Real Information Research Articles

CryoSamba: self-supervised deep volumetric denoising for cryo-electron tomography data.

Cryogenic electron tomography (cryo-ET) has rapidly advanced as a high-resolution imaging tool for visualizing subcellular structures in 3D with molecular detail. Direct image inspection remains challenging due to inherent low signal-to-noise ratios (SNR). We introduce CryoSamba, a self-supervised deep learning-based model designed for denoising cryo-ET images. CryoSamba enhances single consecutive 2D planes in tomograms by averaging motion-compensated nearby planes through deep learning interpolation, effectively mimicking increased exposure. This approach amplifies coherent signals and reduces high-frequency noise, substantially improving tomogram contrast and SNR. CryoSamba operates on 3D volumes without needing pre-recorded images, synthetic data, labels or annotations, noise models, or paired volumes. CryoSamba suppresses high-frequency information less aggressively than do existing cryo-ET denoising methods, while retaining real information, as shown both by visual inspection and by Fourier shell correlation analysis of icosahedrally symmetric virus particles. Thus, CryoSamba enhances the analytical pipeline for direct 3D tomogram visual interpretation.

Study on intelligent anti-occlusion tracking algorithm for infrared ground targets

In response to the issue of infrared ground target tracking failure caused by background occlusion, a novel anti-occlusion tracker for infrared ground targets is proposed based on an enhanced trajectory prediction network. Initially, an occlusion assessment criterion is proposed to accurately assess the occlusion status of infrared ground targets. Subsequently, enhancements are made to the BiTrap trajectory prediction network. On one hand, velocity information is introduced through a Siamese network structure, adopting a unidirectional prediction method, building the SiamTrap trajectory prediction network that improves trajectory prediction accuracy. On the other hand, refining both the training and application methods enables more precise predictions of ground target trajectories. For short-term occlusion, the SiamTrap network uses temporal context information to predict the occluded position of the target. For long-term occlusion, a search expansion strategy is introduced to address prediction errors accumulated due to a lack of real target information. Finally, a "second verification" criterion is introduced, realizing accurate target capture and normal tracking. Comparative tests are conducted on infrared target tracking sequences with occlusion. Compared to baseline trackers, the proposed algorithm shows a 5.2% improvement in success rate and a 5.9% improvement in accuracy under the OPE evaluation metric. This indicates the robustness of the proposed algorithm in handling occlusion scenarios for infrared ground targets.

Research on paging enhancements for 5G-A downlink transmission energy saving

5G-Advanced (5G-A), an evolutionary iteration of 5G, effectively enhances 5G services. The increasing complexity in downlink services scenarios stresses the necessity for research into the integration of efficient communication with low-carbon solutions. Historically, there has been an emphasis on reliability and precision, at the expense of power consumption. Although energy-saving technologies like Idle mode-Discontinuous Reception (IDRX) and Paging Early Indication (PEI) have been introduced to reduce power consumption in UE, they have not been fully tailored to the paging characteristics of 5G-A downlink services. In this paper, we take full account of the impact of paging message density on energy saving measures and propose an enhanced paging technology, termed Predictive-PEI (PPEI), which is designed to reduce UE overhead while minimizing latency whenever possible. Towards this end, we design a dual threshold decision framework founded on machine learning, mainly involving two steps. We first use the LSTM-FNN neural network to forecast the arrival moment of upcoming paging messages based on past real information. Then, the output of the initial prediction is as the input of the next dual threshold decision algorithm, to determine the optimal moment for transmitting the PEI. The restrictive factors, encompass average delay threshold and cache capacity threshold, playing a role in decisions regarding paging message caching and decoding. Compared to the existing schemes, our PPEI scheme flexibly sends efficient PEI according to the actual paging characteristics by introducing machine learning, resulting in substantial power savings of up to 38.89% while concurrently ensuring effective latency control.

Range selective digital holographic imaging of vibrating objects using FMCW lidar

The use of frequency modulated continuous wave (FMCW) chirped transmit and reference waveforms in digital holographic (DH) imaging has enabled range selectivity. By frequency shifting the reference beam to compensate for the typical FMCW lidar beat frequency associated with a particular range, a temporally stable holographic image is formed for objects at the selected range and coherently integrates on a short wave infrared (SWIR) sensor. For vibrating objects, longitudinal movements of the object greater than half of an optical wavelength during the exposure time of the sensor array induce phase shifts that can wash out the hologram. An analog feedback system was designed and constructed whereby a lidar subassembly provides real time phase compensation information to a DH subassembly in order to stabilize the range selective digital holographic recording of the object. The design and characterization of the feedback system, as well as the results demonstrating the performance for vibrating objects that move over 17 wavelengths during the sensor exposure, are discussed.

Does Geopolitical Risk Matter for Cross‐Industry Risk Contagion: The Roles of Real Linkage and Information Channels*

AbstractUsing data from 34 Chinese industries from January 2007 to February 2024, we examine risk spillover in the economic system and the channels through which geopolitical risk (GPR) influences contagion effects. Risk spillover measurements are calculated for both the short and long term by combining variational mode decomposition with the variance decomposition of vector autoregression. Our findings provide evidence of significant and time‐varying spillovers among industries over time. The results on densities and assortativity coefficients indicate the presence of structural changes in the risk network during geopolitical crises. Furthermore, we find that the real linkage (input–output nexus) is an important determinant of long‐term spillovers during periods of high GPR, and the information channel (market sentiment) is associated with risk contagion caused by GPR in both the short and long term. Overall, this paper offers valuable insights to prevent and mitigate the negative consequences triggered by geopolitical events on the stability of the economic system.

Analysis of the current state and forecasts of the development of the residential real estate market of the Republic of Kazakhstan

Purpose of the study – describe the state of the Kazakhstan residential real estate market, identify the features and trends of its development. The authors of the study pay special attention to the analysis of the main indicators characterizing the residential real estate market, as well as the processes taking place in the mortgage lending market. Methodology – the study was conducted using methods of economic and statistical analysis, graphic method, method of description and generalization. The research sources were offi cial statistical materials of the National Bank of Kazakhstan, analytical reviews of consulting companies and fi nancial organizations, the «Krisha.kz» real estate platform and the «Kn.kz» real estate information portal, scientifi c research by Kazakh and foreign authors, as well as materials of scientifi c and practical conferences. Originality / value of the study – the authors carried out a detailed analysis of the state and problems of the residential real estate market of the Republic of Kazakhstan in recent years and gave forecasts for its future development. Findings. According to the results of research and analysis of the current state of the Kazakh residential real estate market in recent years, the authors identifi ed problems and identifi ed current trends in the development of the housing market, as well as forecasts of its future development. The results of the scientifi c article can be useful for real estate agencies, industry experts, as well as the scientifi c community involved in studying the real estate market.

More Familiar or Unfamiliar? Exploring the Impact of Adding Avatars to Three-Dimensional Collaborative Virtual Environments on Peer Social Presence

Remote maintenance refers to the maintenance tasks performed by off-site personnel using communication devices. The quality of collaboration among off-site personnel has been a focus of research on remote maintenance. With the rapid development of virtual technology, remote collaboration has gradually transformed into a three-dimensional (3D) collaborative virtual environment (CVEs) based on mixed reality technology. 3D CVEs has improved the efficiency of the staff using real-time screen sharing and fusion of virtual and real information. The interaction modes provided by 3D CVEs also have deepened the sense of task participation of off-site personnel. Nevertheless, the addition of new technologies has had a complex impact on trust, communication, cohesion, and task performance among off-site personnel. In this study, we investigated the combined effects of intimacy between collaborators and the avatar interaction mode provided by a 3D CVEs on social presence, interpersonal trust, task performance, and task load during collaboration. The results showed that acquaintances were able to quickly establish good interpersonal trust among themselves and had better task performance even in a CVEs. For strangers, performing tasks through CVEs enhanced their social presence. In addition, the avatar had a more complex effect on the performance of members with different levels of intimacy in 3D CVEs.

A Comparation Study of Transmission Line Routing Based on A* and RRT Algorithms

Transmission line design is an important part of power grid engineering design, and transmission line path selection is the basis of line design. Transmission line route selection is to select a line path that can not only meet the requirements of power transmission and consumption, but also consider the cost control of the line, and avoid the farmland, highways, ecological exclusion zone between the start and end of the transmission line. The path scheme is constrained by geographical factors, project cost, construction conditions, operation and maintenance conditions, which makes the path selection show the characteristics of comprehensive diversity, spatial complexity, policy influence and so on. In the traditional route selection of transmission lines, line design specialists need to go through blind route selection, site survey and other steps to determine the path scheme, but because of insufficient effectiveness of the map, it is difficult to reflect the real environmental information, resulting in repeated route selection work to determine the final path scheme. In this paper, the selection principle of transmission line path is introduced first. Then, two path optimization algorithms, i.e. A* algorithm and rapidly expanding random tree (RRT) algorithm, are used to select transmission lines respectively. Finally, the advantages and disadvantages of these two algorithms in practical engineering line selection design are comparatively analyzed. Compared with the way of A* algorithm, RRT algorithm is more suitable for practical application of power system. It aims to provide a reference for the method of intelligent line selection in subsequent transmission design.

Implementing the multiple capitals model in integrated reporting: insight from a case study

Purpose This study aims to explore the process of identifying and defining multiple capitals in the integrated report (IR) of a government-owned tourism company. Design/methodology/approach Interventionist research was conducted using a case study design. The researcher was directly involved in developing the first IR of Zètema, a heritage and tourism company owned by the Municipality of Rome. The research team analyzed internal reports, business model (BM), strategic plan and marketing plan, and collected data through semistructured interviews and participation in company meetings. Findings A template based on a step-by-step deductive process to select and define relevant capitals was derived. Following this process, an appropriate form of capital emerged: “cultural capital”. Furthermore, this study emphasizes a novel awareness of the different meanings that capitals can assume as inputs and outcomes of a BM. Originality/value This study meets the demand for empirical research that investigates real information in integrated reports intended for those for whom value is created. Thus, the paper contributes to the existing knowledge on integrated reporting by examining the partially explored concept of capital, particularly its identification process. Furthermore, this study provides support to preparers of integrated reports by defining a conceptual reference model for the disclosure of significant capitals and underlining the importance of distinguishing capitals as input or outcome.

Changes in Social Media Real Estate Information and Apartment Prices in Small and Medium-Sized Cities: A Case Study of Donghae City

Changes in Social Media Real Estate Information and Apartment Prices in Small and Medium-Sized Cities: A Case Study of Donghae City

부동산 거래 및 정보 플랫폼 활용에 관한 연구

The purpose of this study was to analyze how the benefits provided to users in the utilization of real estate transaction and information platforms affect user engagement and trust in the platform. The analysis methods used in this study included basic statistical analysis, factor analysis, and structural equation modeling, utilizing SPSS 27.0 and AMOS 24.0 programs as analytical tools. In April 2024, a survey was conducted among men and women in their 20s to 50s residing in various regions, and 602 final valid samples were selected and analyzed. The following research results have been derived: First, information acquisition benefits have a significant impact on trust. Second, psychological benefits have a significant impact on trust. Third, economic benefits have a significant impact on trust. Fourth, customization benefits have impact on trust. Finally, usage trust has a significant impact on immersion. Through the above research, implications were presented on which benefits to enhance in order for users to stay longer and develop trust in the real estate platform.

REAL ESTATE PORTAL

Abstract — We propose to build a real estate portal that allows users to post property for sale as well as search sort property and contact its owner online. It also consists of an innovative feature that allows users to post reviews of the dealing process with a buyer after their deal. This allows the buyers to verify a sellers profile and view his past deal experiences from other user reviews on his profile. Every user on the portal has a profile that can be viewed by to check his past deals status. A user may post bad reviews about another user he has dealt with if any payment issues are encountered during the deal. So users may now verify that particular user before dealing with him. Users may look for property as per area, Bhk, Location as well as other amenities like lift, furnished, semi-furnished etc. The system sorts property and displays results list as desired by user. Users may also post a property for sale along with desired price, location and contact details. Dealers can advertise their new projects to the users with the permission of admin. New users can register with permission of admin. The product also provides also lists available loan strategies at present and directs user to the desired bank’s website. Keywords : Spatial Database, Information Visualization, Real Estate Information

Fake News Detection and Classification with Recurrent Neural Network Based Deep Learning Approaches

Events happening in the world are transmitted to the end user through the news channel. The information transmitted from the news is generally considered to be accurate. However, there may be errors or lies in the information that circulates on the news channels. At the same time, this news has an impact on serious environments, such as the economy. In social networks where data sharing is increasing, news data is piling up uncontrollably. In these data piles, there is real information as well as different information that is not real commercial, political, or sales-orientated. False information and data expand faster as a result of sharing false information by users. This news directly affects users, causing erroneous transactions, misinformation, or financial loss. For the stated reasons, automatic fake news classification systems are proposed in this article by combining natural language processing with Recurrent Neural Network (RNN) based deep learning methods. The proposed systems were tested on a dataset containing 23,481 fake news and 21,417 real news using general performance metrics. As a result of the test processes, the proposed BiLSTM method provided 99,72% accuracy, while the proposed GRU method accessed 97,50% accuracy.

Autonomous navigation decision-making for ships in complex estuarine waters: Methodology and validation

This study focused on key scientific issues in the autonomous navigation for ships, including constraints on environmental and navigation rules, limitations on ship maneuverability, and collision avoidance mechanisms, to address the challenges of autonomous navigation decision-making for ships in complex estuarine waters and validate the feasibility and accuracy of the proposed methodology. A novel autonomous navigation decision-making method that can dynamically adapt to residual errors in a system and the random movements of target ships was proposed. The predictive model used to calculate decision plans and the simulation model used to execute and validate decision plans were separated. Environmental constituent elements were classified and modeled to digitize the environment. A navigation scenario was constructed based on the fusion of virtual and real information. The control and process prediction methods were proposed based on the nonlinear ship maneuvering characteristics. Navigation rules were summarized, analyzed, and integrated into the decision-making and execution process. The collision avoidance mechanism and calculation method for feasible course and speed range were studied. Simulation experiments were conducted using the North Channel of the Yangtze River Estuary as an example. The results demonstrated that the proposed method can accurately make autonomous navigation decisions, safely avoid collisions, and timely track routes in nearly realistic scenarios.

Immersive E-learning mode application in Chinese language teaching system based on big data recommendation algorithm

With the rapid development of information technology, E-Learning has become an innovative teaching method. However, in the field of Chinese teaching, how to provide effective learning resources and recommendation algorithms in E-Learning mode is still a challenge. This study aims to improve the effectiveness of Chinese teaching system and students’ learning outcomes through an immersive E-Learning model based on big data interest recommendation algorithm. This paper adopts an immersive E-Learning model based on big data interest recommendation algorithm, and constructs a Chinese teaching system. The web crawler is used to fully collect the experimental data and collate it in a targeted manner, and the required data is screened out by using a more efficient separation method. Adding big data recommendation algorithm to the system of this paper can not only record and analyze historical behaviors of users, but also recommend data information according to users’ interests, so that users can clarify their real information needs. By testing the system’s professional ability and recording the experimental data, this paper finds that the overall performance of this Chinese language teaching system is very good, and can achieve the original expected design purpose. In addition, the system largely solves the problem that the traditional system based on data recommendation algorithm is difficult to carry out effective recommendation smoothly when the total amount of data is too large.

Peningkatan Keterampilan Menulis Teks Prosedur Melalui Penggunaan Media Video

The aim of this research is to determine the improvement of procedure text writing skills through the use of video media. The research method used is a literature review which collects descriptive information and data regarding improving writing skills using video media. The research results show that using video media can improve students' writing skills because videos can display real information in an interesting and not boring presentation so that students' interest in learning increases and helps expand vocabulary for writing procedural texts.

A Method for Identifying False News using Deep Learning Approach

The quick access to information on social media networks as well as its exponential rise also made it difficult to distinguish between fake information or real information. The fast dissemination by way of sharing has enhanced its falsification exponentially. It is also important for the credibility of social media networks to distribute fake information. It thus became a study challenge to automatically check for misstatement of information through its source, content, or publisher. This paper demonstrates an approach to the identification by the artificial intelligence of false statements made by public figures. As a software system, two algos are being applied and a series of data tested. The highest result obtained for binary (true or false) labeling is 99 percent. Python 3.6 is the simulation method used here

Decomposing geographical judgments into spatial, temporal and linguistic components

When mentally exploring maps representing large-scale environments (e.g., countries or continents), humans are assumed to mainly rely on spatial information derived from direct perceptual experience (e.g., prior visual experience with the geographical map itself). In the present study, we rather tested whether also temporal and linguistic information could account for the way humans explore and ultimately represent this type of maps. We quantified temporal distance as the minimum time needed to travel by train across Italian cities, while linguistic distance was retrieved from natural language through cognitively plausible AI models based on non-spatial associative learning mechanisms (i.e., distributional semantic models). In a first experiment, we show that temporal and linguistic distances capture with high-confidence real geographical distances. Next, in a second behavioral experiment, we show that linguistic information can account for human performance over and above real spatial information (which plays the major role in explaining participants’ performance) in a task in which participants have to judge the distance between cities (while temporal information was found to be not relevant). These findings indicate that, when exploring maps representing large-scale environments, humans do take advantage of both perceptual and linguistic information, suggesting in turn that the formation of cognitive maps possibly relies on a strict interplay between spatial and non-spatial learning principles.

Distributed Nash equilibrium seeking in noncooperative game with partial decision information of neighbors.

In the real world, individuals may conceal some of their real decision information to their neighbors due to competition. It is a challenge to explore the distributed Nash equilibrium when individuals play the noncooperative game with partial decision information in complex networks. In this paper, we investigate the distributed Nash equilibrium seeking problem with partial decision information of neighbors. Specifically, we construct a two-layer network model, where players in the first layer engage in game interactions and players in the second layer exchange estimations of real actions with each other. We also consider the case where the actions of some players remain unchanged due to the cost of updating or personal reluctance. By means of the Lyapunov function method and LaSalle's invariance principle, we obtain the sufficient conditions in which the consensus of individual actions and estimations can be achieved and the population actions can converge to the Nash equilibrium point. Furthermore, we investigate the case with switched topologies and derive the sufficient conditions for the convergence of individual actions to Nash equilibrium by the average dwell time method. Finally, we give numerical examples for cases of fixed and switched topologies to verify our theoretical results.

Investigating embedded data distribution strategy on reconstruction accuracy of flow field around the crosswind-affected train based on physics-informed neural networks

PurposePhysics-informed neural networks (PINNs) have become a new tendency in flow simulation, because of their self-advantage of integrating both physical and monitored information of fields in solving the Navier–Stokes equation and its variants. In view of the strengths of PINN, this study aims to investigate the impact of spatially embedded data distribution on the flow field results around the train in the crosswind environment reconstructed by PINN.Design/methodology/approachPINN can integrate data residuals with physical residuals into the loss function to train its parameters, allowing it to approximate the solution of the governing equations. In addition, with the aid of labelled training data, PINN can also incorporate the real site information of the flow field in model training. In light of this, the PINN model is adopted to reconstruct a two-dimensional time-averaged flow field around a train under crosswinds in the spatial domain with the aid of sparse flow field data, and the prediction results are compared with the reference results obtained from numerical modelling.FindingsThe prediction results from PINN results demonstrated a low discrepancy with those obtained from numerical simulations. The results of this study indicate that a threshold of the spatial embedded data density exists, in both the near wall and far wall areas on the train’s leeward side, as well as the near train surface area. In other words, a negative effect on the PINN reconstruction accuracy will emerge if the spatial embedded data density exceeds or slips below the threshold. Also, the optimum arrangement of the spatial embedded data in reconstructing the flow field of the train in crosswinds is obtained in this work.Originality/valueIn this work, a strategy of reconstructing the time-averaged flow field of the train under crosswind conditions is proposed based on the physics-informed data-driven method, which enhances the scope of neural network applications. In addition, for the flow field reconstruction, the effect of spatial embedded data arrangement in PINN is compared to improve its accuracy.