Analysis Of Continuous Data Research Articles

The analysis of continuous data from n-of-1 trials using paired cycles: a simple tutorial

N-of-1 trials are defined and the popular paired cycle design is introduced, together with an explanation as to how suitable sequences may be constructed.Various approaches to analysing such trials are explained and illustrated using a simulated data set. It is explained how choosing an appropriate analysis depends on the question one wishes to answer. It is also shown that for a given question, various equivalent approaches to analysis can be found, a fact which may be exploited to expand the possible software routines that may be used.Sets of N-of-1 trials are analogous to sets of parallel group trials. This means that software for carrying out meta-analysis can be used to combine results from N-of-1 trials. In doing so, it is necessary to make one important change, however. Because degrees of freedom for estimating variances for individual subjects will be scarce, it is advisable to estimate local standard errors using pooled variances. How this may be done is explained and fixed and random effect approaches to combining results are illustrated.

Distinguishing Infection- Vs. Non-Infection-Related Febrile Neutropenia in HCT Patients By Machine Learning Analysis of Continuous Body Temperature Data Collected Using a Wearable Sensor

Distinguishing Infection- Vs. Non-Infection-Related Febrile Neutropenia in HCT Patients By Machine Learning Analysis of Continuous Body Temperature Data Collected Using a Wearable Sensor

What factors affect the quality of life of employed physically disabled people? A qualitative exploration of their experiences.

According to the literature, the majority of People With Physical Disabilities (PWPDs) have a low Quality of Life (QOL). The goal of this study was to look at the experiences of a group of employed PWPDs in Iran in terms of the factors influencing their QOL. A conventional qualitative content study was carried out in Qazvin City, Iran, from February to August 2020. Twenty-eight semi-structured, in-depth interviews with 20 employed PWPDs (6 men and 14 women), 5 five family members, and 3 of the participants' employers (n = 3) were conducted. Participants were asked about their experiences with factors that impacted the QOL of employed PWPDs. Continuous analysis of data began and continued up to data saturation. The participants' experiences on influencing variables QOL were merged into four major categories: psychological state, socioeconomic problems, structural obstructions, and physical constraints. Conducting psychological therapy to reduce stress and using adaptive coping strategies, changing perceptions of the community about PWPDs via the media, and also improving environmental infrastructure may help to increase QOL in PWPDs.

HEARJACK - The Ultimate Posture Enhancement and Skin Health Jacket for Military Excellence

HEARJACK is an innovative smart jacket designed to enhance the health and well-being of military personnel. This groundbreaking project addresses the crucial need for maintaining proper posture, monitoring heart health, and managing humidity-related skin issues. Spinal alignment and posture play a fundamental role in overall well-being, and deviations from a healthy alignment can lead to various health issues. HEARJACK integrates sensors like the MPU6050 for real-time posture monitoring, DHT11 for humidity insights, and a heart rate sensor for stress assessment. These sensors, combined with an IoT algorithm, enable continuous monitoring and data analysis. The results demonstrate a significant improvement in posture, reduced skin-related health concerns, and enhanced stress management, underscoring the potential of IoT-driven wearables in military healthcare. Further research and development promise to revolutionize health and military technology

Exploring nurses' experiences of recommended patient care: a descriptive phenomenological study.

Caring for recommended patients creates work and emotional challenges for nurses. Nurses are obligated to provide care regardless of the patient's situation. Therefore, knowing the experiences of nurses in dealing with recommended patients in order to provide quality and effective care can be the basis for increasing patient satisfaction. The present study was conducted aimed to explain nurses' experiences of caring for recommended patients. This was a qualitative study with descriptive phenomenological approach. Participants were 12 nurses working in different wards of hospitals affiliated to Kermanshah University of Medical Sciences, selected by purposive sampling method with maximum diversity. The data collected using semi-structured interviews in face-to-face and audio-recorded methods. MAXQDA 2020 software was used for data management. The analysis of the data was done using the Colaizzi's 7-step method. In order to verify the trustworthiness of the data, Lincoln and Guba criteria were used. After continuous data analysis, 110 initial codes were extracted. These codes emerged in 18 sub-themes and 6 main themes including: catastrophe, be in decline, be in progress, discrimination, work overload, and poor prognosis. The results showed information about the presence of recommended patients in the hospital, which can have consequences for patients and nurses. Therefore, it is advised that nurses provide standard care and avoid any kind of discrimination against all patients regardless of whether the patient is recommended or not.

Learning from Power Signals: An Automated Approach to Electrical Disturbance Identification within a Power Transmission System.

As power quality becomes a higher priority in the electric utility industry, the amount of disturbance event data continues to grow. Utilities do not have the required personnel to analyze each event by hand. This work presents an automated approach for analyzing power quality events recorded by digital fault recorders and power quality monitors operating within a power transmission system. The automated approach leverages rule-based analytics to examine the time and frequency domain characteristics of the voltage and current signals. Customizable thresholds are set to categorize each disturbance event. The events analyzed within this work include various faults, motor starting, and incipient instrument transformer failure. Analytics for fourteen different event types have been developed. The analytics were tested on 160 signal files and yielded an average accuracy of 99%. Continuous nominal signal data analysis was performed using an approach called the cyclic histogram. The cyclic histogram process is intended to be integrated into the digital fault recorders themselves in order to facilitate the detection of subtle signal variations that are too small to trigger a disturbance event and that can occur over hours or days. In addition to reducing memory requirements by a factor of 320, it is anticipated that cyclic histogram processing will aid in identifying incipient events and identifiers. This project is expected to save engineers time by automating the classification of disturbance events and increasing the reliability of the transmission system by providing near real-time detection and identification of disturbances as well as prevention of problems before they occur.

Statistical Packages and Algorithms for the Analysis of Continuous Glucose Monitoring Data: A Systematic Review.

Continuous glucose monitoring (CGM) measures glucose levels every 1 to 15 minutes and is widely used in clinical and research contexts. Statistical packages and algorithms reduce the time-consuming and error-prone process of manually calculating CGM metrics and contribute to standardizing CGM metrics defined by international consensus. The aim of this systematic review is to summarize existing data on (1) statistical packages for retrospective CGM data analysis and (2) statistical algorithms for retrospective CGM analysis not available in these statistical packages. A systematic literature search in PubMed and EMBASE was conducted on September 19, 2023. We also searched Google Scholar and Google Search until October 12, 2023 as sources of gray literature and performed reference checks of the included literature. Articles in English and Danish were included. This systematic review is registered with PROSPERO (CRD42022378163). A total of 8731 references were screened and 46 references were included. We identified 23 statistical packages for the analysis of CGM data. The statistical packages could calculate many metrics of the 2022 CGM consensus and non-consensus CGM metrics, and 22/23 (96%) statistical packages were freely available. Also, 23 statistical algorithms were identified. The statistical algorithms could be divided into three groups based on content: (1) CGM data reduction (eg, clustering of CGM data), (2) composite CGM outcomes, and (3) other CGM metrics. This systematic review provides detailed tabular and textual up-to-date descriptions of the contents of statistical packages and statistical algorithms for retrospective analysis of CGM data.

Hybrid Probabilistic Slow Feature Analysis of Continuous and Binary Data for Dynamic Process Monitoring

Hybrid Probabilistic Slow Feature Analysis of Continuous and Binary Data for Dynamic Process Monitoring

Guarding Digital Health: Deep Learning for Attack Detection in Medical IoT

The Internet of Medical Things (IoMT) has transformed the healthcare sector by allowing for the continuous gathering, transfer, and analysis of medical data via networked devices.However, as IoMT has become more integrated into healthcare systems, it has been vulnerable to cybersecurity attacks, posing dangers to patient safety and data privacy. This study tackles the essential challenge of safeguarding the IoMT ecosystem by implementing cutting-edge deep learning approaches for threat identification and avoidance.Our study begins by looking at a variety of cyber risks that the IoMT faces, including as backdoors, DDoS assaults, MITM assaults, ransomware and SQL injections.These hostile operations may impair medical services and jeopardize critical med- ical data.To combat these risks, we offer a strong intrusion detection system, that constantly monitors the IoMT architecture for any unusual activity.The findings of this research contribute significantly to the development of a secure and resilient medical IoT ecosystem.The performance metrics of the proposed intrusion detection system, including accuracy, precision, recall, and F1 Score, were evaluated for four different models: CNN, Autoencoder, Transformer Network, and LSTM Network.The LSTM Network ex- hibited exceptional performance, achieving an accuracy of 97%, precision of 93%, recall of 96%, and an F1 Score of 94%.The compiled model, named Adam-LSTM, leverages the strengths of the Adam optimizer and LSTM architecture. Comparative anal- ysis with alternative algorithms demonstrated the superiority of the proposed approach in accurately and efficiently identifying intrusions.The IoMT can supply dependable healthcare services to patients while preserving their sensitive medical information by proactively securing digital health through deep learning-based threat detection.This study provides the groundwork for future breakthroughs in medical IoT cybersecurity solutions and promotes the idea of a smarter and safer healthcare landscape.

3D Monitoring Model for Real‐Time Displacement of Metro Tunnel under “Dual Carbon” Background

Real‐time automatic displacement monitoring of metro tunnels is vital for ensuring operational safety and contributes to carbon reduction goals by improving system efficiency. This study focuses on key monitoring elements such as displacement, settlement, convergence, and cracking. Through the analysis of continuous monitoring data, a real‐time displacement monitoring model for metro tunnels based on robotic total stations is proposed. This model can timely identify potential risks, thereby ensuring the safe operation of tunnels and reducing carbon emissions from unnecessary maintenance operations, thereby reducing the carbon footprint of metro operations. This article takes the Jinan Metro Tunnel Displacement Real‐time Monitoring Project in China as a case study and constructs a comprehensive monitoring framework using robotic total stations, intelligent automated deformation monitoring data collectors, and cloud servers. The implementation details of the project, displacement monitoring principles, monitoring system construction, and data analysis processes are elaborated in detail. Taking the monitoring data of Jinan Metro Line 2 from April 1, 2022, to May 31, 2023, as an example, the results show that the tunnel displacement is within the safe range, verifying the practical application value of the method proposed in this paper. It can effectively ensure the safe operation of the metro and promote sustainable development and low‐carbon metro construction.

Construction of Electricity Load Forecasting Model Based on Electricity Data Analysis

Abstract This paper builds a time series prediction model of recurrent neural networks based on time series electricity load forecasting. In this paper, the household electricity consumption record data of some residents in urban area S is taken as the research object, and the laws and characteristics of users’ electricity consumption behavior are analyzed in depth based on the real residential electricity consumption data. External factors such as temperature conditions, holidays, etc. The arithmetic cases are also analyzed using real load data sets. In the short-term continuous electricity data analysis, the smaller the time interval is, the closer its corresponding electricity consumption ratio is to 1. There is a negative correlation between long-term continuous electricity consumption. When the temperature is 30~35oC versus -5~0oC, electricity consumption rises significantly. Comparing and analyzing the time series decomposition-RNN with several models, the time series decomposition-RNN model has the highest fit at 10:00-12:00 and 12:00-14:00, and the result verifies the validity of the model proposed in this paper.

The Potential of Machine Learning for Future Mars Exploration

The pursuit of understanding Mars, our neighboring planet, is rife with challenges that range from treacherous conditions for potential human astronauts to the vast distances that complicate communication. However, a beacon of hope emerges in the form of machine learning, a technological frontier that promises to transform the landscape of Martian exploration. As we embark on this interplanetary journey, the recognition of machine learning's potential is growing. It offers innovative solutions to some of the most pressing challenges, ushering in a new era of autonomous exploration. Imagine rovers and orbiter spacecraft equipped with the ability to analyze Martian data on-site, reducing the need for slow communications with Earth. This revolutionary approach is already in action with rovers like Curiosity, where machine learning enables self-directed exploration and continuous data analysis on the Martian surface. The applications of machine learning extend beyond mere autonomy. They hold the promise of addressing communication limitations, providing greater operational autonomy, and unlocking the mysteries that shroud the Red Planet. From identifying sources of atmospheric gases, such as oxygen and methane, to interpreting geological features like cloud distributions and weather patterns, machine learning is proving itself to be a versatile and indispensable tool in unraveling the complexities of Mars. Venturing deeper into the Martian climate, machine learning becomes a powerful ally. By leveraging this technology to analyze climate data, we have the potential to generate predictive models crucial for planning future surface missions and assessing the habitability of Mars. Additionally, the application of machine learning on Earth offers a unique opportunity to decode uncertainties related to Martian atmospheric interactions, the dynamics of dust storms, and conditions beneath the surface. Anticipating the wealth of data that future Mars missions will yield, the integration of machine learning emerges as a game-changer. Its efficiency in discerning intricate patterns within extensive datasets has the potential to revolutionize our scientific understanding of Mars. As we delve deeper into the mysteries of the Red Planet, machine learning stands as a pivotal catalyst, promising not just incremental but transformative discoveries. It becomes the linchpin in our ongoing quest to answer the age-old question: Did life ever exist on Mars? In the realm of Martian exploration, machine learning is proving to be the technological cornerstone that propels us towards unprecedented scientific revelations.

Investigation of Medium-Term Performance of Porous Asphalt and Its Impacts on Tire/Pavement Noise

To assess the medium-term performance of porous asphalt pavement during service and its influence on tire/pavement noise level, a seven-year continuous observation and data analysis study was conducted. Key performance indicators were measured and calculated by using automated pavement technology testing equipment. The noise levels were tested by using the on-board sound intensity (OBSI) method on three types of porous asphalt pavements (PUC-10, PAC-13, and PUC-10 + PAC-13) and one dense thin layer course (DTC) for comparison. The findings indicated that the Damage Rate (DR) and Surface Friction Coefficient (SFC) of porous asphalt pavements diminished greatly over time, while the International Roughness Index (IRI) and Rut Depth (RD) remained relatively stable. The two-layer porous asphalt pavement showed the largest noise reduction over the medium-term. Compared to DTC, the OBSI noise levels of these structures were lower by 2.09 dB, 1.53 dB, and 2.88 dB, respectively. The OBSI was found to be closely correlated with the SFC, IRI, test speed, lane, and pavement type. The RD had a notable effect on the OBSI in PUC-10 pavements. In PUC-10 + PAC-13 pavements, a significant linear relationship was observed between the OBSI and SFC. This is mainly because of the polishing of the coarse aggregates, which leads to micro-texture reduction, high frequency noise increase, and SFC decrease. This study makes a valuable contribution to understanding the laws of porous asphalt pavement performance changes and the relationship between tire/pavement noise and pavement characteristics.

Development of intelligent monitoring system based on Internet of Things and wearable technology and exploration of its clinical application mode

Wearable monitoring, which has the advantages of continuous monitoring for a long time with low physiological and psychological load, represents a future development direction of monitoring technology. Based on wearable physiological monitoring technology, combined with Internet of Things (IoT) and artificial intelligence technology, this paper has developed an intelligent monitoring system, including wearable hardware, ward Internet of Things platform, continuous physiological data analysis algorithm and software. We explored the clinical value of continuous physiological data using this system through a lot of clinical practices. And four value points were given, namely, real-time monitoring, disease assessment, prediction and early warning, and rehabilitation training. Depending on the real clinical environment, we explored the mode of applying wearable technology in general ward monitoring, cardiopulmonary rehabilitation, and integrated monitoring inside and outside the hospital. The research results show that this monitoring system can be effectively used for monitoring of patients in hospital, evaluation and training of patients' cardiopulmonary function, and management of patients outside hospital.

A meta-analysis of adverse effects of retinopathy of prematurity on neurodevelopment in preterm infants.

Retinopathy of prematurity (ROP) increases with the survival of late preterm infants, but its relationship with neurodevelopmental outcomes in preterm infants remains controversial. To investigate the relationship between ROP and its severity and adverse neurodevelopmental outcomes in preterm infants. We conducted a meta-analysis. All relevant literature before November 2022 were retrieved from PubMed, Embase, Cochrane Library Web of Science, CNKI, CBM, Wan fang Data, and VIP Database. According to the inclusion criteria and exclusion criteria, eligible literature were included to conduct clinical trial quality assessment, and the Newcastle-Ottawa scale was used to evaluate the quality of evidence. Meta-analysis was performed using RevMan5.3. Data extraction, quality assessment, and meta-analysis were performed independently by 2 people. Mean difference or standardized mean difference of motor, language and cognitive scores (Bayley III or Bayley II) were used as effect sizes for continuous data analysis, all of which were represented by 95% CI. For heterogeneity (I2 ≥ 50% or P < .10), a random effects model was used, otherwise a fixed effects model was used. A total of 6 literature were included. The results of the ROP group for motor (comprehensive motor, proportional motor, and fine motor), language and cognitive scores were -5.57 (95%CI, -1.43 to 0.04), -0.95 (95%CI, 1.4-0.50), -1.34 (95% CI, 1.77-0.92), -1.75 (95% CI, 2.26-1.24) and -5.56 (95% CI, 9.56-1.57). Additionally, the results of severe ROP group for motor (comprehensive motor, proportional motor, fine motor), language and cognitive scores were -8.32 (95%CI, -8.91 to 7.74), -1.10 (95%CI, -1.83 to -0.36), -1.08 (95%CI, -1.75 to -0.41), -7.03 (95%CI, -7.71 to 6.35), and -7.96 (95%CI, -8.5 to -7.42). The Bayley Scale scores of the ROP group were lower than those of the not ROP group, and the scores of the severe ROP were significantly lower than those of the not severe ROP group. These findings suggest that ROP can indeed delay motor, language and cognitive, especially in severe cases.

Exploring Factors Influencing the Workload in Nursing Assistants: A Qualitative Study

Background and Objectives: A high workload is a major challenge to health care workers, especially first- line supporters, like nurse assistants, and this has many negative consequences. This study aimed to identify the factors affecting the workload of nurse assistants in one of Tehran hospitals.&#x0D; Methods: The research is descriptive- qualitative using qualitative content analysis, with the participation of 13 nurse assistants selected by purposive sampling. Data were collected through semi- structured interviews and after each interview, the data were loaded and analyzed in MAX QDA software and this process continued until data saturation. Please match the last sentence with the farsi version.&#x0D; Results: In the findings of continuous data analysis, a total of 473 codes were found in the factors affecting workload in the process of nurse assistants' work system. Finally, they were classified into five categories of Systems Engineering Initiative for Patient Safety model (person, task, organization, tools, equipment and environment).&#x0D; Conclusion: Most factors affecting workload are obtained in the organizational component and the least in the environmental component of the Systems Engineering Initiative for Patient Safety model, which indicates the significant role played by interpersonal relationships in the workplace and hospital rules on the workload of nurse assistants.

Beyond central-tendency: If we agree discrete vegetation communities do not exist, should we investigate other methods of clustering?

Clustering is indispensable in the quest for robust vegetation classification schemes that aim to partition, summarise and communicate patterns. However, clustering solutions are sensitive to methods and data and are therefore unstable, a feature that is usually attributed to noise. Viewed through a central-tendency lens, noise is defined as the degree of departure from type, which is problematic since vegetation types are abstractions of continua, and so noise can only be quantified relative to the particular solution at hand. Graph theory models the structure of vegetation data based on the interconnectivity of samples. Through a graph-theoretic lens, the causes of instability can be quantified in absolute terms via the degree of connectivity among objects. We simulated incremental increases in sampling intensity in a dataset over five iterations and assessed classification stability across successive solutions derived using algorithms implementing, respectively, models of central-tendency and interconnectivity. We used logistic regression to model the likelihood of a sample changing groups between iterations as a function of distance to the centroid and degree of interconnectivity. Our results show that the degree to which samples are interconnected is a more powerful predictor of instability than the degree to which they deviate from their nearest centroid. The removal of weakly interconnected samples resulted in more stable classifications, although solutions with many clusters were apparently inherently less stable than those with few clusters, and improvements in stability flowing from the removal of outliers declined as the number of clusters increased. Our results reinforce the fact that clusters abstracted from continuous data are inherently unstable and that the quest for stable, fine-scale classifications from large regional datasets is illusory. Nevertheless, our results show that using models better suited to the analysis of continuous data may yield more stable classifications of the available data.

Monitoring and controlling of the micro-environment in a special exhibition in the Shanghai Museum

Monitoring and controlling of the showcase environment in a special exhibition in Shanghai Museum are described in this work. The long show time started on March 2022 and ended on January 2023. Various items were shown in this exhibition with different combination modes. Air conditioners, humidifiers and three kinds of passive humidity controlling methods were applied to this exhibition. Massive raw monitoring data are given and some series of data are transformed into specific values to conduct various analyses. Factors including outdoor climate, exhibition hall structure, attendance, showcase structure, tightness and volume, monitor position and object combination modes are discussed in order to assess their effect on monitoring and controlling of temperature and relative humidity. The relation between the average value of temperature (or relative humidity) fluctuation in the whole exhibition period and the corresponding range of maximum temperature (or relative humidity) fluctuation in any 24 h is analyzed in order to obtain the probability of abnormally high fluctuation. The results show that the temperature control target is reached in general, while some unreasonable showcase structures and settings are responsible for the difficulty in monitoring and controlling of relative humidity. By conducting the analysis of big continuous historical data and learning the average values and the ranges reached, it is possible to assess the current environmental risk and predict the future risk in a simple way.

Механизм мониторинга социально-экономического развития региона (на примере Республики Татарстан)

In the current dynamic realities and the difficult to predict impact of economic downturns on the development of regional territories, monitoring changes in the socio-economic context in order to quickly adjust the corresponding strategic directions and tactical actions at the regional level becomes of primary importance. For an in-depth study of the influence of various conditions and elements on the progress of the regional economy, a system of continuous data analysis is used. Within the framework of this article, a description of the criteria, algorithms for their determination and a strategy for highlighting the most important aspects that require active action are given. Modern reality emphasizes the need to create a well-developed system of socio-economic indicators at the level of the state and its administrative-territorial units. The composition of this system is largely determined by the ability to obtain relevant and correct data. The object of the study is a set of indicators of the socio-economic development of the region. The goal is to develop a modern mechanism for monitoring the socio-economic development of the Republic of Tatarstan. Research methods - documentary method, analysis of statistical indicators, system analysis, economic and mathematical modeling, tabular method. The scientific novelty lies in the systematization of approaches to monitoring the socio-economic development of the region and the development of a modern mechanism for monitoring the socio-economic development of the Republic of Tatarstan.

Atrial Fibrillation Detection Through ML Approach: A Comparitive Study

Atrial Fibrillation (AF) is a common “cardiac arrhythmia” with significant health implications. Traditional AF detection methods have limitations in continuous monitoring and data analysis. The emergence of machine learning (ML) offers promising solutions for accurate and timely AF detection. This study aims to explore and evaluate various ML techniques for AF detection, considering data quality, clinical validation, and algorithm performance. A diverse dataset of ECG signals and patient information is collected and pre-processed for training and testing ML models. The study implements supervised and unsupervised learning algorithms, deep learning (DL) architectures, and ensemble methods to compare their effectiveness in AF detection. Results demonstrate the potential of ML-based AF detection to revolutionize diagnosis and management, leading to improved patient care and healthcare outcomes in cardiology. The results of our comparative study demonstrate that all ML approaches achieved impressive results in detecting AF from ECG signals. “The logistic regression classifier achieved an accuracy of 92.48% and sensitivity of 91.89%. The Naïve Bayes classifier achieved an accuracy of 90.26% and sensitivity of 89.27%. The SVM classifier achieved an accuracy of 93.87% and sensitivity of 92.43%. The Decision tree achieved an accuracy of 93.87% and sensitivity of 90.63%. Finally, the Random Forest model attained an accuracy of 95.8% and sensitivity of 92.88%”.