Situational Information Research Articles

Overdosing in a Motor Vehicle: Examination of Human, Geographic, and Environmental Factors.

Fentanyl, a type of opioid, in impaired driving cases increased across cities in the United States. No empirical studies have examined motor vehicle overdoses with fentanyl use. We investigated the magnitude of the motor vehicle overdose problem in Providence, RI, and the environmental, socioeconomic, and geographic conditions associated with motor vehicle overdose occurrence. This was a retrospective observational study of emergency medical services data on all suspected opioid overdoses between January 1, 2017, and October 31, 2020. The data contain forced-choice fields, such as age and biological sex, and an open-ended narrative in which the paramedic documented clinical and situational information. The overdoses were geocoded, allowing for the extraction of sociodemographic data from the U.S. Census Bureau's American Community Survey. Seven other data sources were included in a logistic regression to understand key risk factors and spatial patterns of motor vehicle overdoses. Of the 1,357 opioid overdose cases in this analysis, 15.2% were defined as motor vehicle overdoses. In adjusted models, we found a 61% increase in the odds of a motor vehicle overdose involvement for men versus women, a 16.8% decrease in the odds of a motor vehicle overdose for a one-unit increase in distance to the nearest gas station, and a 10.7% decrease in the odds of a motor vehicle overdose for a one-unit increase in distance to a buprenorphine clinic. There is a need to understand the interaction between drug use in vehicles to design interventions for decreasing driving after illicit drug use.

Research on an intelligent assessment method for the physical health of preschool children based on the Artificial Neural Network (ANN) model

This study aims to use the BP neural network model to predict the monthly total number of reported symptoms in preschool children, thereby achieving early warning for the total number of cases in the current month. By analyzing the training results of the neural network and the fitting of the model, the feasibility and effectiveness of the BP neural network in monitoring the physical health of preschool children are validated. The predictive model is of great significance for the prevention of infectious diseases in a border region of a certain province, providing important information for the local incidence situation. The research results indicate that the established BP neural network model demonstrates good accuracy and practicality in predicting the monthly total number of reported symptoms in preschool children’s physical health.

Field detection of indoor fire threat situation based on LSTM-Kriging network

Obtaining the fire development information quickly and accurately is an important part of indoor fire emergency evacuation and rescue. However, at present, buildings only use fire sensors to monitor whether a fire is occurring, and there is a lack of means to obtain real-time threat situation information on the development of fires. An LSTM-Kriging neural network model is proposed in this paper, which can quickly generate an indoor fire threat field using the temporal data of fire sensors when an indoor fire occurs. Firstly, the Kriging algorithm was improved by replacing the Euclidean distance used in the variational function with the nearest passable distance to obtain a more accurate representation of the indoor threat distribution trend, and combined it with the Long Short-Term Memory (LSTM) network to construct the LSTM-Kriging network. Secondly, indoor fire scenarios were constructed in PyroSim, and fire parameters were simulated randomly. After data augmentation, the indoor fire threat situation dataset was generated. Finally, the optimized LSTM-Kriging model was trained and compared with Transformer, LSTM, and Recurrent Neural Network (RNN) on the indoor fire hazardous situation dataset. The experimental results show that LSTM-Kriging is the most effective model compared with several models mentioned above and the prediction of threatening situation reaches an accuracy of 97.8 %, with an average computation time of 0.19s on the GPU, which can meet the requirement of real-time fire situation monitoring. The indoor fire threat situation field detection method proposed in this paper achieves intelligent indoor fire detection and improves the building fire safety.

An Investigation of the Complexity of the Problems Posed by Prospective Teachers: The Case of Whole Number

The aim of this case study is to examine the linguistic and mathematical complexity of the problems that prospective primary school teachers posed related to four operations with whole numbers. A Problem Posing Questionnaire was administered to 64 participants and semi-structured interviews were conducted with 20 of 64 participants. The data was analyzed through frequency analysis and descriptive analysis. The findings of the study revealed that many prospective teachers posed problems based on the single-statement problem root, without any relation/condition. That is, the linguistic complexity of the problems written by the prospective teachers is in the lowest level which is regarded as assignment category, regardless of the complexity of the problem situation. Furthermore, if the problem situations do not contain any relationship, then many prospective teachers had preferred to pose one-step problems. On the other hand, when the information in the problem situation is related to each other, then they were able to pose multi-step problems which could be regarded as complex problems in terms of mathematically. It can be concluded that although the complexity of the problem situation given to the prospective teachers does not affect the linguistic complexity of the problems they pose; it affects the mathematical complexity.

Network awareness of security situation information security measurement method based on data mining

Awareness of Network Security Situation (abbreviated as NSS for short) technology is in a period of vigorous development recently. NSS technology means network security situational awareness technology. It refers to the technology of collecting, processing, and analyzing various real-time information in the network to understand and evaluate the current network security status. It can not only find network security threats, but also reflect the NSS in the system security metrics, and provide users with targeted security protection measures. Based on data mining methods, this paper analyzed and models perceived threats and security events with data mining algorithms, and improved information security measurement methods based on association analysis. This paper proposed network security information analysis and NSS based on data mining, and analyzed the experimental results of network awareness of NSS information security measurement. The experimental results showed that when the Timer was 8, the accuracy of the awareness of NSS information security measurement method based on data mining can reach 92.89%. The data mining model had the highest accuracy of 93.14% in situation understanding and evaluation of KDDCup-99 dataset. The results showed that the model can accurately predict the NSS. When Timer was 6, the highest accuracy of the model was 92.71%. In general, the NSS prediction mining model based on KDDCup-99 can better understand, evaluate and predict the situation.

Constructing the digital ark: How China utilizes shared media to foster online information collaboration and crisis management in public emergency response

With the prevalence of collaborative editing tools, such as shared documents, a digital rescue platform functionality has emerged to address major unforeseen events, displaying notable self-organizing characteristics. This study examines the transformative role of sharing technologies in crisis communication, using documents from the 2021 Zhengzhou flood and adopting an actor-network theory perspective. Investigating how sharing technology influences situational information, the findings highlight its creation of a real-time, collaborative, and detailed crisis information environment, fostering self-rescue initiatives and positive emotional support. The shift from a closed situational information pool to a dynamic digital network underscores the evolving landscape of crisis communication in the digital age. Self-negotiation and self-restraint mechanisms mitigate rumor transmission, providing robust information support for subsequent rescue efforts. This research contributes valuable insights into the intricate dynamics of sharing technologies in crisis communication.

Relational work and accounting: What venture capital analysts do with accounting and other information in situations of uncertainty This paper is forthcoming at European Accounting Review

Relational work and accounting: What venture capital analysts do with accounting and other information in situations of uncertainty This paper is forthcoming at European Accounting Review

Development of an algorithm for the operation of railway station infrastructure in unconventional situations

The expansion of intelligent transport systems is a notable trend in the current stage of transport infrastructure development. The use of intelligent transport systems will enhance the quality of transport process management and ensure strict compliance with safety requirements in the transportation process. The Russian Federation’s transport strategy up to 2030 envisages the development of intelligent transport systems, which is one of the directions for forming a unified transport space in Russia based on balanced development of an efficient transport infrastructure. The introduction of innovative informational and educational technologies is one of the promising and relevant issues in the context of the digital transformation of the transport complex. The situational information and training model developed in this study will enable operational management personnel to minimize the time required to find the necessary route, prepare it, and reduce or completely avoid train delays at entry signals, thereby enhancing train traffic safety. This article presents an algorithm of measures that will enable the development of specific practical solutions for improving the technology of railway station operations in the event of failures in existing railway automation and telemechanics systems. The use of a situational information and training model “Management of switches in the route” in conjunction with the developed “Route Tables” adapted for various extra-class railway stations will allow eliminating errors made by station duty officers when preparing routes and reducing the time spent on these operations..

Multi-objective pedestrian tracking method based on YOLOv8 and improved DeepSORT.

A multi-objective pedestrian tracking method based on you only look once-v8 (YOLOv8) and the improved simple online and real time tracking with a deep association metric (DeepSORT) was proposed with the purpose of coping with the issues of local occlusion and ID dynamic transformation that frequently arise when tracking target pedestrians in real complex traffic scenarios. To begin with, in order to enhance the feature extraction network's capacity to learn target feature information in busy traffic situations, the detector implemented the YOLOv8 method with a high level of small-scale feature expression. In addition, the omni-scale network (OSNet) feature extraction network was then put on top of DeepSORT in order to accomplish real-time synchronized target tracking. This increases the effectiveness of picture edge recognition by dynamically fusing the collected feature information at various scales. Furthermore, a new adaptive forgetting smoothing Kalman filtering algorithm (FSA) was created to adapt to the nonlinear condition of the pedestrian trajectory in the traffic scene in order to address the issue of poor prediction attributed to the linear state equation of Kalman filtering once more. Afterward, the original intersection over union (IOU) association matching algorithm of DeepSORT was replaced by the complete-intersection over union (CIOU) association matching algorithm to fundamentally reduce the target pedestrians' omission and misdetection situation and to improve the accuracy of data matching. Eventually, the generalized trajectory feature extractor model (GFModel) was developed to tightly merge the local and global information through the average pooling operation in order to get precise tracking results and further decrease the impact of numerous disturbances on target tracking. The fusion algorithm of YOLOv8 and improved DeepSORT method based on OSNet, FSA and GFModel was named YOFGD. According to the experimental findings, YOFGD's ultimate accuracy can reach 77.9% and its speed can reach 55.8 frames per second (FPS), which is more than enough to fulfill the demands of real-world scenarios.

The Evolution of Transfer Theory in Second Language Acquisition: From Behaviorism to Sociocultural Perspectives

This study explores the dynamic interaction and developmental perspectives of transfer theory (TT) in the context of second language acquisition (SLA). First proposed by psychologist Edward Thorndike in 1913, transfer theory focuses on describing how learners utilize existing knowledge or skills to acquire information or skills in new learning situations. Over time, research on transfer theory has deepened from the early behaviorist perspective of unidirectional transfer to the role of bidirectional transfer and situational factors under cognitive and sociocultural theories. This paper explores the complex dynamics of transfer theory in second language acquisition and its impact on language teaching by synthesizing the early and recent research results to provide theoretical guidance and strategic suggestions for teaching practice. By analyzing the phenomenon of language transfer among different learners in diverse contexts, the research aims to reveal the broad applicability of transfer theory and its important implications for educational strategy development. The findings will provide educators and researchers with deeper insights to optimize language teaching methods.

German-Albanian Intercultural communication and interaction in the workplace

Remote work has brought societies and cultures more together than ever. The workplace has been taken to another level and understanding the cultures and behaviours of different countries has become essential, so that there can be a harmonious working environment. Different ways of conveying the information in the same situation and context but at the same time in two different cultural environments, often lead to inconsistencies or problems in different situations in everyday life. In this thesis, German-Albanian communication and interaction in the workplace is observed from a cultural point of view. Communication in the workspace environment has quite a high influence on the working atmosphere, satisfaction, productivity of the employees or companies worldwide management success. The field of study focuses on the workspace since the reason why many Albanian citizens stay in Germany or have work relations with German companies is the offer of a working contract with higher income possibilities. The purpose of this study is to achieve understanding from both sides of the cultures or even let other cultures understand them both at the same time. Beside the theoretical solution of this problem, an illustrated book with funny and simple illustration has been designed to bring better understanding for the cultural differences independent from their age, gender or intellectual level.

The effects of coronavirus news on travel and leisure stocks: Evidence from the NARDL model

This study employs the NARDL model to investigate the asymmetric effects of COVID-19 news on the return and volatility of travel and leisure stocks in both the United States and the global context. Our findings reveal distinct longand short-term impacts of COVID-19 news on these stocks, exhibiting asymmetry and lag effects. Various types of news, including panic-inducing reports, entity-related coronavirus news, and misleading information, contribute to adjustments in travel and leisure stock returns over the long term. Notably, panic-inducing news has a more pronounced long-term impact on stock volatility compared to a higher volume of news sources reporting coronavirus-related information in similar situations. We argue that regulatory bodies can influence the stock market by ensuring the accuracy and reliability of news content.

Integration of Takaful Insurance in the Tunisian Market: Analysis of consumers’ perceptions

The study examines the impact of situational factors, subjective criteria, and information quality on Islamic insurance applications. Drawing inspiration from the theory of logical action, this research introduces a model to analyze key factors within the Islamic insurance application context. The model undergoes testing with data obtained from a survey involving 100 participants. The findings emphasize the significance of attitude, subjective considerations, and the quality of available information in shaping the demand for Islamic insurance. Additionally, the study sheds light on the level of acceptance of Islamic insurance among Tunisian consumers, offering valuable insights for operators to enhance the effective management of Islamic Takaful Insurance Services. By elucidating the intricate interplay between individual perceptions and contextual factors, this research contributes to a deeper understanding of consumer behavior within the Islamic insurance domain. These insights serve as invaluable guideposts for operators seeking to navigate the complex terrain of Islamic Takaful Insurance Services, empowering them to devise tailored strategies that resonate with consumer preferences and foster greater engagement with Islamic financial solutions.

Construction of Enterprise Green Accounting Information Disclosure System under the “Double Carbon” Goal

Abstract Enterprises are the key participants in realizing the development of a green and low-carbon economy and are the main promoters of the environmental transformation and healthy development of the industry, and the green accounting information disclosure truly reflects the important indexes of green development of enterprises. In this paper, the relevant data of 15 listed companies in the paper industry from 2013 to 2023 are selected as samples to carry out empirical analysis, and their green accounting disclosure reports are used as the basis for calculating the GAID scores of the companies. A multiple linear regression model is created to test the disclosure-related research hypotheses, with the goal of discovering the factors that influence the current quality of enterprises’ green accounting disclosure. The fluctuation range of GAID scores of most of the fifteen listed enterprises in the paper industry is between 40 and 70, and there are also 20% of enterprises with GAID scores lower than 40. Enterprise size, proportion of outstanding shares, enterprise value, and enterprise development ability are significantly positively correlated with the quality of corporate green accounting disclosure at the 1% or 5% level, and the quality of disclosure increases by 0.151% for every 1% increase in enterprise size. In the enterprise green accounting information disclosure, enterprises need to be, based on their situation, reasonable and comprehensive information released in order to effectively enhance their corporate image and promote the green and sustainable development of enterprises.

Assessment of Higher-order Thinking Skills in Conversation Situation

Assessment of students’ higher-order thinking skills is a key issue in education, and data-driven multimodal learning analytics is the way forward. Mining the essential content of collaborative conversations is the key to the assessment of higher-order thinking skills, and the integration of learning situations is crucial. In this paper, we provide digital collaborative learning situations through a virtual simulation platform. Based on the conversation data, decision-making behaviour data and environmental data generated in the collaborative learning process, a set of data-driven higher-order thinking skills identification methods integrating situation and conversation are designed. The method includes four steps: situation identification and modelling, text extraction, situational semantic analysis, and semantic flow-based modelling. Compared with traditional methods such as questionnaires and interviews, the ability identification driven by conversation data realizes the direct representation of higher-order thinking skills, which has real-time performance and higher reliability. What’s more, integrating situational information into the learning analysis model improves the accuracy of ability identification results from the pragmatic level.

Revenge Porn: On the Problem of Linguistic Analysis of Pornographic Content

The paper discusses the problem of forensic analysis of pornographic information in revenge porn situation (non-consensual public placement of materials of sexual explicit content). The paper presents a list of social practices related to revenge porn, specifies the participants of the communicative act (actor, addressee- person caught on camara, addressee-reference group, addressee-third-party participant), determines the intentional component of the actor's communicative activity, gives a list of the actor's communicative goals in conditions of multiple addressee-targeting . The features of the recontextualization of pornographic material are revealed. A weak dependence of pornographic discourse on extralinguistic background is established. It is noted that in the situation of revenge porn sexual explicit information is subjected to recontextualization, which makes it difficult to establish linguistic (semiotic) signs of pornographic information in forensic analysis. It is established that under the conditions of the transfer of a sexual explicit text into a conflict-triggering context, pornographic meanings are preserved. At the same time, the actualization of the taboo aspect of sexual interaction underlies the transformation of the emotive potential and determines the nature of the relationship between the participants in the communicative situation. The authors conclude that in order to establish the linguistic features of sexual explicit information, it is necessary to decontextualize the text submitted for forensic research. Analysis of the distribution context is necessary to establish the extralinguistic background of the actor's communication activity.

Особенности моделирования функционирования ВУЗов как сложных организационных систем силовых структур

The article considers an approach to solving the problem of improving the indicators characterizing the state of the educational group of a modern university, which is currently difficult to imagine without the use of systems operating on the basis of mathematical models of SED. The elements of assessing approaches to improving the effectiveness of management decision-making are considered, which cannot always be characterized quantitatively. To increase the effectiveness of the R&D, it is proposed to use methods and tools that implement a quantitative assessment of the state of the management system, which is able to justify the decision made. When modeling managerial decision-making in complex organizational systems of law enforcement agencies, a conceptual approach is proposed that is based on situational modeling and provides information to the decision-maker in this system about his current state. Based on this situational information, the decision-maker will make recommendations in the interests of situational management and ensure its adaptation to real needs. The situational approach uses a set-theoretic representation of models, which will allow combining the spatial and structural interpretation of this model. Determining the relationships between employees in complex organizational systems of law enforcement agencies will allow you to link tasks with employees based on input-output information flows using a multi-criteria assessment of alternative.

The impact of digital household budgets on online purchase decision‐making processes

AbstractRecent literature found positive effects of digital household budget feedback when used over longer timespans. This current study, in turn, investigates whether digital budget feedback also influences consumer behavior in single online purchase decisions. The related research question states: Do transparent technology‐mediated household budgets influence consumers' willingness to pay and payment pain in single, individual online purchase decision processes? The underlying study used a quantitative‐, mixed‐design approach and simulated online purchases for one hedonic and one utilitarian product. Three hundred fourteen study subjects were assigned to either a control group or one of three treatment groups manipulated with specific budget statuses to examine differences in consumer reactions. Critical measures of willingness to pay and payment pain have been taken to analyze potential significant effects. Results found significant differences in consumer willingness to pay and payment pain between and within groups concerning both products. The study could, however, not confirm differences to occur between utilitarian and hedonic purchases. The findings extend existing literature by demonstrating that consumers adjust their online spending based on individual household budget feedback. Consumers that wish to align spending with their current budget status might thus rely on app‐mediated budget updates. At the same time, marketers could offer individualized pricing offers to consumers based on situational budget status information.

Death Due to Anaphylactic Reaction: The Role of the Forensic Pathologist in an Accurate Postmortem Diagnosis.

Background and Objectives: The diagnosis of anaphylaxis comprehensively depends on both situational information and laboratory investigations. For this purpose, serum tryptase concentration is examined as an indicator of systemic mast cell mediator release, linked to an underlying anaphylactic process. Increased levels of tryptase may occur in some events different from anaphylaxis, but usually information from crime scene investigations is lacking and autoptic findings are not specific. For legal reasons, it is required to achieve a definite diagnosis of mast cell degranulation that can lead to a certain diagnosis of death from anaphylaxis. Immunohistochemistry seems to be a relatively simple, reliable, and easily repeatable method that can assist the forensic pathologist in the differential diagnosis of death from anaphylaxis. Materials and Methods: This work provides an overview of the current literature on immunohistochemical methods useful in the determination process of anaphylactic-related deaths. A systematic search, according to the PRISMA statement, was performed in databases to identify studies investigating immunohistochemical targets related to anaphylaxis death. Results: This work underscores the importance of anaphylaxis mediators such as tryptase, CD117, and chymase in the immunohistochemical analysis of anaphylactic deaths. Conclusions: According to the reviewed literature, the diagnosis of death due to anaphylaxis should depend not just on the suspicion of an anaphylactic reaction but also on confirming mast cell degranulation through the identification of IHC positivity for inflammatory mediators, particularly in the respiratory tract.

Multi-agent-based human cognition simulation of Situation-aware earthquake emergency evacuation

The salvation of at-risk people from urban areas with earthquake prognosis or evacuation is one of the important activities of rescue teams. Due to the roles of different groups of individuals (crisis managers, pedestrians, and vehicles) with their special spatial cognition, as well as the various characteristics of transportation infrastructure, multi-agent-based modeling is an effective way of situation-aware simulation. The complexity and dynamic nature of emergencies are owing to the interactions between agents, evacuation environment, and multiple risk levels. This paper aims to develop a new approach to infer different situations before the occurrence of an earthquake based on human spatial cognition with an emphasis on the routing process. This study proposed a situation-aware evacuation framework using multi-agent modeling to examine the agents (pedestrians, vehicles, and controlling agents), their behaviors, and agent-agent and agent-environment interactions. The initial routing was performed using the A* search algorithm, then route correction towards safe zones as action and decision-making on the part of the agent was done using the Bayesian inference method. The study area was District 6 of the Tehran metropolis, which is located very near active faults. The results were assessed under four different scenarios according to levels of agent cognition and access to environmental situation information. The research findings indicated that evacuation time in a state where agents are aware of the environmental situation and have sufficient knowledge was significantly less compared to other scenarios, which can contribute immensely to saving people's lives and reducing human casualties in emergencies and crises.