Are You Being Tracked? Discover the Power of Zero-Shot Trajectory Tracing with LLMs!

There is a burgeoning discussion around the capabilities of Large Language Models (LLMs) in acting as fundamental components that can be seamlessly incorporated into Artificial Intelligence of Things (AIoT) to interpret complex trajectories. This study introduces LLMTrack, a model that illustrates how LLMs can be leveraged for Zero-Shot Trajectory Recognition by employing a novel single-prompt technique that combines role-play and think step-by-step methodologies with unprocessed Inertial Measurement Unit (IMU) data. We evaluate the model using real-world datasets designed to challenge it with distinct trajectories characterized by indoor and outdoor scenarios. In both test scenarios, LLMTrack not only meets but exceeds the performance benchmarks set by traditional machine learning approaches and even contemporary state-of-the-art deep learning models, all without the requirement of training on specialized datasets. The results of our research suggest that, with strategically designed prompts, LLMs can tap into their extensive knowledge base and are well-equipped to analyze raw sensor data with remarkable effectiveness

A novel decomposed-ensemble time series forecasting framework: capturing underlying volatility information

Time series forecasting represents a significant and challenging task across various fields. Recently, methods based on mode decomposition have dominated the forecasting of complex time series because of the advantages of capturing local characteristics and extracting intrinsic modes from data. Unfortunately, most models fail to capture the implied volatilities that contain significant information. To enhance the prediction of contemporary diverse and complex time series, we propose a novel time series forecasting paradigm that integrates decomposition with the capability to capture the underlying fluctuation information of the series. In our methodology, we implement the Variational Mode Decomposition algorithm to decompose the time series into K distinct sub-modes. Following this decomposition, we apply the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to extract the volatility information in these sub-modes. Subsequently, both the numerical data and the volatility information for each sub-mode are harnessed to train a neural network. This network is adept at predicting the information of the sub-modes, and we aggregate the predictions of all sub-modes to generate the final output. By integrating econometric and artificial intelligence methods, and taking into account both the numerical and volatility information of the time series, our proposed framework demonstrates superior performance in time series forecasting, as evidenced by the significant decrease in MSE, RMSE, and MAPE in our comparative experimental results

Analysis of Multiple Scattering Characteristics of Cable-Stayed Bridges with Multi-band SAR

Accurate localization of multi-scattering features of cable-stayed bridges in multi-band Synthetic Aperture Radar (SAR) imagery is crucial for intelligent recognition of bridge targets within images, as well as for precise water level extraction. This study focuses on the Badong Yangtze River Bridge, utilizing Unmanned Aerial Vehicle (UAV) LiDAR data of the bridge, and analyzes the multi-scattering characteristics of different bridge structural targets based on Geometric Optics (GO) methods and the Range-Doppler principle. Furthermore, the study integrates LiDAR data of the bridge's cable-stays to examine their multi-scattering phenomena, finding that the undulations of the Yangtze River's surface waves significantly contribute to the pronounced double scattering features of the bridge's cable-stays. Additionally, statistical analysis of multi-source SAR data indicates that this phenomenon is not directly correlated with radar wavelength, implying no direct connection to surface roughness. Utilizing LiDAR point cloud data from the bridge's street lamps, this paper proposes a novel method for estimating water level elevation by identifying the center position of spots formed by double scattering from lamp posts. The results show that using TerraSAR ascending and descending orbit images, this method achieves a water level elevation accuracy of approximately 0.2 meters

Harnessing Geometric Frustration to Form Band Gaps in Acoustic Channel Lattices

We demonstrate both numerically and experimentally that geometric frustration in two-dimensional periodic acoustic networks consisting of arrays of narrow air channels can be harnessed to form band gaps (ranges of frequency in which the waves cannot propagate in any direction through the system). While resonant standing wave modes and interferences are ubiquitous in all the analyzed network geometries, we show that they give rise to band gaps only in the geometrically frustrated ones (i.e. those comprising of triangles and pentagons). Our results not only reveal a new mechanism based on geometric frustration to suppress the propagation of pressure waves in specific frequency ranges, but also opens avenues for the design of a new generation of smart systems that control and manipulate sound and vibrations

Spiral Complete Coverage Path Planning Based on Conformal Slit Mapping in Multi-connected Domains

Generating a smooth and shorter spiral complete coverage path in a multi-connected domain is an important research area in robotic cavity machining. Traditional spiral path planning methods in multi-connected domains involve a subregion division procedure; a deformed spiral path is incorporated within each subregion, and these paths within the subregions are interconnected with bridges. In intricate domains with abundant voids and irregular boundaries, the added subregion boundaries increase the path avoidance requirements. This results in excessive bridging and necessitates longer uneven-density spirals to achieve complete subregion coverage. Considering that conformal slit mapping can transform multi-connected regions into regular disks or annuluses without subregion division, this paper presents a novel spiral complete coverage path planning method by conformal slit mapping. Firstly, a slit mapping calculation technique is proposed for segmented cubic spline boundaries with corners. Then, a spiral path spacing control method is developed based on the maximum inscribed circle radius between adjacent conformal slit mapping iso-parameters. Lastly, the spiral path is derived by offsetting iso-parameters. The complexity and applicability of the proposed method are comprehensively analyzed across various boundary scenarios. Meanwhile, two cavities milling experiments are conducted to compare the new method with conventional spiral complete coverage path methods. The comparation indicate that the new path meets the requirement for complete coverage in cavity machining while reducing path length and machining time by 12.70% and 12.34%, respectively.Comment: This article has not been formally published yet and may undergo minor content change

High strength mullite-bond SiC porous ceramics fabricated by digital light processing

Fabricating SiC ceramics via the digital light processing (DLP) technology is of great challenge due to strong light absorption and high refractive index of deep-colored SiC powders, which highly differ from those of resin, and thus significantly affect the curing performance of the photosensitive SiC slurry. In this paper, a thin silicon oxide (SiO2) layer was in-situ formed on the surface of SiC powders by pre-oxidation treatment. This method was proven to effectively improve the curing ability of SiC slurry. The SiC photosensitive slurry was fabricated with solid content of 55 vol% and viscosity of 7.77 Pa s (shear rate of 30 s-1). The curing thickness was 50 μm with exposure time of only 5 s. Then, a well-designed sintering additive was added to completely convert low-strength SiO2 into mullite reinforcement during sintering. Complexshaped mullite-bond SiC ceramics were successfully fabricated. The flexural strength of SiC ceramics sintered at 1550 °C in air reached 97.6 MPa with porosity of 39.2 vol%, as high as those prepared by spark plasma sintering (SPS) techniques.</p

V2C MXene-modified g-C3N4 for enhanced visible-light photocatalytic activity

Increasing the efficiency of charge transfer and separation efficiency of photogenerated carriers are still the main challenges in the field of semiconductor-based photocatalysts. Herein, we synthesized g-C3N4@V2C MXene photocatalyst by modifying g-C3N4 using V2C MXene. The prepared photocatalyst exhibited outstanding photocatalytic performance under visible light. The degradation efficiency of methyl orange by g-C3N4@V2C MXene photocatalyst was as high as 94.5%, which is 1.56 times higher than that by g-C3N4. This was attributed to the V2C MXene inhibiting the rapid recombination of photogenerated carriers and facilitating rapid transfer of photogenerated electrons (e) from g-C3N4 to MXene. Moreover, g-C3N4@V2C MXene photocatalyst showed good cycling stability. The photocatalytic performance was higher than 85% after three cycles. Experiments to capture free radicals revealed that superoxide radicals (02) are the main contributors to the photocatalytic activity. Thus, the proposed g-C3N4@V2C MXene photocatalyst is a promising visible-light catalyst.Comment: 20 pages, 9 figure

The impact of emissions controls on atmospheric nitrogen inputs to Chinese river basins highlights the urgency of ammonia abatement

Excessive nitrogen (N) deposition affects aquatic ecosystems worldwide, but effectiveness of emissions controls and their impact on water pollution remains uncertain. In this modeling study, we assess historical and future N deposition trends in Chinese river basins and their contributions to water pollution via direct and indirect N deposition (the latter referring to transport of N to water from N deposited on land). The control of acid gas emissions (i.e., nitrogen oxides and sulfur dioxide) has had limited effectiveness in reducing total N deposition, with notable contributions from agricultural reduced N deposition. Despite increasing controls on acid gas emissions between 2011 and 2019, N inputs to rivers increased by 3%, primarily through indirect deposition. Simultaneously controlling acid gas and ammonia emissions could reduce N deposition and water inputs by 56 and 47%, respectively, by 2050 compared to 2019. Our findings underscore the importance of agricultural ammonia mitigation in protecting water bodies

Nurse Participation in Colonoscopy Observation versus the Colonoscopist Alone for Polyp and Adenoma Detection: A Meta-Analysis of Randomized, Controlled Trials

The role of nurse participation (NP) in colonoscopy observation for polyp and adenoma detection is unclear. This study aimed to evaluate whether nurse participation can improve polyp and adenoma detection. Patients and Methods. The PUBMED, EMBASE, and Cochrane Library databases were searched for randomized controlled trials (RCTs) published in English. The outcome measurements included (1) the polyp and adenoma detection rate (PDR and ADR); (2) the advanced lesions detection rate; and (3) the mean polyp and adenoma detection rate per colonoscopy. Results. Three RCTs with a total of 1676 patients were included. The pooled data showed a significantly higher ADR in the NP group than colonoscopist alone (CA) (45.7% versus 39.3%; RR 1.16; 95% CI, 1.04–1.30). And it showed no significant difference in the PDR and advanced lesions detection rate between the two groups (RR: 1.14, 95% CI: 0.95–1.37; RR: 1.35, 95% CI: 0.91–2.00; resp.). Conclusions. Nurse participation during a colonoscopy can improve the ADR, whereas no benefit for the PDR and advanced lesions detection rate was observed. All RCTs included in the meta-analysis had high risk of bias. Thus, there is a need for new research that uses sound methodology to definitively address the research question under study