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Background: Metabolomics is a tool to study the pathogenesis of diseases and their associated metabolites, but there are still insufficient metabolomic studies on severe knee osteoarthritis.To investigate the differences in serum metabolites between healthy populations and knee osteoarthritis (KOA) patients in Southern China using widely targeted metabolomics, and to explore biomarkers and their metabolic pathways that could be associated with the severity of KOA. Methods: There were 10 healthy individuals in the control group and 32 patients with KOA. According to the Kellgren–Lawrence (KL) grading system, KOA was further divided into mild (n = 13, KL grade 1 and 2) and severe (n = 19, KL grade 3 and 4). Serum samples from all participants were collected and analyzed metabolomics based on ultra-performance liquid chromatography/electrospray ionization/tandem mass spectrometry. We screened for differential metabolites between patients and controls, and between mild and severe KOA. We explored the metabolic pathways involved in differential metabolism using the Kyoto Encyclopedia of Genes and Genomes database. Results: Sixty-one metabolites were differentially expressed in the sera of the patient group compared with the control group (45 upregulated and 16 downregulated). Analysis of the mild and severe KOA groups showed a total of 12 differential metabolites. Receiver operating characteristic curve analysis showed N-alpha-acetyl-L-asparagine was a good predictor of advanced osteoarthritis(OA).Differential metabolites are enriched in multiple pathways such as arachidonic acid metabolism. Conclusion: Widely targeted metabolomics found that upregulation of the amino acid metabolite N-α-acetyl-L-asparagine was significantly associated with severe KOA and could be a biomarker for predicting severity of KOA. Arachidonic acid metabolism may play an important role in patients with severe KOA. [ABSTRACT FROM AUTHOR]

Thermo-chemical treatment methods are pivotal for effectively managing waste tire rubber (WTR) while promoting energy efficiency and emission reduction. This study investigates the liquefaction of WTR within a thermal ethanol environment, exploring temperature ranges (200–300 °C) and residence times (0–80 min) to discern the role of ethanol in tire degradation. Additionally, the study includes liquefaction experiments with cyclohexane as a solvent to contrast the effect of ethanol, and comparison between ethanol and water to elucidate solvent impact. Results indicate that at 300 °C for 40 min, oil yields peak at 53.05 % for a WTR/ethanol mass ratio of 1:8 and at 51.64 % for a ratio of 1:6, notably surpassing hydrolysis outcomes. By contracting with the results from the liquefaction with cyclohexane as solvent, the involvement of ethanol can facilitate liquid phase products, primarily alkenes and oxygen-containing compounds, attributed to aromatization inhibition. Heavy oils and heteroatomic compounds are characterized through Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS), detecting hydrocarbons and heteroatom compounds like NxOy, Ox, Sx, NxOySz, NxSy, and Nx. Notably, sulfur, a significant pollutant, predominantly manifests as sulfide in chars. These findings offer valuable insights into advancing waste tire recycling technologies. • The ethanol can facilitate liquid phase product. • Supercritical ethanol inhibits the aromatization of cyclic alkenes. • Decomposition of WTR in supercritical ethanol surpasses that in subcritical water. • A synergistic effect was observed for mixture of ethanol and water. • Sulfur stays as sulfide in char. [ABSTRACT FROM AUTHOR]

In this paper, we develop a robust image zero-watermarking algorithm based on multiple circular statistical features. First of all, we detect stable feature points from the host image, and construct circular region partitions from the identified feature points, which contain the critical information of the host image. Second, the statistics and differences of circular region partitions are calculated, which contain both the local differences and the global information of the host image. Third, we apply discrete cosine transform (DCT) and singular value decomposition (SVD) in statistics and differences in order to construct the feature image. The stability of DCT and SVD further warrants the robustness of the algorithm. Moreover, Arnold transform is used to scramble the watermark image to enhance the security of the algorithm. Finally, we obtain the zero watermark by performing the exclusive-or (XOR) operation between the feature image and the scrambled watermark image, and the zero watermark is stored in the copyright authentication database for copyright authentication. Moreover, to combat geometric attacks, we develop a blind image correction algorithm without using the original image to accurately correct the attacked image. Numerous experiments and comparisons with the state-of-the-art (SOTA) watermarking algorithms confirm that the proposed algorithm affords good robustness against various attacks, such as filtering attacks, noise attacks, geometric attacks, and even a random combination of the above-stated attacks. [ABSTRACT FROM AUTHOR]

In the past decades, bi-dimensional empirical mode decomposition (BEMD) algorithms have been developed for the image watermarking task with good robustness and imperceptibility; however, the non-negligible algorithm efficiency has received little attention at the same time. In this paper, we devise a robust and efficient image watermarking algorithm based on 1D empirical mode decomposition (EMD) and dimensional reduction via Hilbert curve. Converting the problem of two-dimensional image watermarking into the problem of one-dimensional signal watermarking promises to enhance the efficiency and the robustness of the proposed algorithm. Specifically, host image is first reduced into one-dimensional signal by the Hilbert curve. Our key insight is a dimensionality reduction strategy based on the Hilbert curve that preserves the spatial local relationship to the greatest extent possible. Second, the one-dimensional signal is segmented into several short signal intervals, and each of them is decomposed into several intrinsic mode functions (IMFs) and a residue by 1D EMD, which is much faster than BEMD. Third, the maximum or minimum points of the first IMF are chosen as the watermark embedding positions. For the watermark image, it is first encrypted by Arnold transform, which improves the security of the algorithm. And then, it is also transformed into one-dimensional signal correspondingly. A repeated embedding strategy is used in the embedding process to improve the algorithm's robustness. The final watermarked image can be reconstructed by the inverse Hilbert curve transform after integrating the modified first IMF, the remaining IMFs, and the residual. To improve the security and reduce the length of the key, Arnold transform and Huffman coding are adopted. The watermark extraction is the inverse of the embedding process without using the host image and watermark image. Comprehensive experimental results confirm that our new algorithm exhibits good robustness, efficiency, and high imperceptibility. Compared with the actual watermarking algorithms, the newly proposed watermarking algorithm not only reduces the computational expense, but also shows better performance in combating the cropping attacks, Gaussian noise, median filter, image enhancement attacks, etc. [ABSTRACT FROM AUTHOR]

A novel robust zero-watermarking algorithm for medical images is presented in this paper. The multi-scale decomposition of bi-dimensional empirical mode decomposition (BEMD) has exhibited many attractive properties that enable the proposed algorithm to robustly detect the tampering regions and protect the copyright of medical images simultaneously. Given a medical image, we first decompose a medical image adaptively into a finite number of intrinsic mode functions (IMFs) and a residue, by taking a full advantage of BEMD. The first IMF starts with the finest scale retaining fragile information and is best suitable for tampering detection, while the residue includes robust information at the coarser scale and is applied to the protection of intellectual property rights of medical images. Next, the feature matrices are extracted from the first IMF and the residue via singular value decomposition, which achieves robust performance subject to most attacks. For a given watermark image, it is encrypted by Arnold transform to enhance the security of the watermark. Then, the feature images are constructed by performing the exclusive-or operation between the encrypted watermark image and the extracted feature matrices. Finally, the feature images are securely stored in the copyright authentication database to be further used for copyright authentication and tampering detection. A large number of experimental results and comparisons with existing watermarking algorithms confirm that the newly proposed watermarking algorithm not only has strong ability on tampering detection, but also has better performance in combating various attacks, including cropping, Gaussian noise, median filtering, image enhancement attacks, etc. The newly developed algorithm also shows great promise in processing natural images. [ABSTRACT FROM AUTHOR]

The rising use of 3D digital products has increased the demand for copyright protection. In this paper, we propose a novel and robust 3D watermarking method with high imperceptibility based on EMD (empirical mode decomposition) on surfaces. It first defines a normalized modulus signal on a 3D host model so as to involve EMD into 3D watermarking effectively. And then, it extracts different scale features of the defined signal by using EMD to locate the proper embedding positions. After this, the watermark signal is embedded repeatedly and cyclically into the 3D host model to enhance the robustness. The embedding strength is optimized according to a predefined fidelity parameter to control the imperceptibility of the watermark. Many experiment results show that the proposed method can obtain good results against various attacks while maintaining high invisibility, such as pseudo-random noise, Laplacian smoothing, simplification, subdivision, cropping, and similarity transformation. Furthermore, it is very competitive with the current state-of-the-art 3D watermarking techniques in terms of robustness and invisibility. [ABSTRACT FROM AUTHOR]

In the present study, we pyrolyzed a waste tire at various temperatures under an N 2 atmosphere and a water environment in an autoclave reactor to investigate the effect of water on tire degradation. The analysis involved a comparison of product distribution, char properties, oil composition, and the behavior of heteroatom elements (especially oxygen, nitrogen, and sulfur) under different atmospheres. Elemental analysis, functional-group identification, and chemical state analysis of sulfur were performed for chars. In addition, the chemical composition, elemental composition, and molecular weight of the produced oils were evaluated. The heavy fraction of oils, not detectable by gas chromatography-mass spectrometry (GC-MS), was analyzed through Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS). The findings revealed that high temperatures promoted oil cracking, resulting in the formation of light oils in both pyrolysis and hydrolysis processes. Compared to pyrolysis, hydrolysis generated a higher yield of low molecular–weight oil. Elevated hydrolysis temperatures promoted aromatization, yielding an oil with a low H/C ratio and a high double bond equivalent number. Consequently, the concentration of aromatics in the light fraction of oils generated from the hydrolysis process exceeded that in oils from the pyrolysis process. Temperature exhibited a limited impact on oil composition during the pyrolysis process. Hydrolysis promoted the release of heteroatom-containing compounds at low temperatures. During pyrolysis, nitrogen was gradually released from the solid phase, whereas nitrogen-containing compounds were released early during hydrolysis, with gas-phase nitrogen accounting for more than 50 wt% at 320 °C. A maximum D-limonene yield of 45.58% was obtained at 360 °C within 0 min of hydrolysis, with the potential conversion of D-limonene into aromatics at higher hydrolysis temperatures. These results contribute to the understanding of tire valorization via hydrolysis. [Display omitted] • Higher temperatures can promote the formation of aromatics. • Hydrolysis promote the early release of nitrogen-containing compounds. • Higher hydrolysis temperatures can promote the formation of sulfide. • Temperature has a weak effect on oil composition for pyrolysis process. • Extensive of NxOy, NxOySz, NxSy, Nx and Ox classes formed. [ABSTRACT FROM AUTHOR]

In this paper, a robust and blind image watermarking algorithm via circular embedding and bidimensional empirical mode decomposition (BEMD) is developed. First, the watermark image is scrambled by Arnold transform to increase the security of the algorithm. Second, the Hilbert curve is adopted to reduce the scrambled 2D watermark image to one-dimensional watermark signal. Third, the host image is decomposed by BEMD to obtain the multi-scale representation in the forms of intrinsic mode functions (IMFs) and a residue. Then, the extreme points of the IMFs are extracted as the embedding locations. Finally, the one-dimensional watermark signal is repeatedly and cyclically embedded in the extreme locations of the first IMF according to the texture masking characteristics of the human visual system, which greatly improves the ability of our algorithm against various attacks. The final watermarked image is reconstructed by combining the modified first IMF and the residual. The watermark can be successfully extracted without resorting to the original host image. Furthermore, image correction can be applied before image watermarking extraction if there are geometric attacks in watermarked image. A large number of experimental results and thorough evaluations confirm that our method can obtain higher imperceptibility and robustness under different types of attacks, and achieve better performance than the current state-of-the-art watermarking algorithms, especially in large-scale cropping attack, JPEG compression, Gaussian noise, sharpening, Gamma correction, scaling, histogram equalization, and rotation attacks. [ABSTRACT FROM AUTHOR]

• The ammonia decomposition activity of A/Ce x Zr 1-x O 2 (A = Ni or Co, Fe) catalysts are analyzed. • The ammonia decomposition activity is improved effectively with electric field assistance. • Ni-based catalysts have the highest activity, while Fe-based catalyst had the highest energy efficiency with electric field. • The synergistic effects of basicity enhancement and proton hopping increase the activity of catalysts. It is a good strategy to improve the combustion performance of ammonia in engines by onboard hydrogen mixing through ammonia decomposition. To utilize the exhaust heat from engines, a novel technology to decompose ammonia at low temperature (<350 °C) was studied, which is based on the synergistic effect between electric field and Ce-doped catalysts. A series of non-noble metals (Fe/Co/Ni) supported on Ce-doped ZrO 2 semiconductor (Ce x Zr 1-x O 2) were synthesized by solution combustion method to investigate the electric field-assisted catalytic activity. We achieved an ammonia conversion of 60% at 300 °C for Ni/Ce 0.25 Zr 0.75 O 2 catalyst and the maximum energy efficiency is close to 20% for Fe/Ce 0.25 Zr 0.75 O 2 catalyst with electric field. The synergistic effects of basicity enhancement and proton hopping between the electric field and Ce-doped catalysts were found by a series of characterizations. Electric field facilitated the generation of strong basic sites, which was beneficial to the recombinative desorption of nitrogen. And the proton hopping helped to reduce hydrogen poisoning. [ABSTRACT FROM AUTHOR]

Plasma-catalytic synthesis of ammonia has the potential to store hydrogen on a decentralized scale under mild conditions by consuming the distributed renewable energy power. Suitable catalyst supports can promote plasma-catalytic ammonia synthesis (PCAS). The impact of the support (Al 2 O 3 , Si-MCM-41 and SiO 2) on the performance of the Ni catalysts for PCAS was investigated under ambient pressure without heating under various N 2 :H 2 ratios, discharge powers, and flow rates. Results indicated that Ni/Al 2 O 3 (Ni–Al) had an energy yield of 0.47 g NH 3 /kWh at an NH 3 concentration of 13,670 ppm or 1.15 g NH 3 /kWh at an NH 3 concentration of 3086 ppm and Ni/Si-MCM-41 (Ni-SiM) had a highest ammonia production rate of 6311 μmol/(g cat ·h) with the NH 3 concentration and energy yield closed to those of Ni–Al. The performances of the Ni–Al and Ni-SiM catalysts were maintained for at least 50 h, and the structures of the catalysts undergoing the 50-h reaction did not change significantly. The role of the support was revealed via several characterizations, suggesting that the number of weak acid sites, Ni dispersion, and Ni-support interactions of the catalysts, which are vital for PCAS, can be affected by the type and structure of the support. In situ and ex situ spectral measurements for some important species in PCAS were performed to test the hypothesis that the reaction takes place over the catalyst and in the gas phase. According to the in-situ DRIFTS in the N 2 –H 2 plasma, ex-situ N 1s XPS, and NH 3 -TPD, it was speculated that the generated NH 3 absorbed on the weak acid site could desorb in a timely manner to accelerate the regeneration of the active sites of the catalyst in the N 2 –H 2 plasma, which resulted in a higher ammonia concentration for PCAS. • Plasma-catalytic ammonia synthesis using Ni catalysts provides a novel idea on hydrogen storage using renewable energy. •The type and structure of the support affect the performance of the catalysts for plasma-catalytic ammonia synthesis. •In situ and ex situ spectral measurements of some important species for plasma-catalytic ammonia synthesis were performed. •The performance of the Ni/Al 2 O 3 and Ni/Si-MCM-41 catalysts were maintained for at least 50 h.. [ABSTRACT FROM AUTHOR]

In this paper, to recover the missing geometry details on 3D surfaces, we develop a novel geometry detail recovery algorithm for 3D surfaces based on Empirical Mode Decomposition (EMD). EMD is a powerful tool for processing non-linear and non-stationary signals and has been successfully used in 3D surface analysis and processing. Given a signal defined on 3D surface, EMD could represent the signal in a multi-scale fashion and decompose the signal into a number of Intrinsic Mode Functions (IMFs) and a residue, which usually encode the multi-level finer-scale details and the overall shape of the signal, respectively. Benefiting from the multi-scale representation of geometry details, the EMD-based multi-scale geometry detail recovery algorithm is developed. Our strategy starts from an initial smooth filling of a hole and then transfers the desirable details from the most similar region to the smoothly-filled surface within the framework of EMD. Taking the advantages of EMD, we first apply EMD on the whole completed surface to obtain the multi-scale representation of geometry details. Then, the most similar region corresponding to the hole region is located by the patch descriptor constructed from Heat Kernel Signature (HKS). Finally, the missing geometry details can be effectively recovered by transferring the geometry details from the found similar region to the smoothly-filled surface. Traditional methods, such as context-based methods or example-based methods, usually cut the similar patch and paste them onto the hole region, and they require to match with the hole boundary, are complex in general. In contrast, our method is simple and can transfer different scale details individually or in a concerted way, which makes our algorithm more flexible and can achieve versatile detail recovery results. Comprehensive experiments and quantitative comparisons on popular models have been used to demonstrate the effectiveness of our EMD-based multi-scale geometry detail recovery algorithm. [ABSTRACT FROM AUTHOR]

Point clouds have been extensively employed to represent 3D shapes with the increasing availability of various data acquisition devices/technologies. As a result, more novel techniques are urgently needed for point clouds' analysis and processing. To date, empirical mode decomposition (EMD) has become a powerful and effective analytical tool for non-stationary, non-linear signals, and has been widely applied to time series processing. Despite the fact that EMD has exhibited its potential in 3D geometry processing, extending the existing techniques of EMD to operate directly on point clouds remains to be extremely challenging. This is primarily because of imperfect point clouds, as well as their absence of topological information. In this paper, we develop a multi-scale mesh-free EMD algorithm for point clouds and their analysis and processing. The multi-scale mesh-free EMD is achieved by iteratively extracting the detail level from the input signal and leaving the overall shape in residue. Furthermore, in order to preserve sharp features during point-based EMD analysis/processing, we devise an anisotropic structure measurement assisted envelope computation scheme. The structure measurement is computed by the eigenvalue decomposition of voting tensor, which could faithfully characterize the structure of any input model. Under the guidance of the structure measurement, the envelope is computed in a structure-aware manner and the sharp features are well preserved. Unlike previous feature-preserving EMD methods for meshed models, our algorithm does not explicitly resort to sharp feature detection, which is more suitable for complex geometric models. With the well decomposed multi-scale representation, we could explore various applications of point clouds, such as detail enhancement and smoothing, feature points extraction, and feature-preserving denoising. We showcase comprehensive experimental results to demonstrate the utility of our novel multi-scale mesh-free EMD algorithm. [ABSTRACT FROM AUTHOR]

In this paper we develop an interactive modeling system for complex geometric details transformation based on empirical mode decomposition (EMD) on multi-scale 3D shapes. Given two models, we aim to transfer geometric details from one model to another one in an interactive manner. Taking full advantages of the multi-scale representation computed via EMD, different-scale geometric details can be transferred individually or in a concerted way, which makes our algorithm much more flexible than cut-and-paste and cloning based methods in transferring geometry details. In this process, the target surface with new transferred details could be generated by a mesh reconstruction method widely used in Laplacian surface editing. With the original positions of target surface serving as the soft constraints, the overall shape of the target model will be fully preserved. Our method can also support real-time continuous details transfer. Compared with state-of-the-art algorithms, our method provides an easier-to-use modeling tool and produces varied modeling results. We demonstrate the effectiveness of our modeling tool with various applications, such as detail transfer and enrichment, model reuse and recreation, and detail recovery for shape completion. [ABSTRACT FROM AUTHOR]

Hilbert–Huang Transform (HHT) has proven to be extremely powerful for signal processing and analysis in 1D time series, and its generalization to regular tensor-product domains (e.g., 2D and 3D Euclidean space) has also demonstrated its widespread utilities in image processing and analysis. Compared with popular Fourier transform and wavelet transform, the most prominent advantage of Hilbert–Huang Transform (HHT) is that, it is a fully data-driven, adaptive method, especially valuable for handling non-stationary and nonlinear signals. Two key technical elements of Hilbert–Huang transform are: (1) Empirical Mode Decomposition (EMD) and (2) Hilbert spectra computation. HHT's uniqueness results from its capability to reveal both global information (i.e., Intrinsic Mode Functions (IMFs) enabled by EMD) and local information (i.e., the computation of local frequency, amplitude (energy) and phase information enabled by Hilbert spectra computation) from input signals. Despite HHT's rapid advancement in the past decade, its theory and applications on surfaces remain severely under-explored due to the current technical challenge in conducting Hilbert spectra computation on surfaces. To ameliorate, this paper takes a new initiative to compute the Riesz transform on 3D surfaces, a natural generalization of Hilbert transform in higher-dimensional cases, with a goal to make the theoretic breakthrough. The core of our theoretic and computational framework is to fully exploit the relationship between Riesz transform and fractional Laplacian operator, which can enable the computation of Riesz transform on surfaces via eigenvalue decomposition of Laplacian matrix. Moreover, we integrate the techniques of EMD and our newly-proposed Riesz transform on 3D surfaces by monogenic signals to compute Hilbert spectra, which include the space-frequency-energy distribution of signals defined over 3D surfaces and characterize key local feature information (e.g., instantaneous frequency, local amplitude, and local phase). Experiments and applications in spectral geometry processing and prominent feature detection illustrate the effectiveness of the current computational framework of HHT on 3D surfaces, which could serve as a solid foundation for upcoming, more serious applications in graphics and geometry computing fields. [ABSTRACT FROM AUTHOR]

Background: Lupus nephritis (LN) is a serious complication of systemic lupus erythematosus (SLE), and its pathogenesis is not fully understood. Previously, we showed that fractalkine (FKN) expression was positively correlated with the severity of LN. Here, we aimed to study the role of the Hippo signaling pathway (HSP) and its interaction with FKN in LN in an attempt to provide novel strategies for LN treatment. Methods: In this study, lipopolysaccharide (LPS)/interferon-γ (IFN-γ)-stimulated THP-1 cells were co-cultured with FKN up-regulated or down-regulated kidney epithelial cells Hkb20. FKN-knockout (KO-FKN) mice were used to construct LN model. Flow cytometric analysis, quantitative real-time polymerase chain reaction (qRT-PCR), enzyme-linked immunosorbent assay (ELISA), pathological staining, Western blot, and immunofluorescence (IF) staining were employed to investigate the role of FKN and its interaction with the Hippo signaling pathway (HSP) in LN. Results: Up-regulation of FKN in kidney epithelial cells was associated with increased macrophage activation. FKN overexpression in kidney epithelial cells suppressed apoptosis, inflammation levels, and M1 polarization of THP-1 cells and inhibited the HSP. Oppositely, FKN knockdown in kidney epithelial cells increased apoptosis, inflammation, and M1 polarization and activated the HSP. HSP inhibitor reversed the effect of FKN knockdown on THP-1 cells. In LN mice, FKN knockout and YAP inhibitor decreased the levels of renal function markers, alleviated kidney injury induced by LN, and inhibited macrophage activation in LN mice. Conclusions: FKN down-regulation reduced the activation of macrophages in renal tissue and alleviated kidney damage by activating HSP. The regulatory effect of FKN on HSP should be confirmed in patients with LN, and the mechanism of FKN in LN should be further explored. [ABSTRACT FROM AUTHOR]

This paper presents a novel and fast EMD-based (empirical mode decomposition-based) image fusion approach via morphological filter. Firstly, we develop a multi-channel bidimensional EMD method based on morphological filter to conduct image fusion. It uses the morphological expansion and erosion filters to compute the upper and lower envelopes of a multi-channel image in the sifting processing, and can decompose the input source images into several intrinsic mode functions (IMFs) with different scales and a residue. It significantly improves the computation efficiency of EMD for multi-channel images while maintaining the decomposition quality. Secondly, we adopt a patch-based fusion strategy with overlapping partition to fuse the IMFs and residue instead of the pixel-based fusion way usually used in EMD-based image fusion, where an energy-based maximum selection rule is designed to fuse the IMFs, and the feature information extracted by IMFs is used as a guide to merge the residue. Such strategy can extract the salient information of the source images well and can also reduce the spatial artifacts introduced by the noisy characteristics of the pixel-wise maps. A large number of comparative experiments on the fusion of several commonly used image data sets with multi-focus and multi-modal images, show that our newly proposed fusion method can obtain much better results than the existing EMD-based image fusion approaches. Furthermore, it is very competitive with the state-of-the-art image fusion methods in visualization, objective metrics, and time performance. The code of the proposed method can be downloaded from: https://github.com/neepuhjp/MFMBEMD-ImageFusion. [ABSTRACT FROM AUTHOR]

Finally, we compare our algorithm with the SOTA embedding-based watermarking algorithms and zero-watermarking algorithms to further demonstrate the advantages of the proposed algorithm. The following text should not be included in the figure legend of figure 3 but below the heading "5 Experimental results": In this section, we first discuss the image and parameter selection as well as the metrics used to evaluate the performance of the watermarking algorithms. [Extracted from the article]

Empirical Mode Decomposition (EMD) has proved to be an effective and powerful analytical tool for non-stationary time series and starts to exhibit its modeling potential for 3D geometry analysis. Yet, existing EMD-based geometry processing algorithms only concentrate on multi-scale data decomposition by way of computing intrinsic mode functions. More in-depth analytical properties, such as Hilbert spectra, are hard to study for 3D surface signals due to the lack of theoretical and algorithmic tools. This has hindered much more broader penetration of EMD-centric algorithms into various new applications on 3D surface. To tackle this challenge, in this paper we propose a novel and efficient EMD and Hilbert spectra computational scheme for 3D geometry processing and analysis. At the core of our scheme is the strategy of dimensionality reduction via space-filling curve. This strategy transforms the problem of 3D geometry analysis to 1D time series processing, leading to two major advantages. First, the envelope computation is carried out for 1D signal by cubic spline interpolation, which is much faster than existing envelope computation directly over 3D surface. Second, it enables us to calculate Hilbert spectra directly on 3D surface. We could take advantages of Hilbert spectra that contain a wealth of unexploited properties and utilize them as a viable indicator to guide our EMD-based 3D surface processing. Furthermore, to preserve sharp features, we develop a divide-and-conquer scheme of EMD by explicitly separating the feature signals from non-feature signals. Extensive experiments have been carried out to demonstrate that our new EMD and Hilbert spectra based method is both fast and powerful for 3D surface processing and analysis. [ABSTRACT FROM AUTHOR]

Steganography is critical in traceability, authentication, and secret delivery for multimedia. In this paper, we propose a novel image steganography framework, named StegaEdge, via learning edge-guidance network to simultaneously address three challenges, capacity, multi-task, and invisibility. First, we use an upsampling strategy to expand the embedding space and thus increase the capacity of the embedded message. Second, our algorithm improves the embedding way of messages so that it can handle different messages embedded in the same image and achieve split-task recovery completely. Different information can be embedded in one cover image without affecting each other. Third, we innovatively propose an edge-guidance strategy to solve the problem of poor invisibility in smooth regions. The human eye is significantly less perceptive of intensity changes in edges than in smooth areas. Unlike traditional steganography methods, our edge-guidance steganography can appropriately embed part of the information into non-edge regions when the amount of embedded information is too large. Experimental results on three datasets show that the newly proposed StegaEdge algorithm achieves satisfactory results in terms of capacity, multi-task, imperceptibility, and security compared to the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose an automatic hole-filling method, particularly for recovering missing feature curves and corners. We first extract the feature vertices around a hole of a CAD model and classify them into different feature sets. These feature sets are then automatically paired, using ordered double normals, Gaussian mapping and convex/concave analysis, to produce missing feature curves. Additionally, by minimizing a newly defined energy, the missing corners can be efficiently recovered as well. The hole is consequently divided into simple sub-holes according to the produced feature curves and recovered corners. Finally, each sub-hole is filled by a modified advancing front method individually. The experiments show that our approach is simple, efficient, and suitable for CAD systems. [Copyright &y& Elsevier]

The microstructure and mechanical properties of Cu-12.5Ni-5Sn-xY alloys with different Y addition amounts prepared using spark plasma sintering (SPS) were investigated, and the relationship was discussed. Results indicate that the addition of Y could significantly improve the mechanical properties. When 0.4 wt% Y is added, the maximum hardness and yield strength are approximately 329.5 HB and 691.9 MPa, which are approximately 31 % and 48 % higher than that without Y, respectively. The addition of appropriate amount of Y can significantly refine the grains, promote the transformation of γ-phase in the α + γ coexisting structure from lamellar-shape to needlelike-shape, and significantly inhibit the nucleation and growth of discontinuous precipitation (DP). Especially, when 0.4 wt% Y is added, the nano-scale NiY 3 particles precipitated at the grain boundary can hinder the migration of the grain boundary and occupy the nucleation sites of the γ-phases. In general, grain refinement, nano-precipitation strengthening and inhibition of DP are the main reasons for the improvement of mechanical properties. [Display omitted] • The relationship between microstructure and properties of Cu-12.5Ni-5Sn-xY alloys is discussed. • The maximum hardness and yield strength of alloy are 31 % and 48 % higher than that without Y. • When the addition of Y is 0.4 wt%, its existence is nano-scale NiY 3. • Almost no DP is observed when the addition of Y is 1.0 wt%. [ABSTRACT FROM AUTHOR]

• Non-thermal plasma-catalytic ammonia synthesis is investigated in a DBD reactor to store hydrogen. • Fe-Al-900 showed the optimal ammonia concentration of 11833 ppm, which was 37 % higher than Fe-Al-800. • Utilization of alumina calcined at different temperatures as supports could affect dispersion, surface acidity, and oxygen vacancy of Fe catalysts to improve activity. Non-thermal plasma-catalytic ammonia synthesis (NTPCAS) has been regarded as a promising route to store hydrogen by utilizing renewable electricity. Alumina is a suitable support of the catalysts in NTPCAS and its properties can influence the performance of catalysts remarkably. In this study, the influence of the alumina calcination temperatures (T c) on the catalytic performance of Fe-Al-x (x represents the value of T c) was analyzed in a dielectric barrier discharge (DBD) reactor for NTPCAS. The results suggested that an increase in T c transforms the alumina crystalline from γ phase to α phase. The best performance with a synthesized ammonia concentration of 11833 ppm was achieved over Fe-Al-900 with coexisting γ and θ phases of alumina under N 2 : H 2 = 1:1 and SEI = 37.05 kJ/L conditions, which was 37 % higher than that of Fe-Al-800 with only γ phase. In addition, the larger T c (i.e. 1100 and 1200 °C) decreased the specific surface area, increased the mean catalyst particle size, and led to the non-uniform dispersion of Fe species. The NH 3 -TPD and XPS studies suggested that an increase in T c could influence the surface acidity and oxygen vacancy concentration of Fe catalysts, which were crucial to adjusting the catalytic activity for NTPCAS. This study clarifies the value of a suitable design of support of catalysts for improving NTPCAS. [Display omitted] [ABSTRACT FROM AUTHOR]

Optical parametric amplification (OPA) is a promising method of producing extremely intense light. A new OPA scheme with comprehensively high performance is urgently required for future development. In this study, an amplification scheme known as crossed‐Fabry‐Perot‐cavity OPA (XOPA) is proposed. It is based on the principle of periodic idler elimination, which prevents energy back‐conversion among the three coupling waves, resulting in a monotonically increasing overall conversion efficiency. Using a signal at 808 nm and a pump at 532 nm, a chirped pulse XOPA is experimentally demonstrated with a conversion efficiency of 56.28% and a gain bandwidth of 120 nm. The measured pulse duration after compression is 19.2 fs, which is comparable to the Fourier‐transform‐limited 16.8 fs. Further investigations revealed several advantages. Stable pulse shaping in spatial, temporal, and frequency domains is realized by a spatiotemporally modulated pump. Pulse contrast adjustability on the front edge of the signal is verified in the XOPA of different Fabry‐Perot cavity lengths. These results indicate astringency and precise regulation of output in nonlinear processes. Considering numerous crystals suitable for noncollinear configurations from the near‐infrared to mid‐infrared regions, XOPA has a universal potential application in laser systems with extreme intensity, few‐cycle duration, and internal confinement fusion drivers. [ABSTRACT FROM AUTHOR]

Traditional image watermarking algorithms directly modify the host image by watermark embedding, which is hard to balance the contradiction between the robustness and imperceptibility. Inspired by the human brain's associative memory, this paper proposes a non-embedding and blind image watermarking algorithm via mapping based Residual Convolution Neural Network (Mapping-based RCNN). For preprocessing, median filter is applied on the host image to enhance the robustness of the algorithm to against various attacks. After that, Discrete Cosine Transform and Singular Value Decomposition are adopted to extract the corresponding image information matrix. To obtain the mapping relationship between host image and watermark image, the information matrix is input into the designed Mapping-based RCNN structure for network training. The Mapping-based RCNN is a non-embedding watermarking algorithm, which not only overcomes the imperceptibility shortcoming but also wins good robustness compared with traditional watermarking algorithms. Experimental results show that the proposed algorithm can successfully extract the watermark images under various attacks, and is more robust than existing watermarking algorithms. [ABSTRACT FROM AUTHOR]

The publication of this article unfortunately contained mistakes. The affiliation and the biography of Ling Du were not correct. The corrected affiliation and bibliography is given below. [ABSTRACT FROM AUTHOR]

• A nonlinear coupling dynamic model of a rail vehicle and axle box bearing is built. • A novel and complete model of bearings with multitype defects is proposed. • Bearing modeling strategy has a simple algorithm and high computational efficiency. • Coupling model accurately simulates bearing vibration in train running conditions. • Coupling model provides accurate simulated vibration signals for bearing diagnosis. Axle box bearings are important parts of rail vehicle running gear. Currently, there is limited research on the coupling modeling of an axle box bearing and rail vehicle. In this paper, a complete method for bearing modeling with multitype defects is proposed. The bearing kinematics with three translation and one rotation degrees of freedom are analyzed and modeled, and defect modeling algorithms for the inner raceway, outer raceway and rollers are proposed. The theoretical rolling tracks of rollers passing through the defect points are analyzed and formulated. By coupling the bearing model to the motion joint of the shaft end and axle box, a closed-loop bearing-vehicle model is established. Model simulations show that the calculated results for normal bearings, different types of bearing defects and the bearing-vehicle coupling model are consistent with the theoretical values. The novel and complete coupling model proposed in this paper has the advantages of having a simple algorithm, high computational efficiency, and accurate simulation of the bearing vibration response under train running conditions, which can provide important guidance for research on the response of an axle box bearing in train running gear. By establishing a bearing-vehicle coupling model that simulates the bearing vibration response, especially when the bearings have faults, the performance of the defective bearing can be better understood and applied to develop diagnostic algorithms for the condition monitoring of axle box bearings in a rail vehicle. [ABSTRACT FROM AUTHOR]

The fault response signals of an axle-box bearing of a rail vehicle have strongly non-linear and non-stationary characteristics, which can reflect the operating state of the running gears. This paper proposes a novel method for bearing fault diagnosis based on frequency-domain energy feature reconstruction (EFR) and composite multiscale permutation entropy (CMPE). First, a wavelet packet transform (WPT) is applied to decompose the vibration signals into multiple frequency bands. Then, considering that the bearing-localized defects cause the axle-box bearing system to resonate at a high frequency, which will lead to uneven energy distribution of the signal in the frequency domain, the energy factors of each frequency band are calculated by an energy feature extraction algorithm, from which the frequency band with maximum energy factor (which contains abundant fault information) is reconstructed to the time-domain signal. Next, the complexity of the reconstructed signals is calculated by CMPE as fault feature vectors. Finally, the feature vectors are input into a medium Gaussian support vector machine (MG-SVM) for bearing condition classification. The proposed method is validated by a public bearing data set and a wheelset-bearing system test bench data set. The experimental results indicate that the proposed method can effectively extract bearing fault features and provides a new solution for condition monitoring and fault diagnosis of rail vehicle axle-box bearings. [ABSTRACT FROM AUTHOR]

Cu-xNi-5Sn (wt %) alloys with a different Ni content were prepared by a powder metallurgy method. The effect of Ni content on the hardness and yield strength of Cu-xNi-5Sn (wt %) alloys was investigated. The microstructure, composition, and morphology of Cu-xNi-5Sn (wt %) alloys were observed by X-ray diffraction (XRD), scanning electron microscopy (SEM) with energy dispersive spectroscopy (EDS), and cold field emission scanning electron microscope (FESEM), respectively. Results indicate that the hardness and yield strength firstly increase and then decrease with the increase of Ni content and reach up to a maximum when Ni content is 12.5 wt %. Furthermore, the formation of the sandwich structure and needle-like phase is found in the grain, the grain boundary and intragranular precipitates are rich in both the Ni and Sn phase. The formation of the inerratic and suitable lamellar precipitates of sandwich structure and needle-like phase can be responsible for the good mechanical properties of the Cu-12.5Ni-5Sn alloy after aging treatment. The sandwich structure and need-like phase that were observed by FESEM can contribute to clarify the morphology of Cu-Ni-Sn alloys. [ABSTRACT FROM AUTHOR]

Identifying a suitable water-soluble sacrificial layer is crucial to fabricating large-scale freestanding oxide membranes, which offer attractive functionalities and integrations with advanced semiconductor technologies. Here, we introduce a water-soluble sacrificial layer, “super-tetragonal” Sr4Al2O7 (SAOT). The low-symmetric crystal structure enables a superior capability to sustain epitaxial strain, allowing for broad tunability in lattice constants. The resultant structural coherency and defect-free interface in perovskite ABO3/SAOT heterostructures effectively restrain crack formation during the water release of freestanding oxide membranes. For a variety of nonferroelectric oxide membranes, the crack-free areas can span up to a millimeter in scale. This compelling feature, combined with the inherent high water solubility, makes SAOT a versatile and feasible sacrificial layer for producing high-quality freestanding oxide membranes, thereby boosting their potential for innovative device applications. [ABSTRACT FROM AUTHOR]

Highlights • A double-pass Yb fiber amplifier with over 50-dB gain and good SNR for low-duty-cycle, small signal amplification at 1053 nm is proposed. • Compared with the conventional Faraday-based DP amplifier, the CW background noise of our amplifier is only a half at the same maximum output energy, while the pump efficiency is doubled. • The amplifier is well designed and optimized to suppress the ASE and nonlinear effects. No nonlinear effect has been observed even at the maximum output. Abstract A high-gain fiber amplifier at 1053 nm with a double-pass configuration is proposed. Compared to the Faraday-based double-pass structures, the pump efficiency is almost doubled, and the optical signal-to-noise ratio is improved as well. The effects of fiber length and the insertion loss of elements between two passes on the efficiency are studied experimentally, which is meaningful for the optimization of double-pass amplifiers. Using the fibered polarized beam splitters allow for the orthogonal propagation of forward and the backward signals on the same polarization-maintaining fiber, the polarization mode dispersion in the amplifier is hence totally avoided. With this structure, over 50-dB gain is obtained at pump power of 800 mW for a 1053-nm, 1-Hz nanosecond pulse. [ABSTRACT FROM AUTHOR]

Natural gas is an eco-friendly energy source with low carbon emissions, making it attractive globally. Understanding gas reservoirs is crucial for sustainable extraction and optimizing potential. However, the complicated fluid flow and production dynamics within intricate gas reservoirs, particularly those characterized by abnormally high pressures and tight porous media, remain partially understood and demand further investigation. In a tight porous medium subjected to high pressure, the assumption of constant permeability is no longer valid. Consequently, a novel composite seepage model has been developed in this study, which considers the responsiveness of permeability to stress. Perturbation theory is employed to address the inherent non-linearity demonstrated by the permeability modulus. The solution of dimensionless pressure responses under constant production conditions is accomplished in the Laplace domain by implementing integral transformation methods. Overall, a comprehensive model is provided to understand the production behaviors of tight gas reservoirs. Moreover, in order to comprehend the transient flow characteristics of tight gas reservoirs, log–log plots are generated through the Stehfest numerical inversion approach, with the flow regimes categorized based on the normalized time phases of the pressure curves. Parametric investigations reveal that stress sensitivity detrimentally affects permeability, resulting in more pronounced pressure declines during the intermediate and late flow phases. The transient seepage model elaborated in this study is able to consider the pertinent formation and well parameters. These interpreted parameters bear significance in designing hydraulic fracturing operations, assessing the potential of tight gas reservoirs, and ultimately enhancing gas production. The presented model not only enhances our understanding of the behavior of horizontal wells in stress-sensitive tight gas reservoirs but also makes a valuable contribution to the broader discussion on transient flow phenomena in petroleum engineering. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis (OA) is a chronic degenerative joint disease characterized by loss of joint function, which seriously reduces the quality of life of the elderly and imposes a heavy socioeconomic burden worldwide. Monotropein (MON), the main active ingredient of Morinda officinalis F.C. How, has exhibited therapeutic effects in different disease models. However, its potential effects on chondrocytes in an arthritic model remain unclear. This study aimed to evaluate the effects of MON in chondrocytes and a mouse model of OA, and explore the potential mechanisms. Materials and methods: Murine primary chondrocytes were pretreated with 10 ng/ml interleukin (IL)-1β for 24 h to establish an in vitro model of OA, and then treated with different concentrations of MON (0, 25, 50 and 100 μM) for 24 h. The proliferation of the chondrocytes was assayed using ethynyl-deoxyuridine (EdU) staining. Immunofluorescence staining, western blotting and TUNEL staining were performed to assess the effects of MON on cartilage matrix degradation, apoptosis and pyroptosis. The mouse model of OA was constructed by surgical destabilization of the medial meniscus (DMM), and the animals were randomly divided into the sham-operated, OA and OA + MON groups. Following OA induction, the mice were given intraarticular injection of 100 μM MON or equal volume of normal saline twice a week for 8 weeks. The effects of MON on cartilage matrix degradation, apoptosis and pyroptosis were assessed as indicated. Results: MON significantly accelerated the proliferation of chondrocytes, and inhibited cartilage matrix degradation, apoptosis and pyroptosis in the IL-1β-stimulated cells by blocking the nuclear factor-kappa B (NF-κB) signaling pathway. In the mouse model as well, MON treatment alleviated OA progression and promoted cartilage repair by inhibiting cartilage matrix degradation, and chondrocyte apoptosis and pyroptosis through the inactivation of the NF-κB signaling pathway. Furthermore, the MON-treated arthritic mice exhibited better articular tissue morphology and lower OARSI scores. Conclusions: MON alleviated OA progression by inhibiting cartilage matrix degradation, and the apoptosis and pyroptosis of chondrocytes via NF-κB pathway inactivation, and is a promising alternative for the treatment of OA. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis (OA) is a chronic degenerative joint disease characterized by loss of joint function, which seriously reduces the quality of life of the elderly and imposes a heavy socioeconomic burden worldwide. Monotropein (MON), the main active ingredient of Morinda officinalis F.C. How, has exhibited therapeutic effects in different disease models. However, its potential effects on chondrocytes in an arthritic model remain unclear. This study aimed to evaluate the effects of MON in chondrocytes and a mouse model of OA, and explore the potential mechanisms. Materials and methods: Murine primary chondrocytes were pretreated with 10 ng/ml interleukin (IL)-1β for 24 h to establish an in vitro model of OA, and then treated with different concentrations of MON (0, 25, 50 and 100 μM) for 24 h. The proliferation of the chondrocytes was assayed using ethynyl-deoxyuridine (EdU) staining. Immunofluorescence staining, western blotting and TUNEL staining were performed to assess the effects of MON on cartilage matrix degradation, apoptosis and pyroptosis. The mouse model of OA was constructed by surgical destabilization of the medial meniscus (DMM), and the animals were randomly divided into the sham-operated, OA and OA + MON groups. Following OA induction, the mice were given intraarticular injection of 100 μM MON or equal volume of normal saline twice a week for 8 weeks. The effects of MON on cartilage matrix degradation, apoptosis and pyroptosis were assessed as indicated. Results: MON significantly accelerated the proliferation of chondrocytes, and inhibited cartilage matrix degradation, apoptosis and pyroptosis in the IL-1β-stimulated cells by blocking the nuclear factor-kappa B (NF-κB) signaling pathway. In the mouse model as well, MON treatment alleviated OA progression and promoted cartilage repair by inhibiting cartilage matrix degradation, and chondrocyte apoptosis and pyroptosis through the inactivation of the NF-κB signaling pathway. Furthermore, the MON-treated arthritic mice exhibited better articular tissue morphology and lower OARSI scores. Conclusions: MON alleviated OA progression by inhibiting cartilage matrix degradation, and the apoptosis and pyroptosis of chondrocytes via NF-κB pathway inactivation, and is a promising alternative for the treatment of OA. [ABSTRACT FROM AUTHOR]

VNbTa refractory complex concentrated alloy (RCCA) possesses broad application prospects in a wide temperature window owing to its remarkable strength-ductility synergy and super rolling formability, whereas poor oxidation resistance will give rise to excessive wear above 800 ℃. In this contribution, we aim to fabricate a protective silicide coating on VNbTa RCCA for the purposes of enhancing high-temperature oxidation as well as wear resistance. Microstructural characterizations revealed that a uniform and intact coating was obtained under the optimized processing condition of pack cementation. The coating is mainly composed of a single-phase (VNbTa)Si 2 , along with a thin M 5 Si 3 -type interdiffusion zone. Compared with bare alloy, the coated specimens exhibit remarkable oxidation resistance at 1000 ℃ due to the preferential formation of protective SiO 2 scale. Meanwhile, the coated specimens maintain desirable wear resistance from 800 ℃ to 1000 ℃. The results of this study would offer a valuable reference to expedite the future engineering applications of RCCAs. • A novel silicide coating was fabricated on the ductile VNbTa RCCA by pack cementation. • The coating is mainly composed of single-phase (VNbTa)Si 2 , along with a thin M 5 Si 3 -type interdiffusion zone. • At 1000 ℃, the coated specimens exhibit remarkable oxidation resistance. • The antioxidant coating protects the alloy from excessive wear up to 1000 ℃. [ABSTRACT FROM AUTHOR]

Extensive research has been conducted on image steganography and watermarking algorithms, owing to their crucial rules in secret data transmission, copyright protection, and traceability. Despite promising results and numerous surveys proposed in the literature, there is still a lack of comprehensive analysis dedicated to deep learning-based image steganography and watermarking algorithms. In this paper, we focus on investigating three important aspects: neural networks, structure models, and training strategies. Our review covers the vast literature in this field. Furthermore, we provide a comprehensive statistical analysis from diverse perspectives, including models, loss functions, platforms, datasets, and attacks. Moreover, we conduct in a thorough comparative analysis and evaluation of existing representative algorithms, assessing their effectiveness within the context of deep learning. Finally, the challenges and potential research directions in the domain of deep-learning image steganography and watermarking algorithms are discussed to facilitate future research. • Comprehensive review of deep learning-based image steganography and watermarking. • In-depth analysis of neural networks, structure models, and training strategies. • Extensive statistical analysis of current models, datasets, attack, and platform. [ABSTRACT FROM AUTHOR]

Mesenchymal stem cells (MSCs) play a critical role in tumor metastasis. However, the dynamic process of MSCs-mediated cancer cell invasion remains inconclusive. In breast cancer mouse models, we observed that MSCs promoted lung metastasis. We constructed a microfluidic-based 3D co-culture device to monitor MSCs-mediated cancer cell invasion in a nutrient-deficient hypoxic microenvironment. On biomimetic microfluidic devices, MSCs guided cancer cell migration in a "cluster-sprout-infiltrating" mode. Importantly, hypoxic conditions significantly promoted MSCs migration at the infiltration stage, leading to accelerated breast cancer cell invasion. Moreover, hypoxia related LncRNA analysis showed that H19 was dramatically upregulated in response to hypoxic conditions. Conversely, H19 depletion impaired MSCs-directed breast cancer cell invasion. Mechanistically, H19 functions as a competitive endogenous RNA (ceRNA) which sequesters miRNA let-7 to release its target matrix metalloproteinase-1 (MMP1). Intriguingly, aspirin dramatically suppressed H19 and MMP1 expression and blocked MSCs infiltration under hypoxic conditions, resulting in alleviated breast cancer cell invasion. These findings point to the metastatic promoting role of MSCs in tumor stroma and suggest that MSCs might be a therapeutic target for metastatic breast cancer. [ABSTRACT FROM AUTHOR]

Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of ‘denoising, decomposition and ensemble’. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models. [ABSTRACT FROM AUTHOR]

VNbTa refractory medium-entropy alloy (RMEA) with extraordinary strength-ductility synergy and rolling formability was deemed as an ideal structural material suitable for industrial applications, whereas the tribological performance has been still unclear. Therefore, this work employed a ball-on-disc tribometer to evaluate friction and wear properties of VNbTa alloy at various temperatures. It was surprisingly found that, at 800 °C, the alloy exhibits exceptional tribological performance with the coefficient of friction (COF) of 0.13 and wear rate of 3.9 × 10−6 mm3/Nm. Our results indicated that the mild friction and wear at 800 °C are mainly attributed to the durable lubrication of in-situ molten V 2 O 5 as well as good load-bearing capacity of hard oxide scale. • Tribological performance of the ductile VNbTa RMEA was investigated from RT to 900 °C. • The unexpectedly low COF of 0.13 and wear rate of 3.9 × 10−6 mm3/Nm were available at 800 °C. • The durable lubrication of in-situ molten V 2 O 5 and good load-bearing capacity of hard oxide scale are mainly responsible for the mild friction and wear. [ABSTRACT FROM AUTHOR]

We demonstrate a regenerative laser amplifier based on laser-diode-pumped Nd, Y:SrF 2 crystals with an output of approximately 9.78 mJ/10 Hz. A specially designed birefringent filter was inserted into the cavity to compensate the gain narrowing and amplify the seed pulse with a center wavelength of 1053.3 nm. To optimize the design of the resonator, the thermal wavefront of the laser gain module was simulated and experimentally measured. When a laser pulse with a duration of 9 ns and energy of 1 nJ was injected into the regenerative amplifier, the output energy was 9.78 mJ with peak power of 1.1 MW. The energy stability was 0.72 % RMS. The horizontal and vertical beam quality M2 was 1.18 and 1.23 respectively. The results show the potential of the developed amplifier for application in high-power laser facilities. • A nanosecond Nd, Y:SrF 2 regenerative amplifier is demonstrated. • The gain bandwidth of the regenerative amplifier is tunable. • The output high beam quality and energy stability was obtained. [ABSTRACT FROM AUTHOR]

Based on the pulse-shaping unit in the front end of high-power laser facilities, we propose a new hybrid scheme in a closed-loop control system including wavelet threshold denoising for pretreatment and a first derivative adaptive smoothing filter for smooth pulse recovery, so as to effectively restrain the influence of electrical noise and FM-to-AM modulation in the time–power curve, and enhance the calibration accuracy of the pulse shape in the feedback control system. The related simulation and experiment results show that the proposed scheme can obtain a better shaping effect on the high-contrast temporal shape in comparison with the cumulative average algorithm and orthogonal matching pursuit algorithm combined with a traditional smoothing filter. The implementation of the hybrid scheme mechanism increased the signal-to-noise ratio of the laser pulse from about 11 dB to 30 dB, and the filtered pulse is smooth without modulation, with smoothness of about 98.8%. [ABSTRACT FROM AUTHOR]

In this paper, we report on the influences of gain non-uniformity, Lyot filter effect and transmission spectrum of the passive fiber components on the spectral profile of a semiconductor saturable absorber mirror (SESAM)-based ytterbium (Yb)-doped mode-locked fiber laser. Simulation and experimental results prove that the adjustment of the Lyot filter will be an effective way to control the output spectrum during the actual application and manufacture of the mode-lock fiber laser. • Gain non-uniformity inside the Yb-doped fiber laser. • Influence of the passive fiber components on the mode-locked laser. • Lyot filter will be an effective way to optimize the spectrum characteristics. [ABSTRACT FROM AUTHOR]

Interference noise affects the ranging capability of terahertz radar signal detection. Aiming at the above problem, a method based on adaptive empirical mode decomposition and stochastic resonance system is proposed. With the preprocessing method of twice sampling, adaptive empirical mode decomposition, stochastic resonance system, and scale recovery, the optimal intrinsic mode function can be screened automatically, the optimal parameters can be obtained automatically and the ranging calculation can be completed. Experimental results demonstrate the effectiveness and superiority of the proposed adaptive system in achieving weak fault signal detection under heavy background noise. [ABSTRACT FROM AUTHOR]

• Tanshinone I inhibits RANKL-induced osteoclastogenesis in vitro. • Tanshinone I impedes osteoclast formation via the upregulation of antioxidant gene expressions and inhibition of the RANKL-induced MAPK and NF-κB signaling pathways. • Tanshinone I prevents estrogen-deficiency bone loss in vivo. This study aims to reveal the role of Tanshinone I (TI) in inhibiting osteoclast activity and bone loss in vitro and in vivo , as well as elucidate its underlying molecular mechanism. A mouse model of estrogen deficiency was used to assess the inhibitory effect of TI on osteoclast activity and subsequent bone loss. To validate the impact of TI on osteoclast formation, TRAcP staining and pseudopodia belt staining were conducted. The expressions of osteoclast-specific genes and proteins were evaluated using RT-PCR and Western Blot analyses. Additionally, immunofluorescence staining was employed to examine the effect of TI on p65 nuclear translocation and the expression level of reactive oxygen species (ROS). TI demonstrated significant efficacy in alleviating bone mass loss and suppressing osteoclast activity and function in ovariectomized mice. This outcome was predominantly ascribed to a decrease in ROS levels, thereby impeding the NF-κB signaling pathway and the translocation of p65 to the nucleus. Additionally, TI hindered the RANKL-induced phosphorylation of the MAPK signaling pathway. Moreover, TI played a role in the reduction of osteoclast-specific genes and proteins. To summarize, this study sheds light on TI's capacity to modulate various signaling pathways triggered by RANKL, effectively impeding osteoclast formation and mitigating bone loss resulting from estrogen deficiency. Consequently, TI emerges as a promising therapeutic option for estrogen-deficiency bone loss. [ABSTRACT FROM AUTHOR]

Refractory high entropy alloys (RHEAs) have attracted a great deal of attention for the competitive properties at elevated temperatures, whereas the ambient-temperature brittleness has been still a bottleneck for practical applications. Herein, we report on a newly-designed Ti 30 Hf 20 Nb 20 Ta 10 V 10 Mo 7 W 3 RHEA which exhibits an excellent combination of mechanical properties and wear resistance in a wide temperature range. The present RHEA possesses a yield strength of 1225 ± 17 MPa together with a fracture strain of > 45 % at room temperature, showing an excellent strength-ductility synergy. The alloy also maintains considerable yield strength and ductility at elevated temperatures, outperforming numerous ductile RHEAs. In addition, the coefficient of friction and wear rate decrease with increasing temperature from room temperature to 800 ℃. Interestingly, the alloy exhibits a wear rate as low as (5.50 ± 0.11) × 10−6 mm3 N−1 m−1 at 800 ℃, which is related to the formation of continues and dense oxide layer. • A novel Ti 30 Hf 20 Nb 20 Ta 10 V 10 Mo 7 W 3 RHEA with a single BCC phase was designed and fabricated. • Mechanical and tribological behaviors at various temperatures were investigated. • The RHEA presents an excellent strength-ductility trade-off at room temperature, while it maintains considerable yield strength even up to 1000 ℃. • At 800 ℃, the RHEA possesses the lowest wear rate on the order of 10−6 mm3 N−1 m−1, which is related to the formation of continues and dense oxide layer. [ABSTRACT FROM AUTHOR]

A Ni–P composite coating with MoS 2 and CaF 2 was prepared via electroless plating. The effect of temperature on the tribological behavior of the composite coatings was investigated. The results show that the tribological behavior of coatings largely depends on the temperature. The friction coefficient shows a decreasing trend with increasing temperature; the wear rate first decreases and then increases with increasing temperature. As the temperature increases from room temperature (RT) to 200 °C and 390 °C, the wear rate decreases by one order of magnitude compared to that at RT. The composite coating exhibits stable friction coefficient during the entire sliding in the temperature range of 200–570°C. Lubricating mechanisms were discussed. [ABSTRACT FROM AUTHOR]

In the field of multimedia security, image hashing is an effective technology for solving image content identification. Image hashing algorithm aims to map the high dimensional input features onto perceptual hashing values in terms of their perceptual content, in which visually identical images are mapped to similar digital representations. In this paper, we focus on multi-view dimensionality reduction based hashing generation, and propose a novel method, called Multi-view Dimensionality Reduction based Robust Image Hashing (MDRIH). Our MDRIH scheme maps the multi-view features into a compact binary space by considering the complementarity and similarity between image features across these different views. For preprocessing, bilinear interpolation is applied on the host image to adjust to a standard size, and ensure the different images with same hash size. Specifically, in order to improve the robustness of the algorithm against content-preserving operations, multi-view features, including structural features, edge features, and color features are extracted. After that, multi-view dimension reduction is adopted to obtain meaningful low-dimensional information from multi-feature fusion. Finally, the data in low-dimensional subspace is encoded into a compact binary sequence. The experimental results indicate that the proposed hashing method not only preserves the correlation between multiple views and the similarity among different data points within each view, but also achieves a better balance between robustness and discriminability compared to existing hashing methods. [ABSTRACT FROM AUTHOR]

• The CuO x @SiO 2 catalyst with excellent performance by the FSP method. • The CuO x @SiO 2 catalyst has good thermal stability due to strong sintering resistance. • Catalytic ammonia oxidation over the CuO x @SiO 2 catalyst follows fast i-SCR mechanism. • Reaction path at low temperature is different from that at high temperature. Ammonia has a great application prospect to reach zero-carbon emissions in the combustion society. However, the poor ignition and combustion performance and NOx emissions of ammonia limit its utilization, and catalytic combustion may be a reliable way to utilize ammonia. In this study, the catalysts were prepared by the flame spray pyrolysis method (FSP), and the catalytic combustion performance of ammonia was investigated in a wide temperature range (100–1000 °C). The results indicated that CuO x and core–shell CuO x @SiO 2 had high catalytic activity and N 2 selectivity under different equivalence ratios compared with other catalysts (FeO x and MnO x), and the thermal stability of CuO x @SiO 2 were more prominent. These two catalysts before and after ammonia catalytic combustion tests were characterized to reveal their acting mechanism. At low temperature (<600 °C), the catalytic ammonia combustion over CuO x followed the internal-selective catalytic reduction (i-SCR) mechanism, while that on CuO x @SiO 2 followed the fast i-SCR mechanism, resulting in the outstanding N 2 selectivity of CuO x @SiO 2. At high temperature (600–1000 °C), CuO x @SiO 2 with a better thermal stability hardly sintered. Hydrogen was generated at high temperature and then reacted with oxygen under low equivalence ratio or with the lattice oxygen of the fresh catalyst under high equivalence ratio. The core–shell CuO x @SiO 2 catalyst synthesized by the FSP method provides a novel idea on the catalyst design for catalytic ammonia combustion. [ABSTRACT FROM AUTHOR]

• The CuO x @SiO 2 catalyst with excellent performance by the FSP method. • The CuO x @SiO 2 catalyst has good thermal stability due to strong sintering resistance. • Catalytic ammonia oxidation over the CuO x @SiO 2 catalyst follows fast i-SCR mechanism. • Reaction path at low temperature is different from that at high temperature. Ammonia has a great application prospect to reach zero-carbon emissions in the combustion society. However, the poor ignition and combustion performance and NOx emissions of ammonia limit its utilization, and catalytic combustion may be a reliable way to utilize ammonia. In this study, the catalysts were prepared by the flame spray pyrolysis method (FSP), and the catalytic combustion performance of ammonia was investigated in a wide temperature range (100–1000 °C). The results indicated that CuO x and core–shell CuO x @SiO 2 had high catalytic activity and N 2 selectivity under different equivalence ratios compared with other catalysts (FeO x and MnO x), and the thermal stability of CuO x @SiO 2 were more prominent. These two catalysts before and after ammonia catalytic combustion tests were characterized to reveal their acting mechanism. At low temperature (<600 °C), the catalytic ammonia combustion over CuO x followed the internal-selective catalytic reduction (i-SCR) mechanism, while that on CuO x @SiO 2 followed the fast i-SCR mechanism, resulting in the outstanding N 2 selectivity of CuO x @SiO 2. At high temperature (600–1000 °C), CuO x @SiO 2 with a better thermal stability hardly sintered. Hydrogen was generated at high temperature and then reacted with oxygen under low equivalence ratio or with the lattice oxygen of the fresh catalyst under high equivalence ratio. The core–shell CuO x @SiO 2 catalyst synthesized by the FSP method provides a novel idea on the catalyst design for catalytic ammonia combustion. [ABSTRACT FROM AUTHOR]

A novel Hf 30 Nb 25 Ta 25 Ti 15 Mo 5 refractory high entropy alloy (RHEA) with BCC structure has been proposed. The compressive yield strength, fracture strain and fracture toughness at room temperature are 964 ± 16 MPa, > 50% and 94.7 ± 1.4 MPa m1/2, respectively. Compared with most BCC-type RHEAs, this alloy exhibits the superior combination of strength, ductility and fracture toughness. [ABSTRACT FROM AUTHOR]