Acceptability and deliverability of an auditory rhythmical cueing (ARC) training programme for use at home and outdoors to improve gait and physical activity post-stroke

Background Although laboratory studies demonstrate that training programmes using auditory rhythmical cueing (ARC) may improve gait post-stroke, few studies have evaluated this intervention in the home and outdoors where deployment may be more appropriate. This manuscript reports stakeholder refinement of an ARC gait and balance training programme for use at home and outdoors, and a study which assessed acceptability and deliverability of this programme. Methods Programme design and content were refined during stakeholder workshops involving physiotherapists and stroke survivors. A two-group acceptability and deliverability study was then undertaken. Twelve patients post-stroke with a gait related mobility impairment received either the ARC gait and balance training programme or the gait and balance training programme without ARC. Programme provider written notes, participant exercise and fall diaries, adverse event monitoring and feedback questionnaires captured data about deliverability, safety and acceptability of the programmes. Results The training programme consisted of 18 sessions (six supervised, 12 self-managed) of exercises and ARC delivered by a low-cost commercially available metronome. All 12 participants completed the six supervised sessions and 10/12 completed the 12 self-managed sessions. Provider and participant session written records and feedback questionnaires confirmed programme deliverability and acceptability. Conclusion An ARC gait and balance training programme refined by key stakeholders was feasible to deliver and acceptable to participants and providers. Trial registration ISCTRN 12/03/2018.

Event Monitoring and Intelligence Gathering Using Twitter Based Real-Time Event Summarization and Pre-Trained Model Techniques

Recently, an emerging application field through Twitter messages and algorithmic computation to detect real-time world events has become a new paradigm in the field of data science applications. During a high-impact event, people may want to know the latest information about the development of the event because they want to better understand the situation and possible trends of the event for making decisions. However, often in emergencies, the government or enterprises are usually unable to notify people in time for early warning and avoiding risks. A sensible solution is to integrate real-time event monitoring and intelligence gathering functions into their decision support system. Such a system can provide real-time event summaries, which are updated whenever important new events are detected. Therefore, in this work, we combine a developed Twitter-based real-time event detection algorithm with pre-trained language models for summarizing emergent events. We used an online text-stream clustering algorithm and self-adaptive method developed to gather the Twitter data for detection of emerging events. Subsequently we used the Xsum data set with a pre-trained language model, namely T5 model, to train the summarization model. The Rouge metrics were used to compare the summary performance of various models. Subsequently, we started to use the trained model to summarize the incoming Twitter data set for experimentation. In particular, in this work, we provide a real-world case study, namely the COVID-19 pandemic event, to verify the applicability of the proposed method. Finally, we conducted a survey on the example resulting summaries with human judges for quality assessment of generated summaries. From the case study and experimental results, we have demonstrated that our summarization method provides users with a feasible method to quickly understand the updates in the specific event intelligence based on the real-time summary of the event story.

Event Monitoring of Transformer Discharge Sounds based on Voiceprint

This paper investigates the operation inspection and anomaly diagnosis of transformers in substations, and carries out an application study of artificial intelligence-based sound recognition technology in transformer discharge diagnosis to improve the timeliness and diagnostic capability of intelligent monitoring of substation equipment operation. In this study, a sound parameterization technology in the field of sound recognition is used to implement automatic discharge sound detections. The sound samples are pre-processed and then Mel-frequency cepstrum coefficients (MFCCs) are extracted as features, which are used to train Gaussian mixture models (GMMs). Finally, the trained GMMs are used to detect discharge sounds in the place of transformers in substations. The test results demonstrate that the audio anomaly detection based on MFCCs and GMMs can be used to effectively recognize anomalous discharge in the high scenario of transformers.

ОПЕРАЦІЙНИЙ ЦЕНТР БЕЗПЕКИ ЯК ПОСЛУГА НА ОСНОВІ SIEM

This study examines the Security Operations Center, which provides detection and analysis of cybersecurity, rapid response, and prevention of cyber attacks. Security Operations Center technologies are used to provide visibility and enable analysts to protect against attacks. The algorithm of presenting the topic «Security Center» during the teaching of the discipline «Security of programs and data» at the Volodymyr Vynnychenko Central Ukrainian State Pedagogical University is shown, namely the problems of implementation of event monitoring systems «Security information and event management», types of operational centers, methods of building internal operational security centers. Subject competencies are formed in students: to classify, identify and protect information processing facilities from unauthorized access and computer viruses, to develop individual access control and information protection systems. The process of implementing Security information and event management systems at the enterprise is shown, the main mechanisms of this system using a hierarchical model, the main tasks of the security operational center, the key parameters of the Security Operations Center (organizational model, performance of functions that go beyond the tasks, level of authority), basic rules of correlation. The commercial security operations center SOC as a Service is considered, which is designed to help work with a huge amount of information, real-time monitoring and response to attacks. During the laboratory classes, the students analyzed the companies that provide security operations center services (Information Systems Security Partners, Octave Cybersecurity, Infopulse, Omega Security Service) and studied the factors that affect companies when choosing the type Security Operations Center. Key words: Security Operations Center, SEIM-systems, cybersecurity, SOC as a Service.

Clinical and Laboratory Improvement in Hyperadrenergic Postural Orthostatic Tachycardia Syndrome (POTS) after COVID-19 Infection

A 32-year-old woman with a history of symptomatic supraventricular tachycardia, inappropriate sinus tachycardia, and hyperadrenergic POTS was treated with ivabradine and metoprolol. She then presented with bradycardia and Mobitz II second-degree AV block on event monitoring six weeks after COVID-19 infection. Her post-viral workup revealed normalization of catecholamine levels and significant symptomatic improvement in heart rate. To the authors’ knowledge, this is the first reported case of improvement in POTS after COVID-19 infection. As our understanding of COVID-19 continues to improve, it will be vital to better understand the impact of COVID-19 dysautonomia on cardiac patients.

Performance Evaluation of a Nowcasting Modelling Chain Operatively Employed in Very Small Catchments in the Mediterranean Environment for Civil Protection Purposes

The Hydro-Meteorological Centre (CMI) of the Environmental Protection Agency of Liguria Region, Italy, is in charge of the hydrometeorological forecast and the in-event monitoring for the region. This region counts numerous small and very small basins, known for their high sensitivity to intense storm events, characterised by low predictability. Therefore, at the CMI, a radar-based nowcasting modelling chain called the Small Basins Model Chain, tailored to such basins, is employed as a monitoring tool for civil protection purposes. The aim of this study is to evaluate the performance of this model chain, in terms of: (1) correct forecast, false alarm and missed alarm rates, based on both observed and simulated discharge threshold exceedances and observed impacts of rainfall events encountered in the region; (2) warning times respect to discharge threshold exceedances. The Small Basins Model Chain is proven to be an effective tool for flood nowcasting and helpful for civil protection operators during the monitoring phase of hydrometeorological events, detecting with good accuracy the location of intense storms, thanks to the radar technology, and the occurrence of flash floods.