Event Handling Research Articles

External comparator studies and the joint application of the estimand and target trial emulation frameworks

The estimand framework (EF) and the target trial emulation framework (TTEF) are two important frameworks that can inform the design and analysis of external comparator (EC) studies. The EF helps clarifying the quantity to be estimated (the “estimand”), especially regarding the handling of post-baseline intercurrent events which interfere with the existence or interpretation of endpoints. Similarly, the TTEF is instrumental for specifying key design components of a hypothetical randomized trial and identifying which of these an EC study can and cannot emulate. We offer considerations about the joint application of both frameworks by combining the five EF attributes— treatment, population, endpoint, intercurrent events, and population-level summary—with the seven TTEF components— eligibility criteria, treatment strategies, assignment procedures, follow-up period, outcomes, causal contrasts, and data analysis plan. Any overlap is identified, as well as omissions and unique contributions from both frameworks. Furthermore, we highlight specific considerations when applying these joint elements to EC studies.

SemConvTree: Semantic Convolutional Quadtrees for Multi-Scale Event Detection in Smart City

The digital world is increasingly permeating our reality, creating a significant reflection of the processes and activities occurring in smart cities. Such activities include well-known urban events, celebrations, and those with a very local character. These widespread events have a significant influence on shaping the spirit and atmosphere of urban environments. This work presents SemConvTree, an enhanced semantic version of the ConvTree algorithm. It incorporates the semantic component of data through semi-supervised learning of a topic modeling ensemble, which consists of improved models: BERTopic, TSB-ARTM, and SBert-Zero-Shot. We also present an improved event search algorithm based on both statistical evaluations and semantic analysis of posts. This algorithm allows for fine-tuning the mechanism of discovering the required entities with the specified particularity (such as a particular topic). Experimental studies were conducted within the area of New York City. They showed an improvement in the detection of posts devoted to events (about 40% higher f1-score) due to the accurate handling of events of different scales. These results suggest the long-term potential for creating a semantic platform for the analysis and monitoring of urban events in the future.

Software patterns and data structures for the runtime coordination of robots, with a focus on real-time execution performance.

This paper introduces software patterns (registration, acquire-release, and cache awareness) and data structures (Petri net, finite state machine, and protocol flag array) to support the coordinated execution of software activities (also called "components" or "agents"). Moreover, it presents and tests an implementation for Petri nets that supports real-time execution in shared memory for deployment inside one individual robot and separates event firing and handling, enabling distributed deployment between multiple robots. Experimental validation of the introduced patterns and data structures is performed within the context of activities for task execution, control and perception, and decision making for an application on coordinated navigation.

Ultra-Low-Power Sensor Nodes for Real-Time Synchronous and High-Accuracy Timing Wireless Data Acquisition.

This paper presents an energy-efficient and high-accuracy sampling synchronization approach for real-time synchronous data acquisition in wireless sensor networks (saWSNs). A proprietary protocol based on time-division multiple access (TDMA) and deep energy-efficient coding in sensor firmware is proposed. A real saWSN model based on 2.4 GHz nRF52832 system-on-chip (SoC) sensors was designed and experimentally tested. The obtained results confirmed significant improvements in data synchronization accuracy (even by several times) and power consumption (even by a hundred times) compared to other recently reported studies. The results demonstrated a sampling synchronization accuracy of 0.8 μs and ultra-low power consumption of 15 μW per 1 kb/s throughput for data. The protocol was well designed, stable, and importantly, lightweight. The complexity and computational performance of the proposed scheme were small. The CPU load for the proposed solution was <2% for a sampling event handler below 200 Hz. Furthermore, the transmission reliability was high with a packet error rate (PER) not exceeding 0.18% for TXPWR ≥ -4 dBm and 0.03% for TXPWR ≥ 3 dBm. The efficiency of the proposed protocol was compared with other solutions presented in the manuscript. While the number of new proposals is large, the technical advantage of our solution is significant.

Clinical practice guidelines for telesurgery 2022

Telesurgery is expected to improve medical access in areas with limited resources, facilitate the rapid dissemination of new surgical procedures, and advance surgical education. While previously hindered by communication delays and costs, recent advancements in information technology and the emergence of new surgical robots have created an environment conducive to societal implementation. In Japan, the legal framework established in 2019 allows for remote surgical support under the supervision of an actual surgeon. The Japan Surgical Society led a collaborative effort, involving various stakeholders, to conduct social verification experiments using telesurgery, resulting in the development of a Japanese version of the “Telesurgery Guidelines” in June 2022. These guidelines outline requirements for medical teams, communication environments, robotic systems, and security measures for communication lines, as well as responsibility allocation, cost burden, and the handling of adverse events during telesurgery. In addition, they address telementoring and full telesurgery. The guidelines are expected to be revised as needed, based on the utilization of telesurgery, advancements in surgical robots, and improvements in information technology.

Application of the Estimand Framework to Anesthesia Trials.

Events occurring after randomization, such as use of rescue medication, treatment discontinuation, or death, are common in randomized trials. These events can change either the existence or interpretation of the outcome of interest. However, appropriate handling of these intercurrent events is often unclear. The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) E9(R1) addendum introduced the estimand framework, which aligns trial objectives with the design, conduct, statistical analysis, and interpretation of results. This article describes how the estimand framework can be used in anesthesia trials to precisely define the treatment effect to be estimated, key attributes of an estimand, common intercurrent events in anesthesia trials with strategies for handling them, and use of the estimand framework in a hypothetical anesthesia trial on postoperative delirium. When planning anesthesia trials, clearly defining the estimand is vital to ensure that what is being estimated is clearly understood, is clinically relevant, and helps answer the clinical questions of interest.

Handling of external risks, including launch and re-entry events, in the aviation and maritime sector

An important part of a flight safety analysis for launch and re-entry events is to ensure safety to air and sea traffic, and maritime infrastructure. Thus, hazard areas are defined based on a risk assessment and risk criteria. Traffic impact depends on size and duration of those areas. Current risk criteria for air and sea traffic as well as maritime infrastructure are reviewed in this paper. Furthermore, this paper will take a closer look on other external risk factors in the aviation or maritime sector. The paper analyses their influence on aviation and maritime operations together with its associated safety measures and compares them with the risk posed by space vehicles. Understanding these relationships can support the conduct of safe space operations and efficient integration of space activities.

Updates to Protex for Simulating Proton Transfers in an Ionic Liquid.

The Python-based program Protex was initially developed for simulating proton transfers in a pure protic ionic liquid via polarizable molecular dynamics simulations. This method employs a single topology approach wherein deprotonated species retain a dummy atom, which is transformed into a real hydrogen atom during the protonation process. In this work, we extended Protex to include more intricate systems and to facilitate the simulation of the Grotthuss mechanism to enhance alignment with the empirical findings. The handling of proton transfer events within Protex was further refined for increased flexibility. In the original model, each deprotonated molecule contained a single dummy atom connected to the hydrogen acceptor atom. This model posed limitations for molecules with multiple atoms that could undergo protonation. To mitigate this issue, Protex was extended to execute a proton transfer when one of these potential atoms was within a suitable proximity for the transfer event. For the purpose of maintaining simplicity, Protex continues to utilize only a single dummy atom per deprotonated molecule. Another new feature pertains to the determination of the eligibility for a proton transfer event. A range of acceptable distances can now be defined within which the transfer probability is gradually turned off. These modifications allow for a more nuanced approach to simulating proton transfer events, offering greater accuracy and control of the modeling process.

Time-to-event estimands and loss to follow-up in oncology in light of the estimands guidance.

Time-to-event estimands are central to many oncology clinical trials. The estimands framework (addendum to the ICH E9 guideline) calls for precisely defining the treatment effect of interest to align with the clinical question of interest and requires predefining the handling of intercurrent events (ICEs) that occur after treatment initiation and "affect either the interpretation or the existence of the measurements associated with the clinical question of interest." We discuss a practical problem in clinical trial design and execution, that is, in some clinical contexts it is not feasible to systematically follow patients to an event of interest. Loss to follow-up in the presence of intercurrent events can affect the meaning and interpretation of the study results. We provide recommendations for trial design, stressing the need for close alignment of the clinical question of interest and study design, impact on data collection, and other practical implications. When patients cannot be systematically followed, compromise may be necessary to select the best available estimand that can be feasibly estimated under the circumstances. We discuss the use of sensitivity and supplementary analyses to examine assumptions of interest.

ImGame Project

The study presents the authors' research for the purpose of designing ImGame, a virtual environment inviting users to playfully learn about the concept of immersion and its historical antecedents. The authors describe ImGame's current pre-production stage and examine the basic characteristics of the feeling of immersion. They intend to deepen the current understanding of the immersive experience in art, taking into account its broad cultural connotations. The article suggests that the aesthetics of immersion can be classified into two psychological modes that have not been explicitly defined in the discourse of immersivity: a calm reflection and one of awe. From the technical standpoint, the project offers a simple handling of triggering animations and events of the game as well as storing of their state in order to create gamification elements, interactions for quizzes and other activities for ImGame or any other game using the a-frame framework to create WebXR experiences.

Illusion of Control: Psychological Characteristics as Moderators in Financial Decision Making

Financial decision making requires a sound handling of chance events. However, various studies have suggested that people are prone to illusion of control, i.e., the belief that prospects of a chancy event are better if they are involved in the randomisation process. This paper reports results from an experiment (N=420) suggesting that psychological characteristics moderate risk-taking behaviour under such circumstances. For example, we find that subjects high in sensation seeking buy more tickets of a risky lottery if they determine the winning numbers themselves and the random event lies in the future. The findings suggest that “illusion of control” effects are at least partly driven by underlying (idiosyncratic) emotions/preferences rather than an actual belief in control. Regarding applications, the results emphasise the importance of individual characteristics for the behaviour of decision makers in a financial context.

External comparators and estimands

The estimand framework as defined by the ICH E9(R1) addendum aims to clearly define “the treatment effect reflecting the clinical question posed by the trial objective”. It intends to achieve this goal of a clear definition by specifying the 5 estimand attributes: treatment conditions, population, endpoints, handling of intercurrent events (IEs), and population-level summary. However, hybrid clinical/observational research like External Comparators (ECs) leads to new reflections on existing attributes and to considerations for additional ones. Specifically, treatment conditions and exposure may be more difficult to handle in the EC, and especially Standard of Care (SoC) treatment needs detailed attention. The external population typically cannot be based on the classical Intention-to-treat population and constitutes also an approximation only. Endpoints may not be comparable across cohorts, and IEs may be more different than in an RCT setting, such that the hypothetical treatment policy according to the ICH E9(R1) addendum may become of greater interest especially for long-term endpoints. Finally, the necessary assumptions for some population-level summaries (e.g., the proportional hazards assumption) can become more fragile when joining data from different sources due to induced heterogeneity. Finally, it is shown that the baseline definition and the marginal estimator are candidates for additional estimand attributes in case the estimand framework is revised to account for observational study needs.

Automatic sign language translation system using neural network technologies and 3D animation

Implementation of automatic sign language translation software in the process of social inclusion of people with hearing impairment is an important task. Social inclusion for people with hearing disabilities is an acute problem that must be solved in the context of the development of IT technologies and legislative initiatives that ensure the rights of people with disabilities and their equal opportunities. This substantiates the relevance of the research of assistive technologies, in the context of software tools, such as the process of social inclusion of people with severe hearing impairment in society. The subject of research is methods of automated sign language translation using intelligent technologies. The purpose of the work is the development and research of sign language automation methods to improve the quality of life of people with hearing impairments in accordance with the "Goals of Sustainable Development of Ukraine" (in the "Reduction of Inequality" part). The main tasks of the research are the development and testing of methods of converting sign language into text, converting text into sign language, as well as automating translation from one sign language to another sign language using modern intelligent technologies. Neural network modeling and 3D animation methods were used to solve these problems. The following results were obtained in the work: the main problems and tasks of social inclusion for people with hearing impairments were identified; a comparative analysis of modern methods and software platforms of automatic sign language translation was carried out; a system combining the SL-to-Text method is proposed and investigated; the Text-to-SL method using 3D animation to generate sign language concepts; the method of generating a 3D-animated gesture from video recordings; method of implementing the Sign Language1 to Sign Language2 technology. For gesture recognition, a convolutional neural network model is used, which is trained using imported and system-generated datasets of video gestures. The trained model has a high recognition accuracy (98.52%). The creation of a 3D model for displaying the gesture on the screen and its processing took place in the Unity 3D environment. The structure of the project, executive and auxiliary files used to build 3D animation for the generation of sign language concepts includes: event handler files; display results according to which they carry information about the position of the tracked points of the body; files that store the characteristics of materials that have been added to certain body mapping points. Conclusions: the proposed methods of automated translation have practical significance, which is confirmed by the demo versions of the software applications "Sign Language to Text" and "Text to Sign Language". A promising direction for continuing research on the topic of the work is the improvement of SL1-to-SL2 methods, the creation of open datasets of video gestures, the joining of scientists and developers to fill dictionaries with concepts of various sign languages.

Utilizing Flink and Kafka Technologies for Real-Time Data Processing: A Case Study

In today's very competitive business world, being able to use data to its fullest in real time has become a key differentiation. This paper looks at how two cutting-edge technologies, Apache Flink and Apache Kafka, work together and how they are changing the way real-time data is processed and analyzed. With its fault-tolerant framework made for collecting data from many sources, Apache Kafka is a leader in reliability and scalability when it comes to ingesting data. Apache Flink is the perfect partner for Kafka because it is great at stream processing and low-latency event handling. This paper carefully explains how these technologies work together to create a complete set of tools for handling and analyzing data in real time. The paper goes into detail about how Flink and Kafka can work together, showing how data streams can be handled and intelligently put together to produce insights that can be used. This set of tools, which was created after a lot of study and real-world experience, helps organizations that want to start using real-time data in new ways. Evaluations of performance, scalability, and real-world applications show that this integrated method has a real effect. Beyond just talking about ideas, this study paper gives organizations a step-by-step plan for how to use real-time data to improve their decision-making. By taking advantage of how well Flink and Kafka work together, companies can become more flexible, quick to respond, and creative.

Automated translation and accelerated solving of differential equations on multiple GPU platforms

We demonstrate a high-performance vendor-agnostic method for massively parallel solving of ensembles of ordinary differential equations (ODEs) and stochastic differential equations (SDEs) on GPUs. The method is integrated with a widely used differential equation solver library in a high-level language (Julia’s DifferentialEquations.jl) and enables GPU acceleration without requiring code changes by the user. Our approach achieves state-of-the-art performance compared to hand-optimized CUDA-C++ kernels while performing 20–100× faster than the vectorizing map (vmap) approach implemented in JAX and PyTorch. Performance evaluation on NVIDIA, AMD, Intel, and Apple GPUs demonstrates performance portability and vendor-agnosticism. We show composability with MPI to enable distributed multi-GPU workflows. The implemented solvers are fully featured – supporting event handling, automatic differentiation, and incorporation of datasets via the GPU’s texture memory – allowing scientists to take advantage of GPU acceleration on all major current architectures without changing their model code and without loss of performance. We distribute the software as an open-source library, DiffEqGPU.jl.

Short- and Medium-Term Electricity Consumption Forecasting Using Prophet and GRU

Electricity consumption forecasting plays a crucial role in improving energy efficiency, ensuring stable power supply, reducing energy costs, optimizing facility management, and promoting environmental conservation. Accurate predictions help optimize energy system operations, reduce energy wastage, cut costs, and decrease carbon emissions. Consequently, the research on electricity consumption forecasting algorithms is thriving. However, to overcome challenges like data imbalances, data quality issues, seasonal variations, and event handling, recent forecasting models employ various approaches, including probability and statistics, machine learning, and deep learning. This study proposes a short- and medium-term electricity consumption prediction algorithm by combining the GRU model suitable for long-term forecasting and the Prophet model suitable for seasonality and event handling. (1) The preprocessed data propose the Prophet model in the first step for seasonality and event handling prediction. (2) In the second step, seven multivariate data are experimented with using GRU. Specifically, the seven multivariate data consist of six meteorological data and the residuals between the predicted data from the proposed Prophet model in Step 1 and the observed data. These are utilized to predict electricity consumption at 15 min intervals. (3) Electricity consumption is predicted for short-term (2 days and 7 days) and medium-term (15 days and 30 days) scenarios. The proposed approach outperforms both the Prophet and GRU models, reducing prediction errors and offering valuable insights into electricity consumption patterns.

Power System Analysis of Moving from HVAC to HVDC in the Presence of Renewable Energy Resources

As global energy consumption continues to increase, increased utilization and adaptation of renewable energy resources have tremendously increased over the last decades. Unfortunately, despite the many benefits of renewable energy resources, the intermittent nature of generation and the far distance of large installations from demand centers have tremendous effects on the connecting grid’s stability. In this study, high-voltage direct current (HVDC) systems are proposed as a solution for stable and reliable grid operation in the presence of large renewable energy installations. This research investigates the deployment of an HVDC system into an entire network rather than studying it as an isolated radial system. Various power system analysis functions for both static and dynamic conditions are used to study the effect of integrating an HVDC system on the overall network’s stability. To verify the proposed approach, Jordan’s national electric grid was modeled and used as a case study. The results show when deploying HVDC transmission, losses are reduced by 70% from the baseline case, in addition to better handling of contingency events and enhanced grid’s stability when examining the generator’s rotor angle and speed. Rigorous modeling and simulations of the proposed system structure show the feasibility and prove the advantages of modern HVDC systems over HVAC counterparts.

Learning from the Experiences in the COVID-19 Pandemic - Impact on Quality of Life and Challenges for the Staff of a Dermatological Hospital and Rehabilitation Center in Germany: A Retrospective Analysis.

During the COVID-19 pandemic, infections could also be detected among the staff and patients of the dermatological hospital Bad Bentheim (Germany). This retrospective analysis aims to better understand the impact of the pandemic on health care workers. The results could help improve future pandemic plans and measures to protect health care workers. In 2020, the whole staff (460 participants) of the dermatological hospital Bad Bentheim had been offered the option to be tested with respect to the antibody status on SARS-CoV-2 (IgG, IgM). The data were collected by means of a blood sample and subsequent questionnaires (22 questions for employees with positive SARS-CoV-2 serology) regarding disease severity, symptoms, disease duration, chains of infection, psychological and physical burden. Both groups were divided by positive or negative serology and data analysis was performed using an independent t-test. It was shown that a COVID-19 Infection clinically presented itself as a respiratory tract infection, differed significantly in severity and duration, but also the long-term consequences in employees with proven COVID-19 disease (n=14, 3.7%) from the employees with non-COVID-19 respiratory diseases (33.6% of the seronegative employees). In addition, there was a significant psychological impairment and burden of COVID-19-affected employees. Our study showed unique insights into infection chains, disease courses, disease severity, symptoms and a significant psychological impairment and burden of COVID-19-affected employees among the COVID-19 positive staff. Our study shows deep insights into infection chains, disease courses, disease severity and symptoms among the COVID-19 positive staff and led to change of behavior with the disease itself and among the health care professionals. This knowledge has the potential to positively influence the handling of similar future events.

The Implementation of Mobile Technology in The Process of Reporting Disasters and Events

Telecommunications have an important role in facilitating communication and information exchange, especially in emergency situations such as natural disasters and unexpected community events. Implementing mobile technology is a promising solution to improve the response and handling of in-kind problems. Mobile technology allows the public to quickly report incidents of disaster or security issues through applications or short message services. The implementation of mobile technology facilitates real-time communication between the community and Babinsa. The public can send reports quickly, convey important information, and share photos or videos as evidence of events. Babinsa, on the other hand, can respond more efficiently to these reports and take necessary actions based on the information received. Apart from that, mobile technology also supports two-way communication between Babinsa and the community. The public can also get the latest information about emergencies, efforts handling, or evacuation via app or direct message notification. Thus, the implementation of mobile technology can make a significant contribution to improving communication, response, and handling of disasters and community events.

Decision Support Systems for Coastal Resilience: The Role of Early Warning Systems and Validation

This study presents an advanced Integrated Ocean Forecast Decision Support System (DSS) crafted to elevate ocean forecasting capabilities and enable well-informed decision-making, with a specific focus on the Indian Ocean region. The DSS has a visualization module for scientific inference and spatial analysis. It also has a validation component where each weather model is checked against observations from coastal buoys, open ocean buoys, and ASCAT and SCATSAT satellite data. The system integrates the operational forecasts and observation data and automatically informs the users of the best-correlated model. In cases of extreme conditions like cyclones, the system automatically pulls information from some of the meteorological agencies in real-time for further assessment and refining the models. With the forecasts and observations at its disposal, the DSS suggests the most accurate model to be taken into consideration when issuing alerts. Metrics like the scatter index, correlation coefficient, and root mean square error are displayed on a web interface to generate recommendations. This system facilitated decision-makers in issuing alerts for high winds, elevated waves, and other conditions, particularly during various cyclones in the Arabian Sea and Bay of Bengal, including notable events such as Tauktae and Gulab. The implementation details, the handling of extreme events, and the process of informed decision-making are thoroughly discussed in detail.