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Trail bridges can improve access to critical services such as health care, schools, and markets. In order to evaluate the impact of trail bridges in rural Rwanda, it is helpful to objectively know how and when they are being used. In this study, we deployed motion-activated digital cameras across several trail bridges installed by the non-profit Bridges to Prosperity. We conducted and validated manual counting of bridge use to establish a ground truth. We adapted an open source computer vision algorithm to identify and count bridge use reflected in the digital images. We found a reliable correlation with less than 3% error bias of bridge crossings per hour between manual counting and those sites at which the cameras logged short video clips. We applied this algorithm across 186 total days of observation at four sites in fall 2019, and observed a total of 33,800 daily bridge crossings ranging from about 20 to over 1,100 individual uses per day, with no apparent correlation between daily or total weekly rainfall and bridge use, potentially indicating that transportation behaviors, after a bridge is installed, are no longer impacted by rainfall conditions. Higher bridge use was observed in the late afternoons, on market and church days, and roughly equal use of the bridge crossings in each direction. These trends are consistent with the design-intent of these bridges.

Massive amounts of information about news events are published on the Internet every day in online newspapers, blogs, and social network messages. While search engines like Google help retrieve information using keywords, the large volumes of unstructured search results returned by search engines make it hard to track the evolution of an event. A story chain is composed of a set of news articles that reveal hidden relationships among different events. Traditional keyword-based search engines provide limited support for finding story chains. In this paper, we propose a random walk based algorithm to find story chains. When breaking news happens, many media outlets report the same event. We have two pruning mechanisms in the algorithm to automatically exclude redundant articles from the story chain and to ensure efficiency of the algorithm. We further explore how named entities and word relevance can help find relevant news articles and improve algorithm efficiency by creating a co-clustering based correlation graph. Experimental results show that our proposed algorithm can generate coherent story chains without redundancy. The efficiency of the algorithm is significantly improved on the correlation graph.

Limb repositioning is necessary for individuals with severe physical disabilities to sustain muscle strength and prevent pressure sores. As robotic technologies become ubiquitous, these tools offer promise to support the repositioning process. However, research has yet to focus on ways in which individuals with severe physical disabilities can control robots for these tasks. This paper presents a study that examines the needs and attitudes of potential users with physical disabilities to control a robotic aid for limb repositioning. Subjects expressed interest in using brain–computer interface (BCI) and speech recognition technologies for purposes of executing robotic tasks. The performance of four subjects controlling arm movements on an avatar through the keyboard, mouse, BCI, and Dragon NaturallySpeaking speech recognition was evaluated. Although BCI and speech technologies may limit physical fatigue, more challenges were faced using BCI and speech conditions compared to the keyboard and mouse. This research promotes accessibility into mainstream robotic technologies and represents the first step in the development of a robotic prototype using a BCI and speech recognition technologies for limb repositioning.

Background: Airway wall remodelling and inflammation are features of chronic asthma. Transforming growth factor β (TGF-β) has been implicated in these processes. Aim: To determine the effect of allergen challenge on airway inflammation and remodelling and whether TGF-β isoforms and the Smad signalling pathways are involved. Methods: Thirteen patients with atopic asthma underwent inhalational challenge with 0.9% saline, followed by allergen 3–4 weeks later. After both challenges, fibreoptic bronchoscopy was undertaken to obtain bronchial biopsies and tissue samples were processed for immunohistochemistry and examined by microscopy. Results: Forced expiratory volume in 1 s (FEV1) fell after allergen challenge (mean (SE) −28.1 (0.9)% at 30 min with a late response at 7 hours (−23.0 (1.2)%). Allergen challenge caused an increase in neutrophils and eosinophils in the bronchial mucosa compared with saline. Sub-basement membrane (SBM) thickness did not change after allergen, but tenascin deposition in SBM was increased. Intranuclear (activated) Smad 2/3 and Smad 4 detected by immunohistochemistry were increased after allergen challenge in epithelial and subepithelial cells of bronchial biopsies. No inhibitory Smad (Smad 7) protein was detected. TGF-β isoforms 1, 2 and 3 were expressed predominantly in bronchial epithelium after saline and allergen challenges, but only TGF-β2 expression was increased after allergen. Double immunostaining showed an increase in TGF-β2 positive eosinophils and neutrophils but not in TGF-β1 positive eosinophils and neutrophils after allergen challenge. Conclusions: TGF-β2 may contribute to the remodelling changes in allergic asthma following single allergen exposure.

Recent years have seen a resurgence of interest in the use of metacognition in intelligent systems. This article is part of a small section meant to give interested researchers an overview and sampling of the kinds of work currently being pursued in this broad area. The current article offers a review of recent research in two main topic areas: the monitoring and control of reasoning (metareasoning) and the monitoring and control of learning (metalearning).

Indoor navigation remains a critical challenge for people with visual impairments. The current solutions mainly rely on infrastructure-based systems, which limit their ability to navigate safely in dynamic environments. We propose a novel navigation approach that utilizes a foundation model to transform floor plans into navigable knowledge graphs and generate human-readable navigation instructions. Floorplan2Guide integrates a large language model (LLM) to extract spatial information from architectural layouts, reducing the manual preprocessing required by earlier floorplan parsing methods. Experimental results indicate that few-shot learning improves navigation accuracy in comparison to zero-shot learning on simulated and real-world evaluations. Claude 3.7 Sonnet achieves the highest accuracy among the evaluated models, with 92.31%, 76.92%, and 61.54% on the short, medium, and long routes, respectively, under 5-shot prompting of the MP-1 floor plan. The success rate of graph-based spatial structure is 15.4% higher than that of direct visual reasoning among all models, which confirms that graphical representation and in-context learning enhance navigation performance and make our solution more precise for indoor navigation of Blind and Low Vision (BLV) users.

Theoretical entities are aspects of the world that cannot be sensed directly but that, nevertheless, are causally relevant. Scientific inquiry has uncovered many such entities, such as black holes and dark matter. We claim that theoretical entities, or hidden variables, are important for the development of concepts within the lifetime of an individual and present a novel neural network architecture that solves three problems related to theoretical entities: (1) discovering that they exist, (2) determining their number, and (3) computing their values. Experiments show the utility of the proposed approach using discrete time dynamical systems, in which some of the state variables are hidden, and sensor data obtained from the camera of a mobile robot, in which the sizes and locations of objects in the visual field are observed but their sizes and locations (distances) in the three-dimensional world are not. Two different regularization terms are explored that improve the network's ability to approximate the values of hidden variables, and the performance and capabilities of the network are compared to that of Hidden Markov Models.

Indoor navigation remains a critical accessibility challenge for the blind and low-vision (BLV) individuals, as existing solutions rely on costly per-building infrastructure. We present an agentic framework that converts a single floor plan image into a structured, retrievable knowledge base to generate safe, accessible navigation instructions with lightweight infrastructure. The system has two phases: a multi-agent module that parses the floor plan into a spatial knowledge graph through a self-correcting pipeline with iterative retry loops and corrective feedback; and a Path Planner that generates accessible navigation instructions, with a Safety Evaluator agent assessing potential hazards along each route. We evaluate the system on the real-world UMBC Math and Psychology building (floors MP-1 and MP-3) and on the CVC-FP benchmark. On MP-1, we achieve success rates of 92.31%, 76.92%, and 61.54% for short, medium, and long routes, outperforming the strongest single-call baseline (Claude 3.7 Sonnet) at 84.62%, 69.23%, and 53.85%. On MP-3, we reach 76.92%, 61.54%, and 38.46%, compared to the best baseline at 61.54%, 46.15%, and 23.08%. These results show consistent gains over single-call LLM baselines and demonstrate that our workflow is a scalable solution for accessible indoor navigation for BLV individuals.