Diverse Behaviors Research Articles

A Container Escape Detection Method Based on a Dependency Graph

With the rapid advancement in edge computing, container technology has gained widespread adoption. This is due to its lightweight isolation mechanisms, high portability, and fast deployment capabilities. Despite these advantages, container technology also introduces significant security risks. One of the most critical is container escape. However, current detection research is incomplete. Many methods lack comprehensive detection coverage or fail to fully reconstruct the attack process. To address these gaps, this paper proposes a container escape detection method based on a dependency graph. The method uses various nodes and edges to describe diverse system behaviors. This approach enables the detection of a broader range of attack types. It also effectively captures the contextual relationships between system events, facilitating attack traceability and reconstruction. We design a method to identify container processes on the dependency graph through label generation and propagation. Based on this, container escape detection is implemented using file access control within the graph. Experimental results demonstrate the effectiveness of the proposed method in detecting container escapes.

Exploring giant dielectric constant in Dy2CoMnO6: evidence of non-overlapping small polaron hopping

Abstract Perovskites, both natural and synthetic, form a large class of materials are a vast class of materials with significant technological relevance due to their remarkable physical properties, such as superconductivity, magnetoresistance, ionic conductivity, and diverse dielectric behaviors. In this study, the dielectric relaxation and transport properties of the double perovskite Dy2CoMnO6(DCMO) synthesized via high-energy ball milling are investigated. DCMO exhibits a notably large dielectric constant, attributed to a combination of intrinsic and extrinsic mechanisms. Frequency-dependent dielectric studies reveal non-Debye-like behavior, validated by augmented Havriliak-Negami function fitting. Impedance spectroscopy confirms the semiconducting nature of DCMO, showing a negative temperature coefficient of resistance, and identifies two distinct relaxation processes corresponding to grain boundaries and grain interiors thereby highlighting the impact of microstructure and defects. The Cole-Cole plot further supports the non-Debye behavior, while thermally activated relaxation suggests damped charge carrier dynamics at grain boundaries. Conduction analysis using augmented Jonscher's power law reveals non-overlapping small polaron tunneling as the dominant mechanism driving both the dielectric response and transport properties, with DC conductivity suggesting a three-dimensional variable range hopping model. These results provide significant insights into the dielectric and transport properties of DCMO, highlighting its promising potential for advanced electronic applications.

Cognitive Models for Machine Theory of Mind

AbstractSome of the required characteristics for a true machine theory of mind (MToM) include the ability to (1) reproduce the full diversity of human thought and behavior, (2) develop a personalized model of an individual with very limited data, and (3) provide an explanation for behavioral predictions grounded in the cognitive processes of the individual. We propose that a certain class of cognitive models provide an approach that is well suited to meeting those requirements. Being grounded in a mechanistic framework like a cognitive architecture such as ACT‐R naturally fulfills the third requirement by mapping behavior to cognitive mechanisms. Exploiting a modeling paradigm such as instance‐based learning accounts for the first requirement by reflecting variations in individual experience into a diversity of behavior. Mechanisms such as knowledge tracing and model tracing allow a specific run of the cognitive model to be aligned with a given individual behavior trace, fulfilling the second requirement. We illustrate these principles with a cognitive model of decision‐making in a search and rescue task in the Minecraft simulation environment. We demonstrate that cognitive models personalized to individual human players can provide the MToM capability to optimize artificial intelligence agents by diagnosing the underlying causes of observed human behavior, projecting the future effects of potential interventions, and managing the adaptive process of shaping human behavior. Examples of the inputs provided by such analytic cognitive agents include predictions of cognitive load, probability of error, estimates of player self‐efficacy, and trust calibration. Finally, we discuss implications for future research and applications to collective human–machine intelligence.

Compositional effect on pressure-induced polymorphism in high-entropy alloys

Recently, pressure-induced polymorphic phase transitions were recently discovered in several fragmented high-entropy alloys (HEAs), offering a valuable opportunity to deepen our understanding of these materials. However, the chemical and physical factors that govern these transitions are still unclear. Here, we combined in situ high-pressure and high-temperature synchrotron X-ray diffraction, X-ray emission spectroscopy (XES), and high-resolution transmission electron microscopy (HRTEM) to systematically study the evolution of the atomic and electronic structures in the Cantor alloy and its face-centered-cubic (fcc) subset alloys (CoCrFeMnNi, CoCrFeNi, CoCrMnNi, CoFeMnNi, CoCrNi, CoFeNi, CoMnNi, CrFeNi, and FeMnNi). Surprisingly, diverse behavior was observed among these closely related alloys during compression and decompression, which includes irreversible, reversible fcc to hexagonal close-packed (hcp) phase transitions, or even no detectable phase transitions up to ∼40 GPa. HRTEM measurements confirmed that the fcc and hcp phases abided by the classic Shoji-Nishiyama orientation relationship during the transitions. XES data indicated that high-pressure suppresses the local magnetic moments in all the studied alloys, suggesting that magnetic states do not significantly influence the polymorphic transitions. By comparing the effects of the atomic size difference, entropy, valence electron concentration, and stacking fault energy across all the compositions studied, only the stacking fault energy shows a strong correlation with the phase transitions, indicating it plays a key role in inducing polymorphism in HEAs.

Synchronization of extraction and chromatographic conditions of diverse physicochemical behavior of pseudoephedrine, loratadine and desloratadine for their simultaneous determination in human plasma: Application to pharmacokinetic study

A selective, sensitive, and rapid LC-MS/MS method has been developed, optimized, and validated for the simultaneous determination of pseudoephedrine (PSE), loratadine (LOR), and LOR active metabolite, desloratadine (DES) in K3EDTA human plasma. A simple liquid–liquid extraction method was adopted for extraction of the 3 analytes and their internal standards; chlorpheniramine (CLP), loratadine D4 (LOR-D4), and desloratadine D4 (DES-D4), respectively, giving consistent recovery results (78.38––88.23 %) using diethyl ether: dichloromethane (80:20, v/v) as the extraction solvent. The chromatographic separation was challenging due to the polarity differences between PES, which is more polar, and LOR and DES, which are less polar that affected their physicochemical properties including the retention of the drugs on C18 column. The separation was achieved using of isocratic elution of 20 mM ammonium formate and 2 mM trifluoracetic acid ammonium salt: acetonitrile containing 0.15 % formic acid (12:88, v/v) as a mobile phase pumped onto a Supelco Discovery® C18 (4.6 mm x 100 mm, 5 µm) at 35 °C with a flow rate of 0.8 mL/min. The use of trifluoracetic acid ammonium salt as an ion pair reagent in the selected mobile phase allowed good retention of the polar PSE on the used C18 column with a reasonable retention time. Positive Electrospray ionization was employed followed by detection using a tandem mass spectrometer in multiple reactions monitoring (MRM) mode. The sample analysis of this study was conducted in compliance with ICH guideline M10 on bioanalytical method validation and study sample analysis. The method was linear over the concentration range of (4–1200) ng/mL for PSE, (50–15000) pg/mL for both LOR, and DES. The developed method was accurate (91.14–110.30 %), precise (0.53–7.86 %), and was applied to study the pharmacokinetic profile of the cited drugs in the plasma of healthy human volunteers after single oral administration of the fixed-dose combination Claritin D® tablet, (240/10 mg) of PSE, and LOR, respectively.

Network structure and time delays shape synchronization patterns in brain network models.

In this paper, we investigate synchronization patterns and coherence for a network of delayed Wilson-Cowan nodes. To capture information processing across different brain regions, our model incorporates two distinct delays: an intra-nodal delay that reflects the time signals take to travel within a cortical region due to local circuitry and an inter-nodal delay representing the longer communication times associated with white matter connections between brain areas. To investigate the role of network topology, we consider a range of toy network structures as well as the known (macro-scale) cortical structure of the Macaque monkey. We examine how global network dynamics are shaped by a combination of network configuration, coupling strength, and time delays. Our focus lies on two dynamic measures: synchrony and metastability, the latter reflecting the temporal variation of the former, both crucial for the brain's real-time functionality. Our investigation identifies extensive regions within the system's parameter space where the synchronized state exhibits transverse instabilities. These instabilities give rise to diverse dynamical behaviors contingent upon the network architecture and the interplay between coupling strength and time delay. While similar complex partially synchronized states existed for all network topologies considered, the cortical network demonstrated time-dependent behaviors, such as phase cluster dynamics, which were absent in the toy network architectures, and which are considered crucial in its ability to orchestrate complex information processing and behavior. Additionally, we illustrate how delays can regulate a cortical network with chaotic local dynamics, thus emphasizing the potential importance of delays in suppressing pathological spreading dynamics.

The mechanism of selective separation of stibnite and arsenopyrite by Cu2+ coordination assembly KBX collector

The similar physicochemical properties of stibnite and arsenopyrite resulted in the difficult separation by traditional collectors in flotation. Hence, this study utilized Cu2+ and potassium butyl xanthate (KBX) coordinate assembly to form a novel Cu-KBX complex collector, investigating its properties, conformation, and its role in flotation separation of stibnite and arsenopyrite, as well as its diverse adsorption behavior on mineral surfaces. The stable Cu-KBX complex solution was formed when the molar ratio of Cu2+ to KBX was 1:2, exhibiting a network-like structure at that time. The micro-flotation results showed that at pH 5, the grade of Sb in the concentrate was as high as 61.08 %, with As content at only 4.65 %, enabling effective separation of stibnite and arsenopyrite. Furthermore, the Cu-KBX complex exhibited a reticulated structure at the stibnite surface, while it adhered in a granular fashion on the arsenopyrite surface. FTIR analysis confirmed stronger chemisorption of Cu-KBX onto stibnite compared to arsenopyrite. XPS results indicated that Cu-S was the main collecting component. However, a weak Fe(II)-S substance was found on the arsenopyrite interface, likely due to Fe3+ oxidizing in the slurry with the Cu-KBX complex. Therefore, this disruption of the Cu-KBX complex structure by Fe3+ in arsenopyrite sharply reduced its adsorption on arsenopyrite, enhancing the selective separation of stibnite and arsenopyrite.

A dual-optimization building energy prediction framework based on improved dung beetle algorithm, variational mode decomposition and deep learning

Accurately predicting building energy consumption is essential for enhancing energy utilization efficiency in buildings. However, the inherent volatility and noise in building energy data, caused by diverse user behaviors and potential sensor errors, make significant challenges to energy consumption prediction. To address these issues, a dual-optimization framework (IDBO-VMD-IDBO-BiLSTM) for building energy consumption prediction, which incorporates improved dung beetle optimization algorithm (IDBO), variational mode decomposition (VMD), and bidirectional long short-term memory network (BiLSTM), was proposed. In this framework, IDBO firstly optimizes the VMD by adaptively determining its optimal parameters to decompose the original building energy consumption series into multiple intrinsic modal functions (IMFs) with smoother characteristics, thereby the effect of mitigating data noise. Then, each IMF component is predicted using the BiLSTM model, with IDBO selecting the optimal hyperparameters for BiLSTM. Finally, the individual predictions of each IMF are superimposed and reconstructed to yield the final predictions. To verify the framework’s effectiveness, real energy consumption data from an office building in Shanghai was collected and analyzed in a comprehensive comparison with seven other comparative models. Experimental results suggested that the proposed framework outperformed the comparative models in terms of root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R2), showing both high predictive accuracy and strong robustness. Therefore, the proposed framework can be an effective tool for predicting building energy consumption

Designer Helical Fibers and Tubes: Self-assembling Hybrid Peptides via Leu/Ile-Phe Zipper.

Here, we present a family of simple peptides that show diverse self-assembling behaviors. We used aliphatic (Leu/Ile) and aromatic (Phe) amino acids to delineate our design. The design consists of phenylene urea at the N-terminus of the peptide. The urea peptides with sequence Phe-LeuOMe (1) or Phe-IleOMe (2) associate to form polygonal peptide tubes via zipper arrangements, supported by microscopic and single crystal X-ray diffraction studies. The peptide with Phe placed away from the phenylene urea (3 and 4), showed fibrous assembly. All the peptides showed autofluorescence and red edge excitation behavior upon self-assembly.

Efficient Simulator for P2P Energy Trading: Customizable Bid Preferences for Trading Agents

Given the accelerating global movement towards decarbonization, the importance of promoting renewable energy (RE) adoption and ensuring efficient transactions in energy markets is increasing worldwide. However, renewable energy sources, including photovoltaic (PV) systems, are subject to output fluctuations due to weather conditions, requiring large-scale backup power to balance supply and demand. This makes trading electricity from large-scale PV systems connected to the existing grid challenging. To address this, peer-to-peer (P2P) energy markets where individual prosumers can trade excess power within their local communities have been garnering attention. This study introduces a simulator for P2P energy trading, designed to account for the diverse behaviors and objectives of participants within a market mechanism. The simulator incorporates two risk aversion parameters: one related to transaction timing, expressed through order prices, and another related to forecast errors, managed by adjusting trade volumes. This allows participants to customize their trading strategies, resulting in more realistic analyses of trading outcomes. To explore the effects of these risk aversion settings, we conduct a case study with 120 participants, including both consumers and prosumers, using real data from household smart meters collected on sunny and cloudy days. Our analysis shows that participants with higher aversion to transaction timing tend to settle trades earlier, often resulting in unnecessary transactions due to forecast inaccuracies. Furthermore, trading outcomes are significantly influenced by weather conditions: sunny days typically benefit buyers through lower settlement prices, while cloudy days favor sellers who execute trades closer to their actual needs. These findings demonstrate the trade-off between early execution and forecast error losses, emphasizing the simulator’s ability to analyze trading outcomes while accounting for participant risk aversion preferences.

Particle-stabilized foams with fatty acid salts as stabilizers triggered by transition metal ions

Particle-stabilized foams have been becoming the focus on foaming field because of their excellent stability due to the high stiffness of interface membrane. However, the modification of particle surfaces to reach an appropriate wettability needs complicated process, resulting in accompanied inconvenient foaming-defoaming regulation. In this work, we reported a series of foams stabilized by insoluble particles generated from fatty acid anions with metal ions. Different hydrocarbon chains endowed the formed particles diverse wettability and different aggregation behaviors at the gas–liquid interface, e.g., different foamability and the formation of the “dry water”. The foams exhibited ultra-stable properties and were responsive to environmental acidity, thus possessing a smart foaming-defoaming transition tuned by pH. The combination ability of fatty acid anions with metal ions changed with the chain-length and the nature of metal ions, leading to a choice for the extraction or separation of some metal ions in a fast and simple way. This new aggregation behavior of fatty acid salts can be explored to expand the practical applications.

Organogels of FmocFF: Exploring the Solvent-Dependent Gelmorphic Behavior.

FmocFF (9-fluorenyl methoxycarbonyl-phenylalanine) is an extensively studied low-molecular-weight hydrogel. Although there have been numerous studies on FmocFF hydrogel, its potential to form organogels has not been well explored. In this work, we systematically explore the organogels of FmocFF in a wide range of organic solvents. FmocFF is found to be a robust organogeltor, and the subsequent organogels exhibit diverse gelmorphic behavior exhibiting various degrees of crystallinity and morphology depending on the solvent used. The mechanical strength of the organogels is evaluated using rheology. A novel technique, in situ SHG microscopy, is introduced to study the gel structure in its native state. In addition to the solvent-solute interactions that are typically used to predict gelmorphic behavior, we observed indications that the degree of crystallinity also plays a significant role in determining the mechanical properties and structure of FmocFF organogels.

Sexual and reproductive health of Sudanese refugee girls in Chad: mixed methods study with perspectives from 12-19 year old girls, parents, and health workers.

In humanitarian settings, refugee girls' vulnerability to negative sexual and reproductive health (SRH) outcomes and the barriers they face to access to SRH services increase. Despite global guidelines on adolescent sexual and reproductive health and rights (SRHR) in humanitarian settings, evidence on the diverse knowledge, attitudes, and behaviors of refugee adolescents are limited. This mixed methods study used a cross-sectional survey and participatory research activities to explore the knowledge, attitudes, and behaviors of 12-19 year old refugee girls from Darfur living in two refugee camps in Wadi-Fira, Chad. Focus group discussions with parents of adolescents and in-depth interviews with health workers were conducted to better understand community attitudes toward adolescent SRHR and barriers to accessing services. Overall, SRH knowledge, including of contraceptive methods, was mixed, but older girls had better knowledge than younger girls. Despite stigma around adolescent sexual activity expressed in this community, 20.9% of girls had already had sex. The majority of girls believed that health workers would maintain confidentiality if they sought contraception. Among girls who had ever had sex, 18.0% were currently using a modern contraceptive. None were using a long-acting method, but most obtained their method at the camp health center. Parents and health workers described how social stigma toward premarital sex and unintended pregnancy impeded adolescent access to SRHR information and services, although the midwives described helping girls to seek contraception. Despite community stigma towards premarital sex and contraception for adolescents, some girls in the camps successfully managed to receive a contraceptive method, demonstrating both their interest in and need for contraception. Although midwives were largely supportive of adolescent access, expanding contraceptive service delivery channels and making services more adolescent-responsive would further increase adolescent access. Gender transformative programming engaging girls and boys, should be expanded to improve adolescent knowledge and self-efficacy with respect to SRHR. These efforts must also engage parents and community members to create an enabling environment for adolescent SRHR and reduce stigma.

Evolutionary Game Theory: A General Review

This article explores the Evolutionary Game Theory (now EGT), encompassing its historical underpinnings, recent advancements, and future potential. Originating in the 1970s through the pioneering work of John Maynard Smith and George R. Price, EGT leverages game-theoretic concepts to elucidate the evolution of strategies within various populations across biological, economic, and social domains. Notably, recent progress has seen the integration of advanced large language models (LLMs) such as GPT-3.5 and GPT-4 into agent-based simulations, thereby enriching the authenticity and intricacy of strategic interactions. Additionally, the study addresses the complexities associated with modeling diverse behaviors and bridging the insights derived from LLMs to practical applications in fields like biology, healthcare, education, and social sciences. Furthermore, it underscores the significance of interdisciplinary collaboration and innovative methodologies in addressing the multifaceted challenges within EGT. Finally, the article contemplates the potential avenues for future research, emphasizing the fusion of EGT with real-world applications and the necessity for comprehensive models that encompass the complexities of evolutionary dynamics in adaptive systems.

Global dynamics of a generalized arbitrary order Van der Pol–Duffing Oscillator

We study the global bifurcation diagram and corresponding global phase portraits in the Poincaré disc for a generalized van der Pol-Duffing oscillator, which has four nonlinear terms with arbitrary orders. This nonlinear oscillator possesses more diverse and complicated dynamical behaviours, including the heteroclinic bifurcation, generalized Hopf bifurcation and pitchfork bifurcation. Moreover, theoretical results are exhibited via numerical simulations.

The diversity of evolution behavior between stoichiometric and non-stoichiometric AlTM intermetallics in Mg melt

In this study, Al-5Ti, Al-18Si-5Ti, Al-12Si-2Sc and Al-12Si-1Sc-1Zr alloys, containing the Al3Ti, Ti7Al5Si12, AlSi2Sc2 and AlSi2(Sc, Zr)2 particles, respectively, were introduced into a Mg melt to understand the morphological and structural evolution behaviors of AlTM intermetallic compounds. Deposition layers at the bottom of the Mg melt were obtained due to the particle settlement. Scanning electron microscope and X-ray diffraction were employed to analyze the phase morphologies and structures of the AlTM intermetallic compounds in the deposition layers. A morphological transformation, characterized by the cracking of particles into fine clusters, was observed in all these AlTM intermetallic compounds, which is promoted by the inward diffusion of Mg atoms. The phase structures of the final particles remained Al3Ti and AlSi2Sc2 when Al-5Ti and Al-12Si-2Sc alloys were introduced into the Mg melt, indicating no structural evolution. However, structural evolutions were detected from Ti7Al5Si12 to Ti5Si4 and from AlSi2(Sc, Zr)2 to SiScZr when they were introduced in the Mg melt. It is hypothesized that the crystal structure, whether stoichiometric or non-stoichiometric, is closely related to the structural evolution behavior of AlTM intermetallic compounds. This work may provide new insights into phase control by introducing one type of metal matrix master alloy into another metal melt.

SPathDB: a comprehensive database of spatial pathway activity atlas.

Spatial transcriptomics sequencing technology deepens our understanding of the diversity of cell behaviors, fates and states within complex tissue, which is often determined by the fine-tuning of regulatory network functional activities. Therefore, characterizing the functional activity within tissue space is helpful for revealing the functional features that drive spatial heterogeneity, and understanding complex biological processes. Here, we describe a database, SPathDB (http://bio-bigdata.hrbmu.edu.cn/SPathDB/), which aims to dissect the pathway-mediated multidimensional spatial heterogeneity in the context of functional activity. We manually curated spatial transcriptomics datasets and biological pathways from public data resources. SPathDB consists of 1689868 spatial spots of 695 slices from 84 spatial transcriptome datasets of human and mouse, which involves 36 tissues, and also diseases such as cancer, and provides interactive analysis and visualization of the functional activities of 114998 pathways across these spatial spots. SPathDB provides five flexible interfaces to retrieve and analyze pathways with highly variable functional activity across spatial spots, the distribution of pathway functional activities along pseudo-space axis, pathway-mediated spatial intercellular communications and the associations between spatial pathway functional activity and the occurrence of cell types. SPathDB will serve as a foundational resource for identifying functional features and elucidating underlying mechanisms of spatial heterogeneity.

Furniture Design of Smart Art Classroom Based on Interactive Analysis of Intelligent Sensing and ITIAS

This paper explores the design and optimization of smart art classroom furniture, addressing the evolving needs of modern art education environments. With the integration of intelligent technologies and sensor networks, the research aims to enhance the functionality, flexibility, and user experience of classroom furniture. It highlights the limitations of traditional classroom furniture and the need for adaptive, multifunctional designs that accommodate diverse learning behaviors and physical requirements. Through a combination of ITIAS interactive analysis and mathematical modeling, the study proposes a framework for developing furniture that supports privacy protection, dynamic updates, and better engagement in art education. The findings emphasize the importance of incorporating ergonomic, aesthetic, and technological considerations to improve teaching and learning outcomes in art classrooms.

Mapping multi-regional functional connectivity of astrocyte-neuronal networks during behaviors.

Diverse behaviors rely on coordinated activity and multi-regional functional connectivity within astrocyte-neuronal networks. However, current techniques for simultaneously measuring astrocytic and neuronal activities across multiple brain regions during behaviors remain limited. We propose a multi-fiber solution that can simultaneously record activities of astrocyte-neuronal networks across multiple regions during behaviors. We employed cell-specific dual-color genetically encoded calcium indicators (GECIs) and multi-fiber photometry to simultaneously measure astrocytic and neuronal Ca2+ transients across multiple brain regions in freely behaving animals. Our findings demonstrate that both movements and sensory stimuli induce synchronized and highly correlated Ca2+ transients in astrocytes and neurons of freely behaving mice. In addition, we recorded astrocytic and neuronal Ca2+ transients from multiple brain regions during mouse behaviors. Our observations reveal heightened synchronization of astrocytic and neuronal Ca2+ transients across different brain regions during movements or sensory stimuli, indicating enhanced functional connectivity within brain-wide astrocyte-neuronal networks. Multi-fiber photometry, combined with cell-specific dual-color GECIs, represents a powerful approach for investigating astrocytic and neuronal activities across different brain regions during behaviors. This technique serves as a versatile tool for analyzing the multi-regional functional connectivity map of astrocyte-neuronal networks associated with specific behaviors.

Using non-continuous accelerometry to identify cryptic nesting events of Galapagos giant tortoises

BackgroundTriaxial accelerometers have revolutionized wildlife research by providing an unprecedented understanding of the behavior of free-living animals. Machine learning is often applied to acceleration data to classify diverse animal behaviors across taxa. However, the high frequency, continuous data collection typically favored for behavioral classification studies often generates very large data sets, which may inhibit remote data acquisition and make data storage challenging. Coarse-frequency sampling or non-continuous bursts of acceleration data reduce these problems. To analyze such data, a suite of variables that summarize key features of the behavior of interest can be generated. These variables can then be used in numerous classification approaches, accommodating variation in data collection methods or sampling regimes. We demonstrate the potential for non-continuous accelerometer data to identify long-duration behavior and employ machine learning to classify the nesting behaviors of the critically endangered eastern Santa Cruz giant tortoise (Chelonoidis donfaustoi).ResultsWe field validated 112 nesting events from 21 giant tortoises. We then derived summary statistics based on accelerometry (e.g., overall dynamic body acceleration, metrics comparing acceleration before and after the probable event) and used them as inputs for Random Forest and Boosted Regression Tree classification algorithms. Our models produced a harmonic mean of precision and sensitivity (F1-score) of 0.91. We tested the generality of our model and found that the model performs well when applied to both novel individuals and years. The most important variable in accurately classifying data sequences was the proportion of acceleration data bursts above an activity threshold followed by the average overall dynamic body acceleration value of the bursts.ConclusionsThese results demonstrate the feasibility and efficacy of using non-continuous accelerometer data to identify prolonged, biologically relevant behaviors in free-living wildlife. By using summary variables that do not require continuous sampling, this approach facilitates long-term monitoring of animal behavior. Similar methodology has potential to inform priority questions in ecology and conservation, such as predicting wildlife responses to climate change and identifying critical habitats, with applications across diverse species and behaviors.