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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.

In this paper, a data mining approach is proposed for monitoring the conditions leading to a rail wheel high impact load. The proposed approach incorporates logical analysis of data (LAD) and ant colony optimization (ACO) algorithms in extracting patterns of high impact loads and normal loads from historical railway records. In addition, the patterns are employed in establishing a classification model used for classifying unseen observations. A case study representing real-world impact load data is presented to illustrate the impact of the proposed approach in improving railway services.

Application of artificial intelligence and machine learning approaches becomes an essential tool in improving the performance of railway transportation systems. By using these approaches, the knowledge extracted from historical data can be employed in railway assets monitoring to maintain the assets in a reliable state and to improve the service provided by the railway network.

Results achieved by the proposed approach provide a prognostic system used for monitoring the conditions surrounding rail wheels. Incorporating this prognostic system in surveilling the rail wheels indeed results in better railway services as trips with no-delay or no-failure can be realized. A comparative study is conducted to evaluate the performance of the proposed approach versus other classification algorithms. In addition to the highly interpretable results obtained by the generated patterns, the comparative study demonstrates that the proposed approach provides classification accuracy higher than other common machine learning classification algorithms.

The methodology followed in this research employs ACO algorithm as an artificial intelligent technique and LDA as a machine learning algorithm in analyzing wheel impact load alarm-collected datasets. This new methodology provided a promising classification model to predict future alarm and a prognostic system to guide the system while avoiding this alarm.

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.

Ambient-assisted living (AAL) is one solution to the challenges of healthcare systems in an aging population. Using the “ecosystem adoption of practices over time” (EAPT) as a theoretical lens, this study explores and describes three elements of AAL adoption: (1) the AAL practices in which the technology is embedded (i.e. object of adoption), (2) the older adult's adoption ecosystem (i.e. subject of adoption) and (3) the change of adoption practices over time (i.e. temporality of adoption).

Qualitative interviews with three actor groups in the ecosystem: clients, relatives and home nurses.

The study identifies six categories of AAL practices. Clients, relatives and nurses interact and integrate their resources in carrying out these practices. Some of the practices have developed, or are expected to develop, over time.

The study applies a novel theoretical perspective on how AAL technology is embedded in practices performed by different actors in the adoption ecosystem. This broadens the conceptualization of what is being adopted compared to traditional adoption research.

The nature and amount of data that public organizations have to monitor to counteract corruption lead to a phenomenon called “KPI overload”, consisting of the business analyst feeling overwhelmed by the amount of information and resulting in the absence of appropriate control. The purpose of this study is to develop a solution based on Artificial Intelligence technology to avoid data overloading and, at the same time, under-controlling in business process monitoring.

The authors adopted a design science research approach. The authors started by observing a specific problem in a real context (a healthcare organization); then conceptualized, designed and implemented a solution to the problem with the goal to develop knowledge that can be used to design solutions for similar problems. The proposed solution for business process monitoring integrates databases and self-service business intelligence for outlier detection and artificial intelligence for classification analysis.

The authors found the solution powerful to solve problems related to KPI overload in process monitoring. In the specific case study, the authors found that the combination of Business Intelligence and Artificial Intelligence can provide a significant contribution to the detection of fraud, corruption and/or policy misalignment in public organizations.

The authors provide a big-data-based solution to the problem of data overload in business process monitoring that does not sacrifice any monitored Key Performance Indicators and that also reduces the workload of the business analyst. The authors also developed and implemented this automated solution in a context where data sensitivity and privacy are critical issues.

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.