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The use of Light Amplification by Stimulated Emission of Radiation (i.e., LASERs or lasers) by the U.S. Department of Defense is not new and includes laser weapons guidance, laser-aided measurements, and even lasers as weapons (e.g., Airborne Laser). Lasers in the support of telecommunications is also not new. The use of laser light in fiber optics has shattered thoughts on communications bandwidth and throughput. Even the use of lasers in space is no longer new. Lasers are being used for satellite-to-satellite crosslinking. Laser communication can transmit orders-of-magnitude more data using orders-of-magnitude less power and can do so with minimal risk of exposure to the sending and receiving terminals. What is new is using lasers as the uplink and downlink between the terrestrial segment and the space segment of satellite systems. More so, the use of lasers to transmit and receive data between moving terrestrial segments (e.g., ships at sea, airplanes in flight) and geosynchronous satellites is burgeoning. This manuscript examines the technological maturation of employing lasers as the signal carrier for satellite communications linking terrestrial and space systems. The purpose of the manuscript is to develop key performance parameters (KPPs) to inform the U.S. Department of Defense initial capabilities documents (ICDs) for near-future satellite acquisition and development. By appreciating the history and technological challenges of employing lasers, rather than traditional radio frequency sources for satellite uplink and downlink signal carriers, this manuscript recommends ways for the U.S. Department of Defense to employ lasers to transmit and receive high bandwidth, and large-throughput data from moving platforms that need to retain low probabilities of detection, intercept, and exploit (e.g., carrier battle group transiting to a hostile area of operations, unmanned aerial vehicle collecting over adversary areas). The manuscript also intends to identify commercial sector early-adopter fields and those fields likely to adapt to laser employment for transmission and receipt.

Three means are investigated for further increasing the accuracy of the characterization of a thin film on a substrate, from the transmittance spectrum T(λ) of the specimen, based on the envelope method. Firstly, it is demonstrated that the accuracy of characterization, of the average film thickness d¯ and the thickness non-uniformity ∆d over the illuminated area, increases, employing a simple dual transformation utilizing the product T(λ)xs(λ), where Tsm(λ) is the smoothed spectrum of T(λ) and xs(λ) is the substrate absorbance. Secondly, an approach is proposed for selecting an interval of wavelengths, so that using envelope points only from this interval provides the most accurate characterization of d¯ and ∆d, as this approach is applicable no matter whether the substrate is transparent or non-transparent. Thirdly, the refractive index n(λ) and the extinction coefficient k(λ) are computed, employing curve fitting by polynomials of the optimized degree of 1/λ, instead of by previously used either polynomial of the optimized degree of λ or a two-term exponential of λ. An algorithm is developed, applying these three means, and implemented, to characterize a-Si and As98Te2 thin films. Record high accuracy within 0.1% is achieved in the computation of d¯ and n(λ) of these films.

This article presents the results of two studies investigating increased intra-individual variability (IIV) in the performance of individuals with attention deficit hyperactivity disorder (ADHD), in two cognitive domains: numerosity judgments and quantitative and verbal reasoning. Study 1, a pre-registered experiment, involved approximately 200 participants (42.66% female; mean age: 36.86; standard deviation of age: 10.70) making numerical judgments at two time-points. ADHD-symptom severity was assessed on a continuous scale. In Study 2, we collected the data of approximately 3000 examinees who had taken a high-stakes admissions test for higher education (assessing quantitative and verbal reasoning). The sample comprised only people formally diagnosed with ADHD. The control group consisted of approximately 200 000 examinees, none of whom presented with ADHD. The results of Study 1 revealed a positive correlation between IIV (distance between judgments at the two time-points) and ADHD symptom severity. The results of Study 2 demonstrated that IIV (distance between the scores on two test chapters assessing the same type of reasoning) was greater among examinees diagnosed with ADHD. In both studies, the findings persisted even after controlling for performance level. The results indicate that individuals with ADHD, those without, exhibit less consistent numerosity judgments and greater fluctuation in performance on verbal and quantitative reasoning. The measurement of the same psychological constructs appears to be less precise among individuals with ADHD compared to those without. We discuss the theoretical contributions and practical implications of our results for two fields: judgment and decision-making, and assessment.

Given the recent increase in demand for electricity, it is necessary for renewable energy sources (RESs) to be widely integrated into power networks, with the two most commonly adopted alternatives being solar and wind power. Nonetheless, there is a significant amount of variation in wind speed and solar irradiance, on both a seasonal and a daily basis, an issue that, in turn, causes a large degree of variation in the amount of solar and wind energy produced. Therefore, RES technology integration into electricity networks is challenging. Accurate forecasting of solar irradiance and wind speed is crucial for the efficient operation of renewable energy power plants, guaranteeing the electricity supply at the most competitive price and preserving the dependability and security of electrical networks. In this research, a variety of different models were evaluated to predict medium-term (24 h ahead) wind speed and solar irradiance based on real-time measurement data relevant to the island of Crete, Greece. Illustrating several preprocessing steps and exploring a collection of “classical” and deep learning algorithms, this analysis highlights their conceptual design and rationale as time series predictors. Concluding the analysis, it discusses the importance of the “features” (intended as “time steps”), showing how it is possible to pinpoint the specific time of the day that most influences the forecast. Aside from producing the most accurate model for the case under examination, the necessity of performing extensive model searches in similar studies is highlighted by the current work.

In the case of the inductive voltage transformer (VT), the resonance phenomenon may be the main reason for its poor transformation accuracy of the non-sinusoidal voltage. This problem mainly results from the leakage inductance and the parasitic capacitance of its primary winding. The application of the sinusoidal voltage with a frequency from 20 Hz to 20 kHz presented in this study ensures proper identification of the resonance frequencies of the medium-voltage (MV) inductive VTs. The results are consistent with the values obtained in the reference condition at their nominal primary voltage. Therefore, it is proven that the proposed solution is effective in all cases. The influence of the main frequency variation of the non-sinusoidal primary voltage on the resonance properties of the inductive VT is also studied. Moreover, the tests indicate that the capacitance of the load of the secondary winding may cause a decrease in their resonance frequency.

Papilledema is often difficult to detect in children. Ocular point-of-care ultrasound (POCUS) measurement of the optic nerve sheath diameter (ONSD) is a non-invasive test for increased intracranial pressure (ICP), but no consensus exists on normal pediatric ONSD values. Detection of optic disc elevation (ODE, a component of papilledema) using POCUS has recently been qualitatively described. We sought to establish the diagnostic accuracy of different ODE cutoffs to detect increased ICP in children who underwent ocular POCUS in our pediatric emergency department (PED). We retrospectively reviewed charts of patients ages 0–18 years who received ocular POCUS in our tertiary PED between 2011 and 2016. Patients were included if their archived POCUS examinations were deemed high-quality by a POCUS expert and they underwent ICP determination within 48 h after ocular POCUS. A blinded POCUS expert measured ODE, optic disc width at mid-height (ODWAMH), and ONSD. Receiver-operator curve analysis was performed for various cutoffs for these measurements in detecting increased ICP. 76 eyes from 40 patients met study criteria. 26 patients had increased ICP. The mean ODE of both eyes (ODE-B) generated the largest area under the curve (0.962, 95% CI 0.890–1). The optimal ODE-B cutoff was 0.66 mm, with a sensitivity of 96% (95% CI 79–100%) and a specificity of 93% (95% CI 79–100%). 1/40 (2.5%) of patients with ODE-B < 0.66 had increased ICP. ODE-B may represent the optimal ocular POCUS measurement for detecting increased ICP in children, and future prospective studies could more accurately describe the diagnostic performance of different pediatric ODE-B cutoffs.

To identify a specific sensorimotor impairment feature of autism spectrum disorder (ASD), we focused on temporal processing with millisecond accuracy. A synchronized finger-tapping task was used to characterize temporal processing in individuals with ASD as compared to typically developing (TD) individuals. We found that individuals with ASD showed more variability in temporal processing parameters than TD individuals. In addition, temporal processing instability was related to altered motor performance. Further, receiver operating characteristic (ROC) curve analyses indicated that altered temporal processing can be useful for distinguishing between individuals with and without ASD. These results suggest that instability of temporal processing with millisecond accuracy is a fundamental feature of sensorimotor impairments in ASD.

Emergence of deep neural networks in the healthcare has transformed the process of analyzing medical images especially when it comes to diagnose brain tumor disease. As patients are asked to undergo computed tomography, magnetic resonance imaging, etc. following traditional approaches by medical experts which consume a lot of time and abnormal growth of tissue inside brain may remain undiagnosed due to its extremely tiny size at initial stage. Therefore, developing an automated system utilizing artificial intelligence, deep learning in internet of healthcare can overcome drawbacks associated with traditional approaches to achieve efficient, accurate and quick outcomes in the healthcare to save billions of lives worldwide. Consideration of 3264 brain MRI images has been done which have been acquired from kaggle comprising of 2764 images having tumor and 500 healthy brain MRI images. In this paper, a novel approach has been presented comprising of a pre-trained model namely visual geometry group having 16 layers, a well-established convolutional neural network to extract significant features from input data which are further fed to the support vector classifier for distinguishing infected images from health ones. A process of transfer learning has also been deployed with VGG16 due to acquiring its pre-trained features from keras to save a lot of time while training the proposed model. Moreover, Internet of Healthcare framework can aid radiologists in making decisions on real-time applications to provide timely recommendation as well as treatment to the patients. Thus, validation is performed on unseen data to ensure efficient performance of the model and model gained 98.16% accuracy, 99.09% precision, 98.73% recall and 98.91% F 1-score which outperforms the existing approaches and demonstrated potential of revolutionizing neuroimaging field and patient care.

Background: The cost of heart failure hospitalizations in the US alone is over USD 10 billion per year. Over 4 million Americans are hospitalized every year due to heart failure (HF), with a median length of stay of 4 days and an in-hospital mortality rate that exceeds 5%. Hospitalizations of patients with HF can be prevented by early detection of lung congestion. Our study assessed a new contact-free optical medical device used for the early detection of lung congestion. Methods: The Gili system is an FDA-cleared device used for measuring chest motion vibration data. Lung congestion in the study was assessed clinically and verified via two cardiologists. An algorithm was developed using machine learning techniques, and cross-validation of the findings was performed to estimate the accuracy of the algorithm. Results: A total of 227 patients were recruited (101 cases vs. 126 controls). The sensitivity and specificity for the device in our study were 0.91 (95% CI: 0.86–0.93) and 0.91 (95% CI: 0.87–0.94), respectively. In all instances, the observed estimates of PPVs and NPVs were at least 0.82 and 0.90, respectively. The accuracy of the algorithm was not affected by different covariates (including respiratory or valvular conditions). Conclusions: This study demonstrates the efficacy of a contact-free optical device for detecting lung congestion. Further validation of the study results across a larger and precise scale is warranted.