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A set of colour tuning K2Ba(MoO4)2: Dy3+/Eu3+ (KBM: Dy3+/Eu3+) phosphors were fabricated by solid-state method. The phase purity of as-prepared materials was validated by XRD and DRS analyses. The KBM: 0.03Dy3+ sample exhibits two major luminescence lines at 481 and 572 nm when excited by 351 nm light, corresponding to Dy3+ ions' typical transitions of 4F9/2 → 6HJ (J = 15/2, 13/2). By introducing Eu3+ ions into KBM: 0.03Dy3+ phosphor, the ET from Dy3+ to Eu3+ ions arises, leading to colour tuning from green via yellowish green to pink with changing Eu3+ ions' concentration, along with the lowest CCT of KBM: 0.03Dy3+/0.03Eu3+ sample. It can be verified by PLE and PL spectra analysis combined with the decay times of KBM: 0.03Dy3+/yEu3+ samples. The energy transfer mechanism is also expounded. Meanwhile, the emission intensities at 423 K still maintain 72.8% for Dy3+ ions and 91.2% for Eu3+ ions relative to 303 K of the KBM: 0.03Dy3+/0.03Eu3+ sample, displaying its brilliant thermal stability. All these results connote that KBM: Dy3+/Eu3+ phosphors are prospected to be used for the near-UV excited white light-emitting diodes. [ABSTRACT FROM AUTHOR]

Human liver-derived metabolically competent HepaRG cells have been successfully employed in both two-dimensional (2D) and 3D spheroid formats for performing the comet assay and micronucleus (MN) assay. In the present study, we have investigated expanding the genotoxicity endpoints evaluated in HepaRG cells by detecting mutagenesis using two error-corrected next generation sequencing (ecNGS) technologies, Duplex Sequencing (DS) and High-Fidelity (HiFi) Sequencing. Both HepaRG 2D cells and 3D spheroids were exposed for 72 h to N-nitrosodimethylamine (NDMA), followed by an additional incubation for the fixation of induced mutations. NDMA-induced DNA damage, chromosomal damage, and mutagenesis were determined using the comet assay, MN assay, and ecNGS, respectively. The 72-h treatment with NDMA resulted in concentration-dependent increases in cytotoxicity, DNA damage, MN formation, and mutation frequency in both 2D and 3D cultures, with greater responses observed in the 3D spheroids compared to 2D cells. The mutational spectrum analysis showed that NDMA induced predominantly A:T → G:C transitions, along with a lower frequency of G:C → A:T transitions, and exhibited a different trinucleotide signature relative to the negative control. These results demonstrate that the HepaRG 2D cells and 3D spheroid models can be used for mutagenesis assessment using both DS and HiFi Sequencing, with the caveat that severe cytotoxic concentrations should be avoided when conducting DS. With further validation, the HepaRG 2D/3D system may become a powerful human-based metabolically competent platform for genotoxicity testing. [ABSTRACT FROM AUTHOR]

Seismic images are essential for understanding the subsurface geological structure and resource distribution. However, the accuracy and certainty of geological analysis using seismic images are limited by the resolution and signal-to-noise ratio. Simultaneously improving resolution and suppressing random noise with traditional methods can be quite challenging. This research proposes a new approach called SeisGAN which leverages a generative adversarial network to address the challenge at hand. Due to the lack of high-resolution noiseless and low-resolution noisy seismic data, stochastic parameter control is employed to simulate a vast range of diverse, paired seismic data for SeisGAN training. The results on the synthetic dataset demonstrate that the proposed method is effective in enhancing the resolution and suppressing the random noise in the original images. Spectrum analysis shows that the proposed method increases the bandwidth of the original data, primarily at high frequencies. Ablation experiments reveal that, under similar conditions, SeisGAN outperforms traditional convolutional neural networks. Incorporating the VGG loss in the generator loss function improves the model's ability to recover high-frequency details. The application of the technique on two publicly available field seismic datasets indicates SeisGAN's excellent generalizability, despite being trained only on synthetic seismic data. Compared with bicubic interpolation and traditional noise suppression and resolution enhancement methods, SeisGAN is capable of effectively suppressing the random noise and enhancing the dominant frequency of field seismic data, making it easier to identify adjacent thin layers and fault features, even for small-scale faults. The zoomed images are clearer and easier to interpret. Furthermore, an example of automatic machine fault identification demonstrates the significant contribution of the SeisGAN-enhanced image to accurate fault recognition. [ABSTRACT FROM AUTHOR]

Purpose: Higher dietary intake or higher circulating levels of the trace element boron have been associated with beneficial effects on human health. However, the relationship between plasma boron levels and survival in the general population is not known. Therefore, we aimed to assess the association between plasma boron concentrations and all-cause mortality in a population-based cohort from northern Germany (n = 863 individuals; median age 62.3 years, 42.8% women). Methods: Plasma boron concentrations (median 31.9 µg/L [22.9; 43.5]) were measured by inductively coupled plasma-mass spectrometry. Cox proportional hazards regression models adjusted for relevant confounders were used to associate plasma boron concentrations with all-cause mortality. Results: After a median follow-up time of 11 years, n = 99 individuals had died. In the overall sample, plasma boron concentrations were associated with all-cause mortality in the crude model (HR: 1.07 [95% CI 1.03–1.11] per 5-unit-increment). However, multivariable adjustment rendered the association non-significant (HR: 1.03 [95% CI 0.99–1.07]). Sex-stratified analyses revealed slightly higher mortality hazards with increasing plasma boron concentrations in women (HR: 1.11 [95% CI 1.03–1.18], pInteraction = 0.034), but not in men (HR: 1.00 [95% CI 0.95–1.06]). Conclusion: We conclude that in a moderate-sized sample from the general population, higher plasma boron concentrations were associated with a higher risk of all-cause mortality in women, but not in men. Due to the low number of events in the female subsample (n = 27), this observation has to be interpreted with caution. [ABSTRACT FROM AUTHOR]

A simple glass system [Li2O–B2O3–NiO] was prepared via the melt-quenching method. This article attempts to outline the structural-optical relationships of Ni ions-bearing lithium borate glass. XRD confirmed the glassy nature of the samples. The increase in density confirmed the formation of a rigid network constructed by BO 4 units. Infrared spectra analysis confirmed a strong structure conversion from BO 3 to BO 4 units, with observed mitigation in non-bridging oxygen bonds. Optical absorbance spectra were analyzed to extract optical features. Nickel ions were exploited to disclose the surrounding environment in glass. A set of optical transitions of nickel ions in octahedral forms were assigned at 1287, 815, 487 and 425 nm, which are exploited to probe ligand field effects. A decrease in splitting energy values and increased interelectronic repulsion values were obtained. This is owing to the weak intensity of the interaction of Ni ions with surrounding ligands and the localization of d-electrons, respectively. These variations led to a strong enhancement in the ionicity of bonding nature. Furthermore, the brown color of the samples confirmed the occupancy of Ni 2 + in octahedral sites. Moreover, the indirect optical band gap increased due to the harsh reduction in NBO. These findings pave the way toward developing optical materials with the desired optical features. [ABSTRACT FROM AUTHOR]

Krylov complexity has been proposed as a diagnostic of chaos in non-integrable lattice and quantum mechanical systems, and if the system is chaotic, Krylov complexity grows exponentially with time. However, when Krylov complexity is applied to quantum field theories, even in free theory, it grows exponentially with time. This exponential growth in free theory is simply due to continuous momentum in non-compact space and has nothing to do with the mass spectrum of theories. Thus by compactifying space sufficiently, exponential growth of Krylov complexity due to continuous momentum can be avoided. In this paper, we propose that the Krylov complexity of operators such as O = Tr[FμνFμν] can be an order parameter of confinement/deconfinement transitions in large N quantum field theories on such a compactified space. We explicitly give a prescription of the compactification at finite temperature to distinguish the continuity of spectrum due to momentum and mass spectrum. We then calculate the Krylov complexity of N = 4, 0 SU(N) Yang-Mills theories in the large N limit by using holographic analysis of the spectrum and show that the behavior of Krylov complexity reflects the confinement/deconfinement phase transitions through the continuity of mass spectrum. [ABSTRACT FROM AUTHOR]

The High Dam is one of the world's biggest embankments dams. Moreover, the new city of Aswan, which locates on the western side of the Nile River, is one of the cities that was established to overcome the growing population problem. Therefore, the detailed geophysical studies for the active faults are of more importance for assessing the seismic stability for both of them. Indeed, the emergency spillway fault was documented as a normal and inactive fault. While, a moderate earthquake (ML = 4.6) was recorded along this fault in 2010, about 4.5 km away from the Dam. Hence, its activity must be re-evaluated. The seismic activity along the fault and its extension has been studied. The seismicity distributions and the fault plane solution indicate normal faulting with a strike-slip component and shallow focal depth. Moreover, pore pressure and fluid diffusion play an essential role in fault activation process. On the other hand, the magnetic data for the research area was subjected to a detailed analysis. 2D spectrum analysis and 3D Euler deconvolution methods, were used to analyze and interpret the aeromagnetic anomaly data so as to better understand the tectonic framework of the study region. Finally, the integrated geophysical data delineate the trend of the emergency spillway fault which extends NW–SE. This fault could assist in updating the current seismic source model around the High Dam and new Aswan city for evaluating the seismic hazard for both of them. [ABSTRACT FROM AUTHOR]

Based on the Galerkin method with Chebyshev polynomials, we numerically solve the Orr-Sommerfeld problem with high accuracy. We calculate the spectra of the planeparallel Poisseuille and Couette flows, the Blasius flow, and flows with pressure gradient above a flat semi-infinite plate for different values of the wave number and the Reynolds number. [ABSTRACT FROM AUTHOR]

In this paper, we discuss properties of the singular value decomposition (SVD-decomposition) within the framework of the analysis of the numerical rank in ill-posed problems for determining frequency properties of discrete sequences consisting of trigonometric binomials. The properties of the numerical rank of the SVD-decomposition are indicated. We propose an algorithm for determining the frequencies of trigonometric binomials involved in the original function that forms a discrete sequence; this algorithm is based on estimates of the numerical rank. We obtain a stable criterion for estimating the numerical rank based on the arithmetic-mean estimators of the pseudo-null-space of the trajectory matrix. Also, we present the results of numerical experiments that demonstrate the consistency of the arithmetic-mean estimator of the pseudo-zero-space for the analysis of the spectrum of noisy sequences. [ABSTRACT FROM AUTHOR]

Purpose: Diet-related diseases are advancing as the leading cause of death globally. As self-reporting of diet by patients can be associated with errors, stable isotopes of human tissues can be used to diagnose diseases, understand physiology, and detect change in diet. This study investigates the effect of type and amount of food on the nitrogen and carbon concentration (Nconc and Cconc) and isotopic composition (δ15N and δ13C) in human scalp hair and fingernails. Methods: A total of 100 residents participated in the study whereas only 74 individuals provided complete diet history. Sixty-six food items majorly available to them were also collected. The Nconc, Cconc, δ15N and δ13C values of human hair, nails and food items were determined. Results: The Nconc, Cconc, δ15N and δ13C values between plant-sourced and animal-sourced food items, as well as human hair and nail tissue were significantly different (p < 0.05). The δ15N value of human tissues was distinct between lacto-vegetarians and omnivores by 0.9‰. The δ15N and δ13C values of human tissues increased by 0.4–0.5‰ with every 5% increase in the consumption of animal protein. Conclusions: The study helps to demarcate lacto-vegetarians from omnivores, and estimate the percentage of animal protein in diet based on the dual isotope values of human tissues. It also acts as a reference to determine isotopic composition of hair tissue provided the isotope value of nail tissue is known and vice versa. [ABSTRACT FROM AUTHOR]

Background: After esophagectomy, the postoperative rate of anastomotic leakage is up to 30% and is the main driver of postoperative morbidity. Contemporary management includes endoluminal vacuum sponge therapy (EndoVAC) with good success rates. Vacuum therapy improves tissue perfusion in superficial wounds, but this has not been shown for gastric conduits. This study aimed to assess gastric conduit perfusion with EndoVAC in a porcine model for esophagectomy. Material and methods: A porcine model (n = 18) was used with gastric conduit formation and induction of ischemia at the cranial end of the gastric conduit with measurement of tissue perfusion over time. In three experimental groups EndoVAC therapy was then used in the gastric conduit (− 40, − 125, and − 200 mmHg). Changes in tissue perfusion and tissue edema were assessed using hyperspectral imaging. The study was approved by local authorities (Project License G-333/19, G-67/22). Results: Induction of ischemia led to significant reduction of tissue oxygenation from 65.1 ± 2.5% to 44.7 ± 5.5% (p < 0.01). After EndoVAC therapy with − 125 mmHg a significant increase in tissue oxygenation to 61.9 ± 5.5% was seen after 60 min and stayed stable after 120 min (62.9 ± 9.4%, p < 0.01 vs tissue ischemia). A similar improvement was seen with EndoVAC therapy at − 200 mmHg. A nonsignificant increase in oxygenation levels was also seen after therapy with − 40 mmHg, from 46.3 ± 3.4% to 52.5 ± 4.3% and 53.9 ± 8.1% after 60 and 120 min respectively (p > 0.05). An increase in tissue edema was observed after 60 and 120 min of EndoVAC therapy with − 200 mmHg but not with − 40 and − 125 mmHg. Conclusions: EndoVAC therapy with a pressure of − 125 mmHg significantly increased tissue perfusion of ischemic gastric conduit. With better understanding of underlying physiology the optimal use of EndoVAC therapy can be determined including a possible preemptive use for gastric conduits with impaired arterial perfusion or venous congestion. [ABSTRACT FROM AUTHOR]

The study was aimed at developing a new automatic search technique for specific invariant patterns of movement-related brain potentials reflected in multidimensional electroencephalogram (EEG) signals. An adaptive band-pass filter with bandwidth closely matching the spectrum of the desired EEG pattern at the observed moment was synthesized based on the Singular Spectrum Analysis methodology. The preliminary filtering of the original EEG signals provides the required sensitivity for subsequent searching of time boundaries in patterns. The correctness of the developed method was confirmed with standard machine learning tools through the validation of the adaptive search method carried out on the general set of initial data. It is shown that the synthesized method has provided a reliable automatic search for induced pre-movement EEG patterns and the correct determination of their time boundaries (accuracy up 29% on average and reached maximum values to 100% for some individuals). The developed method expands the existing tools to improve the functionality and reliability of various Brain-computer interfaces for various purposes, including medical applications for paralyzed patients. [ABSTRACT FROM AUTHOR]

Nanocomposites comprising (V2O5)x. (Mn0.4Fe2.6O4)(2−x), where x = [0.2, 0.4, 0.6, 0.8, and 1], were successfully synthesized by milling each single phase of V2O5 and Mn0.4Fe2.6O4 nanostructured samples. We studied the structure and morphology of the prepared samples through X-ray diffraction and scanning electron microscopy. UV–Vis. spectrum analysis was used to study the optical characteristics. As an attempt to improve the optical and nonlinear optical properties of the PVDF blend, the prepared nanocomposites were used as nanofillers for the poly (vinylidene fluoride) (PVDF) polymer. Optical and nonlinear optical properties of the PVDF polymer were tuned using (V2O5)x. (Mn0.4Fe2.6O4)(2−x), where x = [0.2, 0.4, 0.6, 0.8, and 1] nanocomposite. The obtained results showed a tunable spectrum, absorption, refractive index, and dielectric constant at wavelengths ranging from 200 to 800 nm. However, the results showed that the (V2O5)0.6. (Mn0.4Fe2.6O4)1.4, sample was the best for obtaining the lowest transmittance percentage and bandgap energy. The nonlinear optical properties and Kerr effect (quadratic electro-optic effect) showed a significant response, which can be tuned in the range from 200 to 1200 nm of wavelength using the investigated samples. Finally, incorporating (V2O5)x. (Mn0.4Fe2.6O4)(2−x), where x = [0.2, 0.4, 0.6, 0.8, and 1], nanoparticles in the PVDF polymer improved the hydrophilic behavior of the PVDF films. This study suggests a promising nanocomposite material for tunable optoelectronics and useful polymer membranes. [ABSTRACT FROM AUTHOR]

The inspection of fabric defects is of great importance, as undetected and uncorrected defects entail poor production quality and expensive compensation. Due to the variety of defect types and sizes, it is a very tedious task to perform inspection manually. There are numerous automated systems in the literature; however, most of them require a training scheme where clean and defective fabric samples are manually fed to the system. Because of the diversity of fabric patterns and defect classes, supervised systems reduce convenience and ease of use in real practice. In this study, we propose an unsupervised, robust fabric defect detection method using spectral domain analysis. The proposed algorithm has a very simple flow and can run without any prior training scheme. First, the algorithm splits the input textile image into smaller patches and computes a generic spectral representation of the fabric pattern. Then, the method detects defective regions by measuring dissimilarities between the spectral representation and all local patches of the input fabric. We also introduce a textile fabric dataset, i.e., Ten Fabrics Dataset, which consists of ten different types of fabrics with 27 of the most common textile defects. According to the extensive set of experiments on two different datasets, the proposed method outperforms the state-of-the-art by achieving up to 94% accuracy. [ABSTRACT FROM AUTHOR]

Background: The bimolecular fluorescence complementation (BiFC) assay is commonly used for investigating protein–protein interactions. While several BiFC detection systems have been developed, there is a limited amount of research focused on using laser scanning confocal microscope (LSCM) techniques to observe protoplasts. Protoplasts are more susceptible to damage and instability compared to their original cell state due to the preparation treatments they undergo, which makes it challenging for researchers to manipulate them during observation under LSCMs. Therefore, it is crucial to utilize microscope techniques properly and efficiently in BiFC assays. Results: When the target fluorescence is weak, the autofluorescence of chloroplast particles in protoplasts can interfere with the detection of BiFC signals localized in the nuclear region. Spectrum analysis revealed that chloroplast autofluorescence can be excited by lasers of various types, with the highest fluorescence signal observed at around 660 nm. Furthermore, our investigation into the impact of different pipette tips on the integrity of protoplast samples indicated that the utilization of cut tips with larger openings can mitigate cell breakage. We presented a workflow of LSCM techniques for investigating protoplast BiFC and discussed the microscopic manipulation involved in sample preparation and image capturing. Conclusion: When the BiFC signals are weak, they may be affected by chloroplast autofluorescence. However, when used properly, the autofluorescence of chloroplasts can serve as an excellent internal marker for effectively distinguishing other signals. In combination with other findings, this study can provide valuable reference for researchers conducting BiFC assays and related studies. [ABSTRACT FROM AUTHOR]

The characteristic time scales of the response of the globally averaged climate model with the carbon cycle to external forcing with analysis of the spectrum of the linearized evolution operator of the corresponding dynamic system are evaluated. The model exhibits response time scales of about 4–6 years (related to the carbon dynamics in vegetation) and in the range of 20–100 years (related to the carbon dynamics in nonhumified soil reservoirs). When taking into account the effect of humification, the model reveals the time scale of the response, which is on the order of several millennia. For a closed carbon cycle, the time scale is 102 years, which characterizes the joint variations in the atmospheric and oceanic reservoirs. The proposed approach is highly universal and can be used for a wide range of tasks. [ABSTRACT FROM AUTHOR]

Streamflow predictions play a crucial role in the planning and management of water structures. However, accurately predicting streamflow data, which exhibits nonlinear and nonstationary characteristics, is a challenging problem. In this study, a novel approach was proposed for the prediction of both overall and peak streamflows, aiming to achieve high performance. The data used included precipitation and streamflow time series, as well as lagged data from the empirical mode decomposition (EMD), variational mode decomposition (VMD), and circulant spectrum analysis (ciSSA) subbands. The minimum redundancy maximum relevance (MRMR) method was employed for feature selection from these datasets. The selected features were used to develop daily streamflow prediction models using Gaussian process regression (GPR), ensemble (boosting and bagging), support vector regression (SVR), and artificial neural network (ANN) methods. The performance of the developed MRMR-, EMD-MRMR-, VMD-MRMR-, and ciSSA-MRMR-machine learning models was evaluated using mean squared error (MSE), mean absolute error (MAE), correlation coefficient (R), and determination coefficient (R2) metrics. Additionally, the Bland-Altman plots and the Kruskal–Wallis test were used to determine the statistical significance of the results. According to the results, the ciSSA-MRMR-machine learning models achieved higher performance compared to the other models (R2 value of 0.956, an MSE of 0.0001, and an MAE of 0.0049 for overall streamflow prediction). For peak streamflow prediction, the ciSSA-MRMR-ANN model yielded an R2 value of 0.956, an MSE of 0.0002, and an MAE of 0.0217. It was observed that the proposed method significantly improved the prediction of not only overall streamflow but also peak streamflow values. [ABSTRACT FROM AUTHOR]

We characterize the energy spectrum in far-from-equilibrium inhomogeneously trapped Bose-Einstein condensate using the power spectrum technique. We demonstrate that pseudo-momentum fluctuations can be used to characterize turbulence and we compare the obtained exponents to one measured by well established techniques such as the momentum distribution in time of flight. The power spectrum of pseudo-momentum fluctuations and the momentum distribution reveals a power-law dependence into the inertial range of momentum for the turbulent out-of-equilibrium system. We analyze the evolution of the power-law exponent for different far-from-equilibrium regimes and both methods show that the power-law exponent reaches the same stationary value when the cloud is in the developed turbulent regime. These results provide a better understanding of out-of-equilibrium states, which is a new frontier in the study of cold trapped atoms. [ABSTRACT FROM AUTHOR]

In this paper, we present a comparative analysis of the Raman spectra of two heterostructures: Si/SiO2/HfO2/Graphene and Si/SiO2/Graphene/HfO2. In the former configuration, the Graphene layer is transferred onto the 5 nm-thick HfO2 layer, while the latter configuration involves the growth of à 5 nm-thick HfO2 layer by atomic layer deposition (ALD) on the Graphene layer. The analysis of the Raman spectra has demonstrated significant differences between the interfaces Graphene/HfO2 and HfO2/Graphene. The distinct Raman signatures observed emphasize the critical role played by the specific order and arrangement of the Graphene and HfO2 layers within the heterostructure, which impacts the charge transfer between these two materials and the presence or absence of strains. The Raman spectra corresponding to different thickness of the HfO2 layer (5, 10, 15, and 20 nm) in Si/SiO2/Graphene/HfO2 heterostructure shows the appearance of a small shoulder on the low-frequency side of the G band around 1523 cm-1. Furthermore, it was observed that the intensity of this shoulder diminishes as the thickness of the HfO2 layer increases. The observed discrepancies between spectra, related to the experimental conditions employed for sample preparation, are attributed to the interplay of two phenomena: the transfer of charges among Graphene, HfO2, and SiO2, as well as the emergence of constraints. [ABSTRACT FROM AUTHOR]

The dynamics of shock-induced unsteady separated flow past a three-dimensional square-faced protuberance are investigated through wind tunnel experiments. Time-resolved schlieren imaging and unsteady surface pressure measurements are the diagnostics employed. Dynamic mode decomposition (DMD) of schlieren snapshots and analysis of spectrum and correlations in pressure data are used to characterize and resolve the flow physics. The mean shock foot in the centreline is found to exhibit a Strouhal number of around 0.01, which is also the order of magnitude of the Strouhal numbers reported in the literature for two-dimensional shock–boundary layer interactions. The wall pressure spectra, in general, shift towards lower frequencies as one moves away (spanwise) from the centreline with some variation in the nature of peaks. The cross-correlation analysis depicts the strong dependence of the mean shock oscillations and the plateau pressure region, and disturbances are found to travel upstream from inside the separation bubble. Good coherence is observed between the spanwise mean shock foot locations till a Strouhal number of about 0.015 indicating that the three-dimensional shock foot largely moves to-and-fro in a coherent fashion. [ABSTRACT FROM AUTHOR]

We present the results of spectral observations and consequent analysis of six long-period double-lined eclipsing binaries (DLEBs) with main-sequence (MS) components from a sample formed with the aim of testing the "mass–luminosity" relation (MLR) for stars in the mass range. We analyzed all the obtained spectra using a technique that allows one to reveal the binary nature of the system and determine and for each component, as well as the system metallicity [Fe/H] and line-of-sight extinction . We computed the absolute parameters of the systems under consideration. An analysis of the obtained spectra shows that for three of the six objects (V1156 Cyg, EU Gem and V733 Per) we can clearly establish their binary nature and determine the spectral type and class for each component. Both components of the V733 Per system have already left the MS, and therefore the system must be excluded from our sample, whereas studies of V1156 Cyg and EU Gem should continue. OT And did not demonstrate a binary spectrum, however, the main component of the system is a hot A6 V star, and therefore, OT And should remain in our sample. We also revealed no binarity in the IM Del and LX Gem systems. Their brighter components turned out to be a cool giant and a supergiant, and these systems should be excluded from the sample based on the results of our analysis. We used the same technique to analyze the two spectra obtained for systems EU Gem and LX Gem using LAMOST. We have shown that the parameters determined from LAMOST spectra are in good agreement with the parameters determined for spectra obtained at the Caucasian Mountain Observatory (CMO) of SAI MSU. Our analysis allowed us to plot a first approximation of the V1156 Cyg velocity curves and show that the cool component in this system has a larger mass. [ABSTRACT FROM AUTHOR]

Background Cyclooxygenase enzyme is frequently overexpressed in various types of cancer and found to play a crucial role in poor prognosis in cancer patients. In current research, we have reported the new COX-2 inhibitors for cancer treatment using computer-aided drug design and experimental validation. Methods A total of 12,795 compounds from the different databases were used to screen against the COX-2 enzyme. It perceived three new compounds with better binding affinity to the enzyme. Afterwards, physicochemical properties and in silico bioactivity were assessed for efficacy, safety, and structural features required for binding. The molecules were synthesized and confirmed by spectroscopic techniques. Later on, molecules were evaluated for their anti-cancer activity using MCF-7, MDA-MB-231 and SiHa cancer cell lines. Results Compound ZINC5921547 and ZINC48442590 (4a, and 4b) reduced the MCF-7, MDA-MB-231, and SiHa cells proliferation potently than parent compounds. The PG-E2 estimation shown, both compounds act through the COX-2 PGE2 axis. Compound 4a and 4b block the cell cycle at G1-S phase and induce cancer cell death. Conclusions We concluded that compounds 4a and 4b effectively promotes cancer cell death via COX-2 PGE2 axis, and further in vivo studies can be evaluated for development in both compounds as anticancer agents. The compilation of this information will help us to generate better outcome through robust computational methods. The high-quality experimental results may pave the way for identifying effective drug candidates for cancer treatment. [ABSTRACT FROM AUTHOR]

Quasinormal modes and frequencies are the eigenvectors and eigenvalues of a non-Hermitian differential operator. They hold crucial significance in the physics of black holes. The analysis of quasinormal modes of black holes in asymptotically Anti-de Sitter geometries plays also a key role in the study of strongly coupled quantum many-body systems via gauge/gravity duality. In contrast to normal Sturm-Liouville operators, the spectrum of non-Hermitian (and non-normal) operators generally is unstable under small perturbations. This research focuses on the stability analysis of the spectrum of quasinormal frequencies pertaining to asymptotically planar AdS black holes, employing pseudospectrum analysis. Specifically, we concentrate on the pseudospectra of scalar and transverse gauge fields, shedding light on their relevance within the framework of gauge/gravity duality. [ABSTRACT FROM AUTHOR]

Until now, the photoacoustic spectrum (PAS) analysis technology cannot face the need for SF6 gas-decomposition products due to the high noise level, big shock and low accuracy. In this manuscript, we present a portable sulfur hexafluoride (SF6) gas-decomposition PAS gas analysis system based on mid-infrared quantum cascade laser (MI-QCL). Because the narrow linewidth, high speed tunable and stability wavelength of MI-QCL, our PAS gas analysis system has a good performance. A platform for MI-QCL PAS system is set up in our work. The accuracy of quantitative detection for sulfur dioxide (SO2), hydrogen sulfide (H2S) and carbon monoxide (CO) in SF6 gas background mixture gas is 0.5, 0.1 and 0.1 ppm, respectively. Experiment results demonstrate the MI-QCL PAS system not only has a high detection accuracy, but also has a small system volume. This work gives a novel solution method for PAS system miniaturization in the future. [ABSTRACT FROM AUTHOR]

The precise prediction of polar motion parameters is needed for the astrogeodynamics, navigation and positioning of the deep space probe. However, the current prediction methods are limited to predicting polar motion for specific periods, either short- or long-term. In this study, a sliding multilayer perceptron (MLP) method combined singular spectrum analysis (SSA) and autoregressive moving average (ARMA) for short- and long-term polar motion prediction was proposed. MLP was introduced into PM prediction due to its automatic learning characteristics and its ability to effectively process nonlinear and multi-dimensional data. The SSA was used to extract and predict the principal components of polar motion, while the remaining components were predicted using ARMA. In the meantime, SSA and ARMA were used to provide training data and target learning data for the MLP model. MLP input data were constructed by sliding processing with a window of 7 days, composed of n series of the same length (18 years). Finally, MLP was employed to predict the residuals generated during SSA and ARMA prediction. To evaluate the accuracy of the proposed method, the polar motion prediction was applied for a 364-day lead time based on the IERS EOP 14C04 product. The method outperformed the IERS Bulletin A, as demonstrated by the mean-absolute errors of the x and y components of polar motion on the 30th day, which were lower (5.14 mas and 3.37 mas, respectively) than those predicted by IERS Bulletin A (6.66 mas and 3.94 mas). Similarly, the mean-absolute errors on the 364th day were 17.79 mas and 16.29 mas, respectively, compared to the 19.24 mas and 18.81 mas predicted by IERS Bulletin A. [ABSTRACT FROM AUTHOR]

Tyrosinase, the rate-limiting enzyme of melanogenesis, plays a crucial role in hyperpigmentation. As a result, in this study, a novel class of thiazolopyrimidine derivatives was developed and synthesized as tyrosinase inhibitor. The structure of derivatives was characterized using various spectroscopy techniques, including FTIR, Mass, 1H-NMR, and 13C-NMR. Next, the inhibitory activities of all derivatives were examined against tyrosinase, and, 6a as the most potent compound, exhibited an IC50 value of 28.50 µM. Furthermore, the kinetic study of 6a was performed to better understand the inhibitory mechanism and its type of inhibition. The UV/Vis spectra analysis was also executed to provide valuable evidence supporting the inhibitory mechanism of compound 6a in the context of tyrosinase inhibition. Also, molecular docking and dynamic molecular study of 6a were executed to study its interactions within the enzyme's binding site. [ABSTRACT FROM AUTHOR]

Raman spectroscopy shows great potential as a diagnostic tool for thyroid cancer due to its ability to detect biochemical changes during cancer development. This technique is particularly valuable because it is non-invasive and label/dye-free. Compared to molecular tests, Raman spectroscopy analyses can more effectively discriminate malignant features, thus reducing unnecessary surgeries. However, one major hurdle to using Raman spectroscopy as a diagnostic tool is the identification of significant patterns and peaks. In this study, we propose a Machine Learning procedure to discriminate healthy/benign versus malignant nodules that produces interpretable results. We collect Raman spectra obtained from histological samples, select a set of peaks with a data-driven and label independent approach and train the algorithms with the relative prominence of the peaks in the selected set. The performance of the considered models, quantified by area under the Receiver Operating Characteristic curve, exceeds 0.9. To enhance the interpretability of the results, we employ eXplainable Artificial Intelligence and compute the contribution of each feature to the prediction of each sample. [ABSTRACT FROM AUTHOR]

We use an agnostic information-theoretic approach to investigate the statistical properties of natural images. We introduce the Multiscale Relevance (MSR) measure to assess the robustness of images to compression at all scales. Starting in a controlled environment, we characterize the MSR of synthetic random textures as function of image roughness H and other relevant parameters. We then extend the analysis to natural images and find striking similarities with critical ( H ≈ 0 ) random textures. We show that the MSR is more robust and informative of image content than classical methods such as power spectrum analysis. Finally, we confront the MSR to classical measures for the calibration of common procedures such as color mapping and denoising. Overall, the MSR approach appears to be a good candidate for advanced image analysis and image processing, while providing a good level of physical interpretability. [ABSTRACT FROM AUTHOR]

Allocating and routing resources in optical networks is a critical process that has a major bearing on the efficiency and scalability of the network's resources. There has been a lot of study put into it, and several high-quality algorithms have been developed. The authors of this paper propose a novel approach to distributing optical network channels and assessing their spectrum for 5G uses. In this scenario, network channel allocation is performed by means of a reinforcement Markov Fourier spatial convolutional network. By analysing the channel allocation, the spectral efficiency of the network may be increased. Software defined networking, network function virtualization, and network slicing (NS) are just a few examples of the technologies gaining traction in academia and the IT industry that will fuel 5G. Network slicing (NS) is a network management technique that allows a physical topology to be partitioned into many independent virtual networks. Network resources, such as bandwidth, may be dynamically delivered on demand in line with specific quality of service and service level agreement requirements, and many service function chains can be established within a single slice. An experimental analysis of SNR, spectral efficiency, throughput, and packet delivery ratio is conducted based on the number of optical channels, users, and radio networks. [ABSTRACT FROM AUTHOR]

Undoubtedly, Machine Learning (ML) for Optical Communication (OC) has been a popular topic in recent years and is likely to remain so for the foreseeable future. Recent years have seen a tremendous increase in the pace of research in this area. The innovative nature of this study field is related more to the unusual nature of the application domain than to the employment of state-of-the-art ML algorithms in the methodologies used. This study suggests a new method for optimising optical networks for 5G services by using channel allocation and spectral analysis. In this setup, we use a Gaussian reinforcement convolutional learning model to allocate channels for optical network signals, and a multilayer adversarial markov encoder to improve spectrum efficiency. Measurements of spectrum efficiency, bit error rate (BER), channel efficiency, energy consumption, and training correctness are all taken as part of the experimental investigation. According to the findings, the plan was feasible, and decision-making methods based on machine learning algorithms were able to determine the most suitable route of communication. [ABSTRACT FROM AUTHOR]

To solve the problem where the space vector pulse width modulation (SVPWM) of a three-phase inverter produces large harmonic components near the switching frequency (fs) and its doubling frequency, a frequency-optimized random switching frequency SVPWM (FORSF-SVPWM) control strategy is proposed in this paper. In this strategy, the basic principle of the chaotic ant colony algorithm in path optimization is used to determine the optimized scheme of the switching frequency distribution in the FORSF-SVPWM. Research shows that the frequency sample formed by the sigmoid function curve in the switching frequency range can cause the energy that was originally concentrated on the switching frequency and its doubling frequency to be more evenly distributed in the whole frequency range. Moreover, the amplitude of each harmonic wave is shown to be suppressed. The proposed strategy reduces the high-frequency noise and conducted electromagnetic interference (EMI) existing in power switching circuits. Thus, this strategy is obviously better than the traditional random switching frequency SVPWM (RSF-SVPWM) algorithm with its approximately uniform frequency distribution. Simulation and experimental results show that this strategy can work well in the hardware platform of a three-phase inverter without changing the topology of the main circuit of the system. In addition, this strategy is easy to implement. [ABSTRACT FROM AUTHOR]

We have carried out a comprehensive study of the poorly investigated eclipsing polar 1RXS J184542.4 483134 with a short orbital period min. An analysis of its long-term light curves points to a change in the position and sizes of the accretion spot as the accretion rate changes. Narrow and broad components, which are probably formed on the ballistic segment of the accretion stream and on the magnetic trajectory, respectively, are identified in the emission line profiles. An inversion of the line profiles from emission to absorption due to the obscuration of the accretion spot by the accretion stream is observed. Based on the eclipse duration and the radial velocities of the narrow line component, we impose constraints on the white dwarf mass, , and the orbital inclination, . An analysis of the cyclotron spectra points to the presence of two accretion spots with magnetic field strengths MG and MG. The main spot has a complex structure that apparently has a dense core and a less dense periphery emitting a spectrum with cyclotron harmonics. Polarization observations reveal a circular polarization sign reversal during the orbital period and an anticorrelation of the polarization with the brightness of the polar. Our modeling of polarization observations using the simple model of an accreting white dwarf shows that the polarization properties can be interpreted in terms of two-pole accretion with different optical depths of the accretion spots (). An analysis of the Swift/XRT observations points to a predominance of bremsstrahlung in the X-ray radiation from the system. [ABSTRACT FROM AUTHOR]

Background: Reliably measuring sharp details in displacement and strain maps returned by full-field measurement techniques remains an open question in the photomechanics community. Objective: The primary objective of this study is to improve and fine-tune a deconvolution algorithm in order to limit the blur that obscures the details in displacement and strain maps. Methods: Checkerboard patterns are used and processed with a spectral method, namely the Localized Spectrum Analysis (LSA), and the raw maps returned by this technique are deconvolved. The influence of various settings on the quality of the results is studied by using synthetic images deformed through a well-vetted reference displacement field. Results: It is shown that linking the size of the analysis window used in LSA on the one hand, and the size of the second derivative kernel employed in the deconvolution algorithm on the other hand, ensures the convergence of the deconvolution algorithm in all cases. This was not the case with the initial version. The ratio between these sizes, which optimizes the metrological performance of LSA followed by deconvolution, is identified. The influence of the sampling density of the checkerboard pattern in the images is also examined. The efficiency of the deconvolution algorithm employed with optimized settings is illustrated with strain maps obtained on two specimens, one in shape memory alloy, and the other in wood. Conclusions: It is shown in this study that deconvolution with optimized settings is an effective tool to enhance small and sharp details in strain maps obtained with LSA. [ABSTRACT FROM AUTHOR]

The structure of a welded joint of stainless steel 316L is studied by the methods of scanning electron microscopy including microprobe x-ray spectrum analysis and diffraction of backscattered electrons (EBSD). It is shown that a double-phase structure forms in the region of the weld including high-temperature δ-ferrite and austenite. The austenite and the ferrite undergo recrystallization in direction ⟨ 100 ⟩ parallel to the heat removal. The presence of high-temperature δ-ferrite in the steel results in nonuniform distribution of the alloying elements (Cr, Ni, Mo) and in appearance of phase boundaries, which can reduce the corrosion resistance of the welded joint. [ABSTRACT FROM AUTHOR]

As a new promising detection technology in the terahertz research field, the terahertz time-domain spectroscopy (THz-TDS) has very broad application potential in many fields because its advantage on the characteristic spectrum, wide spectrum and non-destructive analysis of interested substances. In this paper, the terahertz absorption spectra of gases mixed with 12CO and 13CO in the spectrum range of 0.5–2.5 THz are measured by terahertz time-domain spectroscopy for the first time. Several isotopologues can be clearly distinguished based on the difference in their rotational energies and the consequent terahertz spectrum. The experimental results show that 12CO and 13CO have obvious characteristic absorption peaks in the spectrum range of 0.5–2.5 THz due to the difference in rotational energy, and the rotational constant B can be calculated according to the experimental values to distinguish the two gaseous isotopologues. The frequency positions of the characteristic absorption peak measured by this experiment and the rotation constant B calculated according to the experimental values are compared with those previous theoretical calculations and experimental results, and they are in good agreement. This result lays a foundation for developing more sophisticated terahertz instruments to the detection of different isotopologues. [ABSTRACT FROM AUTHOR]

Real-time estimates of the Earth orientation parameters (EOP) are currently unavailable for users owing to the delay caused by complex data processing and heavy computation procedures. Accurate short-term predictions of the EOP are therefore essential for several real-time applications such as navigation and tracking of interplanetary spacecrafts and precise orbit determination of Earth satellites, whilst medium- and long-term predictions are required for Global Navigation Satellite System (GNSS) autonomous satellite navigation, climate forecasting as well as for astrogeodynamic studies. Universal time (UT1 – UTC) or its first time derivative, length of day (ΔLOD), representing the changes of the Earth's rotation rate, are the most challenging to predict among the EOP. Various methods and techniques have been used to improve ΔLOD predictions since the present prediction accuracy is yet unsatisfactory even up a few days into the future. This study employs a popular time-series analysis method, called singular spectrum analysis (SSA), in combination with the neural network (NN) technique for medium- and long-term prediction of ΔLOD up to 2 years in the future. The SSA is first applied to extracting the predominant periodic components including annual and semiannual oscillations and irregular short-period signals in ΔLOD data. These extracted predominant periodic components are then extrapolated by the proposed SSA-based data filling strategy. Next, the residuals (the difference between these predominant components and the data themselves) are modeled and predicted by the NN technique. The predicted ΔLOD value is sum of the extrapolation of the predominant periodic components and the prediction of the residuals. The results show that the accuracy of the 180-day ahead predictions is worse than that by the combination of least squares (LS) extrapolation and a stochastic method including autoregressive and NN technology in terms of the mean absolute prediction error. However, the proposed SSA extrapolation in combination with NN modeling can achieve a noticeably better accuracy for the medium- and long-term predictions out 180 days than the combined LS + stochastic technology. The improvement in the prediction accuracy for lead time of 1 year and 2 years can reach up to 53% and 56%, respectively. The combined SSA extrapolation and NN modeling is thus very promising for medium- and long-term prediction of ΔLOD. [ABSTRACT FROM AUTHOR]

The dynamics of molecular systems can be studied with time-resolved spectroscopy combined with model-based analysis. A Python framework for global and target analysis of time-resolved spectra is introduced with the help of three case studies. The first study, concerning broadband absorption of intersystem crossing in 4-thiothymidine, demonstrates the framework's ability to resolve vibrational wavepackets with a time resolution of ≈10 fs using damped oscillations and their associated spectra and phases. Thereby, a parametric description of the "coherent artifact" is crucial. The second study addresses multichromophoric systems composed of two perylene bisimide chromophores. Here, pyglotaran's guidance spectra and lego-like model composition enable the integration of spectral and kinetic properties of the parent chromophores, revealing a loss process, the undesired production of a radical pair, that reduces the light harvesting efficiency. In the third, time-resolved emission case study of whole photosynthetic cells, a megacomplex containing ≈500 chromophores of five different types is described by a combination of the kinetic models for its elements. As direct fitting of the data by theoretical simulation is unfeasible, our global and target analysis methodology provides a useful 'middle ground' where the theoretical description and the fit of the experimental data can meet. The pyglotaran framework enables the lego-like creation of kinetic models through its modular design and seamless integration with the rich Python ecosystem, particularly Jupyter notebooks. With extensive documentation and a robust validation framework, pyglotaran ensures accessibility and reliability for researchers, serving as an invaluable tool for understanding complex molecular systems. [ABSTRACT FROM AUTHOR]

In this mini-review, some of the most significant, from the authors' point of view, features of the methodology of Fourier transform infrared (FTIR) spectroscopy and interpretation of experimentally obtained spectroscopic data are considered, as applied to the molecular spectrochemical analysis of microbiological objects using examples published in the specialised scientific literature mainly in recent years. Examples of typical inaccuracies and errors both of a methodological nature and arising in the interpretation of spectroscopic data are also presented and briefly discussed. [ABSTRACT FROM AUTHOR]

Raman spectroscopy shows great potential as a diagnostic tool for thyroid cancer due to its ability to detect biochemical changes during cancer development. This technique is particularly valuable because it is non-invasive and label/dye-free. Compared to molecular tests, Raman spectroscopy analyses can more effectively discriminate malignant features, thus reducing unnecessary surgeries. However, one major hurdle to using Raman spectroscopy as a diagnostic tool is the identification of significant patterns and peaks. In this study, we propose a Machine Learning procedure to discriminate healthy/benign versus malignant nodules that produces interpretable results. We collect Raman spectra obtained from histological samples, select a set of peaks with a data-driven and label independent approach and train the algorithms with the relative prominence of the peaks in the selected set. The performance of the considered models, quantified by area under the Receiver Operating Characteristic curve, exceeds 0.9. To enhance the interpretability of the results, we employ eXplainable Artificial Intelligence and compute the contribution of each feature to the prediction of each sample. [ABSTRACT FROM AUTHOR]

This research delves into the intricate relationship between two fascinating phenomena: megastability and strange non-chaotic attractors (SNAs). The study centers on a 4D jerk system that incorporates an additional periodic force, aiming to unravel the interplay between these phenomena and shed light on the underlying mechanisms. By manipulating a control parameter, the system's behavior reveals a spectrum of attractors, including the torus, strange non-chaotic attractors, and chaotic states. This diversity underscores the system's complexity and responsiveness to parameter changes. To validate the observed megastability, the research employs rigorous analytical techniques. Phase portraits visually capture the system's trajectories in its state space, while Poincaré sections reveal its periodic behavior. Basin of attraction analysis provides insights into the reliability of the observed megastable behavior. The study then delves into the transitions between these attractors. Bifurcation analysis identifies critical parameter values where the system's dynamics change qualitatively, while Lyapunov exponents quantify the system's sensitivity to initial conditions. The presence and attributes of complex behavior of the system are confirmed through power spectrum analysis, the exploration of nearby point separations, and the identification of singular continuous spectrum patterns. In conclusion, this comprehensive investigation unveils the intricate fusion of complex behaviors within the 4D jerk system. The study's methodologies, ranging from validation to transition analysis and confirmation of these intricate properties, deepen our understanding of complex dynamical systems. Infinite strange non-chaotic attractors. [ABSTRACT FROM AUTHOR]

We use an agnostic information-theoretic approach to investigate the statistical properties of natural images. We introduce the Multiscale Relevance (MSR) measure to assess the robustness of images to compression at all scales. Starting in a controlled environment, we characterize the MSR of synthetic random textures as function of image roughness H and other relevant parameters. We then extend the analysis to natural images and find striking similarities with critical ( H ≈ 0 ) random textures. We show that the MSR is more robust and informative of image content than classical methods such as power spectrum analysis. Finally, we confront the MSR to classical measures for the calibration of common procedures such as color mapping and denoising. Overall, the MSR approach appears to be a good candidate for advanced image analysis and image processing, while providing a good level of physical interpretability. [ABSTRACT FROM AUTHOR]

Flow separation is a critical aerodynamic phenomenon impacting flight envelope of various flying objects. Its efficient detection and subsequent control are vital for enhanced manoeuvring, better fuel economy, superior flight controls, and even for the survival of small air vehicles such as MAVs and UAVs. Over the past 3 decades, MEMS technology has enabled a paradigm shift from macro-level conventional sensing techniques to micro-scale sensors. Critical aerodynamics phenomenon such as flow separation, boundary-layer transitioning, shock, local flow and vortex dynamics, etc. can now be captured with better spatial as well as temporal resolution. Analysis spectrum has also been stretched by compensating the limitations of conventional techniques. This review paper is focused on the use of MEMS technology in the field of flow separation detection. We first briefly introduce the flow separation phenomenon and the limitations of conventional sensing techniques. Then, the application of MEMS to flow separation detection will be discussed in detail with emphasis on the prominent research work and performance review of various MEMS sensors employed in recent past. [ABSTRACT FROM AUTHOR]

Raman spectroscopy provides a vibrational profile of the molecules and thus can be used to uniquely identify different kinds of materials. This sort of molecule fingerprinting has thus led to the widespread application of Raman spectrum in various fields like medical diagnosis, forensics, mineralogy, bacteriology, virology, etc. Despite the recent rise in Raman spectra data volume, there has not been any significant effort in developing generalized machine learning methods targeted toward Raman spectra analysis. We examine, experiment, and evaluate existing methods and conjecture that neither current sequential models nor traditional machine learning models are satisfactorily sufficient to analyze Raman spectra. Both have their perks and pitfalls; therefore, we attempt to mix the best of both worlds and propose a novel network architecture RamanNet. RamanNet is immune to the invariance property in convolutional neural networks (CNNs) and at the same time better than traditional machine learning models for the inclusion of sparse connectivity. This has been achieved by incorporating shifted multi-layer perceptrons (MLP) at the earlier levels of the network to extract significant features across the entire spectrum, which are further refined by the inclusion of triplet loss in the hidden layers. Our experiments on 4 public datasets demonstrate superior performance over the much more complex state-of-the-art methods, and thus, RamanNet has the potential to become the de facto standard in Raman spectra data analysis. [ABSTRACT FROM AUTHOR]

In the present work, biocompatible CdS nanoparticles were synthesized using Schiff base ligand, 3-((2-(-(1-(2hydroxyphenyl)ethylidene)amino)ethyl)imino)-2-pentone, by a simple ultrasonic irradiation method. The structural, morphological, and optical properties were studied using XRD, SEM, TEM, UV–visible absorption, and photoluminescence (PL) spectra. The quantum confinement effect of the Schiff base capped CdS nanoparticles was confirmed by using UV–visible and PL spectrum analysis. This CdS nanoparticles were an effective photocatalyst for degrading rhodamine 6G and methylene blue with a 70% and 98% degradation capacity, respectively. Furthermore, the disc-diffusion method demonstrated that CdS nanoparticles inhibit G-positive bacteria and G-negative bacteria more effectively. These Schiff base capped CdS nanoparticles were taken for an in-vitro experiment with HeLa cells to exhibit the possibility of providing optical probes in biological applications and observed under a fluorescence microscope. In addition, MTT cell viability assays were carried out to investigate the cytotoxicity for 24 h. As a result of this study, 2.5 µg/ml doses of CdS nanoparticles are suitable for imaging and are effective in destroying HeLa cells. The present study suggests that the synthesized Schiff base capped CdS nanoparticles could be a potential photocatalyst, antibacterial agent, and biocompatible nanoparticle for bioimaging applications. [ABSTRACT FROM AUTHOR]

The analysis of seasonal and long-term changes of the Caspian Sea level examined from historical long time tide gauge data in order to consider the influence of climate factors on sea level changes in this lake system using spectral analysis method. The major peak at spectra corresponds to the annual cycle and semiannual oscillations peak is located at next vigorous. Effects of different global and regional factors on lake level changes are investigated by signal processing methods. Results show that annual cycle of Siberian High and Volga River discharge have the considerable effects on the Caspian Sea level changes in global and regional scales, respectively. Analyzing of seasonal cycle revealed that Volga river discharge is significant on sea level fluctuations by 1-month lag in northern and central sub-basins and 2 months delay for southern sub-basin. The results of long-term cycle show ascending trend of evaporation in Caspian central sub-basin, which it is the main driver of decreasing trend of the Caspian Sea level since 1990s. [ABSTRACT FROM AUTHOR]

In recent years, the Global Navigation Satellite System (GNSS) has witnessed rapid development. However, during the sandstorm season, the precipitable water vapor (PWVGNSS) determined from the GNSS data produces large fluctuations due to the influence of particulate matter, which can indirectly reflect the change in particulate matter concentration. To study the variations of PWVGNSS during the sandstorm season, daily data of PWVGNSS, particulate matter (PM10), and precipitation in Wuhai from 2017 to 2021 were used in this study. The principal components of PWV residual (PWVRPC) were obtained by using the least-squares linear fitting, singular spectrum analysis, and least-squares spectral analysis on PWVGNSS. The principal components of PM10 (PM10PC) were obtained by using least squares linear fitting and singular spectrum analysis for PM10. This study performed a correlation analysis of PWVRPC with PM10PC and precipitation data. The results showed a strong correlation between PWVRPC and PM10PC, with a correlation coefficient greater than 0.6. However, it was found that the correlation between PWVRPC and precipitation was not significant. This indicates that during the sandstorm season, PM10 affects PWV determined from GNSS data. [ABSTRACT FROM AUTHOR]

The analysis of gamma spectra obtained from the decay of Au indicated that a ten times higher value was adopted for the quantum yield of the transition of 521.4 keV. Based on the intensity of gamma lines corresponding to well-known transitions, the quantum yield for the 521.4 keV transition was calculated. The obtained value was used to correct the adopted value of the intensity of the beta transition that populates 877.0 keV energy state from which gamma radiation of 521.4 keV is emitted. [ABSTRACT FROM AUTHOR]

The high-resolution spectrum of the 72GeH4 molecule was recorded on a Bruker IFS 125HR Fourier spectrometer with an optical resolution of 0.003 cm–1. The line positions were analyzed for ten interacting vibrational-rotational bands 3ν4 (1F2, F1 2F2), ν2 + 2ν4 (1E, F1, F2, 2E) and 2ν2 + ν4 (1F2, F1, 2F2) in the range 2350–2750 cm–1. As a result of the analysis, 1726 experimental lines were identified with the maximum value of the quantum number Jmax = 17; then used in the fitting procedure with parameters of the effective Hamiltonian. The resulting set of 35 spectroscopic parameters describes the vibrational-rotational structure of the spectrum with drms= 7.5 × 10–4 cm–1. [ABSTRACT FROM AUTHOR]

The singular spectrum analysis (SSA) is a powerful tool to de-noise the geodetic time series and extract geophysical signals of interest. However, when missing data exists in geodetic time series, the ordinary SSA cannot be directly used to process them. Moreover, the heterogeneous properties of the geodetic time series are usually not considered in ordinary SSA, though their formal errors are provided beforehand. In this contribution, we develop an extended singular spectrum analysis (ESSA) to directly process the incomplete and heterogeneous geodetic time series. To validate the proposed approach, we select the 27 vertical position time series of GNSS permanent stations located in the Chinese mainland for analysis, and the results are compared with that of the improved SSA (ISSA, Shen et al. in Nonlinear Process Geophys 22(4):371–376, 2015) which is an effective approach for analyzing the time series with missing data. The results show that (i) our ESSA method performs faster than ISSA, with mean reductions in computation time by 40.14% for 27 stations; moreover, the time consumption of ISSA is more sensitive to the window size and the length of observations than that of ESSA; (ii) the ESSA method can extract more signals than ISSA in terms of fitting errors and root-mean-square ratios of extracted signals over residuals, specifically for the consideration of formal errors. The power spectrum analysis shows that the power of annual oscillation extracted by ESSA is stronger than that by ISSA; (iii) the repeated simulations based on the real data further demonstrate that the signals extracted by ESSA are closer to the true signals than ISSA, and the accuracy improvement is portal to the percentage of data missing. [ABSTRACT FROM AUTHOR]

Geometric and electronic structures of molecular (AH), cationic , zwitterionic (A–H+), and anionic (A–) forms of substituted naphthalenesulfonic (1-aminonaphthalene-8-sulfonic and 1-(N-phenyl)aminonaphthalene-8-sulfonic) acids are considered by the B3LYP DFT method. The theoretical electronic absorption and emission spectra of various forms of the studied sulfonic acids are recorded and analyzed. It is shown on the example of the molecular form of 1-aminonaphthalene-8-sulfonic acid that the absorption bands of conformer I are shifted to the short-wavelength region due to the presence of intramolecular hydrogen bonding, in contrast to the conformers containing no such bonds. The relationship between the energy and shape of frontier orbitals of various forms of sulfonic acids and the features of their electronic absorption spectra is analyzed. It is shown that the theoretical emission spectra of the cationic and zwitterionic forms of 1-(N-phenyl)aminonaphthalene-8-sulfonic acid reproduce well the tendency of the experimental spectra where the band with the maximum wavelength shifts to the long-wavelength region upon the transition from an acidic medium to weakly acidic and neutral ones. [ABSTRACT FROM AUTHOR]