Capture Time Research Articles

Incorporating the Third Law of Geography with Spatial Attention Module–Convolutional Neural Network–Transformer for Fine-Grained Non-Stationary Air Quality Predictive Learning

Accurate air quality prediction is paramount in safeguarding public health and addressing air pollution control. However, previous studies often ignore the geographic similarity among different monitoring stations and face challenges in dynamically capturing different spatial–temporal relationships between stations. To address this, an air quality predictive learning approach incorporating the Third Law of Geography with SAM–CNN–Transformer is proposed. Firstly, the Third Law of Geography is incorporated to fully consider the geographical similarity among stations via a variogram and spatial clustering. Subsequently, a spatial–temporal attention convolutional network that combines the spatial attention module (SAM) with the convolutional neural network (CNN) and Transformer is designed. The SAM is employed to extract spatial–temporal features from the input data. The CNN is utilized to capture local information and relationships among each input feature. The Transformer is applied to capture time dependencies across long-distance time series. Finally, Shapley’s analysis is employed to interpret the model factors. Numerous experiments with two typical air pollutants (PM2.5, PM10) in Haikou City show that the proposed approach has better comprehensive performance than baseline models. The proposed approach offers an effective and practical methodology for fine-grained non-stationary air quality predictive learning.

Structured timeline mapping as a data collection methodology: a new perspective for research on environmental adaptation

ABSTRACT The process of adaptation is dynamic and involves a temporal component. However, conventional methods of data collection have mostly been used in research on environmental adaptations and provide a rather static view. The structured timeline mapping method presented here is grounded in social sciences, designed for the West African environment, and constructed to capture time. The different steps for its construction and implementation are outlined along with the underlying reasoning that led to the choices made. It appears both respondents and interviewers benefit from this method since the recall process is facilitated for the first and the visual support gives a global view of the interview at all stages for the latter. Additionally, this methodology offers four major benefits. First, temporality is the central information collected, with both the timing and the duration of the elements. Second, this approach enables the observation of linkages and interrelations between adaptations or between adaptations and perceived changes in the environment. Third, the participants can self-reflect on their situation and bring elements to the analysis. Lastly, structured timeline mapping results in a more holistic view of the respondent’s adaptation journey. Therefore, this data collection methodology provides a complementary view of the adaptation journey.

Line-of-sight Initial Pointing Model of Space Dynamic Optical Network and Its Verification

In dynamic space networks, achieving high precision and fast initial pointing of the optical line of sight (LOS) is the key goal in developing this technology. It is the premise and basis of realizing optical LOS capture. Based on the composition and working principle of space optical networking systems, and the effect of real-time position and attitude changes on LOS initial pointing between networks, the matrix transformation and transfer principle is used to establish a multi-link LOS initial pointing model and analyze the factors affecting the size of the field of uncertainty (FOU). In a dynamic space optical networking experiment, the “one-to-two” simultaneous LOS pointing test is carried out, which shows that the model can realize the function of multi-link LOS initial pointing. The sizes of the FOU of the test terminal are 8.67 mrad and 8.34 mrad, respectively, with an average capture time of 18.3 s.

Coulomb effect in hybrid double quantum dot-metal nanoparticle systems considering the wetting layer

Many body effects in the wetting layer (WL)-double quantum dot (DQD)-metal nanoparticle (MNP) structure have been studied by modeling the Coulomb scattering rates in this structure. The strong coupling between WL-DQD-MNPs was considered. An orthogonalized plane wave (OPW) is assumed between WL-QD transitions. The transition momenta are calculated accordingly to specify the normalized Rabi frequency on this structure, considering the strong coupling between the WL-DQD-MNP structures. This approach is important for realizing scattering rates, including in-and-out capture and relaxation rates, which are essential for specifying the type of structure used depending on the optimum value of the scattering time required to fit the application. The QD hole capture rate is the highest, and the hole capture times are the shortest. The relaxation times are less than the electron capture times by one order, while they are half of the hole capture times. The capture rates increase with increasing distance R between the DQDs and the MNP. High tunneling increases hole-capture rates and changes the relaxation rates, showing the importance of tunneling in controlling the scattering rates.

ECL cytosensor for sensitive and label-free detection of circulating tumor cells based on hierarchical flower-like gold microstructures

The development of sensitive and efficient cell sensing strategies to detect circulating tumor cells (CTCs) in peripheral blood is crucial for the early diagnosis and prognostic assessment of cancer clinical treatment. Herein, an array of hierarchical flower-like gold microstructures (HFGMs) with anisotropic nanotips was synthesized by a simple electrodeposition method and used as a capture substrate to construct an ECL cytosensor based on the specific recognition of target cells by aptamers. The complex topography of the HFGMs array not only catalyzed the enhancement of ECL signals, but also induced the cells to generate more filopodia, improving the capture efficiency and shortening the capture time. The effect of topographic roughness on cell growth and adhesion propensity was also investigated, while the cell capture efficiency was proposed to be an important indicator affecting the accuracy of the ECL cytosensor. In addition, the capture of cells on the electrode surface increased the steric hindrance, which caused ECL signal changes in the Ru(bpy)32+ and TPrA system, realizing the quantitative detection of MCF-7 cells. The detection range of the sensor was from 102 to 106 cells mL−1 and the detection limit was 18 cells mL−1. The proposed detection method avoids the process of separation, labeling and counting, which has great potential for sensitive detection in clinical applications.

Terminal Congestion Analysis of Container Ports Using Satellite Images and AIS

This study proposes the use of satellite images and a vessel’s automatic identification system (AIS) data to evaluate the congestion level at container ports for operational efficiency analysis, which was never attempted in previous studies. The congestion level in container yards is classified by developing a convolutional neural network (CNN) model and an annotation tool to reduce the workload of creating training data. The annotation tool calculates the number of vertically stacked containers and the reliability of each container cell in a detection area by focusing on the shadows generated by the containers. Subsequently, a high-accuracy CNN model is developed for end-to-end processing to predict congestion levels. Finally, as an example of dynamic efficiency analysis of container terminals using satellite images, the relationship of the estimated average number of vertically stacked containers in the yard with the elapsed time between the image capture time and vessel arrival or departure time obtained from the automatic identification system data is analyzed. This study contributes to representing a prototype for dynamically estimating the number of vertically stacked containers and congestion level of container terminals using satellite images without statistical information, as well as its relationship with the timing of vessel arrival acquired from AIS data.

Confined Transport Behavior of Biomolecules within Tilted Nanopores.

The transport behavior of biomolecules at the confined nanoscale is very different from that of the bulk state. Numerous disease diagnostics and targeted drug treatments are performed based on nanochannels in cells. The specific structure and shape of nanochannels play an important role in the behavior and efficiency of substance transport. In this paper, we fabricated nanopores with different tilt angles and the same diameters using focused ion beam. The capture frequency and the blocking current amplitude of λ-DNA within large-angle nanopores decrease obviously, suggesting an increase in the energy barrier of large-angle nanopores and the fact that they stretch biomolecules to thinness. Most importantly, large-angle nanopores slow down λ-DNA transport by 2-4 times. MD simulations find that the sloped electroosmotic flow inside the tilted nanopores is the main factor contributing to the transport phenomena. The increase in the capture time of biomolecules by nanopores assists in obtaining more biological information from the current trajectories. Our study provides a new understanding of substance transport in specially shaped nanopores, which can be instrumental in providing fresh inspiration and approaches to the biomedical field.

DRL-Searcher: A Unified Approach to Multirobot Efficient Search for a Moving Target.

This article studies the multirobot efficient search (MuRES) for a nonadversarial moving target problem, whose objective is usually defined as either minimizing the target's expected capture time or maximizing the target's capture probability within a given time budget. Different from canonical MuRES algorithms, which target only one specific objective, our proposed algorithm, named distributional reinforcement learning-based searcher (DRL-Searcher), serves as a unified solution to both MuRES objectives. DRL-Searcher employs distributional reinforcement learning (DRL) to evaluate the full distribution of a given search policy's return, that is, the target's capture time, and thereafter makes improvements with respect to the particularly specified objective. We further adapt DRL-Searcher to the use case without the target's real-time location information, where only the probabilistic target belief (PTB) information is provided. Lastly, the recency reward is designed for implicit coordination among multiple robots. Comparative simulation results in a range of MuRES test environments show the superior performance of DRL-Searcher to state of the arts. Additionally, we deploy DRL-Searcher to a real multirobot system for moving target search in a self-constructed indoor environment with satisfying results.

Person group detection with global trajectory extraction in a disjoint camera network

Person group detection refers to the grouping of people with similar spatio-temporal trajectories. In this work, we address the problem of automatically detecting groups of people from disjoint camera views, which has an essential application in public safety but has not been seriously studied. The main challenge of this task is that the sparse distribution of cameras in a large surveillance area makes it difficult to infer and match people’s trajectories across cameras. To address this challenge, we propose a CCRF (Cyclic Conditional Random Fields) based model for cross-camera trajectory extraction, which takes both visual appearance and heterogeneous spatio-temporal information (including camera locations, video capture times, and the map of the surveillance area) as input and infers multiple candidate cross-camera trajectories for each person. Then, for each pair of people, we propose to use a dynamic trajectory warping (DTW) method to measure the similarity of their trajectories. DTW uses visual features to optimize the selection of trajectories and addresses the problem of trajectory length matching. Since there is no existing dataset that can directly support our research, we enrich our previously built Person Trajectory Dataset by adding the person group annotation and then verify the effectiveness of the proposed method on this dataset. The dataset and code are released at https://github.com/zhangxin1995/PTD_GROUP.

New Approach for Enhancing Sensitivity in Liquid‐Gated Nanowire FET Biosensors Under Optical Excitation

AbstractThe results obtained for liquid‐gated (LG) nanowire (NW) field‐effect transistor (FET) sensors analyzed in divalent ion (MgCl2) solutions, which play a crucial role in the human body, are reported. Liquid‐gated NW FET sensors fabricated in‐house are investigated using I–V characteristics and noise spectroscopy in a wide range of voltages and different intensities of optical infrared irradiation. A new effect is revealed under the influence of external optical excitation. The effect allows for the control of characteristic times of single‐trap phenomena: capture time to a single trap, emission time from the trap, and its ratio represented by the dimensionless ratio parameter (R‐factor). These parameters enabled an improvement in sensor sensitivity. The findings open prospects for controlling charge states in LG NW FET structures to optimize new parameters for achieving ultrahigh sensitivity in biosensors.

MotionRender: A simple Python implementation of video motion visualization for 3D motion capture data

We describe a Python library project for generating animations of simple 3D motion capture data points. The motion capture method is agnostic with respect to this visualizaiton tool, expecting a time series of time stamped coordinates captured in a 3-dimensional space. One of the advantages of this tool is the simple data format design. This tool was developed originally for a Kinect system implementing skeleton tracking of 15 joint positions, where each data point consists of an accurate time of capture (time stamp), and 3 accurate coordinate (x, y, z) positions of each of the 15 joints at each time step. In addition to the motion capture time series, all that is needed is a joint graph file describing the relationship of the joint edges to one another. This library can be extended to visualize and create animations of motion capture data that can be reformulated using this basic structure of a time series of time stamped 3D coordinates and a joint graph description file. This library should be useful as is, or with easy modification, for many such visualization requirements of similar motion capture data.

Research on intelligent legal decision support system based on big data analysis

Abstract This paper constructs an intelligent decision support system using text combination feature extraction, case similarity, XBoost sentencing prediction model, and other related technologies. The performance of the smart decision support system is tested for grabbing responses and monitoring response reliability. The XGBoost algorithm is used to construct the sentencing prediction model to achieve the intelligence of sentencing prediction. The effectiveness of the sentencing prediction model is examined by comparing the XGBoost model with different algorithms (Random Forest, CBDT, CNN). The practical application of intelligent decision support systems was summarized, highlighting the positive and negative effects. The results show that the overall responsiveness performance of the intelligent decision support system’s magisterial document capture and retrieval is reliable, and the number of captured and retrieved magisterial documents rises as the capture time increases. In XGBoost’s sentencing prediction, nine overlapping parts of the ten keywords were extracted for sentence and fine. In contrast, the keyword similarities are all higher than 0.5, and the difference between the predicted and real sentence values and fine is small. The intelligent decision support system has resulted in a gradual decrease in the number of court cases and an improvement in efficiency. The re-sentencing and retrial initiation rate decreased by over 20% from 2019 to 2023.

Minimum-Time Control for the Test Mass Release Phase of Drag-Free Spacecraft

The capture control of test mass by means of the electrostatic suspensions is crucial for drag-free spacecraft. The test mass must be released to the cage center of the inertial sensor accurately and quickly. This paper proposes a minimum-time capture control method for the test mass release phase of drag-free spacecraft. An analytical solution of optimal control is derived based on Pontryagin’s minimum principle and the linearized dynamics model of the test mass during the release phase. The parameters of the analytical solution are initially guessed with an approximate linear solution of the test mass dynamics model and are slightly modified by using differential correction. Compared with the exact numerical solution by the hp -adaptive pseudospectral method, the analytical solution is proved to be minimum-time. Numerical simulation shows that the proposed control method quickly captures the test mass to the cage center of the inertial sensor. The capture time to stabilization is only half that of the traditional controller.

A Method for Designing an Optical Clock Beacon to Detect Highly Accurate Image Capture Time

A Method for Designing an Optical Clock Beacon to Detect Highly Accurate Image Capture Time

High-effective fog collection on metal surfaces by temperature gradients

The acquisition of global freshwater resources has always been the direction of people's efforts. At present, metal materials based on special wettability and special structure have become an effective way to collect fog. However, it is still a bottleneck to further improve the water collection efficiency by carefully designing the wettability and special structure of materials. Therefore, we propose to use temperature gradient to enhance fog collection in metal surface. The mechanism of temperature gradient enhancement improves fog collection efficiency by 54 % and surface refresh rate by about 541 %. Further scattering measurements show that the capture time of fog particles at low cooling surface is shortened by about 40 %. Based on the theory of heat transfer and thermophoresis, the temperature gradient around the metal and the behavior of fog particles are analyzed, and it is concluded that the enhancement of fog collection performance on the metal surface can be caused by the temperature gradient caused by thermophoresis force. This work provides ideas for designing better fog collection equipment and materials.

Microplastic removal from water using modified maifanite with rotating magnetic field affected

Magnetic separation of microplastics from water is a new generation technology that is cost effective and environmentally friendly, and can avoid secondary pollution. The influence of magnetic Maifanite on polystyrene removal from water was investigated under a magnetic field action time, solution pH, magnetic carrier dosage, and polystyrene concentration, using a developed rotating magnetic field generator. Additionally, the polystyrene removal mechanism by the rotating magnetic field was revealed based on characterization and process simulation. The results showed that Maifanite can increase the number of surface adsorption sites and improve the adsorption performance after high temperature and acid impregnated modification. Specifically, the surface morphology of the modified Maifanite exhibited skirted stripes and a multilayered pore structure. And the specific surface area increased from 3.26 to 20.83 m2/g. The removal process is based on chemical adsorption, supplemented by physical adsorption. The magnetic carrier is tightly wrapped around the polystyrene surface by π bonds between the metal and hydroxy groups, ensuring agglomeration strength. Additionally, the rotating magnetic field generator designed can provide a rotating dynamic magnetic field, and the magnetic particles in the mixed system present a multi-directional helical motion trajectory, thus the walking displacement increases and the capture time decreases. And the probability of collision with non-magnetic particles and the formation of magnetic agglomerates are greatly increased. Thus the adsorption and removal of microplastics are enhanced theoretically. The polystyrene removal efficiency from water using modified Maifanite by rotating magnetic field affected increased by 19.49 %, reaching 98.46 %, and stabilizing at 86.53 % over 25 operation cycles. This study holds promise for advancing magnetic separation technology for microplastics from water.

An Odor Trace Visualization System Using a Two-Dimensional Backside Scattering Localized Surface Plasmon Resonance Gas Sensor

Odor information fills every corner of our lives yet obtaining its spatiotemporal distribution is a difficult challenge. Localized surface plasmon resonance has shown good sensitivity and a high response/recovery speed in odor sensing and converts chemical information such as odor information into optical information, which can be captured by charge-coupled device cameras. This suggests that the utilization of localized surface plasmon resonance has great potential in two-dimensional odor trace visualization. In this study, we developed a two-dimensional imaging system based on backside scattering from a localized surface plasmon resonance substrate to visualize odor traces, providing an intuitive representation of the spatiotemporal distribution of odor, and evaluated the performance of the system. In comparative experiments, we observed distinct differences between odor traces and disturbances caused by environmental factors in differential images. In addition, we noted changes in intensity at positions corresponding to the odor traces. Furthermore, for indoor experiments, we developed a method of finding the optimal capture time by comparing changes in differential images relative to the shape of the original odor trace. This method is expected to assist in the collection of spatial information of unknown odor traces in future research.

Economy and carbon emissions optimization of different provinces or regions in China using an improved temporal attention mechanism based on gate recurrent unit

With the implementation of 14th Five-Year Plan in China and the completion of the poverty alleviation task, the economy in China has made great progress. However, the carbon dioxide (CO2) emission has also increased, which will lead to the greenhouse effect for threatening economic development and affecting people's lives. Global warming has become the greatest challenge to contemporary economic and social development, and the transition to low-carbon economy has become a general trend of world economic development. Meanwhile, the traditional neural network method for predicting the economy and CO2 emissions is not accurate and objective because of the unbalanced labeling of training data and the inability to capture the relationship between time series data. Therefore, an economy and CO2 emission prediction model based on temporal attention Gate Recurrent Unit (GRU) (TA-GRU) with loss function Balanced Mean Square Error (BMSE) (BTA-GRU) mechanism is proposed to analyze and optimize the energy structure of 27 provincial-level administrative regions in China. Due to the unbalanced data labels in the different energy structure of different regions, the improved loss function BMSE is used to solve the problem of unbalanced labels from the perspective of statistics. Then, with coal, gasoline, petroleum, coke, fuel oil, diesel, natural gas and crude oil as inputs, the per capita Gross Domestic Product (GDP) as the desired output and the CO2 as the undesired output, the GRU can better capture time step dependencies in time series, and the attention mechanism can assign different weights to each time step, so that the proposed method can better learn data features and improve the accuracy of the an economic and CO2 emission prediction model. Finally, the CO2 emission and economic prediction model of different provincial-level administrative regions in China is established based on this proposed method. The experimental results show that compared with other mainstream time series data prediction models, this proposed model has better stability and practicability, and its mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) reach 3.43, 3.28% and 2449.57, respectively. Furthermore, according to the actual situation of efficient provincial administrative regions, the energy structure of inefficient provincial administrative regions can be adjusted and optimized. Such as the energy structure of Hebei Province can be adjusted according to the energy structure of Beijing, it is estimated that the CO2 emissions of Hebei will be reduced by 82,972.43 ×10000 tons (WT) and its per capita GDP will be increased by 115,856 Yuan. Moreover, Chinese provincial governments should vigorously promote the use of clean energy, accelerate the establishment of a sound green low-carbon circular development economic system, and help achieve the goal of carbon neutrality.

150 Repeatability of Rangeland Behavioral and Social Traits Derived from Gps Collars

Abstract The ability of an individual to cope with stressors in their environment is going to be paramount in the face of climate change. Rangeland livestock experience harsh conditions including heat and/or cold stress, water and feed restrictions and risk of predation. In extensive management systems, the ability to capture the capacity to cope with stressors of an individual in its environment is encumbered by remote locations with difficult terrain and no access to power or the internet. GPS collars can provide insights into individual land-use behavior, e.g., their impact on the environment or ability to cope with stressors, and social interactions, e.g., mothering ability. GPS collars (n = 112) developed at the University of Idaho were deployed on 57 ewes and 55 lambs (45 ewe-lamb pairs) in an extensive rangeland environment at the Great Basin Research and Extension Center in Eureka, NV. The collars recorded location data every 10 minutes from July 27th to August 18, 2022. Coordinate fixes were successfully recorded 67.11 ± 27.12% of the time and resulted in a kept record occurring every 60 ± 202 minutes. Of the ewe-lamb pair devices, 43 pairs contributed 801 ± 703 paired records (i.e., records that occurred within 5 minutes of each other). GPS coordinate locations, and their respective capture times, allow for the analysis of distance traveled, water usage, dispersion, and ewe-lamb distance. Distance traveled was analyzed on a daily basis with a weighted median distance imputed for all travel times that lasted more than 900 seconds. Dispersion was defined as the distance of an individual from the centroid of the flock in meters, and was transformed to a Z-score based on the position of individuals with GPS coordinates captured within 10 minutes of the measurement of the individual. Ewe-lamb pair distances were cube root transformed. Twin status, dam line, age, and day were fitted as fixed classes to estimate the repeatability of records for mature ewes. Repeatability estimates of daily distance traveled, daily water usage, and daily dispersion were 0.22 ± 0.05, 0.30 ± 0.06 and 0.10 ± 0.03, respectively. Daily ewe-lamb distances had a repeatability estimate of 0.45 ± 0.08. Day of recording was significant for daily distance traveled (P-value = 2.72×10-4) and water usage (P-value = 1.06×10-3). No other effects were significant in any of the models. In conclusion, PLF tools are an enabling technology that allow for passive data capture in remote locations. Our findings suggest that GPS collars can elucidate a variety of land use and social behavior traits that could serve as meaningful criteria for selection in extensive livestock production settings. Selection on these indicators of resilience may increase livestock productivity and welfare by increasing heat stress tolerance via the analysis of novel behavioral traits seen on extensive rangelands.

Life cycle assessment and carbon neutrality analysis of 'waste plastics - upcycling plastics' system based on adsorption carbon capture

Integrated with carbon capture and utilization (CCU), the waste plastic upcycling is a promising solution for mitigating environmental issues associated with CO2 emissions and plastic waste. However, there still exists a knowledge gap on how to assess reasonably the coupling environmental performance. In this paper, a 'waste plastics - upcycling plastics' system integrated with CCU is proposed through a three-step process: activated carbon is produced from waste PET plastics, CO2 is captured using PET-AC as the adsorbent, and upcycling plastics are synthesized through CO2 utilization. The life cycle assessment and carbon neutrality analysis are employed to evaluate the environmental performance of proposed system. Our findings show that the CO2 utilization process contributes the most to the system's global warming potential (GWP), accounting for approximately 68% of the total impact. By selecting renewable energy sources in the CO2 capture process and extending the capture time, the degree of carbon neutrality (DCN) could be increased from 0.985 to 1.235 and 0.991, respectively. The GWP and DCN are also influenced by the yield of activated carbon and the type of upcycling plastics.