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Measuring postural control in an upright standing position is the standard method. However, this diagnostic method has floor or ceiling effects and its implementation is only possible to a limited extent. Assessing postural control directly on the trunk in a sitting position and consideration of the results in the spectrum in conjunction with an AI-supported evaluation could represent an alternative diagnostic method quantifying neuromuscular control. In a prospective cross-sectional study, 188 subjects aged between 18 and 60 years were recruited and divided into two groups: "LowBackPain" vs. "Healthy". Subsequently, measurements of postural control in a seated position were carried out for 60 s using a modified balance board. A spectrum per trail was calculated using the measured CoP tracks in the range from 0.01 to 10 Hz. Various algorithms for data classification and prediction of these classes were tested for the parameter combination with the highest proven static influence on the parameter pain. The best results were found in a frequency spectrum of 0.001 Hz and greater than 1 Hz. After transforming the track from the time domain to the image domain for representation as power density, the influence of pain was highly significant (effect size 0.9). The link between pain and gender (p = 0.015) and pain and height (p = 0.012) also demonstrated significant results. The assessment of postural control in a seated position allows differentiation between "LowBackPain" and "Healthy" subjects. Using the AI algorithm of neural networks, the data set can be correctly differentiated into "LowBackPain" and "Healthy" with a probability of 81%. [ABSTRACT FROM AUTHOR]

The time-varying gravity field models derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission suffer from pronounced longitudinal stripe errors in the spatial domain. A potential way to mitigate such errors is to combine GRACE data with observations from other sources. In this study, we investigate the impacts on GRACE monthly gravity field solutions of incorporating the GPS data collected by the Gravity Field and Steady-State Ocean Circulation Explorer (GOCE) mission. To that end, we produce GRACE/GOCE combined monthly gravity field solutions through combination on the normal equation level and compare them with the GRACE-only solutions, for which we have considered the state-of-the-art ITSG-Grace2018 solutions. Analysis in the spectral domain reveals that the combined solutions have a notably lower noise level beyond degree 30, with cumulative errors up to degree 96 being reduced by 31%. A comparison of the formal errors reveals that the addition of GOCE GPS data mainly improves (near-) sectorial coefficients and resonant orders, which cannot be well determined by GRACE alone. In the spatial domain, we also observe a significant reduction by at least 30% in the noise of recovered mass changes after incorporating the GOCE GPS data. Furthermore, the signal-to-noise ratios of mass changes over 180 large river basins were improved by 8–20% (dependent on the applied Gaussian filter radius). These results demonstrate that the GOCE GPS data can augment the GRACE monthly gravity field solutions and support a future GOCE-type mission for tracking more accurate time-varying gravity fields. [ABSTRACT FROM AUTHOR]

Although the rapid expansion of urban rail transit offers convenience to citizens, the issue of subway vibration cannot be overlooked. This study investigates the spatial distribution characteristics of vibration in the Fayuan Temple historic and cultural reserve. It involves using a V001 magnetoelectric acceleration sensor capable of monitoring low amplitudes with a sensitivity of 0.298 V/(m/s2), a measuring range of up to 20 m/s2, and a frequency range span from 0.5 to 100 Hz for in situ testing, analyzing the law of vibration propagation in this area, evaluating the impact on buildings, and determining the vibration reduction scheme. The reserve is divided into three zones based on the vertical vibration level measured during the in situ test as follows: severely excessive, generally excessive, and non-excessive vibration. Furthermore, the research develops a dynamic coupling model of vehicle–track–tunnel–stratum–structure to verify the damping effect of the wire spring floating plate track and periodic pile row. It compares the characteristics of three vibration reduction schemes, namely, internal vibration reduction reconstruction, periodic pile row, and anti-vibration reinforcement or reconstruction of buildings, proposing a comprehensive solution. Considering the construction conditions, difficulty, cost, and other factors, a periodic pile row is recommended as the primary treatment measure. If necessary, anti-vibration reinforcement or reconstruction of buildings can serve as supplemental measures. [ABSTRACT FROM AUTHOR]

The proposed "Guanlan" ocean science satellite, led by China's Laoshan Laboratory, includes an interferometric radar altimeter (IRA) as a key payload. As an integral part of its development, an airborne IRA experiment was conducted on 6 November 2021, with a flight path of approximately 90 km in the South China Sea. This study investigates the IRA's ability to observe ocean sea surface height (SSH) across scales ranging from meters to mesoscale. The sea surface height anomaly (SSHA) of the IRA is aligned with the SSHA of the AVISO at scales greater than 30 km, but also demonstrates the ability to capture small-scale SSHA changes in two dimensions. We analyzed wavenumber spectra of SSHA obtained from the airborne IRA, ICESat-2, and SARAL/AltiKa satellite for this region. The results show a good agreement in power spectral density (PSD) levels between ICESat-2, SARAL/AltiKa and IRA at scales larger than 30 km. Within the submesoscale range of 1–10 km, the IRA SSHA spectrum exhibits a distinctly negative slope and the lowest energy level. The minimum PSD level of the IRA fell in the range of 10−4–10−3 m2/cycle/km, at scales around 1 km, which is more than an order of magnitude lower than that of ICESat-2, forming a spectral gap that is in agreement with the theoretical expectation. Furthermore, IRA-derived wave direction and significant wave height matched well with the MFWAM wave data. The results of this study underscore the considerable potential of airborne IRA in capturing SSHA across a range of scales, from oceanic waves to submesoscale. [ABSTRACT FROM AUTHOR]

The overall gain of a scintillation detector is temperature-dependent, leading to a drift in the measured gamma energy spectrum with changes in temperature. To mitigate this effect, a temperature drift correction is essential prior to conducting gamma spectrum analysis. In this study, the detector gain ratio is determined by comparing the positions of the same background peak across different spectra. Subsequently, the original spectrum is adjusted accordingly to obtain a gamma spectrum free from temperature drift. Experimental results demonstrate that after implementing this correction, the relative deviation of the 57Co characteristic peak positions in the gamma spectrum measured by the NaI(Tl) detector is reduced from 18.64% to 0.91%. Furthermore, by performing energy calibration beforehand, the characteristic peak position can be utilized for secondary correction, further minimizing temperature drift. Our findings indicate that the relative deviation of the 22Na characteristic peak positions was reduced, respectively, to 0.51% and 0.46% through secondary correction. This approach, which utilizes the background peak for correction, avoids the need for additional radioactivity or circuitry and effectively mitigates peak drift. Overall, this method holds significant implications for enhancing the accuracy of gamma spectrum analysis. [ABSTRACT FROM AUTHOR]

Vibration signal analysis is an important means for bearing fault diagnosis. Affected by the vibration of other machine parts, external noise and the vibration transmission path, the impulses induced by a bearing defect in the measured vibrations are very weak. Blind deconvolution (BD) methods can counteract the effect of the transmission path and enhance the fault impulses. Most BD methods highlight fault features of the filtered signals by impulse-featured objective functions (OFs). However, residual noise in the filtered signals has not been well tackled. To overcome this problem, a fuzzy entropy-assisted deconvolution (FEAD) method is proposed. First, FEAD takes advantage of the high noise sensitivity of fuzzy entropy (FuzzyEn) and constructs a weighted FuzzyEn–kurtosis OF to enhance the fault impulses while suppressing noise interference. Then, the PSO algorithm is used to iteratively solve the optimal inverse deconvolution filter. Finally, envelope spectrum analysis is performed on the filtered signal to realize bearing fault diagnosis. The feasibility of FEAD was first verified by the bearing fault simulation signals at constant and variable speeds. The bearing test signals from Case Western Reserve University (CWRU), the railway wheelset and the test bench validated the good performance of FEAD in fault feature enhancement. A comparison with and quantitative results for the other state-of-the-art BD methods indicated the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 has accumulated many mutations since its emergence in late 2019. Nucleotide substitutions leading to amino acid replacements constitute the primary material for natural selection. Insertions, deletions, and substitutions appear to be critical for coronavirus's macro- and microevolution. Understanding the molecular mechanisms of mutations in the mutational hotspots (positions, loci with recurrent mutations, and nucleotide context) is important for disentangling roles of mutagenesis and selection. In the SARS-CoV-2 genome, deletions and insertions are frequently associated with repetitive sequences, whereas C>U substitutions are often surrounded by nucleotides resembling the APOBEC mutable motifs. We describe various approaches to mutation spectra analyses, including the context features of RNAs that are likely to be involved in the generation of recurrent mutations. We also discuss the interplay between mutations and natural selection as a complex evolutionary trend. The substantial variability and complexity of pipelines for the reconstruction of mutations and the huge number of genomic sequences are major problems for the analyses of mutations in the SARS-CoV-2 genome. As a solution, we advocate for the development of a centralized database of predicted mutations, which needs to be updated on a regular basis. [ABSTRACT FROM AUTHOR]

The study presents a bioindication complex and a technology of the experiment based on a submersible digital holographic camera with advanced monitoring capabilities for the study of plankton and its behavioral characteristics in situ. Additional mechanical and software options expand the capabilities of the digital holographic camera, thus making it possible to adapt the depth of the holographing scene to the parameters of the plankton habitat, perform automatic registration of the "zero" frame and automatic calibration, and carry out natural experiments with plankton photostimulation. The paper considers the results of a long-term digital holographic experiment on the biotesting of the water area in Arctic latitudes. It shows additional possibilities arising during the spectral processing of long time series of plankton parameters obtained during monitoring measurements by a submersible digital holographic camera. In particular, information on the rhythmic components of the ecosystem and behavioral characteristics of plankton, which can be used as a marker of the ecosystem well-being disturbance, is thus obtained. [ABSTRACT FROM AUTHOR]

In the realm of Earth observation and remote sensing data analysis, the advancement of hyperspectral imaging (HSI) classification technology is of paramount importance. Nevertheless, the intricate nature of hyperspectral data, coupled with the scarcity of labeled data, presents significant challenges in this domain. To mitigate these issues, we introduce a self-supervised learning algorithm predicated on a spectral transformer for HSI classification under conditions of limited labeled data, with the objective of enhancing the efficacy of HSI classification. The S3L algorithm operates in two distinct phases: pretraining and fine-tuning. During the pretraining phase, the algorithm learns the spatial representation of HSI from unlabeled data, utilizing a masking mechanism and a spectral transformer, thereby augmenting the sequence dependence of spectral features. Subsequently, in the fine-tuning phase, labeled data is employed to refine the pretrained weights, thereby improving the precision of HSI classification. Within the comprehensive encoder–decoder framework, we propose a novel spectral transformer module specifically engineered to synergize spatial feature extraction with spectral domain analysis. This innovative module adeptly navigates the complex interplay among various spectral bands, capturing both global and sequential spectral dependencies. Uniquely, it incorporates a gated recurrent unit (GRU) layer within the encoder to enhance its ability to process spectral sequences. Our experimental evaluations across several public datasets reveal that our proposed method, distinguished by its spectral transformer, achieves superior classification performance, particularly in scenarios with limited labeled samples, outperforming existing state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

This work explores the evolution of auditory analysis in NMR spectroscopy, tracing its journey from a supplementary tool to visual methods such as oscilloscopes, to a technique sidelined due to technological advancements. Despite its renaissance in the late 1990s with artistic and scientific applications, widespread adoption was hindered by the necessity for hardware modifications and reliance on specialized software. Addressing these barriers, this paper introduces a new feature in Mnova NMR software that facilitates the easy auditory interpretation of NMR signals. We discuss new applications of this tool, emphasizing its utility in aiding the identification of specific functional groups by auditory analysis of the spectrum's multiplets, such as distinguishing between aromatic, olefinic, or aliphatic protons, thereby enriching the interpretative capabilities of NMR data. [ABSTRACT FROM AUTHOR]

This paper introduces a frequency-domain false data injection attack called Frequency Spectrum Attack (FSA) and explores its effects on load forecasting and the energy management system (EMS) in a microgrid. The FSA analyzes time-series signals in the frequency domain to identify patterns in their frequency spectrum. It learns the distribution of dominant frequencies in a dataset of healthy signals. Subsequently, it manipulates the amplitudes of dominant frequencies within this healthy distribution, ensuring a stealthy attack against statistical analysis of the signal spectrum. We evaluated the performance of FSA on LSTM, a state-of-the-art network for load forecasting. The results show that FSA can triple the Mean Absolute Error (MAE) of predictions compared to the normal case and increase it by 70% compared to noise injection attacks. Furthermore, FSA indirectly enhances battery utilization in the EMS by 45%. We then proposed a detection method that combines statistical analysis and machine-learning-based classification techniques with features. The model effectively distinguishes FSA from healthy and noisy signals, achieving an accuracy of 98.7% and an F1-score of 98.1% on a load dataset, covering healthy, FSA, and noisy load data. Finally, a countermeasure was introduced based on the statistical analysis of the frequency spectrum of healthy signals to mitigate the impact of FSA. This countermeasure successfully reduces the MAE of the attacked model from 0.135 to 0.053, validating its effectiveness in mitigating FSA. [ABSTRACT FROM AUTHOR]

Investigating the distribution of different Zn species on Zn-containing zeolite catalysts is crucial for identifying the active sites and establishing the relationship between the catalyst's structure and its activity in the process of ethylene aromatization. By utilizing X-ray absorption near edge spectra (XANES) of various reference samples, this study employed linear combination fitting (LCF) analysis on XANES spectra of real samples to accurately measure the changes in the distribution of Zn species in Zn-containing HZSM-5 zeolites under different Zn sources and loadings. The results showed that ZnOH+, ZnO clusters, and ZnO crystalline structures coexist in Zn/HZSM-5 catalysts prepared through physical mixing and incipient wet impregnation methods. A similar trend was observed for catalysts prepared using different methods, with an increase in Zn content resulting in a decrease in the proportion of ZnOH+ and a significant increase in the amount of larger ZnO crystals. Furthermore, ZnO clusters were confined within the zeolite pores. The findings of this study established a direct correlation between the amount of ZnOH+ determined through LCF analysis and both the rate of hydrogen production and the rate of aromatics formation, providing strong evidence for the catalytic role of ZnOH+ as an active center for dehydrogenation, which plays a key role in promoting the formation of aromatics. The method of LCF analysis on XANES spectra allows for the determination of the local structure of Zn species, facilitating a more precise analysis based on the distribution of these species. This method not only provides detailed information about the Zn species but also enhances the accuracy of the overall analysis. [ABSTRACT FROM AUTHOR]

Simple Summary: Esophageal carcinoma (EC) is a major cause of cancer deaths since it is first undetectable in its early stages. Narrow-band imaging (NBI) detects EC more accurately, sensitively, and specifically than white light imaging (WLI), according to many studies. This work uses a color space connected to décor to change WLIs into NBIs, improving early EC identification. The YOLOv5 algorithm was utilized to train WLI and NBI images separately, demonstrating the versatility of sophisticated object identification approaches in medical image analysis. Based on the confusion matrix and the trained model's precision, recall, specificity, accuracy, and F1-score, the model's performance was assessed. The model was trained to reliably identify dysplasia, squamous cell carcinoma (SCC), and polyps, demonstrating a detailed and focused examination of EC pathology for a better understanding. Dysplasia cancer, a pre-cancerous stage that may increase five-year survival, was detected with higher recall and accuracy by the NBI model. Although the NBI and WLI models recognized the polyp identically, the SCC category lowered its accuracy and recall rate. The NBI model had an accuracy of 0.60, 0.81, and 0.66 in dysplasia, SCC, and polyp categories, and recall rates of 0.40, 0.73, and 0.76. In dysplasia, SCC, and polyp categories, the WLI model was 0.56, 0.99, and 0.65 accurate. Additionally, it had recall rates of 0.39, 0.86, and 0.78 in the same categories. The NBI model performs poorly due to a small collection of training pictures. Increasing the dataset can improve performance. Esophageal carcinoma (EC) is a prominent contributor to cancer-related mortality since it lacks discernible features in its first phases. Multiple studies have shown that narrow-band imaging (NBI) has superior accuracy, sensitivity, and specificity in detecting EC compared to white light imaging (WLI). Thus, this study innovatively employs a color space linked to décor to transform WLIs into NBIs, offering a novel approach to enhance the detection capabilities of EC in its early stages. In this study a total of 3415 WLI along with the corresponding 3415 simulated NBI images were used for analysis combined with the YOLOv5 algorithm to train the WLI images and the NBI images individually showcasing the adaptability of advanced object detection techniques in the context of medical image analysis. The evaluation of the model's performance was based on the produced confusion matrix and five key metrics: precision, recall, specificity, accuracy, and F1-score of the trained model. The model underwent training to accurately identify three specific manifestations of EC, namely dysplasia, squamous cell carcinoma (SCC), and polyps demonstrates a nuanced and targeted analysis, addressing diverse aspects of EC pathology for a more comprehensive understanding. The NBI model effectively enhanced both its recall and accuracy rates in detecting dysplasia cancer, a pre-cancerous stage that might improve the overall five-year survival rate. Conversely, the SCC category decreased its accuracy and recall rate, although the NBI and WLI models performed similarly in recognizing the polyp. The NBI model demonstrated an accuracy of 0.60, 0.81, and 0.66 in the dysplasia, SCC, and polyp categories, respectively. Additionally, it attained a recall rate of 0.40, 0.73, and 0.76 in the same categories. The WLI model demonstrated an accuracy of 0.56, 0.99, and 0.65 in the dysplasia, SCC, and polyp categories, respectively. Additionally, it obtained a recall rate of 0.39, 0.86, and 0.78 in the same categories, respectively. The limited number of training photos is the reason for the suboptimal performance of the NBI model which can be improved by increasing the dataset. [ABSTRACT FROM AUTHOR]

A Hybrid Photovoltaic Thermal (PVT) system is one of the most emerging and energy-efficient technologies in the area of solar energy engineering. This review paper provides a comprehensive review of hybrid PVT systems in the context of the history of PVT, general classification, and parameter analysis. Several cell technologies with spectrum analysis are discussed to understand the application's ability and energy efficiency. Hybrid PVT concept, characteristics, and structure analysis is also discussed in this study. An extensive analysis on the classifications of hybrid PVT systems from the recent literature is also presented here. These literatures are identified based on several criteria. In order to provide a complete and energy-efficient technology, an innovative classification of the hybrid PVT system is proposed in this paper. This proposed classification is a combination and upgrade of various existing classifications mentioned in recent research studies. Parameters have a significant and unavoidable impact on the performance and efficiency of the hybrid PVT system. A brief analysis of different parameters and the optimization of the system is conducted after reviewing recent research articles. This analysis provides insights into the impact of parameter variations on the system. A novel parameter model comprising parametric and optimistic analyses is also presented in this paper. It provides a detailed parametric description that significantly affects the performance and efficiency of the hybrid PVT system. Finally, the assessment focuses on a critical analysis of the main challenges in adopting PVT technology and suggests ways to overcome these barriers. [ABSTRACT FROM AUTHOR]

Sub-hourly load forecasting can provide accurate short-term load forecasts, which is important for ensuring a secure operation and minimizing operating costs. Decomposition algorithms are suitable for extracting sub-series and improving forecasts in the context of short-term load forecasting. However, some existing algorithms like singular spectrum analysis (SSA) struggle to decompose high sampling frequencies and rapidly changing sub-hourly load series due to inherent flaws. Considering this, we propose an empirical mode decomposition-based hybrid model, named EMDHM. The decomposition part of this novel model first detrends the linear and periodic components from the original series. The remaining detrended long-range correlation series is simplified using empirical mode decomposition (EMD), generating intrinsic mode functions (IMFs). Fluctuation analysis is employed to identify high-frequency information, which divide IMFs into two types of long-range series. In the forecasting part, linear and periodic components are predicted by linear and trigonometric functions, while two long-range components are fitted by long short-term memory (LSTM) for prediction. Four forecasting series are ensembled to find the final result of EMDHM. In experiments, the model's framework we propose is highly suitable for handling sub-hourly load datasets. The MAE, RMSE, MARNE, and R2 of EMDHM have improved by 20.1%, 26.8%, 22.1%, and 5.4% compared to single LSTM, respectively. Furthermore, EMDHM can handle both short- and long-sequence, sub-hourly load forecasting tasks. Its R2 only decreases by 4.7% when the prediction length varies from 48 to 720, which is significantly lower than other models. [ABSTRACT FROM AUTHOR]

Free-standing archaeological walls are significantly exposed to horizontal actions (e.g., earthquakes) as they lack connections provided by floors or roofs. In such cases, the dynamic response governs the activation of local mechanisms of collapse, determining the shape of the macroblocks and their position. Engineering models of archaeological walls are developed according to the results of extensive visual inspections and on-site testing, including modal identification for calibration purposes. A modal response spectrum analysis on the calibrated model identifies the zones where the tensile stress is exceeded, which are more likely to detach as rigid macroblocks and subsequently overturn due to the expected ground spectra. The macroblocks are then assessed according to limit analysis. The case studies are the north and the apse walls of the church of San Giovanni Evangelista in Castelseprio (Varese, Lombardy), a 5th century Longobard fortified settlement, a part of UNESCO World Heritage. The construction quality of the apse was poorer than the north wall, but the masonry of both is very compact thanks to the good mortar. The macroblocks are identified mostly in the upper crests of the walls, and their acceleration of activation is two to six times larger than the demand (considering the dynamic amplification that the structure applies to the ground motion); therefore, no particular intervention is needed. The proposed method will require additional calibration, e.g., through nonlinear dynamic analyses, and a more precise treatment of uncertainties in masonry mechanical properties to determine the shape of the macroblocks. [ABSTRACT FROM AUTHOR]

Biomolecular abundance detection of fermentation microorganisms is significant for the accurate regulation of fermentation, which is conducive to reducing fermentation costs and improving the yield of target products. However, the development of an accurate analytical method for the detection of biomolecular abundance still faces important challenges. Herein, we present a non-invasive biomolecular abundance detection method based on Raman spectra combined with target extraction and multimodel fitting. The high gain of the eXtreme Gradient Boosting (XGBoost) algorithm was used to extract the characteristic Raman peaks of metabolically active proteins and nucleic acids within E. coli and yeast. The test accuracy for different culture times and cell cycles of E. coli was 94.4% and 98.2%, respectively. Simultaneously, the Gaussian multi-peak fitting algorithm was exploited to calculate peak intensity from mixed peaks, which can improve the accuracy of biomolecular abundance calculations. The accuracy of Gaussian multi-peak fitting was above 0.9, and the results of the analysis of variance (ANOVA) measurements for the lag phase, log phase, and stationary phase of E. coli growth demonstrated highly significant levels, indicating that the intracellular biomolecular abundance detection was consistent with the classical cell growth law. These results suggest the great potential of the combination of microbial intracellular abundance, Raman spectra analysis, target extraction, and multimodel fitting as a method for microbial fermentation engineering. [ABSTRACT FROM AUTHOR]

Polysaccharides are the main nutritional constituents in the Armillaria mellea fruiting bodies. The structure and immunoactivity of Armillaria mellea polysaccharide are valuable to be determined for development. In the present study, two polysaccharides, including Armillaria mellea neutral polysaccharide (AMPN) and Armillaria mellea acidic polysaccharide (AMPA), were prepared and determined. AMPN and AMPA were separated and refined by the ion exchange column and gel chromatographycolumn. Analysis of AMPN and AMPA revealed molecular weights of 4.432 × 103 Da and 7.323 × 103 Da. The monosaccharide composition analysis revealed that AMPN was mainly composed of 68.3%glucose, while AMPA consisted primarily of glucose, mannose, and galactose, accompanied by 8.9% galacturonic acid and 3% fucose. Then, infrared spectra analysis, Congo red staining, methylation, and NMR spectroscopy analysis were conducted as a means to clarify the structure of AMPN and AMPA. The NMR spectra demonstrated that the two polysaccharides included both α and β-configuration glycosidic bonds. The Congo red experiment suggests that AMPN and AMPA all had obvious triple helix structures.The effects of AMPN and AMPA on immune repair ability were compared by immune deficiency mice. The activity experiment showed that AMPN and acidic polysaccharides extracted from the Armillaria fruiting body have biological immune activity. Among them, AMPA showed higher immune activity. These findings suggest that Armillaria mellea fruiting bodies may be used as a source of dietary supplements and functional products. [ABSTRACT FROM AUTHOR]

Detrital zircon geochronology plays a crucial role in provenance analysis, serving as one of the fundamental strategies. The age spectrum of detrital zircons collected from the sedimentary unit of interest is often compared or correlated with that of potential source terranes. However, biases in the age data can arise due to factors related to detrital sampling, analysis techniques, and nonlinear geological mechanisms. The current study reviewed two sets of detrital zircon datasets established in 2011 and 2021 to discuss the origins of the Tibetan Plateau. These datasets collected from different media effectively demonstrate a progressive understanding of provenance affinity among the main terranes on the Tibetan Plateau. This highlights issues regarding weak and unclear temporal connections identified through analyzing the age spectrum for provenance analysis. Within this context, a local singularity analysis approach is currently employed to address issues associated with unclear and weak provenance information by characterizing local variations in nonlinear behaviors and enhancing detection sensitivity towards subtle anomalies. This new graphical approach effectively quantifies temporal variations in detrital zircon age populations and enhances identification of weak provenance information that may not be readily apparent on conventional age spectra. [ABSTRACT FROM AUTHOR]

The quantification of the irregular morphology and distribution pattern of mineral grains is an essential but challenging task in ore-related mineralogical research, allowing for tracing the footprints of pattern-forming geological processes that are crucial to understanding mineralization and/or diagenetic systems. In this study, a large model, namely, the Segmenting Anything Model (SAM), was employed to automatically segment and annotate quartz, lepidolite and albite grains derived from Yichun rare-metal granite (YCRMG), based on which a series of fractal and multifractal methods, including box-counting calculation, perimeter–area analysis and multifractal spectra, were implemented. The results indicate that the mineral grains from YCRMG show great scaling invariance within the range of 1.04~52,300 μm. The automatic annotation of mineral grains from photomicrographs yields accurate fractal dimensions with an error of only 0.6% and thus can be utilized for efficient fractal-based grain quantification. The resultant fractal dimensions display a distinct distribution pattern in the diagram of box-counting fractal dimension (Db) versus perimeter–area fractal dimension (DPA), in which lepidolites are sandwiched between greater-valued quartz and lower-valued albites. Snowball-textured albites, i.e., concentrically arranged albite laths in quartz and K-feldspar, exhibit characteristic Db values ranging from 1.6 to 1.7, which coincide with the fractal indices derived from the fractal growth model. The zonal albites exhibit a strictly increasing trend regarding the values of fractal and multifractal exponents from core to rim, forming a featured "fractal-index banding" in the radar diagram. This pattern suggests that the snowball texture gradually evolved from rim to core, thus leading to greater fractal indices of outer zones, which represent higher complexity and maturity of the evolving system, which supports a metasomatic origin of the snowball texture. Our study demonstrates that fractal analyses with the aid of a large model are effective and efficient in characterizing and understanding complex patterns of mineral grains. [ABSTRACT FROM AUTHOR]

Hyperspectral classification is a task of significant importance in the field of remote sensing image processing, with attaining high precision and rapid classification increasingly becoming a research focus. The classification accuracy depends on the degree of raw HSI feature extraction, and the use of endless classification methods has led to an increase in computational complexity. To achieve high accuracy and fast classification, this study analyzes the inherent features of HSI and proposes a novel spectral–spatial feature extraction method called window shape adaptive singular spectrum analysis (WSA-SSA) to reduce the computational complexity of feature extraction. This method combines similar pixels in the neighborhood to reconstruct every pixel in the window, and the main steps are as follows: rearranging the spectral vectors in the irregularly shaped region, constructing an extended trajectory matrix, and extracting the local spatial and spectral information while removing the noise. The results indicate that, given the small sample sizes in the Indian Pines dataset, the Pavia University dataset, and the Salinas dataset, the proposed algorithm achieves classification accuracies of 97.56%, 98.34%, and 99.77%, respectively. The classification speed is more than ten times better than that of other methods, and a classification time of only about 1–2 s is needed. [ABSTRACT FROM AUTHOR]

Simple Summary: This research revolutionizes dermatological diagnostics by integrating artificial intelligence (AI) and hyperspectral imaging (HSI) to identify skin cancer lesions, particularly Mycosis fungoides (MF). It differentiates MF from conditions such as psoriasis and atopic dermatitis using a dataset of 1659 skin images. This study employs a novel AI algorithm alongside advanced techniques for precise lesion segmentation and classification, moving diagnosis from color to spectral analysis. This non-invasive and efficient method marks a significant advancement in the early and accurate detection of skin malignancies. The model's high performance is validated by its sensitivity, specificity, and accuracy, making it a vital tool in dermatology for identifying skin cancers and inflammatory conditions. This study pioneers the application of artificial intelligence (AI) and hyperspectral imaging (HSI) in the diagnosis of skin cancer lesions, particularly focusing on Mycosis fungoides (MF) and its differentiation from psoriasis (PsO) and atopic dermatitis (AD). By utilizing a comprehensive dataset of 1659 skin images, including cases of MF, PsO, AD, and normal skin, a novel multi-frame AI algorithm was used for computer-aided diagnosis. The automatic segmentation and classification of skin lesions were further explored using advanced techniques, such as U-Net Attention models and XGBoost algorithms, transforming images from the color space to the spectral domain. The potential of AI and HSI in dermatological diagnostics was underscored, offering a noninvasive, efficient, and accurate alternative to traditional methods. The findings are particularly crucial for early-stage invasive lesion detection in MF, showcasing the model's robust performance in segmenting and classifying lesions and its superior predictive accuracy validated through k-fold cross-validation. The model attained its optimal performance with a k-fold cross-validation value of 7, achieving a sensitivity of 90.72%, a specificity of 96.76%, an F1-score of 90.08%, and an ROC-AUC of 0.9351. This study marks a substantial advancement in dermatological diagnostics, thereby contributing significantly to the early and precise identification of skin malignancies and inflammatory conditions. [ABSTRACT FROM AUTHOR]

A spectral analysis of the time dynamics of seismicity occurring in the Enguri area of Georgia from 1978 to 2021 is performed by means of Schuster's spectrum analysis, periodogram analysis, and empirical mode decomposition. The results of our analysis suggest that earthquakes around the reservoir (within a 50 km radius from the center of the dam) may be due to changes in water level, featured by the yearly cycle of loading and unloading operations of the reservoir. It is observed that the impacts of water fluctuations are more pronounced in shallower strata (down to 10 km) than deeper ones (down to 20 km); this could indicate that earthquakes occurring at deeper levels may primarily result from tectonic forces, whereas those at shallower depths may be predominantly triggered by reservoir-induced factors. [ABSTRACT FROM AUTHOR]

As an important component of N-linked glycoproteins, the core pentasaccharide is highly crucial to the potential application prospect of glycoprotein. However, the gas phase conformation study is a challenging one due to the size and complexity of the molecule, together with the necessity to rely on quantum chemistry modeling for relevant energetics and structures. In this paper, the structures of the trisaccharides and core pentasaccharides in N-linked glycans in the gas phase were constructed by a three-step tree-based (TSTB) sampling. Since single point energies of all the conformers are calculated at the temperature of zero, it is necessary to evaluate the stability at a high temperature. We calculate the Gibbs free energies using the standard thermochemistry model (T = 298.15 K). For trimannose, the energetic ordering at 298.15 K can be strongly changed compared to 0 K. Moreover, two structures of trimannose with high energies at 0 K are considered to provide a much better match of IR vibration signatures with the low Gibbs free energies. On this basis, the core pentasaccharide was constructed in three ways. The building configurations of core pentasaccharide were optimized to obtain reasonable low-energy stable conformers. Fortunately, the lowest-energy structure of core pentasaccharide is eventually the minimum at 0 K and 298.15 K. Furthermore, spectrum analysis of core pentasaccharide was carried out. Although poorly resolved, its contour from the experiment was in qualitative correspondence with the computed IR spectrum associated with its minimum free energy structure. A large number of strongly and weakly hydrogen-bonded hydroxyl and acetylamino groups contribute to a highly congested set of overlapping bands. Compared with traditional conformation generators, the TSTB sampling is employed to efficiently and comprehensively obtain preferred conformers of larger saccharides with lower energy. [ABSTRACT FROM AUTHOR]

The electricity consumption behavior of the inhabitants is a major contributor to the uncertainty of the residential load system. Human-caused uncertainty may have a distributional component, but it is not well understood, which limits further understanding the stochastic component of load forecasting. This study proposes a short-term load-interval forecasting method considering the stochastic features caused by users' electricity consumption behavior. The proposed method is composed of two parts: load-point forecasting using singular spectrum analysis and long short-term memory (SSA-LSTM), and load boundaries forecasting using statistical analysis. Firstly, the load sequence is decomposed and recombined using SSA to obtain regular and stochastic subsequences. Then, the load-point forecasting LSTM network model is trained from the regular subsequence. Subsequently, the load boundaries related to load consumption consistency are forecasted by statistical analysis. Finally, the forecasting results are combined to obtain the load-interval forecasting result. The case study reveals that compared with other common methods, the proposed method can forecast the load interval more accurately and stably based on the load time series. By using the proposed method, the evaluation index coverage rates (CRs) are (17.50%, 1.95%, 1.05%, 0.97%, 7.80%, 4.55%, 9.52%, 1.11%), (17.95%, 3.02%, 1.49%, 5.49%, 5.03%, 1.66%, 1.49%), (19.79%, 2.79%, 1.43%, 1.18%, 3.37%, 1.42%) higher than the compared methods, and the interval average convergences (IACs) are (−18.19%, −8.15%, 3.97%), (36.97%, 21.92%, 22.59%), (12.31%, 21.59%, 7.22%) compared to the existing methods in three different counties, respectively, which shows that the proposed method has better overall performance and applicability through our discussion. [ABSTRACT FROM AUTHOR]

Parameter estimation is important in weather radar signal processing. Time-domain processing (TDP) and frequency-domain processing (FDP) are two basic parameter estimation methods used in the weather radar field. TDP is widely used in operational weather radars because of its high efficiency and robustness; however, it must be assumed that the received signal has a symmetrical or Gaussian power spectrum, which limits its performance. FDP does not require assumptions about its power spectrum model and has a seamless connection to spectrum analysis; however, its application performance has not been fully validated to ensure its robustness in an operational environment. In this study, we introduce several technical details in FDP, including window function selection, aliasing correction, and noise correction. Additionally, we evaluate the performance of FDP and compare the performance of FDP and TDP based on simulated and measured weather in-phase/quadrature (I/Q) data. The results show that FDP has potential for operational applications; however, further improvements are required, e.g., windowing processing for signals mixed with severe clutter. [ABSTRACT FROM AUTHOR]

Quinoline and isoquinoline moieties occur in many natural and synthetic compounds exhibiting high biological activity. The purpose of this study was to analyze the chemical structures of 5,8-quinolinedione and 5,8-isoquinoline derivatives using FT-IR spectroscopy supplemented with theoretical DFT calculations. Spectroscopic measurements were conducted using the attenuated total reflection (ATR) mode in the frequency range of 4000–400 cm−1. An analysis of FT-IR spectra was carried out, assigning the characteristic vibration frequencies of various functional groups to individual peaks. It was found that the experimental and calculated FT-IR spectra showed a good correlation for all the compounds under study. The most significant difference in the spectra occurred in the region of carbonyl bands. For compounds with the 5,8-quinolinedione moiety, two separated C=O vibration peaks were observed, while for compounds with the 5,8-isoquinolinedione moiety, the carbonyl vibrations created only one peak. This difference makes it possible to distinguish between the 5,8-quinolinedione and 5,8-isoquinolinedione derivatives. [ABSTRACT FROM AUTHOR]

We present first results of the analysis of optical spectra of two complementary samples of Seyfert galaxies (Seyferts). The first sample was extracted from a selection of the 4th Fermi Gamma-ray Large Area Telescope (4FGL) catalog and consists of 11 γ -ray-emitting jetted Seyfert galaxies. The second one was extracted from the Swift-BAT AGN Spectroscopic Survey (BASS) and is composed of 38 hard-X-ray-selected active galactic nuclei (AGN). These two samples are complementary, with the former being expected to have smaller viewing angles, while the latter may include objects with larger viewing angles. We measured emission-line ratios to investigate whether the behavior of these Seyferts can be explained in terms of obscuration, as suggested by the Unified Model (UM) of AGN, or if there are intrinsic differences due to the presence of jets or outflows, or due to evolution. We found no indications of intrinsic differences. The UM remains the most plausible interpretation for these classes of objects, even if some results can be challenging for this model. [ABSTRACT FROM AUTHOR]

Simple Summary: Recent advancements in this field have shown continuous improvement, with CAD algorithms enhancing diagnosis accuracy. The study systematically analyzes various aspects of HSI research for skin cancer detection, emphasizing global efforts and the need for more collaboration and data sharing. The results suggest promising advancements in skin cancer identification through HSI, offering potential for early diagnosis and tailored treatments. Integrating CAD algorithms improves diagnostic accuracy. Despite challenges such as limited patient involvement and data availability, HSI offers non-invasive, precise lesion characterization, reducing false positives and negatives. It holds the potential to improve early detection and treatment planning. With ongoing research and collaboration, HSI holds the potential to save lives and improve patient care by facilitating early diagnosis and personalized treatment. Skin cancer, a malignant neoplasm originating from skin cell types including keratinocytes, melanocytes, and sweat glands, comprises three primary forms: basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and malignant melanoma (MM). BCC and SCC, while constituting the most prevalent categories of skin cancer, are generally considered less aggressive compared to MM. Notably, MM possesses a greater capacity for invasiveness, enabling infiltration into adjacent tissues and dissemination via both the circulatory and lymphatic systems. Risk factors associated with skin cancer encompass ultraviolet (UV) radiation exposure, fair skin complexion, a history of sunburn incidents, genetic predisposition, immunosuppressive conditions, and exposure to environmental carcinogens. Early detection of skin cancer is of paramount importance to optimize treatment outcomes and preclude the progression of disease, either locally or to distant sites. In pursuit of this objective, numerous computer-aided diagnosis (CAD) systems have been developed. Hyperspectral imaging (HSI), distinguished by its capacity to capture information spanning the electromagnetic spectrum, surpasses conventional RGB imaging, which relies solely on three color channels. Consequently, this study offers a comprehensive exploration of recent CAD investigations pertaining to skin cancer detection and diagnosis utilizing HSI, emphasizing diagnostic performance parameters such as sensitivity and specificity. [ABSTRACT FROM AUTHOR]

Bifidobacterium longum subsp. longum XZ01 (BLSL1) is a new strain (isolated from the intestines of healthy people and deposited with the preservation number GDMCC 61618). An exopolysaccharide, S-EPS-1, was successfully isolated from the strain and then systematically investigated for the first time. Some structural features of S-EPS-1 were analyzed by chemical component, HPLC, ultraviolet, infrared, and nuclear magnetic resonance spectrum analyses. These analyses revealed that S-EPS-1 is a neutral heteropolysaccharide with an α-configuration. It contains mainly mannose and glucose, as well as small amounts of rhamnose and galactose. The molecular weight of S-EPS-1 was calculated to be 638 kDa. Several immunoregulatory activity assays indicated that S-EPS-1 could increase proliferation, phagocytosis, and NO production in vitro. In addition, S-EPS-1 could upregulate the expression of cytokines at the mRNA level through TLR4-mediated activation of the NF-κB signaling pathway in RAW 264.7 cells. Finally, S-EPS-1 was demonstrated to exhibit antioxidant activity by ABTS+• scavenging, DPPH• scavenging, and ferric-ion reducing power assays. Furthermore, S-EPS-1 can protect cells from oxidative stress and shows no cytotoxicity. These beneficial effects can be partly attributed to its antioxidant ability. Thus, the antioxidant S-EPS-1 may be applied as a functional food in the future. [ABSTRACT FROM AUTHOR]

The traditional phased array radar is unable to distinguish mainlobe deceptive interference from the true target when they share an identical angle. To address this issue, a method is developed to suppress the interference in the joint transmit–receive–Doppler frequency domain, using secondary compensation and quadratic phase code (QPC) with the frequency diversity array multiple-input multiple-output (FDA-MIMO) radar. The QPC technique is employed by encoding the transmitted pulses in various spatial channels, which extends the degrees-of-freedom (DOFs) of the system. To distinguish and suppress the false targets behind the true target by at least one pulse, Doppler compensation is performed and Doppler filters are employed. To distinguish the fast-generated false targets, which are located in the same pulse with the true target, the spatial frequency compensation is implemented, which equivalently relocates the mainlobe interference to the sidelobes. Via the singular spectrum analysis (SSA) technique and the data-dependent beamforming, the interferences within the ambiguity region are suppressed. Simulation results verify the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Generative models, such as Variational Autoencoders (VAEs), are increasingly employed for atypical pattern detection in brain imaging. During training, these models learn to capture the underlying patterns within "normal" brain images and generate new samples from those patterns. Neurodivergent states can be observed by measuring the dissimilarity between the generated/reconstructed images and the input images. This paper leverages VAEs to conduct Functional Connectivity (FC) analysis from functional Magnetic Resonance Imaging (fMRI) scans of individuals with Autism Spectrum Disorder (ASD), aiming to uncover atypical interconnectivity between brain regions. In the first part of our study, we compare multiple VAE architectures—Conditional VAE, Recurrent VAE, and a hybrid of CNN parallel with RNN VAE—aiming to establish the effectiveness of VAEs in application FC analysis. Given the nature of the disorder, ASD exhibits a higher prevalence among males than females. Therefore, in the second part of this paper, we investigate if introducing phenotypic data could improve the performance of VAEs and, consequently, FC analysis. We compare our results with the findings from previous studies in the literature. The results showed that CNN-based VAE architecture is more effective for this application than the other models. [ABSTRACT FROM AUTHOR]

Simple Summary: Video capsule endoscopy (VCE) is a small, patient-friendly tool used for medical imaging, but it lacks narrow band imaging (NBI), which is crucial for detecting various cancers like esophageal cancer (EC). EC is hard to detect early since it often shows no symptoms, leading to a low 5-year survival rate. NBI enhances mucosal features for early cancer identification, but adding it to VCE isn't feasible due to size constraints. This study successfully developed a method to convert traditional white light images (WLI) from VCE into NBI-like images for esophageal examination. The method performed well, with high similarity scores and improved image quality, offering a promising solution for better cancer detection. Video capsule endoscopy (VCE) is increasingly used to decrease discomfort among patients owing to its small size. However, VCE has a major drawback of not having narrow band imaging (NBI) functionality. The current VCE has the traditional white light imaging (WLI) only, which has poor performance in the computer-aided detection (CAD) of different types of cancer compared to NBI. Specific cancers, such as esophageal cancer (EC), do not exhibit any early biomarkers, making their early detection difficult. In most cases, the symptoms are unnoticeable, and EC is diagnosed only in later stages, making its 5-year survival rate below 20% on average. NBI filters provide particular wavelengths that increase the contrast and enhance certain features of the mucosa, thereby enabling early identification of EC. However, VCE does not have a slot for NBI functionality because its size cannot be increased. Hence, NBI image conversion from WLI can presently only be achieved in post-processing. In this study, a complete arithmetic assessment of the decorrelated color space was conducted to generate NBI images from WLI images for VCE of the esophagus. Three parameters, structural similarity index metric (SSIM), entropy, and peak-signal-to-noise ratio (PSNR), were used to assess the simulated NBI images. Results show the good performance of the NBI image reproduction method with SSIM, entropy difference, and PSNR values of 93.215%, 4.360, and 28.064 dB, respectively. [ABSTRACT FROM AUTHOR]

10.1021/acs.analchem.0c04608 18 Kelly J.G., Cheung K.T., Martin C., O'Leary J.J., Prendeville W., Martin-Hirsch P.L., Martin F.L. A spectral phenotype of oncogenic human papillomavirus-infected exfoliative cervical cytology distinguishes women based on age. As healthcare tools increasingly move towards a more digital and computational format, there is an increasing need for sensor-based technologies that allow for rapid screening and/or diagnostics [[1]]. 10.3390/ijms23094859 6 Guo S., Wei G., Chen W., Lei C., Xu C., Guan Y., Ji T., Wang F., Liu H. Fast and deep diagnosis using blood-based ATR-FTIR spectroscopy for digestive tract cancers. [Extracted from the article]

Performing 5G coverage planning across borders introduces real-life challenges related to legalities, intercountry agreements, and binding documents. This work provides RF network modelling exercise results to provide uninterrupted 5G coverage to the Via Baltica and Rail Baltica transport corridors crossing Estonia and Latvia and on the border with Lithuania, as well as the Tallinn–Tartu–Valga and Valka–Valga roads (Latvia–Estonia), capable of cross-border 5G services. The study starts by identifying and interviewing stakeholders from different sectors of operation in the Baltic states and Europe and then provides an overview of some of the main legal acts and documents regulating the electronic communications sector in the Baltic states and Europe. Furthermore, 5G network requirements are proposed. In addition, the necessary and existing passive and active infrastructure is described, including spectrum management-related analysis, where the RF bands 700 MHz and 3500 MHz are analysed. Finally, coverage planning is performed. The network modelling results aim to foresee the number of new sites that need to be built on the said transport corridors, also examining the existing infrastructure for such purposes. Additionally, an estimated timeline for building the new sites is provided. [ABSTRACT FROM AUTHOR]

An efficient processing approach is essential for increasing identification accuracy since the electroencephalogram (EEG) signals produced by the Brain–Computer Interface (BCI) apparatus are nonlinear, nonstationary, and time-varying. The interpretation of scalp EEG recordings can be hampered by nonbrain contributions to electroencephalographic (EEG) signals, referred to as artifacts. Common disturbances in the capture of EEG signals include electrooculogram (EOG), electrocardiogram (ECG), electromyogram (EMG) and other artifacts, which have a significant impact on the extraction of meaningful information. This study suggests integrating the Singular Spectrum Analysis (SSA) and Independent Component Analysis (ICA) methods to preprocess the EEG data. The key objective of our research was to employ Higher-Order Linear-Moment-based SSA (HOL–SSA) to decompose EEG signals into multivariate components, followed by extracting source signals using Online Recursive ICA (ORICA). This approach effectively improves artifact rejection. Experimental results using the motor imagery High-Gamma Dataset validate our method's ability to identify and remove artifacts such as EOG, ECG, and EMG from EEG data, while preserving essential brain activity. [ABSTRACT FROM AUTHOR]

In Global Navigation Satellite System Reflectometry (GNSS-R), SNR spectrum analysis is widely used for surface altimetry inversion because of its low cost and easy operation. However, this method is somewhat limited in environmental situations with large tidal variations in sea level. In this paper, we implemented a machine learning approach to retrieve sea level height using three feature parameters of frequency, amplitude, and phase extracted by GNSS-R as inputs for the support vector regression (SVR) model, achieving better robustness in environments with large tidal variations. In this experiment, two stations, SC02 and BRST, were selected for research comparison, in which the sea surface fluctuation at the SC02 station was smaller at around 3 m while the sea surface fluctuation at the BRST station was larger at around 7 m. Global Navigation Satellite System (GNSS) observations were selected for 6 months for use to perform the assessment. The SC02 station improved 25.64% and 24.05% in the accuracy of RMSE (14.5 cm) and MAE (12.0 cm), respectively, using the SVR model compared to the conventional method (CM). In the environment with large sea level tidal fluctuations, the BRST station improved accuracy by 17.32% and 15.81% using the SVR model compared to the CM for RMSE (25.3 cm) and MAE (21.3 cm), respectively. It is shown that the SVR model is robust for sea level height retrieval with large tidal variations and that these three feature parameters, including frequency, amplitude, and phase extracted by GNSS-R, are crucial for optimizing sea surface height retrieval. [ABSTRACT FROM AUTHOR]

Simple Summary: Meningiomas are the most common primary central nervous system tumors, and surgery is their primary treatment of choice. The surgical intervention aims for the maximum extent of tumor resection whilst preserving the brain's neurological functions. The effectiveness of such intervention thus depends on the capability to intraoperatively discriminate between the meningioma tissue and the surrounding brain white matter and gray matter. This study investigated dielectric contrast as a potential physical biomarker for meningioma discrimination. Dielectric contrast is based on the difference in complex permittivity, a physical property of tissues, measurable by dielectric spectroscopy. This study shows that the dielectric contrast is relevant as a potential physical biomarker to discriminate the meningioma tissue from the surrounding brain tissues. Such a biomarker has a meaningful potential to make a positive impact on the management of neurooncological patients in terms of intraoperative tumor discrimination, as well as future diagnostic or therapeutic applications. The effectiveness of surgical resection of meningioma, the most common primary CNS tumor, depends on the capability to intraoperatively discriminate between the meningioma tissue and the surrounding brain white and gray matter tissues. Aiming to find a potential biomarker based on tissue permittivity, dielectric spectroscopy of meningioma, white matter, and gray matter ex vivo tissues was performed using the open-ended coaxial probe method in the microwave frequency range from 0.5 to 18 GHz. The averages and the 95% confidence intervals of the measured permittivity for each tissue were compared. The results showed the absence of overlap between the 95% confidence intervals for meningioma tissue and for brain white and gray matter, indicating a significant difference in average permittivity (p ≤ 0.05) throughout almost the entire measured frequency range, with the most pronounced contrast found between 2 GHz and 5 GHz. The discovered contrast is relevant as a potential physical biomarker to discriminate meningioma tissue from the surrounding brain tissues by means of permittivity measurement, e.g., for intraoperative meningioma margin assessment. The permittivity models for each tissue, developed in this study as its byproducts, will allow more accurate electromagnetic modeling of brain tumor and healthy tissues, facilitating the development of new microwave-based medical devices and tools. [ABSTRACT FROM AUTHOR]

Greyish-purple tremolite jade has become well known in the past few years, and the origin of its color has attracted the attention of gemologists. In this study, FT-IR spectra, EPMA, EPR spectra, micro-XRF, UV–Vis–NIR spectra, and LA-ICP-MS in situ mapping were analyzed to investigate the chromophore elements. The study sample was chosen from the Sanchahe mine, Qinghai Province, NW China, which has the typical characteristics of a gradual color change. The FT-IR and EPMA results revealed that the mineral composition of the dark and light greyish-purple regions of the sample are primarily composed of tremolite. UV–Vis–NIR spectra demonstrated that the greyish-purple color is mainly due to strong absorptions at 560 nm and 700 nm and weak absorption at 745 nm in the visible range. The EPR spectra presented ~3400 G six hyperfine lines resulting from the hyperfine interactions of the unpaired electron with the Mn2+ nucleus in the octahedral site. The UV–Vis–NIR and EPR spectra analyses demonstrated that Mn2+ is the origin of the purple color. A comparison of the major elements in the light and dark regions indicated that the chromogenic elements have strong positive correlations with Mn, Cu, and Fe. LA-ICP-MS mapping used to analyze the first transition metals indicated possible positive correlations between the greyish-purple color and the trace chromogenic elements. This suggested that the Mn, Cu, and Fe contents are significantly high in the dark band region. Combining in situ LA-ICP-MS mapping of trace elements, UV–Vis spectra, and EPR analysis results, it was suggested that Mn, Cu, and Fe are the major contributors to the greyish-purple color. This study provides a reference for the specific experimental methods to determine chromophores and the origin of color in tremolite jades. [ABSTRACT FROM AUTHOR]

The question of at what resolution the large eddy simulations (LESs) of a tropical cyclone (TC) high wind area may converge largely remains unanswered. To address this issue, LESs with five resolutions of 300 m, 100 m, 60 m, 33 m, and 20 m are performed in this study to simulate a high wind area near the radius of maximum wind of Typhoon Chanthu (2021) using the Weather Research and Forecasting (WRF) model. The results show that, for a limited area LES, model grid resolution may alter the local turbulence structure to generate significantly different extreme values of temperature, moisture, and winds, but it only has a marginal impact on the median values of these variables throughout the vertical column. All simulations are able to capture the turbulent roll vortices in the TC boundary layer, but the structure and intensity of the rolls vary substantially in different resolution simulations. Local hectometer-scale eddies with vertical velocities exceeding 10 m s−1 are only observed in the 20 m resolution simulation but not in the coarser resolution simulations. The ratio of the resolved turbulent momentum fluxes and turbulent kinetic energies (TKEs) to the total momentum fluxes and TKEs appears to show some convergence of LESs when the grid resolution reaches 100 m or finer, suggesting that it is an acceptable grid resolution for LES applications in TC simulations. [ABSTRACT FROM AUTHOR]

Efforts to delineate the influence of atmospheric variability on regional wildfire activity have previously been complicated by the stochastic occurrence of ignition and large fire events, particularly for Southern California, where anthropogenic modulation of the fire regime is extensive. Traditional metrics of wildfire activity inherently contain this stochasticity, likely weakening regional fire–climate relationships. To resolve this complication, we first develop a new method of quantifying regional wildfire activity that aims to more clearly capture the atmospheric fire regime component by aggregating four metrics of fire activity into an annual index value, the Annual Fire Severity Index (AFSI), for the 27-year period of 1992–2018. We then decompose the AFSI into trend and oscillatory components using singular spectrum analysis (SSA) and relate each component to a set of five climate predictors known to modulate macroscale fire activity in Southern California. These include the Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO), El Niño–Southern Oscillation (ENSO), and Santa Ana wind (SAW) events, and marine layer frequency. The results indicate that SSA effectively isolates the individual influence of each predictor on AFSI quantified by generally moderate fire–climate correlations, | r | > 0.4 , over the full study period, and | r | > 0.5 over select 13–15-year periods. A transition between weaker and stronger fire–climate relationships for each of the oscillatory PC–predictor pairs is centered around the mid-2000s, suggesting a significant shift in fire–climate variability at this time. Our approach of aggregating and decomposing a fire activity index yields a straightforward methodology to identify the individual influence of climatic predictors on macroscale fire activity even in fire regimes heavily modified by anthropogenic influence. [ABSTRACT FROM AUTHOR]

Granulometric aggregate fractions (20 µm–2 mm) of chernozem soils with different agriculture-use histories (native steppe, permanent bare fallow, arable land, and shelterbelt) were investigated in mid-IR and far IR regions (4000–100 cm−1) by heating in the air from 25 to 215 °C, using ATR FTIR and linear discriminant analysis to differentiate the land-use samples without chemical perturbation. The temperature dependences of the band maxima significantly differed for bands of silicate matrix and bands with the contribution of soil organic matter and were more stable to experimental conditions compared to the absolute positions. The thermal behavior of the integral intensities of the IR bands at 790–750 cm−1 and 440–420 cm−1 that was different compared to pure quartz, may be attributed to –C–H bending of alkanes and (poly)aromatic structures and skeletal bending, and could be used to distinguish intact soils from agriculturally used samples. The different temperature behaviors of the bands for fractions of soils with different land use are shown, with the maximum difference in fractions below 20–50 µm and medium fractions (50–200 µm). Changes in the band-maximum frequencies and the integral intensities of the bands were reversible for a heating–cooling cycle. The linear discriminant analysis of the spectra obtained for granulometric fractions of chernozem soils made it possible to separate the samples of native steppe, arable land, bare fallow, and shelterbelt with a high probability based on the type of vegetation and agrogenic load, mainly on the basis of the spectral ranges associated with biogenic forms of quartz and phytoliths. [ABSTRACT FROM AUTHOR]

Recently, the rapid development of deep learning has greatly improved the performance of image classification. However, a central problem in hyperspectral image (HSI) classification is spectral uncertainty, where spectral features alone cannot accurately and robustly identify a pixel point in a hyperspectral image. This paper presents a novel HSI classification network called MS-RPNet, i.e., multiscale superpixelwise RPNet, which combines superpixel-based S3-PCA with two-dimensional singular spectrum analysis (2D-SSA) based on the Random Patches Network (RPNet). The proposed frame can not only take advantage of the data-driven method, but can also apply S3-PCA to efficiently consider more global and local spectral knowledge at the super-pixel level. Meanwhile, 2D-SSA is used for noise removal and spatial feature extraction. Then, the final features are obtained by random patch convolution and other steps according to the cascade structure of RPNet. The layered extraction superimposes the different sparial information into multi-scale spatial features, which complements the features of various land covers. Finally, the final fusion features are classified by SVM to obtain the final classification results. The experimental results in several HSI datasets demonstrate the effectiveness and efficiency of MS-RPNet, which outperforms several current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Millimeter-wave radars are widely used in automotive radars because of their all-weather and all-day operation capability. However, as more and more radar sensors are used, the possibility of mutual interference between radars increases dramatically. Severe interference increases the noise level, affects target detection performance, and can lead to missed detection and wrong detection. In this study, a novel solution to the problem of mutual radar interference is introduced. The method is based on the analysis and synthesis of spectrum sub-bands. Specifically, the received radar signal is partitioned into sub-bands, after which interference mitigation is carried out in each sub-band. Finally, the signals are reconstructed to obtain interference-free data. The effectiveness of this approach is evaluated using both a simulated multi-target scenario and a real-life experimental environment. The results demonstrate that the proposed method outperforms existing techniques in terms of interference mitigation while exhibiting rapid processing speeds. [ABSTRACT FROM AUTHOR]

Singular spectrum analysis (SSA) is a non-parametric adaptive technique used for time series analysis. It allows solving various problems related to time series without the need to define a model. In this study, we focus on the problem of trend extraction. To extract trends using SSA, a grouping of elementary components is required. However, automating this process is challenging due to the nonparametric nature of SSA. Although there are some known approaches to automated grouping in SSA, they do not work well when the signal components are mixed. In this paper, a novel approach that combines automated identification of trend components with separability improvement is proposed. We also consider a new method called EOSSA for separability improvement, along with other known methods. The automated modifications are numerically compared and applied to real-life time series. The proposed approach demonstrated its advantage in extracting trends when dealing with mixed signal components. The separability-improving method EOSSA proved to be the most accurate when the signal rank is properly detected or slightly exceeded. The automated SSA was very successfully applied to US Unemployment data to separate an annual trend from seasonal effects. The proposed approach has shown its capability to automatically extract trends without the need to determine their parametric form. [ABSTRACT FROM AUTHOR]

Many studies available in the literature focus mainly on the mechanical characterization of fiber, leaving out other physicochemical and thermogravimetric analyses that allow for establishing its potential as an engineering material. This study characterizes fique fiber for its potential use as an engineering material. The fiber's chemical composition and physical, thermal, mechanical, and textile properties were analyzed. The fiber has a high holocellulose content and low lignin and pectin content, indicating its potential as a natural composite material for various applications. Infrared spectrum analysis revealed characteristic bands associated with multiple functional groups. The fiber had monofilaments with diameters around 10 μm and 200 μm, as determined by AFM and SEM images, respectively. Mechanical testing showed the fiber could resist a maximum stress of 355.07 MPa, with an average maximum strain at which breakage occurs of 8.7%. The textile characterization revealed a linear density range of 16.34 to 38.83 tex, with an average value of 25.54 tex and a regain of 13.67%. Thermal analysis showed that the fiber's weight decreased by around 5% due to moisture removal in the range of 40 °C to 100 °C, followed by weight loss due to thermal degradation of hemicellulose and glycosidic linkages of cellulose ranging from 250 to 320 °C. These characteristics suggest that fique fiber can be used in industries such as packaging, construction, composites, and automotive, among others. [ABSTRACT FROM AUTHOR]

Gorgonians play a fundamental role in the deep sea (below 200 m depth), composing three-dimensional habitats that are characterized by a high associated biodiversity and playing an important part in biogeochemical cycles. Here we describe the use of a benthic lander to monitoring polyps activity, used as a proxy of gorgonian feeding activity of three colonies of Placogorgia sp. Images cover a period of 22 days with a temporal resolution of 30 min. In addition, this seafloor observatory is instrumented with oceanographic sensors that allows continuous monitoring of the hydrographic conditions in the site. Deep-learning is used for automatic detection of the state of the polyps registered in the images. More than 1000 images of 3 large specimens of gorgonians are analyzed, annotating polyps as extended or retracted, using the semantic segmentation algorithm ConvNeXt. The segmentation results are used to describe the feeding patterns of this species. Placogorgia sp. shows a daily pattern of feeding conduct, depending on the hours of day and night. Using a Singular Spectrum Analysis approach, feeding activity is related to currents dynamics and Acoustic Doppler Current Profile (ADCP) return signal intensity, as proxy of suspended matter, achieving a linear correlation of 0.35 and 0.11 respectively. This is the first time that the behavior of the Placogorgia polyps, directly related to their feeding process, is described. [ABSTRACT FROM AUTHOR]

7α- and 7β-hydroxysteroid dehydrogenases (HSDHs) are enzymes that can catalyze the isomerization of hydroxyl groups at site seven of bile acids. In a previous study, we found that the activities of 7α- and 7β-HSDHs can be inhibited by bilirubin. In order to clarify the impact, the effects of bilirubin on enzymes were studied by kinetics, spectrum, and docking analysis. The relative activity of 7α-HSDH remained less than 40% under 1 mM bilirubin, and only 18% activity of 7β-HSDH kept in the same condition. Using taurochenodeoxycholic acid (TCDCA) as substrate, the Km of 7α-HSDH was up to 0.63 mM from 0.24 mM after binding with bilirubin and the Km of 7β-HSDH rose from 1.14 mM to 1.87 mM for the catalysis of tauroursodeoxycholic acid (TUDCA). The affinity of 7α- and 7β-HSDHs to substrates decreased with the effect of bilirubin. The binding of bilirubin with 7α- or 7β-HSDHs was analyzed by UV–vis, fluorescence, and circular dichroism (CD) spectroscopy. The results reflected that bilirubin caused a slight change in the secondary structure of 7α- or 7β-HSDHs, and the changes were correlated with the ratio of bilirubin to enzymes. Ten candidate molecular docking results were presented to reflect the binding of bilirubin with 7α- or 7β-HSDHs and to explore the inhibition mechanism. This research provides a more in-depth understanding of the effect of bilirubin on 7α- and 7β-HSDHs. [ABSTRACT FROM AUTHOR]

Simple Summary: The non-enhancing peritumoral area (NEPA) is defined as the hyperintense region in T2-weighted and fluid-attenuated inversion recovery (FLAIR) images surrounding a brain tumor. Analyses of the NEPA provide additional information to the magnetic resonance imaging (MRI) evaluation of the enhancing part of brain tumors. NEPA analyses of central nervous system malignancies with conventional and advanced MRI techniques give interesting highlights in the differential diagnosis of solid contrast-enhanced brain tumors regarding the prognostic stratification and treatment response. Considering the rapid technological changes in advanced MRI imaging, we assume that an update on this topic might be of interest. The non-enhancing peritumoral area (NEPA) is defined as the hyperintense region in T2-weighted and fluid-attenuated inversion recovery (FLAIR) images surrounding a brain tumor. The NEPA corresponds to different pathological processes, including vasogenic edema and infiltrative edema. The analysis of the NEPA with conventional and advanced magnetic resonance imaging (MRI) was proposed in the differential diagnosis of solid brain tumors, showing higher accuracy than MRI evaluation of the enhancing part of the tumor. In particular, MRI assessment of the NEPA was demonstrated to be a promising tool for distinguishing high-grade gliomas from primary lymphoma and brain metastases. Additionally, the MRI characteristics of the NEPA were found to correlate with prognosis and treatment response. The purpose of this narrative review was to describe MRI features of the NEPA obtained with conventional and advanced MRI techniques to better understand their potential in identifying the different characteristics of high-grade gliomas, primary lymphoma and brain metastases and in predicting clinical outcome and response to surgery and chemo-irradiation. Diffusion and perfusion techniques, such as diffusion tensor imaging (DTI), diffusional kurtosis imaging (DKI), dynamic susceptibility contrast-enhanced (DSC) perfusion imaging, dynamic contrast-enhanced (DCE) perfusion imaging, arterial spin labeling (ASL), spectroscopy and amide proton transfer (APT), were the advanced MRI procedures we reviewed. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast-conserving surgery (BCS) is an oncological procedure that allows for the excision of breast cancer with a clear margin of healthy tissue whilst optimising the cosmetic appearance. However, BCS is associated with up to a 19% re-excision rate due to incomplete excision ("positive margins") in the United Kingdom. Optical spectroscopy and the optical imaging of BCS specimens could be a potential intraoperative margin assessment tool to help reduce re-excision rates. Hyperspectral sensing is based on the premise that light illuminating biological tissues undergoes several processes that reflect the composition of tissue, thus helping to differentiate between normal and malignant tissues. This review assesses the current literature on the use of hyperspectral sensing in breast cancer. We divide the techniques into either point-based (spectroscopy) or whole field-of-view (imaging) methods. A comparison is made of the effectiveness of these modalities in discriminating between normal and malignant tissue, and we reflect on the usability of these modalities in the intraoperative setting. Up to 19% of patients require re-excision surgery due to positive margins in breast-conserving surgery (BCS). Intraoperative margin assessment tools (IMAs) that incorporate tissue optical measurements could help reduce re-excision rates. This review focuses on methods that use and assess spectrally resolved diffusely reflected light for breast cancer detection in the intraoperative setting. Following PROSPERO registration (CRD42022356216), an electronic search was performed. The modalities searched for were diffuse reflectance spectroscopy (DRS), multispectral imaging (MSI), hyperspectral imaging (HSI), and spatial frequency domain imaging (SFDI). The inclusion criteria encompassed studies of human in vivo or ex vivo breast tissues, which presented data on accuracy. The exclusion criteria were contrast use, frozen samples, and other imaging adjuncts. 19 studies were selected following PRISMA guidelines. Studies were divided into point-based (spectroscopy) or whole field-of-view (imaging) techniques. A fixed-or random-effects model analysis generated pooled sensitivity/specificity for the different modalities, following heterogeneity calculations using the Q statistic. Overall, imaging-based techniques had better pooled sensitivity/specificity (0.90 (CI 0.76–1.03)/0.92 (CI 0.78–1.06)) compared with probe-based techniques (0.84 (CI 0.78–0.89)/0.85 (CI 0.79–0.91)). The use of spectrally resolved diffusely reflected light is a rapid, non-contact technique that confers accuracy in discriminating between normal and malignant breast tissue, and it constitutes a potential IMA tool. [ABSTRACT FROM AUTHOR]