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.

Following Entman’s observation that policy frames define social problems, diagnose causes and suggest remedies, we examined the strategies that 12 U.S. governors (from states matched according to population size and density, demographic composition, per capita incomes, geographic proximity, and COVID-19 incidence) used to frame COVID-19 policy agendas. After scraping the governors’ statements about COVID-19 from press releases issued from January 2020 to May 2023 (N = 14,629), we leveraged ChatGPT (GPT) to identify and assess the intensity of public health, economic stability, and civic vitality frames. Subsequent analysis explored differences in the framing strategies according to the governors’ political party and gender. In the process, this study underscores the importance of AI prompt engineering to realize GPT’s transformative potential to facilitate communication research by efficiently identifying and assessing the content of policy frames.

Neuromorphic vision-based robotic tactile sensors fuse touch and vision, enabling manipulators to efficiently grip and identify objects. Precise robotic manipulation requires early detection of slips on the grasped object, which is crucial for maintaining grip stability and safety. Modern closed-loop feedback technologies use measurements from neuromorphic vision-based tactile sensors to control and prevent object slippage. Unfortunately, most of these sensors measure and report data-based rather than model-based information, resulting in less efficient control capabilities. This work proposes physical and mathematical modeling of an in-house-developed neuromorphic vision-based robotic tactile sensor that utilizes a protruded marker design to demonstrate the model-based approach. This sensor is mounted on the UR10 robotic manipulator, enabling manipulation tasks such as approaching, pressing, and slipping. The neuromorphic vision-based robotic tactile sensor-derived mathematical model revealed first-order system behavior for three manipulation-related actions under study. Experimental robotic manipulator grasping work is conducted to verify and validate the sensor’s derived mathematical FOS model. Two data analysis approaches, temporal and spatial–temporal model based, are adopted to classify the manipulator-sensor actions. A long short-term memory (LSTM) temporal classifier is engineered to exploit the sensor’s derived model. Also, the LSTM spatial–temporal classifier is designed using an event-weighted centroid of the region-of-interest features. Both LSTM methods successfully identified the robotic actions performed with an accuracy of more than 99%. Additionally, quantitative slip rate estimation is carried out based on centroid estimation, and qualitative assessment of pressing force is performed using a fuzzy logic classifier.

Lorenz dominance is a classical criterion for comparing income distributions with respect to inequality and social welfare. However, its binary nature, in which one distribution either dominates another or does not, often leads to inconclusive results when empirical Lorenz curves intersect. To overcome this limitation, we introduce the Lorenz dominance index (LDI), a continuous measure that quantifies the extent to which one Lorenz curve lies above another. The LDI provides an interpretable assessment based on the population, allowing for the evaluation of partial or near dominance and improving its usefulness in empirical settings. We derive the asymptotic distribution of the LDI and propose a nonparametric bootstrap procedure to construct confidence intervals and perform inference. Monte Carlo simulations confirm the estimator’s strong performance in finite samples and its nominal coverage. An application to household income data from China highlights the practical value of the LDI in distributional analysis.

Polychrony is a virtual or artificial tempor[e]ality that is constructed by the fine augmentation or tempering of a natural set of latencies that articulate a complex networked acoustic. The art is to optimise the alignment of these disjunct temporalities as they merge in a new chronotopic fusion. This fooling with Mother Nature, however, does not come without consequences: due to the significant latency effects intrinsic to a planetary-scale network, a phenomenon called topo-rhythmia emerges. Toporhythms are derived simply as a feature of communication over distance; they are the multiple versions of a rhythm that occur at each node of a networked piece due to the temporal offsets caused by delay. To work with this feature more intentionally, rather than as an accident of relativity, we must tune or temper the network latency. Tempering is a general tactic for ontological negotiation, bringing observers and complex systems into some kind of coherency. The purpose of this article is to explore the tempering of musical time-space on networks and how that underlies the notational practices (and the alien compositional assumptions) built upon this novel orientation.