Search

To improve energy savings and emission reduction in industrial heating furnaces, this study investigated the impact of various molar fractions of hydrogen on natural gas combustion and compared the results of the Non-Premixed Combustion Model with the Eddy Dissipation Combustion Model. Initially, natural gas combustion in an industrial heating furnace was investigated experimentally, and these results were used as boundary conditions for CFD simulations. The diffusion flame and combustion characteristics of natural gas were simulated using both the non-premixed combustion model and the Eddy Dissipation Combustion Model. The results indicated that the Non-Premixed Combustion Model provided simulations more consistent with experimental data, within acceptable error margins, thus validating the accuracy of the numerical simulations. Additionally, to analyze the impact of hydrogen doping on the performance of an industrial gas heater, four gas mixtures with varying hydrogen contents (15% H2, 30% H2, 45% H2, and 60% H2) were studied while maintaining constant fuel inlet temperature and flow rate. The results demonstrate that the Non-Premixed Combustion Model more accurately simulates complex flue gas flow and chemical reactions during combustion. Moreover, hydrogen-doped natural gas significantly reduces CO and CO2 emissions compared to pure natural gas combustion. Specifically, at 60% hydrogen content, CO and CO2 levels decrease by 70% and 37.5%, respectively, while NO emissions increase proportionally; at this hydrogen content, NO concentration in the furnace chamber rises by 155%.

Introduction: Ulva lactuca polysaccharide (ULP) is green algae extract with numerous biological activities, including anticoagulant, anti-inflammatory, and antiviral effects. However, the inhibitory ability of ULP in the development of hepatocellular carcinoma warrants further studies. Objectives: To elucidate the anti-tumor mechanism of ULP action and evaluate its regulatory effect on gut microbiota and metabolism in H22 hepatocellular carcinoma tumor-bearing mice. Methods: An H22 tumor-bearing mouse model was established by subcutaneously injecting H22 hepatoma cells. The gut microbiota composition in cecal feces was assessed and subjected to untargeted metabolomic sequencing. The antitumor activity of ULP was verified further by western blot, RT-qPCR, and reactive oxygen species (ROS) assays. Results: Administration of ULP alleviated tumor growth by modulating the compositions of the gut microbial communities (Tenericutes, Agathobacter, Ruminiclostridium, Parabacteroides, Lactobacillus, and Holdemania) and metabolites (docosahexaenoic acid, uric acid, N-Oleoyl Dopamine, and L-Kynurenine). Mechanistically, ULP promoted ROS production by inhibiting the protein levels of JNK, c-JUN, PI3K, Akt, and Bcl-6, thereby delaying the growth of HepG2 cells. Conclusion: ULP attenuates tumor growth in H22 tumor-bearing mice by modulating gut microbial composition and metabolism. ULP inhibits tumor growth mainly by promoting ROS generation.

To elucidate the atomic mechanisms of the chemical mechanical polishing (CMP) of silicon carbide (SiC), molecular dynamics simulations based on a reactive force field were used to study the sliding process of silica (SiO2) abrasive particles on SiC substrates in an aqueous H2O2 solution. During the CMP process, the formation of Si-O-Si interfacial bridge bonds and the insertion of O atoms at the surface can lead to the breakage of Si-C bonds and even the complete removal of SiC atoms. Furthermore, the removal of C atoms is more difficult than the removal of Si atoms. It is found that the removal of Si atoms largely influences the removal of C atoms. The removal of Si atoms can destroy the lattice structure of the substrate surface, leading the neighboring C atoms to be bumped or even completely removed. Our research shows that the material removal during SiC CMP is a comprehensive result of different atomic-level removal mechanisms, where the formation of Si-O-Si interfacial bridge bonds is widespread throughout the SiC polishing process. The Si-O-Si interfacial bridge bonds are the main removal mechanisms for SiC atoms. This study provides a new idea for improving the SiC removal process and studying the mechanism during CMP.

Ulva polysaccharide (ULP) as main active ingredient of Ulva lactuca, has good pharmacological activity. This study further explored its anti-tumor mechanisms in hepatocellular carcinoma (HCC) with wet experiments and bioinformatic analyses. Results indicate that the differential proteins after ULP treatment are mainly enriched in immunomodulatory and complement pathways, among which the immune molecule CD5L and the key proteins of the membrane attack complex (MAC) are significantly upregulated, and the expression of MAC proteins are positively correlated with the expression of CD5L. Moreover, results from TIMER2.0 indicated that CD5L expression had positive correlation with CD8 + T cell infiltration in HCC, and single cell RNA-Seq analysis showed CD5L positive macrophage cells have higher expression of CFP, C1QA, C1QB, and C1QC, which are key molecular of complement cascades. Take together, the results suggest that ULP may exerts its anti-tumor activity by induces the expression of CD5L in HCC.

Abstract As a result of global warming, the area of the polar pack ice is diminishing, making merchant travel more practical. Even if Arctic ice thickness reduced in the summer, fractured ice is still presenting operational risks to the future navigation. The intricate process of ship-ice interaction includes stochastic ice loading on the vessel hull. In order to properly construct a vessel, the severe bow forces that arise must be accurately anticipated using statistical extrapolation techniques. This study examines the severe bow forces that an oil tanker encounters when sailing in the Arctic Ocean. Two stages are taken in the analysis. Then, using the FEM program ANSYS/LS-DYNA, the oil tanker bow force distribution is estimated. Second, in order to estimate the bow force levels connected with extended return periods, the average conditional exceedance rate approach is used to anticipate severe bow forces. The vessel’s itinerary was planned to take advantage of the weaker ice. As a result, the Arctic Ocean passage took a meandering route rather than a linear one. As a result, the ship route data that was investigated was inaccurate with regard to the ice thickness data encountered by a vessel yet skewed with regard to the ice thickness distribution in the region. This research intends to demonstrate the effective application of an exact reliability approach to an oil tanker with severe bow forces on a particular route.

Abstract In contrast to well-known bivariate statistical approach, which is known to properly forecast extreme response levels for two-dimensional systems, the research validates innovative structural reliability method, which is particularly appropriate for multi-dimensional structural responses. The disadvantage of dealing with large system dimensionality and cross-correlation across multiple dimensions is not a benefit of traditional dependability approaches that deal with time series. Since offshore constructions are built to handle extremely high wind and wave loads, understanding these severe stresses is essential, e.g. wind turbines should be built and operated with the least amount of inconvenience. In the first scenario, the blade root flapwise bending moment is examined, whereas in the second, the tower bottom fore-aft bending moment is examined. The FAST simulation program was utilized to generate the empirical bending moments for this investigation with the load instances activated at under-rated, rated, and above-rated speeds. The novel reliability approach, in contrast to conventional reliability methods, does not call for the study of a multi-dimensional reliability function in the case of numerical simulation. As demonstrated in this work, it is now possible to assess multi-degree-of-freedom nonlinear system failure probability, in the case when only limited system measurements are available.

Abstract This research presents two unique techniques for engineering system reliability analysis of multi-dimensional non-linear dynamic structures. First, the structural reliability technique works best for multi-dimensional structural responses that have been either numerically simulated or measured over a long enough length to produce an ergodic time series. Second, a novel extreme value prediction method that can be used in various engineering applications is proposed. In contrast to those currently used in engineering reliability methodologies, the novel method is easy to use, and even a limited amount of data can still be used to obtain robust system failure estimates. As demonstrated in this work, proposed methods also provide accurate confidence bands for system failure levels in the case of real-life measured structural response. Additionally, traditional reliability approaches that deal with time series do not have the benefit of being able to handle a system's high dimensionality and cross-correlation across several dimensions readily. Container ship that experiences significant deck panel pressures and high roll angles when travelling in bad weather was selected as the example for this study. The main concern for ship transportation is the potential loss of cargo owing to violent movements. Simulating such a situation is difficult since waves and ship motions are non-stationary and complicatedly non-linear. Extreme movements greatly enhance the role of nonlinearities, activating effects of second and higher order. Furthermore, laboratory testing may also be called into doubt due to the scale and the choice of the sea state. Therefore, data collected from actual ships during difficult weather journeys offer a unique perspective on the statistics of ship movements. This work aims to benchmark state-of-the-art methods, making it possible to extract necessary information about the extreme response from available on-board measured time histories. Both suggested methods can be used in combination, making them attractive and ready to use for engineers. Methods proposed in this paper open up possibilities to predict simply yet efficiently system failure probability for non-linear multi-dimensional dynamic structure.

Abstract Green seaweed, as the most abundant species of macroseaweeds, is an important marine biological resource. It is a rich source of several amino acids, fatty acids, and dietary fibers, as well as polysaccharides, polyphenols, pigments, and other active substances, which have crucial roles in various biological processes such as antioxidant activity, immunoregulation, and anti-inflammatory response. In recent years, attention to marine resources has accelerated the exploration and utilization of green seaweeds for greater economic value. This paper elaborates on the main nutrients and active substances present in different green seaweeds and provides a review of their biological activities and their applications for high-value utilization. Graphical abstract

Abstract This paper demonstrates the validity of the Naess–Gadai method for extrapolating extreme value statistics of second-order Volterra series processes through application on a representative model of a deep water small size tension leg platform (TLP), with specific focus on wave sum frequency effects affecting restrained modes: heave, roll and pitch. The wave loading was estimated from a second order diffraction code WAMIT, and the stochastic TLP structural response in a random sea state was calculated exactly using Volterra series representation of the TLP corner vertical displacement, chosen as a response process. Although the wave loading was assumed to be a second order (non-linear) process, the dynamic system was modelled as a linear damped mass-spring system. Next, the mean up-crossing rate based extrapolation method (Naess–Gaidai method) was applied to calculate response levels at low probability levels. Since exact solution was available via Volterra series representation, both predictions were compared in this study, namely the exact Volterra and the approximate one. The latter gave a consistent way to estimate efficiency and accuracy of Naess–Gaidai extrapolation method. Therefore the main goal of this study was to validate Naess–Gaidai extrapolation method by available analytical-based exact solution. Moreover, this paper highlights limitations of mean up-crossing rate based extrapolation methods for the case of narrow band effects, such as clustering, typically included in the springing type of response.

Background Novel coronavirus disease has been recently a concern for worldwide public health. To determine epidemic rate probability at any time in any region of interest, one needs efficient bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of novel coronavirus infection rate. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the multi-dimensionality advantage, that suggested methodology offers, namely dealing efficiently with multiple regions at the same time and accounting for cross-correlations between different regional observations. Methods Modern multi-dimensional novel statistical method was directly applied to raw clinical data, able to deal with territorial mapping. Novel reliability method based on statistical extreme value theory has been suggested to deal with challenging epidemic forecast. Authors used MATLAB optimization software. Results This paper described a novel bio-system reliability approach, particularly suitable for multi-country environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of extreme novel coronavirus death rate probability. Namely, accurate maximum recorded patient numbers are predicted for the years to come for the analyzed provinces. Conclusions The suggested method performed well by supplying not only an estimate but 95% confidence interval as well. Note that suggested methodology is not limited to any specific epidemics or any specific terrain, namely its truly general. The only assumption and limitation is bio-system stationarity, alternatively trend analysis should be performed first. The suggested methodology can be used in various public health applications, based on their clinical survey data.

Path planning is crucial for unmanned surface vehicles (USVs) to navigate and avoid obstacles efficiently. This study evaluates and contrasts various USV path-planning algorithms, focusing on their effectiveness in dynamic obstacle avoidance, resistance to water currents, and path smoothness. Meanwhile, this research introduces a novel collective intelligence algorithm tailored for two-dimensional environments, integrating dynamic obstacle avoidance and smooth path optimization. The approach tackles the global-path-planning challenge, specifically accounting for moving obstacles and current influences. The algorithm adeptly combines strategies for dynamic obstacle circumvention with an eight-directional current resistance approach, ensuring locally optimal paths that minimize the impact of currents on navigation. Additionally, advanced artificial bee colony algorithms were used during the research process to enhance the method and improve the smoothness of the generated path. Simulation results have verified the superiority of the algorithm in improving the quality of USV path planning. Compared with traditional bee colony algorithms, the improved algorithm increased the length of the optimization path by 8%, shortened the optimization time by 50%, and achieved almost 100% avoidance of dynamic obstacles.

Abstract This article provides two unique methodologies that may be coupled to study the dependability of multidimensional nonlinear dynamic systems. First, the structural reliability approach is well suited for multidimensional environmental and structural reactions and is either measured or numerically simulated over sufficient time, yielding lengthy ergodic time series. Second, a unique approach to predicting extreme values has technical and environmental implications. In the event of measurable environmental loads, it is also feasible to calculate the probability of system failure, as shown in this research. In addition, traditional probability approaches for time series cannot cope effectively with the system's high dimensionality and cross-correlation across dimensions. It is common knowledge that wind speeds represent a complex, nonlinear, multidimensional, and cross-correlated dynamic environmental system that is always difficult to analyze. Additionally, global warming is a significant element influencing ocean waves throughout time. This section aims to demonstrate the efficacy of the previously mentioned technique by applying a novel method to the Norwegian offshore data set for the greatest daily wind cast speeds in the vicinity of the Landvik wind station. This study aims to evaluate the state-of-the-art approach for extracting essential information about the extreme reaction from observed time histories. The approach provided in this research enables the simple and efficient prediction of failure probability for the whole nonlinear multidimensional dynamic system.

Abstract CO2 capture and storage (CCS) is an important strategy to reduce global CO2 emissions. This work presents both cutting‐edge carbon storage tanker design, as well as novel reliability method making possible to extract useful information about the lifespan distribution of carbon capture systems from their recorded time history. The method outlined may be applied on more complex sustainable systems that are exposed to environmental stresses throughout the whole period of their planned service life. The latter is of paramount importance at the design stage for complex engineering systems. Novel design for CCS system is discussed and accurate numerical simulation results are used to apply suggested novel reliability methodology. Furthermore, traditional reliability approaches that deal with complex energy systems are not well suited for handling high dimensionality and cross‐correlation between various system components of innovative dynamic CO2 storage subsea shuttle tanker. This study has two distinctive key features: the state of art CCS design concept, and the novel general purpose reliability method, recently developed by authors, and particularly suitable for operational safety study of complex energy systems.

Abstract Background Colon cancer is a common gastrointestinal tumor with a poor prognosis, and thus new therapeutic strategies are urgently needed. The antitumor effect of Plasmodium infection has been reported in some murine models, but it is not clear whether it has an anti-colon cancer effect. In this study, we investigated the anti-colon cancer effect of Plasmodium infection and its related mechanisms using a mouse model of colon cancer. Methods An experimental model was established by intraperitoneal injection of Plasmodium yoelii 17XNL-infected erythrocytes into mice with colon cancer. The size of tumors was observed dynamically in mice, and the expression of Ki67 detected by immunohistochemistry was used to analyze tumor cell proliferation. Apoptosis was assessed by terminal deoxynucleotidyl transferase (TdT) dUTP nick-end labeling (TUNEL) staining, and the expression of apoptosis-related proteins including Bax, Bcl-2, caspase-9, and cleaved caspase-3 was detected by western blot and immunohistochemistry, respectively. Transmission electron microscopy (TEM) was used to observe the ultrastructural change in colon cancer cells, and the expression of mitochondrial biogenesis correlative central protein, PGC-1α, and mitophagy relevant crucial proteins, PINK1/Parkin, were detected by western blot. Results We found that Plasmodium infection reduced the weight and size of tumors and decreased the expression of Ki67 in colon cancer-bearing mice. Furthermore, Plasmodium infection promoted mitochondria-mediated apoptosis in colon cancer cells, as evidenced by the increased proportion of TUNEL-positive cells, the upregulated expression of Bax, caspase-9, and cleaved caspase-3 proteins, and the downregulated expression of Bcl-2 protein. In colon cancer cells, we found destroyed cell nuclei, swollen mitochondria, missing cristae, and a decreased number of autolysosomes. In addition, Plasmodium infection disturbed mitochondrial biogenesis and mitophagy through the reduced expression of PGC-1α, PINK1, and Parkin proteins in colon cancer cells. Conclusions Plasmodium infection can play an anti-colon cancer role in mice by inhibiting proliferation and promoting mitochondria-mediated apoptosis in colon cancer cells, which may relate to mitochondrial biogenesis and mitophagy. Graphical Abstract

Polysaccharides are usually composed of more than ten monosaccharide units, which are connected by linear or branched glycosidic bonds. The immunomodulatory effect of natural polysaccharides is one of the most important bioactive function. In this review, molecular weight, monosaccharide (including galactose, mannose, rhamnogalacturonan-I arabinogalactan and uronic acid), functional groups (namely sulfate, selenium, and acetyl groups), types of glycoside bond connection (including β-1,3-D-glucosyl, α-1,4-D-glucosyl, β-1,4-D-glucosyl, α-1,6-D-glucosyl, β-1,4-D-mannosyl, and β-1,4-D-Xylopyranosyl), conformation and the branching degrees are systematically identified as their contribution to the immunostimulatory activity of polysaccharides. At present, studies on the structure-activity relationships of polysaccharides are limited due to their low purity and high heterogeneity. However, it is an important step in providing useful guidance for dietary supplements with polysaccharides. The chemical structures and the process of immune responses induced are necessary to be discussed. Polysaccharides may bind with the cell surface receptors to modulate immune responses. This review mainly discusses the structure-activity relationship of dietary polysaccharides.

This study proposes an innovative method for predicting extreme values in offshore engineering. This includes and is not limited to environmental loads due to offshore wind and waves and related structural reliability issues. Traditional extreme value predictions are frequently constructed using certain statistical distribution functional classes. The proposed method differs from this as it does not assume any extrapolation-specific functional class and is based on the data set's intrinsic qualities. To demonstrate the method's effectiveness, two wind speed data sets were analysed and the forecast accuracy of the suggested technique has been compared to the Naess-Gaidai extrapolation method. The original batch of data consisted of simulated wind speeds. The second data related to wind speed was recorded at an offshore Norwegian meteorological station.

In this study, molecular dynamics simulation is adopted to investigate the friction and wear mechanism of Fe-based amorphous composited coatings. It was found that the ceramic phase can improve plasticity of the composite coatings to reduce wear. In addition, when the Al2O3 content is 15 wt%, due to the stress concentration between ceramic phase and amorphous phase, the viscosity flow in the amorphous phase results in plastic deformation. Through this, the best wear resistance has been obtained. In addition, with the increase of compression depth, the number of atoms accumulated constantly increases, and the degree of plastic deformation becomes higher in the composite coatings. Finally, a high sliding speed results in the reduction of the wear resistance of coatings.

Physics-Informed Neural Networks (PINNs) improve the efficiency of data utilization by combining physical principles with neural network algorithms and thus ensure that their predictions are consistent and stable with the physical laws. PINNs open up a new approach to address inverse problems in fluid mechanics. Based on the single-relaxation-time lattice Boltzmann method (SRT-LBM) with the Bhatnagar–Gross–Krook (BGK) collision operator, the PINN-SRT-LBM model is proposed in this paper for solving the inverse problem in fluid mechanics. The PINN-SRT-LBM model consists of three components. The first component involves a deep neural network that predicts equilibrium control equations in different discrete velocity directions within the SRT-LBM. The second component employs another deep neural network to predict non-equilibrium control equations, enabling the inference of the fluid’s non-equilibrium characteristics. The third component, a physics-informed function, translates the outputs of the first two networks into physical information. By minimizing the residuals of the physical partial differential equations (PDEs), the physics-informed function infers relevant macroscopic quantities of the flow. The model evolves two sub-models that are applicable to different dimensions, named the PINN-SRT-LBM-I and PINN-SRT-LBM-II models according to the construction of the physics-informed function. The innovation of this work is the introduction of SRT-LBM and discrete velocity models as physical drivers into a neural network through the interpretation function. Therefore, the PINN-SRT-LBM allows a given neural network to handle inverse problems of various dimensions and focus on problem-specific solving. Our experimental results confirm the accurate prediction by this model of flow information at different Reynolds numbers within the computational domain. Relying on the PINN-SRT-LBM models, inverse problems in fluid mechanics can be solved efficiently.

Wind turbines and their associated parts are subjected to cyclical loads, such as bending, torque, longitudinal stresses, and twisting moments. The novel spatiotemporal reliability technique described in this research is especially useful for high-dimensional structural systems that are either measured or numerically simulated during representative observational time span. As this study demonstrates, it is possible to predict risks of dynamic system failure or damage given the in situ environmental load pattern. As an engineering example for this reliability, the authors have chosen 10-MW floating wind turbines and their dynamic responses, under environmental loadings, caused by wind and waves. The aim of this study was to benchmark a state-of-the-art approach suitable for the reliable study of offshore wind turbines. Existing reliability methods do not easily cope with dynamic system high dimensionality. The advocated reliability technique enables accurate and efficient assessment of dynamic system failure probability, accounting for system nonlinearities and high dimensionality as well as cross-correlations between different system components.

The goal of this paper is to strengthen the supervision of fishing behavior in the East China Sea and effectively ensure the sustainable development of fishery resources. Based on AIS data, this paper analyzes three types of fishing boats (purse seine operation, gill net operation and trawl operation) and uses the cubic spline interpolation algorithm to optimize the ship trajectory and construct high-dimensional features. It proposes a new coding method for fishing boat trajectory sequences. This method uses the Geohash algorithm to divide the East China Sea into grids and generate corresponding numbers. Then, the ship trajectory is mapped to the grid, the fishing boat trajectory points are associated with the divided grid, and the ship trajectory ID is extracted from the corresponding grid. The extracted complete trajectory sequence passes through the CBOW (continuous bag of words) model, and the correlation of trajectory points is fully learned. Finally, the fishing boat trajectory is converted from coordinate sequence to trajectory vector, and the processed trajectory sequence is trained by the LightGBM algorithm. In order to obtain the optimal classification effect, the optimal superparameter combination is selected. We put forward a LightGBM algorithm based on the Bayesian optimization algorithm, and obtained the classification results of three kinds of fishing boats. The final result was evaluated using the F1_score. Experimental results show that the F1_score trained with the proposed trajectory vectorization method is the highest, with a training accuracy of 0.925. Compared to XgBoost and CatBoost, the F1_score increased by 1.8% and 1.2%, respectively. The results show that this algorithm demonstrates strong applicability and effectiveness in fishery area evaluations and is significant for strengthening fishery resource management.

It is important to understand the mechanical properties of diamond-like carbon (DLC) for use not only in frictionand wear-resistant coatings, but also in vibration reduction and damping increase at the layer interfaces. However, the mechanical properties of DLC are influenced by the working temperature and its density, and the applications of DLC as coatings are limited. In this work, we systematically studied the deformation behaviors of DLC under different temperatures and densities using compression and tensile testing of DLC by molecular dynamics (MD) methods. In our simulation results, the values of tensile stress and compressive stress decreased and tensile strain and compressive strain increased as the temperature increased from 300 K to 900 K during both tensile and compressive processes, indicating that the tensile stress and tensile strain depend on the temperature. During the tensile simulation, Young’s modulus of DLC models with different densities had a different sensitivity to the increase in temperature, and the DLC model with a high density was more sensitive than that with a low density, which was not seen in the compression process. We conclude that the Csp3-Csp2 transition leads to tensile deformation, while the Csp2-Csp3 transition and relative slip dominate compressive deformation.

The traditional multi-level grid multiple-relaxation-time lattice Boltzmann method (MRT-LBM) requires interpolation calculations in time and space. It is a complex and computationally intensive process. By using the buffer technique, this paper proposes a new multi-level grid MRT-LBM which requires only spatial interpolation calculations. The proposed method uses a center point format to store multi-level grid information. The grid type determination in the flow field calculation domain is done using the axis aligned bounding box (AABB) triangle overlap test. According to the calculation characteristics of MRT-LBM, the buffer grid is proposed for the first time at the interface of different levels of grids, which is used to remove the temporal interpolation calculation and simplify the spatial interpolation calculation. The corresponding multi-level grid MRT-LBM algorithm is also presented for two-dimensional and three-dimensional flow field calculation problems. For the two-dimensional problem of flow around a circular cylinder, the simulation results show that a four-level grid MRT-LBM proposed in this paper can accurately obtain the aerodynamic coefficients and Strouhal number at different Reynolds numbers, and it has about 1/9 of the total number of grids as a single-level grid MRT-LBM and is 6.76 times faster. For the three-dimensional flow calculation problem, the numerical experiments of flow past a sphere are simulated to verify the numerical precision of the presented method at Reynolds numbers = 100, 200, 250, 300, and 1000. With the streamlines and velocity contours, it is demonstrated that the multi-level grid MRT-LBM can be calculated accurately even at the interface of different size grids.

Mesh quality is a major factor affecting the structure of computational fluid dynamics (CFD) calculations. Traditional mesh quality evaluation is based on the geometric factors of the mesh cells and does not effectively take into account the defects caused by the integrity of the mesh. Ensuring the generated meshes are of sufficient quality for numerical simulation requires considerable intervention by CFD professionals. In this paper, a Transformer-based network for automatic mesh quality evaluation (Gridformer), which translates the mesh quality evaluation into an image classification problem, is proposed. By comparing different mesh features, we selected the three features that highly influence mesh quality, providing reliability and interpretability for feature extraction work. To validate the effectiveness of Gridformer, we conduct experiments on the NACA-Market dataset. The experimental results demonstrate that Gridformer can automatically identify mesh integrity quality defects and has advantages in computational efficiency and prediction accuracy compared to widely used neural networks. Furthermore, a complete workflow for automatic generation of high-quality meshes based on Gridformer was established to facilitate automated mesh generation. This workflow can produce a high-quality mesh with a low-quality mesh input through automatic evaluation and optimization cycles. The preliminary implementation of automated mesh generation proves the versatility of Gridformer.

Hepatocellular carcinoma (HCC) accounts for 70–85% of liver cancer, and about 85% of HCC are hepatitis B virus-related (HBV-HCC) in China. Transarterial chemoembolization (TACE) combined with traditional Chinese medicine (TCM) has been reported as an effective treatment. Potential biomarkers to stratify patients who may benefit from this treatment are needed. In this study, we aimed to evaluate whether circulating cell-free mitochondrial DNA (ccf-mtDNA) content was associated with the outcome of HCC patients, especially of those who received the combination treatment of TACE and TCM. Univariate and multivariate Cox analyses were conducted to evaluate the association between ccf-mtDNA content and the overall survival of HBV-HCC patients. Kaplan–Meier analysis was used to compare the survival differences between patients with low and high ccf-mtDNA content. In a hospital-based cohort with 141 HBV-HCC patients, there was no statistically significant association between the ccf-mtDNA content and the overall survival of HBV-HCC patients in the univariate analysis, but a borderline significant association was found in the multivariate analyses. In a subcohort of 50 HBV-HCC patients who received TACE and TCM treatment, high ccfDNA content conferred an increased death risk with a hazard ratio of 4.01 (95% confidence interval: 1.25–12.84, p = 0.019) in the multivariate analysis. Kaplan–Meier survival analysis also showed that patients with high ccf-mtDNA content had unfavorable survival (log rank p = 0.097). Our findings suggest that ccf-mtDNA content is a potential non-invasive prognostic biomarker in HCC patients receiving TACE and TCM treatment.

The large-scale numerical simulation of complex flows has been an important research area in scientific and engineering computing. The lattice Boltzmann method (LBM) as a mesoscopic method for solving flow field problems has become a relatively new research direction in computational fluid dynamics. The multi-layer grid-refinement strategy deals with different-level of computing complexity through multi-scale grids, which can be used to solve the complex flow field of the non-uniform grid LBM without destroying the parallelism of the standard LBM. It also avoids the inefficiencies and waste of computational resources associated with standard LBMs using uniform and homogeneous Cartesian grids. This paper proposed a multi-layer grid-refinement strategy for LBM and implemented the corresponding parallel algorithm with load balancing. Taking a parallel scheme for two-dimensional non-uniform meshes as an example, this method presented the implementation details of the proposed parallel algorithm, including a partitioning scheme for evaluating the load in a one-dimensional direction and an interpolation scheme based on buffer optimization. Simply by expanding the necessary data transfer of distribution functions and macroscopic quantities for non-uniform grids in different parallel domains, our method could be used to conduct numerical simulations of the flow field problems with complex geometry and achieved good load-balancing results. Among them, the weak scalability performance could be as high as 88.90% in a 16-threaded environment, while the numerical simulation with a specific grid structure still had a parallel efficiency of 77.4% when the parallel domain was expanded to 16 threads.

Cancer is a complex disease, driven by a combination of genetic and epigenetic alterations. DNA and RNA methylation modifications are the most common epigenetic events that play critical roles in cancer development and progression. Bisulfite converted sequencing is a widely used technique to detect base modifications in DNA methylation, but its main drawbacks lie in DNA degradation, lack of specificity, or short reads with low sequence diversity. The nanopore sequencing technology can directly detect base modifications in native DNA as well as RNA without harsh chemical treatment, compared to bisulfite sequencing. Furthermore, CRISPR/Cas9-targeted enrichment nanopore sequencing techniques are straightforward and cost-effective when targeting genomic regions are of interest. In this review, we mainly focus on DNA and RNA methylation modification detection in cancer with the current nanopore sequencing approaches. We also present the respective strengths, weaknesses of nanopore sequencing techniques, and their future translational applications in identification of epigenetic biomarkers for cancer detection and prognosis.

The traditional single-shot multiBox detector (SSD) for the recognition process in sea cucumbers has problems, such as an insufficient expression of features, heavy computation, and difficulty in application to embedded platforms. To solve these problems, we proposed an improved algorithm for sea cucumber detection based on the traditional SSD algorithm. MobileNetv1 is selected as the backbone of the SSD algorithm. We increase the feature receptive field by receptive field block (RFB) to increase feature details and location information of small targets. Combined with the attention mechanism, features at different depths are strengthened and irrelevant features are suppressed. The experimental results show that the improved algorithm has better performance than the traditional SSD algorithm. The average precision of the improved algorithm is increased by 5.1%. The improved algorithm is also more robust. Compared with YOLOv4 and the Faster R-CNN algorithm, the performance of this algorithm on the P-R curve is better, indicating that the performance of this algorithm is better. Thus, the improved algorithm can stably detect sea cucumbers in real time and provide reliable feedback information.

Abstract Wear of contact materials results in energy loss and device failure. Conventionally, wear is described by empirical laws such as the Archard's law; however, the fundamental physical and chemical origins of the empirical law have long been elusive, and moreover empirical wear laws do not always hold for nanoscale contact, collaboratively hindering the development of high‐durable tribosystems. Here, a non‐empirical and robustly applicable wear law for nanoscale contact situations is proposed. The proposed wear law successfully unveils why the nanoscale wear behaviors do not obey the description by Archard's law in all cases although still obey it in certain experiments. The robustness and applicability of the proposed wear law is validated by atomistic simulations. This work affords a way to calculate wear at nanoscale contact robustly and theoretically, and will contribute to developing design principles for wear reduction.

Due to high strength, high wear resistance and high corrosion resistance, the amorphous metallic glasses were investigated widely. In the present study, the corrosion resistance of amorphous coating and composite coatings with various proportions of AT13 (Al2O3–13 wt.% TiO2) ceramic as additions in 3.5 wt.% NaCl solution were studied. The corrosion resistance was improved obviously as the addition of AT13, and when the content of AT13 was 15 wt.%, the composite coating had the lowest corrosion current density (1.75 × 10−6 A cm−2) and the highest corrosion potential (−411 mV), which was 5.14 × 10−5 A cm−2 and −580 mV for Fe-based metallic glassy coating, respectively. The corrosion mechanism was proposed according to the long-time immersion corrosion test.

Background Novel coronavirus disease has been recently a concern for worldwide public health. To determine epidemic rate probability at any time in any region of interest, one needs efficient bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of novel coronavirus infection rate. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the multi-dimensionality advantage, that suggested methodology offers, namely dealing efficiently with multiple regions at the same time and accounting for cross-correlations between different regional observations. Methods Modern multi-dimensional novel statistical method was directly applied to raw clinical data, able to deal with territorial mapping. Novel reliability method based on statistical extreme value theory has been suggested to deal with challenging epidemic forecast. Authors used MATLAB optimization software. Results This paper described a novel bio-system reliability approach, particularly suitable for multi-country environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of extreme novel coronavirus death rate probability. Namely, accurate maximum recorded patient numbers are predicted for the years to come for the analyzed provinces. Conclusions The suggested method performed well by supplying not only an estimate but 95% confidence interval as well. Note that suggested methodology is not limited to any specific epidemics or any specific terrain, namely its truly general. The only assumption and limitation is bio-system stationarity, alternatively trend analysis should be performed first. The suggested methodology can be used in various public health applications, based on their clinical survey data.

Background: Novel coronavirus disease has been recently a concern for worldwide public health. To determine epidemic rate probability at any time in any region of interest, one needs efficient bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of novel coronavirus infection rate. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the multi-dimensionality advantage, that suggested methodology offers, namely dealing efficiently with multiple regions at the same time and accounting for cross-correlations between different regional observations. Methods: Modern multi-dimensional novel statistical method was directly applied to raw clinical data, able to deal with territorial mapping. Novel reliability method based on statistical extreme value theory has been suggested to deal with challenging epidemic forecast. Authors used MATLAB optimization software. Results: This paper described a novel bio-system reliability approach, particularly suitable for multi-country environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of extreme novel coronavirus death rate probability. Namely, accurate maximum recorded patient numbers are predicted for the years to come for the analyzed provinces. Conclusions: The suggested method performed well by supplying not only an estimate but 95% confidence interval as well. Note that suggested methodology is not limited to any specific epidemics or any specific terrain, namely its truly general. The only assumption and limitation is bio-system stationarity, alternatively trend analysis should be performed first. The suggested methodology can be used in various public health applications, based on their clinical survey data.

Background: Novel coronavirus disease has been recently a concern for worldwide public health. To determine epidemic rate probability at any time in any region of interest, one needs efficient bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of novel coronavirus infection rate. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the multi-dimensionality advantage, that suggested methodology offers, namely dealing efficiently with multiple regions at the same time and accounting for cross-correlations between different regional observations. Methods: Modern multi-dimensional novel statistical method was directly applied to raw clinical data, able to deal with territorial mapping. Novel reliability method based on statistical extreme value theory has been suggested to deal with challenging epidemic forecast. Authors used MATLAB optimization software. Results: This paper described a novel bio-system reliability approach, particularly suitable for multi-country environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of extreme novel coronavirus death rate probability. Namely, accurate maximum recorded patient numbers are predicted for the years to come for the analyzed provinces. Conclusions: The suggested method performed well by supplying not only an estimate but 95% confidence interval as well. Note that suggested methodology is not limited to any specific epidemics or any specific terrain, namely its truly general. The only assumption and limitation is bio-system stationarity, alternatively trend analysis should be performed first. The suggested methodology can be used in various public health applications, based on their clinical survey data.