This study aims to evaluate the thermal behaviors of surface materials in arid climates to enhance environmental sustainability and energy efficiency. Conducted over 1 year at Dokumapark in Antalya, Turkey, it examines surface temperatures of asphalt, concrete, granite, wood, grass, and soil using thermal using a FLIR-C5 thermal camera. Measurements were taken in the morning, noon, and evening, capturing images from sunny and shaded areas, which were processed with custom Python software. A total of 1728 temperature values were statistically and visually analyzed based on surface–air temperature differences.

Seven machine learning models were used for evaluation, with the neural network model achieving the highest accuracy (R2: 0.9848) and minimal error. The model assessed thermal variations across different periods. Grass and wood exhibited low heat retention, while asphalt and brick reached higher temperatures, with asphalt predicted to exceed 50 oC in summer, potentially impacting thermal comfort. Grass was the most efficient material with minimal temperature fluctuations.

This study highlights the importance of thermal properties in enhancing energy efficiency and user comfort, as well as the necessity of selecting materials for sustainable cities. It suggests that combining artificial intelligence and thermal imaging techniques can be a beneficial tool for ecological and sustainable architectural design.

Legged robots operating in irregular environments are often subjected to compound disturbances such as tilts and vibrations, which can degrade attitude stability and motion reliability. This paper presents a real-time disturbance-adaptive control framework for a hexapod robot. The proposed system integrates quaternion-based attitude estimation using an extended Kalman filter (EKF), a double-threshold pose classifier, and a modular gait library and is implemented on an embedded controller with a 2 ms control-loop latency. Analytical verification and laboratory experiments demonstrate that the proposed control loop achieves uniform ultimate boundedness (UUB) under deterministic hybrid disturbances composed of controlled tilt and vibration, with a mean recovery time of 5.7 s. These results demonstrate that a lightweight rule-based controller can ensure reliable posture recovery within the experimentally validated laboratory scenarios, providing a foundation for future extensions to more complex environments. The main contributions of this work are (1) a disturbance-adaptive gait selection architecture for quasi-static stabilization, (2) a noise-robust EKF-based attitude estimation and double-threshold pose determination scheme, and (3) a concise Lyapunov-based stability analysis demonstrating UUB of the closed-loop system.

Speculative xenomusicology explores alternative music theories, imagining the physical and cognitive affordances of alien musical life. Exoplanets are actively studied in astronomy, and though there is no direct evidence of xenobiology, particularly of more advanced musical intelligences, potential alien music may still be considered in advance in the same way that exobiologists speculate on the conditions for alien life. In particular, a generative system is presented which creates imagined xenomusic based on altering human memory constraints and links the organisation of the sound to the parallel generation of an alien language. Microtonal pitch, complex rhythm, timbral material and spatialisation within putative alien architectures are all considered. This alien ‘analysis by synthesis’ can provide new musical adventures and new understanding of the possibilities of music theoretical space, regardless of any eventual ontological resolution of xenocultures.

The ability of multirotor unmanned aerial vehicles (UAVs) to perform accurately in windy environments is crucial for extended use in outdoor applications. To design UAVs to operate in these environments, most studies have focused on static performance metrics such as thrust-to-weight ratio and endurance, without directly considering closed-loop control performance. This work develops a simplified metric that serves as a predictor for achievable disturbance rejection performance, enabling efficient UAV design selection without requiring full-scale nonlinear simulations. A reduced-order model is introduced to capture key aerodynamic and actuation characteristics, allowing for rapid evaluation of UAV configurations. The metric is validated against high-fidelity nonlinear simulations, demonstrating strong correlation with actual control performance. By bridging the gap between UAV structural optimization and closed-loop control behavior, this approach provides a practical tool for integrating disturbance rejection capabilities into UAV design processes. The practical utility of this metric is supported by experimental findings from related wind tunnel studies of fully-actuated UAVs, which demonstrate that actual disturbance rejection performance aligns with the trends predicted by the simplified correlation function.

Traced monoidal categories model processes that can feed their outputs back to their own inputs, abstracting iteration. The category of finite-dimensional Hilbert spaces with the direct sum tensor is not traced. But surprisingly, in 2014, Bartha showed that the monoidal subcategory of isometries is traced. The same holds for coisometries, unitary maps, and contractions. This suggests the possibility of feeding outputs of quantum processes back to their own inputs, analogous to iteration. In this paper, we show that Bartha’s result is not specifically tied to Hilbert spaces, but works in any dagger additive category with Moore–Penrose pseudoinverses (a natural dagger-categorical generalization of inverses).

In unstructured environments, Delta robots face challenges in achieving high vision-guided grasping precision due to dynamic lighting conditions and workpiece diversity. This paper designs an integrated solution that combines RGB-D multimodal learning with an enhanced Mask R-CNN framework. Initially, a dual-stream ResNet50-FPN backbone network is designed to achieve cross-modal adaptive alignment via hierarchical feature fusion. Subsequently, a depth-guided attention module is incorporated to bolster robustness against material ambiguity and reflective interference. Moreover, a dynamic depth estimation algorithm is employed to significantly improve target localization accuracy and stability. Finally, real-time trajectory tracking is realized by integrating PD control with Jacobian mapping. Experimental results validate the efficacy of the proposed method, offering an efficient and reliable approach for industrial robotic applications.

Efficient memory management is essential for the stability and long-term performance of mobile robots in Simultaneous Localization and Mapping (SLAM). However, existing methods often struggle to control redundancy in keyframes and map points, leading to reduced efficiency, increased latency, and potential system failure due to resource constraints. Achieving high accuracy in both mapping and trajectory estimation while maintaining a compact state representation remains a key challenge for scalable and efficient SLAM systems. To address this issue, this paper proposes an efficient long-term visual SLAM method based on sparse prior embedding and nonlinear score-guided sparsification for memory-constrained environments. The approach embeds keyframe information into sparse prior factors, avoiding global coupling while preserving system sparsity and consistency. Additionally, a nonlinear scoring function combining parallax and descriptor uniqueness is introduced to guide map point sparsification within the sliding window. This strategy enables efficient state graph management, achieving compact global map representations and effective observation constraints. The proposed method has been implemented in a complete visual SLAM system and evaluated through long-term real-world mapping experiments on an embedded robotic platform. Experimental results demonstrate that the approach significantly reduces memory consumption while maintaining trajectory and mapping accuracy. Furthermore, the method ensures real-time execution and deployment potential, indicating its suitability for large-scale SLAM tasks in resource-constrained and long-duration operational scenarios.

Designing complex products increasingly requires integrative methodologies that address the rising challenges of multi-disciplinary complexity and functional inter-dependencies. This article proposes a conceptual design framework that combines the abstractional design method (ADM) with a novel inter-coupling index (ICX) to model and manage inter-component dependencies within cyber-physical vehicle (CPV) systems. The ADM provides a unified object-based representation of system components through functional and attribute abstraction, facilitating shared understanding across disciplines. The ICX quantitatively captures the degree of inter-dependency among system elements, offering a new metric for evaluating design complexity. A case study of a CPV acceleration module demonstrates how indirect coupling and cascading failure risks can be identified and mitigated in the early design process. The methodology supports the decomposition and synthesis of design architectures while preserving functional intent and reducing system vulnerability. This research contributes a transferable and scalable approach to conceptual system design in multi-disciplinary domains.

For efficient wind farm management and optimized power generation under adverse weather conditions, understanding the causal meteorological drivers is essential. In this paper, we investigate the temporal causal influences of wind speed-related meteorological processes within a wind farm using the Heterogeneous Graphical Granger model (HMML). HMML is applied to synthetically generated wind power production data from Eastern Austria. To assess the plausibility of the identified causal processes, we compare the results with those obtained using the state-of-the-art LiNGAM method. Both methods are applied and evaluated across six different scenarios, each defined by distinct hydrological periods. The scenarios are defined by a set of time intervals characterized by either low/high extreme wind speeds or moderate wind speeds. We applied both methods across these scenarios and conducted causal reasoning to identify potential causes of extreme wind speeds within the wind farm. The sets of causal parameters obtained using HMML were found to be more realistic than those derived from LiNGAM. Combining the knowledge of causal variables affecting wind speed at the turbine hub, identified by HMML in each scenario, with weather forecasts can offer practical guidance for wind farm operators. Specifically, this knowledge can support more informed planning regarding when wind turbines should or should not be generating energy. For instance, the strong Granger-causal linkage identified between wind speed and temperature can inform curtailment strategies. In scenarios where rising temperatures are predictive of declining wind speeds, operators may preemptively adjust turbine output or schedule maintenance to optimize efficiency and reduce wear. Moreover, such predictive insights can feed into energy market models, where anticipated curtailment due to meteorological dependencies affects both generation forecasts and pricing strategies. By integrating these causal relationships into operational planning, the proposed tool offers a pathway toward more adaptive and economically efficient wind energy management.

To solve the problems of precise operation and real-time interaction during the spraying process of industrial robots, a new spraying method based on digital twin technology is proposed. In view of the limitations of traditional spraying processes in complex geometric shape processing, spraying uniformity control, and operational flexibility, this study built a highly simulated virtual environment based on digital twin and human–machine collaboration technology, allowing operators to guide the robot in real time for precise spraying operations. The use of multisensor fusion technology achieves a high degree of consistency between the physical and virtual environments, ensuring that the system can maintain high-precision spraying on complex workpiece surfaces. The experimental designed spraying tasks for different geometric shapes and evaluated the performance of the system’s interactive spraying method in terms of real-time feedback guidance and path planning. The results show that the proposed method significantly improves the accuracy and efficiency of the spraying process, especially showing obvious advantages when processing complex geometric workpieces, and provides a new technical approach for future high-precision manufacturing.