible in various recent research studies. Hence, in this work, a deep learning-based model is employed for the citation recommendation system. Primarily, the input dataset is taken and the function words are removed using stochastic matching pattern scheme at which the library is created. Then, the canonicalization of document is carried by means of probabilistic min-match algorithm. The data is classified finally using Multi-cell RNN (Recurrent neural network) approach after which the rank is set for citation. At last, the performance is assessed for various metrics like MAP, MRR, precision, recall, and F1-measure. The obtained results are then compared with traditional models to validate and show the enhancement of proposed model over existing schemes. It is revealed from the analysis that the proposed scheme shows improvement in results.

Energy management of smart homes over fog-based IoT architecture

Existing research studies on home automation systems mostly conserve energy by modeling the occupancy of users within home. Some others apply statistical approaches on the survey data about usage of appliances. Consequently, these research works either reduce wastage of electricity through automation or achieve energy efficiency based on appliances’ usage estimations. However, they do not provide energy consumption modeling which is human comfort centric and also validated through practical implementation in real-world smart homes. We present a Markov-chain-based probabilistic model to obtain users stochastic activity patterns which are used to forecast the energy consumption in a smart home environment. These predictions are then leveraged by our novel comfort aware energy saving mechanism named as prediction- and feedback-based proactive energy conservation (PF-PEC) algorithm. The PF-PEC algorithm reduces the total energy consumption while ensuring standard human comfort. Furthermore, a fog-based Internet of Things (IoT) architecture is implemented and deployed in a smart home to efficiently incorporate the proposed algorithm in real-world scenarios. Experimental results show up to 36% energy conservation, marking substantial reduction in daily electricity usage.

Spatial metapopulation dynamics with local and global colonization

We revisit a spatial metapopulation model on continuous space as a stochastic point pattern dynamics. In the model, local patches as points are distributed with a certain spatial configuration and status of each patch changes stochastically between occupied and empty: an occupied patch becomes empty by local extinction and an empty patch becomes occupied both by local and global colonization. We carry out simulation analysis and derive an analytical model in terms of singlet, pair and triplet probabilities that describe the stochastic dynamics. Using a simple closure that approximates triplet probabilities by singlet and pair probabilities, we show that equilibrium singlet and pair probabilities can be analytically derived. The derived equilibrium properties successfully describe simulation results under a certain condition where the range of local colonization and the proportion of global colonization play key roles. Our model is an extension of the classical non-spatial Levins model to a spatially explicit metapopulation model. We appeal the advantage of point pattern approach to study spatial dynamics in general ecology and call for the need to deepen our understanding of mathematical tools to explore point pattern dynamics.