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Understanding how older adults organize their daily lives is crucial for developing person-centered homecare systems. This study proposes an interpretable framework for modeling daily life and detecting behavioral anomalies using data from 18 CASAS smart homes. The dataset contains several weeks of continuous sensor recordings from residents living independently. Daily activity patterns were analyzed in 15-minute intervals using principal component analysis (PCA) to identify key temporal patterns shared by the population. For each resident, a personal baseline routine was defined as the median of their daily activity profiles over a 14-day baseline period, and deviations from this baseline were compared with global deviations derived from the PCA model. The results revealed explainable behavioral differences among residents and highlighted three lifestyle archetypes like active bimodal, stable routine, and early resting. By linking the difference scores to contextual activities such as sleep, hygiene, and computer use, the framework provides relevant explanations for daily irregular behaviors.

In today's landscape of rapidly evolving software engineering, managers face a multitude of challenges and complex decision-making scenarios. Within the Swedish Transport Administration (STA), software engineering managers take on dual roles as both strategists and traditional managers, increasing the complexity of their decision-making environment. We investigate how management teams in this context can use and adopt agile practices for better decision-making. The aim is to explore if agile software development team practices can be used for software engineering management teams, with the goal of identifying agile success factors that can be mapped to management success. We employ an industrial case study with a mixed-method research approach, combining quantitative agile data using project tracking software, and qualitative data using structured interviews with the management teams. Unlike previous research that has primarily examined agile adoption within software development teams or emphasized the manager's supportive role, this work investigates management teams themselves as adopters of agile practices and metrics. To our knowledge, this is the first study to develop a mapping model that systematically connects Scrum-based practices, roles, and metrics to the context of software engineering management teams. Our study showed that Scrum-based agile practices, such as stand-ups and retrospectives, can be adapted to software engineering management teams, and that certain agile software development metrics can be transformed into a managerial setting using our proposed model. © 2026 The Author(s). Journal of Software: Evolution and Process published by John Wiley & Sons Ltd.

Introduction This study examines the role of interactivity in virtual learning environments (VLEs), focusing on the integration of synchronous and asynchronous modalities in shaping educational presence.Methods A mixed-methods design was applied, combining survey data, observational analysis, and interaction records from a Moodle-based course with 115 undergraduate Communication students. Variables included participation patterns, cognitive engagement, and access to technological resources.Results Synchronous interaction enhances teaching and social presence through immediacy and real-time feedback, while asynchronous interaction supports cognitive presence by fostering reflection and deeper engagement. These effects are conditioned by communication strategies and contextual factors, including technological access and socioeconomic conditions.Discussion Interactivity emerges as a context-dependent process rather than a direct causal mechanism. The study proposes the concept of Strategic Educational Presence, extending the Community of Inquiry framework by incorporating a communicational and strategic dimension. This approach underscores the alignment of interaction modalities, pedagogical design, and contextual conditions to support meaningful learning. Limitations include a single institutional context and reliance on self-reported data. Future research should test this framework across disciplines using longitudinal and multivariate approaches.

While humanity stands at a critical point—one future leading toward sustainability, equity, and resilience, the other toward escalating conflicts, ecological collapse, and irreversible loss—climate change emerges as one of the most urgent challenges of the 21st century. The Global South, specifically the northwestern South American region, lacks model confidence and reports on current climatic conditions due to gaps in historical data. This study, therefore, presents temperature and precipitation trends in the highest city on the equator, Quito, Ecuador, from 2004–2024. Six different districts were analyzed for maximum, average, and minimum temperatures, as well as cumulative precipitation, in terms of monthly and annual statistics, using Seasonal-Trend Decomposition. Over the past two decades, this Andean city has warmed by an average of +0.95 °C, with minimum temperatures rising at rates twice the global urban average of extreme urban heat islands (+2.47 °C), while precipitation has nearly doubled in rapidly developing parts of the city. These profound changes, shaped by urban expansion, El Niño–Southern Oscillation variability, and climate change, demand urgent adaptation in water management, urban planning, and climate resilience strategies, as well as comparative studies with rural Ecuador to differentiate local vs. regional climate signatures.

The planet is experiencing global warming, with an increasing number of heat waves worldwide. Cities are particularly affected by the high temperatures because of the urban heat island (UHI) effect. This phenomenon is mostly explained by the land cover changes, reduced green spaces, and the concentration of infrastructure in urban settings. However, the reasons for the UHI are complex and involve multiple factors still understudied. Air pollution is one of them. This work investigates the link between particulate matter ≤2.5 μm (PM_2.5) and air temperature by convergent cross-mapping (CCM), a statistical method to infer causation in dynamic non-linear systems. A positive correlation between the concentration of fine particulate matter and urban temperature is observed. The causal relationship between PM_2.5 and temperature is confirmed in the most urbanized areas of the study site (Quito, Ecuador). The results show that (i) the UHI is present even in the most elevated capital city of the world, and (ii) air quality is an important contributor to the higher temperatures in urban than outlying areas. This study supports the hypothesis of a non-linear threshold effect of pollution concentration on urban temperature.

INTRODUCTION: This study investigates the bidirectional causal interplay between PM_2.5 and relative humidity (RH) in Quito, Ecuador. Focusing on a high-altitude city with complex terrain, the objective is to understand pollution-climate feedbacks over a two-decade span.

METHODS: The study employs Convergent Cross Mapping (CCM), a nonlinear empirical dynamic modeling approach. Hourly data were analyzed across four districts in Quito across two distinct time periods: 2004-2005 versus 2022-2024. Robustness of causality was confirmed using surrogate testing techniques.

RESULTS: The analysis reveals statistically significant, nonlinear, and time-variant couplings. While RH influenced PM_2.5 in the early 2000s, the relationship inverted, with PM_2.5 increasingly driving RH by the early 2020s. Partial-derivative analyses indicate shifting interaction signs and strengths. Notably, pollution was found to increasingly suppress RH, particularly in northern districts.

DISCUSSION: The observed suppression of RH by pollution is consistent with urban heat island amplification and radiative effects. These findings underscore the necessity of nonlinear causality frameworks for understanding environmental feedbacks in complex terrains. The study highlights the need for integrated air quality and climate strategies. Future research should expand variables and monitoring sites to further generalize these findings.

The global growth of the older adult’s population highlights the urgent need for intelligent privacy-preserving homecare systems that can monitor daily activities and detect behavioral deviations. We propose a comprehensive framework that combines a Transformer-based deep learning model for human activity recognition with a rule-based, interpretable routine deviation detection system. Leveraging the SDHAR-HOME dataset, which contains multi-sensor time series data from two users, the framework first classifies daily activities using a transformer encoder and then constructs a personalized behavioral baseline to identify deviations such as missed meals, sleep disturbances, and unusual hygiene habits. Results demonstrate high classification accuracy (up to 98.5%) and validate the effectiveness of conventional monitoring methods through detailed visualization and semantic deviation labeling. This dual-strategy framework is particularly suitable for assistive monitoring applications in homecare settings.

Among the challenges the human population needs to address are threats of global pandemics, increasing socioeconomic inequality, especially in developing countries, and anthropogenic climate change. The latter's effect has been amplified with the arrival of 2023/24 El Ni & ntilde;o, causing an exceptional drought in the Amazon basin, significantly affecting fire conditions and hydroelectric power production in several South American countries, including Ecuador. This study analyzes five criteria pollutants-carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and particulate matter <= 2.5 mu m (PM2.5)-during 2019-2024 in Quito, Ecuador, a high-elevation tropical metropolis. Despite long-term efforts to regulate emissions, air pollution levels continue to rise, driven by overlapping crises, including energy shortages, political unrest, and extreme weather events. The persistent failure to improve air quality underscores the vulnerability of developing nations to climate change-induced energy instability and the urgent need for adaptive, diversified, and resilient future energy planning. Without immediate shifts in climate adaptation policies, cities like Quito will continue to experience worsening air quality, with severe implications for public health and environmental sustainability.

The concept of digital twin has captured significant attention in recent years and its potential application within the domain of home care has been explored in several studies. This review endeavors to provide a comprehensive overview of digital twin technology and its applications in the realm of home care, delineating the key attributes and challenges entailed in their implementation. A systematic search was conducted across five databases, namely ACM digital library, IEEE Xplore, PubMed, Scopus, and Web of Science. Findings from forty-five included articles were categorized employing a systematic approach, highlighting the technology’s deployment in remote older adults’ care monitoring, health issue prediction and personalized treatment planning. Furthermore, this review identified challenges of integrating digital twins into the home care sector. Despite recognition of its potential, there is a distinct lack in the literature of in-depth studies specifically exploring the implementation of digital twin technology in home care, highlighting the need for further research.