bluuebunny/crossref_metadata_2025_split huggingface dataset:
Examining the challenges of blood pressure estimation via photoplethysmogram
Authors: Mehta Suril , Kwatra Nipun , Jain Mohit , McDuff Daniel 
Published: August 2024

AbstractThe use of observed wearable sensor data (e.g., photoplethysmograms [PPG]) to infer health measures (e.g., glucose level or blood pressure) is a very active area of research. Such technology can have a significant impact on health screening, chronic disease management and remote monitoring. A common approach is to collect sensor data and corresponding labels from a clinical grade device (e.g., blood pressure cuff) and train deep learning models to map one to the other. Although well intentioned, this approach often ignores a principled analysis of whether the input sensor data have enough information to predict the desired metric. We analyze the task of predicting blood pressure from PPG pulse wave analysis. Our review of the prior work reveals that many papers fall prey to data leakage and unrealistic constraints on the task and preprocessing steps. We propose a set of tools to help determine if the input signal in question (e.g., PPG) is indeed a good predictor of the desired label (e.g., blood pressure). Using our proposed tools, we found that blood pressure prediction using PPG has a high multi-valued mapping factor of 33.2% and low mutual information of 9.8%. In comparison, heart rate prediction using PPG, a well-established task, has a very low multi-valued mapping factor of 0.75% and high mutual information of 87.7%. We argue that these results provide a more realistic representation of the current progress toward the goal of wearable blood pressure measurement via PPG pulse wave analysis. For code, see our project page: https://github.com/lirus7/PPG-BP-Analysis
Comparison of psychopathology, purpose in life and moral courage between nursing home and hospital healthcare workers during the COVID-19 pandemic
Authors: Echeverria Iván , Bonet Lucía , Benito Ana , López Javier , Almodóvar-Fernández Isabel , Peraire Marc , Haro Gonzalo 
Published: August 2024

AbstractThe COVID-19 pandemic deeply affected healthcare workers, although the impact may have differed according to different workplace contexts. The aim of this current research was to compare the psychopathology presented by hospital versus nursing home healthcare workers during the COVID-19 pandemic and to analyse the predictive role of purpose in life and moral courage in the appearance of psychopathology. This was an observational, cross-sectional study carried out on a sample of 108 healthcare workers, 54 each from a hospital or nursing homes, who were recruited during the 5 and 6th waves of the COVID-19 pandemic in Spain. Various self-reported scales were used to assess anxiety, depression, acute/post-traumatic stress disorder, drug and alcohol abuse, burnout, purpose in life, and moral courage. Compared to the hospital healthcare workers, nursing home healthcare workers had higher scores and a higher prevalence of anxiety (74.1% vs. 42%), depression (40.7% vs. 14.8%), and post-traumatic stress disorder (55.6% vs. 25.9). In the overall sample, purpose in life was a protective factor against psychopathology (OR = 0.54) and burnout (OR = 0.48); moral courage was a protective factor against depression (OR = 0.47) and acute stress (OR = 0.45); and exposure of family/friends to SARS-CoV-2 was a risk factor for acute stress (OR = 2.24), post-traumatic stress disorder (OR = 1.33), and higher burnout depersonalisation subscale scores (OR = 1.84). In conclusion, the increased presence of psychopathology in nursing home healthcare workers may be influenced by workplace and occupational contexts, personal factors such as exposure of family/friends to SARS-CoV-2, or internal dimensions such as purpose in life and moral courage. This knowledge could be useful for understanding how a future epidemic or pandemic might affect the mental health of healthcare workers in different labour contexts.
H-plane gap-RGW horn antenna with very low side lobe level
Authors: Mohammadpour Mohammad , Mohajeri Farzad , Parizi Seyed Ali Razavi 
Published: August 2024

AbstractThis paper presents and applies a new concept of gap-RGW to develop a new configuration for RGW H-plane horn antennas. The proposed antenna is fully metallic and can generate a very low side-lobe level along with a flat gain response over a reasonable bandwidth, which is one of the advantages of this antenna. A prototype of the presented structure is designed and fabricated which provides the impedance bandwidth (S11 < − 10 dB) of about 25% with SLL < − 20 dB and 2 dB gain variation over the operating bandwidth.
Both partners’ negative emotion drives aggression during couples’ conflict
Authors: McCurry Annah G. , May Robert C. , Donaldson David I. 
Published: August 2024

AbstractResearchers examining conflict between intimate partners believe that the experience and expression of emotion drives aggressive behaviour. Intra-personally, increases in negative affect make aggression more likely. Inter-personally, theoretical models suggest that each individuals’ perception of their partners’ emotion also influences aggression, potentially creating a Violence Escalation Cycle. Here, using a lab-based aggression task across a primary study (n = 104, number of trials = 3095) and a replication (n = 58, number of trials = 3167), we show that both intra- and inter-personal experiences of negative emotion predict reactive aggression within couples, revealing retaliation but not escalation. Critically, analyses of facial affect reveal that prototypic displays of negative emotions have a compounding effect, leading to dramatic changes in aggression depending on whether one, both, or neither partner expressed negative emotion. We propose a mechanism by which temporal delays (i.e., experimentally imposed forced breaks) reduce aggression by decreasing negative emotional arousal and limiting impulsive action. Our results show that both forced breaks and elective breaks (i.e., extra participant-initiated extensions of the forced break time) reduce aggression, providing exciting evidence that interventions focused on preventing impulsive action when people are in a provoked state can reduce aggression within couples.
RNA helicase SKIV2L limits antiviral defense and autoinflammation elicited by the OAS-RNase L pathway
Authors: Yang Kun , Dong Beihua , Asthana Abhishek , Silverman Robert H , Yan Nan 
Published: August 2024

AbstractThe OAS-RNase L pathway is one of the oldest innate RNA sensing pathways that leads to interferon (IFN) signaling and cell death. OAS recognizes viral RNA and then activates RNase L, which subsequently cleaves both cellular and viral RNA, creating “processed RNA” as an endogenous ligand that further triggers RIG-I-like receptor signaling. However, the IFN response and antiviral activity of the OAS-RNase L pathway are weak compared to other RNA-sensing pathways. Here, we discover that the SKIV2L RNA exosome limits the antiviral capacity of the OAS-RNase L pathway. SKIV2L-deficient cells exhibit remarkably increased interferon responses to RNase L-processed RNA, resulting in heightened antiviral activity. The helicase activity of SKIV2L is indispensable for this function, acting downstream of RNase L. SKIV2L depletion increases the antiviral capacity of OAS-RNase L against RNA virus infection. Furthermore, SKIV2L loss exacerbates autoinflammation caused by human OAS1 gain-of-function mutations. Taken together, our results identify SKIV2L as a critical barrier to OAS-RNase L-mediated antiviral immunity that could be therapeutically targeted to enhance the activity of a basic antiviral pathway.
Statistical Learning Among Preschoolers With and Without Developmental Language Disorder: Examining Effects of Language Status, Age, and Prior Learning
Authors: Kapa Leah L. , Mettler Heidi M. 
Published: August 2024

Purpose:
Our goal was to compare statistical learning abilities between preschoolers with developmental language disorder (DLD) and peers with typical development (TD) by assessing their learning of two artificial grammars.

Method:

Four- and 5-year-olds with and without DLD were compared on their statistical learning ability using two artificial grammars. After learning an
aX
grammar, participants learned a relatively more complex
abX
grammar with a nonadjacent relationship between
a
and
X
. Participants were tested on their generalization of the grammatical pattern to new sequences with novel
X
elements that conformed to (
aX, abX
) or violated (
Xa, baX
) the grammars.

Results:

Results revealed an interaction between age and language group. Four-year-olds with and without DLD performed equivalently on the
aX
and
abX
grammar tests, and neither of the 4-year-old groups' accuracy scores exceeded chance. In contrast, among 5-year-olds, TD participants scored significantly higher on
aX
tests compared to participants with DLD, but the groups'
abX
scores did not differ. Five-year-old participants with DLD did not exceed chance on any test, whereas 5-year-old TD participants' scores exceeded chance on all grammar learning outcomes. Regression analyses indicated that
aX
performance positively predicted learning outcomes on the subsequent
abX
grammar for TD participants.

Conclusion:
These results indicate that preschool-age participants with DLD show deficits relative to typical peers in statistical learning, but group differences vary with participant age and type of grammatical structure being tested.

Supplemental Material:

https://doi.org/10.23641/asha.26487376
Application of symmetric uncertainty and emperor penguin–grey wolf optimisation for feature selection in motor fault classification
Authors: Lee Chun‐Yao , Le Truong‐An , Chien Wei‐Lun , Hsu Shih‐Che 
Published: August 2024

AbstractThe authors present a model for diagnosing motor faults based on machine learning, demonstrating advantages over other algorithms in terms of both improved fitness values and reduced running time. The structure of the model involves three primary phases: feature extraction, feature selection and classification. During the feature extraction phase, crucial features are identified using empirical mode decomposition, fast Fourier transform and multiresolution analysis, resulting in a total of 144 features. The feature selection stage employs a new strategy that combines symmetrical uncertainty in the filter approach with the binary grey wolf optimiser and emperor penguin optimiser in the wrapper approach. Finally, a support vector machine is used for classification to generate fitness values. To validate the model's effectiveness and accuracy, motor fault current signal datasets, case Western Reserve University (CWRU) benchmark datasets and mechanical failure prevention technology benchmark datasets are utilised. In the motor fault current signal dataset, the highest average accuracy achieved is 99.95%, with a minimum average running time of 88.02 s obtained under ∞dB conditions. Regarding benchmark datasets and mechanical failures at CWRU, using the prevention technology benchmark dataset resulted in classification accuracies of 99.54% and 99.52%, respectively. Comparative analysis with traditional algorithms reveals that symmetric uncertainty and emperor penguin–grey wolf optimisation model outperforms traditional models in terms of performance.
Peer‐to‐peer electricity trading via a bi‐level optimization approach for renewable energy‐driven microgrids connected to the distribution grid
Authors: Helmi Hamid , Abedinzadeh Taher , Beiza Jamal , Shahmohammadi Sima , Daghigh Ali 
Published: August 2024

AbstractThis study employs a sophisticated bi‐level optimization methodology to model the most efficient operation of microgrids (MGs) within the operational framework of distribution companies (DCs). In this bi‐level optimization problem, the upper level strives to maximize the profits of both MGs owners and DCs, while the lower level is dedicated to ensuring load balance, managing distributed generation, and implementing load curtailment strategies. The coordination of power transmission is facilitated by the DCs. At the upper level of decision‐making, the optimal pricing strategies for power transactions are determined, accounting for various factors such as market prices, demand response programs, and uncertainties in wind speed. Through the utilization of a bi‐level optimization framework, this study comprehensively captures the complex interactions between MGs and DCs, taking into consideration the objectives and constraints of both entities. This approach offers a more precise representation of the decision‐making process in retail electricity markets, thereby providing valuable insights into the optimal operation of MGs within the DCs setting.
Dynamic load shedding and system restoration using wide area distribution management system
Authors: Jose Shirley Jennie Angela , Manivelan Harini , Khare Prashant , Reddy Maddikara Jaya Bharata 
Published: August 2024

AbstractThe demand for electric power has consistently been on the rise, owing to urbanisation and technological improvements. On the generation side, renewable sources have been favoured over their polluting, exhaustible, non‐renewable counterparts. These changes in the power system have necessitated a system for maintaining the supply‐demand balance, to maintain system stability. A complex power system is also more prone to blackouts and grid failure. Islanding helps in provision of supply to consumers in a microgrid, reducing the possibility of a blackout. Depending on the power demand and generation, loads need to be shed or restored to mitigate power imbalances. A wide area distribution management system (WADMS) is proposed to dynamically shed and restore loads in the islanded mode, with the aid of micro phasor measurement units (µPMUs). A priority and consumer‐based load shedding and restoration (PCLS) algorithm is proposed in the WADMS that preferentially sheds or restores loads based on their assigned load priority indices and number of consumers. The algorithm has been tested on a modified IEEE 13 bus system, incorporated with a solar photovoltaic (PV) system, diesel generators (DGs) and an energy storage system (ESS) in MATLAB Simulink.
Driving a safer future: Exploring cross‐country perspectives in automated vehicle adoption by considering cyber risks, liability, and data concerns
Authors: Khan Shah Khalid , Shiwakoti Nirajan , Stasinopoulos Peter , Warren Matthew 
Published: August 2024

AbstractThere is a significant lack of comprehensive research that systematically examines public perceptions of liability (related to cyber risks), consumer data, and how these factors influence the adoption of automated vehicles (AVs). To fill this knowledge gap, the authors' research used a survey of 2062 adults across Australia, New Zealand, the UK, and the US to develop a scale for Liability, Data concerns, Data sharing and Patching and updates. This analytical approach employed various statistical methods to analyze the data (summarizing, finding patterns, measuring relationships). The results indicate that 70% of respondents express concerns about AV liability based on cyber risks, highlighting a significant level of liability anxiety. Individuals with high liability concerns also exhibit heightened concerns about AV data, are less comfortable sharing AV data, and display lower intent to adopt AVs. Conversely, individuals comfortable with data sharing are more willing to engage in patching and express a greater intent to adopt AVs. Interestingly, individuals with AV data concerns do not exhibit a negative correlation with their intent to adopt AVs. Additionally, those willing for patches also show a stronger intent to adopt AVs, challenging the notion that software updates hinder AV adoption.
Orthogonal double V‐linear frequency modulation waveform for simultaneous polarimetric measurement and its non‐linear processing
Authors: Shen Biao , Zhang Runzhe , Yang Ziyuan , Liu Tao 
Published: August 2024

AbstractIt has become an interesting topic for modern radar possessing capabilities of polarimetric measurement, high‐resolution imaging, and good delay‐Doppler resolution. The linear frequency modulation (LFM) scheme is widely used in radar systems as it can achieve both high Doppler tolerance and resolution. Based on the LFM scheme, a novel waveform is proposed, namely, the orthogonal double V‐linear frequency modulation (ODV‐LFM), which exhibits the capability to mitigate the influence of delay‐Doppler coupling and reduces the risk of false targets and ghosts in multiple‐target scenes. Based on the ODV‐LFM signal, a simultaneous polarimetric measurement method is proposed to obtain fully polarimetric information of the target. According to the unique chirp rate characteristic of the ODV‐LFM signal, the pointwise linear and non‐linear processing methods are proposed to enhance delay‐Doppler resolution, suppress sidelobes, and achieve high‐resolution imaging without substantially compromising the signal detection performance. Finally, simulated experiments are conducted to demonstrate the superiority of the proposed waveform and methods.
A new code for low-resolution spectral identification of white dwarf binary candidates
Authors: Liu Genghao , Tang Baitian , Ren Liangliang , Li Chengyuan , Cheng Sihao , Zong Weikai , Fu Jianning , Ma Bo , Xu Cheng , Hu Yiming 
Published: August 2024

Context. Close white dwarf binaries (CWDBs) are considered to be progenitors of several exotic astronomical phenomena (e.g., type Ia supernovae, cataclysmic variables). These violent events are broadly used in studies of general relativity and cosmology. However, obtaining precise stellar parameter measurements for both components of CWDBs is a challenging task given their low luminosities, swift time variation, and complex orbits. High-resolution spectra (R > 20 000) are preferred but expensive, resulting in a sample size that is insufficient for robust population study. Recently, studies have shown that the more accessible low-resolution (R ~ 2000) spectra (LRS) may also provide enough information for spectral decomposition. To release the full potential of the less expensive low-resolution spectroscopic surveys, and thus greatly expand the CWDB sample size, it is necessary to develop a robust pipeline for spectra decomposition and analysis.
Aims. We aim to develop a spectroscopic fitting program for white dwarf binary systems based on photometry, LRS, and stellar evolutionary models. The outputs include stellar parameters of both companions in the binary including effective temperature, surface gravity, mass, radius, and metallicity in the case of MS stars.
Methods. We used an artificial neural network (ANN) to build spectrum generators for DA/DB white dwarfs and main-sequence stars. Characteristic spectral lines were used to decompose the spectrum of each component. The best-fit stellar parameters were obtained by finding the least χ2 solution to these feature lines and the continuum simultaneously. Compared to previous studies, our code is innovative in the following aspects: (1) implementing a sophisticated binary decomposition technique in LRS for the first time; (2) using flux-calibrated spectra instead of photometry plus spectral lines, in which the latter requires multi-epoch observations; (3) applying an ANN in binary decomposition, which significantly improves the efficiency and accuracy of generated spectra.
Results. We demonstrate the reliability of our code with two well-studied CWDBs, WD 1534+503 and PG 1224+309. We also estimate the stellar parameters of 14 newly identified CWDB candidates, most of which are fitted with double component models for the first time. Our estimates agree with previous results for the common stars and follow the statistical distribution in the literature.
Conclusions. We provide a robust program for fitting binary spectra of various resolutions. Its application to a large volume of white dwarf binary candidates will offer important statistic samples to stellar evolution studies and future gravitational wave monitoring.
Das schwere Thoraxtrauma: Indikationen und Kontraindikationen für operative und nicht operative Versorgungsstrategien
Authors: Spering Christopher , Lehmann Wolfgang 
Published: August 2024

ZusammenfassungDas schwere Thoraxtrauma kann einerseits als penetrierende oder stumpfe Monoverletzung oder im Rahmen einer Polytraumatisierung auftreten. Bei der Hälfte der im TraumaRegister DGU dokumentierten Schwerverletzten ist ein schweres Thoraxtrauma, definiert nach Abbreviated Injury Scale (AIS) mit ≥ 3, diagnostiziert worden. Im eigenen Kollektiv betrug der Anteil 60%. Die Notfallthorakotomie im Schockraum oder innerhalb der 1. Stunde nach Aufnahme ist in Deutschland mit 0,9% der schweren Thoraxverletzungen eine äußerst seltene Intervention. Das differenzierte Management von komplexen Polytraumata und umfangreichen pathophysiologischen Reaktionen auf die Traumatisierung haben ebenso wie das Wissen über die Entstehung von Sekundärschaden dazu geführt, dass durch minimalinvasive Verfahren wie videoassistierte Thorakoskopien (VATS) oder das Einlegen einer Thoraxdrainage der Großteil der schweren Thoraxverletzungen erfolgreich therapiert werden kann. Dabei sind penetrierende
Verletzungen des Thorax mit < 4% eine Seltenheit. Mit steigender Tendenz weisen unter den stumpfen Thoraxverletzungen > 6% eine instabile Thoraxwand auf, die einer operativen Rekonstruktion bedarf. Die demografische Entwicklung in Deutschland führt dazu, dass zunehmend auch Niedrigenergietraumata zu höhergradigen Thoraxwandverletzungen mit penetrierenden Rippenfrakturen in Pleura, Lunge, Peri- bzw. Myokard und Zwerchfell führen. Eine Thoraxwandinstabilität mit hochgradiger Einschränkung der Atemmechanik kann zu fulminanten Pneumonien bis hin zum ARDS (Acute Respiratory Distress Syndrome) führen. In der vergangenen Dekade hat vor diesem Hintergrund die operative Thoraxwandrekonstruktion stark an Bedeutung gewonnen. Auf der Grundlage der überzeugenden Evidenz ist die klinische Beurteilung des Umfangs und der Schwere des Traumas und der Grad der atemmechanischen Einschränkung die Basis für eine strukturierte Entscheidung über eine nicht operative oder
operativ-rekonstruktive Strategie sowie den OP-Zeitpunkt, die OP-Art und den OP-Umfang. Durch die frühzeitige OP innerhalb von 72 h können Morbidität (Pneumonierate, Intensivliegedauer und Beatmungsdauer) sowie Letalität verringert werden. In dem folgenden Artikel werden evidenzbasierte Algorithmen zur operativen und nicht operativen Versorgungsstrategie von schweren Thoraxverletzungen diskutiert. Dazu wurden in einer selektiven Literaturrecherche die wegweisenden Veröffentlichungen zu Indikationsstellung, Versorgungsstrategie und Therapieempfehlungen der schweren Thoraxverletzung sowie Thoraxwandrekonstruktion eingeschlossen.