Epochs Of Data Research Articles

Detection of Prenatal Cardiac Disease using Computer Vision and Artificial Intelligence

Prenatal cardiac anomalies, commonly referred to as congenital heart defects (CHDs), comprise a spectrum of pathologies that adversely affect cardiac function. There is a correlation between the numerous risks of cardiovascular diseases and the pressing requirement for precise, reliable, and efficient methods of early detection. The contemporary epoch of voluminous data presents a plethora of novel prospects for clinicians to utilize artificial intelligence in order to identify and enhance treatment for pediatric patients and those afflicted with congenital heart disease. Machine learning, a prevalent technique in the field of artificial intelligence, has been utilized to forecast various outcomes in obstetrics. The application of artificial intelligence in real-time electronic health recording and predictive modelling has demonstrated promising outcomes in the domain of fetal monitoring. The present research provides an in-depth review of recent advancements and challenges in the application of artificial intelligence techniques, such as deep learning and computer vision, for the detection of congenital heart disease.

Generating X-Ray Transit Profiles with Batman

We present an adaptation of the exoplanet transit model code batman, in order to permit the generation of X-ray transits. Our underlying extended coronal model assumes an isothermal plasma that is radially symmetric. While this ignores the effect of bright, active regions, observations of transits in X-rays will require averaging across multiple epochs of data for the foreseeable future, significantly reducing the importance of more complex modeling. Our publicly available code successfully generates the predicted W-shaped transit profile in X-rays due to the optically thin nature of the emission, which concentrates the expected observational emission around the limb of the photospheric stellar disk. We provide some examples based on the best known X-ray transit target, HD 189733 b, and examine the effect of varying the planet size, coronal temperature, and impact parameter on the resulting transit profile. We also derived scaling relationships for how the overall transit detectability is affected by changing these parameters. Over most of the parameter space, we find that the detectability scales linearly with the cross-sectional area of the planet in X-rays. The relationship with increasing coronal temperature is less fixed, but averages out to a power law with slope −1/4 except when the impact parameter is high. Indeed, varying impact parameter has little effect on detectability at all until it approaches unity.

Investigation of the nature of the wind interaction in HD 93205 based on multi-epoch X-ray observations

The study of the X-ray emission from massive binaries constitutes a relevant approach to investigate shock physics. The case of short period binaries may turn out to be quite challenging, especially in very asymmetric systems where the primary wind may overwhelm that of the secondary in the wind interaction. Our objective consists in providing an observational diagnostic of the X-ray behavior of HD\,93205, which is a very good candidate with which to investigate these aspects. We analyzed 31 epochs of XMM-Newton X-ray data spanning about two decades to investigate its spectral and timing behavior. The X-ray spectrum is very soft along the full orbit, with a luminosity exclusively from the wind interaction region in the range of 2.3 -- 5.4\,times $. The light curve peaks close to periastron, with a rather wide pre-periastron low state coincident with the secondary's body hiding a part of the X-ray emitting region close to its surface. We determined a variability timescale of 6.0807\,pm \,0.0013\,d, in full agreement with the orbital period. Making use of a one-dimensional approach to deal with mutual radiative effects, our results point to a very likely hybrid wind interaction, with a wind photosphere occurring along most of the orbit, while a brief episode of wind-wind interaction may still develop close to apastron. Besides mutual radiative effects, the radiative nature of the shock that leads to some additional pre-shock obliquity of the primary wind flow certainly explains the very soft emission. HD\,93205 constitutes a relevant target to investigate shock physics in short period, asymmetric massive binary systems, where various mutual radiative effects and radiative shocks concur to display an instructive soft X-ray behavior. HD\,93205 should be considered as a valid, though challenging target for future three-dimensional modeling initiatives.

The SDSS-V Black Hole Mapper Reverberation Mapping Project: A Kinematically Variable Broad-line Region and Consequences for the Masses of Luminous Quasars

We present a velocity-resolved reverberation mapping analysis of the hypervariable quasar RM160 (SDSS J141041.25+531849.0) at z = 0.359 with 153 spectroscopic epochs of data representing a 10 yr baseline (2013–2023). We split the baseline into two regimes based on the 3× flux increase in the light curve: a “low state” phase during the years 2013–2019 and a “high state” phase during the years 2022–2023. The velocity-resolved lag profiles (VRLPs) indicate that gas with different kinematics dominates the line emission in different states. The Hβ VRLP begins with a signature of inflow onto the broad-line region (BLR) in the low state, while in the high state it is flatter with less signature of inflow. The Hα VRLP begins consistent with a virialized BLR in the low state, while in the high state shows a signature of inflow. The differences in the kinematics between the Balmer lines and between the low state and the high state suggests complex BLR dynamics. We find that the BLR radius and velocity (both FWHM and σ) do not obey a constant virial product throughout the monitoring period. We find that the BLR lags and continuum luminosity are correlated, consistent with rapid response of the BLR gas to the illuminating continuum. The BLR kinematic profile changes in unpredictable ways that are not related to continuum changes and reverberation lag. Our observations indicate that nonvirial kinematics can significantly contribute to observed line profiles, suggesting caution for black hole mass estimation in luminous and highly varying quasars like RM160.

A Search for Collisions and Planet–Disk Interactions in the Beta Pictoris Disk with 26 Years of High-precision HST/STIS Imaging

β Pictoris's well-studied debris disk and two known giant planets, in combination with the stability of the Hubble Space Telescope’s Space Telescope Imaging Spectrograph (HST/STIS) (and now also JWST), offers a unique opportunity to test planet–disk interaction models and to observe recent planetesimal collisions. We present HST/STIS coronagraphic imaging from two new epochs of data taken between 2021 and 2023, complementing earlier data taken in 1997 and 2012. This data set enables a temporal comparison with the longest baseline and highest precision of any debris disk to date, with sensitivity to variations in temporal surface brightness of sub-percent levels in the midplane of the disk. While no localized changes in surface brightness are detected, which would be indicative of a recent planetesimal collision, there is a tentative brightening of the southeast side of the disk over the past decade. We link the constraints on surface brightness variations to dynamical models of the planetary system’s evolution and to the collisional history of planetesimals. Using a coupled collisional model and injection/recovery framework, we estimate sensitivity to expanding collisional debris down to a Ceres mass per progenitor in the most sensitive regions of the disk midplane. These results demonstrate the capabilities of long-baseline, temporal studies with HST (and also soon with JWST) for constraining the physical processes occurring within debris disks.

Detection of the Fe K lines from the binary AGN in 4C+37.11

We report the discovery of the Fe K line emission at $ $ keV with a width of $ $ keV using two epochs of Chandra archival data for the nucleus of the galaxy 4C+37.11, which is known to host a binary supermassive black hole (BSMBH) system where the SMBHs are separated by $ mas or sim 7pc. Our study reports the first detection of the Fe K line from a known binary AGN, which has an F-statistic value of 20.98 and a probability of $2.47 $. Stacking two spectra reveals another Fe K line component at $ $ keV. Different model scenarios indicate that the lines originate from the combined effects of accretion disk emission and circumnuclear collisionally ionized medium. The observed low column density favors a gas-poor merger scenario, where the high temperature of the hot ionized medium may be associated with the shocked gas in the binary merger and not with star formation activity. The estimated total BSMBH mass and disk inclination are $ $ M$_ and $ indicating that the BSMBH is probably a high-inclination system. We were not able to tightly constrain the spin parameter using the present data sets. Our results draw attention to the fact that detecting the Fe K line emissions from BSMBHs is important for estimating the individual SMBH masses and the spins of the binary SMBHs, as well as for exploring their emission regions.

Quantifying Physical Activity and Sedentary Behavior in Adults with Intellectual Disability: A Scoping Review of Assessment Methodologies.

Background/Objectives: Methodologies for assessing behavior form the foundation of health promotion and disease prevention. Physical activity (PA) and sedentary behavior (SB) assessment methodologies have predominantly been developed for adults without an intellectual disability (ID), raising credibility concerns for adults with ID. The purpose was to synthesize the current state of assessment methodologies for quantifying PA and SB volume in the free-living setting for adults with an ID. Methods: Following PRISMA guidelines, eleven databases were searched through December 2023, yielding 8174 records. Data were extracted in Covidence (v.2.0), obtaining quantified PA and SB volume and assessment methodology characteristics across data collection and analysis, including tool(s) and technique(s) used, preparatory actions taken, instructions provided, and behavioral strategies employed during data collection. Results: Of the 8174 articles screened, 91 met the inclusion criteria. Common metrics included minutes/hours per day/week and steps per day/week. Despite 80% of the studies using objective techniques, substantial variation existed across studies regarding wearable models, sampling frequency and epoch length settings, calibration protocols, wearable placements, and data processing techniques. Limited studies provided instructions that did not exclusively rely on spoken language. Behavioral strategies varied, including self-monitoring, providing assistance or supervision, administering questionnaires verbally, issuing reminders, and offering monetary incentives. Conclusions: This review underscores the need for greater consistency and accessibility in PA and SB assessment methodology for adults with ID. Tailored preparation, instruction, and behavioral strategies may enhance assessment viability and suitability for adults with ID, with or without caregiver or researcher involvement in the free-living setting.

Evaluation of indoor activities for moderate-to-vigorous physical activity in preschoolers

ABSTRACT The study objectives were 1) to determine the feasibility and acceptability of indoor activities to achieve moderate-to-vigorous physical activity (MVPA) in preschoolers, and 2) compare MVPA estimates between direct observation (DO) and various accelerometry placements. In this cross-sectional study, 35 preschoolers (51% female, 54% 3-year-olds) performed six, 6-minute activities (dancing to video, balloon/bubbles, stationary exergame cycling, circuits, running-in-place, and cleaning up) in sequential order, facilitated by a trained staff member. Triaxial accelerometers (Actigraph Gt3×BT) at the ankle, waist, and wrist measured MVPA using age-specific cut-points. Total activity and MVPA time were quantified via DO of video recordings. Feasibility and acceptability were assessed via parent and child report. Preschoolers contributed 4339, 15-second epochs of accelerometry and DO data (~31.0 minutes/preschooler). Preschoolers achieved MVPA ≥ 50% of the time while engaging in balloon/bubbles, cycling, and circuits; but not while dancing to video (15%), running-in-place (48.5%), or cleaning up (8%). There were no differences in MVPA by age, sex, or between screen and non-screen activities. Parents and preschoolers reported most activities were feasible (≥4.0/5.0). Waist and ankle accelerometry had strong agreement with DO (ICCs range: 0.70–0.84) while wrist had fair to low agreement (ICCs: 0.22–0.58). Multiple indoor activities show promise to increase preschoolers’ MVPA.

Intra- and Inter-Rater Reliability of Linear and Nonlinear Measures of Short-Term Heart Rate Variability Following Combat-Related Traumatic Injury.

Heart rate variability (HRV) is a marker of autonomic function. However, the reliability of short-term HRV measurement in individuals with combat-related traumatic injury (CRTI) remains undetermined. An intra- and inter-rater reliability study was conducted using a subsample (n = 35) of British servicemen with CRTI enrolled in the ongoing ADVANCE study. A five-minute epoch of single-lead electrocardiogram data collected during spontaneous breathing was used to measure HRV. HRV analyses were independently performed by two examiners using Kubios. Intraclass correlation coefficient (ICC), standard error of measurement (SEM), minimum detectable change (MDC), and coefficient of variance were calculated for linear [root mean square of successive difference (RMSSD), standard deviation of NN interval, low-frequency, high-frequency, total power] and nonlinear (SD1-2, acceleration and deceleration capacities, sample entropy) measures. Bland-Altman %plots were used to assess bias in intra- and inter-rater HRV data. The mean age of participants was 39.3 ± 6.3 years. An excellent ICC score of 0.9998 (95% CI 0.9997, 0.9999) was observed for intra-rater analyses of RMSSD, and similar excellent ICC scores were seen for all other HRV measures. The inter-rater reliability analyses produced an excellent ICC score (range 0.97-1.00). Comparatively, frequency-domain measures produced higher MDC% and SEM% scores than time-domain and nonlinear measures in both inter- and intra-rater analyses. The Bland-Altman plots revealed relatively higher bias for frequency-domain and nonlinear measures than time-domain measures. ECG-related short-term HRV measures were reliable in injured servicemen under spontaneous breathing. However, the reliability appeared better with the time-domain measure than frequency-domain and nonlinear measures in this sample.

Glimmers in the Cosmic Dawn: A Census of the Youngest Supermassive Black Holes by Photometric Variability* * This research is based on observations made with the NASA/ESA Hubble Space Telescope obtained from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 526555. These observations are associated with

We report the first results from a deep near-infrared campaign with the Hubble Space Telescope to obtain late-epoch images of the Hubble Ultra Deep Field, 10–15 yr after the first epoch data were obtained. The main objectives are to search for faint active galactic nuclei (AGN) at high redshifts by virtue of their photometric variability and measure (or constrain) the comoving number density of supermassive black holes (SMBHs), n SMBH, at early times. In this Letter, we present an overview of the program and preliminary results concerning eight objects. Three variables are supernovae, two of which are apparently hostless with indeterminable redshifts, although one has previously been recorded as a z ≈ 6 object precisely because of its transient nature. Two further objects are clear AGN at z = 2.0 and 3.2, based on morphology and/or infrared spectroscopy from JWST. Three variable targets are identified at z = 6–7 that are also likely AGN candidates. These sources provide a first measure of n SMBH in the reionization epoch by photometric variability, which places a firm lower limit of 3 × 10−4 cMpc−3. After accounting for variability and luminosity incompleteness, we estimate n SMBH ≳ 8 × 10−3 cMpc−3, which is the largest value so far reported at these redshifts. This SMBH abundance is also strikingly similar to estimates of n SMBH in the local Universe. We discuss how these results test various theories for SMBH formation.

Differentiating loss of consciousness causes through artificial intelligence-enabled decoding of functional connectivity

Differential diagnosis of acute loss of consciousness (LOC) is crucial due to the need for different therapeutic strategies despite similar clinical presentations among etiologies such as nonconvulsive status epilepticus, metabolic encephalopathy, and benzodiazepine intoxication. While altered functional connectivity (FC) plays a pivotal role in the pathophysiology of LOC, there has been a lack of efforts to develop differential diagnosis artificial intelligence (AI) models that feature the distinctive FC change patterns specific to each LOC cause. Three approaches were applied for extracting features for the AI models: three-dimensional FC adjacency matrices, vectorized FC values, and graph theoretical measurements. Deep learning using convolutional neural networks (CNN) and various machine learning algorithms were implemented to compare classification accuracy using electroencephalography (EEG) data with different epoch sizes. The CNN model using FC adjacency matrices achieved the highest accuracy with an AUC of 0.905, with 20-s epoch data being optimal for classifying the different LOC causes. The high accuracy of the CNN model was maintained in a prospective cohort. Key distinguishing features among the LOC causes were found in the delta and theta brain wave bands. This research advances the understanding of LOC's underlying mechanisms and shows promise for enhancing diagnosis and treatment selection. Moreover, the AI models can provide accurate LOC differentiation with a relatively small amount of EEG data in 20-s epochs, which may be clinically useful.

A machine learning artefact detection method for single-channel infant event-related potential studies

Objective. Automated detection of artefact in stimulus-evoked electroencephalographic (EEG) data recorded in neonates will improve the reproducibility and speed of analysis in clinical research compared with manual identification of artefact. Some studies use very short, single-channel epochs of EEG data with little recorded EEG per infant—for example because the clinical vulnerability of the infants limits access for recording. Current artefact-detection methods that perform well on adult data and resting-state and multi-channel data in infants are not suitable for this application. The aim of this study was to create and test an automated method of detecting artefact in single-channel 1500 ms epochs of infant EEG. Approach. A total of 410 epochs of EEG were used, collected from 160 infants of 28–43 weeks postmenstrual age. This dataset—which was balanced to include epochs of background activity and responses to visual, auditory, tactile and noxious stimuli—was presented to seven independent raters, who independently labelled the epochs according to whether or not they were able to visually identify artefacts. The data was split into a training set (340 epochs) and an independent test set (70 epochs). A random forest model was trained to identify epochs as either artefact or not artefact. Main results. This model performs well, achieving a balanced accuracy of 0.81, which is as good as manual review of data. Accuracy was not significantly related to the infant age or type of stimulus. Significance. This method provides an objective tool for automated artefact rejection for short epoch, single-channel EEG in neonates and could increase the utility of EEG in neonates in both the clinical and research setting.

Exploring Ultra-short Heart Rate Variability Metrics in Patients with Diabetes Mellitus: A Reliability Analysis.

Ultra-short heart rate variability (HRV) metrics represent autonomic tone parameters derived using small epochs of interbeat interval data. These measures have risen in popularity with the advent of wearable devices that can capture interbeat interval data using electrocardiography (ECG) or photoplethysmography. Autonomic neuropathy in diabetes mellitus (DM) is well established, wherein 5-min HRV is conventionally used. Ultra-short measures have the potential to serve as markers of reduced autonomic tone in this patient population. Data of patients with Type I and Type II DM who had presented to our laboratory for autonomic neuropathy assessment were chosen for analysis. One-minute and 2-min epochs were chosen from 5 min of ECG data using standard software. Time domain, frequency domain, and nonlinear measures were computed from 1 to 2 min epochs, and reliability was compared with measures derived from 5-min HRV using intraclass correlation coefficients (ICCs). Data of 131 subjects (79 males, 52 females; mean age = 53.3 ± 12.16 years) were analyzed. All ultra-short HRV measures derived from 1 min to 2 min data showed good to excellent reliability (median ICC values ranging from 0.83 to 0.94) when compared with 5-min metrics. The notable exception was very low frequency (VLF) power, which showed poor reliability (median ICC = 0.43). Ultra-short HRV metrics derived from 1 to 2 min epochs of ECG data can be reliably used as predictors of autonomic tone in patients with DM. VLF power is poorly reproducible in these small epochs, probably due to variability in respiratory rates. Our findings have implications for ultra-short HRV estimation using short epochs of ECG data.

Probing the broad line region geometry and size of the gravitationally lensed quasar Q2237+0305 with microlensing time series

Lensed quasars are powerful cosmic laboratories; they are used to simultaneously probe various astrophysical phenomena. Microlensing by stars within distant galaxies acts as strong gravitational lenses of multiply imaged quasars, and provides a unique and direct measurement of the lensed quasar internal structure. Microlensing of the continuum emitting region as well as the broad-line region (BLR) is well characterized by four observable indices, μcont, μBLR, WCI (wing-core), and RBI (red-blue), measured directly from the spectra. During the 2004−2007 monitoring period, image A of the quadruply lensed system Q2237+0305 underwent a strong microlensing amplification, while image D remained unaffected. We used 35 epochs of archival spectrophotometric data of Q2237+0305 obtained with the Very Large Telescope of the European Southern Observatory to develop an independent microlensing method for estimating the geometry and size of the BLR. We measured the index time series for the C IV line and the continuum emission at 1450 Å. We built a library of the simulated microlensing index time series that reproduce the observed times series based on three representative BLR models: Keplerian disk (KD), polar wind (PW), and equatorial wind (EW). After sampling the model parameter space, we find that KD is the predominant model, while PW and EW are less likely. We infer that the system is viewed at an intermediate viewing angle i ∼ 35°, and we estimate the most likely C IV BLR half-light radius r1/2 = 51 ± 23 light days. Our results are in good agreement with previous findings in the literature and extend the validity of the index-based approach to a temporal domain.

Mixed-Input Residual Network for Air Temperature Forecasting by Low-Power Embedded Devices

Accuracy and model compactness are essential requirements for weather forecasting models designed for operation on low-power embedded devices. This study developed Mixed-Input Residual Network (MIRNet), a compact temperature-forecasting deep neural network model. MIRNet integrates stacked bidirectional long short-term memory layers using concatenated 1-dimensional and 2-dimensional convolutional layers to improve model accuracy. MIRNet was trained and tested on two datasets: one, IfeData, comprising historical weather data from Ile-Ife, Nigeria and the other a standard weather forecasting dataset called the Jena dataset. Training was carried out using 100 epochs of data partitioned in the standard 80:20 ratio, with an adaptable learning rate strategy. The model was tested for Nth-hour-ahead prediction for 1N24; where N are natural numbers, and performance quantified using metrics such as mean absolute percentage error (MAPE) and mean square error (MSE). The model was also implemented on a Raspberry Pi 4 device with a 1.8 GHz 64-bit quad-core ARM Cortex-A72 processor. The model achieved a MSE of 1.00 x 10-3 on the IfeData dataset, and 1.23x10-4 on the Jena dataset for 1-hour ahead forecasting. This is currently the best verifiable result achieved on the Jena dataset by any prediction model globally. For Nth hour ahead forecasting, MIRNet achieved an MSE generally below 2.0x10-3 for all values of N on the standard Jena dataset. The MSE of MIRNet for N-sequential 1-hour ahead and single Nth hour predictions using the Jena dataset reveal quadratic and linear relationships with N respectively. The model compares favourably with existing models for multi-hour predictions. The developed model is compact and has good forecasting properties.

The Impact of Data Media on Customer Spending Behavior and Consumer Psychology

In the epoch of big data and digital media, consumer behavior has experienced impressive changes. This paper explores the impact of data media on consumer behavior and psychology, particularly concerning social media and digital platforms. The transition from traditional data analytics to the diverse world of big data and digital media is analyzed in this paper as well as how this shift has changed the way companies understand consumer needs and adjust their marketing strategies. The paper also explores how big data enables companies to accurately predict consumer behavior and raises key questions about customers' and firms privacy, data security, and the ethical use of consumer information. In addition, the paper discusses how the COVID-19 pandemic is accelerating digital transformation and changing consumer behavior. Understanding these changes is critical for businesses, marketers, and researchers to gain deeper insights into the current and future trends in consumer behavior in the digital age.

Soft-state optical spectroscopy of the black hole MAXI J1305-704

The X-ray dipper MAXI J1305-704 is a dynamically confirmed black hole (BH) X-ray binary discovered a decade ago. While its only outburst has been studied in detail in X-rays, follow-up at other wavelengths has been scarce. We report here the results from an optical spectroscopy campaign across the outburst of MAXI J1305-704. We analysed two epochs of data obtained by theMagellan ClayTelescope during two consecutive nights, when the source was in a soft X-ray spectral state. We identified typical emission lines from outbursting low-mass X-ray binaries, such as the hydrogen Balmer series, He II4686 Å and the Bowen blend. We focused our analysis on the prominent Hαline, which exhibits asymmetric emission and variable absorption components. We applied both traditional analytical methods and machine-learning techniques in order to explore the association of the absorption features with outflowing phenomena, and we conclude that they are best explained by broad absorption. This result is consistent with reports from other outbursting BHs, where optical outflows have predominantly been observed in the hard state. Further observations at different X-ray states are key to properly test whether this behaviour is universal and to determine the implications for the disc wind physics.

Multi-night cortico-basal recordings reveal mechanisms of NREM slow-wave suppression and spontaneous awakenings in Parkinson's disease.

Sleep disturbance is a prevalent and disabling comorbidity in Parkinson's disease (PD). We performed multi-night (n = 57) at-home intracranial recordings from electrocorticography and subcortical electrodes using sensing-enabled Deep Brain Stimulation (DBS), paired with portable polysomnography in four PD participants and one with cervical dystonia (clinical trial: NCT03582891). Cortico-basal activity in delta increased and in beta decreased during NREM (N2 + N3) versus wakefulness in PD. DBS caused further elevation in cortical delta and decrease in alpha and low-beta compared to DBS OFF state. Our primary outcome demonstrated an inverse interaction between subcortical beta and cortical slow-wave during NREM. Our secondary outcome revealed subcortical beta increases prior to spontaneous awakenings in PD. We classified NREM vs. wakefulness with high accuracy in both traditional (30 s: 92.6 ± 1.7%) and rapid (5 s: 88.3 ± 2.1%) data epochs of intracranial signals. Our findings elucidate sleep neurophysiology and impacts of DBS on sleep in PD informing adaptive DBS for sleep dysfunction.

Searching for pairs of actively accreting supermassive black holes in the Gaia FPR GravLens catalog

Observational data on dual supermassive black holes (SMBHs) are scarce, even though they should be common according to the current hierarchical formation model for galaxies. We present hereby a method to detect such dual systems, involving an already known quasar, as two luminosity peaks with angular separations smaller than one arcsecond (1″). It is based on the analysis of the spatial distribution of the Gaia data delivered in the Focused Product Release (FPR), that published for the first time detections made by Gaia at the epoch level. This approach relies on the selection of elongated spatial distributions of epoch measurements, due to two clumps of detections, checked against images coming from the PanSTARRS1 and 2MASS surveys. We have analyzed the 3.8 million of quasars flagged as single in the Gaia FPR extra-galactic table and we currently end up with only 8 dual AGN candidates, applying very conservative thresholds. G-band magnitude of each of the two luminosity peaks is provided for these candidates. The mean value of the angular separation between these two components is found to be equal to 0.3″. This demonstrates the ability of Gaia epoch data to detect previously unknown dual sources, which might be compact dual AGN.

Forest Stand Growth Forecasting in the Context of Changes in the Insolation of Building Roofs

This article analyzed the long-term impact of tree growth on the decrease in sunlight of a planned photovoltaic installation. As trees grow, they can obstruct sunlight and reduce the amount of insolation reaching the PV panels, and knowledge about the degree of this reduction is crucial when assessing the long-term economic effects of the investment. Currently, when planning PV installation, the roof facing, latitude, topography, and neighboring buildings are taken into account. However, there is no tool to assess the impact of tree growth over time on changes in the level of sunlight on the roof surface. The authors propose a tool for performing such an assessment using geospatial analysis techniques. The data from airborne laser scanning (ALS) and unmanned aerial vehicles with laser scanning (ULS) were used to model trees in two epochs. The authors used two epochs of data to evaluate mathematical models of tree growth. The evaluated tree growth model was then used to predict forest stand growth over a 30-year period and to assess the change in sunlight due to the modeled growth. For the case study, two test sites have been taken into account. At site 1 and site 2, respectively, 25 and 12 points representing the centers of potential photovoltaic panels were designed, for which the annual sum of minutes during which the point remained exposed to sunlight was calculated. The results showed that the use of ALS and ULS provided valuable data for determining current and predicted shading of trees. Moreover, the presented studies showed that the changes in forest stand growth had a significant impact on decreasing the insolation of building construction. In the case of both test sites, the change in tree height after 30 years resulted in a reduction in the number of minutes of sunlight by more than 50%. The authors suggest that the developed technique should be incorporated into PV installation planning tools to ensure reliable prediction of the long-term profitability of designed PV installations.