Scale Variability Research Articles

Evolution of parsec-scale jet directions in active galaxies

Abstract We analyze the variability of the parsec-scale jet directions in active galactic nuclei (AGNs). Our analysis involves 317 AGNs at frequencies ranging from 2 to 43 GHz, and is made possible by developing an automatic jet direction measurement procedure. We find strong significant variations in a one quarter of these AGNs; the effect is likely ubiquitous, and not detected in the rest due to a limited sensitivity and observations epoch coverage. Apparent jet rotation speeds range from 0.21 deg yr−1 at 2 GHz to 1.04 deg yr−1 at 43 GHz. This strong frequency dependence indicates that the variability cannot be explained by jet components propagating ballistically without acceleration: more complex jet shapes or motion patterns are required. Still, we demonstrate that the apparent direction changes are predominantly caused by the jet nozzle rotations, and not by individual components propagating transversely to the jet. In this work, we focus on variability scales much longer than the times of observations, that is ≳ 50 years. Using our measurements, we bound potential periods to less than 1000 years in the source rest frame for 90% AGNs in the sample. These timescales constrain jet direction variation mechanisms, with the most likely explanations being the plasma instabilities, the precession caused by the accretion disk with density ∼r−1, and the orbital motion of binary systems.

Integrative supporting techniques for the taxonomy of Schistorchis carneus Lühe, 1906 (Digenea: Megaperidae) with perspective for the existence of species complexes

The morphology and host-parasite data of Schistorchis carneus Lühe, 1906 indicate low host specificity and geographical range variation, accompanied by a large scale of intraspecific variability and ambiguous molecular phylogeny, confusing in determining biodiversity extent and specificity to a particular host and/or locality. To address the challenging circumstances of records of S. carneus, a detailed morphological description, molecular characterization and species delimitation analyses were conducted using a combination of comparative morphology, molecular phylogeny, multivariate analyses, and host-parasite data. Several morphological features exhibited an extensive intra-variation, as well as a wide morphometric range in many measurements within and across records. Phylogenetic analyses showed that the classification of schistorchiines is highly correlated with the morphology/nature of the oral sucker and highlighted the limitations of ribosomal 28S rDNA and ITS2 rDNA in distinguishing morphologically close/related taxa. Species delimitation analyses reflected the critical importance of host distinction in schistorchiines recognition/differentiation, even those that are indistinct morphologically. The clustering of schistorchiines into certain groups was driven by host differences. Additionally, host variation typically corresponds to a distinct schistorchiine species, even if it is morphologically identical to another. Cluster analysis associated with host-parasite data revealed high morphometric convergence and significant diversity among Egyptian and Sri-Lankan records of S. carneus. It also confirmed the distinction between Indian records from the Bay of Bengal and those from the Gulf of Mannar (previously mentioned as Manaar), each in their own distant group. In conclusion, the records of S. carneus represent a repository for a group of closely related cryptic species. The restricted concept of S. carneus should include only records from the white-spotted puffer, Arothron hispidus (Linnaeus) and the stellate puffer, Arothron stellatus (Anonymous). The Australian specimens from the narrow-lined puffer, Arothron manilensis (Marion de Procé) and the Indian record from the lunartail puffer, Lagocephalus lunaris (Bloch & Schneider) likely represent a free-standing cryptic species with limited distribution, requiring further characterization.

Dual career athletes and psycho-physiological stress

Introduction: The high-level athletic careers frequently conclude significantly earlier than those in other fields, and at times, despite the considerable efforts exerted, they do not consistently flourish as anticipated. Consequently, numerous athletes concurrently pursue academic pathways to adequately prepare for alternative professional opportunities beyond the realm of sports. Objective: Accordingly, the objective of the current investigation was to examine the efficacy of Yoga in ameliorating the stress levels experienced by student-athletes. Methodology: A cohort of 50 dual-career athletes with ages ranging from 18 to 25 years, was selected from five universities. The participants in the experimental group participated in a Yoga regimen in addition to their standard training protocol. In contrast, the participants within the control group continued to engage in conventional sport-specific training. Preceding and after the Yoga intervention, the students underwent psycho-physiological assessments, including the State Mindfulness Scale (SMS) and vagus-mediated heart rate variability (HRV). Results: The results indicated a significant Time x Group interaction effect for both the SMS and HRV, suggesting a substantial enhancement in the treatment groups. Discussion: This study confirmed that meditative practice through Yoga can be a source of improvement of psycho-physiological parameters in the context of interventions aimed at improving the well-being of dual career students Conclusions: In conclusion, there is a need to support and increase research in this area to have new and adequate information about the reality of the Dual Career and to be able to help student-athletes to reconcile school and sports paths.

The JAGUAR-DAS whole neutral atmosphere reanalysis: JAWARA

Using the Japanese Atmospheric General circulation model for the Upper Atmosphere Research-Data Assimilation System (JAGUAR-DAS), a whole neutral atmosphere reanalysis dataset (JAWARA) over about 19 years from September 2004 to December 2023 is produced. JAWARA is the first long-period reanalysis covering the height region from the surface to the lower thermosphere (~ 110 km). This wide height coverage is a notable advantage over other operational reanalysis datasets, which cover up to the middle mesosphere. Key dynamical characteristics are compared between JAWARA and two satellite observations and three other operational reanalysis datasets in their covered height regions. The seasonal variations of zonal mean temperature and zonal wind are similar between JAWARA and the datasets used for comparison. The climatologies of zonal mean temperature, zonal wind, residual-mean circulation, and E-P flux in the meridional cross section are also broadly consistent with other reanalysis datasets. The analyzed residual-mean vertical flow in the northern high latitudes in the middle atmosphere exhibits the well-known patterns of upwelling in summer and downwelling in winter. JAWARA also shows a prominent feature of strong downward propagating anomalies from the lower thermosphere to the upper stratosphere after sudden stratospheric warmings. This analysis takes full advantage of the JAWARA data, which cannot be made using satellite observations and other reanalysis datasets. This reanalysis product is expected to contribute broadly to future research on the characteristics of observed mesospheric phenomena, thermosphere–ionosphere coupling, space weather, and improvement of middle atmospheric meteorological systems, including their interannual and decadal scale variability.

FAMHE-Net: Multi-Scale Feature Augmentation and Mixture of Heterogeneous Experts for Oriented Object Detection

Object detection in remote sensing images is essential for applications like unmanned aerial vehicle (UAV)-assisted agricultural surveys and aerial traffic analysis, facing unique challenges such as low resolution, complex backgrounds, and the variability of object scales. Current detectors struggle with integrating spatial and semantic information effectively across scales and often omit necessary refinement modules to focus on salient features. Furthermore, a detector head that lacks a meticulous design may face limitations in fully understanding and accurately predicting based on the enriched feature representations. These deficiencies can lead to insufficient feature representation and reduced detection accuracy. To address these challenges, this paper introduces a novel deep-learning framework, FAMHE-Net, for enhancing object detection in remote sensing images. Our framework features a consolidated multi-scale feature enhancement module (CMFEM) with integrated Path Aggregation Feature Pyramid Network (PAFPN), utilizing our efficient atrous channel attention (EACA) within CMFEM for enhanced contextual and semantic information refinement. Additionally, we introduce a sparsely gated mixture of heterogeneous expert heads (MOHEH) to adaptively aggregate detector head outputs. Compared to the baseline model, FAMEH-Net demonstrates significant improvements, achieving a 0.90% increase in mean Average Precision (mAP) of the DOTA dataset and a 1.30% increase in mAP12 of HRSC2016 datasets. These results highlight the effectiveness of FAMEH-Net in object detection within complex remote sensing images.

Exceptional X-ray activity in BL Lacertae

BL Lacertae is a unique blazar for which the X-ray band can cover either the synchrotron or the inverse Compton, or both parts of the broadband spectral energy distribution. In the latter case, when the spectral upturn is located in the X-ray range, it allows contemporaneous study of the low- and high-energy ends of the electron distribution function. In this work, we study spectral and temporal variability using X-ray and optical observations of the blazar performed with the Neil Gehrels Swift Observatory from 2020 to 2023. The large set of observational data reveals intensive flaring activity, accompanied by spectral changes in both spectral branches. We conclude that the low-energy and high-energy ends of the particle distribution function are characterised by similar variability scales. Additionally, the hard X-ray observations of BL Lacertae performed with the Nuclear Spectroscopic Telescope Array (NuSTAR) confirm a concave spectral curvature for some epochs of the blazar activity and reveal that it can be shifted up to energies of as high as 8 keV. The time-resolved spectral analysis allows us to disentangle X-ray spectral variability features of the synchrotron from inverse Compton components. Despite significant variability of both spectral components, we find only small changes in the position of the spectral upturn. The different slopes and shapes of the X-ray spectrum of BL Lacertae demonstrate that the classification of this source is not constant, and BL Lacertae can exhibit features of either high-, intermediate-, or low-energy peaked blazar in different epochs of observation. This also indicates that the spectral upturn for this blazar can be located not only in the X-ray range of 0.3−10 keV, but also at lower or higher energies.

An evaluation approach to PM2.5 policy effectiveness over South Korea based on a newly proposed scalable spatial decomposition method

Abstract Air quality management policies often exhibit spatial inconsistencies in effectiveness due to the diverse spatial scales of air pollution variability, which result from source characteristics as well as geographical and meteorological factors. To address this, the present study proposes a scalable spatial decomposition method to separate spatiotemporal air pollution data into background (nationwide), intercity-scale (tens of kilometers), and neighborhood-scale (several kilometers) components. This decomposition was achieved by introducing spatially varying effective ranges for intercity-scale variability at each station, based on the correlation coefficient distance of the background-removed component. Applying this approach to hourly fine particulate matter (PM2.5) concentrations from 535 monitoring stations across South Korea for 2021–2022, we evaluated the effectiveness of PM2.5 management policies. During the polluted cold season (December to March), the intercity-scale component contributed an average of approximately 18% of the total PM2.5 concentration in the Seoul Metropolitan Area (SMA) and Central Area (CA), which are densely populated and industrialized regions. In contrast, this component helped reduce PM2.5 levels in southeastern coastal areas, where high winds facilitate dispersion. The neighborhood-scale component contributed positively to PM2.5 levels near industrial complexes and ports but negatively in residential and commercial areas. The results demonstrate the effectiveness of central government-led intercity-scale regulations on total emissions allowances in the SMA and CA and highlight the need for additional local management targeting individual point sources near industrial complexes and ports. This study provides intuitive spatial decomposition tools for understanding PM2.5 pollution across spatial scales and offers policymakers a foundation for developing multi-scale mitigation strategies.

Inferring flow energy, space scales, and timescales: freely drifting vs. fixed-point observations

Abstract. A novel method for the inference of spatiotemporal decomposition of oceanic surface flow variability is presented and its performance assessed in a synthetic idealized configuration with horizontally divergentless flow. Inference methodology is designed for observations of surface velocity. The ability of networks of surface drifters and moorings to infer the spatiotemporal scales of surface ocean flow variability is quantified. The sensitivity of inference performance for both types of platforms to the number of observations, geometrical configurations, and flow regimes is presented. As drifters simultaneously sample spatial and temporal variability, they are shown to be able to capture both spatial and temporal flow properties even when deployed in isolation. Moorings are particularly adept for the characterization of the flow's temporal variability and may also capture spatial scales provided they are deployed as arrays. In particular, we show that our method correctly identifies whether drifters are preferentially sampling spatial vs. temporal variability. Pending further developments, this method opens novel avenues for the analysis of existing datasets as well as the design of future experimental campaigns targeting the characterization of small-scale (e.g., <100 km) ocean variability.

Ensemble reconstruction of missing satellite data using a denoising diffusion model: application to chlorophyll a concentration in the Black Sea

Abstract. Satellite observations provide a global or near-global coverage of the World Ocean. They are however affected by clouds (among others), which severely reduce their spatial coverage. Different methods have been proposed in the literature to reconstruct missing data in satellite observations. For many applications of satellite observations, it has been increasingly important to accurately reflect the underlying uncertainty of the reconstructed observations. In this paper, we investigate the use of a denoising diffusion model to reconstruct missing observations. Such methods can naturally provide an ensemble of reconstructions where each member is spatially coherent with the scales of variability and with the available data. Rather than providing a single reconstruction, an ensemble of possible reconstructions can be computed, and the ensemble spread reflects the underlying uncertainty. We show how this method can be trained from a collection of satellite data without requiring a prior interpolation of missing data and without resorting to data from a numerical model. The reconstruction method is tested with chlorophyll a concentration from the Ocean and Land Colour Instrument (OLCI) sensor (aboard the satellites Sentinel-3A and Sentinel-3B) on a small area of the Black Sea and compared with the neural network DINCAE (Data-INterpolating Convolutional Auto-Encoder). The spatial scales of the reconstructed data are assessed via a variogram, and the accuracy and statistical validity of the reconstructed ensemble are quantified using the continuous ranked probability score and its decomposition into reliability, resolution, and uncertainty.

Complex multivariate model predictions for coral diversity with climatic change

AbstractModels of the future of coral reefs are potentially sensitive to theoretical assumptions, variable selectivity, interactions, and scales. A number of these aspects were evaluated using boosted regression tree models of numbers of coral taxa trained on ~1000 field surveys and 35 spatially complete influential environmental proxies at moderate scales (~6.25 km2). Models explored influences of climate change, water quality, direct human‐resource extraction, and variable selection processes. We examined the predictions for numbers of coral taxa using all variables and compared them to models based on variables commonly used to predict climate change and human influences (eight and nine variables). Results indicated individual temperature variables alone had lower predictive ability (R2 < 2%–7%) compared to human influence variables (6%–18%) but overall climate had a higher training–testing fit (70%) than the human influence (63%) model. The full variable model had the highest fit to the full data (27 variables; R2 = 85%) and indicated the strongly interactive and complex role of environmental and human influence variables when making moderate‐scale biodiversity predictions. Projecting changes using Coupled Model Intercomparison Project (CMIP) 2050 Representative Concentration Pathways (RCP2.6 and 8.5) water temperature predictions indicated high local variability and fewer negative effects than predictions made by coarse scale threshold and niche models. The persistence of coral reefs over periods of rapid climate change is likely to be caused by smaller scale variability that is poorly simulated with coarse scale modeled predictions.

Resilient fast convolutional sparse filtering with convergence directivity for the bearing fault diagnosis

The fast nonlinear convolutional sparse filtering (FNCSF), which detects fault features 42 h earlier than traditional methods in the intelligent maintenance system dataset and takes only 0.12 s, is widely considered a powerful tool for early fault diagnosis. However, random shocks caused by the structure or external interference pose challenges to the extraction accuracy of FNCSF. In addition, the extraction reliability of FNCSF is affected by computational instability and complex parameter settings. To address the above defects, resilient fast convolutional sparse filtering (RFCSF) is proposed in this study. First, the collected vibration signal is normalized by Z-score to eliminate the scale variability of the nonlinear transformation. Then, the frequency components of the signal are evenly divided by the initialized filters to indicate the convergence direction of fault and improve random shock resistance and extraction stability. Next, the nonlinear [Formula: see text] norm, which can effectively distinguish fault features from irrelevant disturbances, is regarded as the target of blind deconvolution. In the process of deconvolution, the filter converging to random shocks and noise is adaptively eliminated to improve the extraction efficiency and intelligence. Finally, the filtered signal is envelope demodulated to obtain fault information. In the simulation analysis, features under noise with −16 dB and random shocks with 50 m/s2 are accurately extracted by RFCSF with parameters robustness. Several experimental cases demonstrate that RFCSF with these advantages is a promising feature extraction tool.

Towards accurate L4 ocean colour products: Interpolating remote sensing reflectance via DINEOF

Ocean colour (OC) remote sensing benefits society by providing continuous biological and ecological parameters relevant to sustainable marine resource exploitation. It enhances our understanding of climate change and allows us to monitor oceanographic phenomena over various scales of variability. However, significant data gaps occur daily due to cloud cover, atmospheric correction failures, sun-glint contamination, and satellite coverage limitations. Level 4 (L4) gap-free images are generally created by averaging over specific periods (e.g., weekly, monthly, seasonal) or re-gridding data with coarser resolution to overcome these limitations. These approaches, however, often fail to capture anomalous events or fine-scale resolution processes, calling for more advanced methods. The Data Interpolating Empirical Orthogonal Function (DINEOF) method has proved effective in reconstructing missing OC data and capturing smaller-scale features in noisy fields. To the best of authors knowledge, DINEOF is here used for the first time to interpolate multispectral Remote Sensing Reflectance (Rrs) to produce a consistent and gap-free L4 Rrs dataset, minimizing errors in inferred ocean products, such as Chlorophyll-a (Chl), the most widely used proxy for phytoplankton biomass. Specifically, using a multivariate approach, we assessed the DINEOF technique’s capability to reconstruct Rrs, focusing on six bands (412, 443, 490, 510, 555, and 670 nm) and validating the results using extensive in situ datasets. Our outcomes show that this “upstream interpolation” method can generate a consistent Rrs dataset, thereby improving the accuracy of L4 Chl predictions when used as input in algorithms for remote Chl estimation. We anticipate further improvements in L4 Rrs accuracy using richer spectral information from upcoming hyperspectral satellite missions. This study highlights the effectiveness of using Rrs as a standalone dataset for DINEOF interpolation. Operationally, it can derivate various gap-free and consistent biogeochemical parameters with reduced uncertainty, thus providing a more reliable and versatile method.

Feeding ecology of two wild sympatric canids in protected areas of northeastern Argentina

AbstractThe role of mesocarnivores in wildlife communities tends to be overlooked, which has led to a large gap in natural history knowledge. In northeastern Argentina, two mesocarnivore foxes, Cerdocyon thous and Lycalopex gymnocercus, live in sympatry in noncompetitive coexistence. Their feeding habits have been studied in many occasions; however, the studies related to trophic variability in a wider spatial scale remain scarce. In this study, we aimed to analyze how the diet of these foxes varies in three protected areas in Argentina: the Mburucuyá National Park (MNP), the “San Nicolás Portal” (SN), and the Natural Reserve Rincón de Santa María (RSM). We collected fecal samples in each area, identified them using thin layer chromatography at species level, and classified the found feeding items to obtained two parameters: occurrence and relative frequency. Furthermore, we compared the diet of the two species in each site and between sites using chi‐square and estimated trophic niche breadth and overlap between the species. We found that both species consumed different food categories, with variable frequencies across study sites. We observed that in each area, the foxes differed in diet composition. However, we found no differences between the diet compositions of C. thous and L. gymnocercus, showing high niche overlap in each site. Here, we show the plasticity of both fox species in changing their diets even with small geographical variation. Both species can coexist even with a high diet overlap, possibly explained by their noncompetitive coexistence which may occur due to the current changes in the ecosystem interactions, or partitioning may exist but on a smaller niche level.

Channel and Spatial Enhancement Network for human parsing

The dominant backbones of neural networks for scene parsing consist of multiple stages, where feature maps in different stages often contain varying levels of spatial and semantic information. High-level features convey more semantics and fewer spatial details, while low-level features possess fewer semantics and more spatial details. Consequently, there are semantic-spatial gaps among features at different levels, particularly in human parsing tasks. Many existing approaches directly upsample multi-stage features and aggregate them through addition or concatenation, without addressing the semantic-spatial gaps present among these features. This inevitably leads to spatial misalignment, semantic mismatch, and ultimately misclassification in parsing, especially for human parsing that demands more semantic information and more fine details of feature maps for the reason of intricate textures, diverse clothing styles, and heavy scale variability across different human parts. In this paper, we effectively alleviate the long-standing challenge of addressing semantic-spatial gaps between features from different stages by innovatively utilizing the subtraction and addition operations to recognize the semantic and spatial differences and compensate for them. Based on these principles, we propose the Channel and Spatial Enhancement Network (CSENet) for parsing, offering a straightforward and intuitive solution for addressing semantic-spatial gaps via injecting high-semantic information to lower-stage features and vice versa, introducing fine details to higher-stage features. Extensive experiments on three dense prediction tasks have demonstrated the efficacy of our method. Specifically, our method achieves the best performance on the LIP and CIHP datasets and we also verify the generality of our method on the ADE20K dataset.

Sensitivity of Sentinel-1 C-band SAR backscatter, polarimetry and interferometry to snow accumulation in the Alps

The physical drivers of Sentinel-1 C-band backscatter observations during snow accumulation are still uncertain. To investigate these, backscatter fluctuations (in co-polarization VV, cross-polarization VH, and cross-polarization ratio VH-VV) were temporally and spatially linked to modeled surface (0–10 cm) soil moisture (SM) and soil temperature (T) (here referred to as soil dynamics) and modeled snow depth (SD) and snow water equivalent (SWE) (snow dynamics) in the bare and herbaceous regions of the Alps at a spatial resolution of 1 km. Results demonstrate that, during snow accumulation and at a regional scale, VH and VH-VV variability is primarily influenced by SD and SWE, whereas VV fluctuations are driven by a combination of soil and snow dynamics. At low local incidence angles, VV is driven by snow dynamics rather than by soil dynamics, which results in a decreased sensitivity of VH-VV to snow accumulation, potentially degrading VH-VV based SD retrieval. Additionally, polarimetric and interferometric Sentinel-1 observations are generated to assess their sensitivity to snow dynamics. Results show that polarimetric α (from entropy-α dual-pol decomposition) and the first Stokes parameter are more sensitive to SD than VH-VV and VV, respectively, suggesting the potential for improved SD retrievals. Finally, results show that interferometric 6-day coherence observations respond to modeled SWE accumulation, with low coherence values after significant SWE accumulation and higher values in case of minor SWE changes.

Terrain Analysis According to Multiscale Surface Roughness in the Taklimakan Desert

Surface roughness, interpreted in the wide sense of surface texture, is a generic term referring to a variety of aspects and scales of spatial variability of surfaces. The analysis of solid earth surface roughness is useful for understanding, characterizing, and monitoring geomorphic factors at multiple spatiotemporal scales. The different geomorphic features characterizing a landscape exhibit specific characteristics and scales of surface texture. The capability to selectively analyze specific roughness metrics at multiple spatial scales represents a key tool in geomorphometric analysis. This research presents a simplified geostatistical approach for the multiscale analysis of surface roughness, or of image texture in the case of images, that is highly informative and interpretable. The implemented approach is able to describe two main aspects of short-range surface roughness: omnidirectional roughness and roughness anisotropy. Adopting simple upscaling approaches, it is possible to perform a multiscale analysis of roughness. An overview of the information extraction potential of the approach is shown for the analysis of a portion of the Taklimakan desert (China) using a 30 m resolution DEM derived from the Copernicus Glo-30 DSM. The multiscale roughness indexes are used as input features for unsupervised and supervised learning tasks. The approach can be refined both from the perspective of the multiscale analysis as well as in relation to the surface roughness indexes considered. However, even in its present, simplified form, it can find direct applications in relation to multiple contexts and research topics.

Insights Into Magma Reservoir Dynamics From a Global Comparison of Volcanic and Plutonic Zircon Trace Element Variability in Individual Hand Samples

AbstractTrace element compositional trends in zircons separated from single hand samples have been used to infer dynamic processes in magma reservoirs. Here, we compile published zircon trace element chemistry to quantify any systematic difference between the range of compositions observed in zircon from individual volcanic and plutonic hand samples and compare these results with geochemical modeling to derive implications for magma reservoir dynamics. We find that both rock types span a wide range of hand‐sample scale variability (i.e., wide range of coefficients of variation), but there is no systematic difference in the average variability between plutonic and volcanic samples (i.e., no difference in the mean coefficient of variation). This indicates that dynamic processes related to eruption are not necessarily required as a fundamental process to create hand sample‐scale compositional heterogeneity beyond what is present due to dynamic processes in the reservoir recorded in plutonic samples. Modeling of felsic systems (>68.5 wt.% SiO2) indicates that the similar average variability in felsic volcanic and plutonic hand samples cannot be reproduced by closed‐system crystallization of compositionally distinct melts locally within a magma reservoir (i.e., isolated melt pockets in a crystal mush) but requires mixing of at least two felsic melt compositions at a small spatial scale. This study provides a framework for focused studies on individual volcanic‐plutonic systems exploring how plutonic and volcanic zircon compositional variability records the time and length scales of magma reservoir processes.

Does radiofrequency radiation impact sleep? A double-blind, randomised, placebo-controlled, crossover pilot study.

The most common source of Radiofrequency Electromagnetic Field (RF-EMF) exposures during sleep includes digital devices, yet there are no studies investigating the impact of multi-night exposure to electromagnetic fields emitted from a baby monitor on sleep under real-world conditions in healthy adults. Given the rise in the number of people reporting to be sensitive to manmade electromagnetic fields, the ubiquitous use of Wi-Fi enabled digital devices and the lack of real-world data, we investigated the effect of 2.45 GHz radiofrequency exposure during sleep on subjective sleep quality, and objective sleep measures, heart rate variability and actigraphy in healthy adults. This pilot study was a 4-week randomised, double-blind, crossover trial of 12 healthy adults. After a one-week run-in period, participants were randomised to exposure from either an active or inactive (sham) baby monitor for 7 nights and then crossed over to the alternate intervention after a one-week washout period. Subjective and objective assessments of sleep included the Pittsburgh Insomnia Rating Scale (PIRS-20), electroencephalography (EEG), actigraphy and heart rate variability (HRV) derived from electrocardiogram. Sleep quality was reduced significantly (p < 0.05) and clinically meaningful during RF-EMF exposure compared to sham-exposure as indicated by the PIRS-20 scores. Furthermore, at higher frequencies (gamma, beta and theta bands), EEG power density significantly increased during the Non-Rapid Eye Movement sleep (p < 0.05). No statistically significant differences in HRV or actigraphy were detected. Our findings suggest that exposure to a 2.45 GHz radiofrequency device (baby monitor) may impact sleep in some people under real-world conditions however further large-scale real-world investigations with specified dosimetry are required to confirm these findings.

Effects of a 30-min rest with a nap chair on task performance, sleepiness, and neurophysiological measures in men with suspected brain fatigue: a randomized controlled crossover trial

BackgroundIt has been suggested that a short nap in the afternoon may improve sleepiness, alertness, and task performance. The present study evaluated the effects of a 30-min rest with a new nap chair on task performance, sleepiness, and neurophysiological measures.MethodsA randomized controlled crossover trial with a 1-week interval was carried out at the BOOCS Clinic Fukuoka in Japan. The subjects were male workers aged 20 to 64 years with suspected brain fatigue, which was defined by the Profile of Mood Status 2. The intervention was a 30-min rest with an office chair or a nap chair. The primary outcome was the performance in the Uchida-Kraepelin test. The secondary outcomes included the Karolinska Sleepiness Scale and 15-min heart rate variability (HRV). The changes after the nap-chair rest and office-chair rest were compared. Repeated measures analysis of variance with nesting was used in the statistical analysis.ResultsTwenty participants were eligible and entered the crossover trial. The overall 15-min score in the Uchida-Kraepelin test improved after the nap-chair rest and after the office-chair rest to almost the same extent (5.9 vs. 5.5 points, P = 0.68). The Karolinska Sleepiness score significantly decreased after the nap-chair rest, and the between-treatment difference in the decrease was highly significant (P = 0.0004). The average duration of sleep during rest was prominently longer in the nap-chair rest than in the office-chair rest (19.0 vs. 7.6 min, P = 0.002). No participants experienced REM sleep during the rest. LF and HF powers of the HRV were greater during the nap-chair rest than during the office-chair rest, the difference in the HF power being substantial.ConclusionA 30-min rest with the nap chair did not appreciably improve the performance in the Uchida-Kraepelin test as compared with the office-chair rest. The nap-chair rest induced a substantially longer sleep accompanied with a parasympathetic activation, thereby resulting in a material improvement in sleepiness after the rest.

Mapping intertidal microphytobenthic biomass with very high-resolution remote sensing imagery in an estuarine system

Microphytobenthos (MPB) contributes significantly to estuarine primary production, so that quantifying its biomass is crucial for assessing their ecosystem functioning. Conventional sampling methods are labour-intensive, logistically challenging, and cannot provide a comprehensive spatial distribution map of MPB biomass. Satellite imagery has offered a feasible alternative for mapping large areas at various temporal and spatial resolutions. However, no imaging device with a spatial resolution consistent with the few square centimetres sampled in-situ has been used in the field. This makes it challenging to accurately relate field biomass measurements with remotely sensed radiometric observations. In this study, two similar multispectral sensors were mounted on an unmanned aerial vehicle (UAV) at different altitudes, as well as on a custom-built device specifically designed to acquire images at ∼1 m altitude, in order to collect very-high spatial resolution reflectance data of MPB biofilms at the Guadalquivir Estuary (Spain) mudflats. In addition, a hyperspectral spectroradiometer acquiring in-situ field reflectance was used for validation. Simultaneously, MPB samples were collected using a 2 mm depth contact corer method, which were analysed through high-performance liquid chromatography (HPLC) to measure the concentrations of major MPB pigments. To assess the relationship between the MPB pigments and different reflectance-based spectral indices, generalised linear mixed effects models (GLMMs) were used, achieving a significant positive relationship between chlorophylls and all spectral indices tested. These models were used to map microphytobenthic biomass, yielding a mean biomass in the range of 30–50 mg Chl-a m−2 in the Guadalquivir estuary during late winter. This study demonstrates the potential of low-altitude/high spatial resolution radiometric imaging as an efficient, rapid, and non-destructive addition to in-situ measurements of MPB biomass, providing exciting perspectives for the monitoring of estuarine systems on a millimetric scale of variability.