Information To Flow Research Articles

Spatial and temporal evolution of tourism flows among 296 Chinese cities in the context of COVID-19: a study based on Baidu Index

COVID-19 repeated outbreaks in China have significantly impacted residents’ lives, particularly in the tourism sector. Accurately assessing the spatio-temporal evolution of tourism flow under the disturbance of COVID-19 is essential for scientific tourism planning and management. This study analyzes the tourism information search index of 296 cities in China to represent tourist flow tendencies and explores the distribution characteristics and formation reasons of tourism flow from both tourist source and destination perspectives. The results are as follows. (1) The COVID-19 has severely affected Chinese tourists’ willingness to travel, with a decline of over 50% from 2020 to 2022 compared to 2019. Although a rebound is observed in 2023, it has not yet returned to pre-epidemic levels. (2) COVID-19 has impacted the spatial distribution pattern without altering the essential characteristic of “high in the East and low in the West.” The distribution of core tourist sources is “bow-shaped”, while that of popular destinations is “U-shaped”. (3) In both tourism information inflow and outflow, the number of “head” and “tail” cities is relatively small, with most cities situated in the “middle” echelon. Additionally, the “Matthew effect” has emerged among tourist destination cities, which may aggravate the unbalanced nature of tourism distribution in China. Therefore, future tourism development should be strategically planned, respecting the principles of evolutionary patterns.

Asymmetry of Directed Brain Connectivity at Birth in Low-Risk Full-Term Newborns.

Functional connectivity hubs were previously identified at the source level in low-risk full-term newborns by high-density electroencephalography (HD-EEG). However, the directionality of information flow among hubs remains unclear. The aim of this study was to study the directionality of information flow among source level hubs in low-risk full-term newborns using HD-EEG. A retrospective analysis of HD-EEG collected from a prospective study. Subjects included 112 low-risk full-term (37-41 weeks' gestation) newborns born in a large delivery center and studied within 72 hours of life by HD-EEG. The directionality of information flow between hubs at the source level was quantified using the partial directed coherence in the delta frequency band. Descriptive statistics were used to identify the maximum and minimum information flow. Differences in information flow between cerebral hemispheres were assessed using Student t-test. There was higher information flow from the left hemisphere to the right hemisphere hubs (p < 0.05, t-statistic = 2). The brainstem had the highest information inflow and lowest outflow among all the hubs. The left putamen received the lowest information, and the right pallidum had the highest information outflow to other hubs. In low-risk full-term newborns, there is a significant information flow asymmetry already present, with left hemisphere dominance at birth. The relationship between these findings and the more prevalent left hemisphere dominance observed in full-term newborns, particularly in relation to language, warrants further study.

Water Budget Input Linked to Atmospheric Rivers in British Columbia's Nechako River Basin

ABSTRACTThis study explores the contribution of atmospheric rivers (ARs) to the water budget input of the Nechako River Basin (NRB) in British Columbia (BC), western Canada. The study quantifies the fraction of precipitation, rainfall, snowfall, and snow water equivalent (SWE) associated with ARs at multiple scales and tests for trends using the Mann–Kendall (MK) test. AR‐related totals for 1950–2021 were created by linking AR events to water budget input variables of the ERA5‐Land reanalysis product on a daily scale. Associations with different phases of the El Niño‐Southern Oscillation (ENSO) climate pattern and AR‐related contributions to the NRB are also investigated. Results indicate an increasing fractional contribution of rain in ARs landfalling in the NRB in the last two decades (2000–2019). Moreover, 21% of the total annual precipitation in the NRB is associated with ARs, with decreasing contributions from west to east. October has higher AR‐related total precipitation than other months, while March, May and June are the least affected. ARs contribute disproportionately more to mid‐ and high‐intensity daily precipitation totals, and provide up to 45% and 24% of the seasonal rainfall and snowfall, respectively. AR‐related SWE is relatively higher in autumn due to the increased frequency and intensity of ARs, resulting in a greater fractional contribution of ARs to the snowpack compared to winter. ARs influence snowpack accumulation during fall (18%) and winter (13%) but also increase the risk of natural hazards. The MK test for AR‐related water budget variables on the annual scale identified no significant trends. However, AR‐related snowfall shows decreasing trends in the NRB, more specifically in the Upper Nechako, Lower Nechako and Stellako sub‐basins during the summer. Over the study period, ARs consistently contribute up to one‐fifth of the annual input to the NRB's water budget. This study provides the first quantitative assessment and trend analyses of AR contributions to the water budget input of a reservoir‐regulated watershed in north‐central BC, yielding valuable information for hydropower production, ecological flows, irrigation, domestic and industrial water use.

Resting state connectivity biomarkers of seizure freedom after epilepsy surgery

Alterations in brain networks may cause the lowering of the seizure threshold and hypersynchronization that underlie the recurrence of unprovoked seizures in epilepsy. The aim of this work is to estimate functional network characteristics, which may help predicting outcome of epilepsy surgery. Twenty patients were studied (11 females, 9 males, mean age 33years) with scalp-recorded HD-EEG in resting state (eyes closed, no interictal discharges) before intracranial evaluation, which allowed the precise determination of the epileptogenic zone. Dipole source time courses in the brain were estimated using Weighted Minimum Norm Estimate based on HD-EEG signals. Information inflow and outflow of atlas-based brain regions were computed using partial directed connectivity. A set of graph measures for pairwise connections in standard EEG frequency bands was calculated. After epilepsy surgery 10 patients were seizure-free (Engel 1a) and 10 patients continued suffering from seizures (Engel outcome worse than 1a). Inflow of the regions containing the epileptogenic zone in the beta and delta frequency bands was significantly lower in patients who achieved seizure-freedom after surgery, compared with patients who continued to have seizures (p=0.012, and p=0.026, respectively). Average path length in the beta frequency band was significantly higher in patients who achieved seizure freedom (p=0.012). In the delta frequency band, local efficiency and clustering coefficient were significantly higher in patients who achieved seizure freedom (0.033, 0.046). In patients who achieved seizure freedom after surgery, the preoperative analysis of the epileptic network exhibited stronger separation of the region containing the seizure onset zone, with less inflow of information. In contrast, shorter paths within the epileptic network may facilitate hypersynchronous neuronal activity and thus the recurrence of seizures in non-seizure free patients. This study supports the hypothesis that epileptic network properties might help to define suitable candidates for epilepsy surgery.

Formation of reading literacy of schoolchildren in history lessons: historiographical aspect

The article analyzes approaches to the formation of reading literacy of students at school in history lessons in both domestic and foreign historiography over the past two decades. History is one of the main disciplines for the development of reading literacy. Reading literacy is included in functional literacy, that is, a person's ability to find and apply the acquired knowledge in life, adapt to constantly changing conditions, and learn throughout life. The most important skills are to find the necessary information in a huge information flow, critically evaluate it and use it for practical purposes. The studied works present both the theoretical aspect, the components of reader's literacy, and the practical component, methodological techniques, tasks are shown, methodological recommendations aimed at the development of reader's literacy are developed. In history lessons, as part of the educational process, it is necessary to form reading literacy skills using different types of text – solid and non–solid, with different information presentation – legislative acts, memoirs of contemporaries, excerpts from newspapers, fiction, as well as actively use historical paintings, maps, objects with inscriptions (coins, medals, monetary bills, stamps, etc.). This will allow the student to receive information from various sources, compare them with each other, and critically evaluate. Using different types of tasks, including personal assessment, students learn to analyze information and apply it in life.

Advancing airborne Doppler lidar wind profiling in turbulent boundary layer flow – an LES-based optimization of traditional scanning-beam versus novel fixed-beam measurement systems

Abstract. There is a need for improved wind measurements inside the planetary boundary layer (PBL), including the capability to sample turbulent flow. Airborne Doppler lidar (ADL) provides unique capabilities for spatially resolved and targeted wind measurements in the PBL. However, ADL wind profiling in the PBL is challenging, as turbulence violates the flow homogeneity assumption used in wind profile retrieval and thereby introduces error in the retrieved wind profiles. As turbulence is a dominant source of error it is necessary to investigate and optimize ADL wind profiling capabilities in turbulent PBL flow. This study investigates the potential of a novel multiple-fixed-beam ADL system design to provide improved wind information in turbulent PBL flow compared to traditional single-scanning-beam ADL systems. To achieve this, an LES-based (LES: large eddy simulation) airborne Doppler lidar simulator presented in Gasch et al. (2020) is employed and extended in this study. Results show that a multiple-fixed-beam system with settings comparable to those of commonly used single-scanning-beam systems offers distinct advantages. Advantages include overall reduced wind profile retrieval error due to turbulence and improved spatial representation alongside higher wind profile availability. The study also offers insight into the dependence of the retrieval error on system setup parameters and retrieval parameters for both fixed-beam and scanning-beam systems. When using a fixed-beam system, an order of magnitude higher wind profile resolution appears possible compared to traditional scanning systems at comparable retrieval accuracy. Thus, using multiple-fixed-beam systems opens the door to better sampling of turbulent PBL flow. Overall, the simulator provides a cost-effective tool to investigate and optimize wind profile error characteristics due to turbulence and to optimize system setup and retrieval strategies for ADL wind profiling in turbulent flow.

Multivariate Time Series Prediction Based on Temporal Change Information Learning Method.

In the multivariate time series prediction tasks, the impact information of all nonpredictive time series on the predictive target series is difficult to be extracted at different time stages. Through the emphasis on optimal-related sequences in the target series, the deep learning model with the attention mechanism achieves a good predictive performance. However, temporal change information in the objective function and optimization algorithm is completely ignored in these models. To this end, a temporal change information learning (CIL) method is proposed in this article. First, mean absolute error (MAE) and mean squared error (MSE) losses are contained in the objective function to evaluate different amplitude errors. Meanwhile, the second-order difference technology is used in the correlation terms of the objective function to adaptively capture the impact of the abrupt and slow change information in each series on the target series. Second, the long short-term memory (LSTM) network with the transformation mechanism is used in the method so that temporal dependence information can be fully extracted (i.e., avoiding the supersaturation region). Third, to effectively obtain the optimal model parameters, the current and historical moment estimation information is adaptively memorized without the introduction of additional hyperparameters, and therefore, the acquisition ability of temporal change information in the error gradient flow is greatly enhanced by the proposed optimization algorithm. Finally, three datasets with different scales are used to verify the advantages of the CIL method in computational overhead and prediction effect.

Dynamic relationship between volume and volatility in the Chinese stock market: evidence from the MS-VAR model

Dynamic relationship between volume and volatility in the Chinese stock market: evidence from the MS-VAR model

A study of brain networks associated with Freezing of gait in Parkinson’s disease using transfer entropy analysis

Parkinson's disease (PD) is a common neurodegenerative disease in the elderly. Freezing of Gait (FOG) is one of the common motor symptoms of PD, but the potential mechanism remains unclear. This study aimed to investigate the changes of brain functional network topology in PD patients with FOG. The resting electroencephalogram (EEG) were acquired from15 PD patients with FOG (PD-FOG), 13 PD patients without FOG (PD-nFOG), and 16 healthy control (HC). Cognitive and motor functions were assessed using subjective scales. The whole-brain functional networks were constructed based on transfer entropy. Transfer entropy was used to analyse the information flow and causality in the network and the network connectivity was analyzed by graph theory. The characteristics of PD-FOG and PD-nFOG were compared by receiver operator characteristic (ROC) curve analysis. The θ bands brain network of PD-FOG, PD-nFOG and HC group was significantly different (P<0.05). The average characteristic path length of the θ bands brain network was positively correlated with FOG Questionnaire (FOGQ). PD-FOG and PD-nFOG get high classification accuracy according to this feature. The information inflow in the frontal and occipital lobes and information outflow in the temporal lobe of PD-FOG patients in the θ bands increased significantly. The whole-brain functional network characteristics of PD-FOG in the θ bands can serve as potential biomarkers for early diagnosis of PD-FOG. Abnormal information flow of the frontal, occipital, and temporal lobes in the θ bands may be an important factor leading to FOG.

Modeling and analysis of car-following models incorporating multiple lead vehicles and acceleration information in heterogeneous traffic flow

Modeling and analysis of car-following models incorporating multiple lead vehicles and acceleration information in heterogeneous traffic flow

TSDTVOS: Target-guided spatiotemporal dual-stream transformers for video object segmentation

TSDTVOS: Target-guided spatiotemporal dual-stream transformers for video object segmentation

Accurate and Reliable Classification of Unstructured Reports on Their Diagnostic Goal Using BERT Models.

Understanding the diagnostic goal of medical reports is valuable information for understanding patient flows. This work focuses on extracting the reason for taking an MRI scan of Multiple Sclerosis (MS) patients using the attached free-form reports: Diagnosis, Progression or Monitoring. We investigate the performance of domain-dependent and general state-of-the-art language models and their alignment with domain expertise. To this end, eXplainable Artificial Intelligence (XAI) techniques are used to acquire insight into the inner workings of the model, which are verified on their trustworthiness. The verified XAI explanations are then compared with explanations from a domain expert, to indirectly determine the reliability of the model. BERTje, a Dutch Bidirectional Encoder Representations from Transformers (BERT) model, outperforms RobBERT and MedRoBERTa.nl in both accuracy and reliability. The latter model (MedRoBERTa.nl) is a domain-specific model, while BERTje is a generic model, showing that domain-specific models are not always superior. Our validation of BERTje in a small prospective study shows promising results for the potential uptake of the model in a practical setting.

Artificial intelligence to boost traceability systems for fraud prevention in the meat industry

Traceability was introduced about twenty years ago to face the worldwide spread of food safety crises. Traceability data flow associated with each lot of food products during any production and/or delivery phases can also be used to guarantee product authenticity. For this purpose, it is necessary to protect the data from cyber intrusions and, at the same time, to guarantee the integrity of the bond between the physical product and the data. Price grading related to quality perceivable or credence attributes attracts criminals to attempt item substitution fraud. Improved track and trace technologies supported by artificial intelligence (AI) could highly enhance systems’ capability to detect authenticity violations by product substitution. This paper proposes an innovative method based on AI, to reinforce traceability systems in detecting possible counterfeiting by product substitution. It is an item-based mass balance method that analyses the congruity of the traceability data flows not by using explicit (even stochastic) rules but by exploiting the learning capabilities of a neural network. The system can then detect suspect information in a traceability data flow, alerting a possible profit-driven crime. The AI-based method was applied to a pork slaughtering and meat cutting chain case study.

Folk theories of false information: A mixed-methods study in the context of Covid-19 in Turkey

This study explores how media users define false information in the daily flow of their lives against a backdrop of sociopolitical contexts. We focus on the vernacular definitions of false information through the concept of folk theories, which are the intuitive explanatory tools users develop to make sense of and act in the world around them. Based on mixed-method research conducted in Turkey during the Covid-19 pandemic, we identify three prevailing folk theories of false information. First, users consider text-based characteristics, such as the presence of evidence as a flag of accuracy/inaccuracy. Second, users assume that people in their social networks distinguish between the accurate and the inaccurate, and thus the information coming from these circles is accurate. Finally, users imagine that people whose worldviews conflict with theirs spread inaccurate information. Despite users’ overarching references to textual traits of news, it appears that the latter two folk theories drive users’ information processing practices in daily life.

Barriers for data management as an enabler of circular economy: an exploratory study of the Norwegian AEC-industry

Effective data management can enable the utilisation of new tools and technologies and, ultimately the creation of circular business models in the building industry. To accomplish this, a targeted mapping and collection of data must take place. However, several challenges hinder the exchange of information in a seamless digital flow through the value chain and building life cycle. This exploratory study contributes to bridging the gap in the research, providing an overview of existing barriers related to mapping, collecting, and storing data about materials and products in existing buildings. The findings are obtained through 18 semi-structured interviews with experts working with circular economy and digitalisation in the Norwegian AEC-industry. Some of the identified barriers, such as the lack of data availability and interoperability, lack of competencies and unwillingness to share data, are strongly interrelated; a collaborative approach will be essential across the value chain. The research sets the basis for developing a framework for data management that can facilitate the reuse of materials and products from a building at the end of life to new construction or refurbishment projects. Ultimately, effective data management opens for developing and implementing innovative circular business models, enhancing strategic data-based asset management.

Selected Issues Associated with the Operational and Power Supply Reliability of Fire Alarm Systems

The article reviews issues associated with the use of electronic fire alarm systems (FAS). They are operated in various environments and buildings with varying volumes. FAS have to function properly under different operating conditions associated with their operation, as well as power supply and information inflow. Due to their functions, i.e., ensuring the safety of people, vehicles, logistics bases, airports, etc., FAS have to exhibit an appropriately organized reliability structure associated with their implementation and power supply. Operational studies involving FAS operated in various facilities were conducted to this end. The authors determined damage and recovery time intensities. FAS reliability indicators were also determined. The article presents graphs associated with developing the energy balance for selected FAS. The graphs are consistent with the latest and applicable legal regulations. The next stage of the work related to this article was developing an FAS operation process model and conducting computer simulations in order to determine reliability indicators. Such an approach to the FAS operation process enables a rational selection of technical and organizational solutions aimed at guaranteeing reliability in the course of executing operational tasks associated with ensuring fire safety. FAS operational analysis, developing balance graphs and models, as well as the computer simulation, enabled inferring conclusions that might be useful to the process of engineering and operating such systems.

Toward Privacy Preserving Federated Learning in Internet of Vehicular Things: Challenges and Future Directions

The Internet of vehicular things (IoVT) is turning into an indubitably evolving area of interest in either industrial or academic domains. The tremendous information exchanging between IoVT devices enable the development of a wide variety of vehicular applications i.e., intelligent transportation systems and autonomous driving system, etc. However, the sensitivity of this information resulted in growing security privacy concerns. Remarkably, federated learning (FL) is a promising paradigm of distributed learning from vehicular data of distinct agents without communicating the raw data among them. FL can appropriately use the computation power of manifold agents to develop efficient and privacy-preserving solutions for IoVT environment. Thus, this study figures out the potential of the FL approach in developing efficient decentralized solutions that consider the security and privacy concerns of the IoVT system. A federated graph convolutional recurrent network (Fed-GCRN) is introduced to learn spatial-temporal information for traffic flows forecasting. The Fed-GCRN introduce an adaptive differential privacy mechanism to realize a better privacy performance tradeoff. Finally, the current challenges related to FL are discussed along with the hopeful future directions that enable the development of more intelligent, secure, and private IoVT applications.

Time-evolution of local information: thermalization dynamics of local observables

Quantum many-body dynamics generically result in increasing entanglement that eventually leads to thermalization of local observables. This makes the exact description of the dynamics complex despite the apparent simplicity of (high-temperature) thermal states. For accurate but approximate simulations one needs a way to keep track of essential (quantum) information while discarding inessential one. To this end, we first introduce the concept of the information lattice, which supplements the physical spatial lattice with an additional dimension and where a local Hamiltonian gives rise to well-defined locally conserved von Neumann information current. This provides a convenient and insightful way of capturing the flow, through time and space, of information during quantum time-evolution, and gives a distinct signature of when local degrees of freedom decouple from long-range entanglement. As an example, we describe such decoupling of local degrees of freedom for the mixed-field transverse Ising model. Building on this, we secondly construct algorithms to time-evolve sets of local density matrices without any reference to a global state. With the notion of information currents, we motivate algorithms based on the intuition that information for statistical reasons flows from small to large scales. Using this guiding principle, we construct an algorithm that, at worst, shows two-digit convergence in time-evolutions up to very late times for diffusion process governed by the mixed-field transverse Ising Hamiltonian. While we focus on dynamics in 1D with nearest-neighbor Hamiltonians, the algorithms do not essentially rely on these assumptions and can in principle be generalized to higher dimensions and more complicated Hamiltonians.

OBD-Data-Assisted Cost-Based Map-Matching Algorithm for Low-Sampled Telematics Data in Urban Environments

A myriad of connected vehicles collects large-scale telematics data throughout cities, enabling data-based infrastructure planning. To truly benefit from this emerging technology, it is important to integrate pervasive telematics data with map data to produce more tractable and readable information for traffic flows and safety. Map-matching algorithms enable the projection of noisy trajectory data onto map data as a means of integrating telematics data. However, map-matching poses challenges due to higher levels of positioning errors and complex road networks. The authors propose a novel map-matching algorithm that can fuse in-vehicle data with trajectory data to improve the efficiency and accuracy of the algorithm. The proposed algorithm combines the probabilistic and weight-based map-matching frameworks. The novelty of the proposed algorithm includes (i) an adaptive segment candidate search mechanism based on in-vehicle speed information, (ii) adaptive matching parameters to reflect the variations in the Global Positioning System (GPS) noise levels, (iii) a novel transition probability that uses in-vehicle speed data, and (iv) a backend data query system for the shortest routes. Map-matching results were validated based on ground-truth data collected using an in-vehicle sensing device developed by the authors, as well as comparing with a commonly-used off-the-shelf map-matching platform. The proposed algorithm is proven to be robust, with an accuracy of 97.45%, particularly where map data are denser and GPS noise is high.

Sources of information and analytical activity

Due to the imposition of state of war in Ukraine on February 24, 2022 after the beginning of the armed attack of the Russian Federation, the criminal situation in Ukraine has worsened, as is evidenced by some increase in the number of crimes committed and public danger. To form an effective system for combating crime, it is necessary to study and conduct a criminological analysis of the state of crime at the present stage of development of Ukrainian society.&#x0D; In addition, the intense dynamics of growth of social processes generates huge uncontrolled, contradictory flows of information that require appropriate research, analysis, control, which cannot be done without an effective modern analytical technologies.&#x0D; In order to successfully solve the tasks related to improving the effectiveness of the fight against crime, it is necessary that anti-crime authorities have comprehensive, accurate and timely information on the status, structure, dynamics of crime and other anti-social manifestations, the circumstances that contribute to their fight against crimes of various categories.&#x0D; Within the limits of their powers, criminal police units receive a sufficient amount of information about criminal activity, in particular, about representatives of organized crime.&#x0D; For law enforcement, large amounts of information that always mean quality efficiency. Law enforcement agencies usually have a surplus of information, and they need to process it in such a way that it facilitates procedural decisions, increases efficiency, allows to develop strategic directions of their activities.&#x0D; In this regard, an important task is to use tools that would produce significant amounts of available data. One of such tools in the activities of operational units of the National Police of Ukraine is criminal analysis - a specific type of information-analytical activity, which consists in identifying and determining as accurately as possible internal links between information (information, data) related to criminal offenses and others data obtained from various sources, their use in favor of operational and investigative and investigative activities, their analytical support.&#x0D; The purpose of criminal analysis is to find important information in a large flow of data and to disseminate this information among law enforcement officers to assist them in stopping criminal activity and apprehending criminals.&#x0D; In today's world, criminal analysis, as a separate type of professional activity, is carried out by law enforcement agencies of most developed countries and is an essential component of tasks to achieve long-term goals, prioritize and general direction of law enforcement.