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The effect of Sm on thermally stimulated decomposition of SiOx and the formation of silicon nanocrystals during high-temperature air annealing was studied by measuring the spectra of photoluminescence (PL), infrared (IR) reflection and transmission and Micro-Raman scattering (RS). The SiOx: Sm films were produced by thermal co-evaporation of silicon monoxide and metallic samarium powders in a vacuum onto polished c-Si and silica substrates. The Sm concentration varied from 0.8 to 2.0 wt. %. In the SiOx: Sm films annealed at 970 ºC, an intense PL band of silicon nanocrystals was found in the 850–900 nm region, the position and intensity of which depend on the samarium concentration. The formation of Si nanocrystals in those films is also confirmed by studying their Raman spectra. At the same time, no Si nanocrystals were found in undoped SiOx films annealed under similar conditions. The analysis of these spectra made it possible to determine the sizes of silicon nanocrystals: 3.7 nm from the PL spectra and 3.6 nm from the Raman spectra. A possible mechanism of thermally stimulated interaction of Sm atoms with the SiOx matrix is discussed. [ABSTRACT FROM AUTHOR]

This paper formulates the image super resolution problem as various denoising problems. In particular, the discrete cosine transform zero padding approach is used to generate an initial high resolution image. Then, three different time frequency analysis based denoising methods are applied iteratively to improve the quality of the super resolution image. In particular, the two dimensional quaternion valued singular spectrum analysis (2DQSSA) based denoising method, the empirical mode decomposition (EMD) based denoising method and the discrete cosine transform based denoised method are applied. For the 2DQSSA based denoising method, the luminance plane is used as the real part of the quaternion valued image. Since different color planes in the quaternion valued image are fused together via the quaternion valued operation, some high frequency information missing in one color plane can be generated using those in other color planes. On the other hand, for the EMD based denoising method, the selection of the intrinsic mode functions (IMFs) is formulated as a binary linear programming problem. Here, the high frequency components generated by the aliasing are removed by discarding some IMFs. The computer numerical simulation results show that our proposed method can achieve the super resolution performance better than those without performing any one of the above three time frequency analysis based denoising. [ABSTRACT FROM AUTHOR]

Among several complications related to physiotherapy, osteosarcopenia is one of the most frequent in elderly patients. This condition is limiting and quite harmful to the patient's health by disabling several basic musculoskeletal activities. Currently, the test to identify this health condition is complex. In this study, we use mid-infrared spectroscopy combined with chemometric techniques to identify osteosarcopenia based on blood serum samples. The purpose of this study was to evaluate the mid-infrared spectroscopy power to detect osteosarcopenia in community-dwelling older women (n = 62, 30 from patients with osteosarcopenia and 32 healthy controls). Feature reduction and selection techniques were employed in conjunction with discriminant analysis, where a principal component analysis with support vector machines (PCA–SVM) model achieved 89% accuracy to distinguish the samples from patients with osteosarcopenia. This study shows the potential of using infrared spectroscopy of blood samples to identify osteosarcopenia in a simple, fast and objective way. [ABSTRACT FROM AUTHOR]

This paper presents a digital low computation detection method for signal nonlinear distortion. After presetting ADC sampling rate and completing data sampling, the low sampling rate data extracted from the sampling data complete the autocorrelation analysis to correct and calculate the accurate fundamental frequency. According to the relationship between the fundamental frequency and the sampling rate, the complete periodic segment data of the signal cutting out from the sampling data carry out the digital quadrature detection operation to obtain the value of each harmonic component and nonlinear distortion. Experiments show that the calculation of this method is less than that of FFT spectrum analysis method when measuring the THD lower than 15 order. When calculating the 15-order THD at 1024 points, the calculation time is reduced by 44.26%. Moreover, the digital quadrature detection method has high measurement accuracy with less than 0.6% error of detecting 5-order THD. [ABSTRACT FROM AUTHOR]

That the speed of light in vacuum is constant is a cornerstone of modern physics. However, recent experiments have shown that when the light field is confined in the transverse plane, the observed propagation speed of the light is reduced. This effect is a consequence of the transverse structure which reduces the component of wavevector of the light in the direction of propagation, thereby modifying both the phase and group velocity. Here, we consider the case of optical speckle, which has a random transverse distribution and is ubiquitous with scales ranging from the microscopic to the astronomical. We numerically investigate the plane-to-plane propagation speed of the optical speckle by using the method of angular spectrum analysis. For a general diffuser with Gaussian scattering over an angular range of 5°, we calculate the slowing of the propagation speed of the optical speckle to be on the order of 1% of the free-space speed, resulting in a significantly higher temporal delay compared to the Bessel and Laguerre–Gaussian beams considered previously. Our results have implications for studying optical speckle in both laboratory and astronomical settings. [ABSTRACT FROM AUTHOR]

Advancements in ground- and space-based telescopes have resulted in an acute need for and improvement in the breadth and accuracy of the atomic data vital for the analysis of astronomical spectra. Many data, even for astrophysically important elements such as the iron group, have analyses dating back over 50 years, which are no longer suitable for the interpretation of modern, high-resolution astrophysical spectra. The Imperial College Spectroscopy group is addressing this need by measuring new atomic data and improving the accuracy of existing data using Fourier transform spectroscopy from the infrared to the vacuum ultraviolet. Analysis of these high-resolution spectra has led to new and improved atomic data for a wide range of astrophysically important elements. Reductions in uncertainties of transition wavelengths and energy levels are routinely over an order of magnitude and transition probabilities are measured to a few percent of uncertainty. Our work has increased both the quantity and quality of available atomic data. In this paper, we provide an update of our recent investigations and research plans. We also detail the improvements being made to our world-class laboratory to ensure we are able to meet the future data needs of the astrophysical community. [ABSTRACT FROM AUTHOR]

In this study, we present a new method for measuring the tilt of an image sensor to the lens. The assembly accuracy of the lens and image sensor units has a significant influence on the image quality for users. Any misalignment causes non-uniform defocus and distortion in the camera image. Our approach measures the tilt of the image sensor based on the amplitude (i.e., contrast or defocus) and phase (i.e., distortion) included in the camera image without special devices and precisely calibrated references. The amplitude and phase were separated in the frequency domain. The validity of the proposed method was confirmed through a numerical simulation using optical design software. The sensor tilt can be measured from the chart tilt, which does not cause any non-uniform defocus or distortion. This study achieved the simultaneous measurement and analysis of two parameters and can contribute to increase the productivity of manufacturers and improve the image quality for users. [ABSTRACT FROM AUTHOR]

To achieve more efficient and accurate measurement of low- and intermediate-level radioactive waste (LILW), a room-temperature semiconductor detector CdZnTe (CZT) can be used to replace the high-purity germanium detector. The traditional method cannot identify nuclides since the energy resolution of CZT detectors is poor, and the spectrum have more overlapping peaks, compared with HPGe detectors. Therefore, in this study, we proposed a gamma spectrum analysis method based on an intelligent dynamic library. Simulation spectrum experiment and standard-source spectrum experiment achieved LILW all-nuclide quantitative analysis based on a CZT detector for the first time globally. [ABSTRACT FROM AUTHOR]

The twin-roll casting (TRC) method was used to prepare 6061 plates with a content of 0.4 wt.% Er. The refining influence of Er on the homogenization process of the 6061 plates was studied by means of metallographic observation, scanning electron microscopy, transmission electron microscopy, and energy spectrum analysis. The results show that during the homogenization process, Mg2Si dissolves, and Si can be easily combined with Er to form the AlSiEr eutectic compound, and the refining effect is significant. The addition of Er can obviously promote the homogenization process of the 6061 TRC plates and shorten its duration from 12 to 6 h. The nanoscale Al3Er second phase dispersedly precipitates in the matrix and gives rise to pinning dislocations and subgrain boundaries, which improve the comprehensive properties of the 6061 TRC plates after homogenization. After homogenization, the tensile strength, yield strength, and elongation increased by 22.66 MPa, 17.64 MPa, and 4.91%, respectively, compared with those of the 6061 cast-rolled plate without Er. [ABSTRACT FROM AUTHOR]

A complex analysis was made of histological preparations of the large intestine without pathology, chronic colitis, and surgical material of primary colorectal adenocarcinoma with due allowance for recovery of the patients after standard treatment by confocal Raman spectroscopy with spatial resolution. It was shown that the main difference in the spectra of the normal and pathologically changed tissues are the positions and relative intensities of the vibrational bands at frequencies of 1248, 1537, 1587, and 1620 cm–1. The spectral changes were analyzed in terms of the Stark approach on the assumption of change of the local electric field in the epithelial cells during oncogenesis. [ABSTRACT FROM AUTHOR]

Failures of bearing would cause sudden downtime and severe accidents that may require significant maintenance and economic consequences. Therefore, condition monitoring of bearing is extremely important for ensuring reliable and efficient operation. The analysis of electric power supply signals offers a non-intrusive and cost-effective method for detecting various incipient faults in rotary machines. However, using inverter or variable speed drive would influence the electric power supply signals, causing difficulties in detection and diagnosis results. To reduce these influences and develop effective detection techniques, this paper focuses on using electric power supply signals as an approach to detect incipient faults in the bearing of driving induction motor based on high-order bispectral analysis. It takes the advantage of providing abundant information from electric signatures of downstream driving motors to improve the performance of bearing's health monitoring. Modulation signal bispectrum (MSB) and cyclic bispectrum (CCB) exhibit significance to detect normal and abnormal bearing conditions, whereas remarkable effort is dedicated from MSB result due to the inclusion of phase information with amplitude modulation in both lower and higher sidebands. It can conclude that effective measurements of electrical power supply signals utilize reliable fault diagnosis information in the mechanical components of the driving motor. [ABSTRACT FROM AUTHOR]

The interaction between nanosecond laser and glass is complicated, and defects such as micro-cracks and fractures are prone to be generated during laser scribing. Acoustic emission (AE) technique is applied in this paper to monitor laser scribing of float glass. Strong consistency between AE signals and width of laser ablation area is found. With laser power increasing from 11.2 to 26.9 W, root mean square (RMS) values increase from 0.160 to 0.634 V, and width of heat affected zone (HAZ) increase from 12.396 to 53.711 μ m . Correlation between AE signals and laser scribing quality is investigated by characteristic analysis. When scanning speed is kept constant (200 mm/s) and laser power is gradually increasing from 11.2 to 24.2 W, RMS values and MARSE are increasing (0.3–1.2 V, 600–1500 correspondingly), and scribing quality is declining continuously ( W C W G increase from 0 to 0.343). Time-frequency spectrum analysis by CWT could be applied in defects diagnosis. Three prominent frequency bands contents (150 ~ 180 kHz, 200 ~ 250 kHz, and 280 ~ 320 kHz) were generated during laser scribing process. Higher frequency bands contents (200 ~ 250 kHz, and 280 ~ 320 kHz) are demonstrated to reflect defects by analyzing distribution characteristic of frequency contents. Intensity of higher frequency compositions is vigorous (≥ 0.5) when there are defects, while less than 0.5 with no defects. [ABSTRACT FROM AUTHOR]

Background: Accurate estimation of the imminent fragility fracture risk currently represents a challenging task. The novel Fragility Score (FS) parameter, obtained during a Radiofrequency Echographic Multi Spectrometry (REMS) scan of lumbar or femoral regions, has been developed for the non-ionizing estimation of skeletal fragility. Aims: The aim of this study was to assess the performance of FS in the early identification of patients at risk for incident fragility fractures with respect to bone mineral density (BMD) measurements. Methods: Data from 1989 Caucasians of both genders were analysed and the incidence of fractures was assessed during a follow-up period up to 5 years. The diagnostic performance of FS to discriminate between patients with and without incident fragility fracture in comparison to that of the BMD T-scores measured by both Dual X-ray Absorptiometry (DXA) and REMS was assessed through ROC analysis. Results: Concerning the prediction of generic osteoporotic fractures, FS provided AUC = 0.811 for women and AUC = 0.780 for men, which resulted in AUC = 0.715 and AUC = 0.758, respectively, when adjusted for age and body mass index (BMI). For the prediction of hip fractures, the corresponding values were AUC = 0.780 for women and AUC = 0.809 for men, which became AUC = 0.735 and AUC = 0.758, respectively, after age- and BMI-adjustment. Overall, FS showed the highest prediction ability for any considered fracture type in both genders, resulting always being significantly higher than either T-scores, whose AUC values were in the range 0.472–0.709. Conclusion: FS displayed a superior performance in fracture prediction, representing a valuable diagnostic tool to accurately detect a short-term fracture risk. [ABSTRACT FROM AUTHOR]

During high wind events with dry weather conditions, electric power systems can be the cause of catastrophic wildfires. In particular, conductor-vegetation contact has been recognized as the major ignition cause of utility-related wildfires. There is a urgent need for accurate wildfire risk analysis in support of operational decision making, such as vegetation management or preventive power shutoffs. This work studies the ignition mechanism caused by transmission conductor swaying out to nearby vegetation and resulting in flashover. Specifically, the studied limit state is defined as the conductor encroaching into prescribed minimum vegetation clearance. The stochastic characteristics of the dynamic displacement response of a multi-span transmission line are derived through efficient spectral analysis in the frequency domain. The encroachment probability at a specified location is estimated by solving a classical first-excursion problem. These problems are often addressed using static-equivalent models. However, the results show that the contribution of random wind buffeting to the conductor dynamic displacement is appreciable under turbulent strong winds. Neglecting this random and dynamic component can lead to an erroneous estimation of the risk of ignition. The forecast duration of the strong wind event is an important parameter to determine the risk of ignition. In addition, the encroachment probability is found highly sensitive to vegetation clearance and wind intensity, which highlights the need of high resolution data for these quantities. The proposed methodology offers a potential avenue for accurate and efficient ignition probability prediction, which is an important step in wildfire risk analysis. [ABSTRACT FROM AUTHOR]

During the 2015 "summer of welcome", the mass arrival of refugees to Germany triggered widely publicised acts of pro-refugee solidarity among citizens. To date, scholarship has largely focused on hostility towards immigrants—including refugees-, and few studies shed light on the determinants of acceptance, so that refugee solidarity remains poorly understood. In this paper, we hypothesise that pro-refugee engagement does not just reflect humanitarian concerns, but also a more general acceptance of socio-cultural diversity in German society. Relying on unique survey data gathered in 2019–2020, we show that a majority of urban Germans indeed engaged in some form of support. We employ latent class analysis to capture the spectrum of diversity attitudes within the urban population. A series of regression models show that diversity attitudes are powerful predictors of pro-refugee engagement. Overall, our study helps advancing immigration scholarship by demonstrating that widespread support for refugees is strongly associated with more general diversity assent. [ABSTRACT FROM AUTHOR]

The review summarizes the results of publications on methods for determining arsenic in metallurgical materials from 2000 to 2022. [ABSTRACT FROM AUTHOR]

The aim of this study is accomplished by utilizing the H/V spectral ratio method (Nakamura technique) to investigate the site response at fifteen seismic site stations. Regarding this method, some significant local site parameters such as the fundamental frequencies (f0) of soft sediments, peak amplitudes (A0) corresponding to H/V spectral ratios, spectrum amplitude, and spectrum rotation were computed over fifteen seismic site stations in the study area. A site response study has been done by using microtremor data from fifteen seismic site stations. The fundamental frequency response varies from 0.67 to 11.54 Hz within the study area. Resulting from this analysis, the fundamental frequency response is low in and around the KHAT, PNGL, and SBLA seismic site stations, which indicates the possibility of thick sedimentary soil cover at greater depth. Similarly, the peak amplification at these seismic site stations is found to be in the range of 2–4. Further, the contour map is prepared with the help of fundamental frequency response values obtained from the H/V curve to observe the area of seismic hazard potential in the study region. Also, obtained H/V results from different seismic site stations were verified by considering the SESAME guidelines and conditions for reliability. Then, spectrum rotation and spectrum amplitude analysis were carried out to know the characteristics of H/V with respect to azimuth and frequency in the sedimentary soil. [ABSTRACT FROM AUTHOR]

Radon-222 is considered an important earthquake precursor because it is a noble gas and for its radioactive decay characteristics. However, radon anomalies are controlled not only by seismic activity, but also by environmental and other related natural phenomena such as atmospheric pressure, atmospheric temperature, and Earth tides. These effects must be assessed and eliminated from the radon signal to enhance its capability as a precursor. To identify these effects and their modes of variability in radon progeny time series, we have applied singular spectrum analysis (SSA). We analyzed hourly measurements of radon progeny for a 6-month period acquired from a 10-m-deep borehole at the Multi-Parametric Geophysical Observatory at Ghuttu, Garhwal Himalaya. The site also has a superconducting gravimeter for acquiring continuous gravity suitable to detect tidal waves with high accuracy. Our results show clear evidence of diurnal and semidiurnal daily periodicities, along with slight signatures of the M2–O1 waves in the radon time signal. Results show that even at 10 m depth these effects play a part in controlling radon release from the soil. [ABSTRACT FROM AUTHOR]

In this paper, we prove that the Morse index of the critical Möbius band in the 4-dimensional Euclidean ball B 4 equals 5. It is conjectured that this is the only embedded non-orientable free boundary minimal surface of index 5 in B 4 . One of the ingredients in the proof is a comparison theorem between the spectral index of the Steklov problem and the energy index. The latter also enables us to give another proof of the well-known result that the index of the critical catenoid in B 3 equals 4. [ABSTRACT FROM AUTHOR]

Urban atmospheric environmental problems have raised increasing attention in recent years. To confirm the impact of plant dust deposition capacity on urban atmosphere and spectral characteristics, this study carried out experiments in Xuhui District and Minhang District of Shanghai, and 4 common greening species were selected as research objects. In order to explore the changes in vegetation spectral characteristics, ASD FieldSpec 3 Spectrometer and 1/10000 electronic balance were used to measure the spectral data and dust data of samples. The results show as follows: (1) 380–680 nm and 750–1350 nm are the best spectral wavelengths to analyze the influence of dust deposition on spectrum. (2) The canopy reflectance spectra of tree species decrease with the increase of dust deposition, especially in the wavelength range of 750–1350 nm. (3) The first derivative and the second derivative are beneficial to observe the spectral changes and judge the position of the red edge. The red edge position of some tree species is easy to move under the interference of dust deposition. (4) Among the four tree species, the spectrum of Osmanthus fragrans is relatively undisturbed by dust deposition, and Osmanthus fragrans is a great tree species for urban greening. The research made a foundation for the future use of spectral information to estimate vegetation dust. [ABSTRACT FROM AUTHOR]

The Infrared (IR) spectra usually assume the samples are 3D materials. Thus, it is difficult to identify functional groups in 2D materials at the edge and the center of the 2D surface. Therefore, it is crucial to introduce analysis methods that enable the investigation of 2D carbon materials such as graphene and its derivatives using IR spectra. This study calculates the infrared spectra of pyrene-like molecules as an insight for a simple analysis of graphene quantum dots using a semi-empirical method. These IR spectra were correlated to the electronic transition and charge distribution associated with functional groups. The IR spectra analysis focuses on comparing the pristine and functionalized molecule at the wavenumber 1400–2000 cm−1, especially to identify the C=C stretching mode and 3000–3500 cm−1 for C–H and OH stretching. Moreover, the determination of excitation spectra was carried out to analyze the electronic transition of the molecules in the ultraviolet–visible region (200–900 nm) calculated using ZINDO method. The investigation of the pyrene-like GQD permitted the identification of the edge and center surface functionalization in 2D carbon materials. [ABSTRACT FROM AUTHOR]

The study was aimed to evaluate the performance of a newly developed spectroscopy-based non-invasive and noncontact device (SAMIRA) for the simultaneous measurement of hemoglobin, bilirubin and oxygen saturation as an alternative to the invasive biochemical method of blood sampling. The accuracy of the device was assessed in 4318 neonates having incidences of either anemia, jaundice, or hypoxia. Transcutaneous bilirubin, hemoglobin and blood saturation values were obtained by the newly developed instrument which was corroborated with the biochemical blood tests by expert clinicians. The instrument is trained using Artificial Neural Network Analysis to increase the acceptability of the data. The artificial intelligence incorporated within the instrument determines the disease condition of the neonate. The Pearson's correlation coefficient, r was found to be 0.987 for hemoglobin estimation and 0.988 for bilirubin and blood gas saturation respectively. The bias and the limits of agreement for the measurement of all the three parameters were within the clinically acceptance limit. [ABSTRACT FROM AUTHOR]

The paper, which consists of two parts, considers in detail the method of discrete mobility spectrum analysis (DMSA) proposed by the authors as well as its application to determine the parameters of charge carriers in CdHgTe. The first part of the work was a brief review of the existing methods for analyzing the field dependences of the Hall coefficient and conductivity in structures with a multi-carrier spectrum of charge carriers. In the second part of the work, the physical properties of the mobility spectrum envelope in the mobility spectrum analysis (MSA) method are analyzed and the main idea, features, and algorithm of the DMSA method are presented. On the example of studying the electrical properties of numerous samples of CdHgTe epitaxial films, the high sensitivity of the DMSA was confirmed. Using DMSA, we analyzed a number of dependences of the conductivity tensor components σxx and σxy on the magnetic field B, available in the literature, and compared the results of the analysis with those obtained with other methods. [ABSTRACT FROM AUTHOR]

At present, existing research works on action recognition are still not ideal, when most of the video content is redundant such as video clips without any object motion, and human actions in the video are complex. The reasons are as follows: (1) Most of them lack attention to key-motion information of the video, thus irrelevant information will be input into the model. (2) And there is a lack of interaction between video spatial and temporal information, which may cause the loss of detailed motion information in the video. In this paper, we propose a Key-detail Motion Capturing Network (K-MCN) to solve these problems, which contains two modules. The first one is the Video Key-motion Spectrum Analyzer (VKSA) module. In this module, the video optical flow can be subjected to frequency spectrum analysis, filtering and clustering to extract the key-motion frames. The second one is the Multiscale Motion Spatiotemporal Interaction module, which allows multi-scale modeling and fusion of spatial and temporal features extracted from key-motion frames, enabling the network to realize the interaction and supplement of multiscale spatiotemporal information. Finally, we conducted extensive experiments on the UCF101, HMDB51 and Something-SomethingV1 datasets, and the results showed that our method achieves better performance compared with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Accurate electricity demand forecasting can provide a timely and effective reference for economic control and facilitate the secure production and operation of power systems. However, electricity data are well known for their nonlinearity and multi-seasonal features, making it challenging to construct forecasting models. This study investigates the combination of singular spectrum analysis to facilitate the construction of decomposition-based forecasting approaches for electricity load. First, we demonstrate and emphasize the importance of separability for specifically extracting different features hidden in the original data; moreover, only by using the separable feature subseries, the constructed individual model can capture the inner and distinct characteristics of original series more effectively. Second, this study decomposes the electricity load into several significant features using singular spectrum analysis. Each feature series is predicted separately to construct aggregate results. In particular, we propose SSA-based period decomposition to not only perform separable decomposition but also overcome the border effect, which has received little attention in previous work. Finally, to verify the effectiveness of the proposed method, we conduct an empirical study and compare the performance of the discussed models. The empirical results show that the proposed approach can obtain the expected forecasting performance and is a reliable and promising tool for extracting different features. [ABSTRACT FROM AUTHOR]

Our main goal in the present paper is to expand the known class of noncompact manifolds over which the L 2 -spectrum of a general Dirac operator and its square is maximal. To achieve this, we first find sufficient conditions on the manifold so that the L p -spectrum of the Dirac operator and its square is independent of p for p ≥ 1 . Using the L 1 -spectrum, which is simpler to compute, we generalize the class of manifolds over which the L p -spectrum of the Dirac operator is the real line for all p. We also show that by applying the generalized Weyl criterion, we can find large classes of manifolds with asymptotically nonnegative Ricci curvature, or which are asymptotically flat, such that the L 2 -spectrum of a general Dirac operator and its square is maximal. [ABSTRACT FROM AUTHOR]

A Duistermaat–Guillemin–Gutzwiller trace formula for Dirac-type operators on a globally hyperbolic spatially compact stationary spacetime is achieved by generalising the recent construction by Strohmaier and Zelditch (Adv Math 376:107434, 2021) to a vector bundle setting. We have analysed the spectrum of the Lie derivative with respect to a global timelike Killing vector field on the solution space of the Dirac equation and found that it consists of discrete real eigenvalues. The distributional trace of the time evolution operator has singularities at the periods of induced Killing flow on the space of lightlike geodesics. This gives rise to the Weyl law asymptotic at the vanishing period. [ABSTRACT FROM AUTHOR]

This paper mainly concerns the area growth and bottom spectrum of complete stable minimal surfaces in a three-dimensional manifold with scalar curvature bounded from below. When the ambient manifold is the Euclidean space, by an elementary argument, it is shown directly from the stability inequality that the area of such minimal surfaces grows exactly as the Euclidean plane. Consequently, such minimal surfaces must be flat, a well-known result due to Fisher-Colbrie and Schoen as well as do Carmo and Peng. In the case of general ambient manifold, explicit area growth estimate is also derived. For the bottom spectrum, a self-contained argument involving positive Green's function is provided for its upper bound estimates. The argument extends to stable minimal hypersurfaces in a complete manifold of dimension up to six with sectional curvature bounded from below. [ABSTRACT FROM AUTHOR]

Micro-holes were prepared by using metal wire coated graphite paint, and microstructure and morphology nearby micro-hole wall were observed and analysis by scanning electron microscope and energy spectrum analysis and so on. The results of showed that morphology of micro-hole wall was not nearly changed when pouring temperature range was 700 ~ 800 °C. However, carbon element was detected on the micro-hole wall when using metal wire coated graphite paint hole core to prepare micro-hole. The diffusion of carbon from surface of hole cores increased with the increasing of temperature. And then, the amount of carbon or carbides retained on the surface of the micro-holes wall also increased. They will form tiny bulges on the surface of the micro-holes after the molten metal cooling, which result in affecting the surface quality of micro-holes. [ABSTRACT FROM AUTHOR]

Coastal areas are among the densely populated regions in the world with growing population and subsequent increasing demands for water. Understanding the long-term variations in available water resources aids in efficient water conservation, management and allocation strategies. The present study investigated the long-term trends in groundwater depths (GWDs) for pre-monsoon and post-monsoon seasons in the coastal district of Kollam during the period 1996∼2017, where groundwater is the primary source for domestic and agricultural uses during summer season. The trends examined using the modified Mann Kendall (mMK), innovative trend analysis (ITA) and Sen's slope estimator indicated a decreasing pre-monsoon GWD trends at an average of −0.5m/decade in 63% of the wells, while increasing post-monsoon GWD trends at an average rate of +0.43m/decade in 72% of the wells at 5% significance level. The singular spectrum analysis (SSA) captured monotonic as well as non-monotonic trend trajectories for the GWDs. About 41% wells exhibited a correlation below — 0.5 (p<0.05) between post-monsoon GWDs and JJASO (June, July, August, September and October) rainfall totals. The increasing post-monsoon GWDs could be related to recent changes in the southwest monsoon patterns over the peninsular India. Adequate planning and management of existing water resources could impart better control on water conservation strategies under the scenario of climate change. [ABSTRACT FROM AUTHOR]

The paper consisting of two parts presents a detailed consideration of the proposed method of discrete mobility spectrum analysis and its application for studying the parameters of charge carriers in CdHgTe. The first part of the paper is a brief review of the existing methods of the analysis of the magnetic-field dependences of the Hall coefficient and conductivity in structures with a multi-carrier spectrum. The underlying principles of various methods are considered, including the original mobility spectrum analysis proposed by Beck and Anderson, the multi-carrier fitting, and the iterative method developed by Dziuba and Górska, as well as more recent developments. The advantages, drawbacks, and limits of applicability of these methods are discussed. [ABSTRACT FROM AUTHOR]

Objectives: Diagnosis of non-convulsive status epilepticus (NCSE) is challenging and outcomes during follow-up are not clear. This study aimed to conduct power spectrum analysis in NCSE and measure outcomes of patients. Methods: We searched continuous EEG monitoring (cEEG) recordings to identify patients of NCSE. An artifact-free cEEG epoch of continuous 60 s was chosen for spectral power analysis. We also collected electronic medical records of the patients for extracting clinical information. Patients recruited were followed up at least every half a year. Results: There were 48 patients with 64 independent NCSE episodes during different course of disease recruited in the study, with a mean age of 40.3 ± 19.1 years (range, 12–72 years), including 24 males (50%) and 24 females (50%). When the spectral power of 60 s equaled to 11.30 μV2 for predicting impairment of consciousness, (sensitivity, specificity) = (0.979, 0.625). When the spectral power of 60 s equaled to 52.70 μV2 for predicting myoclonic jerks, (sensitivity, specificity) = (0.783, 0.756). There were 27 patients (56.3%) followed up with a duration over 12 months. Nineteen patients (70.4%) continued to have seizures. Eleven (40.7%) resisted to at least two kinds of appropriate anti-seizure medication at maximum tolerated levels. Five patients with prolonged NCSE suffered from loss of brain parenchymal volume on follow-up MRI scans. Conclusion: Spectral power analysis can be used to detect mental status and limb jerks. Early diagnosis and treatment of NCSE are important, which can influence outcomes of the patients during follow-up. [ABSTRACT FROM AUTHOR]

As the globe has witnessed the pandemic, epidemic diseases exert a strong impact on human beings and ecosystems. Since the Sun is the primary energy source of the Earth, some scientific pioneers attempted to search for the discernible relation between solar activity and the incidence of epidemics. In this study, the periodic changes and trends of ancient Chinese epidemic data were analyzed in comparison with those of sunspot numbers, a solar activity proxy. The results show that the epidemic and solar activity changes are in good agreement to a certain extent, especially during the Gleissberg and the de Vries cycles. The wavelet coherence shows that the frequency of the epidemic data and sunspot numbers are highly associated. In addition, results from the ensemble empirical mode decomposition illustrate consistent variations in low-frequency decompositions. This study has important implications for further understanding of the potential impact of solar activity on Earth's biosphere, the underlying mechanism of which needs further exploration. [ABSTRACT FROM AUTHOR]

Optoelectrical, electronic, and thermodynamic properties of {6}cycloparaphenylene ({6}CPP) and its derivative, N,N-dimethyl-diaza{6}cycloparaphenylene (DMDA{6}CPP), are theoretically investigated in this work. Using the density functional theory (DFT) and time-dependent DFT (TD-DFT) supported by the APFD and B3LYP-D3 functionals and the 6-311++G (d,p) basis set, the new compound, DMDA{6}CPP, revealed an optical band gap E opt = 1.20 eV. With the lowest energy transition excited state recorded at λ = 1029 nm and associated light-harvesting efficiency (LHE) = 25 % , this derivative demonstrates good characteristics as absorber material in organic solar cell (OSC) devices. The analysis of its charge transport abilities exhibits a ratio of hole/electron reorganization energies λ h / λ e = 0.68 , providing very consistent information on good hole transport abilities. Moreover, this derivative of {6}CPP registered amazing linear and NLO properties with polarizability and first-order hyperpolarizability α = 626.7 a.u and β = 10195.8 a.u, respectively. These values are far higher than those of both para-nitro aniline (p-NA) and urea commonly used as reference molecules in linear and nonlinear optics, positioning this molecule as a perfect candidate for NLO applications and optoelectronics. Molecular atomization energy calculation, spectra analysis, and thermodynamic descriptor calculations have also revealed a very stable structure. [ABSTRACT FROM AUTHOR]

In this paper, we use the fourth-order Runge–Kutta and fast Fourier transform-based spectral analysis to study the power spectrum of the phenomena of multi-periodicity, crenelated, mixed-mode oscillations, and chaos when the values of the bandwidth and the cubic-nonlinear term (CNT) of the filter vary in the cubic-nonlinear optoelectronic oscillator (CN-OEO). On the one hand, when the high and low cut-off frequencies are sufficiently far apart, it is numerically proved that the presence of the CNT reveals the frequency combs generation with a free spectral range equal to the inverse of the time-delay. Likewise, the width of the central peak narrows with the increase of the CNT, showing that the system becomes more and more selective in terms of oscillation frequencies. On the other hand, when the cut-off frequencies are sufficiently close, harmonic and sub-harmonic frequencies are recorded. In either case, CN-OEO displays oscillations whose frequencies remain greater than those of a standard optoelectronic oscillator. [ABSTRACT FROM AUTHOR]

Hyperspectral remote sensing, which retrieves the water quality parameters by direct high-resolution analysis of the electromagnetic spectrum reflected from the water surface, has been widely applied for inland water quality detection. Such a new approach provides an opportunity to generate real-time data from water with the noncontact method, largely improving working efficiency. By summarizing the development and current applications of hyperspectral remote sensing, we compare the relative merits of varying remote sensing platforms, popular inversion models, and the application of hyperspectral monitoring of chlorophyll-a (Chl-a), transparency, total suspended solids (TSS), colored dissolved organic matter (CDOM), phycocyanin (PC), total phosphorus (TP), and total nitrogen (TN) water quality parameters. Most studies have focused on spaceborne remote sensing, which is usually used to monitor large waterbodies for Chl-a and other water quality parameters with optical properties; semiempirical, bio-optical, and semianalytical models are frequently used. With the rapid development of aerospace technology and near-surface remote sensing, the spectral resolution of remote sensing imaging technology has been dramatically improved and has begun to be applied to small waterbodies. In the future, the multiplatform linkage monitoring approach may become a new research direction. Advanced computer technology has also enabled machine learning models to be applied to water quality parameter inversion, and machine learning models have higher robustness than the three commonly used models mentioned above. Although nitrogen and phosphorus, with nonoptical properties, have also received attention and research from some scholars in recent years, the uncertainty of their mechanisms makes it necessary to maintain a cautious attitude when treating such research. [ABSTRACT FROM AUTHOR]

Background: The aim of this real-life cross-sectional explorative study was to compare radiofrequency echographic multi-spectrometry (REMS) with dual-energy X-rays absorptiometry (DXA) in the BMD assessment of patients receiving peritoneal dialysis (PD). Furthermore, we investigated the relationship between lumbar aortic calcifications (AOCs) and the DXA lumbar measurements. Methods: Consecutive patients referring to the PD clinic of our hospital were included. Lumbar spine and femur scans were acquired with both techniques (including lumbar laterolateral DXA scans). The risk assessment of two fracture risk algorithms (FRAX® and DeFRA®) were compared. Cohen's k coefficients were used to assess the inter-technique agreement in the classification of patients as osteoporotic. Lumbar AOCs were estimated semi-quantitatively on laterolateral DXA scans. Results: 41 patients were enrolled. No significant differences were documented between the BMD T-scores measured through DXA or REMS at the femur. At the lumbar spine, the DXA anteroposterior mean T-score (− 0.49 ± 1.98) was significantly higher than both the laterolateral DXA (− 1.66 ± 0.99) and the REMS (− 2.00 ± 1.94) measurements (p < 0.01 vs both). No significant differences were found between the DXA and REMS fracture risk estimates with both algorithms. The inter-technique Cohen's k coefficient (for the worst T-score, any site) was 0.421, p < 0.001. The discrepancy between the DXA laterolateral and anteroposterior lumbar T-score was positively associated with the AOCs extent and severity (r = 0.402, p < 0.01). Conclusions: Our data showed a promising agreement, in a real-life PD setting, between DXA and REMS BMD assessment and in the consequent fracture risk estimation and confirm the AOCs interference on the diagnostic accuracy of lumbar DXA. [ABSTRACT FROM AUTHOR]

Real-time Doppler estimation of sonar signals is of great significance to underwater communication and detection. The Hyperbolic-Frequency-Modulated (HFM) waveform-based speed spectrum estimation is one of the modern methods for underwater Doppler estimation, and this paper presents a detailed analyses of the derivation and the performance of this method. The analyzing results show the robustness of the method on multipath channels and multi-highlight targets and provide the main lobe width of the speed spectrum. Moreover, the paper examines the spectral properties of the HFM signal from the frequency-domain Doppler invariance property, which could provide insights for better understanding and utilization of this Doppler invariant waveform. [ABSTRACT FROM AUTHOR]

In this paper we use data from the Hyperspectral Imager for the Coastal Ocean (HICO, NASA) hyperspectral satellite sensor to identify and qualitatively estimate the content of cyanobacteria (CB) in the coastal waters of the Black and Azov seas. Three data processing algorithms are applied: two use a spectrum shape analysis and one is semianalytical. The first algorithm uses the determination of the minimum of the reflection coefficient spectrum for remote sensing in the spectral region of ~680 nm, the so-called CB presence index. Based on this algorithm, a second was developed to identify the presence of phycocyanin in water, which is a marker pigment for CB. The second algorithm analyzes the minimum of the reflectance spectrum for remote sensing (Rrs) in the region of 620 nm. The third semianalytical algorithm makes it possible to determine the concentration of phycocyanin. The results of automatic identification of the presence of the CB were compared with the visual analysis of the spectra. The results of applying three algorithms to images are compared. It is concluded that phycocyanin is present in the studied water areas and its presence is a consequence of the presence of CB. [ABSTRACT FROM AUTHOR]

An automatic algorithm is proposed for identifying areas of cyanobacteria (CB) blooms in the Azov Sea based on an analysis of the optical spectra of MODIS satellite data. The algorithm has been validated by comparison with high-resolution quasi-synchronous Landsat data. The results obtained with this algorithm were used to study the spatiotemporal variability of CB blooms in the Azov Sea in 2003–2019 and give examples of the evolution of blooms in individual years. CB blooms in the Azov Sea are observed from March to November with a peak in August. The most intense and longest CB blooms are observed in Taganrog Bay. In the spring months, they spread from here eastward along the northern coast. In June–July, CB begin to intensively spread southward along the eastern coast and then, in some years, they penetrate into the center of the basin under the action of anticyclonic currents. An analysis of interannual variability has shown that the intensity of blooms was the highest in 2004–2011, with a peak in 2008–2009, and dropped significantly in 2011–2019. One reason for this drop is the decreased flow of the Don and Kuban rivers, as well as the increased wind speed observed in recent years. [ABSTRACT FROM AUTHOR]

Sarcopenia is defined as decreased skeletal muscle mass and function, and is an important cause of frailty in the elderly, also being associated with vascular lesions and poor microcirculation. The present study aimed to combine noninvasive pulse measurements, frequency-domain analysis, and machine learning (ML) analysis (1) to determine the effects on the pulse waveform induced by sarcopenia and (2) to develop discriminating models for patients with possible sarcopenia. Radial blood pressure waveform (BPW) signals were measured noninvasively for 1 min in 133 subjects who visited Tri-Service General Hospital for geriatric health checkups. They were assigned to a robust group and a possible-sarcopenia group that combined dynapenia, presarcopenia, and sarcopenia. Two classification methods were used: ML analysis and a self-developed scoring system that used 40 harmonic pulse indices as features: amplitude proportions and their coefficients of variation, and phase angles and their standard deviations. Significant differences were found in several spectral indices of the BPW between possible-sarcopenia and robust subjects. Threefold cross-validation results indicated excellent discrimination performance, with AUC equaling 0.77 when using LDA and 0.83 when using our scoring system. The present noninvasive and easy-to-use measurement and analysis method for detecting sarcopenia-induced changes in the arterial pulse transmission condition could aid the discrimination of possible sarcopenia. [ABSTRACT FROM AUTHOR]

As UAV Internet of things applications continue to grow, so does interest in the development of air-to-ground (ATG) systems, which can effectively expand communication system coverage. To maximize utilization of frequency resources, the ATG system adopts frequency reuse with the 5G system on the ground. This research studies the co-channel interference between ATG and 5G at 3.5 GHz as well as the neighboring channel interference with C-band fixed satellite service. We do research on various ATG aircraft flying altitudes and horizontal distances, which yields multiple potential interference reduction strategies. Finally, numerical results are shown to demonstrate the efficacy and productivity of our proposed methods. [ABSTRACT FROM AUTHOR]

The defining of upper crustal structures is an essential process for understanding the tectonic evolution and geodynamics of a region. In this context, the paper aims to determine basement depth and upper crustal structures through the Bouguer gravity anomalies and seismic reflection sections in Denizli Graben located in the western part of Turkey. The gravity data have been analyzed using the power spectrum technique. The results from this technique show that the average Moho depth, basement depth of the middle layer, and the depth of sediment basin in the region have been calculated as 33.6, 12.8, and 3.9 km, respectively. Furthermore, the Moho depth of the region has been estimated using gravity anomalies and is computed to be about 33 km in Denizli Graben. As such, it is shown that the depth values obtained from power spectrum analysis and Moho depth calculations are consistent with each other. Bouguer gravity anomalies have been also modeled three-dimensionally (3D). In the model map, the basement depth of the Denizli Graben has been calculated as approximately 9–10 km. Besides these, by interpreting the seismic section, the depth of the interface in the Denizli Graben has been obtained as approximately 2634 m for one-way travel time. This depth is quite shallow compared to the one (i.e., around 10 km) obtained from the gravity model. Thus, the interface seen in the seismic section is not considered to be the deepest part of the Denizli Graben. From the geothermal and oil exploration perspectives, the obtained basement and interface depths are quite beneficial, especially for the drilling planning. [ABSTRACT FROM AUTHOR]

Typically, gamma spectra are analyzed by fitting individual peaks. In an entirely different approach to analysis of gamma spectra, non-linear least-squares methods have been applied in a program known as VRF to fitting gamma spectra taken as single, self-consistent sets of data. According to theory, uncertainties from non-linear least-squares fitting are obtained directly from the inverse of the second-derivative matrix. There remains a need to independently verify theoretical uncertainties as implemented in VRF. We report results of a numerical experiment in which uncertainties derived from analysis of a large number of spectra are compared to theortical uncertainties from VRF. [ABSTRACT FROM AUTHOR]

Abstract—The article provides a brief description of the software package DECH for processing and analysis of astronomical spectra. DECH supports all stages of processing and analysis of spectral data, including image preprocessing, spectra extraction (including those with a variable tilted slit), wavelength calibration by a two-dimensional polynomial, continuum normalization (manual or automatic), measurement of equivalent widths and radial velocities in various ways, cross-correlation analysis, etc. The DECH software package is actively used by astronomers from different countries and continues to be improved. In particular, utilities for processing and analysis of data from the high-resolution fiber-feed echelle spectrograph installed at 6-m telescope of Special Astrophysical Observatory of Russian Academy of Sciences were added in the latest version. Software provides high-precision measurements of radial velocities, including those for detection of extraterrestrial planets. [ABSTRACT FROM AUTHOR]

A quantitative analysis of the spectrum of HD 108564 is performed. It is a star of the main sequence of spectral class K5V, the atmosphere of which is depleted in metals. The high-quality observed HARPS spectra are downloaded from the ESO archive. Abundances of elements in the atmosphere are obtained by fit of observational profiles of the C I lines and selected lines of the C2 molecules, and the O I, Ca I, Si I, Sc II, Cr I, CI, OI, Na I, Mg I, Si I, Ca I, Sc II, Ti I, Ti II, Cr I, Mn I, Fe I, Fe II, Co I, Ni I, Cu I, and Zn I. Abundances are determined iteratively, with a recalculation of the input parameters, which are effective temperature Teff at a fixed value of gravity logg (or log g for a fixed Teff value). The effect of variations of Teff or log g, which provide the same abundances of A(Fe I) and A(Fe II), on the abundances of other elements are determined. The obtained results indicate an excess of light elements (C, O, and Si) compared to the group of iron. The absence of the lithium line at 670.8 nm is confirmed with an estimate of A(Li) < –12.5 for the upper limit of lithium abundance in the abundance scale, in which the sum of all abundances is 1.0. The determined radial velocity equal to Vrad = 111.21 km/s is consistent with the known estimates of other researchers. Apparent rotation velocity V sin i = 1.12 ± 0.5 km/s is determined. [ABSTRACT FROM AUTHOR]

Recognition of epileptic electroencephalogram (EEG) signals is vital to epileptic seizure detection. Current research on seizure detection mostly focused on generalized seizure analysis. Compared with generalized seizures, childhood focal epilepsy generally originates in one hemisphere of the brain, and the seizure onset patterns vary from patient to patient, making it difficult for analysis. Meanwhile, the frequency, amplitude and rhythm of EEG activities in children of different ages are different, making childhood focal epilepsy detection challenging. In this paper, the channel correlation features (CCF) containing the regional scalp EEG cross correlations and auto-correlations are proposed for children focal epilepsy EEG representation. The spectrum features are also extracted to characterize EEGs in different frequency bands. Further, a convolutional autoencoder (CAE)-based deep feature learning and dimensionality reduction model is proposed for discriminative EEG frequency domain feature extraction. An early seizure detection framework for the childhood focal epilepsy based on the fused CAE and CCF EEG features is finally developed, and an ensemble classification model (ECM) is applied to solve the imbalance issue between ictal, interictal, and preictal. The performance is evaluated on the EEG dataset collected by the Children's Hospital, Zhejiang University School of Medicine (CHZU). Experiments show that the proposed algorithm can reach to the highest accuracy of 93.57% for the early seizure detection in childhood focal epilepsy. [ABSTRACT FROM AUTHOR]

Peripheral object discrimination is hindered by a central dynamic mask presented between 150 and 300 ms after stimulus onset. The mask is thought to interfere with task-relevant feedback coming from higher visual areas to the foveal cortex in V1. Fan et al. (2016) supported this hypothesis by showing that the effect of mask can be further delayed if the task requires mental manipulation of the peripheral target. The main purpose of this study was to better characterize the temporal dynamics of foveal feedback. Specifically, in two experiments we have shown that (1) the effect of foveal noise mask is sufficiently robust to be replicated in an online data collection (2) in addition to a change in sensitivity the mask affects also the criterion, which becomes more conservative; (3) the expected dipper function for sensitivity approximates a quartic with a global minimum at 94 ms, while the best fit for criterion is a quintic with a global maximum at 174 ms; (4) the power spectrum analysis of perceptual oscillations in sensitivity data shows a cyclic effect of mask at 3 and 12 Hz. Overall, our results show that foveal noise affects sensitivity in a cyclic manner, with a global dip emerging earlier than previously found. The noise also affects the response bias, even though with a different temporal profile. We, therefore, suggest that foveal noise acts on two distinct feedback mechanisms, a faster perceptual feedback followed by a slower cognitive feedback. [ABSTRACT FROM AUTHOR]

This study aimed to automatically classify live cells based on their cell type by analyzing the patterns of backscattered signals of cells with minimal effect on normal cell physiology and activity. Our previous studies have demonstrated that label-free acoustic sensing using high-frequency ultrasound at a high pulse repetition frequency (PRF) can capture and analyze a single object from a heterogeneous sample. However, eliminating possible errors in the manual setting and time-consuming processes when postprocessing integrated backscattering (IB) coefficients of backscattered signals is crucial. In this study, an automated cell-type classification system that combines a label-free acoustic sensing technique with deep learning-empowered artificial intelligence models is proposed. We applied an one-dimensional (1D) convolutional autoencoder to denoise the signals and conducted data augmentation based on Gaussian noise injection to enhance the robustness of the proposed classification system to noise. Subsequently, denoised backscattered signals were classified into specific cell types using convolutional neural network (CNN) models for three types of signal data representations, including 1D CNN models for waveform and frequency spectrum analysis and two-dimensional (2D) CNN models for spectrogram analysis. We evaluated the proposed system by classifying two types of cells (e.g., RBC and PNT1A) and two types of polystyrene microspheres by analyzing their backscattered signal patterns. We attempted to discover cell physical properties reflected on backscattered signals by controlling experimental variables, such as diameter and structure material. We further evaluated the effectiveness of the neural network models and efficacy of data representations by comparing their accuracy with that of baseline methods. Therefore, the proposed system can be used to classify reliably and precisely several cell types with different intrinsic physical properties for personalized cancer medicine development. [ABSTRACT FROM AUTHOR]

Time-series data, such as unsteady pressure-sensitive paint (PSP) measurement data, may contain a significant amount of random noise. Thus, in this study, we investigated a noise-reduction method that combines multivariate singular spectrum analysis (MSSA) with low-dimensional data representation. MSSA is a state-space reconstruction technique that utilizes time-delay embedding, and the low-dimensional representation is achieved by projecting data onto the singular value decomposition (SVD) basis. The noise-reduction performance of the proposed method for unsteady PSP data, i.e., the projected MSSA, is compared with that of the truncated SVD method, one of the most employed noise-reduction methods. The result shows that the projected MSSA exhibits better performance in reducing random noise than the truncated SVD method. Additionally, in contrast to that of the truncated SVD method, the performance of the projected MSSA is less sensitive to the truncation rank. The projected MSSA achieves denoising effectively by extracting smooth trajectories in a state space from noisy input data. Expectedly, the projected MSSA will be effective for reducing random noise in not only PSP measurement data, but also various high-dimensional time-series data. [ABSTRACT FROM AUTHOR]