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This study explores the effects of bubble distribution parameters on the noise spectrum of bubble clusters through direct numerical simulations across volume fractions from 0.005% to 40%. Three types of bubble cluster distributions were analyzed: layered (uniformly sized bubbles with layered positioning), random (uniformly sized bubbles with random positioning), and lognormal (log-normally distributed bubble sizes with random positioning). Using the Ffowcs Williams–Hawkings (FW–H) method, we evaluated the sound pressure levels of the clusters. We found that the arrangement of bubble positions has little impact on the collapse times of bubble clusters. At volume fractions greater than 0.5%, bubble size also shows minimal effect on collapse times. However, when the volume fraction is less than 0.5%, the collapse times gradually approach the collapse time of the largest bubble in the cluster in a free field. Noise spectrum analyses showed that the arrangement of bubble positions significantly influences the noise spectra within the volume fraction range of 0.5%–25%, but has minimal impact outside this range. Importantly, the distribution of bubble sizes shows negligible effects on the noise spectrum, demonstrated by the nearly identical sound pressure level octave decay rates for random and lognormal clusters at the same volume fractions. This consistency can be mathematically described by the fitting formula: decay rate (dB/octave) = 18.192 × α − 0.047 −16.264. These findings enhance our understanding of the noise spectrum across varied bubble cluster distributions and provide new insights into the mechanisms of cavitation noise. [ABSTRACT FROM AUTHOR]

Novel zeolitic imidazolate frameworks (ZIFs), classical subtypes of metal organic frameworks (MOFs), and nanostructures are electro-engineered onto carbon fiber (CF), leading to a unique freestanding electrochemical platform of budlike nano Zn-ZIFs decorated CF (BN-Zn-ZIFs/CF). The unique morphology, structure, and composition are characterized by electron microscopy and energy spectrum analysis. Notably, the BN-Zn-ZIFs/CF platform displays superb electrocatalysis towards the oxidation of isoeugenol with encouragingly low overpotential and high current response. The strong electrocatalytic oxidation capability of BN-Zn-ZIFs/CF makes it an excellent sensing platform for isoeugenol detection. BN-Zn-ZIFs/CF sensor exhibits high-performance isoeugenol sensing with an extremely low limit of detection (13 nM) and wide detection range (0.1–700 µM). Besides, the BN-Zn-ZIFs/CF sensor can greatly resist interference from common ions, major biomolecules, and some amino acids. Moreover, excellent reliability, stability, and practicality are obtained. Our work demonstrates that the as-prepared BN-Zn-ZIFs/CF can act as an high-performance electrochemical sensor for the isoeugenol detection, the well-developed ZIF nanocrystal-modified conductive substrates can be a unique platform for the efficient sensing of other molecules, and the electrochemical engineering strategy can be an effective method for the growing of fresh MOF nanocrystals at conductive substrates in various electrochemical applications. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is a neurodegenerative disorder characterized by the presence of neurodegenerative lesions and cognitive impairment. In this study, a series of novel palmatine derivatives were designed and synthesized through the introduction of a heteroatom using carbodiimide-mediated condensation. The synthesized compounds were then screened for toxicity and potency, leading to the identification of compound 2q, which exhibited low toxicity and high potency. Our findings demonstrated that compound 2q displayed significant neuroprotective activity in vitro, emerging as a promising candidate for Alzheimer's disease treatment. [ABSTRACT FROM AUTHOR]

A new 14-membered resorcylic acid lactone (RAL14), chaetolactone A (1), along with three known ones (2−4), was obtained from the fermentation of the soil-derived fungus Chaetosphaeronema sp. SSJZ001. Their structures were established based on extensive spectroscopic data analyses (UV, IR, HRESIMS, 1D, and 2D NMR),13C NMR chemical shifts calculations coupled with the DP4+ probability method, theoretical calculations of ECD spectra, as well as X-ray diffraction analysis. All compounds were evaluated for their cytotoxic effects against A549, HO-8910, and MCF-7 cell lines. [ABSTRACT FROM AUTHOR]

In the present study, a series of tests were conducted on a purpose-built experimental setup to investigate the relationship between the pressure signal and flow regime in horizontal pneumatic conveying processes. The pressure signals and images of the material flow regime during the conveying process were collected and analyzed. Direct observation, power spectrum analysis, and discrete wavelet transform were employed in this work. The results show that as the conveying pressure increases, the time taken to reach the steady conveying stage is shorter. A correlation was discovered among the frequency intervals, pneumatic conveying stages, and flow regimes. The periodicity of the decomposed signal increases with decomposition scale. [ABSTRACT FROM AUTHOR]

We introduce the quantum currents for quantum systems with an SU(1,1) dynamic symmetry group whose evolution is governed by a non-linear Hamiltonian possessing a continuous spectrum and apply them to the analysis of the tunneling dynamics on the hyperbolic phase space. [ABSTRACT FROM AUTHOR]

A novel binary compounds as 3-(1,3-diphenyl-1H-pyrazol-4-yl)-2-phenyl-2H benzo [f] indazole-4,9-dione (10) and fused compounds as 2-hydroxy-3-(4-hydroxy-7-methoxy-2-oxo-1,2 dihydroquinolin-3-yl) naphthalene-1,4-dione (7) and naphtha [2,3-b] furan-3,4,9 (2H)-trione (6) based on Lawsone 'that plays as important precursor' was synthesized by reaction with 1,3-diphenyl-1H-pyrazole-4-carbaldehyde and halo-compounds, such as 2-chloro-acetyl chloride or 3-bromo-4-hydroxy-7-methoxyquinolin-2(1H)-one. Spectra and elemental analysis of newly synthesized compounds were investigated. The target molecules were showed easy access to antioxidant (calorimetrical measurement) and antitumor (Ehrlich ascites carcinoma [EAC] cells) activities. Geometrical isomers (enol, Keto conformers, and syn, anti-conformers) were achieved by density functional theory (DFT) that conformed to the spectral analysis of the investigated compounds. [ABSTRACT FROM AUTHOR]

In this work, 2,2‐bis(3‐amino‐4‐hydroxyphenyl) hexafluoropropane (APAF) was used as the fixed diamine monomer, and 4,4′‐(hexafluoroisopropylidene) diphthalic anhydride (6FDA), 4,4′‐oxydiphthalic anhydride (ODPA), 3,3′,4,4′‐biphenyltetracarboxylic diandhydride (BPDA) and pyromellitic dianhydride (PMDA) were selected as four different dianhydride monomers, respectively, to prepare the thermally rearranged polybenzoxazole (TR‐PBO) gas separation membranes with different structures via a thermal rearrangement reaction at 450°C. The microstructure of TR‐PBO membranes was investigated using X‐ray diffraction and positron annihilation spectroscopy. All the TR‐PBO membranes exhibit bimodal free volume structure, containing ultramicropores (<0.7 nm) and micropores (<2 nm) with size of 3.6–4.6 and 7.6–8.4 Å, respectively. As the steric hindrance of the bridging groups in the dianhydride monomers increases, the radius and number of pores in the membranes increases, leading to an increase in fractional free volume (FFV). The pure gas permeation test shows a positive correlation between gas permeability and FFV in the TR‐PBO membranes, indicating that an increase in FFV is favorable for the diffusion of gas molecules. Particularly, the ultramicropores play a decisive role on the gas selectivity. All the TR‐PBO membranes exhibit excellent gas permeaselectivity. Among them, the H2/CH4 separation performance of APAF‐6FDA and APAF‐ODPA membranes approaches the 2008 Robeson upper bound, which is due to the high number of both micropores and ultramicropores. Our results demonstrate that the free volume structure of polymer membranes can be designed by selecting monomers with different structures and utilizing thermal rearrangement reactions, allowing for the preparation of gas separation membranes with high permeability and selectivity. [ABSTRACT FROM AUTHOR]

Landslide disasters frequently result in significant casualties and property losses, underscoring the critical importance of research on landslide displacement prediction. This paper introduces an approach combining improved empirical mode decomposition (ICEEMDAN) and singular entropy-enhanced singular spectrum analysis (SSA) to predict landslide displacement using a time series short-duration memory network (LSTM). Initially, ICEEMDAN decomposes the landslide displacement time series into trend and periodic terms. SSA is then employed to denoise these components before fitting the trend term with LSTM. Pearson correlation analysis is utilized to identify characteristic factors within the LSTM model, followed by predictions using a multivariate LSTM model. The empirical results from the Baijiabao landslide in the Three Gorges Reservoir area demonstrate that the joint ICEEMDAN-SSA approach, when combined with LSTM modeling, outperforms the separate applications of SSA and ICEEMDAN, as well as other models such as RNN and SVM. Specifically, the ICEEMDAN-SSA-LSTM model achieves an RMSE of 6.472 mm and an MAE of 4.992 mm, which are considerably lower than those of the RNN model (19.945 mm and 15.343 mm, respectively) and the SVM model (16.584 mm and 11.748 mm, respectively). Additionally, the R2 value for the ICEEMDAN-SSA-LSTM model is 97.5%, significantly higher than the RNN model's 72.3% and the SVM model's 92.8%. By summing the predictions of the trend and periodic terms, the cumulative displacement prediction is obtained, indicating the superior accuracy of the ICEEMDAN-SSA-LSTM model. This model provides a new benchmark for precise landslide displacement prediction and contributes valuable insights to related research. [ABSTRACT FROM AUTHOR]

Considering the nonlinear and non-stationary characteristics of sea-level-change time series, this study focuses on enhancing the predictive accuracy of sea level change. The adjacent seas of China are selected as the research area, and the study integrates singular spectrum analysis (SSA) with long short-term memory (LSTM) neural networks to establish an SSA-LSTM hybrid model for predicting sea level change based on sea level anomaly datasets from 1993 to 2021. Comparative analyses are conducted between the SSA-LSTM hybrid model and singular LSTM neural network model, as well as (empirical mode decomposition) EMD-LSTM and (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) CEEMDAN-LSTM hybrid models. Evaluation metrics, including the root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2), are employed for the accuracy assessment. The results demonstrate a significant improvement in prediction accuracy using the SSA-LSTM hybrid model, with an RMSE of 5.26 mm, MAE of 4.27 mm, and R2 of 0.98, all surpassing those of the other models. Therefore, it is reasonable to conclude that the SSA-LSTM hybrid model can more accurately predict sea level change. [ABSTRACT FROM AUTHOR]

Due to the noise uncertainty, the conventional point prediction model is difficult to describe the actual characteristics of wind speed and lacks a description of the wind speed fluctuation range. In this paper, the kernel density estimation according to its error value is given, and then its fluctuation range is found to combine the prediction results of the test set to get its prediction range. Firstly, the singular spectrum analysis (SSA) is introduced to conduct the noise reduction, and variational modal decomposition (VMD) is performed to handle the sequences, then an improved slime mold algorithm (SMA) is proposed to optimize the VMD, and the stochastic configuration networks (SCNs) is applied to perform the prediction. Finally, the interval prediction results are calculated by fusing the point prediction error and kernel density estimation. The experimental results demonstrate that the proposed method can effectively reduce the noise interference in the wind speed prediction. [ABSTRACT FROM AUTHOR]

Objective: Deep brain stimulation (DBS) is a promising approach for the treatment of epilepsy. However, the optimal target for DBS and underlying mechanisms are still not clear. Here, we compared the therapeutic effects of DBS on distinct septal subregions, aimed to find the precise targets of septal DBS and related mechanisms for the clinical treatment. Methods: Assisted by behavioral test, electroencephalography (EEG) recording and analyzing, selectively neuronal manipulation and immunohistochemistry, we assessed the effects of DBS on the three septal subregions in kainic acid (KA)‐induced mouse seizure model. Results: DBS in the medial septum (MS) not only delayed generalized seizure (GS) development, but reduced the severity; DBS in the vertical diagonal band of Broca (VDB) only reduced the severity of GS, while DBS in the horizontal diagonal band of Broca (HDB) subregion showed no anti‐seizure effect. Notably, DBS in the MS much more efficiently decreased abnormal activation of hippocampal neurons. EEG spectrum analysis indicated that DBS in the MS and VDB subregions mainly increased the basal hippocampal low‐frequency (delta and theta) rhythm. Furthermore, ablation of cholinergic neurons in the MS and VDB subregions blocked the anti‐seizure and EEG‐modulating effects of septal DBS, suggesting the seizure‐alleviating effect of DBS was dependent on local cholinergic neurons. Significance: DBS in the MS and VDB, rather than HDB, attenuates hippocampal seizure by activation of cholinergic neurons‐augmented hippocampal delta/theta rhythm. This may be of great therapeutic significance for the clinical treatment of epilepsy with septal DBS. Plain Language Summary: The optical target of deep brain stimulation in the septum is still not clear. This study demonstrated that stimulation in the medial septum and vertical diagonal band of Broca subregions, but not the horizontal diagonal band of Broca, could alleviate hippocampal seizure through cholinergic neurons‐augmented hippocampal delta/theta rhythm. This study may shed light on the importance of precise regulation of deep brain stimulation therapy in treating epileptic seizures. [ABSTRACT FROM AUTHOR]

In recent years, the authors have extended the traditional interval method into the time dimension to develop a new mathematical tool called the "interval process model" for quantifying time-varying or dynamic uncertainties. This model employs upper and lower bounds instead of precise probability distributions to quantify uncertainty in a parameter at any given time point. It is anticipated to complement the conventional stochastic process model in the coming years owing to its relatively low dependence on experimental samples and ease of understanding for engineers. Building on our previous work, this paper proposes a spectrum analysis method to describe the frequency domain characteristics of an interval process, further strengthening the theoretical foundation of the interval process model and enhancing its applicability for complex engineering problems. In this approach, we first define the zero midpoint function interval process and its auto/cross-power spectral density (PSD) functions. We also deduce the relationship between the auto-PSD function and the auto-covariance function of the stationary zero midpoint function interval process. Next, the auto/cross-PSD function matrices of a general interval process are defined, followed by the introduction of the concepts of PSD function matrix and cross-PSD function matrix for interval process vectors. The spectrum analysis method is then applied to random vibration problems, leading to the creation of a spectrum-analysis-based interval vibration analysis method that determines the PSD function for the system displacement response under stationary interval process excitations. Finally, the effectiveness of the formulated spectrum-analysis-based interval vibration analysis approach is verified through two numerical examples. [ABSTRACT FROM AUTHOR]

Cavitation phenomena are accompanied by harsh cavitation noise. Studying cavitation noise is an effective method to monitor cavitation conditions. In order to study the characteristics of cavitation noise, a Venturi test bench experiment platform is built, the cavitation flow field and cavitation noise of Venturi test bench are simulated. The frequency domain analysis of cavitation noise is carried out, and the relationship between cavitation region, flow rate and sound pressure level of cavitation noise is verified by comparison between simulation and experiment. Because the cavitation noise is easily polluted by ambient noise, wavelet transform is used to analyze the cavitation noise and background noise, and the characteristics of cavitation noise are studied by comparing the difference between them. [ABSTRACT FROM AUTHOR]

This study presents a novel method for synthesizing Cu2O nanoparticles by employing ion exchange within a modified nanozeolite 4A matrix. The nanoparticles had a consistent particle size, predominantly around 6nm, with a narrow distribution. Nanosizing of the zeolite was achieved through high-energy milling treatments, thereby enhancing the surface-to-volume ratio. A band close to 700cm−1 was observed in the FTIR spectrum, potentially indicating that zeolite downsizing was related to symmetric stretching of the Si–O bond postmilling. Furthermore, a distinctive band corresponding to Cu(I)–O stretching vibrations was identified at approximately 600cm−1. Additionally, optical absorption analyses of the UV-Vis spectrum revealed two characteristic bands attributable to Cu2O nanoparticles at 370nm and 470nm. These findings have led to advancements in resource use and promoted sustainable and environmentally friendly production practices. [ABSTRACT FROM AUTHOR]

Emulsions in the form of creams, lotions, gels or foams are the most widely used personal care formulations to improve the condition and feel of the skin. Achieving an optimal balance between their performance, effectiveness and sensory profile is essential, with the sensory profile being a key factor in consumer satisfaction and the success of these products in the market. Well‐established methods using highly trained and semi‐trained panels (e.g. Spectrum descriptive analysis, Flash Profile method, Quantitative Descriptive Analysis method and ‘Check‐all‐that‐apply’) are available and commonly used for the sensory assessment of personal care products. Nevertheless, a common drawback among all these methods is their inherent cost, both in terms of financial resources and time requirements. In recent years, research studies have emerged to address this limitation by investigating potential correlations between tactile sensory attributes and instrumental data associated with the physical characteristics of topical formulations. In other words, significant efforts have been invested in the development of robust instrumental methods specifically designed to accurately predict the sensory description that a panel of assessors could establish. These methods are not only faster, cheaper and more objective compared to traditional sensory testing, but they can also be applied to formulations that have not undergone extensive safety and toxicological testing. This review summarizes the most relevant findings, trends and current challenges in predicting tactile sensory attributes of personal care emulsions based on instrumental parameters. [ABSTRACT FROM AUTHOR]

Background: Diffusion kurtosis imaging is an advanced magnetic resonance imaging technique that reveals additional information on the microstructure and micro-dynamics of different brain masses without the need for contrast agents. The aim of this study was to provide a comprehensive analysis of the role of MRI diffusion kurtosis and to compare it with magnetic resonance spectroscopy (MRS) and dynamic susceptibility contrast perfusion (DSC) in characterizing different brain masses, including gliomas, recurrent tumors, radiation necrosis, abscesses, and infarctions. Sixty-six patients with intracranial brain masses were enrolled in this prospective study. All patients were examined by conventional MRI sequences, DSC perfusion, MRS, and diffusion kurtosis imaging, with implemented b values which were 200, 500, 1000, 1500 and 2000s/mm2. Results: Mean kurtosis (MK) was higher (P < 0.001) in recurrent brain tumors than in radiation-induced necrosis; the optimal MK cutoff value for differentiation between them was 642 with 91.3% sensitivity and 85.7% specificity. Mean kurtosis was also higher (P < 0.001) in high-grade gliomas than in low-grade gliomas; the optimal MK cutoff value for differentiation between them was 639 with 91.6% sensitivity and 85.71% specificity. There was a good level of agreement between ADC and MD within the studied cases, with a correlation coefficient r = 0.815. MK had more sensitivity and specificity in differentiation between high- and low-grade gliomas, as well as RIN and tumoral recurrence, than MRS and DSC. Conclusions: Diffusion kurtosis imaging stands as an integral, noninvasive, and noncontrast tool for the characterization of various brain masses. It augments the capabilities of traditional and advanced MRI techniques, providing a deeper understanding of the microstructural changes in brain tissues. [ABSTRACT FROM AUTHOR]

Spectral graph theory explores the relationship between the eigenvalues and the structural properties of graphs. This study focuses on the fundamental matrices associated with simple graphs, specifically the adjacency matrix. This work contributes to the spectral graph theory literature by introducing a new family of simple connected graphs and investigating their eigenvalue properties. We examine conditions under which these graphs satisfy specific spectral properties, such as the strong antireciprocal eigenvalue property. Additionally, we construct another family of graphs from this new family, proving that each graph within this derived family satisfies the strong antireciprocal eigenvalue property. Through these contributions, we aim to deepen the understanding of the spectral characteristics of graph families and their applications in theoretical and applied contexts. [ABSTRACT FROM AUTHOR]

The sleeping stage classification plays an important role in the medical science because it helps the diagnosis of the mental health diseases. The conventional approach for performing the sleeping stage classification is based on the electroencephalograms (EEGs). However, it is worth noting that the EEGs reflect the brain activities. Nevertheless, the brain activities are very complicated even though the subjects are sleeping. Hence, performing the sleeping stage classification via the EEGs may yield the low classification accuracy. On the other hand, the electrooculograms (EOGs) are the voltages between the front eyes and the back eyes which are related to the eye ball movement. As it can directly reflect the various sleeping stages, it can achieve a higher classification accuracy. Therefore, this paper employs the two channel EOGs for performing the sleeping stage classification. The major contribution of this paper is to 1) employ the singular spectrum analysis (SSA) to exploit the latent intrinsic high dimensional dynamics of the one dimensional EOGs for performing the sleeping stage classification, 2) employ the approximate entropy as the features for performing the sleeping stage classification, and 3) assign the same features of different SSA components of different channels of the epochs of the EOGs into the same group and perform the principal component analysis (PCA) on each group of the feature vectors so that the properties of each type of the features are preserved. The results show that our proposed method yields the five sleeping stage classification accuracy at 93.73% and the sensitivity of the stage one non-rapid eye movement (S1) at 78.44%, which achieves the significant improvements compared to the existing methods. Therefore, our proposed method could be used to reduce the workload of the medical officers. [ABSTRACT FROM AUTHOR]

Pipeline transportation is the main means of transportation of oil, natural gas and other energy sources. During transportation, corrosive substances in oil and natural gas can cause damage to the pipeline structure. A non-destructive testing technology for pipeline corrosion based on electromagnetic ultrasound technology was proposed to improve the stability and safety of energy pipeline transportation systems. This technology utilized empirical mode decomposition and singular spectrum analysis to denoise electromagnetic ultrasound signals. The designed electromagnetic signal denoising algorithm completely removed mild noise pollution. When using this method to detect pipeline corrosion, the maximum calculation error of pipeline wall thickness was 0.1906 mm, and the lowest was 0.0015 mm. When detecting small area corrosion deficiency, the amplitude of the detection signal increased with the depth, up to a maximum of around 24 V, which accurately reflected small area defects. This non-destructive testing technology for pipelines can effectively detect the pipeline corrosion, which is helpful for the regular maintenance of pipeline energy transmission systems. [ABSTRACT FROM AUTHOR]

Stellar spectral template libraries play an important role in the automated analysis of stellar spectra. Synthetic template libraries cover a very large parameter space but suffer from poor matching with observed spectra. In this study, we propose a synthetic-to-observed spectral translation (SOST) method based on generative adversarial networks. The SOST method is able to calibrate synthetic spectra by converting them to the corresponding observed spectra. We applied this method to Kurucz synthetic spectra and observed spectra data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). After that, we constructed a stellar spectral library with uniform and broad parameter distributions using the SOST-corrected Kurucz synthetic spectra. Our stellar spectral template library contains 2431 spectra spanning a parameter space of 3500–8000 K for effective temperature (Teff), 0.0–5.0 dex for surface gravity (log g), and −2.0–0.5 dex for metallicity ([Fe/H]). The spectra in the library have a resolution of R ∼ 1800 and cover the wavelength range 3900–8700 Å. In order to verify the accuracy of this template library, we used the template library and the template-matching algorithm to derive the parameters of the PASTEL database. Compared to measurements using the original synthetic template library, the accuracies of the three parameters, Teff, log g, and [Fe/H], are improved, from 140 K, 0.31 dex, and 0.21 dex to 121 K, 0.26 dex, and 0.13 dex, respectively. In addition, we re-parameterised more than six million stellar spectra released by LAMOST DR8. [ABSTRACT FROM AUTHOR]

Background: A parotid abscess (PA) is a complication of an acute bacterial parotitis with a potentially life-threatening course. To date, data on the diagnosis and therapy of PA is sparse and mostly consists of case reports or case series. Therefore, this study aimed at comprehensively analyzing the microbiological spectrum and the therapeutic management in a bi-institutional setting. Methods: A retrospective clinical chart review was performed to identify all patients surgically treated for PA at two tertiary care centers in Germany. Data on demographics, clinical management and microbiological data including species identification, pathogenicity, type of antibiotic therapy, adjustment of antibiotics, antibiotic sensitivity testing, and smear test results were extracted. Intervention-related variables and etiology were analyzed for their statistical association with outcome variables. Results: Overall, 85 patients were included. Most patients (92.9%) underwent surgical incision. Around half of the patients (45.9%) were treated under local anesthesia. No facial nerve palsy was observed. The most frequently detected pathogens were Streptococci (n = 23), followed by Staphylococcus aureus (n = 6) including one case of methicillin-resistant Staphylococcus aureus. Most patients (68.2%) received an aminopenicillin ± beta-lactamase inhibitor as empiric antibiotic therapy. In 6 cases the antibiotic therapy was modified after receiving the antibiogram. Four patients (5.2%) presented with recurrent PA. Etiology was idiopathic (42.4%), followed by tumorous (12.9%), obstructive, and immunosuppressive (each 11.8%). Patients with a dental focus (p = 0.007) had a longer duration of hospitalization. Conclusion: The results show that the surgical therapy of PA under local anesthesia is safe. A dental examination should routinely be performed to rule out a dental focus. Obtaining a microbiological specimen in order to modify antibiotic therapy if necessary and a histopathological specimen to rule out a tumorous etiology is obligate. [ABSTRACT FROM AUTHOR]

Based on the requirements of deep sealing and profile control for the excellent temperature and salt resistance on polyacrylamide microspheres, the Poly‐AM/AMPS was prepared depends on aqueous solution polymerization method to improving oil and gas recovery efficiency in low‐permeability and heterogeneous reservoirs resulted by water channeling. The key technical parameters were established through single‐factor experimental methods, and the chemical structure was studied by infrared spectrum analysis (FTIR), energy spectrum analysis, and x‐ray diffraction analysis, and then the thermal stability was investigated depends on the thermogravimetric (TG) and differential thermogravimetric (DTG) distribution curves, moreover, the particle size and distribution, microstructure, and surface morphology were further studied. As a result, the results confirmed that the molecular structure of Poly‐AM/AMPS meets the expected design, and the quality retention rate at 800°C is 17.4%, the median particle size of Poly‐AM/AMPS particles is 35.88 μm, and the particle size distribution range is about 1.94–497.8 μm. Further, the Poly‐AM/AMPS was modified by maleic anhydride, sodium styrenesulfonate, and dimethyl diallyl ammonium chloride through conventional functional strengthening and modification, modified by nano‐SiO2, GO, and S‐CaCO3 through nano‐functional strengthening and modification. Additionally, the functional strengthening and modification mechanism was studied depends on the FTIR, TG, and DTG methods. [ABSTRACT FROM AUTHOR]

Substrate-free electrodes are promising dry electrodes for long-term physiological electrical signal monitoring due to their ultra-thinness, conformal contact, and stable skin–electrode impedance. However, the response of substrate-free electrodes to various disturbances during electrocardiogram (ECG) monitoring and the corresponding optimization needs to be investigated. This paper investigates the specific effects of various influencing factors on skin–electrode impedance and ECG during electrocardiogram (ECG) detection. The research utilizes substrate-free poly(3,4-ethylenedioxythiophene)/poly(styrene-sulfonate) (PEDOT:PSS) electrodes. The investigation employs several methods, including skin–electrode impedance comparison, ECG waveform analysis, spectrum analysis, and signal-to-noise ratio (SNR) evaluation. To avoid the impact of physiological state differences in subjects at different times, relevant data were only compared with the same group of experiments conducted in the same period. The results demonstrate that the substrate-free conformal contact PEDOT:PSS electrode has more stable skin–electrode impedance and could obtain a more stable ECG than partial contact electrodes (the SNR of the partial contact and conformal contact electrodes are 1.2768 ± 4.0299 dB and 7.2637 ± 1.4897 dB, respectively). Furthermore, the ECG signal quality of the substrate-free conformal contact PEDOT:PSS electrode was independent of the electrode area and shape (the SNRs of the large, medium, and small electrodes are 4.0447 ± 0.4616 dB, 3.9115 ± 0.5885 dB, and 4.1556 ± 0.5557 dB, respectively; the SNRs of the circular, square, and triangular electrodes are 9.2649 ± 0.6326 dB, 9.2471 ± 0.6806 dB, and 9.1514 ± 0.6875 dB, respectively), showing high signal acquisition capability that is the same as microneedle electrodes and better than fabric electrodes. The results of clothing friction effects show that skin–electrode impedance stability was important for ECG stability, while the impedance value was not (the SNRs of friction and non-friction electrodes are 2.4128 ± 7.0784 dB and 9.2164 ± 0.6696 dB, respectively). Moreover, the skin–electrode impedance maintains stability even at a high breathing frequency, but the ECG signal fluctuates at a high breathing frequency. This experiment demonstrates that even when the skin–electrode impedance remains stable, the ECG signal can still be susceptible to interference from other factors. This study suggests that substrate-free PEDOT:PSS that could form conformal contact with the skin has higher skin–electrode impedance stability and could measure a high ECG signal even with a small electrode area, demonstrating its potential as dry ECG electrodes, but the interference from other physiological electrical signals may require better circuit design. [ABSTRACT FROM AUTHOR]

Background: Medicinal plant-mediated combinational therapies have gained importance globally due to minimal side effects and enhanced treatment outcomes compared to single-drug modalities. We aimed to analyze the cytotoxic potential of each conventional treatment i.e., photodynamic therapy (PDT), chemotherapy (doxorubicin hydrochloride; Dox-HCl) with or without various concentrations of medicinal plant extracts (PE) on soft tissue cancer Rhabdomyosarcoma (RD) cell line. Methods: The Rhabdomyosarcoma (RD) cell line was cultured and treated with Photosensitizer (Photosense (AlPc4)), Chemo (Dox-HCl), and their combinations with different concentrations of each plant extract i.e., Thuja occidentalis, Moringa oleifera, Solanum surattense. For the source of illumination, a Diode laser (λ = 630 nm ± 1 nm, Pmax = 1.5 mW) was used. Photosensitizer uptake time (∼ 45 min) was optimized through spectrophotometric measurements (absorption spectroscopy). Drug response of each treatment arm was assessed post 24 h of administration using 3-(4, 5-dimethyl-2-thiazolyl)-2, 5- 5-diphenyl-2 H- tetrazolium bromide (MTT) assay. Results: PE-mediated Chemo-Photodynamic therapy (PDT) exhibited synergistic effects (CI < 1). Moreover, Rhabdomyosarcoma culture pretreated with various plant extracts for 24 h exhibited significant inhibition of cell viability however most effective outcomes were shown by low and high doses of Moringa oleifera compared to other plant extracts. Post low doses treated culture with all plant extracts followed by PDT came up with more effectiveness when compared to all di-therapy treatments. Conclusion: The general outcome of this work shows that the ethanolic plant extracts (higher doses) promote the death of cancerous cells in a dose-dependent way and combining Dox-HCl and photo-mediated photodynamic therapy can yield better therapeutic outcomes. [ABSTRACT FROM AUTHOR]

We propose a new class of high‐order time‐marching schemes with dissipation control and unconditional stability for parabolic equations. High‐order time integrators can deliver the optimal performance of highly accurate and robust spatial discretizations such as isogeometric analysis. The generalized‐α$$ \alpha $$ method delivers unconditional stability and second‐order accuracy in time and controls the numerical dissipation in the discrete spectrum's high‐frequency region. We extend the generalized‐α$$ \alpha $$ methodology to obtain high‐order time marching methods with high accuracy and dissipation control in the discrete high‐frequency range. Furthermore, we maintain the original stability region of the second‐order generalized‐α$$ \alpha $$ method in the new higher‐order methods; we increase the accuracy of the generalized‐α$$ \alpha $$ method while keeping the unconditional stability and user‐control features on the high‐frequency numerical dissipation. The methodology solves k>1,k∈ℕ$$ k>1,k\in \mathbb{N} $$ matrix problems and updates the system unknowns, which correspond to higher‐order terms in Taylor expansions to obtain (3/2k)th$$ \left(3/2k\right)\mathrm{th} $$‐order method for even k$$ k $$ and (3/2k+1/2)th$$ \left(3/2k+1/2\right)\mathrm{th} $$‐order for odd k$$ k $$. A single parameter ρ∞$$ {\rho}^{\infty } $$ controls the high‐frequency dissipation, while the update procedure follows the formulation of the original second‐order method. Additionally, we show that our method is A‐stable, and for ρ∞=0$$ {\rho}^{\infty }=0 $$ we obtain an L‐stable method. Furthermore, we extend this strategy to analyze the accuracy order of a generic method. Lastly, we provide numerical examples that validate our analysis of the method and demonstrate its performance. First, we simulate heat propagation; then, we analyze nonlinear problems, such as the Swift–Hohenberg and Cahn–Hilliard phase‐field models. To conclude, we compare the method to Runge–Kutta techniques in simulating the Lorenz system. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) is often used to detect targets in a construction environment. Due to the different construction environments, the noise exhibits different characteristics on the GPR signal. When the noise is widely distributed on the GPR signal, and its spectrum and the spectrum of the active signal are aliased, it is difficult to separate and suppress the noise by traditional filtering methods. In this paper, we propose a deep learning GPR image noise suppression method based on a recursive guided and dual multi-scale self-attention mechanism neural network (RG-DMSA-NN), which uses a recursive guidance module and a dual multi-scale self-attention mechanism module to improve the feature extraction ability of the image and enhance the robustness and generalization ability in image noise suppression. Through the application of noise suppression on the synthesized test data and the GPR data actually collected by the Macao Science and Technology Museum, the advantages of this method over the traditional filtering, DnCNN and UNet noise suppression methods are demonstrated. [ABSTRACT FROM AUTHOR]

Background: Artemisia argyi is a traditional herbal medicine belonging to the genus Artemisia that plays an important role in suppressing inflammation. However, the chemical constituents and underlying mechanisms of its therapeutic potential in neuroinflammation are still incompletely understood, and warrant further investigation. Methods: Several column chromatography were employed to isolate and purify chemical constituents from Artemisia argyi, and modern spectroscopy techniques were used to elucidate their chemical structures. The screening of monomeric compounds with nitric oxide inhibition led to the identification of the most effective bioactive compound, which was subsequently confirmed for its anti-inflammatory capability through qRT‒PCR. Predictions of compound-target interactions were made using the PharmMapper webserver and the TargetNet database, and an integrative protein-protein interaction network was constructed by intersecting the predicted targets with neuroinflammation-related targets. Topological analysis was performed to identify core targets, and molecular docking and molecular dynamics simulations were utilized to validate the findings. The result of the molecular simulations was experimentally validated through drug affinity responsive target stability (DARTS) and Western blot experiments. Results: Seventeen sesquiterpenoids, including fifteen known sesquiterpenoids and two newly discovered guaiane-type sesquiterpenoids (argyinolide S and argyinolide T) were isolated from Artemisia argyi. Bioactivity screening revealed that argyinolide S (AS) possessed the most potent anti-inflammatory activity. However, argyinolide T (AT) showed weak anti-inflammatory activity, so AS was the target compound for further study. AS may regulate neuroinflammation through its modulation of eleven core targets: protein kinase B 1 (AKT1), epidermal growth factor receptor (EGFR), proto-oncogene tyrosine-protein Kinase (FYN), Janus Kinase (JAK) 1, mitogen-activated protein (MAP) Kinase 1,8 and 14, matrix metalloproteinase 9 (MMP9), ras-related C3 botulinum toxin substrate 1 (RAC1), nuclear factor kappa-B p65 (RELA), and retinoid X receptor alpha (RXRA). Molecular dynamics simulations and DARTS experiments confirmed the stable binding of AS to JAK1, and Western blot experiments demonstrated the ability of AS to inhibit the phosphorylation of downstream Signal transducer and activator of transcription 3 (STAT3) mediated by JAK1. Conclusions: The sesquiterpenoid compounds isolated from Artemisia argyi, exhibit significant inhibitory effects on inflammation in C57BL/6 murine microglia cells (BV-2). Among these compounds, AS, a newly discovered guaiane-type sesquiterpenoid in Artemisia argyi, has been demonstrated to effectively inhibit the occurrence of neuroinflammation by targeting JAK1. [ABSTRACT FROM AUTHOR]

This paper presents a deep-learning-based method to detect recreational vessels. The method takes advantage of existing underwater acoustic measurements from an Estuarine Soundscape Observatory Network based in the estuaries of South Carolina (SC), USA. The detection method is a two-step searching method, called Deep Scanning (DS), which includes a time-domain energy analysis and a frequency-domain spectrum analysis. In the time domain, acoustic signals with higher energy, measured by sound pressure level (SPL), are labeled for the potential existence of moving vessels. In the frequency domain, the labeled acoustic signals are examined against a predefined training dataset using a neural network. This research builds training data using diverse vessel sound features obtained from real measurements, with a duration between 5.0 seconds and 7.5 seconds and a frequency between 800 Hz to 10,000 Hz. The proposed method was then evaluated using all acoustic data in the years 2017, 2018, and 2021, respectively; a total of approximately 171,262 2-minute.wav files at three deployed locations in May River, SC. The DS detections were compared to human-observed detections for each audio file and results showed the method was able to classify the existence of vessels, with an average accuracy of around 99.0%. [ABSTRACT FROM AUTHOR]

This study analyzes coupled atmosphere–ocean variability in the South Atlantic Ocean. To do so, we characterize the spatio-temporal variability of annual mean sea-surface temperature (SST) and sea-level pressure (SLP) using Multichannel Singular Spectrum Analysis (M-SSA). We applied M-SSA to ERA5 reanalysis data (1959–2022) of South Atlantic SST and SLP, both individually and jointly, and identified a nonlinear trend, as well as two climate oscillations. The leading oscillation, with a period of 13 years, consists of a basin-wide southwest–northeast dipole and is observed both in the individual variables and in the coupled analysis. This mode is reminiscent of the already known South Atlantic Dipole, and it is probably related to the Pacific Decadal Oscillation and to El Niño–Southern Oscillation in the Pacific Ocean. The second oscillation has a 5-year period and also displays a dipolar structure. The main difference between the spatial structure of the decadal, 13-year, and the interannual, 5-year mode is that, in the first one, the SST cold tongue region in the southeast Atlantic's Cape Basin is included in the pole closer to the equator. Together, these two oscillatory modes, along with the trend, capture almost 40% of the total interannual variability of the SST and SLP fields, and of their co-variability. These results provide further insights into the spatio-temporal evolution of SST and SLP variability in the South Atlantic, in particular as it relates to the South Atlantic Dipole and its predictability. [ABSTRACT FROM AUTHOR]

The infrared and laser Raman spectra of 4, 5-dimethyl-2-nitroaniline were measured and used for experimental spectrum analysis for current work. On a computational level, Hartree-Fock and density functional theory were employed to perform geometry and vibrational spectral analysis. Here, scaled frequencies are displayed in addition to IR, Raman intensities, and decreased masses. Utilizing HF/6-31+G (d, p), HF/6-311++G (d, p), and B3LYP/6-31+G (d, p) approaches, the various thermodynamical parameters are tabulated. These include entropy, enthalpy, and specific heat. The geometrical footprints, such as bond length and bond angles, are further explored in this paper on a computational level. [ABSTRACT FROM AUTHOR]

Pipelines are an important transportation form in industry. However, pipeline corrosion, particularly that occurring internally, poses a significant threat to safe operations. To detect the internal corrosion of a pipeline, a method utilizing piezoelectric sensors alongside singular spectrum analysis is proposed. Two piezoelectric patches are affixed to the exterior surface of the pipeline, serving the roles of an actuator and a sensor, respectively. During the detection, the signals excited by the actuator are transmitted through the pipeline's wall and are received by PZT2 through different paths, and the corresponding piezoelectric sensor captures the signals. Then, the response signals are denoised by singular spectrum analysis, and the first several wave packets in the response signals are selected to establish a feature for pipeline corrosion detection. At last, the envelope area of the selected packets is calculated as a feature to detect corrosion. To validate the proposed method, corrosion monitoring experiments are performed. The experimental results indicate that the envelope area of the first several wave packets from the response signals, following singular spectrum analysis, can serve as a feature to assess the degree of pipeline corrosion, and the index has a monotonic relationship with the corrosion depth of the pipeline. This method provides an effective way for pipeline corrosion monitoring. [ABSTRACT FROM AUTHOR]

Summary: Diatoms are a diverse group of phytoplankton usually dominating areas characterized by rapidly shifting light conditions. Because of their high growth rates and interesting biochemical profile, their biomass is considered for various commercial applications. This study aimed at identifying strains with superior growth in a photobioreactor (PBR) by screening the natural intraspecific diversity of ecotypes isolated from different habitats. We investigated the effect of PBR light fluctuating on a millisecond scale (FL, simulating the light in a PBR) on 19 ecotypes of the diatom Skeletonema marinoi isolated from the North Sea–Baltic Sea area.We compare growth, pigment ratios, phylogeny, photo‐physiological variables and photoacclimation strategies between all strains and perform qPCR and absorption spectra analysis on a subset of strains.Our results show that the ecotypes responded differently to FL, and have contrasting photo‐physiological and photoprotective strategies. The strains from Kattegat performed better in FL, and shared common photoacclimation and photoprotection strategies that are the results of adaptation to the specific light climate of the Kattegat area.The strains that performed better with FL conditions had a high light (HL)‐acclimated phenotype coupled with unique nonphotochemical quenching features. Based on their characteristics, three strains were identified as good candidates for growth in PBRs. [ABSTRACT FROM AUTHOR]

As an emerging research hotspot in contemporary remote sensing, hyperspectral change detection (HCD) has attracted increasing attention in remote sensing Earth observation, covering land mapping changes and anomaly detection. This is primarily attributable to the unique capacity of hyperspectral imagery (HSI) to amalgamate both the spectral and spatial information in the scene, facilitating a more exhaustive analysis and change detection on the Earth's surface, proving to be successful across diverse domains, such as disaster monitoring and geological surveys. Although numerous HCD algorithms have been developed, most of them face three major challenges: (i) susceptibility to inherent data noise, (ii) inconsistent accuracy of detection, especially when dealing with multi-scale changes, and (iii) extensive hyperparameters and high computational costs. As such, we propose a singular spectrum analysis-driven-lightweight network for HCD, where three crucial components are incorporated to tackle these challenges. Firstly, singular spectrum analysis (SSA) is applied to alleviate the effect of noise. Next, a 2-D self-attention-based spatial–spectral feature-extraction module is employed to effectively handle multi-scale changes. Finally, a residual block-based module is designed to effectively extract the spectral features for efficiency. Comprehensive experiments on three publicly available datasets have fully validated the superiority of the proposed SSA-LHCD model over eight state-of-the-art HCD approaches, including four deep learning models. [ABSTRACT FROM AUTHOR]

In this paper, we construct a new 4 - D hyperchaotic system with a nonlinear term in the form of an exponential function. The system is derived from a modified 3 - D Lü system. Firstly, we discuss the qualitative properties of the proposed system, using tools such as hyperchaotic attractors, symmetry, dissipation, equilibrium points, and Lyapunov spectrum for analysis. Next, we present an adaptive controller for stability analysis of the system. An active controller is designed to achieve complete synchronization between two identical systems. All stability results are established using Lyapunov stability theory. Finally, numerical examples and computer simulations are provided to illustrate the main results. [ABSTRACT FROM AUTHOR]

Background: Hyperspectral imaging (HSI), combined with machine learning, can help to identify characteristic tissue signatures enabling automatic tissue recognition during surgery. This study aims to develop the first HSI-based automatic abdominal tissue recognition with human data in a prospective bi-center setting. Methods: Data were collected from patients undergoing elective open abdominal surgery at two international tertiary referral hospitals from September 2020 to June 2021. HS images were captured at various time points throughout the surgical procedure. Resulting RGB images were annotated with 13 distinct organ labels. Convolutional Neural Networks (CNNs) were employed for the analysis, with both external and internal validation settings utilized. Results: A total of 169 patients were included, 73 (43.2%) from Strasbourg and 96 (56.8%) from Verona. The internal validation within centers combined patients from both centers into a single cohort, randomly allocated to the training (127 patients, 75.1%, 585 images) and test sets (42 patients, 24.9%, 181 images). This validation setting showed the best performance. The highest true positive rate was achieved for the skin (100%) and the liver (97%). Misclassifications included tissues with a similar embryological origin (omentum and mesentery: 32%) or with overlaying boundaries (liver and hepatic ligament: 22%). The median DICE score for ten tissue classes exceeded 80%. Conclusion: To improve automatic surgical scene segmentation and to drive clinical translation, multicenter accurate HSI datasets are essential, but further work is needed to quantify the clinical value of HSI. HSI might be included in a new omics science, namely surgical optomics, which uses light to extract quantifiable tissue features during surgery. [ABSTRACT FROM AUTHOR]

To improve the accuracy of monthly temporal gravity field models for the Gravity Recovery and Climate Experiment (GRACE) and the GRACE Follow-On (GRACE-FO) missions, a new series named HUST-Grace2024 is determined based on the updated L1B datasets (GRACE L1B RL03 and GRACE-FO L1B RL04) and the newest atmosphere and ocean de-aliasing product (AOD1B RL07). Compared to the previous HUST temporal gravity field model releases, we have made the following improvements related to updating the background models and the processing chain: (1) during the satellite onboard events, the inter-satellite pointing angles are calculated to pinpoint outliers in the K-band ranging (KBR) range-rate and accelerometer observations. To exclude outliers, the advisable threshold is 50 mrad for KBR range rates and 20 mrad for accelerations. (2) To relieve the impacts of KBR range-rate noise at different frequencies, a hybrid data-weighting method is proposed. Kinematic empirical parameters are used to reduce the low-frequency noise, while a stochastic model is designed to relieve the impacts of random noise above 10 mHz. (3) A fully populated scale factor matrix is used to improve the quality of accelerometer calibration. Analyses in the spectral and spatial domains are then implemented, which demonstrate that HUST-Grace2024 yields a noticeable reduction of 10 % to 30 % in noise level and retains consistent amplitudes of signal content over 48 river basins compared with the official GRACE and GRACE-FO solutions. These evaluations confirm that our aforementioned efforts lead to a better temporal gravity field series. This data set is identified with the following DOI: 10.5880/ICGEM.2024.001 (Zhou et al., 2024). [ABSTRACT FROM AUTHOR]

Simple Summary: The current devices and methods for dosimetry of personalized photodynamic therapy of tumors have several disadvantages. For instance, they are unable to evaluate the entire tumor volume at great depth, and they lack the capacity to provide an accurate estimate of the effective light dose. Furthermore, they are limited by their ability to evaluate either only the photosensitizer concentration or only the hemoglobin oxygenation at a specific point on the tissue surface or the average value based on the finite tissue volume at little depth. Despite the advances in photodynamic therapy, the development of a precise and dependable methodology to ascertain the optimal light dosage for a given tumor remains a significant challenge. This is due to the pivotal role of light dosage in determining the efficacy of treatment outcomes. Significance: Despite the widespread use of photodynamic therapy in clinical practice, there is a lack of personalized methods for assessing the sufficiency of photodynamic exposure on tumors, depending on tissue parameters that change during light irradiation. This can lead to different treatment results. Aim: The objective of this article was to conduct a comprehensive review of devices and methods employed for the implicit dosimetric monitoring of personalized photodynamic therapy for tumors. Methods: The review included 88 peer-reviewed research articles published between January 2010 and April 2024 that employed implicit monitoring methods, such as fluorescence imaging and diffuse reflectance spectroscopy. Additionally, it encompassed computer modeling methods that are most often and successfully used in preclinical and clinical practice to predict treatment outcomes. The Internet search engine Google Scholar and the Scopus database were used to search the literature for relevant articles. Results: The review analyzed and compared the results of 88 peer-reviewed research articles presenting various methods of implicit dosimetry during photodynamic therapy. The most prominent wavelengths for PDT are in the visible and near-infrared spectral range such as 405, 630, 660, and 690 nm. Conclusions: The problem of developing an accurate, reliable, and easily implemented dosimetry method for photodynamic therapy remains a current problem, since determining the effective light dose for a specific tumor is a decisive factor in achieving a positive treatment outcome. [ABSTRACT FROM AUTHOR]

Simple Summary: The paper describes the statistical analysis of the response of gastric cancer cells and normal cells to broadband terahertz radiation up to 4 THz, both with and without the use of nanostructured contrast agents. A statistical analysis was conducted towards multiple pairwise comparisons in order to identify the narrow THz frequency ranges where the contrast is enhanced and allows a clear identification of tumor cells in the presence of contrast agents. The paper describes the statistical analysis of the response of gastric cancer cells and normal cells to broadband terahertz radiation up to 4 THz, both with and without the use of nanostructured contrast agents. The THz spectroscopy analysis was comparatively performed under the ATR procedure and transmission measurement procedure. The statistical analysis was conducted towards multiple pairwise comparisons, including a support medium (without cells) versus a support medium with nanoparticles, normal cells versus normal cells with nanoparticles, and, respectively, tumor cells versus tumor cells with nanoparticles. When generally comparing the ATR procedure and transmission measurement procedure for a broader frequency domain, the differentiation between normal and tumor cells in the presence of contrast agents is superior when using the ATR procedure. THz contrast enhancement by using contrast agents derived from MRI-related contrast agents leads to only limited benefits and only for narrow THz frequency ranges, a disadvantage for THz medical imaging. [ABSTRACT FROM AUTHOR]

BACKGROUND: Microplastics are a pervasive contaminant cycling through food webs—leading to concerns regarding exposure and risk to humans. OBJECTIVES: We aimed to quantify and characterize anthropogenic particle contamination (including microplastics) in fish caught for human consumption from the Humber Bay region of Lake Ontario. We related quantities of anthropogenic particles to other factors (e.g., fish size) that may help in understanding accumulation of microplastics in fish. METHODS: A total of 45 samples of six fish species collected from Humber Bay in Lake Ontario near Toronto, Ontario, Canada, were examined for anthropogenic particles in their gastrointestinal (GI) tracts and fillets. Using microscopy and spectroscopy, suspected anthropogenic particles were identified and characterized. RESULTS: We observed anthropogenic particles in the GI tracts and fillets of all species. Individual fish had a mean±standard deviation of 138±231 anthropogenic particles, with a single fish containing up to 1,508 particles. GI tracts had 93±226 particles/fish (9.8±32.6 particles/gram), and fillets had 56±61 particles/fish (0.5±0.8 particles/gram). Based on a consumption rate of 2 servings/week, the average yearly human exposure through the consumption of these fish fillets would be 12,800±18,300 particles. DISCUSSION: Our findings suggest that consumption of recreationally caught freshwater fish can be a pathway for human exposure to microplastics. The elevated number of particles observed in fish from Humber Bay highlights the need for large-scale geographic monitoring, especially near sources of microplastics. Currently, it is unclear what the effects of ingesting microplastics are for humans, but given that recreationally caught freshwater fish are one pathway for human exposure, these data can be incorporated into future human health risk assessment frameworks for microplastics. [ABSTRACT FROM AUTHOR]

When compared to traditional seismic data acquisition, irregular blended acquisition significantly promotes the acquisition efficiency. Yet, the blending noise of subsampled blended data introduces new obstacles for the subsequent processing of seismic data. Due to the predictability of linear events in the frequency–space domain, the constructed Hankel matrices exhibit low‐rank characteristics. However, the blending noise of subsampled blended data increases the rank, so deblending and interpolation can be implemented via rank‐reduction algorithms such as the singular spectrum analysis. The significant computing cost of the singular value decomposition, however, makes the traditional singular spectrum analysis inefficient. An alternative algorithm, known as the randomized singular spectrum analysis, employs the randomized singular value decomposition instead of the traditional singular value decomposition for rank‐reduction. The randomized singular spectrum analysis significantly enhances the efficiency of the decomposition process, particularly when dealing with large Hankel matrices. There still remains some random noise when using the singular spectrum analysis or randomized singular spectrum analysis for subsampled blended data, because the noise subspace and signal subspace are coupled together. Thus, we incorporate a damping operator into the randomized singular value decomposition and propose a novel damped randomized singular spectrum analysis method. The damped randomized singular spectrum analysis combines the advantages of the randomized singular value decomposition and the damping operator to enhance the computational efficiency and suppress the remaining noise. Moreover, an iterative projected gradient descent strategy is introduced to achieve deblended and interpolated seismic data for subsequent processing. Examples from synthetic data and field data are used to demonstrate the effectiveness and superiority of the proposed damped randomized singular spectrum analysis method, which enhances the accuracy and efficiency during simultaneous deblending and interpolation. [ABSTRACT FROM AUTHOR]

Stainless steel is a notably beneficial product and is widely used in many applications. Controlling the quality of steel products by determining their trace elemental content is essential. The content of trace elements such as Cr in steel protects the alloy from corrosion and enhances its lifetime, especially in the case of steel embedded in concrete structures. This article presents a quantitative analysis of the trace element Cr in steel samples using advanced laser-induced breakdown spectroscopy (LIBS) with an Nd:YAG laser at wavelengths of 355 and 1064 nm. The LIBS emission intensity of Cr at a laser wavelength of 355 nm was improved compared to that at 1064 nm. Notably, the calibration curves exhibit a zero intercept using the LIBS technique at the third harmonic (355 nm) and fundamental (1064 nm) wavelengths generated by the Nd:YAG laser. The detection limits for Cr in steel were determined as 21 and 36 parts per million using the laser wavelengths of 355 and 1064 nm, respectively. This study demonstrated that the wavelength of 355 nm is significantly more suitable for the quantitative analysis of stainless steel than 1064 nm in laser-induced spectroscopy. [ABSTRACT FROM AUTHOR]

Virtual reality has gained more attention in the physical training field, but few studies focus on the effects of VR on older adults. Based on existing study we suggest that VR-based upper limb training might be more effective for older adults and used functional near inferred spectrum and movement analysis to evaluate the effects of VR-based training on older adults. 20 older and 20 youth adults were recruited to perform VR training by extending their upper limb to reaching the objects, and non-VR training as a contrast. Both age-related and task-related differences were found in cortical activation, showing that the VR training has aroused more cortical activation. The older groups have more intensive movement but perform worse in terms of task completion. Both groups performed better in VR, and the difference in the older group was higher. [ABSTRACT FROM AUTHOR]

Two new triterpenes mayteneri A (1), mayteneri B (2), and seven known compounds (3-9) were isolated from stems of Maytenus hookeri Loes. The chemical structures of compounds 1 and 2 were established by 1D, 2D NMR, HRESIMS analysis, and calculating electronic circular dichroism (ECD). The structures of known compounds 3-9 were determined by comparison of their spectral with those reported. Compounds 4-7 showed significant inhibitory activity for NLRP3 inflammasome, with the IC50 values of 2.36–3.44 μM. [ABSTRACT FROM AUTHOR]

Capillary force self‐assembly (CFSA) technology unfolds great potential in the fabrication of functional micro/nanostructures. To date, most self‐assembly methods focus on monolithic microstructure manipulation yet it remains a challenge to achieve precise localized control over the CFSA process of micro/nanostructures. Herein, a light‐regulated CFSA is proposed to realize the localized precise control of microstructures, leveraging on the synergistic effect of photothermally‐responsive hydrogel and self‐assembly technique. The micropillars with asymmetric cross‐linking densities can readily prepare by applying a rationally designed laser and laser‐exposing dosage, thereby realizing the on‐demand steering of their bending directions. Significantly, owing to the giant capillary force deriving from the evaporating water, above micropillars can successfully assemble into highly‐ordered chiral structures. Simulation coupling with fundamental hydrodynamics enables to shed light on the light steering principle over pillar's actuations. Last, on the basis of vortical dichroism spectra analysis, the chiral characteristics of light‐regulated self‐assembly including the chiral as well as the achiral self‐assembled microstructures are successfully deployed. This strategy provides an avenue for fabricating the localized controllable self‐assembly at the microscale and will bloom the field over metamaterials, microsensors, chiral optics, and so on. [ABSTRACT FROM AUTHOR]

• Automatic selection of embedding dimension in Singular spectrum analysis is achieved. • Singular spectrum analysis yields the most stable and accurate sea level heights among the methods. • Singular spectrum analysis provides a better concentration of spectral power in the periodogram. In order to obtain reliable GNSS-R derived sea level heights (SLHs), it is crucial to extract accurate reflected signals from signal-to-noise ratio (SNR) data. In this study, Quadratic Fitting, Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), and Singular Spectrum Analysis (SSA) are adopted for extracting the reflected signals from SNR data, which are then analyzed with Lomb Scargle Periodogram (LSP) assisted with tidal harmonic analysis for GNSS-R SLHs. Three continuous GNSS stations including Onsala in Sweden, Friday Harbor in the United States, and Brest in France with tidal ranges of approximately 1 m, 3 m, and 7 m, respectively, were used in the study. The derived SLHs were evaluated against the nearby or co-located tide gauge records. The results reveal that the three proposed algorithms effectively cope with the multi-peaks spectrum problems when using the conventional Quadratic Fitting without a priori reflected height constraint, and SSA can provide the solutions with fewer outliers among the proposed methods with the RMSEs of 6.7 cm, 13.4 cm and 39.6 cm at Onsala, Friday Harbor, and Brest stations, respectively. Without implementing tidal harmonic analysis, SSA is the method capable of acquiring reliable SLHs among the stations, as evidenced by comparing with co-located gauge records, particularly at the Brest station. In summary, SSA not only distinguishes signals across different frequencies, but also orders the signal components according to their eigenvalues, demonstrating the potential to extract the reflected signals from SNR and enhance the stability and accuracy of GNSS-R applications. [ABSTRACT FROM AUTHOR]

The viable but non-culturable (VBNC) state is considered a survival strategy employed by bacteria to endure stressful conditions, allowing them to stay alive. Bacteria in this state remain unnoticed in live cell counts as they cannot proliferate in standard culture media. VBNC cells pose a significant health risk because they retain their virulence and can revive when conditions normalize. Hence, it is crucial to develop fast, reliable, and cost-effective methods to detect bacteria in the VBNC state, particularly in the context of public health, food safety, and microbial control assessments. This research examined the biomolecular changes in Escherichia coli W3110 induced into the VBNC state in artificial seawater under three different stress conditions (temperature, metal, and antibiotic). Initially, confirmation of VBNC cells under various stresses was done using fluorescence microscopy and plate counts. Subsequently, lipid peroxidation was assessed through the TBARS assay, revealing a notable increase in peroxidation end-products in VBNC cells compared to controls. ATR-FTIR spectroscopy and chemomometrics were employed to analyze biomolecular changes, uncovering significant spectral differences in RNA, protein, and nucleic acid concentrations in VBNC cells compared to controls. Notably, RNA levels increased, while protein and nucleic acid amounts decreased. ROC analyses identified the 995 cm− 1 RNA band as a consistent marker across all studied stress conditions, suggesting its potential as a robust biomarker for detecting cells induced into the VBNC state under various stressors. [ABSTRACT FROM AUTHOR]

Rooted in dynamic systems theory, convergent cross mapping (CCM) has attracted increased attention recently due to its capability in detecting linear and nonlinear causal coupling in both random and deterministic settings. One limitation with CCM is that it uses both past and future values to predict the current value, which is inconsistent with the widely accepted definition of causality, where it is assumed that the future values of one process cannot influence the past of another. To overcome this obstacle, in our previous research, we introduced the concept of causalized convergent cross mapping (cCCM), where future values are no longer used to predict the current value. In this paper, we focus on the implementation of cCCM in causality analysis. More specifically, we demonstrate the effectiveness of cCCM in identifying both linear and nonlinear causal coupling in various settings through a large number of examples, including Gaussian random variables with additive noise, sinusoidal waveforms, autoregressive models, stochastic processes with a dominant spectral component embedded in noise, deterministic chaotic maps, and systems with memory, as well as experimental fMRI data. In particular, we analyze the impact of shadow manifold construction on the performance of cCCM and provide detailed guidelines on how to configure the key parameters of cCCM in different applications. Overall, our analysis indicates that cCCM is a promising and easy-to-implement tool for causality analysis in a wide spectrum of applications. [ABSTRACT FROM AUTHOR]

This study addresses the possibility of using rank distributions of leukocyte formula and dielectric indicators of blood cell suspensions as invariant characteristics for express monitoring of pathological processes in the organism. Experiments were performed in laboratory mice with Ehrlich ascites carcinoma. [ABSTRACT FROM AUTHOR]

Tunable laser sources with a wide wavelength tuning range, mode-hop-free (MHF) operation, and high spectral purity are essential for applications such as high-resolution spectroscopy, coherent detection, and intelligent fiber sensing. In this paper, we present a wide-range tunable laser source that operates without mode hopping, based on external cavity feedback using a semiconductor gain chip as the laser gain medium. The wavelength, power, and spectral characteristics of the laser are experimentally measured. A wide MHF continuous wavelength tuning range from 1480 nm to 1620 nm with a side-mode suppression ratio of more than 61.65 dB is achieved. An output optical power of more than 11.14 dBm with good power stability can also be realized in the full C+L band. This proposed external-cavity tunable laser source features a narrow intrinsic linewidth and MHF tunable radiation with a maximum sweep speed of 200 nm/s, enabling practical applications such as high-resolution vector spectrum analysis. [ABSTRACT FROM AUTHOR]