Currently, existing drone datasets mainly focus on collecting data in urban environments. However, there are numerous residential and farming areas located alongside the river in rural regions. If there is heavy rain and flooding, the river is likely to overflow, posing a threat to human lives and property. In this paper, we collect a novel drone-captured dataset, called DroneRiver, tailored for water management of pre-disaster scenarios and analysis of preliminary river leakage in rural areas. DroneRiver provides annotations for the objects on both sides of the river, such as people, vehicles, levee roads, farms, vegetation, etc. Additionally, to overcome the difficulty of collecting real-world river leakage scenes without environmental harm, based on the recent generative model, preliminary river leakage images can be generated in our datasets for pre-disaster prevention and alerts. Finally, our dataset annotates the original and generated aerial at the pixel level for semantic segmentation. We also conduct extensive experiments using state-of-the-art segmentation models and evaluate the quality of the generated aerial images. The results demonstrate the significance of the DroneRiver dataset for river leakage analysis.

The paper presents an integrated gain scheduling controller architecture for the system, which could be described by an interpolation of a set of local linear systems at different operating points. The integrated controller design combines a linear parameter varying (LPV) gain scheduling controller with a coprime factorization-based conditional detection mechanism and an interpolation strategy. A three-step procedure is given for the controller synthesis. First, a central nominal controller is designed to guarantee a certain performance index in all operating regions. Second, a Youla parameter including an interpolation strategy between these controllers is designed to guarantee local performance at different operating points. Finally, an integrated strategy is then proposed to combine the central controller and Youla parameter. The proposed integrated controller architecture not only guarantees the quadratic stability and nominal control performance of the closed-loop system in all operating regions but also considers local control performance at different operating points. Furthermore, it also provides an on-line controller tuning architecture for gain scheduling control to achieve the balance between dynamic and steady-state performance. Finally, a numerical example is given to show the design procedure.

Sustaining learner motivation remains a persistent challenge in higher education Japanese-language e-learning, where learners often study with limited social presence and personalized encouragement. This paper proposes a preliminary Design Framework for Motivational Robots in E-Learning (DFMRE), derived through a retrospective, theory-guided synthesis of two previously published empirical studies on robot-assisted Japanese-language learning. The synthesis interprets the prior findings through Self-Determination Theory and self-efficacy theory and formulates five candidate design principles: human-affine compact hardware, multi-level learner-selectable gestures, calibrated vocal encouragement, learner-initiated interaction protocol, and content-independent system integration. To provide an initial external check, nine domain experts with diverse backgrounds in education, educational technology, human–robot interaction, and related fields provided a preliminary appraisal of the principles in terms of clarity, feasibility, transferability, and overall usefulness. The framework received broadly favorable ratings, including a mean overall usefulness score of 4.33 on a 5-point scale, while expert comments highlighted the need for clearer operational definitions and flexible interaction modes. Because the empirical base consists of two small-sample Japanese-language learning studies conducted at one institution using one robot platform, DFMRE should be read as an early, context-grounded, falsifiable proposal rather than as a confirmed model for higher education e-learning in general.

Controlled releases can reveal downstream response timing when calibrated hydraulic routing is unavailable, but sparse stage-only records limit what can be inferred. We analyze one principal 2026 controlled release on the Satsunai River, Japan, using dam outflow and two downstream water-level series. We ask whether dam-referenced lags agree with a direct station-to-station estimate, whether the reach-scale timing persists under alternative preprocessing and dependence assumptions, and what timing can be assigned conditionally to an intermediate ungauged location. Cross-correlation maxima occurred at 3.67 h at Kamisatsunai and 8.00 h at Dainikawabashi; their 4.33 h difference matched the full-record station-to-station maximum and was close to the 4.67 h release-window result. Across detrending and differencing transformations, endpoint separation remained 3.67–4.33 h. Expanded 1–24 h moving-block bootstrap sensitivity yielded a lag-difference envelope of 4.17–4.50 h, corresponding to an apparent celerity of 1.30–1.41 m/s. Linear interpolation gave 5.84 h at Nakajima Shinbashi, with a conditional percentile-interval envelope of 5.51–6.43 h. Taken together, these estimates give a consistent event-specific description of bulk hydrograph alignment. Additional events and hydraulic observations are needed to assess transferability and spatial model-form uncertainty.