Situational Information Research Articles

Emotions as social information in unambiguous situations: role of emotions on procedural justice perception.

The online version contains supplementary material available at 10.1007/s12144-023-04640-y.

An FW–GA Hybrid Algorithm Combined with Clustering for UAV Forest Fire Reconnaissance Task Assignment

The assignment of tasks for unmanned aerial vehicles (UAVs) during forest fire reconnaissance is a highly complex and large-scale problem. Current task allocation methods struggle to strike a balance between solution speed and effectiveness. In this paper, a two-phase centralized UAV task assignment model based on expectation maximization (EM) clustering and the multidimensional knapsack model (MKP) is proposed for the forest fire reconnaissance task assignment. The fire situation information is acquired using the sensors carried by satellites at first. Then, the EM algorithm based on the Gaussian mixture model (GMM) is applied to get the initial position of every UAV. In the end, the MKP is applied for UAV task assignment based on the initial positions of the UAVs. An improved genetic algorithm (GA) based on the fireworks algorithm (FWA) is proposed for faster iteration speed. A simulation was carried out against the background of forest fires in Liangshan Prefecture, Sichuan Province, and the simulation’s results demonstrate that the task assignment model can quickly and effectively address task allocation problems on a large scale. In addition, the FW–GA hybrid algorithm has great advantages over the traditional GA, particularly in solving time, iteration convergence speed, and solution effectiveness. It can reduce up to 556% of the iteration time and increase objective function value by 1.7% compared to the standard GA. Furthermore, compared to the GA–SA algorithm, its solving time is up to 60 times lower. This paper provides a new idea for future large-scale UAV task assignment problems.

Techniques for Finding Analytical Solution of Generalized Fuzzy Differential Equations with Applications

Engineering and applied mathematics disciplines that involve differential equations include classical mechanics, thermodynamics, electrodynamics, and general relativity. Modelling a wide range of real-world situations sometimes comprises ambiguous, imprecise, or insufficient situational information, as well as multiindex, uncertainty, or restriction dynamics. As a result, intuitionistic fuzzy set models are significantly more useful and versatile in dealing with this type of data than fuzzy set models, triangular, or trapezoidal fuzzy set models. In this research, we looked at differential equations in a generalized intuitionistic fuzzy environment. We used the modified Adomian decomposition technique to solve generalized intuitionistic fuzzy initial value problems. The generalized modified Adomian decomposition technique is used to solve various higher-order generalized trapezoidal intuitionistic fuzzy initial value problems, circuit analysis problems, mass-spring systems, steam supply control sliding value problems, and some other problems in physical science. The outcomes of numerical test applications were compared to exact technique solutions, and it was shown that our generalized modified Adomian decomposition method is efficient, robotic, and reliable, as well as simple to implement.

Research on multi-context aware recommendation methods based on tensor factorization

Compared to the traditional recommender systems, context-aware recommender systems are more in line with actual application contexts. However, the existing researches are mostly focused on single context-aware recommendation, such as time-aware recommendation or location-aware recommendation, and lack of in-depth research on multi-context-aware recommendation. Therefore, we proposed a recommendation method of high-order tensor factorization based on multi-context-aware. First, on the basis of analyzing the influence of context on users’ interest preferences, the sensitivity of users to multiple contexts was detected using statistical methods. For context-sensitive users, four-dimensional tensors and feature matrices used to solve data sparsity were constructed based on rating matrix and situational information. And then the stochastic gradient descent algorithm was used for iterative calculation to fill in missing data values and carry out parameter optimization. For context-insensitive users, we used matrix factorization to predict users’ interest preferences. Finally, we tested and validated our method on a multi-context-aware movie dataset, and the experimental results show that the proposed method could effectively reduce the prediction error and improve the recommendation quality.

An extended SFR model based fast frequency security assessment method for power systems

The large-scale integration of renewable energy and the increasing access to power electronic equipment lead to the low inertia of power grid, which raises severe challenges to frequency security. The low inertia makes power system more sensible to power disturbances, and therefore, evaluating frequency stability is crucial. To address this issue, a fast frequency security assessment method for power grid based on the extended SFR (system frequency response) model is proposed. In this method, an extended SFR model is established based on the characteristics of future new power systems, which are comprised of multiple frequency response sources, including induction motor load, renewable energy, and other asynchronous resources. To obtain the parameters of the proposed model, the principle and applicability of model parameter aggregation methods are explained. Considering the difficulty of obtaining all the required information in real-world situations, the parameters are obtained by parameter identification method through optimization algorithms. Finally, the effectiveness of the proposed extended SFR model is verified in the WSCC-9 bus system and the parameter identification method is validated in a regional power grid. The calculation time is less than 0.03s and the accuracy is more precise than conventional SFR. The results demonstrate that the proposed extended SFR model and parameter identification method show characteristics of both rapidity and accuracy in judging the security of power systems.

IH-TCGAN: Time-Series Conditional Generative Adversarial Network with Improved Hausdorff Distance for Synthesizing Intention Recognition Data.

As military technology continues to evolve and the amount of situational information available on the battlefield continues to increase, data-driven deep learning methods are becoming the primary method for air target intention recognition. Deep learning is based on a large amount of high quality data; however, in the field of intention recognition, it often faces key problems such as low data volume and unbalanced datasets due to insufficient real-world scenarios. To address these problems, we propose a new method called time-series conditional generative adversarial network with improved Hausdorff distance (IH-TCGAN). The innovation of the method is mainly reflected in three aspects: (1) Use of a transverter to map real and synthetic data into the same manifold so that they have the same intrinsic dimension; (2) Addition of a restorer and a classifier in the network structure to ensure that the model can generate high-quality multiclass temporal data; (3) An improved Hausdorff distance is proposed that can measure the time order differences between multivariate time-series data and make the generated results more reasonable. We conduct experiments using two time-series datasets, evaluate the results using various performance metrics, and visualize the results using visualization techniques. The experimental results show that IH-TCGAN is able to generate synthetic data similar to the real data and has significant advantages in the generation of time series data.

How to adjust the expected waiting time to improve patient’s satisfaction?

BackgroundLong waiting time in hospital leads to patient’s low satisfaction. In addition to reducing the actual waiting time (AWT), we can also improve satisfaction by adjusting the expected waiting time (EWT). Then how much can the EWT be adjusted to attribute a higher satisfaction?MethodsThis study was conducted though experimental with hypothetical scenarios. A total of 303 patients who were treated by the same doctor from August 2021 to April 2022 voluntarily participated in this study. The patients were randomly divided into six groups: a control group (n = 52) and five experimental groups (n = 245). In the control group, the patients were asked their satisfaction degree regarding a communicated EWT (T0) and AWT (Ta) under a hypothetical situation. In the experimental groups, in addition to the same T0 and Ta as the control group, the patients were also asked about their satisfaction degree with the extended communicated EWT (T1). Patients in five experimental groups were given T1 values with 70, 80, 90, 100, and 110 min respectively. Patients in both control and experiment groups were asked to indicate their initial EWT, after given unfavorable information (UI) in a hypothetical situation, the experiment groups were asked to indicate their extended EWT. Each participant only participated in filling out one hypothetical scenario. 297 valid hypothetical scenarios were obtained from the 303 hypothetical scenarios given.ResultsThe experimental groups had significant differences between the initial indicated EWT and extended indicated EWT under the effect of UI (20 [10, 30] vs. 30 [10, 50], Z = -4.086, P < 0.001). There was no significant difference in gender, age, education level and hospital visit history (χ2 = 3.198, P = 0.270; χ2 = 2.177, P = 0.903; χ2 = 3.988, P = 0.678; χ2 = 3.979, P = 0.264) in extended indicated EWT. As for patient’s satisfaction, compared with the control group, significant differences were found when T1 = 80 min (χ2 = 13.511, P = 0.004), T1 = 90 min (χ2 = 12.207, P = 0.007) and T1 = 100 min (χ2 = 12.941, P = 0.005). When T1 = 90 min, which is equal to the Ta, 69.4% (34/49) of the patients felt “very satisfied”, this proportion is not only significantly higher than that of the control group (34/ 49 vs. 19/52, χ2 = 10.916, P = 0.001), but also the highest among all groups. When T1 = 100 min (10 min longer than Ta), 62.5% (30/48) of the patients felt “very satisfied”, it is significantly higher than that of the control group (30/ 48 vs. 19/52, χ2 = 6.732, P = 0.009). When T1 = 80 min (10 min shorter than Ta), 64.8% (35/54) of the patients felt “satisfied”, it is significantly higher than that of the control group (35/ 54 vs. 17/52, χ2 = 10.938, P = 0.001). However, no significant difference was found when T1 = 70 min (χ2 = 7.747, P = 0.052) and T1 = 110 min (χ2 = 4.382, P = 0.223).ConclusionsProviding UI prompts can extend the EWT. When the extended EWT is closer to the AWT, the patient’s satisfaction level can be improved higher. Therefore, medical institutions can adjust the EWT of patient’s through UI release according to the AWT of hospitals to improve patient’s satisfaction.

Short-range air combat maneuver decision of UAV swarm based on multi-agent Transformer introducing virtual objects

With the development of Unmanned Aerial Vehicle (UAV) swarm technology, there has been a growing interest in using Artificial Intelligence (AI) to drive UAV swarms for short-range air combat. However, due to the complexity of situation information in UAV swarm air combat, making accurate air combat decisions based on air combat situation information has become a challenge. In this paper, we propose the multi-agent Transformer introducing virtual objects (MTVO) to address this issue. First, the proposed approach designs a multi-agent Transformer network structure by exploiting the homogeneity feature of the UAV state information in the swarm. This structure enables the structured processing of complex situation information. Specifically, the local situation information of each UAV is calculated by self-attention, which reduces the size of the swarm situation information while retaining the information of key UAVs. This approach reduces the difficulty of processing UAV swarm situation information. Moreover, we add a virtual object to the UAV swarm information to assist in calculating the weight distribution of the local situation. The weighted fusion of local situations allows us to obtain a more effective representation of the global situation, which serves as the basis for more accurate air combat maneuver decisions. We demonstrate the performance of the proposed method through air combat simulation results using Reinforcement Learning (RL) methods, and validate the applicability and effectiveness of the MTVO network for the short-range air combat problem of UAV swarms.

Assessment of Solar Panel Using Multiattribute Decision-Making Approach Based on Intuitionistic Fuzzy Aczel Alsina Heronian Mean Operator

Multiattribute decision-making (MADM) approach is an effective method for handling ambiguous information in a practical situation. The process of the MADM technique has drawn a lot of interest from various academic and selection processes of extensive analysis. The aggregation operators (AOs) are the best mathematical tools and received a lot of attention from researchers. This article explored the theory of intuitionistic fuzzy IF sets (IFSs) and their certain fundamental operations. The theory of triangular norms also explores Aczel Alsina operations (AAOs) in advanced mathematical tools. The concepts of Heronian mean (HM) and geometric HM (GHM) operators are presented to define interrelationships among different opinions. We developed a list of certain AOs by utilizing AAOs under the system IF information, namely, IF Aczel Alsina HM (IFAAHM), IF Aczel Alsina weighted HM (IFAAWHM), IF Aczel Alsina GHM (IFAAGHM), and IF Aczel Alsina weighted GHM (IFAAWGHM) operators. Some particular characteristics of our invented methodologies are also presented. Solar energy is an effective, efficient resource to enhance electricity production and the country’s economic growth. Therefore, we studied an application of solar panel systems to solve real-life problems under a robust technique of the MADM approach by utilizing our invented approaches of IFAAWHM and IFAAWGHM operators. A numerical example was also given to select more suitable solar panels under our proposed methodologies. To find the competitiveness and feasibility of discussed methodologies, we make an inclusive comparative study in which we contrast the results of existing AOs with the consequences of current approaches.

The experiences of the landslide survivors from Kodagu District, India: Need for community-engaged village/ward level micro disaster management planning

IntroductionKodagu district in India had catastrophic landslides in 2018, the rarest occurrence known to people until 2018. It has become an annual affair radiated to other districts of the Western Ghats range. AimTo explore landslide survivors' experiences, conventional knowledge, and responses during the emergency relief response. MethodsIn the emergency relief response phase, a qualitative study was conducted using the purposive sampling technique of landslide survivors in the Kodagu District. Ninety-nine participants were interviewed in 10 Focus Group Discussions (FGD). The FGDs were audio-recorded, transcribed verbatim and thematically analysed. ResultsIn this study, 35 males and 64 females participated. All have endured horrifying experiences, and fear of life and social triggers have played an essential role in self-evacuation. The effective group work among the survivors was perceptible. The issues include improper communication of situational information, disruption of road and cellular connectivity, physical and mental health problems, direct and indirect losses, and other cross-cutting themes. Themes have been recognised as Experiencing the Darkness in Life; Disputed Communication; community-managed Immediate Disaster Response; Health Concerns of the Landslide Survivors; and Cross-Cutting Themes. ConclusionThe study concludes that community-driven community-managed evacuation minimised the causalities. The response from the government in the immediate disaster response phase was satisfactory; fear of life and social trigger played a vital role in the self-evacuation, subsequently reducing the casualties. With community participation, disaster planning at the village/ward level would be the way forward for the well-coordinated emergency relief response.

Multiple criteria decision analytic methods in management with T-spherical fuzzy information.

With a focus on T-spherical fuzzy (T-SF) sets, the aim of this paper is to create a split-new appraisal mechanism and an innovative decision analytic method for use with multiple-criteria assessment and selection in uncertain situations. The T-SF frame is the latest recent advancement in fuzzy settings and uses four facets (consisting of membership grades of positivity, neutrality, negativity, and refusal) to elucidate complex uncertainties, thereby evidently reducing information loss, in anticipation of fully manifesting indistinct and equivocal information. This paper adds to the body of knowledge regarding multiple criteria choice modeling by raising T-SF correlation-oriented measurements connected to the fixed and displaced ideal/anti-ideal benchmarks and by creating an approachable appraisal mechanism for advancing a T-SF decision analytic methodology. Consider, in particular, the performance ratings of available options in terms of judging criteria under the T-SF type of uncertainties. This research gives correlation-oriented measurements focusing on two varieties of maximum and square root functions in T-SF situations, which serve as a solid foundation for an efficacious appraisal mechanism from two views of anchored judgments corresponding to the fixed and displaced benchmarks. The T-SF Minkowski distance index is generated to integrate the outranking and outranked identifiers relying on correlation-oriented measurements for figuring out the local outranking and outranked indices. The T-SF decision analytic procedures are constructed using a new appraisal significance index that is founded on certain valuable insights of correlation-oriented maximizing and minimizing indices as well as global outranking and outranked indices. Additionally, a concrete location selection dilemma is dealt with in this research to showcase the applicability and efficiency of the suggested T-SF decision analytic methodology. Sensitivity analyses and comparative studies are carried out to investigate substantial modifications in pertinent parameters and to confirm the robustness of the predominance relationships among the available options. The suggested approaches are adaptable, flexible, and reliable, according to the application outcomes and comparison findings. This research provides four scientific contributions: (1) the utilization of T-SF correlation coefficients as the basis for prioritization analysis involving multiple criteria assessments, (2) the evolution of the T-SF Minkowski distance index to model outranking decision-making processes, (3) the creation of a reliable appraisal mechanism based on T-SF correlation-oriented measurements for intelligent decision support, and (4) the advancement of computational tools and procedures (e.g., correlation-oriented maximizing and minimizing indices, global outranking and outranked indices, and appraisal significance indices) to perform the decision analytic procedure in T-SF settings. In terms of managerial implications, the solution findings support the employment of the fixed ideal/anti-ideal benchmarking mechanism, as its measurements and indices are easy to operate and suitably sensitive. Next, in practical implementations of the T-SF decision analytic procedure, it is advised to utilize the T-SF Manhattan distance index for calculating convenience. Finally, the T-SF decision analytic techniques offer fundamental ideas and measurements appropriate for manipulating T-SF information in complex decision situations, thereby increasing the application potential in the area of decision-making with information uncertainty.

Individual Differences and Situational Constraint Predict Information Search in Negotiation Planning

Planning is critical for negotiation success as it facilitates obtaining superior outcomes. However, little empirically is known about this topic. We argue that individual differences are important to understanding planning behavior because planning affords greater opportunities for individual differences to manifest compared to other phases of negotiation. We conducted a series of studies with the aim of understanding how the Big Five factors and gender predict information search and preference while planning. In Study 1, we examined information search behavior using the Mouselab paradigm. When the search space was large, agreeableness predicted persistence in search, and women spent significantly more time on the task, and looked for value creating and relationship related information to a greater extent than men (Study 1a). In a more constrained situation or with a smaller search space, few associations between individual differences and search behavior emerged (Studies 1b and 1c). In Study 2, we used a survey design to elicit preferences for information type. In this task, multiple personality factors predicted preferences. Conscientiousness and openness predicted preferences for value claiming information, and women preferred value creating information. Conscientiousness predicted preference for value claiming choice of information through the preference for value claiming information. How individual differences manifest in information search and preference in negotiation planning is a function of both task type and complexity.

Real-Time Context-Aware Recommendation System for Tourism.

Recently, the tourism trend has been shifting towards the Tourism 2.0 paradigm due to increased travel experiences and the increase in acquiring and sharing information through the Internet. The Tourism 2.0 paradigm requires developing intelligent tourism service tools for positive effects such as time savings and marketing utilization. Existing tourism service tools recommend tourist destinations based on the relationship between tourists and tourist destinations or tourism patterns, so it is difficult to make recommendations in situations where information is insufficient or changes in real time. In this paper, we propose a real-time recommendation system for tourism (R2Tour) that responds to changing situations in real time, such as external factors and distance information, and recommends customized tourist destinations according to the type of tourist. R2Tour trains a machine learning model with situational information such as temperature and precipitation and tourist profiles such as gender and age to recommend the top five nearby tourist destinations. To verify the recommendation performance of R2Tour, six machine learning models, including K-NN and SVM, and information on tourist attractions in Jeju Island were used. As a result of the experiment, R2Tour was verified with accuracy of 77.3%, micro-F1 0.773, and macro-F1 0.415. Since R2Tour trains tourism patterns based on situational information, it is possible to recommend new tourist destinations and respond to changing situations in real time. In the future, R2Tour can be installed in vehicles to recommend nearby tourist destinations or expanded to tasks in the tourism industry, such as a smart target advertising system.

Passenger Counting in Mass Public Transport Systems using Computer Vision and Deep Learning

Estimating the number of people in vehicles and stations in public transport systems is crucial to improve service quality. The TransMilenio system in Bogota has serious drawbacks due to the lack of information in congestion situations. In this work we present a computer vision method that estimates the number of people in TransMilenio stations using deep learning techniques. We offer free use of the TransMilenio-Javeriana database with nearly 900,000 head labels on buses and stations. From these images a deep learning architecture tuned for crowd counting was trained to generate density maps around the heads in the scene. Several head count methods were evaluated on the density maps. After testing the method with 10,800 images, the results show a mean absolute error of 1 head per frame, equivalent to 11% relative error. The accuracy of this method is much better than its manual counterpart. This method is also scalable and low cost, which indicates that it has great potential to provide information for the planning and operation of public transport systems.

JUSTIFICATION OF THE NEED TO CLARIFY THE BASIC CONCEPTS AND FORMULAS OF AUTOMOTIVE EXPERTISE

The reliability of the investigation and the conclusions of the examination depends on how accurate and correct the initial information – the facts, circumstances and parameters of the condition of vehicles, road conditions and the driver accepted for consideration – corresponds to the mechanism of the accident. The reasons for this kind of inconsistencies in the information of the real situation may be different: the ambiguity of the verbal description of the environment and the condition of the pavement, the variance of the values of parameters and coefficients relative to their discrete reference value, the dependence of calculated values on numerous factors, measurement errors. In addition, the result depends on the completeness and accuracy of the formulas used, the accuracy of the definition of certain concepts, in particular, the concepts of the delay time of the brake actuation, the time of the deceleration rise, the vehicle’s use, etc.&#x0D; That is why in the works of many authors devoted to the examination of accidents, it has been repeatedly noted that the information used in the investigation carries elements of uncertainty, which inevitably affects the correctness and reliability of the conclusions of the investigation.&#x0D; Purpose – is to show the need to clarify the basic concepts and formulas used in production&#x0D; Methodology of the work: the methods of formalization and specification of formulas used in the investigation of the examination of an accident were used in the work&#x0D; Results: the examples given in the paper and the theoretical studies carried out show that a significant number of formulas used in automotive research require refinement and refinement.&#x0D; Practical implications: it is advisable to apply the results obtained when conducting automotive research – investigation and examination of road accidents.

The impact of information sharing on the quality of decision-making: From the point of view of employees in Jordanian private hospitals

This paper examines how information sharing impacts the quality of decision-making in Jordanian private hospitals from the point of view of employees. It involved a structured questionnaire, which consisted of demographic data, information sharing, and decision-making. The research results suggest that sharing information in both common and unique situations significantly positively influences the decision-making abilities of teams. Merely possessing unique information does not enhance the ability to make decisions; conversely, common information is crucial to facilitate the decision-making process. Participant characteristics have no effect on decision-making quality, especially as team functional diversity continues to rise, with information sharing even less relevant as a result. Our results indicate that participants’ characteristics do not predict whether the discussion of shared information will contribute to decision-making quality. As a practical contribution, our findings recognise the importance of decision teams in information management, encourage flexibility and openness in organisational relations, and assist policymakers, managers, and regulators through training programs that confirm participation is one of the most important components of generating new ideas.

Legal regulation of electronic evidence and the practice of their use in the judiciary of Ukraine

The article, based on the analysis of judicial practice (criminal, civil, administrative and economic proceedings), analyzes the assessment and use of evidence received in electronic form by courts, and draws a conclusion regarding the lack of unanimity in these issues; in particular, regarding the very concept of "electronic evidence" (in the Criminal Procedural Code of Ukraine, unlike other procedural codes, this term is not defined) and its duplicate or copy, the method of recording and certification (by whom: by the provider or another entity), the use screenshot seizure, etc. Ukraine's signing on November 30, 2022 of the Second Additional Protocol to the Convention on Cybercrime, which is aimed at improving the process of electronic data collection, marks the beginning of a new law-making process to improve Ukraine's national legislation in accordance with international standards. The basis for this is the created legal basis for international direct cooperation with service providers, accelerated forms of cooperation for disclosure of information about the subscriber and traffic data, accelerated cooperation and disclosure of information in emergency situations, additional tools for mutual assistance, data protection and other guarantees of the rule of law are also provided , the definition of "emergency situation" is given for the first time, which should be understood as a situation that creates a significant and unavoidable risk to the life or safety of any natural person, and "personal data" is considered to be information related to the identification of a person. On the basis of updated international standards, current legislation and practice, it is proposed to start an in-depth study of the "Theory of electronic evidence", which will contribute to the development of verified bills and the adoption by the parliament of a law that would specify the unambiguous terms "electronic evidence", a copy/duplicate of electronic evidence, the order of withdrawal, records or certificates, the provider's authority to assist in legal proceedings.

CIMLA: A Modular and Modifiable Data Preparation, Organization, and Fusion Infrastructure to Partially Support the Development of Context-aware MMLA Solutions

Multimodal Learning Analytics (MMLA) solutions aim to provide a more holistic picture of a learning situation by processing multimodal educational data. Considering contextual information of a learning situation is known to help in providing more relevant outputs to educational stakeholders. However, most of the MMLA solutions are still in prototyping phase and dealing with different dimensions of an authentic MMLA situation that involve multiple cross-disciplinary stakeholders like teachers, researchers, and developers. One of the reasons behind still being in prototyping phase of the development lifecycle is related to the challenges that software developers face at different levels in developing context-aware MMLA solutions. In this paper, we identify the requirements and propose a data infrastructure called CIMLA. It includes different data processing components following a standard data processing pipeline and considers contextual information following a data structure. It has been evaluated in three authentic MMLA scenarios involving different cross-disciplinary stakeholders following the Software Architecture Analysis Method. Its fitness was analyzed in each of the three scenarios and developers were interviewed to assess whether it meets functional and non-functional requirements. Results showed that CIMLA supports modularity in developing context-aware MMLA solutions and each of its modules can be reused with required modifications in the development of other solutions. In the&amp;nbsp;future, the current involvement of a developer in customizing the configuration file to consider contextual information can be investigated.

Twitter-Based Disaster Response Using Machine Learning

Twitter, a microblogging platform, receives real-time information via informal conversations, and it has, accordingly, become the main source of data for research studies based on emergency situational awareness. Millions of tweets are posted on Twitter every day, and during disasters, the frequency of tweets relating to an on-going crisis event grows exponentially. This unprecedented increase in the number of tweets during disasters needs to be monitored, identified, processed, and analyzed so that necessary measures can be taken at the earliest to reduce the loss or damage during emergencies. However, due to large voluminous data being available during crisis hours, it is almost impossible for a human to perform these tasks in real time. In this regard, a semi-automated AI-based disaster response system for Twitter data is proposed. The proposed disaster response system would be capable of extracting essential situational awareness information related to a disaster and would also be capable of sketching tentative area of critically affected population.

Modeling unmeasured baseline information in observational time-to-event data subject to delayed study entry.

Unmeasured baseline information in left-truncated data situations frequently occurs in observational time-to-event analyses. For instance, a typical timescale in trials of antidiabetic treatment is "time since treatment initiation", but individuals may have initiated treatment before the start of longitudinal data collection. When the focus is on baseline effects, one widespread approach is to fit a Cox proportional hazards model incorporating the measurements at delayed study entry. This has been criticized because of the potential time dependency of covariates. We tackle this problem by using a Bayesian joint model that combines a mixed-effects model for the longitudinal trajectory with a proportional hazards model for the event of interest incorporating the baseline covariate, possibly unmeasured in the presence of left truncation. The novelty is that our procedure is not used to account for non-continuously monitored longitudinal covariates in right-censored time-to-event studies, but to utilize these trajectories to make inferences about missing baseline measurements in left-truncated data. Simulating times-to-event depending on baseline covariates we also compared our proposal to a simpler two-stage approach which performed favorably. Our approach is illustrated by investigating the impact of baseline blood glucose levels on antidiabetic treatment failure using data from a German diabetes register.