Search results for: event methodology

Authors: Özlem Küçükakça

Abstract:

The world population is rapidly growing. In the last few decades, environmental protection and climate change have been remarked as a global concern. All events have their own ecological footprint. Therefore, all participants who take part in the events, from event organizer to audience should be responsible for reducing carbon emissions. Currently, there is a literature gap which investigates the relationship between events and environment. Hence, this study is conducted to investigate how to implement environmental sustainability in the event management. Therefore, a wide literature and also the UK festivals database have been investigated. Finally, environmental effects and the solution of reducing impacts at events were discussed.

Keywords: ecological footprint, environmental sustainability, events, sustainability

Procedia PDF Downloads 280

Authors: Yi-De Liu

Abstract:

For many European cities, a key motivation in developing event strategies is to use event as a catalyst for urban regeneration. One type of event that is particularly used as a means of urban development is the European Capital of Culture (ECOC) initiative. Based on a case study of the 2008 ECOC Liverpool, this paper aims at conceptualising the significance of major event for a city’s economic, cultural and social regenerations. In terms of economic regeneration, the role of the ECOC is central in creating Liverpool’s visitor economy and reshaping city image. Liverpool planned different themes for eight consecutive years as a way to ensure economic sustainability. As far as cultural regeneration is concerned, the ECOC contributed to the cultural regeneration of Liverpool by stimulating cultural participation and interest from the demand side, as well as improving cultural provision and collaboration within the cultural sector from the supply side. So as to social regeneration, Liverpool treated access development as a policy guideline and considered the ECOC as an opportunity to enhance the sense of place. The most significant lesson learned from Liverpool is its long-term planning and efforts made to integrate the ECOC into the overall urban development strategy. As a result, a more balanced and long-term effect on urban regeneration could be achieved.

Keywords: cultural event, urban regeneration, european capital of culture, Liverpool

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Authors: Vajiheh Javani

Abstract:

Hosting sport event result in both economic and socio-psychological impacts on host communities. Economic impacts, which are considered by many scholars and the social impacts of tourism based on hosting sports events have also been somehow investigated. But, investigating perceived social impacts based on host community perceptions has been paid not with little attention enough. Therefore, this study aims to study the beneficiary social impact of hosting sport event from residents’ perceptions. The participations for this research were 50 residents of Tabriz city who were recruited by judgment sampling method. focused group interviews were used for gathering the data. Then thematic analysis was utilized for interview analysis. Extracted perceived beneficiary social impacts include (1) economic benefits; (2) community pride; (3) community development. This study highlighted the perceived social beneficiary impacts and could contribute to a better understanding of how local residents of the studied community view the impacts associated with a sport event.

Keywords: socio-psychological impacts, sport event, community development, hosting

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Authors: Rosyidah, Permata Wulandari

Abstract:

We investigate the price effect of addition and deletions from the Indonesia Sharia Stock Index (ISSI) and Jakarta Islamic Index (JII). Using event study methodology, we measure abnormal returns for firms over the period June 2019 - to December 2021. Through the sample of 107 additions and 95 deletions, we find evidence to support the theory of Muslim country investment behavior. We find that additions to the Islamic index led to a significant positive stock market reaction and deletions to the Islamic index led to a negative stock market reaction on Jakarta Islamic Index (JII) and there is no significant reaction of addition and deletion on Indonesia Sharia Stock Index (ISSI).

Keywords: abnormal return, abnormal volume, event study, index changes, sharia index

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Authors: Zhuo Jing-Schmidt, Jun Lang

Abstract:

Numeral classifiers are common in Asian languages. Research on numeral classifiers primarily focuses on noun classifiers that quantify and individuate nominal referents. There is a scarcity of research on event quantification using verb classifiers. This study aims to understand the semantic and conceptual basis of event quantification in Chinese. From a usage-based Construction Grammar perspective, this study presents a corpus analysis of event quantification in Chinese. Drawing on a large balanced corpus of contemporary Chinese, we analyze 667 NOUN col-lexemes totaling 31136 tokens of a productive numeral classifier construction in Chinese. Using collostructional analysis of the collexemes, the results show that the construction quantifies and classifies dramatic events using a theater-based conceptual metaphor. We argue that the usage patterns reflect the cultural entrenchment of theater as in Chinese conceptualization and the construal of theatricality in linguistic expression. The study has implications for cognitive semantics and construction grammar.

Keywords: event quantification, classifier, corpus, metaphor

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Authors: Changyin Zhou

Abstract:

Ba constructions are a special type of constructions in Chinese. Their syntactic behaviors are closely related to their event structural properties. The existing study which treats the semantic function of Ba as causative meets difficulty in treating the discrepancy between Ba constructions and their corresponding constructions without Ba in expressing causativity. This paper holds that Ba in Ba constructions is a functional category expressing affectedness. The affectedness expressed by Ba can be positive or negative. The functional category Ba expressing negative affectedness has the semantic property of being 'expected'. The precondition of Ba construction is the boundedness of the event concerned. This paper, holding the parallelism between motion events and change-of-state events, proposes a syntactic model based on the notions of boundedness and affectedness, discusses the transformations between Ba constructions and the related resultative constructions, and derivates the various Ba constructions concerned.

Keywords: affectedness, Ba constructions, boundedness, event structure, resultative constructions

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Authors: Zhibo Xiao, Tharini Nayanika de Silva, Kezhi Mao

Abstract:

In this paper, we present an evolving knowledge extraction system named AKEOS (Automatic Knowledge Extraction from Online Sources). AKEOS consists of two modules, including a one-time learning module and an evolving learning module. The one-time learning module takes in user input query, and automatically harvests knowledge from online unstructured resources in an unsupervised way. The output of the one-time learning is a structured vector representing the harvested knowledge. The evolving learning module automatically schedules and performs repeated one-time learning to extract the newest information and track the development of an event. In addition, the evolving learning module summarizes the knowledge learned at different time points to produce a final knowledge vector about the event. With the evolving learning, we are able to visualize the key information of the event, discover the trends, and track the development of an event.

Keywords: evolving learning, knowledge extraction, knowledge graph, text mining

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Authors: Jawid Ahmad Baktash, Mursal Dawodi, Tomokazu Nagata

Abstract:

With the rapid growth of Cloud Computing, Formal Methods became a good choice for the refinement of message specification and verification for Dynamic Routing in Cloud Computing. Cloud-based Dynamic Routing is becoming increasingly popular. We propose feedback in Formal Methods for Dynamic Routing and Cloud Computing; the model and topologies show how to send messages from index zero to all others formally. The responsibility of proper verification becomes crucial with Dynamic Routing in the cloud. Formal Methods can play an essential role in the routing and development of Networks, and the testing of distributed systems. Event-B is a formal technique that consists of describing the problem rigorously and introduces solutions or details in the refinement steps. Event-B is a variant of B, designed for developing distributed systems and message passing of the dynamic routing. In Event-B and formal methods, the events consist of guarded actions occurring spontaneously rather than being invoked.

Keywords: cloud, dynamic routing, formal method, Pro-B, event-B

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Authors: Noriko Hanakawa, Masaki Obana

Abstract:

A simulation tool has been proposed for big-scale projection mapping events. The tool has four main functions based on Mapbox and Unity utilities. The first function is building a 3D model of real cities by MapBox. The second function is a movie projection to some buildings in real cities by Unity. The third function is a movie sending function from a PC to a virtual projector. The fourth function is mapping movies with fitting buildings. The simulation tool was adapted to a real projection mapping event that was held in 2019. The event has been finished. The event had a serious problem in the movie projection to the target building. The extra tents were set in front of the target building. The tents became the obstacles to the movie projection. The simulation tool can be reappeared the problems of the event. Therefore, if the simulation tool was developed before the 2019 projection mapping event, the problem of the tents’ obstacles could be avoided with the simulation tool. In addition, we confirmed that the simulation tool is useful to make a plan of future projection mapping events in order to avoid obstacles of various extra equipment such as utility poles, planting trees, monument towers.

Keywords: projection mapping, projector position, real 3D map, avoiding obstacles

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Authors: Sachin Singh, Thomas Penzel, Dinesh Nandan

Abstract:

Accurate identification of Sleep Apnea Hypopnea Syndrom Diagnosis is difficult problem for human expert because of variability among persons and unwanted noise. This paper proposes the diagonosis of Sleep Apnea Hypopnea Syndrome (SAHS) using airflow, ECG, Pulse and SaO2 signals. The features of each type of these signals are extracted using statistical methods and ANN learning methods. These extracted features are used to approximate the patient's Apnea Hypopnea Index(AHI) using sample signals in model. Advance signal processing is also applied to snore sound signal to locate snore event and SaO2 signal is used to support whether determined snore event is true or noise. Finally, Apnea Hypopnea Index (AHI) event is calculated as per true snore event detected. Experiment results shows that the sensitivity can reach up to 96% and specificity to 96% as AHI greater than equal to 5.

Keywords: neural network, AHI, statistical methods, autoregressive models

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Authors: Noura Al Akkari, Aurélie Foucquier, Sylvain Lespinats

Abstract:

Load monitoring is a management process for energy consumption towards energy savings and energy efficiency. Non Intrusive Load Monitoring (NILM) is one method of load monitoring used for disaggregation purposes. NILM is a technique for identifying individual appliances based on the analysis of the whole residence data retrieved from the main power meter of the house. Our NILM framework starts with data acquisition, followed by data preprocessing, then event detection, feature extraction, then general appliance modeling and identification at the final stage. The event detection stage is a core component of NILM process since event detection techniques lead to the extraction of appliance features. Appliance features are required for the accurate identification of the household devices. In this research work, we aim at developing a new event detection methodology with accurate load disaggregation to extract appliance features. Time-domain features extracted are used for tuning general appliance models for appliance identification and classification steps. We use unsupervised algorithms such as Dynamic Time Warping (DTW). The proposed method relies on detecting areas of operation of each residential appliance based on the power demand. Then, detecting the time at which each selected appliance changes its states. In order to fit with practical existing smart meters capabilities, we work on low sampling data with a frequency of (1/60) Hz. The data is simulated on Load Profile Generator software (LPG), which was not previously taken into consideration for NILM purposes in the literature. LPG is a numerical software that uses behaviour simulation of people inside the house to generate residential energy consumption data. The proposed event detection method targets low consumption loads that are difficult to detect. Also, it facilitates the extraction of specific features used for general appliance modeling. In addition to this, the identification process includes unsupervised techniques such as DTW. To our best knowledge, there exist few unsupervised techniques employed with low sampling data in comparison to the many supervised techniques used for such cases. We extract a power interval at which falls the operation of the selected appliance along with a time vector for the values delimiting the state transitions of the appliance. After this, appliance signatures are formed from extracted power, geometrical and statistical features. Afterwards, those formed signatures are used to tune general model types for appliances identification using unsupervised algorithms. This method is evaluated using both simulated data on LPG and real-time Reference Energy Disaggregation Dataset (REDD). For that, we compute performance metrics using confusion matrix based metrics, considering accuracy, precision, recall and error-rate. The performance analysis of our methodology is then compared with other detection techniques previously used in the literature review, such as detection techniques based on statistical variations and abrupt changes (Variance Sliding Window and Cumulative Sum).

Keywords: electrical disaggregation, DTW, general appliance modeling, event detection

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Authors: Frazer G. Thompson

Abstract:

The 1990s in America ended with a mass-school-shooting (at least four killed by gunfire excluding the perpetrator(s)) at Columbine High School in Littleton, Colorado. Post-event, many demanded that government and civilian experts develop a ‘profile’ of the potential school shooter in order to identify and preempt likely future acts of violence. This grounded theory research study seeks to explore the validity of the original hypotheses proposed by the Federal Bureau of Investigation (FBI) in 2000, as it relates to the commonality of disclosure by perpetrators of mass-school-shootings, by evaluating fourteen mass-school-shooting events between 2000 and 2019 at locations around the United States. Methods: The strategy of inquiry seeks to investigate case files, public records, witness accounts, and available psychological profiles of the shooter. The research methodology is inclusive of one-on-one interviews with members of the FBI’s Critical Incident Response Group seeking perspective on commonalities between individuals; specifically, disclosure of intent pre-event. Results: The research determined that school shooters do not ‘unfailingly’ notify others of their plans. However, in nine of the fourteen mass-school-shooting events analyzed, the perpetrator did inform the third party of their intent pre-event in some form of written, oral, or electronic communication. In the remaining five instances, the so-called ‘red-flag’ indicators of the potential for an event to occur were profound, and unto themselves, might be interpreted as notification to others of an imminent deadly threat. Conclusion: Data indicates that conclusions drawn in the FBI’s threat assessment perspective published in 2000 are relevant and current. There is evidence that despite potential ‘red-flag’ indicators which may or may not include a variety of other characteristics, perpetrators of mass-school-shooting events are likely to share their intentions with others through some form of direct or indirect communication. More significantly, implications of this research might suggest that society is often informed of potential danger pre-event but lacks any equitable means by which to disseminate, prevent, intervene, or otherwise act in a meaningful way considering said revelation.

Keywords: columbine, FBI profiling, guns, mass shooting, mental health, school violence

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Authors: Ethan Petersen

Abstract:

In this paper, we analyze the volatility of Apple’s stock beginning January 3, 2005 up to October 9, 2014, then focus on a range from 30 days prior to each product announcement until 30 days after. Product announcements are filtered; announcements whose 60 day range is devoid of other events are separated. This filtration is chosen to isolate, and study, a potential cross-effect. Concerning Apple keynotes, there are two significant dates: the day the invitations to the event are received and the day of the event itself. As such, the statistical analysis is conducted for both invite-centered and event-centered time frames. A comparison to the VIX is made to determine if the trend is simply following the market or deviating. Regardless of the filtration, we find that there is a clear deviation from the market. Comparing these data sets, there are significantly different trends: isolated events have a constantly decreasing, erratic trend in volatility but an increasing, linear trend is observed for clustered events. According to the Efficient Market Hypothesis, we would expect a change when new information is publicly known and the results of this study support this claim.

Keywords: efficient market hypothesis, event study, volatility, VIX

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Authors: Abigail M. Csik, Gabriel A. Radvansky

Abstract:

As people read through event-based narratives, they construct an event model that captures information about the characters, goals, location, time, and causality. For many reasons, memory for such narratives is represented at different levels, namely, the surface form, textbase, and event model levels. Rereading has been shown to decrease surface form memory, while, at the same time, increasing textbase and event model memories. More generally, distributed practice has consistently shown memory benefits over massed practice for different types of materials, including texts. However, little research has investigated distributed practice of narratives at different inter-study intervals and these effects on these three levels of memory. Recent work in our lab has indicated that there may be dramatic changes in patterns of forgetting around one week, which may affect the three levels of memory. The present experiment aimed to determine the effects of rereading on the three levels of memory as a factor of whether the texts were reread before versus after one week. Participants (N = 42) read a set of stories, re-read them either before or after one week (with an inter-study interval of three days, seven days, or fourteen days), and then took a recognition test, from which the three levels of representation were derived. Signal detection results from this study reveal that differential patterns at the three levels as a factor of whether the narratives were re-read prior to one week or after one week. In particular, an ANOVA revealed that surface form memory was lower (p = .08) while textbase (p = .02) and event model memory (p = .04) were greater if narratives were re-read 14 days later compared to memory when narratives were re-read 3 days later. These results have implications for what type of memory benefits from distributed practice at various inter-study intervals.

Keywords: memory, event cognition, distributed practice, consolidation

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Authors: Ashok V. Sutagundar, Sunilkumar S. Manvi

Abstract:

Energy, delay and bandwidth are the prime issues of wireless sensor network (WSN). Energy usage optimization and efficient bandwidth utilization are important issues in WSN. Event triggered data aggregation facilitates such optimal tasks for event affected area in WSN. Reliable delivery of the critical information to sink node is also a major challenge of WSN. To tackle these issues, we propose an event driven dynamic clustering and data aggregation scheme for WSN that enhances the life time of the network by minimizing redundant data transmission. The proposed scheme operates as follows: (1) Whenever the event is triggered, event triggered node selects the cluster head. (2) Cluster head gathers data from sensor nodes within the cluster. (3) Cluster head node identifies and classifies the events out of the collected data using Bayesian classifier. (4) Aggregation of data is done using statistical method. (5) Cluster head discovers the paths to the sink node using residual energy, path distance and bandwidth. (6) If the aggregated data is critical, cluster head sends the aggregated data over the multipath for reliable data communication. (7) Otherwise aggregated data is transmitted towards sink node over the single path which is having the more bandwidth and residual energy. The performance of the scheme is validated for various WSN scenarios to evaluate the effectiveness of the proposed approach in terms of aggregation time, cluster formation time and energy consumed for aggregation.

Keywords: wireless sensor network, dynamic clustering, data aggregation, wireless communication

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Authors: Jawid Ahmad Baktash, Tadashi Shiroma, Tomokazu Nagata, Yuji Taniguchi, Morikazu Nakamura

Abstract:

The goal of this paper is to explore the use of formal methods for Dynamic Routing, The purpose of network communication with dynamic routing is sending a massage from one node to others by using pacific protocols. In dynamic routing connections are possible based on protocols of Distance vector (Routing Information Protocol, Border Gateway protocol), Link State (Open Shortest Path First, Intermediate system Intermediate System), Hybrid (Enhanced Interior Gateway Routing Protocol). The responsibility for proper verification becomes crucial with Dynamic Routing. Formal methods can play an essential role in the Routing, development of Networks and testing of distributed systems. Event-B is a formal technique consists of describing rigorously the problem; introduce solutions or details in the refinement steps to obtain more concrete specification, and verifying that proposed solutions are correct. The system is modeled in terms of an abstract state space using variables with set theoretic types and the events that modify state variables. Event-B is a variant of B, was designed for developing distributed systems. In Event-B, the events consist of guarded actions occurring spontaneously rather than being invoked. The invariant state properties must be satisfied by the variables and maintained by the activation of the events.

Keywords: dynamic rout RIP, formal method, event-B, pro-B

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Authors: Eda Orhun

Abstract:

Purpose: This paper analyzes a target firm’s decision to voluntarily disclose information during a takeover event and the effect of such disclosures on the outcome of the takeover. Such voluntary disclosures especially in the form of earnings forecasts made around takeover events may affect shareholders’ decisions about the target firm’s value and in return takeover result. This study aims to shed light on this question. Design/methodology/approach: The paper tries to understand the role of voluntary disclosures by target firms during a takeover event in the likelihood of takeover success both theoretically and empirically. A game-theoretical model is set up to analyze the voluntary disclosure decision of a target firm to inform the shareholders about its real worth. The empirical implication of model is tested by employing binary outcome models where the disclosure variable is obtained by identifying the target firms in the sample that provide positive news by issuing increasing management earnings forecasts. Findings: The model predicts that a voluntary disclosure of positive information by the target decreases the likelihood that the takeover succeeds. The empirical analysis confirms this prediction by showing that positive earnings forecasts by target firms during takeover events increase the probability of takeover failure. Overall, it is shown that information dissemination through voluntary disclosures by target firms is an important factor affecting takeover outcomes. Originality/Value: This study is the first to the author's knowledge that studies the impact of voluntary disclosures by the target firm during a takeover event on the likelihood of takeover success. The results contribute to information economics, corporate finance and M&As literatures.

Keywords: takeovers, target firm, voluntary disclosures, earnings forecasts, takeover success

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Authors: Iqbal Felani

Abstract:

A Coal-Fired Power Plant has some integrated facilities to handle coal from three separated coal yards to eight units power plant’s bunker. But nowadays the facilities are not reliable enough for supporting the system. Management planned to invest some facilities to increase the reliability. They also had a plan to make single spesification of coal used all of the units, called Single Quality Coal (SQC). This simulation would compare before and after improvement with two scenarios i.e First In First Out (FIFO) and Last In First Out (LIFO). Some parameters like stay time, reorder point and safety stock is determined by the simulation. Discrete event simulation based software, Flexsim 5.0, is used to help the simulation. Based on the simulation, Single Quality Coal with FIFO scenario has the shortest staytime with 8.38 days.

Keywords: Coal Yard Management, Discrete event simulation First In First Out, Last In First Out.

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Authors: Tarah Bouaricha

Abstract:

The impact of worst natural disasters is analysed in terms of insured losses which happened between 2010 and 2014 on S&P insurance index. Event study analysis is used to test whether natural disasters impact insurance index stock market price. There is no negative impact on insurance stock market price around the disasters event. To analyse the reaction of insurance stock market, normal returns (NR), abnormal returns (AR), cumulative abnormal returns (CAR), cumulative average abnormal returns (CAAR) and a parametric test on AR and on CAR are used.

Keywords: study event, natural disasters, insurance, reinsurance, stock market

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Authors: Kok Meng Ng, Sulung Veronica

Abstract:

Electronic Dance Music (EDM), a musical event that so sought-after amongst the youth, is getting prevailed around the world. The emergence of this à la mode event has magnetized lots of attentions from the media as well as the public due to its high probabilities in creating social problems and menacing social harmony of one destination, for instance, two death cases occurred during the EDM events in Malaysia caused a feeling of consternation of the society. The arguments over the impacts of such events towards the society are endless. This paper focuses on the study of the impacts of EDM towards social harmony in Klang Valley area, Malaysia by scrutinizing the contradiction of statements from several experts and the local communities. This study sampled 15-20 people that represent different social background with face-to-face and online interview through snowball sampling method. This study helps to understand the social context as a whole based on the impacts of EDM events that take place in Malaysia. It also provides valuable information to EDMs’ organizer as well as local authorities for a proper event management to minimize EDM impacts towards society as part of the sustainable growth of the event industry.

Keywords: electronic dance music, social harmony, impacts, Klang Valley

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Authors: Ping Xu, Gregory T. Golm, Guanghan (Frank) Liu

Abstract:

In the presence of competing events, traditional survival analysis may not be appropriate and can result in biased estimates, as it assumes independence between competing events and the event of interest. Instead, competing risk analysis should be considered to correctly estimate the survival probability of the event of interest and the hazard ratio between treatment groups. The COVID-19 pandemic has provided a potential source of competing risks in clinical trials, as participants in trials may experienceCOVID-related competing events before the occurrence of the event of interest, for instance, death due to COVID-19, which can affect the incidence rate of the event of interest. We have performed simulation studies to compare multiple competing risk analysis models, including the cumulative incidence function, the sub-distribution hazard function, and the cause-specific hazard function, to the traditional survival analysis model under various scenarios. We also provide a general recommendation on conducting competing risk analysis in randomized clinical trials during the era of the COVID-19 pandemic based on the extensive simulation results.

Keywords: competing risk, survival analysis, simulations, randomized clinical trial, COVID-19 pandemic

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Authors: Noura Al Akkari, Aurélie Foucquier, Sylvain Lespinats

Abstract:

Load monitoring is a management process for energy consumption towards energy savings and energy efficiency. Non Intrusive Load Monitoring (NILM) is one method of load monitoring used for disaggregation purposes. NILM is a technique for identifying individual appliances based on the analysis of the whole residence data retrieved from the main power meter of the house. Our NILM framework starts with data acquisition, followed by data preprocessing, then event detection, feature extraction, then general appliance modeling and identification at the final stage. The event detection stage is a core component of NILM process since event detection techniques lead to the extraction of appliance features. Appliance features are required for the accurate identification of the household devices. In this research work, we aim at developing a new event detection methodology with accurate load disaggregation to extract appliance features. Time-domain features extracted are used for tuning general appliance models for appliance identification and classification steps. We use unsupervised algorithms such as Dynamic Time Warping (DTW). The proposed method relies on detecting areas of operation of each residential appliance based on the power demand. Then, detecting the time at which each selected appliance changes its states. In order to fit with practical existing smart meters capabilities, we work on low sampling data with a frequency of (1/60) Hz. The data is simulated on Load Profile Generator software (LPG), which was not previously taken into consideration for NILM purposes in the literature. LPG is a numerical software that uses behaviour simulation of people inside the house to generate residential energy consumption data. The proposed event detection method targets low consumption loads that are difficult to detect. Also, it facilitates the extraction of specific features used for general appliance modeling. In addition to this, the identification process includes unsupervised techniques such as DTW. To our best knowledge, there exist few unsupervised techniques employed with low sampling data in comparison to the many supervised techniques used for such cases. We extract a power interval at which falls the operation of the selected appliance along with a time vector for the values delimiting the state transitions of the appliance. After this, appliance signatures are formed from extracted power, geometrical and statistical features. Afterwards, those formed signatures are used to tune general model types for appliances identification using unsupervised algorithms. This method is evaluated using both simulated data on LPG and real-time Reference Energy Disaggregation Dataset (REDD). For that, we compute performance metrics using confusion matrix based metrics, considering accuracy, precision, recall and error-rate. The performance analysis of our methodology is then compared with other detection techniques previously used in the literature review, such as detection techniques based on statistical variations and abrupt changes (Variance Sliding Window and Cumulative Sum).

Keywords: general appliance model, non intrusive load monitoring, events detection, unsupervised techniques;

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Authors: N. Mehrotra, V. Ahuja, N. Sridharan

Abstract:

Resilience is an all hazard and a proactive approach, require a multidisciplinary input in the inter related variables of the city system. This research based to identify and operationalize indicators for assessment in domain of institutions, infrastructure and knowledge, all three operating in task oriented community networks. This paper gives a brief account of the methodology developed for assessment of Urban Resilience and its prioritized components for a target population within a newly planned urban complex integrating Surajpur and Kasna village as nodes. People’s perception of Urban Resilience has been examined by conducting questionnaire survey among the target population of Greater Noida. As defined by experts, Urban Resilience of a place is considered to be both a product and process of operation to regain normalcy after an event of disturbance of certain level. Based on this methodology, six indicators are identified that contribute to perception of urban resilience both as in the process of evolution and as an outcome. The relative significance of 6 R’ has also been identified. The dependency factor of various resilience indicators have been explored in this paper, which helps in generating new perspective for future research in disaster management. Based on the stated factors this methodology can be applied to assess urban resilience requirements of a well planned town, which is not an end in itself, but calls for new beginnings.

Keywords: disaster, resilience, system, urban

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Authors: Jerry Q. Cheng

Abstract:

Currently, in analyzing large-scale recurrent event data, there are many challenges such as memory limitations, unscalable computing time, etc. In this research, a divide-and-conquer method is proposed using parametric frailty models. Specifically, the data is randomly divided into many subsets, and the maximum likelihood estimator from each individual data set is obtained. Then a weighted method is proposed to combine these individual estimators as the final estimator. It is shown that this divide-and-conquer estimator is asymptotically equivalent to the estimator based on the full data. Simulation studies are conducted to demonstrate the performance of this proposed method. This approach is applied to a large real dataset of repeated heart failure hospitalizations.

Keywords: big data analytics, divide-and-conquer, recurrent event data, statistical computing

Procedia PDF Downloads 137

Authors: Dhanuja S. Patil, Sanjay B. Waykar

Abstract:

Event detection is a standout amongst the most key parts for distinctive sorts of area applications of video data framework. Recently, it has picked up an extensive interest of experts and in scholastics from different zones. While detecting video event has been the subject of broad study efforts recently, impressively less existing methodology has considered multi-model data and issues related efficiency. Start of soccer matches different doubtful circumstances rise that can't be effectively judged by the referee committee. A framework that checks objectively image arrangements would prevent not right interpretations because of some errors, or high velocity of the events. Bayesian networks give a structure for dealing with this vulnerability using an essential graphical structure likewise the probability analytics. We propose an efficient structure for analysing and summarization of soccer videos utilizing object-based features. The proposed work utilizes the t-cherry junction tree, an exceptionally recent advancement in probabilistic graphical models, to create a compact representation and great approximation intractable model for client’s relationships in an interpersonal organization. There are various advantages in this approach firstly; the t-cherry gives best approximation by means of junction trees class. Secondly, to construct a t-cherry junction tree can be to a great extent parallelized; and at last inference can be performed utilizing distributed computation. Examination results demonstrates the effectiveness, adequacy, and the strength of the proposed work which is shown over a far reaching information set, comprising more soccer feature, caught at better places.

Keywords: summarization, detection, Bayesian network, t-cherry tree

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Authors: Ioannis Andrianakis, Manolis Falelakis, Maria Pavlidou, Konstantinos Papakonstantinou, Ermioni Avramidou, Dimitrios Kalogiannis, Nikolaos Milios, Katerina Bountakidou, Kiriakos Chatzidimitriou, Panagiotis Panagiotopoulos

Abstract:

Artistic events, such as concerts and performances, are challenging to organize because they involve many people with different skill sets. Small and medium venues often struggle to afford the costs and overheads of booking and hosting remote artists, especially if they lack sponsors or subsidies. This limits the opportunities for both venues and artists, especially those outside of big cities. However, more and more research shows that audiences prefer smaller-scale events and concerts, which benefit local economies and communities. To address this challenge, our project “PerformAnts: Digital Event Manager-Organizer” aims to develop a smart digital tool that automates and optimizes the processes and costs of live shows and tours. By using machine learning, applying best practices and training users through workshops, our platform offers a comprehensive solution for a growing market, enhances the mobility of artists and the accessibility of venues and allows professionals to focus on the creative aspects of concert production.

Keywords: event organization, creative industries, event promotion, machine learning

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Authors: Kwaku Damoah

Abstract:

This study presents a comprehensive data-driven analysis to understand the evolution of adverse events (AEs) in pharmacovigilance. Utilizing data from the FDA Adverse Event Reporting System (FAERS), we employed three analytical methods: rank-based, frequency-based, and percentage change analyses. These methods assessed temporal trends and patterns in AE reporting, focusing on various drug-active ingredients and patient demographics. Our findings reveal significant trends in AE occurrences, with both increasing and decreasing patterns from 2000 to 2023. This research highlights the importance of continuous monitoring and advanced analysis in pharmacovigilance, offering valuable insights for healthcare professionals and policymakers to enhance drug safety.

Keywords: event analysis, FDA adverse event reporting system, pharmacovigilance, temporal trend analysis

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Authors: Karen J. Polvado, Betty Bowles, Jansen Lauren, Gibson Martha, Robin Lockhart

Abstract:

The purpose of this study was to identify motivation to participate in the Hotter ‘n Hell Hundred (HHH) bicycling event, and the participants’ demographics, health risk factors, and preparation to ride in the event. A convenience sample of adults pre-registered for non-competitive cycling events (N = 7,472) were requested to complete a survey. Of these, 2,645 (35%) responded. Questions identified the participants’ demographics, preparation, previous experience with HHH, and motives for riding. The HHH attracted riders of all ages (18-80), genders, ethnicities, and educational levels. The majority were males, 40-59 years old, married, college graduates, and identified themselves as non-Hispanic whites. The majority (68%) reported no existing medical conditions, and were normal weight (70%), although 52% had been overweight or obese in the past. Preparation to ride in the HHH varied from riding more than five times a week for the last year, to riding 1-2 times per week one month before the event. Most (93%) had ridden in the HHH an average of 5 times. Motivations to ride included: personal challenge (75%); to experience the HHH ride (57%); a chance to ride with family/friends/coworkers (52%); improving health (47%); fun (33%); challenge by others (15%); part of a weight loss plan (11%); training for another event (10%); and raising money for a cause (2%). The motivation to participate appeared to move from extrinsic to intrinsic motivation as age increased. Exploration of the exercise habits and motivations of older adults (70+) is suggested by this study.

Keywords: cycling, motivation, physical activity, training

Procedia PDF Downloads 276

Authors: Mwafak Shakoor

Abstract:

The purpose of this study was to reduce patient waiting times, improve system throughput and improve resources utilization in radiology department. A discrete event simulation model was developed using Arena simulation software to investigate different alternatives to improve the overall system delivery based on adding resource scenarios due to the linkage between patient waiting times and resource availability. The study revealed that there is no addition investment need to procure additional scanner but hospital management deploy managerial tactics to enhance machine utilization and reduce the long waiting time in the department.

Keywords: discrete event simulation, radiology department, arena, waiting time, healthcare modeling, computed tomography

Procedia PDF Downloads 561

Authors: Seokbeen Lim, KyeongSeok Sim, DaKyeong Shin, Gilwon Yoon

Abstract:

Event-related potential (ERP) is one of the useful tools for investigating cognitive reactions. In this study, the potential of ERP components detected after auditory and visual stimuli was examined. Subjects were asked to respond upon stimuli that were of three categories; Target, Non-Target and Standard stimuli. The ERP after stimulus was measured. In the experiment of visual evoked potentials (VEPs), the subjects were asked to gaze at a center point on the monitor screen where the stimuli were provided by the reversal pattern of the checkerboard. In consequence of the VEP experiments, we observed consistent reactions. Each peak voltage could be measured when the ensemble average was applied. Visual stimuli had smaller amplitude and a longer latency compared to that of auditory stimuli. The amplitude was the highest with Target and the smallest with Standard in both stimuli.

Keywords: auditory stimulus, EEG, event related potential, oddball task, visual stimulus

Procedia PDF Downloads 259