Empirical Case Study Research Articles

Beyond terrorist attacks: the impact of jihadist non-state actors on foreign policy

ABSTRACT What is the impact of jihadist non-state actors (JNAs) on foreign policy? We theorize that JNA operations follow a strategic logic, designed to create an unstable environment conducive to JNA survival. Employing an empirical case study using open-source intelligence (OSINT) data on Libyan foreign policy since the Arab Spring (2011), we demonstrate this logic, showing how through their violent and subversive activities, JNAs in Libya have strategically forced government constituents to overtly focus on security and counterterrorism while subsequently marginalizing more salient developmental concerns such as economic, social, and governance issues. The findings highlight how JNAs are incentivized to perpetuate violence and instability in their countries of operation and demonstrate how Western foreign policies are subsequently influenced by the presence and impact of JNAs in other countries as security concerns, particularly the continued threat of international terrorism, are prioritized. The findings have numerous policy implications, demonstrating the significant influence JNAs continue to have on the global foreign policy landscape, particularly on global counterterrorism efforts.

TraitTrainR: Accelerating large-scale simulation under models of continuous trait evolution

Abstract Motivation The scale and scope of comparative trait data are expanding at unprecedented rates, and recent advances in evolutionary modeling and simulation sometimes struggle to match this pace. Well-organized and flexible applications for conducting large-scale simulations of evolution hold promise in this context for understanding models and more so our ability to confidently estimate them with real trait data sampled from nature. Results We introduce TraitTrainR, an R package designed to facilitate efficient, large-scale simulations under complex models of continuous trait evolution. TraitTrainR employs several output formats, supports popular trait data transformations, accommodates multi-trait evolution, and exhibits flexibility in defining input parameter space and model stacking. Moreover, TraitTrainR permits measurement error, allowing for investigation of its potential impacts on evolutionary inference. We envision a wealth of applications of TraitTrainR, and we demonstrate one such example by examining the problem of evolutionary model selection in three empirical phylogenetic case studies. Collectively, these demonstrations of applying TraitTrainR to explore problems in model selection underscores its utility and broader promise for addressing key questions, including those related to experimental design and statistical power, in comparative biology. Availability and implementation TraitTrainR is developed in R 4.4.0 and is freely available at https://github.com/radamsRHA/TraitTrainR/, which includes detailed documentation, quick-start guides, and a step-by-step tutorial.

Design of Oral English Teaching Quality Evaluation System Based on AHP-Fuzzy Algorithm

In the evolving landscape of English oral language instruction, the integration of online and offline (blended) teaching methodologies has emerged as a significant approach to enhance learning outcomes. This study addresses the need for a robust and nuanced evaluation framework capable of assessing the quality of blended English oral language teaching. Motivated by the limitations of traditional evaluation methods in capturing the complexities and subjective nuances of blended teaching environments, we propose a novel evaluation model that leverages the Analytic Hierarchy Process (AHP) and Fuzzy Association Analysis. This paper outlines a systematic approach to construct a comprehensive evaluation indicator system tailored to the specific demands of English oral language teaching. Utilizing AHP, we determine the relative weights of various evaluation indicators, laying a solid foundation for a structured assessment framework. Subsequently, Fuzzy Association Analysis is employed to handle the inherent uncertainties and subjectivities associated with teaching quality assessment, thereby facilitating a more accurate and holistic evaluation. An empirical case study is presented to demonstrate the practical application of our proposed AHP-Fuzzy model in evaluating the quality of a specific blended English oral language teaching program. The results underscore the model’s effectiveness in providing a detailed and nuanced assessment of teaching quality, highlighting its superiority over traditional evaluation methods in addressing the complexities of blended teaching environments.

The Effectiveness of Digital Learning Platforms in Enhancing Student Engagement and Academic Performance

This review explores the factors influencing the effectiveness of digital learning platforms (DLPs) in enhancing student engagement and academic performance. It identifies key factors such as technological infrastructure, the role of instructors, and student characteristics, which significantly impact the effectiveness of DLPs. Technological infrastructure, including platform availability, network connectivity, and device requirements, plays a crucial role in ensuring accessibility and usability. The instructor's role in adopting active teaching strategies and providing personalized support is also essential for maximizing student success in digital learning environments. Furthermore, student characteristics such as prior knowledge, learning preferences, and technological proficiency influence their ability to engage with and benefit from DLPs. The review presents evidence from empirical studies, case studies, and meta-analyses that demonstrate the positive impact of digital platforms on student engagement and academic outcomes, particularly in blended learning environments. The discussion emphasizes the importance of personalized learning and the need for further research in areas such as cross-cultural comparisons, long-term effectiveness, and equity in access to technology. Recommendations for higher education institutions to optimize the use of DLPs and ensure equitable access for all students are also provided.

Abundance-mediated species interactions.

Species interactions shape biodiversity patterns, community assemblage, and the dynamics of wildlife populations. Ecological theory posits that the strength of interspecific interactions is fundamentally underpinned by the population sizes of the involved species. Nonetheless, prevalent approaches for modeling species interactions predominantly center around occupancy states. Here, we use simulations to illuminate the inadequacies of modeling species interactions solely as a function of occupancy, as is common practice in ecology. We demonstrate erroneous inference into species interactions due to error in parameter estimates when considering species occupancy alone. To address this critical issue, we propose, develop, and demonstrate an abundance-mediated interaction framework designed explicitly for modeling species interactions involving two or more species from detection/non-detection data. We present Markov chain Monte Carlo (MCMC) samplers tailored for diverse ecological scenarios, including intraguild predation, disease- or predator-mediated competition, and trophic cascades. Illustrating the practical implications of our approach, we compare inference from modeling the interactions in a three-species network involving coyotes (Canis latrans), fishers (Pekania pennanti), and American marten (Martes americana) in North America as a function of occupancy states and as a function of abundance. When modeling interactions as a function of abundance rather than occupancy, we uncover previously unidentified interactions. Our study emphasizes that accounting for abundance-mediated interactions rather than simple co-occurrence patterns can fundamentally alter our comprehension of system dynamics. Through an empirical case study and comprehensive simulations, we demonstrate the importance of accounting for abundance when modeling species interactions, and we present a statistical framework equipped with MCMC samplers to achieve this paradigm shift in ecological research.

A Comprehensive Analysis of The Retail Giant: An In-Depth Study of Costco

As a leader in the US membership-based warehouse retail chain, Costco provides consumers with high-quality and low-price goods through high-volume procurement and a streamlined supply chain and cultivates a loyal customer base through the membership fee model. This study provides an in-depth analysis of Costco's market performance, competitive advantages, SWOT analysis, and risk assessment to evaluate the company's operating conditions and development prospects comprehensively. The financial data shows that Costco's revenue continues to grow, and profitability is solid. However, it still faces increasing competitive pressure from online retailers and risk factors such as economic volatility and supply chain disruptions. According to the study, Costco should actively invest in digital transformation, explore multichannel retail strategies, and focus on expansion opportunities in emerging markets. At the same time, companies must continue to focus on cost control and operational efficiency and strengthen cybersquatting and sustainability initiatives. Through internal and external training, Costco can consolidate its leading position in the retail industry, navigate the rapidly changing market environment, and achieve long-term stable growth. Based on empirical analysis and case studies, this study aims to objectively evaluate Costco's strengths and weaknesses and provide reference suggestions for its future development.

Application of Mixed-Mode Ventilation to Enhance Indoor Air Quality and Energy Efficiency in School Buildings

Indoor air quality and energy efficiency are instrumental aspects of school facility design and construction, as they directly affect the physical well-being, comfort, and academic output of both pupils and staff. The challenge of balancing the need for adequate ventilation to enhance indoor air quality with the goal of reducing energy consumption has long been a topic of debate. The implementation of mixed-mode ventilation systems with automated controls presents a promising solution to address this issue. However, a comprehensive literature review on this subject is still missing. To address this gap, this review examines the potential application of mixed-mode ventilation systems as a solution to attaining improved energy savings without compromising indoor air quality and thermal comfort in educational environments. Mixed-mode ventilation systems, which combine natural ventilation and mechanical ventilation, provide the versatility to alternate between or merge both methods based on real-time indoor and outdoor environmental conditions. By analyzing empirical studies, case studies, and theoretical models, this review investigates the efficacy of mixed-mode ventilation systems in minimizing energy use and enhancing indoor air quality. Essential elements such as operable windows, sensors, and sophisticated control technologies are evaluated to illustrate how mixed-mode ventilation systems dynamically optimize ventilation to sustain comfortable and healthy indoor climates. This paper further addresses the challenges linked to the design and implementation of mixed-mode ventilation systems, including complexities in control and the necessity for climate-adaptive strategies. The findings suggest that mixed-mode ventilation systems can considerably lower heating, ventilation, and air conditioning energy usage, with energy savings ranging from 20% to 60% across various climate zones, while also enhancing indoor air quality with advanced control systems and data-driven control strategies. In conclusion, mixed-mode ventilation systems offer a promising approach for school buildings to achieve energy efficiency and effective ventilation without sacrificing indoor environment quality.

The Swinging Pendulum of the Game Nations Play in International Relations: A Duplicity Diplomacy

Purpose: This paper endeavours to critically interrogate the concept of "duplicity diplomacy," positing it as an essential framework for understanding the oscillating strategies employed by states amidst the complexities of contemporary international relations. The aim is to unveil the inherent contradictions and strategic necessities that underlie this phenomenon, particularly in the context of an increasingly multipolar global landscape. Materials and Methods: The research employs a qualitative methodology that integrates a multi-faceted analysis of empirical case studies involving significant geopolitical players. This approach encompasses semi-structured interviews with seasoned diplomats to capture experiential insights, a comprehensive content analysis of diplomatic communications to identify patterns and nuances in language and intent, as well as systematic reviews of contemporaneous media portrayals to contextualize public perceptions. By triangulating these methods, the study aims to enrich the understanding of duplicity in statecraft. Findings: The findings indicate that duplicity diplomacy transcends a mere catalogue of deceit, emerging as a strategic necessity reflective of the intricate complexities and contradictions that characterize our current multipolar world. Empirical illustrations reveal how states frequently oscillate between cooperative rhetoric and competitive actions, engaging in ostensible displays of loyalty while simultaneously pursuing self-interested goals through subterfuge. Notably, instances of strategic alliances undermined by covert operations serve to illustrate the double-edged nature of duplicity, where short-term gains often precipitate long-term repercussions that undermine international trust and stability. Implications to Theory, Practice and Policy: The analysis raises substantial questions regarding the ethical frameworks governing diplomatic practice in the context of growing global interdependence. It posits that as the stakes of international interactions escalate, the propensity for duplicity in state behaviour is likely to intensify, necessitating a critical re-evaluation of both scholarly and practical approaches to diplomacy. This discourse invites policymakers and scholars alike to consider how established norms around transparency and accountability can be reconciled with the inherent contradictions of statecraft.

Optimized design and comparative analysis of double-glazed photovoltaic windows for enhanced light harvesting and energy efficiency in cold regions of China

This study investigates the daylighting performance and energy efficiency optimization strategies of double-glazed photovoltaic windows (DS-STPV) in cold regions of China. By conducting a comprehensive comparative analysis with traditional and energy-efficient window systems, this research aims to identify high-efficiency building solutions tailored to extreme climatic conditions. Amidst the escalating global energy demand and the pressing need for energy conservation and emission reduction, Building-Integrated Photovoltaic (BIPV) technology is increasingly recognized for its potential and value as a critical method for harnessing green energy. However, the widespread adoption of BIPV technology faces several challenges, including cost-effectiveness, conversion efficiency, system stability, and architectural aesthetic integration. Utilizing the T&A House from the 3rd International Solar Decathlon as an empirical case study, this research employs Ecotect and DesignBuilder simulation software to systematically evaluate the daylighting effect and energy performance of DS-STPV. The analysis considers various key design parameters, including photovoltaic cell coverage, window orientation, and window-to-wall ratio. Through refined modeling and multi-dimensional analysis, this study aims to identify the optimal design configurations of DS-STPV windows in cold regions, with the goal of simultaneously achieving superior natural lighting quality and significant building energy efficiency. The findings indicate that a south-facing DS-STPV window design with approximately 30% photovoltaic cell coverage and a window-to-wall ratio of 30% effectively balances daylighting requirements and energy efficiency in cold regions of China. This design strategy not only ensures an abundance of natural light in the room, but also significantly reduces the building’s energy consumption, proving the superior performance of DS-STPV windows in cold climates. In addition, the unique optical properties of DS-STPV windows reduce glare, further improving the overall quality of the indoor environment. In summary, this study provides a robust scientific foundation for the application of DS-STPV windows in cold regions, offering practical guidance and reference for optimizing energy efficiency and facilitating green transformation in future building design.

Understanding hybridity governance in Africa: A theoretical framework for hybrid structures, policies, and practices

The amalgamation of traditional and modern governance systems represents a transformative advancement in contemporary African government. This study examines the concept of "hybridity governance" within the African context, providing a comprehensive theoretical framework that integrates modern institutional procedures with traditional governance systems. The paper examines the evolution of African governance from pre-colonial to post-colonial eras, illustrating the emergence of hybrid governance as a response to internal dynamics and external influences. It accomplishes this by utilising historical studies. The philosophical underpinnings are investigated, highlighting organisational and social concepts that elucidate the acceptance of hybrid models. The paper elucidates the applications, accomplishments, and challenges of hybrid government in several African nations through various empirical case studies. A critical examination examines the consequences of these governance paradigms for public administration in Africa. It underscores opportunities for innovation and enhanced flexibility, while also emphasising intrinsic challenges such as job ambiguity and conflict. The report concludes with forecasts regarding the future trajectory of hybrid governance in Africa, considering the interplay of local socio-political dynamics, global interconnectedness, and technological advancements. This article serves as a vital resource for policymakers, researchers, and stakeholders interested in the evolving governance paradigm in Africa, providing valuable applications and recommendations.

Redefining Newsrooms: The Role of Artificial Intelligence in Shaping Journalism's Future

This paper seeks to justify why journalism is at the cutting edge of this revolution with Artificial Intelligence (AI); it has become an essential component of various industries. In this study, the author examines the effects of the integration of AI into the current news media industry with an emphasis on the use of AI in executing analytic functions such as writing and verification of information. As for strong points and prospects, AI contributes improvements in terms of effectiveness and time efficiency, yet it brings into focus certain drawbacks concerning ethical aspects of carrying fake news, distorted information, etc., alongside the problem of algorithms’ credibility of algorism-based journalism. In this empirical qualitative case study, the author examines the benefits and limitations of AI’s implementation in journalism. On this point, it presents literature gaps that exist on the effects of AI on ethical considerations and looks at how such organizations can balance the need to use technology and still produce work of the highest ethical standards. According to the findings, while AI may step up the accuracy and credibility that is required in news dissemination, its abuse may also lead to increased biased and fake news. Overall, this research calls for adopting AI technologies in newsrooms, but only after putting this article’s findings into practice alongside human oversight and ethics. Finally, the paper concludes the future of AI journalism and emphasizes sustainable and more responsible ways to create technological advancement within the field of Journalism to make more truthful and reliable news.

Green, Alternative or Business as Usual? Critical Geographies of Sustainable Finance

AbstractIn recent years, a growing number of contributions on green finance have emerged, not only within economic geography but also increasingly from disciplines beyond it. With this Special Issue, we aim to engage with the ongoing debate around green and sustainable finance and its challenges, including concerns over greenwashing. Our extended editorial provides structure to this complex discussion by identifying four primary strands of literature that frame the field. Two of these strands adopt a more critical stance, combining analytical approaches and rigorous assessments that question the impact and ‘authenticity’ of green and sustainable finance schemes, approaches and policies. Most of the contributions and empirical case studies featured here align with these critical perspectives, which are introduced in greater depth in the second part of this editorial.

Creativity in crisis communication: a systematic review

This systematic review explores the concept of creativity in crisis communication, a widely used but poorly conceptualised phenomenon. It draws on empirical research and case studies to highlight the diversity of creative approaches during major crises such as natural disasters, health crises, or conflicts. The focus is particularly on situations where these crises are protracted, and creativity is required to deal with the “fog of warning” caused by repeated alerts. It is also important to exercise creativity in order to control the meaning of information and combat the spread of misinformation and conspiracy theories. Creativity in crisis communication during major crises is described as the dynamic generation of novel and contextually relevant strategies using unconventional or metaphorical elements to engage audiences, reshape their understanding, and drive behavioural change for effective crisis resolution and recovery. The study provides a framework for understanding creativity in crisis communication by synthesising existing academic knowledge. Using the preferred reporting items for systematic reviews and meta-analyses methodology, 42 articles were analysed and categorised into three areas of communication: meaning making, behaviour framing, and reputation preservation. The framework illustrates the multiple facets of creativity: metaphorical creativity promotes critical reflection; visual representation enhances accessibility; humour and creative solutions engage and motivate diverse audiences; artistic elements, serious games and creative scenarios facilitate behaviour change. This framework underscores the critical role of creativity in crisis contexts, bridging the gap between standard communication and innovative, contextually relevant strategies in crisis communication strategies.

Comparison of Distributed and Parallel Machine Learning: Efficiency and Effectiveness in Large-Scale Data Processing

Abstract. As a matter of fact, with the exponential growth of data, machine learning (ML) techniques have increasingly relied on distributed and parallel computing to handle large-scale problems. With this in mind, this paper provides a comparative analysis of distributed and parallel machine learning methodologies, focusing on their efficiency and effectiveness in processing large datasets. To be specific, this study will discuss as well as contrast key models and frameworks within both paradigms, assessing their performance based on computational cost, scalability, and accuracy. Through empirical evidence and case studies, this research will highlight the strengths and limitations of each approach. According to the analysis, the findings indicate that while distributed machine learning excels in scalability as well as fault tolerance, parallel machine learning offers superior computational speed for smaller-scale tasks. Overall, the insights from this study are crucial for researchers and practitioners seeking to optimize ML workflows for large-scale data environments.

Transforming Public Policy in Developing Countries: A Comprehensive Review of Digital Implementation

This article discusses the process of digitization in the implementation of public policy within developing countries, its critical success factors, challenges, and lessons learned from case studies. Public administration can improve efficiencies, transparency, and effectiveness through the infusion of digital technologies into processes implicated in the making, execution, and evaluation of public policies. Key success factors identified include robust digital infrastructure, clear strategic vision and leadership, effective stakeholder engagement, capacity building, and a supportive regulatory framework. In light of these potential benefits, developing countries experience enormous challenges such as inadequate infrastructure, political instability, socio-political resistance, and gaps in digital literacy. Through a review of empirical case studies from countries such as Estonia, South Korea, China, Bangladesh, and Rwanda, the article identifies key lessons that can inform the digital strategies of other developing nations. The discussion addresses the challenges of sustaining digital infrastructure, including ongoing maintenance costs, the need for continuous technology updates, and the risks of obsolescence. Furthermore, the article explores the significance of capacity building, public-private partnerships, and evolving regulatory frameworks in ensuring the long-term viability of digital policies. The future directions proposed underscore the necessity of adopting adaptable, resilient, and well-governed digital infrastructures to fully realize the benefits of digital public policy in the context of developing countries.

AI Frontier: Envisioning the Future Landscape of Artificial Intelligence in Events and Festivals

Amidst rapid growth and increasing complexity, the events and festivals industry is undergoing a significant transformation through the integration of Artificial Intelligence (AI) technologies. This paper investigates the impact of AI on the events landscape, examining its current applications, emerging trends, and future implications. The research questions address AI's current use in events, emerging trends, and potential future transformations. The study highlights AI's potential to improve attendee experiences, streamline event management, and enhance safety measures, offering deep insights into its substantial industry impact. Using a comprehensive research methodology, this paper explores the complex relationship between AI and events/festivals using empirical research, industry analysis, and case studies. It employs frameworks like AI-driven engagement tools, predictive analytics, and immersive experiences to assess AI's impact on the events landscape. Key findings highlight AI's ability to personalize experiences, optimize logistics, and enhance safety and sustainability. The paper also addresses challenges like data privacy, algorithmic bias, and ethical considerations, emphasizing the need for responsible AI deployment and risk mitigation. By offering best practices and ethical guidelines, this research aims to guide industry stakeholders in integrating AI effectively, steering the events and festivals industry towards greater innovation and efficiency.

Innovative cross-platform health applications to improve accessibility in underserved communities

The development of innovative cross-platform health applications has the potential to transform healthcare delivery in underserved communities by increasing accessibility, reducing disparities, and improving health outcomes. These applications are designed to operate across multiple devices and operating systems, ensuring that people in marginalized or remote areas with limited technological infrastructure can still access essential healthcare services. This study explores the development and implementation of cross-platform mobile health (mHealth) applications that provide features such as telemedicine consultations, remote patient monitoring, health education, and digital medical records. By leveraging cloud computing, artificial intelligence, and data analytics, these applications can offer personalized health solutions, support proactive health management, and facilitate timely medical interventions. Furthermore, cross-platform compatibility reduces barriers related to device ownership and connectivity, which are common challenges in underserved areas. This research also examines user adoption, challenges in technological integration, data privacy concerns, and potential strategies for achieving sustainable engagement within these communities. Through empirical analysis and case studies, the findings highlight how cross-platform health applications can bridge the gap in healthcare access, contributing to a more equitable healthcare system and supporting the United Nations Sustainable Development Goal of health for all. Keywords: Healthcare Accessibility, Digital Health, Cloud Computing, Health Equity, Sustainable Healthcare.

Testing relationships between multiple regional features and biogeographic processes of speciation, extinction, and dispersal.

The spatial and environmental features of regions where clades are evolving are expected to impact biogeographic processes such as speciation, extinction, and dispersal. Any number of regional features (such as elevation, distance, area, etc.) may be directly or indirectly related to these processes. For example, it may be that distances or differences in elevation or both may limit dispersal rates. However, it is difficult to disentangle which features are most strongly related to rates of different processes. Here, we present an extensible Multi-feature Feature-Informed GeoSSE (MultiFIG) model that allows for the simultaneous investigation of any number of regional features. MultiFIG provides a conceptual framework for incorporating large numbers of features of different types, including categorical, quantitative, within-region, and between-region features, along with a mathematical framework for translating those features into biogeographic rates for statistical hypothesis testing. Using traditional Bayesian parameter estimation and reversible-jump Markov chain Monte Carlo, MultiFIG allows for the exploration of models with different numbers and combinations of feature-effect parameters, and generates estimates for the strengths of relationships between each regional feature and core process. We validate this model with a simulation study covering a range of scenarios with different numbers of regions, tree sizes, and feature values. We also demonstrate the application of MultiFIG with an empirical case study of the South American lizard genus Liolaemus, investigating sixteen regional features related to area, distance, and elevation. Our results show two important feature-process relationships: a negative distance/dispersal relationship, and a negative area/extinction relationship. Interestingly, although speciation rates were found to be higher in Andean versus non-Andean regions, the model did not assign significance to Andean- or elevation-related parameters. These results highlight the need to consider multiple regional features in biogeographic hypothesis testing.

Digital transformation in journalism: Navigating big data governance and ethical challenges

Digital transformation is reshaping journalism, driven by advancements in big data analytics, artificial intelligence (AI), and digital platforms. These technologies offer new opportunities for dynamic storytelling, audience engagement, and content personalization. However, they also present challenges related to data governance, privacy, and ethical standards. As news organizations increasingly rely on data-driven practices, they must navigate complex regulatory landscapes and address ethical dilemmas to maintain credibility and public trust. This paper proposes an integrated framework combining big data governance with journalistic ethics and examines empirical case studies of leading news organizations to illustrate the key challenges and strategies for ensuring responsible and effective use of data.

A 'how-to' guide for estimating animal diel activity using hierarchical models.

Animal diel activity patterns can aid understanding of (a) how species behaviourally adapt to anthropogenic and natural disturbances, (b) mechanisms of species co-existence through temporal partitioning, and (c) community or ecosystem effects of diel activity shifts. Activity patterns often vary spatially, a feature ignored by the kernel density estimators (KDEs) currently used for estimating diel activity. Ignoring this source of heterogeneity may lead to biased estimates of uncertainty and misleading conclusions regarding the drivers of diel activity. Thus, there is a need for more flexible statistical approaches for estimating activity patterns and testing hypotheses regarding their biotic and abiotic drivers. We illustrate how trigonometric terms and cyclic cubic splines combined with hierarchical models can provide a valuable alternative to KDEs. Like KDEs, these models accommodate circular data, but they can also account for site-to-site and other sources of variability, correlation amongst repeated measures, and variable sampling effort. They can also more readily quantify and test hypotheses related to the effects of covariates on activity patterns. Through empirical case studies, we illustrate how hierarchical models can quantify changes in activity levels due to seasonality and in response to biotic and abiotic factors (e.g. anthropogenic stressors and co-occurrence). We also describe frequentist and Bayesian approaches for quantifying site-specific (conditional) and population-averaged (marginal) activity patterns. We provide guidelines and tutorials with detailed step-by-step instructions for fitting and interpreting hierarchical models applied to time-stamped data, such as those recorded by camera traps and audio recorders. We conclude that this approach offers a viable, flexible, and effective alternative to KDEs when modelling animal activity patterns.