Dynamic Information Acquisition Research Articles

Study on the monitoring method of debonding between concrete beams and reinforced steel plates based on piezoelectric smart materials

Concrete reinforcement is essential for ensuring the safety and durability of concrete structures. Bonding steel plates to reinforce concrete is widely used to renovate or strengthen concrete beam structures. Due to construction quality and the influence of factors such as environment and fatigue, debonding often occurs between the steel plate and concrete, making monitoring and early warning after concrete structure reinforcement challenging. This paper proposes a novel approach to monitor the degree of debonding between the steel plate and concrete beam using active sensing technology. The method uses lead zirconate titanate (PZT) as an actuator to generate stress waves. It prepares strip sensors with polyvinylidene fluoride as the sensing element to monitor stress waves passing through the steel plate and concrete beam. The monitoring system detects the degree of debonding between the steel plate and the concrete beam by monitoring the change in surface voltage of the sensor. Experiments show that the degree of debonding significantly correlates with the received voltage signal; the higher the debonding, the larger the received voltage signal. It is also observed that, at the same degree of debonding, the actuator and sensor attachment position have a particular impact on the received voltage signal. Through experiments and numerical simulation analysis, it is found that when the sensor is attached to the left side of the steel plate, that is, the bonded section of the steel plate, the amplitude of the voltage signal collected by the dynamic information acquisition system is the smallest, i.e., V_debonded section > V_middle > V_bonded section. Based on the above research, the active sensing technology proposed in this paper has good sensitivity to the degree of debonding between the steel plate and concrete. It is expected to become an effective monitoring and evaluation method for the degree of debonding between steel plates and concrete.

Dynamic wake field reconstruction of wind turbine through Physics-Informed Neural Network and Sparse LiDAR data

Real-time acquisition of dynamic wake field information has garnered substantial attention in the wind farm industry, as it provides a crucial data source for intelligent wind farm monitoring and control. However, the existing wind measurement technologies, such as light detection and ranging (LiDAR), are limited to sparse data point measurements. This paper explores the utilization of a Physics-Informed Neural Network (PINN) to reconstruct wind turbine wake dynamics, specifically focusing on the influence of active wind turbine yaw operation on wake evolution. The methodology involves creating a tailored loss function that combines sparse wake measurement data with the Navier-Stokes (NS) equations. More precisely, the neural network incorporates the NS equations as constraints to guide the prediction of physical quantities in the output, including downwind velocity, crosswind velocity, and pressure. Taking the dynamic wake during yawing as a case study, the proposed method showcases remarkable universality and robustness across diverse scenarios involving varying scanning angle intervals, measurement point spacings, frequencies, and noise levels. It successfully captures the dynamic trends in wake evolution during yawing and accurately forecasts the wake trajectory and deflection. Even when tested with actual wake measurement data, the method can still effectively reconstruct the flow field, indicating significant potential for the real wind farm yaw control.

Humification levels of dissolved organic matter in the eastern plain lakes of China based on long-term satellite observations

Under the influence of intensive human activities and global climate change, the sources and compositions of dissolved organic matter (DOM) in the eastern plain lake (EPL) region in China have fluctuated sharply. It has been successfully proven that the humification index (HIX), which can be derived from three-dimensional excitation-emission matrix fluorescence spectroscopy, can be an effective proxy for the sources and compositions of DOM. Therefore, combined with remote sensing technology, the sources and compositions of DOM can be tracked on a large scale by associating the HIX with optically active components. Here, we proposed a novel HIX remote sensing retrieval (IRHIX) model suitable for Landsat series sensors based on the comprehensive analysis of the covariation mechanism between HIX and optically active components in different water types. The validation results showed that the model runs well on the independent validation dataset and the satellite-ground synchronous sampling dataset, with an uncertainty ranging from 30.85 % to 36.92 % (average ± standard deviation = 33.6 % ± 3.07 %). The image-derived HIX revealed substantial spatiotemporal variations in the sources and compositions of DOM in 474 lakes in the EPL during 1986–2021. Subsequently, we obtained three long-term change modes of the HIX trend, namely, significant decline, gentle change, and significant rise, accounting for 74.68 %, 17.09 %, and 8.23 % of the lake number, respectively. The driving factor analysis showed that human activities had the most extensive influence on the DOM humification level. In addition, we also found that the HIX increased slightly with increasing lake area (R2 = 0.07, P < 0.05) or significantly with decreasing trophic state (R2 = 0.83, P < 0.05). Our results provide a new exploration for the effective acquisition of long-term dynamic information about the sources and compositions of DOM in inland lakes and provide important support for lake water quality management and restoration.

Extraction the Spatial Distribution of Mangroves in the Same Month Based on Images Reconstructed with the FSDAF Model

Mangroves have extremely high economic and ecological value. Through remote sensing, the spatial distribution of and spatiotemporal changes in mangroves can be accurately obtained, providing data support for the sustainable development of coastal wetlands. However, due to the cloudy and rainy conditions in the growing areas of mangroves, there are relatively few high-quality image data available, resulting in a time difference between regional mosaic images, with a maximum difference of several months, which has a certain impact on accuracy when extracting the spatial distribution of mangroves in some regions. At present, most regional mangrove research has ignored the impact of the time difference between mosaic images, which not only leads to inaccurate monitoring results of mangroves’ spatial distribution and dynamic changes but also limits the frequency of monitoring of regional mangrove dynamic changes to an annual scale, making it difficult to achieve more refined time scales. Based on this, this study takes the coastal mangrove distribution area in China as the research area, uses Landsat 8 and MODIS images as basic data, reconstructs the January 2021 images of the research area based on the FSDAF model, and uses a random forest algorithm to extract the spatial distribution of mangrove forests and analyze the landscape pattern. The results showed that the fused image based on the FSDAF model was highly similar to the validation image, with an R value of 0.85, showing a significant positive correlation, indicating that the fused image could replace the original image for mangrove extraction in the same month. The overall accuracy of the spatial distribution extraction of mangroves based on the fused image was 89.97%. The high sample separation and spectral curve changes highly similar to the validation image indicate that the fused image can more accurately obtain the spatial distribution of mangroves. Compared to the original image, the fused image based on the FSDAF model is closer to the validation image, and the fused image can reflect the changes in mangroves in time series, thus achieving accurate acquisition of dynamic change information in a short time span. It provides data and methodological support for future monitoring of dynamic changes in large-scale mangroves. The total area of mangroves in China in January 2021 based on the fused image was 27,122.4 ha, of which Guangdong had the largest mangrove area, with 12,098.34 ha, while Macao had the smallest mangrove area of only 16.74 ha. At the same time, the mangroves in Guangdong and Guangxi had a high degree of fragmentation and were severely disturbed, requiring strengthened protection efforts, while the mangroves in Hong Kong, Zhejiang, and Macao had regular shapes, benefiting from local active artificial restoration.

Near-infrared II fluorescent carbon dots for differential imaging of drug-resistant bacteria and dynamic monitoring of immune system defense against bacterial infection in vivo

In vivo fluorescence imaging allows the accurate acquisition of dynamic biological information, which plays a vital role in the early diagnosis and treatment of bacterial infectious diseases. Herein, a NIR-II-emitting CD-based nanoprobe (CyCDs) is synthesized using Cy7-CH3 as the carbon source and 3-hydroxytyramine hydrochloride as the surface passivator. Notably, the π-conjugated system and rigid planar structure of the carbon source can broaden the wavelength range of absorption and emission. The presence of amino groups enables CyCDs with a positive zeta potential (+0.32 mV), which can facilitate the interactions between CyCDs and bacteria. Interestingly, CyCDs can only stain the cell wall of drug-resistant bacteria, while normal bacteria have a bright fluorescent signal over all of the cell. This phenomenon enables the identification of drug-resistant bacteria in vitro. Furthermore, CyCDs can be used to monitor the interaction between the immune system and infected bacteria in vivo and guide the timely usage of antibiotics

Validity of Variable Message Sign Information: Is It Influenced by Drivers’ Short-Term Memory?

Variable message signs (VMSs), as a key component of intelligent transportation systems, have been frequently used in the management of urban roads to provide drivers with real-time traffic information. Nevertheless, a lack of unified regulations for VMS design, makes it difficult to accurately determine the validity of VMS information. In practice, inappropriately designed VMSs (e.g., VMSs overloaded with information and long setting distances) are common. In this study, laboratory simulation trials were conducted to assess VMS information validity in relation to the short-term memory (STM) capacity of drivers. Specifically, drivers capacity to easily obtain and respond correctly to valid VMS information based on memorized target information. Sixty participants completed the trials and pre-/postquestionnaires. Subjective evaluation, dynamic information acquisition rate, accuracy, and memory reliability were analyzed to evaluate their relationship with VMS information validity. The results confirmed that STM considering the information demand rate significantly affected the validity of the VMS information. Information validity deteriorated as the information volume and VMS setting distance increased. Reasonable validity was found when memory reliability ranged between 0.85 and 1.5 and the information volume was no more than 90 bits, corresponding to a subjective evaluation of 1, an accuracy of at least 80%, and a suggested STM time of 43.46 s. These criteria would satisfy driver information demand and prevent information redundancy or wasting of resources. These findings provide a basis for determining the thresholds for VMS information to promote practical and user-friendly VMS designs on urban roads.

Optimizing Internet of Things-Based Intelligent Transportation System’s Information Acquisition Using Deep Learning

This work first discusses the Intelligent Transportation System (ITS)-oriented dynamic and static Information Acquisition Models (IAMs) and explains the information collection mechanism of the Internet of Things (IoT)-based ITS. The goal is to improve travel conditions and contribute to a better urban environment. In order to do so, the Faster Region-based Convolutional Neural Network (Faster R-CNN) is introduced to extract the IoT-based ITS’s electronic data features. It is observed that the Faster R-CNN has excellent recall and accuracy in extracting the features from the ITS electronic data sets. Specifically, the Faster R-CNN’s average recall and accuracy reach 83.89% and 86.79%. The accuracy is 6.20% higher than the R-CNN method. Thus, the Faster R-CNN algorithm features more robust and reliable performance for collecting and analyzing ITS data. Overall, this work examines ITS-oriented electronic information collection and automatic detection against the technological background of applying Computer Vision, Deep Learning, and IoT in urban traffic management. In particular, it explains the IoT-based ITS’s electronic information collection mechanism under Deep Learning (Faster R-CNN). The finding offers a theoretical foundation for implementing Deep Learning technologies in collecting ITS-oriented big data and smart city construction.

Using HJ-1 CCD and MODIS Fusion Data to Invert HJ-1 NBAR for Time Series Analysis, a Case Study in the Mountain Valley of North China

HJ-1 charge-coupled device (CCD) data with high temporal and medium spatial resolution are widely used in environmental and disaster monitoring in China. However, due to bad weather, it is difficult to obtain sufficient time-continuous HJ-1 CCD data for environmental monitoring. In this study, the mountain valley with farmland and forestland in North China is selected as the experimental area, and HJ-1 CCD and moderate resolution imaging spectroradiometer (MODIS) data are used in the case study. An improved method of fusing data and inversing surface reflectivity is presented to obtain the HJ-1 inversion network-based application resolution (NBAR) data using linear matching of the Ross Thick-Li Sparse Reciprocal (RTLSR) model, and then predicted reflectivity using the seasonal autoregressive integrated moving average (SARIMA) model. The fusion data have advantages of high spatial and temporal resolution, as well as meeting the requirements of high quality and quantity of small-scale regional data. This case study provides a feasibility method for the HJ-1 satellites to produce the secondary products for small-scale remote sensing ground surface research. It also provides a reference for dynamic information acquisition and application of small satellite data, contributing to the improvement in RS estimation of surface environment variables.

Temporal and spatial variation of coastline using remote sensing images for Zhoushan archipelago, China

The acquisition of dynamic information on the coastline is of great significance for the Zhoushan archipelago. However, a large amount of suspended sediments, a tortuous coastline, numerous tidal flats, and so on have brought many challenges to research involving coastline extraction and the analysis of the spatial–temporal dynamics. This study has developed a method for extracting coastline information using multi-temporal remote sensing images based on tasseled cap transformation and mathematical morphology, allowing the temporal and spatial evolution of the coastline to be analyzed. The results showed that the proposed method can effectively attenuate the influence of suspended sediments, winding coastline, and shoals on the extraction of coastline information, and allow researchers to extract the spatial position of the coastline more accurately. The total coastline length of the Zhoushan archipelago increased by 327.36 km during the study period from 2000 to 2018, the average growth in length was 18.19 km, and the average growth rate was 0.72% over the 19-year period. The total area enclosed by the coastline of the Zhoushan archipelago increased by 112.26 km2 during the study period from 2000 to 2018, the average growth in area was 6.24 km2, and the average growth rate was 0.49% over the 19-year period. Construction, reclamation, and ocean engineering are the main causes of changes to the coastline of the Zhoushan archipelago. This study is useful for accurate coastline information extraction using remote sensing images in a complex marine environment and for protecting coastal resources in the archipelago.

Risky Investments Under Static and Dynamic Information Acquisition

Risky Investments Under Static and Dynamic Information Acquisition

Prediction Method of Ship's Track Based on Mathematical Model

Liu, J., 2020. Prediction method of ship's track based on mathematical model. In: Li, L. and Huang, X. (eds.), Sustainable Development in Coastal Regions: A Perspective of Environment, Economy, and Technology. Journal of Coastal Research, Special Issue No. 112, pp. 379-382. Coconut Creek (Florida), ISSN 0749-0208.Real-time and accurate acquisition of ship navigation dynamic information plays an important role in maritime traffic research and management, and ship intelligent collision avoidance. This paper proposes to convert ship automatic identification system (AIS) data into navigation dynamics time series data for training and testing of LSTM network. The prediction results are compared with the prediction results of the traditional track estimation algorithm and the BP (back propagation) neural network method. The results prove that the ship navigation dynamic prediction model based on the mathematical model has high accuracy, strong robustness and good versatility. Features. The prediction results can provide a reference for the supervision of the ship traffic management center, and have high practical application value in early warning of ship collisions and groundings.

Dynamic Information Acquisition and Strategic Trading

We study how dynamic research affects information acquisition in financial markets. In our strategic trading model, the trader performs costly research to generate private information but does not always succeed. Optimal research activity responds to market conditions and generates novel empirical and policy implications. First, more frequent public disclosures can “crowd in” private information acquisition, and increase price informativeness and harm liquidity, instead of “leveling the playing field.” Second, measures of research activity may be negatively associated with informed trading. Finally, policies that improve research effectiveness or increase market participation by uninformed investors can simultaneously improve price informativeness and liquidity.

An Axiomatic Theory of Dynamic Information Acquisition

An Axiomatic Theory of Dynamic Information Acquisition

The Role of Model Uncertainty in Corporate Risk Management

Current corporate risk management theories predict that young firms should hedge more than the established ones. However, the claim is not supported by empirical observations, which also present mixed evidence on whether hedging creates value. This paper attempts to address this puzzle by including model uncertainty as part of risk management process. We develop a dynamic model in which agents learn about a firm’s hedgeability, gauged by the correlation between its operating cash flow and underlying asset of hedging instruments, while weighing the costs and benefits of different risk management tools. The model predicts that resolving model uncertainty accelerates the process of building up hedging positions, but this is not necessarily accompanied with firm value creation. We conclude that dynamic information acquisition is an important determinant of corporate risk management.

Dynamic information acquisition and time-varying uncertainty

This paper studies the role of information acquisition in propagating/stabilizing uncertainty shocks in a dynamic financial market. In a static world, uncertainty raises the value of information, which encourages more information acquisition. In a dynamic world, however, uncertainty can depress information acquisition through a dynamic complementarity channel: More uncertainty induces future investors to trade more cautiously. This renders future resale stock price less informative and reduces the value of information today. Due to the dynamic complementarity, transitory uncertainty shocks can have long-lasting impacts. Direct government purchases can stimulate information production, eliminate equilibrium multiplicity, and attenuate the impacts of uncertainty shocks by raising the effective risk-bearing capacity of the informed investors.

Try before you buy: A theory of dynamic information acquisition

This paper develops a model of dynamic information acquisition where a buyer acquires information about a product, and a monopoly seller sets the price of her product anticipating the buyer's behavior. It finds that the buyer makes a purchase decision when his expected gain from trade hits one of two boundaries which are deterministic functions of time. Those boundaries are independent of the buyer's prior value. The seller's profit is increasing in the buyer's cost of information if and only if the buyer's prior purchase probability is above fifty percent. In analyzing the problem, a close connection between the information acquisition problem and the theory of American options is established and exploited.

Quantitative microimmunohistochemistry for the grading of immunostains on tumour tissues.

Immunohistochemistry is the gold-standard method for cancer-biomarker identification and patient stratification. Yet, owing to signal saturation, its use as a quantitative assay is limited as it cannot distinguish tumours with similar biomarker-expression levels. Here, we introduce a quantitative microimmunochemistry assay that enables the acquisition of dynamic information, via a metric of the evolution of the immunohistochemistry signal during tissue staining, for the quantification of relative antigen density on tissue surfaces. We used the assay to stratify 30 patient-derived breast-cancer samples into conventional classes and to determine the proximity of each sample to the other classes. We also show that the assay enables the quantification of multiple biomarkers (human epidermal growth factor receptor, oestrogen receptor and progesterone receptor) in a standard breast-cancer panel. The integration of quantitative microimmunohistochemistry into current pathology workflows may lead to improvements in the precision of biomarker quantification.

OFFDTAN: A New Approach of Offline Dynamic Taint Analysis for Binaries

Dynamic taint analysis is a powerful technique for tracking the flow of sensitive information. Different approaches have been proposed to accelerate this process in an online or offline manner. Unfortunately, most of these approaches still have performance bottlenecks and thus reduce analytical efficiency. To address this limitation, we present OFFDTAN, a new approach of offline dynamic taint analysis for binaries. OFFDTAN can be described in terms of four stages: dynamic information acquisition, vulnerability modeling, offline analysis, and backtrace analysis. It first records program runtime information and models the stack buffer overflow vulnerabilities and controlled jump vulnerabilities. Then it performs offline analysis and backtrace analysis to locate vulnerabilities. We implement OFFDTAN on the basis of QEMU virtual machine and apply it to off-the-shelf applications. In order to illustrate how our approach works, we first employ a case study. Furthermore, six applications have been verified so as to evaluate our approach. Experimental results demonstrate that our approach is correct and effective. Compared with other offline analysis tools, OFFDTAN has much lower application runtime overhead.

Inverse packet matrix reasoning model-based the intelligent dynamic separation and acquisition of educational information

Inverse packet sets is a set model with dynamic characteristics, which is got by introducing dynamic characteristics into Cantor set $$ X $$ and improving Cantor $$ X $$ . By using inverse packet sets to improve the concept of ordinary augmented matrix, the structure of internal inverse packet augmented matrix, outer inverse packet augmented matrix and inverse packet augmented matrix are given. Internal inverse packet augmented matrix reasoning, outer inverse packet augmented matrix reasoning, inverse packet augmented matrix reasoning and reasoning model are given. Finally intelligent dynamic separating-acquisition of information is given. By using these theoretical results, the application of intelligent dynamic separating-acquisition of educational information is given. Inverse packet matrix reasoning provides a new theory and new method for mining and acquisition of dynamic educational information. This is also a successful teaching demonstration on the research of matrix Teaching.

Investment Decisions and Falling Cost of Data Analytics

Central and local governments around the world are seeking investments from private firms to create smart city solutions. Motivated by this, we model an investor and a local government with a Stackelberg game. The investor has a CARA (or exponential) utility function and she has an option to acquire data dynamically and use that in a Bayesian predictive model to forecast the quality sensitivity of demand before her investment decision. The government chooses the highest price such that demand quantity is above a minimum threshold. We prove comparative statics under the dynamic information acquisition and the Stackelberg game. We show that borrowing-constrained or highly risk-averse investors have low demand for information. Fur- thermore, the falling marginal cost of information driven by smart cities’ datasets and analytics raises investment amounts and leverage, which leads to higher losses during crises. Finally, we show that leverage constraints are effective in mitigating losses during crises.