bluuebunny/crossref_metadata_2025_split huggingface dataset:
Filtering-induced changes of pulse transmit time across different ages: a neglected concern in photoplethysmography-based cuffless blood pressure measurement
Authors: Liao Shangdi , Liu Haipeng , Lin Wan-Hua , Zheng Dingchang , Chen Fei 
Published: July 2023

Background: Pulse transit time (PTT) is a key parameter in cuffless blood pressure measurement based on photoplethysmography (PPG) signals. In wearable PPG sensors, raw PPG signals are filtered, which can change the timing of PPG waveform feature points, leading to inaccurate PTT estimation. There is a lack of comprehensive investigation of filtering-induced PTT changes in subjects with different ages.Objective: This study aimed to quantitatively investigate the effects of aging and PTT definition on the infinite impulse response (IIR) filtering-induced PTT changes.Methods: One hundred healthy subjects in five different ranges of age (i.e., 20–29, 30–39, 40–49, 50–59, and over 60 years old, 20 subjects in each) were recruited. Electrocardiogram (ECG) and PPG signals were recorded simultaneously for 120 s. PTT was calculated from the R wave of ECG and PPG waveform features. Eight PTT definitions were developed from different PPG waveform feature points. The raw PPG signals were preprocessed then further low-pass filtered. The difference between PTTs derived from preprocessed and filtered PPG signals, and the relative difference, were calculated and compared among five age groups and eight PTT definitions using the analysis of variance (ANOVA) or Scheirer–Ray–Hare test with post hoc analysis. Linear regression analysis was used to investigate the relationship between age and filtering-induced PTT changes.Results: Filtering-induced PTT difference and the relative difference were significantly influenced by age and PTT definition (p &lt; 0.001 for both). Aging effect on filtering-induced PTT changes was consecutive with a monotonous trend under all PTT definitions. The age groups with maximum and minimum filtering-induced PTT changes depended on the definition. In all subjects, the PTT defined by maximum peak of PPG had the minimum filtering-induced PTT changes (mean: 16.16 ms and 5.65% for PTT difference and relative difference). The changes of PTT defined by maximum first PPG derivative had the strongest linear relationship with age (R-squared: 0.47 and 0.46 for PTT difference relative difference).Conclusion: The filtering-induced PTT changes are significantly influenced by age and PTT definition. These factors deserve further consideration to improve the accuracy of PPG-based cuffless blood pressure measurement using wearable sensors.
Factors associated with psychiatric and physical comorbidities in bipolar disorder: a nationwide multicenter cross-sectional observational study
Authors: Dragasek Jozef , Minar Michal , Valkovic Peter , Pallayova Maria 
Published: July 2023

BackgroundBipolar disorder (BD) is a chronic and disabling affective disorder with significant morbidity and mortality. Despite the high rate of psychiatric and physical health comorbidity, little is known about the complex interrelationships between clinical features of bipolar illness and comorbid conditions. The present study sought to examine, quantify and characterize the cross-sectional associations of psychiatric and physical comorbidities with selected demographic and clinical characteristics of adults with BD.MethodsA nationwide multicenter cross-sectional observational epidemiological study conducted from October 2015 to March 2017 in Slovakia.ResultsOut of 179 study participants [median age 49 years (interquartile range IQR 38–58); 57.5% females], 22.4% were free of comorbidity, 42.5% had both psychiatric and physical comorbidities, 53.6% at least one psychiatric comorbidity, and 66.5% at least one physical comorbidity. The most prevalent were the essential hypertension (33.5%), various psychoactive substance-related disorders (21.2%), specific personality disorders (14.6%), obesity (14.5%), and disorders of lipoprotein metabolism (14%). The presence of an at least one physical comorbidity, atypical symptoms of BD, and unemployed status were each associated with an at least one psychiatric comorbidity independent of sex, early onset of BD (age of onset &lt;35 years), BD duration and pattern of BD illness progression (p &lt; 0.001). The presence of various psychoactive substance-related disorders, BD duration, atypical symptoms of BD, unemployed status, pension, female sex, and not using antipsychotics were each associated with an at least one physical comorbidity independent of the pattern of BD illness progression (p &lt; 0.001). In several other multiple regression models, the use of antipsychotics (in particular, olanzapine) was associated with a decreased probability of the essential hypertension and predicted the clinical phenotype of comorbidity-free BD (p &lt; 0.05).ConclusionThis cross-national study has reported novel estimates and clinical correlates related to both the comorbidity-free phenotype and the factors associated with psychiatric and physical comorbidities in adults with BD in Slovakia. The findings provide new insights into understanding of the clinical presentation of BD that can inform clinical practice and further research to continue to investigate potential mechanisms of BD adverse outcomes and disease complications onset.
Ebola vaccine uptake and attitudes among healthcare workers in North Kivu, Democratic Republic of the Congo, 2021
Authors: Doshi Reena H. , Garbern Stephanie C. , Kulkarni Shibani , Perera Shiromi M. , Fleming Monica K. , Muhayangabo Rigobert Fraterne , Ombeni Arsene Balene , Tchoualeu Dieula Delissaint , Kallay Ruth , Song Elizabeth , Powell Jasmine , Gainey Monique , Glenn Bailey , Mutumwa Ruffin Mitume , Hans Bateyi Mustafa Stephane , Earle-Richardson Giulia , Gao Hongjiang , Abad Neetu , Soke Gnakub Norbert , Fitter David L. , Hyde Terri B. , Prybylski Dimitri , Levine Adam C. , Jalloh Mohamed F. , Mbong Eta Ngole 
Published: July 2023

IntroductionDuring the 2018–2020 Ebola virus disease (EVD) outbreak in the eastern part of the Democratic Republic of the Congo (DRC), prevention and control measures, such as Ebola vaccination were challenging by community mistrust. We aimed to understand perceptions regarding Ebola vaccination and identify determinants of Ebola vaccine uptake among HCWs.MethodsIn March 2021, we conducted a cross-sectional survey among 438 HCWs from 100 randomly selected health facilities in three health zones (Butembo, Beni, Mabalako) affected by the 10th EVD outbreak in North Kivu, DRC. HCWs were eligible if they were ≥ 18 years and were working in a health facility during the outbreak. We used survey logistic regression to assess correlates of first-offer uptake (i.e., having received the vaccine the first time it was offered vs. after subsequent offers).ResultsOf the 438 HCWs enrolled in the study, 420 (95.8%) reported that they were eligible and offered an Ebola vaccine. Among those offered vaccination, self-reported uptake of the Ebola vaccine was 99.0% (95% confidence interval (CI) [98.5–99.4]), but first-offer uptake was 70.2% (95% CI [67.1, 73.5]). Nearly all HCWs (94.3%; 95% CI [92.7–95.5]) perceived themselves to be at risk of contracting EVD. The most common concern was that the vaccine would cause side effects (65.7%; 95% CI [61.4–69.7]). In the multivariable analysis, mistrust of the vaccine source or how the vaccine was produced decreased the odds of first-time uptake.DiscussionOverall uptake of the Ebola vaccine was high among HCWs, but uptake at the first offer was substantially lower, which was associated with mistrust of the vaccine source. Future Ebola vaccination efforts should plan to make repeated vaccination offers to HCWs and address their underlying mistrust in the vaccines, which can, in turn, improve community uptake.
A qualitative study of the reasons for delayed medical treatment in adolescents with depression based on the health ecology model
Authors: Zhao Yuan , Li Yuling , Zhao Juan 
Published: July 2023

BackgroundDepression is a prevalent mental health issue among adolescents, and timely treatment can prevent it from worsening. However, many adolescent depressed patients delay seeking medical treatment. To clarify the reasons for delayed medical treatment in adolescent depressed patients and to provide reference to help adolescent depressed patients to seek medical treatment on time.MethodsFrom July 2022 to March 2023, a semi-structured interview was conducted using a purposive sampling method with adolescent depressed patients attending the Mental Health Center of the First Hospital of Shanxi Medical University. Based on inclusion and exclusion criteria, 22 adolescent depression inpatients with delayed access to medical care were finally included as the study population. This study applied the phenomenological research method in qualitative research and used the Colaizzi seven-step data analysis method to analyze and refine the interview data.ResultsThe study found that 22 adolescents with depression delayed medical care for 1–6 years, with a mean of 2.86 years. Six themes were identified as the reasons for delayed medical treatment: (1) inadequate levels of mental health literacy; (2) lack of disease-related knowledge and information; (3) negative emotional experiences; (4) negative attitudes toward health care; (5) inadequate medical insurance policy for psychotherapy; (6) unequal distribution of resources for mental health medical services.ConclusionThe phenomenon of delayed medical treatment for adolescent depression patients should not be ignored. Efforts should be made to explore the trajectory of delayed illness in adolescents with depression, improve patient symptom assessment, improve poor patient experience, promote positive patient behavior, and improve the current situation of delayed medical treatment for adolescent depression patients through the joint efforts of individuals, families, schools, and society.
A Comprehensive study on Review of literatures on Tuberculosis
Authors: Kanta B Laxmi , Subburaman C 
Published: July 2023

Background of the study: Tuberculosis is a contagious illness caused by bacteria (Mycobacterium tuberculosis) that mostly affects the lungs. When an infected individual coughs or sneezes, it can spread via the air. Tuberculosis imposes an economic cost not only on the afflicted person, but also on his whole family and entire nations due to lost output during the ill period. Every year, ten million individuals have tuberculosis, resulting in a significant loss in the nation's per capita revenue. Objectives of the study: A greater number of articles by diverse writers demonstrate their interest in researching one economic stage of TB. Few authors have conducted reviews of the literature on the economic morbidity of TB in recent decades; however, given the economic elements in recent times, a more complete assessment is performed here to identify the Research Gap. Findings: The researcher reviewed the literature on economic morbidity of tuberculosis in this paper in order to assist other researchers, academicians, policymakers, and practitioners in taking a closer look at the recent trends and growths, statistical methods, financial gaps, and achievement rates in this studies. Conclusion: The author has examined various papers and proposed a comprehensive summary of various studies conducted in India and abroad, identifying certain recommendations and research gaps, as well as methods that would provide hints for future research in the area of Health Economics under the headings of tuberculosis.
On Shape, Orientation, and Structure of Atmospheric Cells Inside Wind Rolls in Two SAR Images
Authors: zecchetto stefano , De Biasio Francesco 
Published: July 2023

<p>Synthetic aperture radar (SAR) images of the sea surface often show roll-vortex structures and other features which, in general, are spread out over several length scales and may present spatial periodicity as well as intermittence. Standard techniques, such as two-dimensional (2-D) Fourier analysis, are unsuitable both when it is of interest to detect intermittent phenomena and to analyze the spatial disposition of the backscatter structures inside the SAR images. For the above reasons, the 2-D continuous wavelet transform analysis has been applied to two European Remote Sensing mission SAR images over the Mediterranean Sea, showing wind rolls and atmospheric gravity waves. Through the evaluation of the wavelet variance map, which ideally corresponds to the 2-D Fourier spectrum, it has been possible to assess the presence of two main energy areas at large (from 7–28 km) and small (from 0.5–2 km) spatial scales. While the large-scale fluctuations may be ascribed to atmospheric gravity waves and other features induced by the surroundings, the small-scale fluctuations reveal the inner structure of the atmospheric wind rolls. The SAR-like maps, obtained by adding the wavelet coefficient maps pertaining to the small scales, have permitted to highlight the high- and low-intensity backscatter cells associated with the wind rolls. These cells have been statistically characterized by means of the frequency distributions of the size of the cells maximum and minimum axes, of the orientation of the maximum axis, and of their area. The results indicate that high- and low-intensity backscatter cells have similar characteristics in both cases studied: they appear of elliptic shape, with the major axis along the wind roll direction; the average axes ratio is 2.5:1. The frequency distributions of the cells area indicate a continuous distribution of sizes, without significant gaps.</p>
Characteristic-based Design of an LCC Resonant Converter with a Capacitive Filter
Authors: Sengupta Arkadeb , Kundu Utsab , John Vinod 
Published: July 2023

<p> The <u>LCC</u> resonant converter with a <u>capacitive</u> output filter is a popular topology for variable load applications, though the simultaneous design requirements of wide load range, soft switching, and low conduction loss are not clearly tackled in available design strategies. In this paper, the existing time-domain analysis of the converter is extended in a normalized design-oriented fashion. The proposed extension to boundary modes lends itself to the derivation of critical converter characteristics such as the soft switching range. A dimensionless converter and load characteristic-based design algorithm with parameter decoupling is developed to meet the load requirements. The design algorithm reduces converter rms current while meeting practical requirements of zero voltage switching and constraints on switching frequency across the load range. The proposed design method is validated using circuit simulations incorporating series <u>dissipative</u> non-<u>idealities</u> and the transformer magnetizing inductance, and also on a 160 W laboratory prototype with switching frequency within the designated limits and $>$96$\%$ experimental efficiency.</p>
AI-powered Badminton Video Detection: Enhancing Gameplay Analysis and Training
Authors: Cheng Kevin , Kim Jay 
Published: July 2023

<p>This research focuses on the use of AI-powered badminton video analysis to enhance gameplay analysis and training. The technology utilizes artificial intelligence and machine learning algorithms to analyze varJaccard Similarity Index aspects of badminton game footage, including player movements, shot selection, and game strategy. It provides personalized feedback and recommendations for improvement to players and helps coaches identify patterns and trends in their players' performance.</p>
<p>The use of lightweight models, such as YoloV5, is essential for real-time video analysis due to the need for high-speed processing. These models, including MobileNets, EfficientNets, SqueezeNet, ShuffleNet, and Tiny-YOLO, are designed to be lightweight and optimized for speed while maintaining high accuracy.</p>
<p>The Yolo model, a one-shot learning model, is particularly suitable for real-time object detection tasks due to its impressive speed and accuracy. It uses anchor boxes and data augmentation techniques to quickly learn and recognize objects with a high level of accuracy.</p>
<p>The research process involved the collection of badminton match videos, preparation of datasets, and testing of the YoloV5 model's performance. Precision and Jaccard Similarity Index metrics using Jaccard similarity were used to evaluate the model's performance in detecting player positions and court boundaries.</p>
<p>The results showed that the YoloV5 model performed better when tested on the video data with less clear image of judges in the background. The Jaccard Similarity Index metrics using Jaccard similarity demonstrated improved performance when tested on generalized video data was used to accurately evaluate the overlap between predicted and ground truth</p>
<p>Overall, AI-powered badminton video analysis has the potential to revolutionize the way badminton is played and coached. The use of lightweight models like YoloV5 enables faster and more efficient real-time analysis, making it practical for a wide range of applications in badminton.</p>
Spatio-Temporal Associative Memories in Brain-inspired Spiking Neural Networks: Concepts and Perspectives
Authors: Kasabov Nikola 
Published: July 2023

<p>The paper introduces the main principles of spatio-temporal associative memory (STAM) inspired by learning principles in the human brain and implemented on a brain -inspired spiking neural network (SNN) architecture. A STAM is a machine learning model that is trained on a full set of spatio-temporal variables, but can be successfully recalled on only a subset of the variables measured in different time intervals. In addition, a STAM model can be further incrementally trained on a new subset of variables measured at varying times. STAM is based on spatio-temporal learning (STL), where existing spatial or other relevant information from the data is used to structure a SNN model and then temporal information is used to train the model. The model captures evolvable and explainable spatio/specro temporal patterns. A STAM model is validated through the introduced association- and generalization accuracy. Departing from traditional deep neural networks and machine learning methods, where trained models can only be recalled or incrementally trained on the same set of variables using vector-based representation, STAM opens the field of machine intelligence for the development of large-scale global spatio-temporal models that can be recalled and used locally, within the available local spatio-temporal data. Possible applications for STAM include: biological and brain signals; audio-visual data; seismic sensory data; financial and economic data; and other. </p>
Is ChatGPT capable of solving classical Communications and Networking problems?
Authors: Hernandez Jose Alberto , Conde Javier , Reviriego Pedro , Martínez Ruiz de Arcaute Gonzalo 
Published: July 2023

<p>This article overviews OpenAI’s ChatGPT and its capability at solving classical communications and networking problems, ranging from physical layer, medium access control, queueing theory and TCP/IP. We asked ChatGPT v3.5 to solve ten classical problems in this field of science and engineering and we observed that, although ChatGPT knows the theoretical background and sounds convincing, it fails to give the final result due to intermediate mistakes in eight cases. This may be dangerous for students using ChatGPT v3.5 as an assistant in their studies of Computer Science and Engineering and should be therefore used carefully. </p>
Surface Virus Inactivation by Non-Thermal Plasma Flow Reactor
Authors: Tang Anthony , Ong Angelo , Beck Nicola , Meschke John S. , Novosselov Igor V. 
Published: July 2023

<p>Infectious viruses can survive on surfaces for hours; they can be responsible for the fomite transmission mechanism of many diseases. We report the development and evaluation of a compact non-thermal plasma (NTP) flow reactor and its application for disinfection of surfaces. The concentric flow reactor produces a high concentration of reactive species by ionizing air flow passed through the reactor; the ionized jet exiting the reactor is impinged on the surface, exposing pathogens to high concentrations of reactive oxygen and nitrogen species. The reactor performance is characterized by measuring ozone concentration for various geometries and operating conditions. The ozone concentration reaches saturation at the exit for electrical power input above 100 W. The approach was tested for the inactivation of MS2 bacteriophage on surfaces with varied exposure times. We observed a 1.3 log reduction in viable virus counts at the exposure of 30 s and a 5.3 log reduction at 120 s exposure. The ozone production rates suggest that the reactor can be scaled to operate at higher flow rates, producing a concentration of reactive species sufficient for virus inactivation. </p>
Scaling Hosting Capacity Analysis for Large Electrical Distribution Systems
Authors: Pizzimbone Luca 
Published: July 2023

<p>The rapid growth of renewable generation, driven by increasing concerns about climate change, sustainable energy sources, and energy independence, has presented significant challenges for distribution system operators (DSOs). Integrating intermittent sources while ensuring grid stability and reliability demands a robust evaluation of hosting capacity (HC) in power distribution networks. However, prevailing HC analysis methodologies predominantly rely on conservative assumptions or worst-case scenarios, often leading to impractical and unreliable outcomes. Commercial tools, though offer readily available solutions for hosting capacity analysis, suffer from limitations as they typically assess HC of individual nodes independently or focus on only a few distributed scenarios.</p>
<p>More elaborate methods have been proposed, attempting to cope with the complexity and stochastic nature of the HC problem. However, no much consideration has been given to their scalability to large distribution systems.</p>
<p>In response to the complexity and stochastic nature of the HC problem, this paper introduces an efficient methodology based on stochastic power system simulations and statistical analysis. The proposed approach undergoes comprehensive assessments to substantiate its efficacy, with a key focus on validating its accuracy for reliable HC estimates. Utilizing the bootstrap method, a resampling technique, multiple samples are generated to mimic possible Distributed Energy Resources (DERs) deployments, enabling estimation of confidence intervals for HC metrics. </p>
<p>By applying statistical analysis to power system simulation results, insights into the expected variability and uncertainty of the problem are gained. These insights guide the selection of the minimum number of DER deployment scenarios necessary to ensure an efficient and well-informed HC assessment process.</p>
<p>The proposed methodology effectively addresses the challenges of HC analysis, offering scalability to large distribution networks. Its efficiency and enhanced accuracy make it a valuable tool for DSOs in facilitating the integration of renewable generation in a dependable and practical manner.</p>