Source Of Real-time Information Research Articles

Event Identification for Supply Chain Risk Management Through News Analysis by Using Large Language Models

Event identification is important in many areas of the business world. In the supply chain risk management domain, the timely identification of risk events is vital to ensure the success of supply chain operations. One of the important sources of real-time information from across the world is news sources. However, the analysis of large amounts of daily news cannot be done manually by humans. On the other hand, extracting related news depends on the query or the keyword used in the search engine and the news content. Recent advancements in artificial intelligence have opened up opportunities to leverage intelligent techniques to automate this analysis. This paper introduces the LUEI framework, a lightweight framework that, with only the event’s name as input, can autonomously learn all the related phrases associated with that event. It then employs these phrases to search for relevant news and presents the search engine results with a label indicating their relevance. Hence, by conducting this analysis, the LUEI framework is able to identify the occurrence of the event in the real world. The framework’s novel contribution lies in its ability to identify those events (termed as the Contributing Events (CEs)) that contribute to the occurrence of a risk event, offering a proactive approach to risk management in supply chains. Pinpointing CEs from vast news data gives supply chain managers actionable insights to mitigate risks before they escalate.

Tweeting Through the Flood: Application of BERT Topic Modeling for a Comparative Flood Communication Analysis

Floods are prevalent disasters in the United States, posing escalating risks due to climate change-induced factors like rising sea levels and erratic rainfall patterns. Despite governmental efforts, flood risk communication remains inadequate, hindering preparedness and response capacity. While governmental agencies predominantly employ traditional, one-sided information dissemination approaches, social media platforms pose as crucial early warning indicators and sources of real-time information. This study conducts the first part of our intended comparative analysis of social media messages between governmental agencies and communities. We perform a case study on a flooding event in Michigan from May 17-20, 2020. Utilizing advanced topic modeling, we examine Twitter/X message content and sentiment from community-based posts. Insights aim to inform more effective flood communication strategies, bridging the gap between official information and community needs during events. Future work will compare social media messages' content and sentiment from governmental agencies relative to those from the community.

Utilizing microblogs for optimized real-time resource allocation in post-disaster scenarios.

In the aftermath of a disaster event, it is of utmost important to ensure efficient allocation of emergency resources (e.g. food, water, shelter, medicines) to locations where the resources are needed (need-locations). There are several challenges in this goal, including the identification of resource-needs and resource-availabilities in real time, and deciding a policy for allocating the available resources from where they are available (availability-locations) to the need-locations. In recent years, social media, and especially microblogging sites such as Twitter, have emerged as important sources of real-time information on disasters. There have been some attempts to identify resource-needs and resource-availabilities from microblogging sites. However, there has not been much work on having a policy for optimized and real-time resource allocation based on the information obtained from microblogs. Specifically, the allocation of critical resources must be done in an optimal way by understanding the utility of emergency resources at various need-locations at a given point of time. This paper attempts to develop such a utility-driven model for optimized resource allocation in a post-disaster scenario, based on information extracted from microblogs in real time. Experiments show that the proposed model achieves much better allocation of resources than baseline models—the allocation by the proposed model is not only more efficient in terms of quickly bringing down resource-deficits at various need-locations, but also more fair in distributing the available resources among the various need-locations.

Evaluating the reliability of mobility metrics from aggregated mobile phone data as proxies for SARS-CoV-2 transmission in the USA: a population-based study

BackgroundIn early 2020, the response to the SARS-CoV-2 pandemic focused on non-pharmaceutical interventions, some of which aimed to reduce transmission by changing mixing patterns between people. Aggregated location data from mobile phones are an important source of real-time information about human mobility on a population level, but the degree to which these mobility metrics capture the relevant contact patterns of individuals at risk of transmitting SARS-CoV-2 is not clear. In this study we describe changes in the relationship between mobile phone data and SARS-CoV-2 transmission in the USA.MethodsIn this population-based study, we collected epidemiological data on COVID-19 cases and deaths, as well as human mobility metrics collated by advertisement technology that was derived from global positioning systems, from 1396 counties across the USA that had at least 100 laboratory-confirmed cases of COVID-19. We grouped these counties into six ordinal categories, defined by the National Center for Health Statistics (NCHS) and graded from urban to rural, and quantified the changes in COVID-19 transmission using estimates of the effective reproduction number (Rt) between Jan 22 and July 9, 2020, to investigate the relationship between aggregated mobility metrics and epidemic trajectory. For each county, we model the time series of Rt values with mobility proxies.FindingsWe show that the reproduction number is most strongly associated with mobility proxies for change in the travel into counties (0·757 [95% CI 0·689 to 0·857]), but this relationship primarily holds for counties in the three most urban categories as defined by the NCHS. This relationship weakens considerably after the initial 15 weeks of the epidemic (0·442 [−0·492 to −0·392]), consistent with the emergence of more complex local policies and behaviours, including masking.InterpretationOur study shows that the integration of mobility metrics into retrospective modelling efforts can be useful in identifying links between these metrics and Rt. Importantly, we highlight potential issues in the data generation process for transmission indicators derived from mobile phone data, representativeness, and equity of access, which must be addressed to improve the interpretability of these data in public health.FundingThere was no funding source for this study.

Evaluating Real-Time Search over Tweets

Twitter offers a phenomenal platform for the social sharing of information. We describe new resources that have been created in the context of the Text Retrieval Conference (TREC) to support the academic study of Twitter as a real-time information source. We formalize an information seeking task — real-time search — and offer a methodology for measuring system effectiveness. At the TREC 2011 Microblog Track, 58 research groups participated in the first ever evaluation of this task. We present data from the effort to illustrate and support our methodology.

Applications of predictive modelling early in the COVID-19 epidemic

Applications of predictive modelling early in the COVID-19 epidemic

Supervised Sentiment Analysis of Science Topics

Twitter is one of the largest sources of real-time information on the Internet and is continuously fed by millions of users around the world. Each of these users publishes text messages with their opinions, concerns, information, or simply their daily happenings. It is a challenge to address the analysis of massive data in the network, just as it is an objective to look for ways to understand everything that data can offer today in terms of knowledge of society and the market. The sector of science communication is still discovering everything that the web 2.0 and social networks can offer to reach all audiences. This article develops a classification model of messages launched on Twitter, on science topics, in Spanish, with machine learning techniques. The training of this type of models requires the creation of a specific corpus in Spanish for the subject of science, which is one of the most laborious tasks. The classifier is able to predict the sentiment of the message in real time on Twitter, with a confidence interval greater than 80%. The results of its evaluation are at 72% accuracy.

Optimization Model for IoT-Aware Energy Exchange in Energy Communities for Residential Users

In recent years, the distribution of local and renewable generation plants has introduced significant challenges in the management of electrical energy. In order to increase the usage of renewable energy, the prosumers, i.e., the residential users that can act both as producers and consumers, can benefit from joining together and forming energy communities. The deployment of an energy community is based both on technological advancements and on a deep understanding of human decision-making, which in turn requires knowledge about the factors that influence the behavior of residential users. This new scenario calls for great research investigations aimed to improve the management of energy exchanges inside energy communities. An important role in this context is played by the Internet of Things (IoT) technology, as smart IoT objects are used both as a source of real-time information regarding the energy production and the users’ requirements, and as actuators that can help to regulate the distribution and use of energy. In this paper, an IoT-aware optimization model for the energy management in energy communities is presented. The main novelty consists in modeling the entire energy community as a whole, rather than each prosumer separately, with the goal of optimizing the energy sharing and balance at the community level. Experimental results, performed in an university campus, show the advantages of the approach and its capability of reducing the energy costs and increasing the community’s energy autonomy.

L-BiX: incremental sliding-window aggregation over data streams using linear bidirectional aggregating indexes

The number of real-time information sources, or so-called streams, has rapidly increased, leading to a greater demand for complex analyses over streams. Although many stream analysis methods exist, aggregation is fundamental to ascertain higher levels of knowledge from raw data. In particular, sliding-window aggregation, where aggregations over sliding windows are repeatedly computed, is useful in many real-life applications. Two stacks is the state-of-the-art method to compute sliding-window aggregations incrementally with a O(1) time complexity. However, its performance seriously degrades as the window size increases due to the high overhead to maintain its index. To address this problem, this paper proposes a linear bidirectional index (L-BiX) that exploits two kinds of partial aggregations. Specifically, forward (old-to-new) and backward (new-to-old) aggregations allow efficient computations in an incremental manner. The proposed algorithm requires the same time complexity as two stacks (O(1)). Our experimental evaluation shows that the throughput of L-BiX can be faster by up to 1.71 times than that of two stacks with a 50% smaller memory footprint.

Multiagent Simulation of Real-Time Passenger Information on Transit Networks

Modeling and simulation play an important role in transportation networks analysis. With the widespread use of personalized real-time information sources, the status of the simulation depends heavily on individual travelers reactions to the received information. As a consequence, it is relevant for the simulation model to be individual-centered, and agent-based simulation is the most promising paradigm in this context. Information is now personalized, and the simulations have to take into account the interaction of individually guided passengers. In this paper, we present a multiagent simulation model to observe and assess the effects of real-time information provision on the passengers in transit networks. These effects are measured by simulating several scenarios according to the ratio of connected passengers to a real-time information system. We represent the passengers and the vehicles as agents in the system. We analyze the simulated scenarios following their effect on the passengers travel times. The information provided to the connected passengers is based on a space-time representation of the transportation networks. Results show that real-time personalized information may have an increasingly positive impact on overall travel times following the increasing ratio of connected passengers. However, there is a ratio threshold after which the effect of real-time information becomes less positive.

Extracting Resource Needs and Availabilities From Microblogs for Aiding Post-Disaster Relief Operations

Microblogging sites like Twitter are the important sources of real-time information during disaster/emergency events. During such events, the critical situational information posted is immersed in a lot of conversational content; hence, reliable methodologies are needed for extracting the meaningful information. In this paper, we focus on a particular application that is critical for efficient management of post-disaster relief operations—identifying tweets that inform about resource needs and resource availabilities. Two broad types of methodologies can be practically applied to identify such tweets during an ongoing disaster event: 1) supervised classification approaches, where the classifier models are trained on microblogs posted during prior events and applied on those posted during the ongoing event and 2) unsupervised pattern matching and information retrieval approaches that can be directly applied on the microblogs posted during the ongoing event. In this paper, we experiment with several supervised and unsupervised approaches to address the problem, including several neural network-based classification and retrieval models. We also propose two novel neural retrieval models (unsupervised) for the said application, which effectively combine word-level embeddings and character-level embeddings. We conduct experiments on tweets posted during two disaster events and observe that the two approaches perform well in different scenarios. Specifically, if good quality training data are available from prior events, then classification approaches perform better; however, if such training data are not available, then unsupervised retrieval methods outperform supervised classification approaches.

A tale of two epidemics: Contextual Word2Vec for classifying twitter streams during outbreaks

Unstructured tweet feeds are becoming the source of real-time information for various events. However, extracting actionable information in real-time from this unstructured text data is a challenging task. Hence, researchers are employing word embedding approach to classify unstructured text data. We set our study in the contexts of the 2014 Ebola and 2016 Zika outbreaks and probed the accuracy of domain-specific word vectors for identifying crisis-related actionable tweets. Our findings suggest that relatively smaller domain-specific input corpora from the Twitter corpus are better in extracting meaningful semantic relationship than generic pre-trained Word2Vec (contrived from Google News) or GloVe (of Stanford NLP group). However, domain-specific quality tweet corpora during the early stages of outbreaks are normally scant, and identifying actionable tweets during early stages is crucial to stemming the proliferation of an outbreak. To overcome this challenge, we consider scholarly abstracts, related to Ebola and Zika virus, from PubMed and probe the efficiency of cross-domain resource utilization for word vector generation. Our findings demonstrate that the relevance of PubMed abstracts for the training purpose when Twitter data (as input corpus) would be scant during the early stages of the outbreak. Thus, this approach can be implemented to handle future outbreaks in real time. We also explore the accuracy of our word vectors for various model architectures and hyper-parameter settings. We observe that Skip-gram accuracies are better than CBOW, and higher dimensions yield better accuracy.

Two distribution methods for multiagent traffic simulations

Modeling and simulation play an important role in transportation networks analysis. With the widespread use of personalized real-time information sources, the behavior of the simulation depends heavily on individual travelers reactions to the received information. As a consequence, it is relevant for the simulation model to be individual-centered, and multiagent simulation is the most promising paradigm in this context. However, representing the movements of realistic numbers of travelers within reasonable execution times requires significant computational resources. It also requires relevant methods, architectures and algorithms that respect the characteristics of transportation networks. In this paper, we define two multiagent simulation models representing the existing sequential multiagent traffic simulations. The first model is fundamental diagram-based model, in which travelers do not interact directly and use a fundamental diagram of traffic flow to continuously compute their speeds. The second model is car-following based, in which travelers interact with their neighbors to adapt their speeds to their surrounding environment. Then we define patterns to distribute these simulations in a high-performance environment. The first is agent-based and distributes agents equally between available computation units. The second pattern is environment-based and partitions the environment over the different units. The results show that agent-based distribution is more efficient with fundamental diagram-based model simulations while environment-based distribution is more efficient with car following-based simulations.

Extracting and Summarizing Situational Information from the Twitter Social Media during Disasters

Microblogging sites like Twitter have become important sources of real-time information during disaster events. A large amount of valuable situational information is posted in these sites during disasters; however, the information is dispersed among hundreds of thousands of tweets containing sentiments and opinions of the masses. To effectively utilize microblogging sites during disaster events, it is necessary to not only extract the situational information from the large amounts of sentiments and opinions, but also to summarize the large amounts of situational information posted in real-time. During disasters in countries like India, a sizable number of tweets are posted in local resource-poor languages besides the normal English-language tweets. For instance, in the Indian subcontinent, a large number of tweets are posted in Hindi/Devanagari (the national language of India), and some of the information contained in such non-English tweets is not available (or available at a later point of time) through English tweets. In this work, we develop a novel classification-summarization framework which handles tweets in both English and Hindi—we first extract tweets containing situational information, and then summarize this information. Our proposed methodology is developed based on the understanding of how several concepts evolve in Twitter during disaster. This understanding helps us achieve superior performance compared to the state-of-the-art tweet classifiers and summarization approaches on English tweets. Additionally, to our knowledge, this is the first attempt to extract situational information from non-English tweets.

Distributed morality, privacy, and social media in natural disaster response

In this article we note that natural disasters are a destructive force of natural evil that will likely have even greater deleterious effects moving into the future. Whilst natural disasters have catastrophic potential, the advent of social media means that statutory emergency managers have a source of real time information updates to assist decision making in natural disaster response. However, social media feeds do not contain purely relevant information, therefore the task of navigating them in crisis scenarios can be an unwieldy one. As researchers involved in the development of a system that monitors social media for information pertaining to natural disasters (the EU FP 7 funded Slándáil project), we propose that the delegation of this morally loaded task to an autonomous computational artefact can potentially help harness the power of distributed morality and can empower heterogeneous organisations to overcome the phenomenon Luciano Floridi refers to as the tragedy of the Good Will.We note that the collection and processing of social media messages entails privacy intrusion, and consequently argue that the delegation of tasks to autonomous computational artefacts in order to partially mitigate this by using representation to remind emergency managers that action is required (for instance, deletion or anonymization of personal data) is one possible component of a solution.

Distributed Agent-Based Traffic Simulations

Modeling and simulation play an important role in transportation networks analysis. With the widespread of personalized real-time information sources, it is relevant for the simulation model to be individual-centered. The agent-based simulation is the most promising paradigm in this context. However, representing the movements of realistic numbers of travelers within reasonable execution times requires significant computational resources. It also requires relevant methods, architectures and algorithms that respect the characteristics of transportation networks. In this paper, we tackle the problem of using high-performance computing for agent-based traffic simulations. To do so, we define two generic agent-based simulation models, representing the existing sequential agent-based traffic simulations. The first model is macroscopic, in which travelers do not interact directly and use a fundamental diagram of traffic flow to continuously compute their speeds. The second model is microscopic, in which travelers interact with their neighbors to adapt their speeds to their surrounding environment. We define patterns to distribute these simulations in a high-performance environment. The first distributes agents equally between available computation units. The second pattern splits the environment over the different units. We finally propose a diffusive method to dynamically balance the load between units during execution. The results show that agent-based distribution is more efficient with macroscopic simulations, with a speedup of 6 compared to the sequential version, while environmentbased distribution is more efficient with microscopic simulations, with a speedup of 14. Our diffusive load-balancing algorithm improves further the performance of the environment based approach by 150%.

AMI Data for Decision Makers and the Use of Data Analytics Approach

The Advanced Metering Infrastructure (AMI) analytics provide a source of real-time information not only about energy usage, but also as an indicator of various social, demographic, and economic phenomena inside a city, according to the National Electricity Information Administration. As a tool for leveraging the potential of AMI data within the applications in a Smart City, this article proposes a Data Analytics/Big Data framework applied to AMI data as presented in this study. The framework is comprised of three main components. First and foremost, the architectural perspective sets AMI within the context of the Smart Grids Architecture Model-SGAM. Second, the methodological view describes the translation of raw data into knowledge, which is represented by the DIKW hierarchy and the NIST Big Data interoperability model, among other things. The final factor that connects the two perspectives is human expertise and talents, which enable us to gain a better comprehension of the results and translate knowledge into wisdom. Our novel perspective responds to the issues that are emerging in the energy markets by including a binding element that provides assistance for the most optimal and efficient decision-making possible. We created a case study to demonstrate the functionality of our framework. It illustrates how each component of the framework for a load forecasting application at a retail electricity provider is implemented in the instance described here (REP). According to the company, the Mean Absolute Percentage Error (MAPE) for certain of the REP's markets was less than 5 percent. Aside from that, the instance illustrates what happens when the binding element is introduced, since it generates fresh development possibilities and serves as a feedback mechanism for more forceful decision-making.

Extracting Non-Situational Information from Twitter During Disaster Events

Micro blogging sites have become important forums for discussion during disaster events in which Twitter has become one of the important source of real time information. Millions of tweets are posted during disasters, which include not only information about the present situation or relief efforts, but also the emotions or opinion of the masses. Much research has been conducted on extracting situational information from tweets during disaster. However, according to current knowledge, there has not been any prior attempt to study the non-situational tweets posted during disasters, such as those which express the emotions/opinions of the people, political and governmental views, raising charities and event analysis. In this study, the authors characterized the non-situational tweets posted during recent disaster events, the Nepal Earthquake and the Gurudaspur Terrorist attack. They developed a classifier to categorize various types of non-situational tweets into a set of fine-grained classes utilizing state-of-the-art machine learning technique. This system also helps in filtering out communal tweets which can make worst the situation by disrupting communal harmony during certain disaster events.

Extraction of actionable information from crowdsourced disaster data.

Natural disasters cause enormous damage to countries all over the world. To deal with these common problems, different activities are required for disaster management at each phase of the crisis. There are three groups of activities as follows: (1) make sense of the situation and determine how best to deal with it, (2) deploy the necessary resources, and (3) harmonize as many parties as possible, using the most effective communication channels. Current technological improvements and developments now enable people to act as real-time information sources. As a result, inundation with crowdsourced data poses a real challenge for a disaster manager. The problem is how to extract the valuable information from a gigantic data pool in the shortest possible time so that the information is still useful and actionable. This research proposed an actionable-data-extraction process to deal with the challenge. Twitter was selected as a test case because messages posted on Twitter are publicly available. Hashtag, an easy and very efficient technique, was also used to differentiate information. A quantitative approach to extract useful information from the tweets was supported and verified by interviews with disaster managers from many leading organizations in Thailand to understand their missions. The information classifications extracted from the collected tweets were first performed manually, and then the tweets were used to train a machine learning algorithm to classify future tweets. One particularly useful, significant, and primary section was the request for help category. The support vector machine algorithm was used to validate the results from the extraction process of 13,696 sample tweets, with over 74 percent accuracy. The results confirmed that the machine learning technique could significantly and practically assist with disaster management by dealing with crowdsourced data.

Integrated management of inventory and production systems based on floating decoupling point and real-time information: A simulation based analysis

Globalisation has transformed market conditions reducing lead times and increasing both the degree of product differentiation and price competition. To cope with this issue, new hybrid order fulfilment strategies have emerged which integrate the Make to Stock (MTS) and Build to Order (BTO) strategies, and whose aim is to simultaneously improve opposite objectives, such as the stock level and the lead time. Furthermore, companies face frequent challenges of sharing updated information and avoiding the lack of coordination between the different company subsystems. This work focuses on relatively recent fulfilment strategies based on the Floating Decoupling Point (FDP) which uses real time information to operate. The main objectives of this work are the study of the influence of different sources of real-time information about the available products and select the most suitable configuration of these systems, regarding the inventory control policy and the source of real-time information. The stochastic nature of the process makes simulation an appropriate tool to achieve valuable results. The simulation studies have been carried out following a full factorial Design of Experiments (DoE). Experimental results show that the strategies based on the FDP are very sensitive to the real-time information. These systems achieve the best performance when the inventory control policy uses simultaneously demand forecasting and real-time information of available products corresponding to the Work-In-Process (WIP) and Finished Goods Inventory (FGI). In contrast, system performance deteriorates significantly when real-time information of available products in the WIP has not been taken into account.