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.

Balister, the second author, Groenland, Johnston, and Scott recently showed that there are asymptotically $C4^n/n^{3/4}$ many unordered sequences that occur as degree sequences of graphs with $n$ vertices. Combining limit theory for infinitely divisible distributions with a new connection between a class of random walk trajectories and a subset counting formula from additive number theory, we describe $C$ in terms of Walkup’s number of rooted plane trees. The bijection is related to an instance of the Lévy–Khintchine formula. Our main result complements a result of Stanley, that ordered graphical sequences are related to quasi-forests.

To address the challenges of low detection accuracy, missed detections, and high false detection rates for small targets in PCB defect detection tasks, this study proposes an enhanced YOLOv8 methodology incorporating feature focusing and multi-scale fusion techniques. Initially, a lightweight GTADH module is integrated into the detection head of YOLOv8, employing a shared convolution and task alignment mechanism to minimize model parameters while enhancing classification and localization accuracy. Subsequently, an adaptive feature-focusing module is introduced into the feature fusion network to bolster the detection capabilities for small targets via multi-scale feature fusion. Finally, the reverse residual moving block (iRMB) and attention mechanisms are combined within the backbone network to facilitate efficient extraction and fusion of feature information, preserving finer details of small targets. Experimental results demonstrate that the Improved YOLO algorithm achieves a 1.3% increase in detection accuracy and a 7.3% enhancement in mAP50:90 evaluation standards compared to the original YOLOv8s algorithm on the PCB defect dataset, while also reducing model size by 60%, thus showcasing its effectiveness in small target detection tasks.