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A novel method has been proposed to reduce the false alarm rate of arrhythmia patients regarding life-threatening conditions in the intensive care unit. In this purpose, the atrial blood pressure, photoplethysmogram (PLETH), electrocardiogram (ECG) and respiratory (RESP) signals are considered as input signals.

Three machine learning approaches feed-forward artificial neural network (ANN), ensemble learning method and k-nearest neighbors searching methods are used to detect the false alarm. The proposed method has been implemented using Arduino and MATLAB/SIMULINK for real-time ICU-arrhythmia patients' monitoring data.

The proposed method detects the false alarm with an accuracy of 99.4 per cent during asystole, 100 per cent during ventricular flutter, 98.5 per cent during ventricular tachycardia, 99.6 per cent during bradycardia and 100 per cent during tachycardia. The proposed framework is adaptive in many scenarios, easy to implement, computationally friendly and highly accurate and robust with overfitting issue.

As ECG signals consisting with PQRST wave, any deviation from the normal pattern may signify some alarming conditions. These deviations can be utilized as input to classifiers for the detection of false alarms; hence, there is no need for other feature extraction techniques. Feed-forward ANN with the Lavenberg–Marquardt algorithm has shown higher rate of convergence than other neural network algorithms which helps provide better accuracy with no overfitting.

This paper aims to describe the phenomenon of multiple simultaneous un/natural disasters (MSDs). This study also aims to describe the importance and contribution of philosophy in describing MSDs warning and alarming systems.

The paper summarizes topics in the philosophy of MSDs that were covered in detail in previous research in order to continue with the topic of the philosophy of MSDs alarming and warning. A practical solution to conceptual paradoxes is researched by means of conceptual-morphological analysis.

The paper proposes a conceptual idea for MSDs alarming system which is its main topic; namely, it offers a conceptual solution to a series of practical-conceptual paradoxes that occur before, during and after MSDs.

This is only a conceptual research, and it does not deal with particular technological solutions.

The proposed solution of this research could be implemented in various warning and alarm systems.

The proposed concept of a universal alarm system for MSDs was not previously proposed.

This study aims to introduce the achievements and benefits of applying wheel/rail-force–based maintenance interval extension of the C80 series wagon in China.

Chinese wagons' existing maintenance strategy had left a certain safety margin for the characteristics of widely running range, unstable service environment and submission to transportation organization requirements. To reduce maintenance costs, China railway (CR) has attempted to extend the maintenance interval since 2020. The maintenance cycle of C80 series heavy haul wagons is extended by three months (no stable routing) or 50,000 km (regular routing). However, in the meantime, the alarming rate of the running state, a key index to reflect the severe degree of hunting stability, by the train performance detection system (TPDS) for the C80 series heavy haul wagons has increased significantly.

The present paper addresses a big data statistical way to evaluate the risk of allowing the C80 series heavy haul wagons to remain in operation longer than stipulated by the maintenance interval initial set. Through the maintenance and wayside-detector data, which is divided into three stages, the extension period (three months), the current maintenance period and the previous maintenance period, this method reveals the alarming rate of hunting was correlated with maintenance interval. The maintainability of wagons will be achieved by utilizing wagon performance degradation modeling with the state of the wheelset and the often-contact side bearing. This paper also proposes a statistical model to return to the average safety level of the previous maintenance period's baseline through correct alarming thresholds for unplanned corrective maintenance.

The paper proposes an approach to reduce safety risk due to maintenance interval extension by effective maintenance program. The results are expected to help the railway company make the optimal solution to balance safety and the economy.

The purpose of this study is to introduce the top-level design ideas and the overall architecture of earthquake early-warning system for high speed railways in China, which is based on P-wave earthquake early-warning and multiple ways of rapid treatment.

The paper describes the key technologies that are involved in the development of the system, such as P-wave identification and earthquake early-warning, multi-source seismic information fusion and earthquake emergency treatment technologies. The paper also presents the test results of the system, which show that it has complete functions and its major performance indicators meet the design requirements.

The study demonstrates that the high speed railways earthquake early-warning system serves as an important technical tool for high speed railways to cope with the threat of earthquake to the operation safety. The key technical indicators of the system have excellent performance: The first report time of the P-wave is less than three seconds. From the first arrival of P-wave to the beginning of train braking, the total delay of onboard emergency treatment is 3.63 seconds under 95% probability. The average total delay for power failures triggered by substations is 3.3 seconds.

The paper provides a valuable reference for the research and development of earthquake early-warning system for high speed railways in other countries and regions. It also contributes to the earthquake prevention and disaster reduction efforts.

The paper aims to solve the problem of personnel intrusion identification within the limits of high-speed railways. It adopts the fusion method of millimeter wave radar and camera to improve the accuracy of object recognition in dark and harsh weather conditions.

This paper adopts the fusion strategy of radar and camera linkage to achieve focus amplification of long-distance targets and solves the problem of low illumination by laser light filling of the focus point. In order to improve the recognition effect, this paper adopts the YOLOv8 algorithm for multi-scale target recognition. In addition, for the image distortion caused by bad weather, this paper proposes a linkage and tracking fusion strategy to output the correct alarm results.

Simulated intrusion tests show that the proposed method can effectively detect human intrusion within 0–200 m during the day and night in sunny weather and can achieve more than 80% recognition accuracy for extreme severe weather conditions.

(1) The authors propose a personnel intrusion monitoring scheme based on the fusion of millimeter wave radar and camera, achieving all-weather intrusion monitoring; (2) The authors propose a new multi-level fusion algorithm based on linkage and tracking to achieve intrusion target monitoring under adverse weather conditions; (3) The authors have conducted a large number of innovative simulation experiments to verify the effectiveness of the method proposed in this article.

Higher education has experienced some significant changes over the past few years including a highly competitive landscape, use of new technology, managing COVID protocols and guiding students to resources that ensure their success. With prior research highlighting the changes in the workforce and poor working conditions of part-time faculty, this study aims to explore full-time perceptions of several employment-related variables to determine how these significant workplace changes have impacted them.

A mixed-methods approach is used. This includes a questionnaire being sent out via e-mail to faculty at a medium-sized, public, regional university located in the USA. This was sent through two separate listservs: full-time faculty listserv and part-time faculty listserv. The questionnaire included quantitative and qualitative questions. A one-way ANOVA was used to detect significant differences between the two groups of interest for the quantitative components. The qualitative portions of the questionnaire provided deeper insights into employee perceptions of their workplace.

This research uncovers some alarming trends for full-time faculty within higher education. Across several different employment variables, full-time faculty perceptions are significantly worse than part-time faculty. This includes work–family conflict, pay perceptions, compensation opportunities, online teaching experiences, overwhelming work activities, technology provided, travel funding provided, perceived satisfaction of a faculty advocate and perceived benefits of a faculty advocate. Qualitative and quantitative results support these findings and provide additional clarification as to why they have these negative workplace perceptions.

A convenience sample was used, where data was only gathered from one university. Future research could replicate finding with more universities varying in their make-up and location to determine if these results hold across the USA and internationally. Some measures did not use established scales in the literature, and some were single-item measures. Future research could replicate findings using established scales with multi-item measures to provide more confidence the results produced that are reliable and valid.

These results suggest alarming concerns for higher education institutions regarding their full-time faculty. Human resource managers and administrators at universities should respond to “the alarm” from this research and internal employee satisfaction surveys they have conducted with their employees. Changes should be made at higher education institutions to improve employee workplace perceptions in hopes of retaining valuable employees and improving worker morale to increase productivity. The recent workplace changes and challenges for full-time faculty are negatively impacting their workplace perceptions.

As a result of full-time faculty having significantly worse perceptions across all measured employment variables than their part-time colleagues, who already had poor perceptions, the authors may see more “good” employees leaving the industry for other more lucrative options. Others may become “dead wood” in the university and engage in “quite quitting” resulting in less productivity. With the tenure process protecting professors, this may result in universities being “stuck” with many unmotivated professors and hurt the quality of educational services provided. Some professors may even act out negatively toward the university. This could damage the quality of education provided at universities and perceptions of higher education by society.

To the best of the authors’ knowledge, this is the first study comparing full-time and part-time faculty workplace perceptions across several variables. After previous study has highlighted the poor work conditions and perceptions of part-time faculty, this study adds to the discussion showing that significant changes in the workplace have resulted in full-time faculty now perceiving their employment to be significantly worse than their part-time colleagues. This can have significant short-term and long-term ramifications for the industry that will make it more difficult for universities to attract talented individuals to choose a career in education and retaining their best workers based on current employment perceptions.

In the mid-2000s, the operator of New York City’s mass transit network committed more than a half-billion dollars to military contractor Lockheed Martin for a security technology capable, in part, of inferring threats based on analysis of data streams, of developing response strategies, and taking automated action toward alerts and calamities in light of evolving circumstances. The project was a failure. This chapter explores the conceptualization and development of this technology – rooted in cybernetics – and compares its conceptual underpinnings with some situated problems of awareness, communication, coordination, and action in emergencies as they unfold in one of the busiest transport systems in the world, the New York subway. The author shows how the technology, with all the theatrical trappings of a “legitimate” security solution, was apparently conceived without a grounded understanding of actual use-cases, and the degree to which the complex interactions which give rise to subway emergency can be anticipated in – and therefore managed through – a technological system. As a case-study, the chapter illustrates the pitfalls of deploying technology against problems which are not well-defined in the first place, to the neglect of investments against much more fundamental problems – such as inadequate communication systems, and unstable relationships with emergency response agencies – which might offer guaranteed benefits, and indeed lay a firm groundwork for future deployment of more ambitious technology.

This study aims to develop a machine learning model to detect structure fire fatalities using a dataset comprising 11,341 cases from 2011 to 2019.

Exploratory data analysis (EDA) was conducted prior to modelling, in which ten machine learning models were experimented with.

The main fatal structure fire risk factors were fires originating from bedrooms, living areas and the cooking/dining areas. The highest fatality rate (20.69%) was reported for fires ignited due to bedding (23.43%), despite a low fire incident rate (3.50%). Using 21 structure fire features, Random Forest (RF) yielded the best detection performance with 86% accuracy, followed by Decision Tree (DT) with bagging (accuracy = 84.7%).

Limitations of the study are pertaining to data quality and grouping of categories in the data pre-processing stage, which could affect the performance of the models.

The study is the first of its kind to manipulate risk factors to detect fatal structure classification, particularly focussing on structure fire fatalities. Most of the previous studies examined the importance of fire risk factors and their relationship to the fire risk level.

During a pandemic, with businesses implementing social distancing protocols and work-from-home strategies, the use of continuous controls monitoring (CCM) may add value to the internal audit function. This study aims to examine the use of CCM technologies and the impact on the internal audit function during a pandemic.

This study adopted a case study approach for this study because it focuses on questions of “how” and “what.” Case studies provided an opportunity for an in-depth analysis of the phenomena being investigated. Semi-structured interviews were used to collect data. This study did not use sampling. Instead, multiple case studies were used for data collection.

Based on the findings, this study makes several contributions to the literature, for example, in health-care evidence suggests the pandemic has caused internal audit to focus on risk areas. Other industries, such as retail, have invested in CCM. However, in all cases, education and preparedness (or the lack thereof) appeared to significantly influence uptake of CCM. Organizations that made prior investments in CCM technologies experienced greater acceptance in the face of changing demands. Training in emerging technologies is a key competency in supporting audit operations in changing environments.

As the study was conducted with a small sample of cases, findings cannot be extrapolated nor generalized beyond the case study organizations.

This study found that several factors limit adoption, exploitation and further development of CCM technologies, such as lack of top management support, acceptance of CCM technologies and suitable education and training of internal audit staff.

This study addresses the issue of the value that CCM offers organizations and whether it is a silver bullet that the internal audit profession needs, particularly when physical access to organizations may be restricted. The COVID-19 pandemic placed considerable focus on digital access. Better IT systems and more data will allow organizations to better support employees, inform strategic and financial decisions and engage stakeholders. During the recovery phase, leveraging investments in CCM technologies will contribute to internal audits’ ability to help clients to manage organizational risk.