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BACKGROUND: Movement plays a key role in allowing animal species to adapt to sudden environmental shifts. Anthropogenic climate and land use change have accelerated the frequency of some of these extreme disturbances, including megafire. These megafires dramatically alter ecosystems and challenge the capacity of several species to adjust to a rapidly changing landscape. Ungulates and their movement behaviors play a central role in the ecosystem functions of fire-prone ecosystems around the world. Previous work has shown behavioral plasticity is an important mechanism underlying whether large ungulates are able to adjust to recent changes in their environments effectively. Ungulates may respond to the immediate effects of megafire by adjusting their movement and behavior, but how these responses persist or change over time following disturbance is poorly understood. METHODS: We examined how an ecologically dominant ungulate with strong site fidelity, Columbian black-tailed deer (Odocoileus hemionus columbianus), adjusted its movement and behavior in response to an altered landscape following a megafire. To do so, we collected GPS data from 21 individual female deer over the course of a year to compare changes in home range size over time and used resource selection functions (RSFs) and hidden Markov movement models (HMMs) to assess changes in behavior and habitat selection. RESULTS: We found compelling evidence of adaptive capacity across individual deer in response to megafire. Deer avoided exposed and severely burned areas that lack forage and could be riskier for predation immediately following megafire, but they later altered these behaviors to select areas that burned at higher severities, potentially to take advantage of enhanced forage. CONCLUSIONS: These results suggest that despite their high site fidelity, deer can navigate altered landscapes to track rapid shifts in encounter risk with predators and resource availability. This successful adjustment of movement and behavior following extreme disturbance could help facilitate resilience at broader ecological scales.

To secure the privacy, confidentiality, and integrity of Speech Data (SD), the concept of secure Speech Recognition (SR) involves accurately recording and comprehending spoken language while employing diverse security processes. As the Internet of Things (IoT) rapidly evolves, the integration of SR capabilities into IoT devices gains significance. However, ensuring the security and privacy of private SD post-integration remains a critical concern. Despite the potential benefits, implementing the proposed Reptile Search Optimized Hidden Markov Model (RSO-HMM) for SR and integrating it with IoT devices may encounter complexities due to diverse device types. Moreover, the challenge of maintaining data security and privacy for assigned SD in practical IoT settings poses a significant hurdle. Ensuring seamless interoperability and robust security measures is essential. We introduce the Reptile Search Optimized Hidden Markov Model (RSO-HMM) for SR, utilizing retrieved aspects as speech data. Gathering a diverse range of SD from speakers with varying linguistic backgrounds enhances the accuracy of the SR system. Preprocessing involves Z-score normalization for robustness and mitigation of outlier effects. The Perceptual Linear Prediction (PLP) technique facilitates efficient extraction of essential acoustic data from speech sources. Addressing data security, Elliptic Curve Cryptography (ECC) is employed for encryption, particularly suited for resource-constrained scenarios. Our study evaluates the SR system, employing key performance metrics including accuracy, precision, recall, and F1 score. The thorough assessment demonstrates the system's remarkable performance, achieving an impressive accuracy of 96%. The primary objective revolves around appraising the system's capacity and dependability in accurately transcribing speech signals. By proposing a comprehensive approach that combines the RSO-HMM for SR, data preprocessing techniques, and ECC encryption, this study advocates for the wider adoption of SR technology within the IoT ecosystem. By tackling critical data security concerns, this approach paves the way for a safer and more efficient globally interconnected society, encouraging the broader utilization of SR technology in various applications. [ABSTRACT FROM AUTHOR]

Trajectory mapping onto a road network is a complex yet important task. This is because, in the presence of circular sections, Y-shaped intersections, and sections with elevated overlaps with the ground, the conditions of road networks become complicated. Consequently, trajectory mapping becomes challenging owing to the complexities of road networks and the noise generated by high positioning errors. In this study, in response to the difficulty in handling redundant noisy trajectory data in complex road network environments, a complex road network map-matching method based on trajectory structure extraction is proposed. The features of the structure are extracted from the original trajectory data to reduce the effects of redundancy and noise on matching. An adaptive screening candidate method is proposed using driver behavior to estimate the road density and reduce the matching time by selecting effective candidates. A spatiotemporal analysis function is redefined using speed and distance features, and a directional analysis function is proposed for use in combination with directional features to improve the matching accuracy of complex road networks. An experimental evaluation based on real-ground trajectory data collected using in-vehicle sensing devices is conducted to verify the effectiveness of the algorithm. Moreover, extensive experiments are performed on challenging real datasets to evaluate the proposed method, and its accuracy and efficiency are compared with those of two state-of-the-art map-matching algorithms. The experimental results confirm the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Fleming–Viot diffusions are widely used stochastic models for population dynamics that extend the celebrated Wright–Fisher diffusions. They describe the temporal evolution of the relative frequencies of the allelic types in an ideally infinite panmictic population, whose individuals undergo random genetic drift and at birth can mutate to a new allelic type drawn from a possibly infinite potential pool, independently of their parent. Recently, Bayesian nonparametric inference has been considered for this model when a finite sample of individuals is drawn from the population at several discrete time points. Previous works have fully described the relevant estimators for this problem, but current software is available only for the Wright–Fisher finite-dimensional case. Here, we provide software for the general case, overcoming some nontrivial computational challenges posed by this setting. The R package FVDDPpkg efficiently approximates the filtering and smoothing distribution for Fleming–Viot diffusions, given finite samples of individuals collected at different times. A suitable Monte Carlo approximation is also introduced in order to reduce the computational cost. [ABSTRACT FROM AUTHOR]

We propose a copula-based extension of the hidden Markov model (HMM) which applies when the observations recorded at each time in the sample are multivariate. The joint model produced by the copula extension allows decoding of the hidden states based on information from multiple observations. However, unlike the case of independent marginals, the copula dependence structure embedded into the likelihood poses additional computational challenges. We tackle the latter using a theoretically-justified variation of the EM algorithm developed within the framework of inference functions for margins. We illustrate the method using numerical experiments and an analysis of room occupancy. for this article are available online. [ABSTRACT FROM AUTHOR]

Selecting the number of regimes in Hidden Markov models is an important problem. Many criteria are used to do so, such as Akaike information criterion (AIC), Bayesian information criterion (BIC), integrated completed likelihood (ICL), deviance information criterion (DIC), and Watanabe-Akaike information criterion (WAIC), to name a few. In this article, we introduced goodness-of-fit tests for general Hidden Markov models with covariates, where the distribution of the observations is arbitrary, i.e. continuous, discrete, or a mixture of both. A selection procedure is proposed based on this goodness-of-fit test. The main aim of this article is to compare the classical information criterion with the new criterion when the outcome is either continuous, discrete or zero-inflated. Numerical experiments assess the finite sample performance of the goodness-of-fit tests, and comparisons between the different criteria are made. [ABSTRACT FROM AUTHOR]

Based on the Roesser model, the asynchronous sliding‐mode control (SMC) problem for two‐dimensional (2‐D) Markov jump systems (MJSs) with time‐varying delays is studied in this article. Since the controller mode cannot always access the system mode, the asynchronous phenomenon between the system mode and the controller mode will be described by the hidden Markov model (HMM). First, under the framework of HMM, the asynchronous 2D‐SMC law is designed based on 2‐D sliding surface function to ensure closed‐loop 2‐D discrete MJSs asymptotic mean square stability and the reachability of sliding regions around specific sliding surfaces. Second, an algorithm for deducing the law of 2‐D asynchronous SMC is presented. Finally, a numerical example is given to verify the effective of the results. [ABSTRACT FROM AUTHOR]

Hidden Markov models (HMMs) are popular methods for continuous sequential data modeling and classification tasks. In such applications, the observation emission densities of the HMM hidden states are generally continuous, can vary from one model to the other, and are typically modeled by elliptically contoured distributions, namely Gaussians or Student’s t-distributions. In this context, this paper proposes a novel HMM with Bounded Asymmetric Student’s t-Mixture Model (BASMM) emissions. Our new BASMMHMM is introduced in the light of the added robustness guaranteed by the BASMM in comparison to other popular emission distributions such as the Gaussian Mixture Model (GMM). In fact, GMMs generally have a limited performance with outliers in the data sets (observations) that the HMM is fitted to. Also, GMMs cannot sufficiently model skewed populations, which are typical in many fields, such as financial or signal processing-related data sets. An excellent alternative to solve this problem is found in Student’s t-mixture models. They have similar behaviour and shape to GMMs, but with heavier tails. This allows to have more tolerance towards data sets that span extensive ranges and include outliers. Asymmetry and bounded support are also important features that can further extend the model’s flexibility and fit the imperfections of real-world data. This leads us to explore the effectiveness of the BASMM as an observation emission distribution in HMMs, hence the proposed BASMMHMM. We will also demonstrate the improved robustness of our model by presenting the results of three different experiments: occupancy estimation, stock price prediction, and human activity recognition. [ABSTRACT FROM AUTHOR]

The study investigates the potential of anxiety clusters in predicting programming performance in two distinct coding environments. Participants comprised 83 second-year programming students who were randomly assigned to either a block-based or a text-based group. Anxiety-induced behaviors were assessed using physiological measures (Apple Watch and Electrocardiogram machine), behavioral observation, and self-report. Utilizing the Hidden Markov Model and Optimal Matching algorithm, we found three representative clusters in each group. In the block-based group, clusters were designated as follows: "stay calm" (students allocating more of their time to a calm state), "stay hesitant" (students allocating more of their time to a hesitant state), and "to-calm" (those allocating minimal time to a hesitant and anxious state but displaying a pronounced propensity to transition to a calm state). In contrast, clusters in the text-based group were labeled as: "to-hesitant" (exhibiting a higher propensity to transition to a hesitant state), "stay hesitant" (allocating significant time to a hesitant state), and "stay anxious" (remaining persistently anxious in a majority of the coding time). Additionally, our results indicate that novice programmers are more likely to experience anxiety during text-based coding. We discussed the findings and highlighted the policy implications of the study. [ABSTRACT FROM AUTHOR]

While advances continue to be made in model-based clustering, challenges persist in modeling various data types such as panel data. Multivariate panel data present difficulties for clustering algorithms because they are often plagued by missing data and dropouts, presenting issues for estimation algorithms. This research presents a family of hidden Markov models that compensate for the issues that arise in panel data. A modified expectation–maximization algorithm capable of handling missing not at random data and dropout is presented and used to perform model estimation. [ABSTRACT FROM AUTHOR]

Introduction: Mind-wandering is a highly dynamic phenomenon involving frequent fluctuations in cognition. However, the dynamics of functional connectivity between brain regions during mind-wandering have not been extensively studied. Methods: We employed an analytical approach aimed at extracting recurring network states of multilayer networks built using amplitude envelope correlation and imaginary phase-locking value of delta, theta, alpha, beta, or gamma frequency band. These networks were constructed based on electroencephalograph (EEG) data collected while participants engaged in a video-learning task with mind-wandering and focused learning conditions. Recurring multilayer network states were defined via clustering based on overlapping node closeness centrality. Results: We observed similar multilayer network states across the five frequency bands. Furthermore, the transition patterns of network states were not entirely random. We also found significant differences in metrics that characterize the dynamics of multilayer network states between mind-wandering and focused learning. Finally, we designed a classification algorithm, based on a hidden Markov model using state sequences as input, that achieved a 0.888 mean area under the receiver operating characteristic curve for within-participant detection of mind-wandering. Discussion: Our approach offers a novel perspective on analyzing the dynamics of EEG data and shows potential application to mind-wandering detection. [ABSTRACT FROM AUTHOR]

Classifying animal behaviors in their natural environments is both challenging and ecologically important, but the use of biologgers with multiple sensors has significantly advanced this research beyond the capabilities of traditional methods alone. Here, we show how biologgers containing an integrated tri-axial accelerometer, GPS logger and immersion sensor were used to infer behavioural states of a cryptic, freshwater turtle, the Blanding's turtle (Emydoidea blandingii). Biologgers were attached to three males and five females that reside in two undisturbed coastal marshes in northeastern Georgian Bay (Ontario, Canada) between May and July 2023. Raw acceleration values were separated into static and dynamic acceleration and subsequently used to calculate overall dynamic body acceleration (ODBA) and pitch. The unsupervised Hidden Markov Model (HMM) successfully differentiated five behavioural states as follows: active in water, resting in water, active out of water, resting in water, and nesting. Overall accuracy of the classification was 93.8%, and except for nesting (79%), all other behaviours were above 92%. There were significant differences in daily activity budgets between male and female turtles, with females spending a greater proportion of time active out of water, and inactive out of the water, while males spent a greater proportion of time active in water. These differences were likely a result of large seasonal life-history requirements such as nesting and mate finding. Accurate classification of behavioural states is important for researchers to understand fine-scale activities carried out during the active season and how environmental variables may influence the behaviours of turtles in their natural habitats. [ABSTRACT FROM AUTHOR]

AbstractHidden Markov models (HMM) are used in different fields to study the dynamics of a process that cannot be directly observed. However, in some cases, the structure of dependencies of a HMM is too simple to describe the dynamics of the hidden process. In particular, in some applications in finance and in ecology, the transition probabilities of the hidden Markov chain can also depend on the current observation. In this work, we are interested in extending the classical HMM to this situation. We refer to the extended model as the observation-driven hidden Markov model (OD-HMM). We present a complete study of the general non parametric OD-HMM with discrete and finite state spaces. We study its identifiability and the consistency of the maximum likelihood estimators. We derive the associated forward-backward equations for the E-step of the EM algorithm. The quality of the procedure is tested on simulated datasets. We illustrate the use of the model on an application focused on the study of annual plant dynamics. This work establishes theoretical and practical foundations for this framework that could be further extended to the parametric context in order to simplify estimation and to hidden semi-Markov models for more realism. [ABSTRACT FROM AUTHOR]

Humans prefer to convey information through speech utilizing similar language. Speech detection is the capability to recognize the spoken words of the speaking person. Recent work demonstrates the increased attention among researcher workers in this field specially in Brain-Like computing applications and emphasizes the real-world usability of speech for speaker recognition across different applications. Automatic speech recognition (ASR) is the method of identifying human speech and converting it into text. This study has gained much popularity in recent times. It is a crucial area of research for human-to-machine interaction. Pioneer methods are concerned with manual feature extraction and classical algorithms including Hidden Markov Models (HMM), Gaussian Mixture Model (GMM), and the Dynamic Time Warping (DTW) model. In recent years, neural networks, namely convolutional neural networks (CNN), recurrent neural networks (RNN), and Transformers, have been utilized in the context of ASR and reached outstanding performance over the past few years. This study introduces Intelligent Speech Recognition using the Fractal Amended Grasshopper Optimization Algorithm with Deep Learning (ISR-AGODL) approach. The presented ISR-AGODL technique correctly identifies and recognizes speech signals. In the ISR-AGODL technique, the speech signals are transformed into spectrograms. Besides, the features are derived using the deep convolutional neural networks (DCNN) model. Followed by the Fractals AGO technique is utilized for the choosing of hyperparameters. Finally, the recognition of speech signals is achieved using the extreme gradient boosting (XGBoost) model. The simulation outcomes of the ISR-AGODL method can be validated using a benchmark dataset. The experimental results of the ISR-AGODL method portrayed a superior accuracy outcome of 96.34% over other models. [ABSTRACT FROM AUTHOR]

Enzymatic mechanisms are typically inferred from structural data. However, understanding enzymes require unravelling the intricate dynamic interplay between dynamics, conformational substates, and multiple protein structures. Here, we use single-molecule nanopore analysis to investigate the catalytic conformational changes of adenylate kinase (AK), an enzyme that catalyzes the interconversion of various adenosine phosphates (ATP, ADP, and AMP). Kinetic analysis validated by hidden Markov models unravels the details of domain motions during catalysis. Our findings reveal that allosteric interactions between ligands and cofactor enable converting binding energies into directional conformational changes of the two catalytic domains of AK. These coordinated motions emerged to control the exact sequence of ligand binding and the affinity for the three different substrates, thereby guiding the reactants along the reaction coordinates. Interestingly, we find that about 10% of enzymes show altered allosteric regulation and ligand affinities, indicating that a subset of enzymes folds in alternative catalytically active forms. Since molecules or proteins might be able to selectively stabilize one of the folds, this observation suggests an evolutionary path for allostery in enzymes. In AK, this complex catalytic framework has likely emerged to prevent futile ATP/ADP hydrolysis and to regulate the enzyme for different energy needs of the cell. Single molecule nanopore analysis is used to reveal adenylate kinase global dynamics in real time. It was found that allosteric interactions guide domain motions and substrate affinity, with 10% of enzymes displaying alternative active forms, suggesting evolutionary paths for enzyme regulation. [ABSTRACT FROM AUTHOR]

This study proposes an enhanced ship-type classification model that employs a sequential processing methodology integrating hidden Markov model (HMM), deep neural network (DNN), and convolutional neural network (CNN) techniques. Four different ship types—fishing boat, passenger, container, and other ship—were classified using multiple ship trajectory features extracted from the automatic identification system (AIS) and small fishing vessel tracking system. For model optimization, both ship datasets were transformed into various formats corresponding to multiple models, incorporating data enhancement and augmentation approaches. Speed over ground, course over ground, rate of turn, rate of turn in speed, berth distance, latitude/longitude, and heading were used as input parameters. The HMM–DNN–CNN combination was obtained as the optimal model (average F-1 score: 97.54%), achieving individual classification performances of 99.03%, 97.46%, and 95.83% for fishing boats, passenger ships, and container ships, respectively. The proposed approach outperformed previous approaches in prediction accuracy, with further improvements anticipated when implemented on a large-scale real-time data collection system. [ABSTRACT FROM AUTHOR]

ABSTRACT The main thrust of this study is to scrutinize the issues of fault estimation and asynchronous sampled‐data fault‐tolerant control for semi‐Markov jump cyber‐physical systems with external disturbances, faults and deception attacks. To do so, initially, an intermediate variable is framed and then using that variable as a foundation, a mode‐dependent intermediate estimator is constructed, which estimates the fault signals and system's state simultaneously. Due to the unavailability of mode information in the Markov chain for the observer/controller, a hidden Markov model is employed to represent the asynchronous scenario between the mode of the original system and that of the designed observer/controller. Subsequently, benefited by the estimated terms and sampled‐data approach, an asynchronous sampled fault‐tolerant control protocol is offered up that facilitates compensating for the faults occurring in the system. In the meantime, the extended passive performance is used to lessen the negative impact of external disturbances exerting on the system. Besides this, the deception attacks occurring in the system are presumed to have a stochastic nature that adheres to the Bernoulli distribution. Moreover, by constructing mode‐dependent Lyapunov–Krasovskii functional and blending it with integral inequalities, the sufficient condition confirming the intended outcomes is procured in the framework of linear matrix inequalities. Thereafter, on the platform of deduced adequate criteria, an explicit formulation for the requisite gain values can be obtained. Ultimately, simulation results are offered to verify the reliability of presented outcomes. [ABSTRACT FROM AUTHOR]

Background Objectives Methods Results and Conclusions Process data captured by computer‐based assessments provide valuable insight into respondents' cognitive processes during problem‐solving tasks. Although previous studies have utilized process data to analyse behavioural patterns or strategies in problem‐solving tasks, the connection between latent cognitive states and their theoretical interpretation in problem solving remains unclear.This research aims to investigate the connections between similar hidden response states and unfold respondents' transition paths in problem‐solving processes. Analysing process data from the 2012 United States Programme for the International Assessment of Adult Competencies (PIAAC), this study seeks to discern patterns in problem solving among participants.The hidden Markov model was first used to uncover the hidden states based on a sequence of observed actions. Next, Gaussian graphical network analysis was employed to analyse the relationships between hidden response states.Results indicated that correct responders had simpler, clearer state relationships, while incorrect responders displayed more complex connections. Respondents who solved the tasks correctly had clearer thoughts about the problem‐solving process, whereas incorrect respondents struggled to understand the problem and failed to figure out solutions. Cognitive state changes during problem solving also varied between groups. The correct groups showed cohesive, logical transitions, in contrast to the emerged isolated, erratic patterns of the incorrect groups. [ABSTRACT FROM AUTHOR]

ABSTRACT This article addresses the asynchronous guaranteed cost control (GCC) problems for interval type‐2 (IT2) fuzzy continuous‐time Markov jump systems (C‐TMJSs) with quantization and non‐consecutive transmission. A dynamic event‐triggered mechanism (ETM) for IT2 fuzzy C‐TMJSs is designed to address the limited bandwidth and communication burden, which incorporates mode‐dependent thresholds and weighting matrices. An asynchronous IT2 fuzzy guaranteed cost controller possessing unmatched premise variable information is developed to stabilize the considered system, achieving desired GCC performance. The relationship between system mode and controller mode is described by the hidden Markov model (HMM). A quantizer is employed to quantize the signal prior to transmission to the next node, modeled as a stochastic quantization utilizing the HMM. Furthermore, an improved mode‐dependent and fuzzy‐dependent Lyapunov function is constructed for stability analysis, and the conditions of optimal GCC performance are derived. Finally, two examples are performed to validate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Advancing the efficiency and reliability of wireless sensor networks is a paramount pursuit in modern networking research. In this context, we introduce a groundbreaking approach based on Hidden Markov Chain (HMC) modeling with opportunistic routing, harnessed by the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) mechanism. Our innovative algorithm addresses key challenges in Wireless Body Area Networks, including delay, throughput, and bandwidth utilization. By strategically integrating the HMC model, which captures intricate state transitions and emission probabilities, our proposed method introduces a robust solution for optimizing node connections and routing decisions. This fusion of HMC and opportunistic routing capitalizes on the strengths of both paradigms, enhancing the network's ability to make intelligent decisions in dynamic scenarios. Through rigorous simulations, we showcase the algorithm's prowess in achieving efficient data transmission. The empirical evidence from these simulations underscores the algorithm's superiority when juxtaposed against state-of-the-art mechanisms such as Simple Multi-Packet Access (SMPA) and Priority-based Congestion-avoidance Routing Protocol (PCRP). Our algorithm not only outperforms existing solutions in terms of delay, throughput, and bandwidth utilization, but it also showcases its ability to adapt to varying network conditions, making it a versatile tool for diverse applications. [ABSTRACT FROM AUTHOR]

Attempts to understand wildlife population dynamics and implement management practices benefit from understanding animal behavior traits. In Alaska, USA, grizzly bear (Ursus arctos) behavior is important to understand because the species is an apex predator, exerts top-down population effects, and is a cornerstone species across complex landscapes. Our objectives were to examine how environmental and anthropogenic factors affect behavior patterns in grizzly bears in south-central Alaska. We hypothesized that, for a given sex, the time spent by bears resting, foraging, and traveling are similar and show consistent seasonal shifts as resource availability changes throughout their nondenning season. Additionally, we hypothesized that males spend more time traveling than do females because of differences in home range sizes, metabolic needs, and the rearing of cubs associated with females. We used a hidden Markov model (HMM) to test our hypotheses and examine how various dynamic, static, and temporal variables affected bear (n = 12) behavior during the summers of 2021–2022. Males spent the most time foraging and the least time resting while females spent the most time resting and the least time foraging. These patterns shifted as the summer progressed with increases in traveling and foraging and decreases in resting for both sexes. Bears were found to be most likely in a resting state at hotter temperatures and more likely to be traveling at colder temperatures. Additionally, bear behaviors deviated depending on elevation, whereby bears were foraging at higher elevations and resting or traveling at lower elevations. Our findings indicate that male–female differences in behavior are seasonally dependent, being similar in autumn and opposite during the postdenning period. In addition, we see evidence that changes in environmental conditions, such as warming, can have direct effects on behavior. Further studies should explore how future environmental and anthropogenic factors such as predicted changes in climate and increases in land-use changes can affect bear behavior and subsequent demographic effects. [ABSTRACT FROM AUTHOR]

Introduction: As emerging technologies enable measurement of precise details of the activity within microcircuits at ever-increasing scales, there is a growing need to identify the salient features and patterns within the neural populations that represent physiologically and behaviorally relevant aspects of the network. Accumulating evidence from recordings of large neural populations suggests that neural population activity frequently exhibits relatively low-dimensional structure, with a small number of variables explaining a substantial fraction of the structure of the activity. While such structure has been observed across the brain, it is not known how reduced-dimension representations of neural population activity relate to classical metrics of "brain state," typically described in terms of fluctuations in the local field potential (LFP), single-cell activity, and behavioral metrics. Methods: Hidden state models were fit to spontaneous spiking activity of populations of neurons, recorded in the whisker area of primary somatosensory cortex of awake mice. Classic measures of cortical state in S1, including the LFP and whisking activity, were compared to the dynamics of states inferred from spiking activity. Results: A hidden Markov model fit the population spiking data well with a relatively small number of states, and putative inhibitory neurons played an outsize role in determining the latent state dynamics. Spiking states inferred from the model were more informative of the cortical state than a direct readout of the spiking activity of single neurons or of the population. Further, the spiking states predicted both the trial-by-trial variability in sensory responses and one aspect of behavior, whisking activity. Discussion: Our results show how classical measurements of brain state relate to neural population spiking dynamics at the scale of the microcircuit and provide an approach for quantitative mapping of brain state dynamics across brain areas. [ABSTRACT FROM AUTHOR]

Two families of matrix-variate hidden Markov regression models (MV-HMRMs) are here introduced. The distinction between them relies on the role of the covariates, which can be treated as fixed or random. Parsimony is achieved by using the eigen-decomposition of the components' covariance matrices. This generates a different number of parsimonious models between the two families: 98 MV-HMRMs with fixed covariates and 9604 MV-HMRMs with random covariates. ECM algorithms are discussed for parameter estimation and, because of the high number of parsimonious models, convenient initialization, and fitting strategies are proposed. Our models are first applied to simulated data, and then to a four-way real dataset concerning the relationship between unemployment and labor force participation in the Italian provinces. [ABSTRACT FROM AUTHOR]

Background: The single-channel sleep EEG has the advantages of convenient collection, high-cost performance, and easy daily use, and it has been widely used in the classification of sleep stages. Methods: This paper proposes a single-channel sleep EEG classification method based on long short-term memory and a hidden Markov model (LSTM-HMM). First, the single-channel EEG is decomposed using wavelet transform (WT), and multi-domain features are extracted from the component signals to characterize the EEG characteristics fully. Considering the temporal nature of sleep stage changes, this paper uses a multi-step time series as the input for the model. After that, the multi-step time series features are input into the LSTM. Finally, the HMM improves the classification results, and the final prediction results are obtained. Results: A complete experiment was conducted on the Sleep-EDFx dataset. The results show that the proposed method can extract deep information from EEG and make full use of the sleep stage transition rule. The proposed method shows the best performance in single-channel sleep EEG classification; the accuracy, macro average F1 score, and kappa are 82.71%, 0.75, and 0.76, respectively. Conclusions: The proposed method can realize single-channel sleep EEG classification and provide a reference for other EEG classifications. [ABSTRACT FROM AUTHOR]

The consistent decrease in the open ratio of wafers has spurred a demand for advanced endpoint detection (EPD) techniques to ensure accurate plasma etching in nonlinear optical emission spectroscopy (OES) data characterized by a low signal-to-noise ratio. Additionally, precise detection of endpoint is hindered by variations between plasma chambers arising from diverse issues. To address these issues, this study proposes a nonlinear manifold learning-based EPD model and a chamber condition identification framework. The EPD model demonstrates the capability to extract endpoint-related latent variables from complex nonlinear OES data. Moreover, the model exhibits the ability to generalize to larger datasets through density-based time series clustering. The chamber condition identification framework not only classifies plasma conditions but also automates the determination of the conditions for incoming new wafers. Evaluation of the proposed approach, conducted using actual OES data from multiple chambers, demonstrated that the EPD model outperformed other models which are based on diverse dimensionality reduction approaches. Furthermore, the chamber condition identification process successfully identified condition variations and accurately determined the plasma condition of new data. Moreover, conducting EPD modeling for separate conditions rather than collectively for diverse conditions demonstrated superior detection results, underscoring the importance of the chamber condition identification process. [ABSTRACT FROM AUTHOR]

Simple Summary: We propose a behaviour model for West-African pastoral mobility based on a long-term survey of agropastoral cattle herds in Senegal. We combined position and activity statistics with satellite land-use data. Our findings on behaviour and herding practices align with literature and field observations and shed light on nocturnal foraging, transhumance phases and individual fallowing. We describe and discuss our methodology to provide tools for future research. In agrarian systems where animal mobility is crucial for feed management, nutrient cycles and household economy, there is a notable lack of precise data on livestock mobility and herding practices. We introduce a methodology leveraging GPS-based behavioural models to analyse and document pastoral mobility in the Sahel. Over 2.5 years, we conducted a continuous collection of GPS data from transhumant and resident cattle herds in the Senegalese agropastoral semiarid rangelands. We developed a Hidden Markov Model robustly fitted to these data to classify recordings into three states of activity: resting (47% overall), foraging (37%) and travelling (16%). We detail our process for selecting the states and testing data subsets to guide future similar endeavours. The model describes state changes and how temperature affects them. By combining the resulting dataset with satellite-based land-use data, we show the distribution of activities across landscapes and seasons and within a day. We accurately reproduced key aspects of cattle mobility and characterised rarely documented features of Sahel agropastoral practices, such as transhumance phases, nocturnal grazing and in-field rainy season paddocking. These results suggest that our methodology, which we make available, could be valuable in addressing issues related to the future of Sahelian pastoralism. [ABSTRACT FROM AUTHOR]

Predator hunting strategies, such as stalking versus coursing behaviors, are hypothesized to influence antipredator behaviors of prey and can describe the movement behaviors of predators themselves. Predators and prey may alter their movement in relation to predator hunting modes, yet few studies have evaluated how these strategies influence movement behaviors of free‐ranging animals in a multiple‐predator, multiple‐prey system. We fit hidden Markov models (HMM) with movement data derived from >400 GPS‐collared ungulates and large predators in eastern Washington, USA. We used these models to test our hypotheses that stalking (cougars [Puma concolor]) and coursing (gray wolves [Canis lupus]) predators would exhibit different broad‐scale movement behaviors consistent with their respective hunting strategies in areas that increased the likelihood of encountering or capturing ungulate prey (e.g., habitats selected by deer [Odocoileus spp.]). Similarly, we expected that broadscale movement behaviors of prey would change in response to background levels of predation risk associated with each predator's hunting strategy. We found that predators and ungulate prey adjusted their broadscale movements in response to one another's long‐term patterns of habitat selection but not based on differences in predator‐hunting strategies. Predators changed their movement behaviors based on the type of prey, whereas ungulates generally reduced movement in areas associated with large predators, regardless of the predator's hunting strategy. Both predator and prey movements varied in response to landscape features but not necessarily based on habitat that would facilitate specific hunting behaviors. Our results suggest that predators and prey adjust their movements at broad temporal scales in relation to long‐term patterns of risk and resource distributions, potentially influencing their encounter rates with one another at finer spatiotemporal scales. Habitat features further influenced changes in movement, resulting in a complex combination of movement behaviors in multiple‐predator, multiple‐prey systems. [ABSTRACT FROM AUTHOR]

With the popularity of smart meters, the frequent information exchange between smart grids and consumers leads to easy leakage of consumers' electricity consumption data. These leaked electricity consumption data are obtained by some malicious attackers and used to infer consumers' behavioural patterns by non‐intrusive load monitoring (NILM), which seriously threatens consumers' privacy. Therefore, the multi‐agent Hidden Markov energy management model is proposed in this paper to safeguard the privacy of consumers. First, a weighted Bayesian risk model is proposed, which combines privacy leakage risks and energy storage system (ESS) losses in a microgrid with multiple agents. Next, a three‐loop model for lithium batteries is constructed to quantify the capacity degradation and cost issues of the ESS. Finally, the multi‐objective optimization problem is resolved by integrating the Bayesian risk model with a hidden Markov model to simulate attackers. The proposed multi‐agent Markov decision process method is validated on Electricity Consumption and Occupancy (ECO) dataset, and control strategies are evaluated based on different weights in the Bayesian risk model. The results demonstrate that by incorporating the multi‐agent approach and energy storage system capacity degradation into the privacy protection strategy, the lifespan of the energy storage system can be significantly increased. [ABSTRACT FROM AUTHOR]

We present a new method to distinguish between different states (e.g. high and low, quiescent and flaring) in astronomical sources with count data. The method models the underlying physical process as latent variables following a continuous-space Markov chain that determines the expected Poisson counts in observed light curves in multiple passbands. For the underlying state process, we consider several autoregressive processes, yielding continuous-space hidden Markov models of varying complexity. Under these models, we can infer the state that the object is in at any given time. The continuous state predictions from these models are then dichotomized with the help of a finite mixture model to produce state classifications. We apply these techniques to X-ray data from the active dMe flare star EV Lac, splitting the data into quiescent and flaring states. We find that a first-order vector autoregressive process efficiently separates flaring from quiescence: flaring occurs over 30 per cent–40 per cent of the observation durations, a well-defined persistent quiescent state can be identified, and the flaring state is characterized by higher plasma temperatures and emission measures. [ABSTRACT FROM AUTHOR]

Active inference is a theory of perception, learning, and decision making that can be applied to neuroscience, robotics, psychology, and machine learning. Recently, intensive research has been taking place to scale up this framework using Monte Carlo tree search and deep learning. The goal of this activity is to solve more complicated tasks using deep active inference. First, we review the existing literature and then progressively build a deep active inference agent as follows: we (1) implement a variational autoencoder (VAE), (2) implement a deep hidden Markov model (HMM), and (3) implement a deep critical hidden Markov model (CHMM). For the CHMM, we implemented two versions, one minimizing expected free energy, CHMM[EFE] and one maximizing rewards, CHMM[reward]. Then we experimented with three different action selection strategies: the ε -greedy algorithm as well as softmax and best action selection. According to our experiments, the models able to solve the dSprites environment are the ones that maximize rewards. On further inspection, we found that the CHMM minimizing expected free energy almost always picks the same action, which makes it unable to solve the dSprites environment. In contrast, the CHMM maximizing reward keeps on selecting all the actions, enabling it to successfully solve the task. The only difference between those two CHMMs is the epistemic value, which aims to make the outputs of the transition and encoder networks as close as possible. Thus, the CHMM minimizing expected free energy repeatedly picks a single action and becomes an expert at predicting the future when selecting this action. This effectively makes the KL divergence between the output of the transition and encoder networks small. Additionally, when selecting the action down the average reward is zero, while for all the other actions, the expected reward will be negative. Therefore, if the CHMM has to stick to a single action to keep the KL divergence small, then the action down is the most rewarding. We also show in simulation that the epistemic value used in deep active inference can behave degenerately and in certain circumstances effectively lose, rather than gain, information. As the agent minimizing EFE is not able to explore its environment, the appropriate formulation of the epistemic value in deep active inference remains an open question. [ABSTRACT FROM AUTHOR]

Accurate prediction of cloud resource instances is becoming increasingly important for public cloud users and cloud service providers, because it touches on the reasonable reservation of cloud resources with minimize costs. However, current methods do not predict the instance types of cloud resources based on the application workloads from users, and less consider the characteristics of workload data changes in the real-time prediction. To solve these problems, this paper proposes an application workload-dependent cloud resource instance prediction model to predict appropriate cloud instance resource usage in a timely manner. Firstly, we adopt a trend degree (TD) to classify all requested workloads into three types of wave trend patterns. Next, a Hidden Markov model based cloud resource prediction method (HMM-CPM) tracing the requested workload trends is presented. Finally, the reasonable cloud instance types following the patterns of the requested workloads can be predicted. The simulation results show that the proposed method can predict cloud resource instance types in the scenario with certain workload fluctuation, and the prediction accuracy is higher than the existing related approaches. [ABSTRACT FROM AUTHOR]

The problem of asynchronous fault detection filtering for nonhomogeneous Markov jumping systems with dynamic quantization and hybrid cyber attacks is addressed in this paper. The introduction of polytopic-structure-based transition probabilities is employed to describe the nonhomogeneous Markov process. An asynchronous fault detection filter is proposed, which utilizes the hidden Markov model to achieve comprehensive access to the plant mode information. Prior to transmission to the filter, the measurement output of the system undergoes quantization using a dynamic quantizer. The novel hybrid cyber attacks model being discussed involves four types of attacks: deception attacks, denial-of-service attacks, no attack, and hybrid attacks with both deception and denial-of-service attacks. By constructing Lyapunov functional, sufficient conditions are presented for achieving the stochastic stability with H ∞ performance. Under the complex network environment, the industrial application of the presented asynchronous fault detection filtering model is demonstrated on a non-isothermal continuous stirred tank reactor. The simulation results confirm the practicality of the proposed design method. • The dynamic quantizer is adopted to quantify measurement output signals to reduce information exchange frequency and communication burden. • A convex polyhedron is used to describe the time-varying transition probability matrix. • Due to the mode information between the filter and the plant cannot be fully obtained in the networked control system, a HMM-based asynchronous mechanism is proposed. • In order to improve the system performance, a novel hybrid network attack model is proposed, which includes four attack modes. [ABSTRACT FROM AUTHOR]

The GARCH model is the most used technique for forecasting conditional volatility. However, the nearly integrated behaviour of the conditional variance originates from structural changes which are not accounted for by standard GARCH models. We compare the forecasting performance of the GARCH model to three regime switching models: namely, the Markov Switching GARCH, the Hidden Markov Model, and the Gated Recurrent Unit neural network. We define the number of optimal states by means of three methods: piecewise linear regression, Baum–Welch algorithm and Markov Chain Monte Carlo. Since forecasting volatility models face the bias-variance trade-off, we compare their out-of-sample forecasting performance via a walk-forward methodology. Moreover, we provide a robustness check for the results by applying k-fold cross-validation to the original time series. The Gated Recurrent Unit network is the best suited for volatility forecasting, while the Hidden Markov Model is the best at discerning the market regimes. [ABSTRACT FROM AUTHOR]

The state of Colorado's West Slope Basins are critical headwaters of the Colorado River and play a vital role in supporting Colorado's local economy and natural environment. However, balancing the multi‐sectoral water demands in the West Slope Basins while maintaining crucial downstream deliveries to Lake Powell is an increasing challenge for water managers. Internal variability of the hydroclimatic system and climate change complicate future vulnerability assessments. This work contributes a detailed accounting of multi‐sectoral drought vulnerability in the West Slope Basins and the impacts of drought on downstream deliveries. We first introduce a novel multi‐site Hidden Markov Model (HMM)‐based synthetic streamflow generator to create an ensemble of streamflows for all West Slope basins that better characterizes the region's drought extremes. We capture the effects of climate change by perturbing the HMM to generate an ensemble of streamflows reflecting plausible changes in climate. We then route both ensembles through StateMod, Colorado's water allocation model, to evaluate spatially compounding drought impacts across the West Slope Basins. Our results illustrate how drought events emerging from the system's stationary internal variability in the absence of climate change can significantly impact local water uses and deliveries to Lake Powell, exceeding extreme conditions in the historical record. Further, we find that even modest climate change can cause a regime shift where historically low downstream delivery volumes and extreme drought impacts become routine. These results can inform future Colorado River planning efforts, and our methodology can be expanded to other snow‐dominated regions that face persistent droughts. Plain Language Summary: The state of Colorado's West Slope Basins ‐ six watersheds within the Colorado River Basin on the western side of the continental divide ‐ are essential water sources for the Colorado River and play a vital role in supporting the state of Colorado's local economy and natural environment. Water managers in the region are challenged by competing water demands for uses such as agriculture, environmental flows, and downstream deliveries to Lake Powell, a critical regional reservoir. Evaluating drought vulnerability in the region is complicated by the inherent randomness of streamflows and reduced flows from climate change. This work contributes a new analysis of local drought vulnerability in the West Slope Basins and the consequences for water deliveries to Lake Powell. We introduce a method to generate streamflow scenarios and explore drought impacts in the West Slope Basins. We run the scenarios through a model of the human‐natural system to explore local and regional drought vulnerability. Our results reveal elevated drought risks to downstream water users, agriculture, and the environment, even without climate change. Further, we find that even optimistic climate change projections can exacerbate drought risk, leading to unprecedented challenges for local water users and the broader regional system. Key Points: We introduce a multi‐site two‐state Gaussian Hidden Markov model to explore drought impacts in Colorado's West Slope Basins and Lake PowellInternal variability in the hydroclimatic system can cause spatially compounding drought impacts exceeding historically extreme eventsStreamflow declines driven by an optimistic climate change scenario can transition the system to a drier regime and increase drought impacts [ABSTRACT FROM AUTHOR]

Serious concerns exist that the increasing frequency of fires may delay the recovery of ozone given increasing temperatures due to climate change. Australian bushfires from September 2019 to February 2020 were catastrophic. A random forest spatial-temporal (RF sp) analysis using satellite data to detect an association between Australian bushfires and stratosphere ozone on the local depletion of ozone in the vicinity of fires in three regions of Australia (Pacific Ocean, Victoria, NSW) has shown a significant reduction in ozone attributable to aerosols from fires. By intervention analysis, increases in aerosols in all three regions were shown to have a significant and ongoing impact 1–5 days later on reducing ozone (p < 0.0001). Intervention analysis also gave similar periods of aerosol exceedance to those found by Hidden Markov models (HMMs). HMMs established a significant and quantifiable decline in ozone due to bushfire-induced aerosols, with significant lags of 10–25 days between times of aerosol exceedance and subsequent ozone level decline in all three regions. [ABSTRACT FROM AUTHOR]

This letter to the editor discusses a case study involving the use of MLPA testing to detect copy number variations in the BRCA1 gene. The study found that MLPA produced a false-positive result for a heterozygous deletion in BRCA1 exon 17 due to an artifact caused by a microsatellite variant. The article emphasizes the need for standardization and data exchange among professionals to ensure accurate results in molecular testing. It also highlights the importance of integrating exon-level CNV bioinformatics pipelines into HRD analysis for resolving unexpected results. [Extracted from the article]

This paper explores the utilization of sliding mode control, which relies on an asynchronous observer, for Markov jump systems subject to external disturbances. Firstly, given that the system's mode is not directly measurable and could potentially differ from the observer's and controller's mode, the paper constructs an asynchronous observer employing a hidden Markov model. Secondly, a sliding surface is designed to correspond with the asynchronous observer. Moreover, a multi-parameter event-triggered mechanism is incorporated into the observer design to alleviate bandwidth strain. Thirdly, by applying the integrated sliding mode control law, we ensure that the system state trajectories will reach the sliding surface within a finite time. Fourthly, the achievement of H ∞ stability is realized by making use of the Lyapunov function. Lastly, a practical-oriented example is presented to illustrate the efficiency of the established method. [ABSTRACT FROM AUTHOR]

In the rapidly evolving field of insurance, accurate risk measurement is crucial for effective claims management and financial stability. Therefore, this research presented a systematic literature review (SLR) on insurance claims risk measurement using the Hidden Markov Model (HMM). Bibliometric analysis was conducted using VOSviewer 1.6.20 and ResearchRabbit software to map research trends and collaboration networks in this topic. This review explored the implementation of the HMM in predicting the frequency and severity of insurance claims, with a focus on the statistical distribution methods used. In addition, the research emphasized the influence of the number of hidden states in the HMM on claims behavior, both in terms of frequency and magnitude, and provided interpretations of these hidden dynamics. Data sources for this review comprised three databases, namely, Scopus, ScienceDirect, and Dimensions, and additional papers from a website. The article selection process followed updated PRISMA 2020 guidelines, resulting in twelve key papers relevant to the topic. The results offered insights into the application of the HMM for forecasting the frequency and severity of insurance claims and opened avenues for further investigation on distribution models and hidden state modeling. [ABSTRACT FROM AUTHOR]

Background: Protein kinases are a diverse superfamily of proteins common to organisms across the tree of life that are typically involved in signal transduction, allowing organisms to sense and respond to biotic or abiotic environmental factors. They have important roles in organismal physiology, including development, reproduction, acclimation to environmental stress, while their dysregulation can lead to disease, including several forms of cancer. Identifying the complement of protein kinases (the kinome) of any organism is useful for understanding its physiological capabilities, limitations and adaptations to environmental stress. The increasing availability of genomes makes it now possible to examine and compare the kinomes across a broad diversity of organisms. Here we present a pipeline respecting the FAIR principles (findable, accessible, interoperable and reusable) that facilitates the search and identification of protein kinases from a predicted proteome, and classifies them according to group of serine/threonine/tyrosine protein kinases present in eukaryotes. Results: KiNext is a Nextflow pipeline that regroups a number of existing bioinformatic tools to search for and classify the protein kinases of an organism in a reproducible manner, starting from a set of amino acid sequences. Conventional eukaryotic protein kinases (ePKs) and atypical protein kinases (aPKs) are identified by using Hidden Markov Models (HMMs) generated from the catalytic domains of kinases. Furthermore, KiNext categorizes ePKs into the eight kinase groups by employing dedicated Hidden Markov Models (HMMs) tailored for each group. The performance of the KiNext pipeline was validated against previously identified kinomes obtained with other tools that were already published for two marine species, the Pacific oyster Crassostrea gigas and the unicellular green alga Ostreoccocus tauri. KiNext outperformed previous results by finding previously unidentified kinases and by attributing a large proportion of previously unclassified kinases to a group in both species. These results demonstrate improvements in kinase identification and classification, all while providing traceability and reproducibility of results in a FAIR pipeline. The default HMM models provided with KiNext are most suitable for eukaryotes, but the pipeline can be easily modified to include HMM models for other taxa of interest. Conclusion: The KiNext pipeline enables efficient and reproducible identification of kinomes based on predicted amino acid sequences (i.e. proteomes). KiNext was designed to be easy to use, automated, portable and scalable. [ABSTRACT FROM AUTHOR]

Black adults' prior exposure to racial discrimination may be associated with their acute parasympathetic reactivity to and recovery from a new race‐related stressor. Existing analytical approaches to investigating this link obscure nuances in the timing, magnitude, and patterns of these acute parasympathetic nervous system (PNS) responses. In a re‐analysis of a prior study, we utilize an hidden Markov model (HMM) approach to examine how prior experiences of racial discrimination are associated with intraindividual patterns of (1) physiological states of PNS activity and (2) patterns of and variability in transitions between these physiological states. Participants (N = 118) were Black young adults (range 18–29 years; Mage = 19.67, SDage = 2.04) who completed an online survey to index prior racial discrimination exposure, followed by an in‐person lab visit during which their PNS activity in response to a race‐related stress task was measured via electrocardiogram and converted into respiratory sinus arrhythmia. HMMs indicated evidence for two states: baseline and a second state representing a significant reduction in respiratory sinus arrhythmia. Most participants (93.22%) demonstrated a blunted response to the task, indicating that they did not transition from baseline during the procedure. Prior racial discrimination was not associated with HMM states or state transition parameters. Blunted physiological responses may be an important area of future investigation that could inform early life course mental and physical health screenings. [ABSTRACT FROM AUTHOR]

Large carnivores play a crucial role in the ecosystem, though their conservation needs a landscape-level approach due to their wide range of habitats and dispersal events. The study of tigers in a human-dominated landscape matrix and their adaptation and adjustment of movement behaviours during the dispersal phase is essential for long-term conservation planning and management policy. We studied the dispersal event of five VHF/GPS collared individuals during 2009–2020. We investigated movement parameters (step length), and the effects of anthropogenic pressures (distance from village), distance from water and vegetation cover, on behavioural phase under a Hidden Markov Model framework. We also tested the effects of distance from village, vegetation cover, and habitat types on animal movement using an integrated Step Selection Function framework. The mean step length (405.44±10.63 m/hr) varied widely by different time of day. Displacement was high during the night (665.28±21.36 m/hr) compared to day (434.16±17.37 m/hr). Tigers moved fast (872.7m; 95% CI 839.1–906.3m) with longer step length and a directional turning angle in non-forested areas (i.e. the human-dominated landscape), likely to avoid conflict with humans. Individuals distinctly exhibited two behavioural states: encamping (~32% of the time) and travelling (~68% of the time). Further, they avoided the human-dominated landscape and mostly remained in and forested areas, especially during nighttime. Our study is the first attempt to understand behavioural transition of dispersal tigers and their habitat selection. Lesser anthropogenic disturbance and high vegetation cover positively influenced the tiger dispersal, while water availability did not affect their state transitional probability. Additionally, dispersers showed high affinity towards forested land during nighttime for travelling.The findings of this study show the importance of functional corridors and stepping stones (mostly encamping areas), and also provide baseline knowledge for integrated landscape management planning and policymaking for the long-term survival of tigers in metapopulation framework. [ABSTRACT FROM AUTHOR]

Understanding and remembering the complex experiences of everyday life relies critically on prior schematic knowledge about how events in our world unfold over time. How does the brain construct event representations from a library of schematic scripts, and how does activating a specific script impact the way that events are segmented in time? We developed a novel set of 16 audio narratives, each of which combines one of four location-relevant event scripts (restaurant, airport, grocery store, and lecture hall) with one of four socially relevant event scripts (breakup, proposal, business deal, and meet cute), and presented them to participants in an fMRI study and a separate online study. Responses in the angular gyrus, parahippocampal gyrus, and subregions of the medial prefrontal cortex (mPFC) were driven by scripts related to both location and social information, showing that these regions can track schematic sequences from multiple domains. For some stories, participants were primed to attend to one of the two scripts by training them to listen for and remember specific script-relevant episodic details. Activating a location-related event script shifted the timing of subjective event boundaries to align with script-relevant changes in the narratives, and this behavioral shift was mirrored in the timing of neural responses, with mPFC event boundaries (identified using a hidden Markov model) aligning to location-relevant rather than socially relevant boundaries when participants were location primed. Our findings demonstrate that neural event dynamics are actively modulated by top-down goals and provide new insight into how narrative event representations are constructed through the activation of temporally structured prior knowledge. [Display omitted] • Default mode regions represent schematic event scripts during narrative perception • Attending to an event script impacts behavioral event segmentation and memory • Neural dynamics in the mPFC reflect the event structure of an attended script De Soares et al. find that priming participants to attend to a schematic event script can change the way that a narrative stimulus is behaviorally and neurally segmented into perceptual events. [ABSTRACT FROM AUTHOR]

The spatial availability of social resources is speculated to structure animal movement decisions, but the effects of social resources on animal movements are difficult to identify because social resources are rarely measured. Here, we assessed whether varying availability of a key social resource—access to receptive mates—produces predictable changes in movement decisions among bighorn sheep in Nevada, the United States. We compared the probability that males made long-distance 'foray' movements, a critical driver of connectivity, across three ecoregions with varying temporal duration of a socially mediated factor, breeding season. We used a hidden Markov model to identify foray events and then quantified the effects of social covariates on the probability of foray using a discrete choice model. We found that males engaged in forays at higher rates when the breeding season was short, suggesting that males were most responsive to the social resource when its existence was short lived. During the breeding season, males altered their response to social covariates, relative to the non-breeding season, though patterns varied, and age was associated with increased foray probability. Our results suggest that animals respond to the temporal availability of social resources when making the long-distance movements that drive connectivity. This article is part of the theme issue 'The spatial–social interface: a theoretical and empirical integration'. [ABSTRACT FROM AUTHOR]

As humans' additional arms or legs, supernumerary robotic limbs (SRLs) have gained great application prospects in many fields. However, current SRLs lack both rigidity/flexibility adaptability and arm/leg function conversion. Inspired by the muscular hydrostat characteristics of octopus tentacles, fiber-reinforced actuators (FRAs) were employed to develop SRLs simultaneously realizing flexible operation and stable support. In this paper, an SRL with FRAs was designed and implemented. The analytic model of the FRA was established to formulate the movement trajectory and stiffness profile of the SRL. A hierarchical hidden Markov model (HHMM) was proposed to recognize the wearer's motion intention and control the SRL to complete the specific working mode and motion type. Experiments were conducted to exhibit the feasibility and superiority of the proposed robot. [ABSTRACT FROM AUTHOR]

This paper examines power user behavior and the design of marketing strategies, using a case study of Smart Community A. We explore how advanced analytical models are used to enhance energy efficiency and user services. First, we apply spectral clustering to refine user segmentation and identify distinct electricity consumption patterns among different groups. Then, the Hidden Markov Model (HMM) analyzes user behavior, uncovering shifts in consumption habits and enabling personalized service offerings. Next, the ARIMA model predicts electricity consumption trends, guiding grid scheduling and resource allocation. Based on these analyses, we develop targeted marketing strategies, such as dynamic pricing and energy-saving incentives, which boost user engagement and reduce energy usage. Through an IoT and big data-driven interactive marketing platform, we enhance user experience and foster a culture of energy conservation. Finally, a feedback mechanism ensures continuous improvement and maximizes the effectiveness of the marketing strategies. [ABSTRACT FROM AUTHOR]

Immunogenicity is the propensity of a therapeutic protein to generate an immune response to itself. While reporting of antidrug antibodies (ADAs) is increasing, model‐based analysis of such data is seldom performed. Model‐based characterization of factors affecting the emergence and dissipation of ADAs may inform drug development and/or improve understanding in clinical practice. This analysis aimed to predict ADA dynamics, including the potential influence of individual covariates, following subcutaneous satralizumab administration. Satralizumab is a humanized IgG2 monoclonal recycling IL‐6 receptor antagonist antibody approved for treating neuromyelitis optica spectrum disorder (NMOSD). Longitudinal pharmacokinetic (PK) and ADA data from 154 NMOSD patients in two pivotal Phase 3 studies (NCT02028884, NCT02073279) and PK data from one Phase 1 study (SA‐001JP) in 72 healthy volunteers were available for this analysis. An existing population PK model was adapted to derive steady‐state concentration without ADA for each patient. A mixed hidden Markov model (mHMM) was developed whereby three different states were identified: one absorbing Markov state for non‐ADA developer, and two dynamic and inter‐connected Markov states—transient ADA negative and positive. Satralizumab exposure and body mass index impacted transition probabilities and, therefore, the likelihood of developing ADAs. In conclusion, the mHMM model was able to describe the time course of ADA development and identify patterns of ADA development in NMOSD patients following treatment with satralizumab, which may allow for the formulation of strategies to reduce the emergence or limit the impact of ADA in the clinical setting. [ABSTRACT FROM AUTHOR]