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Small mustelids are increasingly recognized as species requiring conservation attention. In recent years, several camera-based methodologies have been developed to study them, but studies comparing different methods are still rare. To identify the most effective method to study small mustelid populations, we compared two camera-based monitoring methods in the Italian Alps. We also examined the effects of sampling session and habitat type on the occupancy probability and tested the “umbrella effect” of these methods for rodents. After superimposing a 700 × 700 m grid on an Alpine valley (Maritime Alps Natural Park, northwestern Italy), we surveyed 36 cells over three separate 45-day sessions from June to October 2023. In each cell, we employed (1) an “Alpine Mostela”, a foldable PVC box containing a camera trap and a PVC 9 cm Ø tube, and (2) a stand-alone trail camera. All devices were located at least 150 m from the others, and salmon oil was used as bait in half of the cells. To compare the methods, we used a single-season Bayesian occupancy model. The detection probability of stoats was higher with unbaited Alpine Mostelas and baited external cameras. We found the highest occupancy probability in the second session and non-forested habitats. Bait use positively affected the number of non-target videos. In this study, unbaited Alpine Mostelas and baited external cameras demonstrated reliable performance in detecting stoats. However, with the Alpine Mostela accomplishing slightly better results with much fewer non-target videos, it emerged as the preferred choice for long-term stoat monitoring.

Inclusion is an area of strategic importance in Aotearoa New Zealand and internationally, requiring that all learners have access to quality education. Nonetheless, children with disabilities in our schools are often excluded from rich learning opportunities, particularly in mathematics. Teaching through inclusive pedagogies, by contrast, supports teachers to plan for and teach all children; however, we know little about how teachers may apply these approaches to their teaching of mathematics. In this article, we report on a case study of one teacher’s practice as we develop a ‘research lesson’ approach that enhances the engagement and learning of all children, including those who are most at risk of being excluded in mathematics lessons. Participants described challenges to inclusive teaching mathematics such as differentiation, engaging contexts, children’s experience of anxiety, and children’s comfort with oral language and discussion. We found that research lesson reflections enabled solutions to be found for these challenges as part of the process of identifying them. We argue that an approach focussed on inclusion is a tool with potential to help teachers to rethink their mathematics classroom organisation and planning. Inclusive teaching may be enacted differently depending on the context; however, we found the key teaching practice to be planning for inclusion at the outset, followed by focussed reflections on teaching. Further, we suggest that research into innovative mathematics pedagogies is most likely to be successful when there is a focus on inclusive pedagogy.

Falls can pose a serious health threat, especially for older people, often leading to fractures, head injuries, or long-term disability. This highlights the need for reliable and non-invasive detection systems. Existing solutions often suffer from limitations such as user discomfort with wearables, constrained coverage of fixed sensors, or environmental challenges in vision-based methods. This paper proposes a dual-stream attention-guided robust indoor fall detection framework to solve the problem. The approach combines a pose-guided stream with a video stream, in which the former captures high-level skeletal features through MediaPipe and simultaneously processes spatiotemporal dynamics from raw RGB frames using a 3D Convolutional Neural Network (CNN) with a temporal attention module. In order to enhance classification precision, features from each stream are adaptively fused in a block based on attention mechanisms, which improves the model’s interpretation of posture and movement semantics. Testing on the KFall dataset reveals that the proposed method achieves 98.71% accuracy, surpassing pre-existing benchmarks. Triggering a visual alert by displaying a red rectangle on the screen upon the occurrence of this event is a subsequent outcome of this work. An ablation study highlights the effectiveness of each component. Finally, the proposed work advances fall detection by combining pose estimation and attention-based deep learning to deliver an accurate, interpretable, and deployable solution.

She's small-town. He's big-league. They broke up years ago… But is it really over?In the small town of Parker Falls, Jessica Parker spends her time serving pie and coming up with ideas to save the failing diner she inherited from her grandfather. A single mom to eight-year-old Wesley, she has enough to keep her busy well into the future. But when her old high school love, Chase Taynor, unexpectedly pops up, Jess finds herself thinking not of the future but of her past with the handsome professional baseball player. After a few bad pitches leave his dream career in limbo, Chase slides back home to Parker Falls…although the town may not be as safe as he'd expected. As Jess and Chase reconnect, old feelings return in a major-league way. With the possibility that Chase could return to the baseball diamond at any time, will their reunion be worth the risk to their hearts? Or will it be déjà vu all over again, with Chase choosing the limelight over the woman he loved before the fame? The book contains a free original recipe for Bacon & White Cheddar Burgers with Spicy Mustard Relish.