Search Results (94)

This paper concerns the experimental validation of optimized near-field (NF) spherical spiral scannings employing a minimum number of samples, when an offset-mounted elongated antenna under test (AUT), i.e., with its center shifted with respect to that of the measurement sphere, is considered. In order to perform the standard NF/far-field transformation (NF/FFT) technique, a non-centered AUT would generally require the collection of a significantly increased amount of voltage data if compared to the onset scenario. This issue is addressed here by using the non-redundant (NR) sampling representations of electromagnetic (EM) fields. These representations, by leveraging the analytical properties of the EM field, allow one to perform the NR NF/FFTs for offset-mounted AUTs by using only a minimum number of (offset acquired) samples, equal to that required by the NR approaches for the onset case (over 85% fewer samples compared to the standard NF spherical scanning). In particular, these NR NF/FFTs are obtained by modeling the AUT with a prolate spheroid or a rounded cylinder and their effectiveness is fully assessed by the reported experimental results. Full article

Reforestation is widely promoted as a nature-based solution for climate change, yet its unintended consequences, such as deforestation leakage, remain under-investigated. This study provides empirical evidence of reforestation-induced leakage in the Brazilian Amazon, using municipality-level panel data from 2000 to 2023 and spatial Durbin panel models to estimate both the magnitude and spatial reach of agricultural displacement. Despite the positive local effects of reforestation projects, we found a significant displacement of deforestation to the vicinity of municipalities. We estimated a statistically significant deforestation leakage effect of approximately 12% from the reforested area, due to the agricultural displacement of cattle ranching activities. Spatial spillovers are strongest within a 150 km radius and within two years after reforestation onset. Sensitivity tests using alternative spatial weight matrices, including distance decay and land rent-weighted specifications, confirm the robustness of these findings. Livestock intensification, proxied by cattle stocking rates, does not significantly mitigate displacement effects, challenging assumptions about land sparing benefits. These results suggest that current carbon market protocols (e.g., Verra, ART-TREES) may improve their leakage analysis to avoid under- or over-estimating net carbon benefits. Incorporating spatial econometric evidence into offset methodologies and reforestation planning can improve climate policy integrity and reduce unintended environmental trade-offs. Full article

The use of antiplatelet agents is essential in percutaneous coronary interventions, both periprocedurally and in the post-interventional period. Procedural antiplatelet therapy, aiming to limit ischemic complications, is mostly administered with oral agents, including aspirin and P2Y12 inhibitors. However, there are several limitations in the use of oral P2Y12 inhibitors, including their difficult administration in patients presenting with cardiogenic shock and their relatively slower onset of action, leaving a significant period of the procedure with a suboptimal antiplatelet effect. These pitfalls could be avoided with the use of cangrelor, the only available intravenous P2Y12 inhibitor, which has a rapid onset and offset antiplatelet effect, as well as a favorable pharmacological profile. The use of cangrelor has been increasing in recent years, with several studies aiming to determine what the optimal patient phenotype to receive such treatment ultimately is and how its use could be adjunctive to oral P2Y12 inhibitors. Therefore, the aim of this review is to provide an overview of the pharmacological profile of cangrelor and an update regarding the clinical evidence supporting its use, as well as to discuss the optimal patient phenotype, related clinical algorithms, and future implications for larger implementation of this agent into everyday clinical practice. Full article

Sleep is an essential part of the recovery process that may be jeopardized during specific contexts across the season. Therefore, this study aimed to examine the impact of key contextual factors—game venue and season period—on sleep in semi-professional, female basketball players. Sleep was monitored in players using wrist-worn activity monitors across the entire regular season. For game venue analyses, nights were categorized as a control, before and after home games, as well as before and after away games. For season period analyses, nights were arranged into evenly distributed four-week blocks as early, middle, and late periods of the regular season. Players slept significantly less on nights before away games (p < 0.05) than on other nights, which was attributed to significantly earlier wake times (p < 0.05). While sleep onset and offset times were significantly later during the middle and later season periods than the early season period (p < 0.05), sleep duration and quality remained consistent across periods. These results suggest players could experience disrupted sleep prior to away games, which has potential implications for performance in upcoming games. Coaches and performance staff may need to consider implementing suitable strategies to safeguard the sleep of their players in these scenarios. Full article

Most research on dialectometry so far primarily focuses on European languages. Within these studies, analyses on the phonetic level predominantly focus on segments. A lack of studies on languages outside of Europe means that the variation in many lesser-studied languages, including tonal languages, is largely unknown. Tonal languages are languages which pitch is used as an indication in the lexical realisations in (at least some) morphemes, and over half of the world’s languages include lexical tones. Despite tones being the inseparable and unneglectable part of the majority of the world’s languages, there is only a handful of quantitative dialectometric studies on the dialectal variation in tonal languages. In this paper, we explore the phonetic and phonological variations in Yue, a lesser-studied tonal language spoken by around 80 million people in Southern China. Using a newly devised tone representation (modified Onset–Contour–Offset) combined with the Levenshtein distance, we explore the patterns of dialectal variation on the tonal level, as well as to what extent tonal variation correlates with segmental variation. Our results show that tones behave rather differently from segments, and thus, we illustrate that studying lesser-studied and tonal languages can contribute immensely to the study of dialect variation in general. Full article

Background/Objectives: Social jetlag, which refers to the misalignment between biological rhythms and social schedule, is linked to an increased risk of metabolic diseases. This cross-sectional study investigated the relationship between social jetlag and workers’ dietary quality. Methods: This secondary data analysis included a sample of workers from the Korea National Health and Nutrition Examination Survey (n = 11,430). Social jetlag was determined by calculating the difference in the sleep midpoint between free days and workdays, based on sleep onset and offset times. The Korean Health Eating Index (KHEI) was calculated based on 24-h recalls, with higher scores indicating superior dietary qualities (range: 0–100). Poor dietary quality was defined as a KHEI below the lowest quartile (<51.6). Linear or logistic regressions were utilized to estimate β or odds ratio (OR), respectively. Results: Among study participants, 12.1% of workers experienced ≥120 min of social jetlag. ≥120 min of social jetlag was associated with a reduced KHEI score compared with 0–59 min (β: −1.23, 95% confidence interval [CI]: −2.16, −0.30). Those with ≥120 min of social jetlag were more likely to have poor dietary quality than those with 0–59 min (OR: 1.27, 95% CI: 1.08, 1.50). Conclusions: Workers experiencing ≥120 min of social jetlag had poorer dietary quality compared with workers with 0–59 min of social jetlag. Therefore, this study suggests that policy efforts are needed to reduce social jetlag among workers in Korea. Full article

Background: Overweight prevalence in Spain reached critical levels before the COVID-19 pandemic, which likely exacerbated this issue. The PESCA (Programa Escolar de Salud Cardio-vascular) program is a multicomponent school-based intervention, launched in 2018 with the aim of tackling this health problem and reducing overweight rates in youth. Objectives: (1) To analyze the efficacy of the PESCA program intervention on body composition, overweight prevalence, physical activity (PA)/sport practice, resting time, and screentime before COVID-19 and (2) to evaluate the impact of COVID-19 and the associated lockdown measures on these parameters in the studied sample. Methods: This longitudinal study included 207 children and adolescents from schools in Madrid (aged 2.82 to 15.84 years; 44.4% girls), with measurements taken at three time points: two before COVID-19 and one after its onset. Overweight prevalence, body fat percentage diagnosis, physical activity, resting time, and screentime were assessed. Cochran’s Q test and repeated-measures ANOVA were used to compare outcomes across the three assessment time points. Results: Overweight prevalence remained stable among children in the PESCA program before COVID-19 (17.87% to 19.81%). However, a significant increase was observed from point 2 to point 3, post-COVID-19 onset (19.81% to 26.57%). Similarly, healthy body composition significantly deteriorated from 63.16% at point 2 to 52.48% at point 3. PA/sport practice prevalence significantly increased until COVID-19 onset (80.19% to 91.22%) but declined thereafter (91.22% to 79.10% from point 2 to point 3). Although the differences were small, resting time significantly decreased post-COVID-19 onset (from 10.18 h at point 2 to 9.96 h at point 3), with no changes in the first period. Non-academic screentime showed a similar pattern: stable before COVID-19 and significantly increased after its onset (1.61 h at point 1; 1.70 h at point 2; 2.29 h at point 3). Conclusions: The PESCA program positively impacted PA/sport practice prevalence and may have provided some protection against overweight and related variables during the pre-COVID period. However, health authorities’ restrictions and lockdown policies during COVID-19 negatively affected the health and lifestyle variables studied, offsetting previous improvements. Full article

Climate change and variability pose significant threats to agricultural production, particularly in regions heavily dependent on rainfed agriculture like Senegal. The problem addressed in this study revolves around the impact of climate-related risks on agricultural yields in the Senegalese Groundnut Basin as a key agricultural region. Daily rainfall, temperatures, and yield over 1991–2020 were used. The data were analyzed using multiple regression, trend analysis, and correlation approaches. The results indicate that the overall seasonal precipitation increases over time (98 mm in the north and 103 mm in the south). However, we found that the south Groundnut Basin has a much slower seasonal precipitation rate than the northern zone. Our results also show that the northern zone exhibits a more consistent and predictable growing season, with onset and offset, in contrast with the southern zone, which shows higher variability. The analysis further reveals that both the northern and southern zones are experiencing a warming trend, with the southern zone showing a more pronounced increase in maximum temperatures (+0.7 °C) than to the northern zone (+0.4 °C). Estimates from the regression analysis revealed that total seasonal precipitation and maximum temperature positively and significantly influence groundnut, millet, and maize yields in the northern and southern zones. All the other weather-related parameters have different influences depending on the zone. These findings highlight the heterogeneous nature of the study area and the significant role climatic factors play in crop yield variability in the Groundnut Basin. Understanding these influences is crucial for developing targeted agricultural strategies and climate adaptation measures to mitigate risks and enhance regional productivity. The study provides valuable insights for policymakers and farmers aiming to improve crop resilience and sustain agricultural outputs amidst changing climatic conditions. Full article

Automatic music transcription (AMT) aims to convert raw audio signals into symbolic music. This is a highly challenging task in the fields of signal processing and artificial intelligence, and it holds significant application value in music information retrieval (MIR). Existing methods based on convolutional neural networks (CNNs) often fall short in capturing the time-frequency characteristics of audio signals and tend to overlook the interdependencies between notes when processing polyphonic piano with multiple simultaneous notes. To address these issues, we propose a dual attention feature extraction and multi-scale graph attention network (DAFE-MSGAT). Specifically, we design a dual attention feature extraction module (DAFE) to enhance the frequency and time-domain features of the audio signal, and we utilize a long short-term memory network (LSTM) to capture the temporal features within the audio signal. We introduce a multi-scale graph attention network (MSGAT), which leverages the various implicit relationships between notes to enhance the interaction between different notes. Experimental results demonstrate that our model achieves high accuracy in detecting the onset and offset of notes on public datasets. In both frame-level and note-level metrics, DAFE-MSGAT achieves performance comparable to the state-of-the-art methods, showcasing exceptional transcription capabilities. Full article

The aim of this contribution is to present a segmentation method for the identification of voluntary movements from inertial data acquired through a single inertial measurement unit placed on the subject’s wrist. Inertial data were recorded from 25 healthy subjects while performing 75 consecutive reach-to-grasp movements. The approach herein presented, called DynAMoS, is based on an adaptive thresholding step on the angular velocity norm, followed by a statistics-based post-processing on the movement duration distribution. Post-processing aims at reducing the number of erroneous transitions in the movement segmentation. We assessed the segmentation quality of this method using a stereophotogrammetric system as the gold standard. Two popular methods already presented in the literature were compared to DynAMoS in terms of the number of movements identified, onset and offset mean absolute errors, and movement duration. Moreover, we analyzed the sub-phase durations of the drinking movement to further characterize the task. The results show that the proposed method performs significantly better than the two state-of-the-art approaches (i.e., percentage of erroneous movements = 3%; onset and offset mean absolute error < 0.08 s), suggesting that DynAMoS could make more effective home monitoring applications for assessing the motion improvements of patients following domicile rehabilitation protocols. Full article

Background/Objectives: Non-vitamin K antagonist oral anticoagulants (NOACs) have demonstrated similar effectiveness and safety profiles to vitamin K antagonists (VKAs) in treating nonvalvular atrial fibrillation (AF). Given their favorable pharmacological profile, including the rapid onset and offset of action, fixed dosing, and predictable pharmacokinetics with a consistent dose-response relationship, reducing the need for frequent blood tests, researchers have investigated the potential of NOACs in patients with AF and valvular heart disease (VHD). Methods: Clinical trials, excluding patients with mechanical prosthetic valves or moderate/severe mitral stenosis, have shown the benefits of NOACs over VKAs in this population. However, there is a need for further research to determine if these findings apply to mechanical valve prostheses and NOACs. Results: Several ongoing randomized controlled trials are underway to provide more definitive evidence regarding NOAC treatment in moderate to severe rheumatic mitral stenosis. Importantly, recent trials that included patients with atrial fibrillation and bioprosthetic valves (also transcatheter heart valves) have provided evidence supporting the safety of NOACs in this specific patient population. Ongoing research aims to clearly define the specific scenarios where NOACs can be safely and effectively prescribed for various types of VHD, including moderate/severe mitral stenosis and mechanical valves. Conclusions: The aim of this review is to accurately identify the specific situations in which NOACs can be prescribed in patients with VHD, with a focus centered on each type of valvulopathy. Full article

Boat acceleration profiles can provide valuable information for coaches and practitioners to make meaningful technical interventions and monitor the determinants of success in rowing. Previous studies have used simple feature detection methods to identify key phases within individual strokes, such as drive onset, drive time, drive offset and stroke time. However, based on skill level, technique or boat class, the hull acceleration profile can differ, making robust feature detection more challenging. The current study’s purpose is to employ the undecimated wavelet transform (UWT) technique to detect individual features in the stroke acceleration profile from a single rowing hull-mounted accelerometer. In this investigation, the temporal and kinematic values obtained using the AdMos^TM sensor in conjunction with the UWT processing approach were strongly correlated with the comparative measures of the Peach™ instrumented oarlock system. The measures for stroke time displayed very strong agreeability between the systems for all boat classes, with ICC values of 0.993, 0.963 and 0.954 for the W8+, W4− and W1x boats, respectively. Similarly, the drive time was also very consistent, with strong to very strong agreeability, producing ICC values of 0.937, 0.901 and 0.881 for the W8+, W4− and W1x boat classes. Further, a Bland–Altman analysis displayed little to no bias between the AdMos^TM-derived and Peach™ measures, indicating that there were no systematic discrepancies between signals. This single-sensor solution could form the basis for a simple, cost-effective and accessible alternative to multi-sensor instrumented systems for the determination of sub-stroke kinematic phases. Full article

Signal detection and classification tasks, especially in the realm of audio, suffer from difficulties in capturing long short-term dependencies and effectively utilizing samples. Firstly, audio signal detection and classification need to classify audio signals and detect their onset and offset times; therefore, obtaining long short-term dependencies is necessary. The methods based on RNNs have high time complexity and dilated convolution-based methods suffer from the “gridding issue” challenge; thus, the HDC-Inception module is proposed to efficiently extract long short-term dependencies. Combining the advantages of the Inception module and a hybrid dilated convolution (HDC) framework, the HDC-Inception module can both alleviate the “gridding issue” and obtain long short-term dependencies. Secondly, datasets have large numbers of silent segments and too many samples for some signal types, which are redundant and less difficult to detect, and, therefore, should not be overly prioritized. Thus, selecting effective samples and guiding the training based on them is of great importance. Inspired by support vector machine (SVM), combining soft margin SVM and cross-entropy loss (CE loss), the soft margin CE loss is proposed. Soft margin CE loss can adaptively select support vectors (effective samples) in datasets and guide training based on the selected samples. To utilize datasets more sufficiently, a hybrid CE loss is proposed. Using the benefits of soft margin CE loss and CE loss, hybrid CE loss guides the training with all samples and gives weight to support vectors. Soft margin CE loss and hybrid CE loss can be extended to most classification tasks and offer a wide range of applications and great potential. Full article

The aim of this study is to address the challenge of 12-lead ECG delineation by different encoder–decoder architectures of deep neural networks (DNNs). This study compares four concepts for encoder–decoders based on a fully convolutional architecture (CED-Net) and its modifications with a recurrent layer (CED-LSTM-Net), residual connections between symmetrical encoder and decoder feature maps (CED-U-Net), and sequential residual blocks (CED-Res-Net). All DNNs transform 12-lead representative beats to three diagnostic ECG intervals (P-wave, QRS-complex, QT-interval) used for the global delineation of the representative beat (P-onset, P-offset, QRS-onset, QRS-offset, T-offset). All DNNs were trained and optimized using the large PhysioNet ECG database (PTB-XL) under identical conditions, applying an advanced approach for machine-based supervised learning with a reference algorithm for ECG delineation (ETM, Schiller AG, Baar, Switzerland). The test results indicate that all DNN architectures are equally capable of reproducing the reference delineation algorithm’s measurements in the diagnostic PTB database with an average P-wave detection accuracy (96.6%) and time and duration errors: mean values (−2.6 to 2.4 ms) and standard deviations (2.9 to 11.4 ms). The validation according to the standard-based evaluation practices of diagnostic electrocardiographs with the CSE database outlines a CED-Net model, which measures P-duration (2.6 ± 11.0 ms), PQ-interval (0.9 ± 5.8 ms), QRS-duration (−2.4 ± 5.4 ms), and QT-interval (−0.7 ± 10.3 ms), which meet all standard tolerances. Noise tests with high-frequency, low-frequency, and power-line frequency noise (50/60 Hz) confirm that CED-Net, CED-Res-Net, and CED-LSTM-Net are robust to all types of noise, mostly presenting a mean duration error < 2.5 ms when compared to measurements without noise. Reduced noise immunity is observed for the U-net architecture. Comparative analysis with other published studies scores this research within the lower range of time errors, highlighting its competitive performance. Full article

Sleep disorders can have harmful consequences in both the short and long term. They can lead to attention deficits, as well as cardiac, neurological and behavioral repercussions. One of the most widely used methods for assessing sleep disorders is polysomnography (PSG). A major challenge associated with this method is all the cables needed to connect the recording devices, making the examination more intrusive and usually requiring a clinical environment. This can have potential consequences on the test results and their accuracy. One simple way to assess the state of the central nervous system (CNS), a well-known indicator of sleep disorder, could be the use of a portable medical device. With this in mind, we implemented a simple model using both the RR interval (RRI) and its second derivative to accurately predict the awake and napping states of a subject using a feature classification model. For training and validation, we used a database providing measurements from nine healthy young adults (six men and three women), in which heart rate variability (HRV) associated with light-on, light-off, sleep onset and sleep offset events. Results show that using a 30 min RRI time series window suffices for this lightweight model to accurately predict whether the patient was awake or napping. Full article