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Abstract This paper examines the mechanisms of differentiated discursive disciplining for immigrants of different religious backgrounds in the French media. Based on a sample of 120 articles on immigration in the French mainstream media, textual analyses and comparisons were conducted. Results show that influenced by religious factors, the French media use secularism as a tool to portray completely different images of immigrants through different discursive logics. Muslim immigrants present a paradoxically biased negative image in the French media discourse as a collection of invaders and the disciplined. In contrast, the media image of Ukrainian immigrants of Orthodox Christian background is positive on the whole, with a certain degree of deliberateness in the details of its construction. The difference in the symbolic characterization between Catholicism and Islam enables the exclusionary media discourse against Muslim immigrants. The paper also discusses French media discourse flaws and policy dilemmas. The logic of French media discourse is paradoxical in its inability to maintain an intact secularist identification and to effectively influence political issues to the detriment of promoting immigrant integration.

With the rapid growth of the electric vehicle industry, the demand for battery fault detection methods is also growing. Effective battery defect detection methods help maintain the performance of the battery pack. In this research, a reconstruction-based model for internal short circuit (ISC) detection in battery packs is presented by combining transformer and long short-term memory (LSTM). LSTM is added to the model to improve the encoding results of the transformer. The optimized decoders have a positive effect on the fitting ability of the model. In this model, the voltage data of the battery packs are taken as input and residual signals are generated by the differences between the reconstructed and true values to detect the ISC. The online parameter update method is proposed to increase the ability of the neural network model to screen out fault signals more efficiently, and the false-positive clipping method is employed to enhance detection accuracy. A large amount of data, including ISC, was collected from the laboratory to test the sensitivity and robustness. The verification results show that our model has better fitting ability and sensitivity. The parameter update method and false-positive clipping method help improve the adaptability and robustness. Our detection method achieves 96.05%, 98.57%, and 96.40% accuracy in ISC detection of 1 ohm, 5 ohms, and 10 ohms respectively.

Understanding the intricate relationship between drought stress and corn yield is crucial for ensuring food security and sustainable agriculture in the face of climate change. This study investigates the subtle effects of drought stress on corn physiological, morphological, and spectral characteristics at different growth stages, in order to construct a new drought index to characterize drought characteristics, so as to provide valuable insights for maize recovery mechanism and yield prediction. Specific conclusions are as follows. Firstly, the impact of drought stress on corn growth and development shows a gradient effect, with the most significant effects observed during the elongation stage and tasseling stage. Notably, Soil and Plant Analyzer Development (SPAD) and Leaf Area Index (LAI) are significantly affected during the silking stage, while plant height and stem width remain relatively unaffected. Secondly, spectral feature analysis reveals that, from the elongation stage to the silking stage, canopy reflectance exhibits peak–valley variations. Drought severity correlates positively with reflectance in the visible and shortwave infrared bands and negatively with reflectance in the near-infrared band. Canopy spectra during the silking stage are more affected by moderate and severe drought stress. Thirdly, LAI shows a significant positive correlation with yield, indicating its reliability in explaining yield variations. Finally, the yield-related drought index (YI) constructed based on Convolutional Neural Network (CNN), Random Forest (RF) and Multiple Linear Regression (MLR) methods has a good effect on revealing drought characteristics (R = 0.9332, p < 0.001). This study underscores the importance of understanding corn responses to drought stress at various growth stages for effective yield prediction and agricultural management strategies.

PTFE coatings were deposited on YT15 carbide substrates using spray technology. A series of examinations were conducted, including the use of surface and cross-section micrographs to analyze the structural integrity of the coatings. The surface roughness, the adhesion force between the PTFE coatings and the carbide substrate, and the micro-hardness of the coated carbide were also evaluated. Additionally, the friction and wear behaviors were assessed through dry sliding friction tests against WC/Co balls. The test results indicated that while the PTFE-coated carbide exhibited a rougher surface and reduced micro-hardness, it also demonstrated a significant reduction in surface friction and adhesive wear. These findings suggest that the PTFE coatings enhance the overall wear resistance of the carbides. The lower surface hardness and shear strength of the coatings influenced the friction performance, leading to specific wear failure mechanisms, such as abrasion wear, coating delamination, and flaking. Overall, the deposition of PTFE coatings on carbide substrates presents a promising strategy to enhance their friction and wear performance. This approach not only improves the durability of carbide materials but also offers potential applications in industries where reduced friction and wear are critical for performance.

Myxomycete communities in Taxodium distichum forests and the influence of substrates and seasons on myxomycete diversity were studied. In total, 80 substrates (40 living barks and 40 litters) were collected from the sample site in winter, spring, summer and autumn of 2023, followed by moist chamber cultures. A total of 733 records representing 34 myxomycete species in 21 genera were obtained. Arcyria cinerea and six other species were abundant (A). Six species were common (C), six species were occasional (O), and fifteen species were rare (R). Species diversity (H’ = 2.04 ± 0.20) of corticolous communities was significantly higher than that (H’ = 1.49 ± 0.31) of litter-inhabiting communities (p < 0.05). Nonmetric Multidimensional Scaling (NMDS) analyses showed significant differences between these two communities (p < 0.01). Species diversity was slightly higher in summer and autumn compared to winter and spring. NMDS analyses indicated no significant differences among seasonal communities. Redundancy Analysis showed that the corticolous species were closely related to wind speed (p < 0.05), while litter-inhabiting species were mainly determined by temperature (p < 0.05). The study revealed that substrates and seasons influenced myxomycete communities in T. distichum forests, with the communities being particularly affected by the substrates in which they live.

To calculate the carrying capacity of spherical hydrostatic bearings, a numerical calculation model was presented. The influence law of dynamic pressure effect on the carrying characteristics of liquid hydrostatic spherical bearings is revealed. Under general working conditions, the dynamic pressure effect caused by the radial eccentricity of the bearing has little influence on the bearing load characteristics parameters; when the minimum width of the gap between the sealing edge is very small and the bearing rotational speed is high, the dynamic pressure effect is more obvious.

Simulation of indoor temperature provides important references for thermal environment not only for buildings at design stage but also for existing buildings. The current thermal environment simulation software tools suit for buildings at design stage, however not for an existing building. A model is proposed to simulate indoor temperature combining Optimization multivariable grey prediction model (OGM(1,N)) and Elman neural network. The proposed model is trained by short-term field measured data. A unit is assembled to measure and record thermal parameters in a case natural ventilated building at half-hourly intervals during 7:00 May 29 and 6:30 June 2010. Programming in Matlab implements the proposed model and referenced models. The maximum mean deviation is 0.46°C, the maximum standard mean square deviation is 0.65°C. Three referenced indoor temperature simulation models, OGM(1,N), Elman neural network, and Designer’s Simulation Toolkit are executed, respectively, in case building to provide comparison. Compared with referenced models, the proposed model has higher accuracy and stronger robustness. It is expected that this study provides important references for thermal environment assessment in existing buildings using short-term field measured data.

The subsynchronous interaction between the wind farm’s grid-side converter and the series-compensated transmission line can trigger subsynchronous oscillations (SSO). The wind power system oscillation frequency depends on the actual operational conditions. This paper proposes a repetitive-PI control strategy to mitigate oscillations under various conditions. The repetitive-PI control offers fast dynamic performance and high steady-state accuracy, which can eliminate steady-state errors and improve the system’s robustness. Firstly, the repetitive-PI composite controller is derived based on the frequency domain expansion of the repetition controller. Then, the stability of the repetitive-PI composite controller is analyzed, and its main parameters of it are designed. Finally, to demonstrate the efficacy of the proposed suppression technique for SSO, simulations are conducted under different wind speeds, series compensation levels, and numbers of operating wind turbines. The time-domain simulation results and impedance model-based analysis demonstrate that the proposed repetitive-PI control strategy enhances the system’s damping performance and improves its disturbance resistance.

Accurate monitoring of crop drought thresholds at different growth periods is crucial for drought monitoring. In this study, the canopy temperature (Tc) of winter wheat (‘Weilong 169’ variety) during the three main growth periods was extracted from high-resolution thermal and multispectral images taken by a complete unmanned aerial vehicle (UAV) system. Canopy-air temperature difference (ΔT) and statistic Crop Water Stress Index (CWSIsi) indicators were constructed based on Tc. Combined experiment data from the field and drought thresholds for the ΔT and CWSIsi indicators for different drought levels at three main growth periods were monitored. The results showed a strong correlation between the Tc extracted using the NDVI-OTSU method and ground-truth temperature, with an R2 value of 0.94. The CWSIsi was more stable than the ΔT index in monitoring the drought level affecting winter wheat. The threshold ranges of the CWSIsi for different drought levels of winter wheat at three main growth periods were as follows: the jointing–heading period, where the threshold ranges for normal, mild drought, moderate drought, and severe drought are 0.48, respectively; the heading–filling period, where the threshold ranges for normal, and mild, moderate, and severe drought are 0.53, respectively; and the filling–maturation period, where the threshold ranges for normal, mild drought, moderate drought, and severe drought are 0.59, respectively. The UAV thermal threshold method system can improve the accuracy of crop drought monitoring and has considerable potential in crop drought disaster identification.

Soil moisture (SM) data can provide guidance for decision-makers in fields such as drought monitoring and irrigation management. Soil Moisture Active Passive (SMAP) satellite offers sufficient spatial resolution for global-scale applications, but its utility is limited in regional areas due to its lower spatial resolution. To address this issue, this study proposed a downscaling framework based on the Stacking strategy. The framework integrated extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost) to generate 1 km resolution SM data using 15 high-resolution factors derived from multi-source datasets. In particular, to test the influence of terrain partitioning on downscaling results, Anhui Province, which has diverse terrain features, was selected as the study area. The results indicated that the performance of the three base models varied, and the developed Stacking strategy maximized the potential of each model with encouraging downscaling results. Specifically, we found that: (1) The Stacking model achieved the highest accuracy in all regions, and the performance order of the base models was: XGBoost > CatBoost > LightGBM. (2) Compared with the measured SM at 87 sites, the downscaled SM outperformed other 1 km SM products as well as the downscaled SM without partitioning, with an average ubRMSE of 0.040 m3/m3. (3) The downscaled SM responded positively to rainfall events and mitigated the systematic bias of SMAP. It also preserved the spatial trend of the original SMAP, with higher levels in the humid region and relatively lower levels in the semi-humid region. Overall, this study provided a new strategy for soil moisture downscaling and revealed some interesting findings related to the effectiveness of the Stacking model and the impact of terrain partitioning on downscaling accuracy.

With the increase in cadmium (Cd) release into the environment, it is necessary to find appropriate solutions to reduce soil Cd pollution. Microorganisms are a green and effective means for the remediation of Cd-contaminated soil. In this study, in a Cd-contaminated farmland, we screened and identified novel Cd-resistant strains, Paenarthrobactor nitroguajacolicus, Lysinibacillus fusiformis, Bacillus licheniformis, and Methyllobacium brachiatum, with minimum inhibitory concentrations of 100, 100, 50, and 50 mg/L, respectively, and added them each to pots containing Cd-contaminated rape plants to explore their remediation ability. The results showed that treatment with each of the four strains significantly increased the abundance of Nitrospirae, Firmicutes, Verrucomicrobia, and Patescibacterium in the rhizosphere soil of the plants. This led to changes in soil physical and chemical indices; pH; and available phosphorus, urease, and catalase activities, which were significantly negatively correlated with bioavailable Cd, reducing 28.74–58.82 % Cd enrichment to plants and 23.72–43.79 % Cd transport within plants, and reducing 5.52–10.68 % available cadmium in soil, effectively reducing the biotoxicity of Cd. Thus, this study suggests microbial remediation as a reliable option, forming a basis for the remediation of Cd-contaminated soil.

The acid sites are important local structures to determine catalytic performances of zeolites. Here, the authors expand the interface engineering to the field of porous zeolites through the lattice mismatch of the intergrown zeolite to enrich the inherent Lewis acid sites at the interface of a mortise-tenon ZSM-5 catalyst.

Here, we report a Na-promoted FeCu-based catalyst with excellent liquid hydrocarbon selectivity and catalytic activity. The physiochemical properties of the catalysts were comprehensively characterized by various characterization techniques. The characterization results indicate that the catalytic performance of the catalysts was closely related to the nature of the metal promoters. The Na-AlFeCu possessed the highest CO2 conversion due to enhanced CO2 adsorption of the catalysts by the introduction of Al species. The introduction of excess Mg promoter led to a strong methanation activity of the catalyst. Mn and Ga promoters exhibited high selectivity for light hydrocarbons due to their inhibition of iron carbides generation, resulting in a lack of chain growth capacity. The Na-ZnFeCu catalyst exhibited the optimal C5+ yield, owing to the fact that the Zn promoter improved the catalytic activity and liquid hydrocarbon selectivity by modulating the surface CO2 adsorption and carbide content. Carbon dioxide (CO2) hydrogenation to liquid fuel is considered a method for the utilization and conversion of CO2, whereas satisfactory activity and selectivity remains a challenge. This method provides a new idea for the catalytic hydrogenation of CO2 and from there the preparation of high-value-added products.

Orthogonal Time Frequency Space (OTFS) as a two-dimensional modulation scheme designed in the delay-Doppler domain, is realized by inverse symplectic finite Fourier transform (ISFFT) and symplectic finite Fourier transform (SFFT), for combating the high Doppler channels toward future wireless communications. Meanwhile, generalized spatial modulation (GSM) offers an efficient implementation for multi-input multi-output (MIMO) systems, by activating only part of the transmit antennas to alleviate inter-channel interference (ICI). In this paper, the combination of the above two concepts is exploited, for bridging their unique advantages. On the other hand, an iterative detector based on generalized approximate message passing (GAMP) is also developed for GSM-OTFS. Simulation results demonstrate that the proposed GAMP detector can achieve better performance than the conventional minimum mean square error (MMSE) detector.

To improve the surface friction characteristics of cemented carbide, a Cr coating was deposited on cemented carbide substrate with the multiple arc plating technique. The surface and cross-section micrographs, adhesion force and micro-hardness of the Cr-coated cemented carbide were tested. The anti-friction and anti-wear behaviors of cemented carbide with and without Cr coating were investigated via the sliding friction test against a WC/Co ball. The tribological properties of cemented carbide were examined using a scanning electron microscope and energy dispersive X-ray analysis. The test results showed that Cr-coated cemented carbide possessed good adhesion properties and lower hardness. The average coefficient of friction for Cr-coated cemented carbide was reduced by 10–20% compared with that of an uncoated one. The primary wear modes of the Cr-coated sample were delamination of coating, flaking and abrasion wear. It can be found that the preparation of a Cr coating is an effective way to enhance the friction and wear performance of traditional cemented carbide.

Accurate estimation of the state of charge plays a very important role in ensuring the safe and effective operation of battery lithium-ion batteries and is one of the most important state parameters. However, the estimation method of state of charge has various limitations, so it is of great significance to improve the accuracy and calculation speed of the method. In this article, we propose an improved recurrent neural network model to estimate lithium-ion battery state of charge. Simple recurrent units are used to replace the traditional recurrent neural network basic unit or long short-term memory unit, and the computation speed is improved by implementing parallel processing. Finally, the prediction results of the model are fed into an unscented Kalman filter module to remove the interference of noise on the prediction. This article studies the prediction accuracy and speed of Samsung INR 18650-20R and INR 18650-25R under various ambient temperatures, initial state of charge values, and electric vehicle drive cycles. The results show that the proposed method can obtain accurate state of charge estimation results in the INR 18650-20R data set. For different temperatures and initial SOC, the root mean square error is less than 0.015 and 0.016, and the prediction speed is about 30% higher than that of long short-term memory. In the INR 18650-25R data set, for three different driving cycles, the root mean square error is less than 0.034, and the average test speed is about 2.7s, which proves the effectiveness of this method in estimating accuracy and speed.

As the country hosting the most significant number of refugees in Europe, Germany’s Merkel government’s refugee policy has been repeatedly adjusted and plagued by inconsistencies and management failures. What factors have influenced the formation of the Merkel government’s refugee policy and its two shifts? The traditional rational decision-making model does not effectively explain government decisions’ motivation in complex challenges. This article develops a framework for analyzing “bounded rational decision-making”. It identifies the three factors that influenced the Merkel government’s refugee policy: (a) strict border controls, (b) welcome culture, and (c) welcome culture under restrained policies. It explains the process and logic of their development. Based on the theory of “bounded rational decision making”, the article examines the “key events and problem identification”, “goal selection”, “national interests”, and “political psychology”. We found that in the early stages of the refugee crisis, (a) public opinions and pressure, (b) the pursuit of a positive national image, and (c) a shift in the leader’s psychology led Merkel to shift away from a pragmatic and rational course. These factors led to the first shift in German refugee policy from “hesitation” to a “welcoming culture”. At the end of 2015, however, the shortcomings of the irrational policy were quickly revealed, and the frequency of refugee-related social problems reversed German public opinion, forcing Merkel’s government to seek a balance between humanitarianism and national interests. As a result, refugee policy was adjusted for the second time, returning to a strict refugee examination system. The development of the Merkel government’s refugee policy exposed the shortcomings of the traditional crisis decision-making model. Moreover, it provided a new perspective for rethinking the governance of the refugee crisis.

Increasing the surface charge density of a triboelectric nanogenerator (TENG) under a single energy source is of wide interest. Here, the authors increase the surface charge density by comprehensively utilizing solar energy and tidal energy to develop a bioinspired photoelectric-electromechanical integrated TENG.

Trunk pests have always been one of the most important species of tree pests. Trees eroded by trunk pests will be blocked in the transport of nutrients and water and will wither and die or be broken by strong winds. Most pests are social and distributed in the form of communities inside trees. However, it is difficult to know from the outside if a tree is infected inside. A new method for the non-invasive detecting of tree interiors is proposed to identify trees eroded by trunk pests. The method is based on electromagnetic inverse scattering. The scattered field data are obtained by an electromagnetic wave receiver. A Joint-Driven algorithm is proposed to realize the electromagnetic scattered data imaging to determine the extent and location of pest erosion of the trunk. This imaging method can effectively solve the problem of unclear imaging in the xylem of living trees due to the small area of the pest community. The Joint-Driven algorithm proposed by our group can achieve accurate imaging with a ratio of pest community radius to live tree radius equal to 1:60 under the condition of noise doping. The Joint-Driven algorithm proposed in this paper reduces the time cost and computational complexity of tree internal defect detection and improves the clarity and accuracy of tree internal defect inversion images.

Since the turn of the 21st century, China has leveraged the international environment in its favour to achieve a spectacularly rapid rise, a success that Chinese strategic policymakers attribute in part to ‘seizing the strategic window of opportunity’. How has China perceived the international environment and shaped the narrative of its macro-strategy? What impact has it had on China and the world? The article explores the evolution of the strategic narrative of the ‘window of opportunity’ in three stages since the 21st century and explores its impact in terms of quantity and keyword mapping by analysing the narrative texts of China’s ‘discourse coalition’ (government, official media and intellectual elites). The study found that China’s macro-strategic narrative is primarily driven by the official political and semi-official intellectual elites and places a premium on domestic political persuasion rather than external displays of strategic intent. It combines classical and modern philosophical thinking and methodology with the two core objectives of ‘maintaining peace’ and an ‘economic priority’. Over 20 years, the trend of narrative production of the ‘strategic opportunity period’ has shown a concentrated emergence and fluctuating decline, and its narrative elements have been enriched, interlinked and aligned with the narrative of the CPC’s core policies. The narrative of the ‘strategic opportunity period’ has been a powerful force in building domestic legitimacy, guaranteeing partial fulfilment of strategic objectives and offering lessons for other countries to learn from. Inevitably, the narrative faces a number of dilemmas and challenges.

Abstract Background The stomata of plants mainly regulate gas exchange and water dispersion between the interior and external environments of plants and play a major role in the plants’ health. The existing methods of stomata segmentation and measurement are mostly for specialized plants. The purpose of this research is to develop a generic method for the fully automated segmentation and measurement of the living stomata of different plants. The proposed method utilizes level set theory and image processing technology and can outperform the existing stomata segmentation and measurement methods based on threshold and skeleton in terms of its versatility. Results The single stomata images of different plants were the input of the method and a level set based on the Chan-Vese model was used for stomatal segmentation. This allowed the morphological features of the stomata to be measured. Contrary to existing methods, the proposed segmentation method does not need any prior information about the stomata and is independent of the plant types. The segmentation results of 692 living stomata of black poplars show that the average measurement accuracies of the major and minor axes, area, eccentricity and opening degree are 95.68%, 95.53%, 93.04%, 99.46% and 94.32%, respectively. A segmentation test on dayflower (Commelina benghalensis) stomata data available in the literature was completed. The results show that the proposed method can effectively segment the stomata images (181 stomata) of dayflowers using bright-field microscopy. The fitted slope of the manually and automatically measured aperture is 0.993, and the R2 value is 0.9828, which slightly outperforms the segmentation results that are given in the literature. Conclusions The proposed automated segmentation and measurement method for living stomata is superior to the existing methods based on the threshold and skeletonization in terms of versatility. The method does not need any prior information about the stomata. It is an unconstrained segmentation method, which can accurately segment and measure the stomata for different types of plants (woody or herbs). The method can automatically discriminate whether the pore region is independent or not and perform pore region extraction. In addition, the segmentation accuracy of the method is positively correlated with the stomata’s opening degree.

Multilevel thresholding has got more attention in the field of image segmentation recently. However, it is still challenging and complicated for color image segmentation in many applications. To mitigate the above conditions, a novel multilevel thresholding algorithm consists of two innovative strategies is proposed on the basis of moth-flame optimization (MFO) to develop the SAMFO-TH algorithm. On one hand, a creative self-adaptive inertia weight scheme is used to enhance both the exploration and exploitation, on the other hand, a newly proposed thresholding (TH) heuristic is embedded into MFO to improve the global performance in multilevel thresholding. To find the optimal threshold values of an image, Otsu's variance, and Kapur's entropy criteria are employed as fitness functions. The experiments have been performed on ten color images including six natural images and four satellite images at different threshold levels with a comparison of other eight meta-heuristic algorithms: multi-verse optimizer (MVO), whale optimization algorithm (WOA), standard MFO, and so on. The experimental results are presented in terms of computational time (CPU time), mean value to reach (MVTR), standard deviation (STD), mean square error (MSE), peak signal to noise ratio (PSNR), structural similarity (SSIM), feature similarity (FSIM), probability rand index (PRI), the variation of information (VoI), and threshold value distortion (TVD). The results demonstrate that the proposed SAMFO-TH outperforms other competitive algorithms and has superiority concerning stability, accuracy, and convergence rate, which can be applied to practical engineering problems.

Hybrid algorithms have attracted more and more attention in the field of optimization algorithms. In this paper, three hybrid algorithms are proposed to solve feature selection problems based on seagull optimization algorithm (SOA) and thermal exchange optimization (TEO). In the first algorithm, we take the roulette wheel to choose one of the two algorithms for located updating. Another method is to join the TEO algorithm for optimization after SOA algorithm iteration. The last method is to adopt TEO algorithm's heat exchange formula to improve the seagull attack mode of SOA algorithm, so as to improve the exploitation ability of SOA algorithm. The performance of proposed methods is evaluated on 20 standard benchmark datasets in the UCI repository and compared with three well-known hybrid optimization feature selection methods in the literature. The experimental results illustrate that the proposed algorithm has high efficiency in improving classification accuracy, ensuring the ability of hybrid SOA algorithm in feature selection and classification task information attribute selection, and reducing the CPU time.

Multithreshold segmentation of color images is an important part of modern image processing. Multithreshold segmentation techniques that use the ideas of optimization in mathematics to process the information contained in the three channels of RGB images have received much attention in recent years. In this paper, a hybrid algorithm of multiverse optimization algorithm with a gravitational search algorithm (GSMVO) is proposed. On the one hand, the gravitational mechanism among individuals in the universe enables particles to share information and move toward particles with large mass (the optimal solution), thus optimizing local optimization. On the other hand, the problem of a slow interpopulation update is improved. On the premise of improving the optimization precision, the convergence speed of the whole system is accelerated and the robustness can be improved. The time complexity of a proposed hybrid algorithm is analyzed. The quality of a segmented image is evaluated by using peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structural similarity index (SSIM), and Wilcoxon test. The experimental results illustrate that the proposed method has obvious advantages in objective function value, image quality measurement, convergence performance, and robustness.

Multithreshold segmentation is an indispensable part of modern image processing. Color images contain more information than gray images, therefore RGB multi-thresholding segmentation techniques have been drawn much attention during recent years. Multiverse optimization (MVO) algorithm has a strong advantage in finding the optimal solution of three channels for RGB. In this paper, an MVO algorithm based on Lévy flight (LMVO) is proposed. Lévy flight is an efficient strategy which can not only increase the population diversity to prevent premature convergence but also improve the ability to jump out of the local optimum. Therefore, LMVO conduces to achieve a better balance between exploration and exploitation of MVO, so that it is faster and more robust than MVO and avoids premature convergence. Further LMVO algorithm is compared with the other eight famous meta-heuristics algorithms, by maximizing the objective function of Kapur's entropy method or of Otsu method to determine the optimal threshold. The maximum objective function, peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structural similarity index (SSIM), CPU calculation time, optimal threshold value, and Wilcoxon's rank-sum test are used to evaluate the quality of the segmented image. The experimental results show that this method has obvious advantages in terms of objective function value, image quality measurement, convergence performance, and robustness.

The purpose of this paper is to propose a new hybrid metaheuristic to solve the problem of feature selection. Feature selection problem is the process of finding the most relevant subset based on some criteria. A hybrid metaheuristic is a new trend in the development of optimization algorithms. In this paper, two different hybrid models are designed based on spotted hyena optimization (SHO) for feature selection problem. The SHO algorithm can find the optimal or nearly optimal feature subset in the feature space to minimize the given fitness function. In the first model, the simulated annealing algorithm (SA) is embedded in the SHO algorithm (called SHOSA-1) to enhance the optimal solution found by the SHO algorithm after each iteration. In the second model, SA enhances the final solution obtained by the SHO algorithm (called SHOSA-2). The performance of these methods is evaluated in 20 datasets in the UCI repository. The experiments show that SHOSA-1 performs better than the native algorithm and SHOSA-2. And then, SHOSA-1 is compared with six state-of-the-art optimization algorithms. The experimental results confirm that SHOSA-1 improves the classification accuracy and reduces the number of selected features compared with other wrapper-based optimization algorithms. That proves the excellent performance of SHOSA-1 in spatial search and feature attribute selection.

In order to realize the multilevel thresholding segmentation of color satellite images, a multi-strategy emperor penguin optimizer (called MSEPO) is proposed to find the optimal threshold values for three channels of RGB images. Masi entropy is utilized as the objective function. Meanwhile, three strategies are introduced, namely highly disruptive polynomial mutation, Levy flight, and thermal exchange operator. Through these, the MSEPO is able to properly balance the exploration and exploitation mechanisms. Moreover, the convergence, accuracy and stability performance have been significantly enhanced. Tests are carried out on color Berkeley images and color satellite images at various threshold levels. The experimental results show that the proposed method achieves higher Peak Signal to Noise Ratio (PSNR), higher Structural Similarity Index (SSIM), higher Feature Similarity Index (FSIM), and shorter CPU time than seven state-of-the-art optimization techniques. To present in a comprehensive manner, the computational complexity has also been analyzed in terms of time and space complexity. Wilcoxon rank sum test and Friedman test are also applied to statistical analysis. To sum up, MSEPO algorithm has achieved significant improvement and superior performance. What's more, the proposed technique is more suitable for high-dimensional segmentation of complex satellite images.

In recent years, deep learning has been applied to intelligent fault diagnosis and has achieved great success. However, the fault diagnosis method of deep learning assumes that the training dataset and the test dataset are obtained under the same operating conditions. This condition can hardly be met in real application scenarios. Additionally, signal preprocessing technology also has an important influence on intelligent fault diagnosis. How to effectively relate signal preprocessing to a transfer diagnostic model is a challenge. To solve the above problems, we propose a novel deep transfer learning method for intelligent fault diagnosis based on Variational Mode Decomposition (VMD) and Efficient Channel Attention (ECA). In the proposed method, the VMD adaptively matches the optimal center frequency and finite bandwidth of each mode to achieve effective separation of signals. To fuse the mode features more effectively after VMD decomposition, ECA is used to learn channel attention. The experimental results show that the proposed signal preprocessing and feature fusion module can increase the accuracy and generality of the transfer diagnostic model. Moreover, we comprehensively analyze and compare our method with state-of-the-art methods at different noise levels, and the results show that our proposed method has better robustness and generalization performance.

Tree height is an essential indicator in forestry research. This indicator is difficult to measure directly, as well as wind disturbance adds to the measurement difficulty. Therefore, tree height measurement has always been an issue that experts and scholars strive to improve. We propose a tree height measurement method based on tree fisheye images to improve the accuracy of tree height measurements. Our aim is to extract tree height extreme points in fisheye images by proposing an improved lightweight target detection network YOLOX-tiny. We added CBAM attention mechanism, transfer learning, and data enhancement methods to improve the recall rate, F1 score, AP, and other indicators of YOLOX-tiny. This study improves the detection performance of YOLOX-tiny. The use of deep learning can improve measurement efficiency while ensuring measurement accuracy and stability. The results showed that the highest relative error of tree measurements was 4.06% and the average relative error was 1.62%. The analysis showed that the method performed better at all stages than in previous studies.

PurposeTo evaluate the therapeutic efficacy of transcatheter arterial chemoembolization (TACE) for hepatocellular carcinoma (HCC) with different areas of right retroperitoneal space (rRPS) invasion and analyze the blood supply.MethodsThis retrospective study enrolled 41 patients with HCC with different areas of rRPS invasion treated with TACE, including 22 HCCs with superior aspect of the right perirenal space (SARPS) invasion and 19 HCCs with right anterior pararenal space (RAPS) invasion. The overall response rate (ORR) and disease control rate (DCR) were analyzed. The prognostic factors for overall survival (OS) after TACE were determined. The blood supply characteristics of HCC with different areas of rRPS invasion were analyzed with arteriograms.ResultsAll patients underwent 2.8 ± 1.8 TACE sessions over 25.0 ± 21.9 months. The median OS was 29.0 months for patients with SARPS invasion and 12.0 months for patients with RAPS invasion (P = 0.004). Only the invaded area of the rRPS was an independent prognostic factor for OS [hazard ratio (HR), 2.833; 95% CI, 1.297–6.188; and P = 0.009). The ORR and DCR were significantly higher in the group with SARPS invasion than in the group with RAPS invasion (ORR: 63.6% vs 31.6%, P = 0.041; DCR: 77.3% vs 47.4%, P = 0.047). Initially, HCC with SARPS invasion were supplied by the hepatic artery (HA; n = 8) and both the HA and extrahepatic collateral vessels (EHCs; n = 14); HCC with RAPS invasion were supplied by the HA (n = 10) and both the HA and EHCs (n = 9); as the TACE sessions increased, the tumor-feeding vessels shifted from the HA to both the HA and EHCs, and even EHCs could be the only blood supply. Rare EHCs appeared earlier and more frequently in the RAPS group than in the SARPS group.ConclusionThe efficacy of TACE differed for HCC with different areas of rRPS invasion, and the median OS, ORR and DCR were significantly better in the SARPS group than in the RAPS group. Different common EHCs supplied HCCs with different areas of rRPS invasion, while other rare EHCs appeared more frequently in the RAPS group.

Background: To compare the diagnostic performance of radiomics models with that of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) perfusion parameters for the preoperative prediction of extramural venous invasion (EMVI) in rectal cancer patients and to develop a preoperative nomogram for predicting the EMVI status.Methods: In total, 106 rectal cancer patients were enrolled in our study. All patients under went preoperative rectal high-resolution MRI and DCE-MRI. We built five models based on the perfusion parameters of DCE-MRI (quantitative model), the radiomics of T2-weighted (T2W) CUBE imaging (R1 model), DCE-MRI (R2 model), clinical features (clinical model), and clinical-radiomics features. The predictive efficacy of the radiomics signature was assessed and internally verified. The area under the receiver operating curve (AUC) was used to compare the diagnostic performance of different radiomics models and DCE-MRI quantitative parameters. The radiomics score and clinical-pathologic risk factors were incorporated into an easy-to-use nomogram.Results: The quantitative parameters Ktrans and Ve were significantly higher in the EMVI-positive group than in the EMVI-negative group (both P =0.02). Ktrans combined with Ve showed a fair degree of accuracy (AUC 0.680 in the training cohort and AUC 0.715 in the validation cohort) compared with Ktrans or Ve alone. The AUCs of the R1 and R2 models were 0.826, 0.715 and 0.872, 0.812 in the training and validation cohorts, respectively. In addition, the R2-C model yielded an AUC of 0.904 in the training cohort and 0.812 in the validation cohort. The nomogram was presented based on the clinical-radiomics model. The calibration curves showed good agreement.Conclusion: The radiomics nomogram that incorporates the radiomics score, histopathological grade and T stage demonstrated better diagnostic accuracy than the DCE-MRI quantitative parameters and may have significant clinical implications for the preoperative individualized prediction of EMVI in rectal cancer patients.

Abstract Liquid marbles represented a significant advance in the manipulation of fluids as they used particle films to confine liquid drops, creating a robust and durable soft solid. We exploit this technology to engineering a bioactive hydrogel marble (BHM). Specifically, pristine bioactive glass nanoparticles were chemically tuned to produce biocompatible hydrophobic bioactive glass nanoparticles (H-BGNPs) that shielded a gelatin-based bead. The designed BHM shell promoted the growth of a bone-like apatite layer upon immersion in a physiological environment. The fabrication process allowed the efficient incorporation of drugs and cells into the engineered structure. The BHM provided a simultaneously controlled release of distinct encapsulated therapeutic model molecules. Moreover, the BHM sustained cell encapsulation in a 3D environment as demonstrated by an excellent in vitro stability and cytocompatibility. The engineered structures also showed potential to regulate a pre-osteoblastic cell line into osteogenic commitment. Overall, these hierarchical nanostructured and functional marbles revealed a high potential for future applications in bone tissue engineering.

Micro-scale cracks on the surface of a magnetic rotor are difficult to detect because of the formation of stains. A method that is based on computer vision and a support vector machine (SVM) that can accurately and rapidly locate micro-scale cracks are proposed. To further highlight the cracks and reduce interference, the image is pre-processed using improved adaptive median filtering, image sharpening, extract feature point, and an expansion based on mathematical morphology. Next, the feature curves of cracks and stains are extracted as training samples using the feature curve extract algorithm and the region orientation algorithm (ROA) for continuous and discontinuous crack curves images, respectively. SVM is later used to distinguish between the two types of image sources. An improved region orientation algorithm (IROA) is proposed to increase the efficiency of discontinuous crack curves images through online detection. Experiment results show that the accuracy of crack detection using ROA is 97.9%, and the accuracy is 94.7% using IROA. However, the IROA algorithm execution time is 3% that of ROA, and IROA's average crack detection time is shorter than that of ROA by 51%. The proposed method provides the basis for the automatic online detection of micro-scale cracks on a magnetic rotor surface and paves the way for the identification of cracks on complex morphologies, such as split cracks and circular cracks.

In this paper, a kind of green triboelectric nano-generator based on natural degradable cellulose is proposed. Different kinds of regenerated cellulose composite layers are prepared by a blending doping method, and then assembled with poly(tetrafluoroethylene) (PTFE) thin films to form tribioelectric nanogenerator (TENG). The results show that the open circuit output voltage and the short circuit output current using a pure cellulose membrane is 7.925 V and 1.095 μA. After adding a certain amount of polyamide (PA6)/polyvinylidene fluoride (PVDF)/barium titanate (BaTiO3), the open circuit output voltage peak and the peak short circuit output current increases by 254.43% (to 20.155 V) and 548.04% (to 6.001 μA). The surface morphology, elemental composition and functional group of different cellulose layers are characterized by Scanning Electronic Microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), and tested by the electrochemical analyze. Moreover, after multiple assembly and rectification processing, the electrical output performance shows that the peak value of open-circuit output voltage and the peak value of short circuit output current increases by 132.06% and 116.13%. Within 500 s of the charge-discharge test, the single peak charge reached 3.114 V, and the two peak charges reached 3.840 V. The results demonstrate that the nano-generator based on cellulose showed good stability and reliability, and the application and development of natural biomaterials represented by cellulose are greatly promoted in miniature electronic sensing area.

In a PV-dominant DC microgrid, the traditional energy distribution method based on the droop control method has problems such as output voltage drop, insufficient power distribution accuracy, etc. Meanwhile, different battery energy storage units usually have different parameters when the system is running. Therefore, this paper proposes an improved control method that introduces a reference current correction factor, and a weighted calculation method for load power distribution based on the parameters of battery energy storage units is proposed to achieve weighted allocation of load power. In addition, considering the variation of bus voltage at the time of load mutation, voltage secondary control is added to realize dynamic adjustment of DC bus voltage fluctuation. The proposed method can achieve balance and stable operation of energy storage units. The simulation results verified the effectiveness and stability of the proposed control strategy.

In this paper, a novel satellite image segmentation technique based on dynamic Harris hawks optimization with a mutation mechanism (DHHO/M) is proposed. Compared with the original Harris hawks optimization (HHO), the dynamic control parameter strategy and mutation operator used in DHHO/M can avoid falling into the local optimum and efficiently enhance the search capability. To evaluate the performance of the proposed method, a series of experiments are carried out on various satellite images. Eight advanced thresholding approaches are selected for comparison. Three criteria are adopted to determine the segmentation thresholds, namely Kapur’s entropy, Tsallis entropy, and Otsu between-class variance. Furthermore, four oil pollution images are used to further assess the practicality and feasibility of the proposed method on real engineering problem. The experimental results illustrate that the DHHO/M based thresholding technique is superior to others in the following three aspects: fitness function evaluation, image segmentation effect, and statistical tests.

This paper proposes a three dimensional pulse coupled neural network (3DPCNN) image segmentation method based on a hybrid seagull optimization algorithm (HSOA) to solve the oil pollution image. The image of oil pollution is taken by the unmanned aerial vehicle (UAV) in the oil field area. The UAV is good at shooting the ground area, but its ability to identify the oil pollution area is poor. In order to solve this problem, a 3DPCNN-HSOA algorithm is proposed to segment the oil pollution image, and the oil pollution area is segmented to identify the dirty oil area and improve the inspection of environmental pollution. The 3DPCNN image segmentation method has simple structure and good segmentation effect, but it has many parameters and poor segmentation effect for complex oil images. Therefore, we apply HSOA algorithm to optimize the parameters of 3DPCNN algorithm, so as to improve the segmentation accuracy and solve the segmentation of oil pollution images. The experimental results show that the 3DPCNN-HSOA model can separate the oil pollution area from the complex background.

An efficient satellite image segmentation method based on a hybrid grasshopper optimization algorithm (GOA) and minimum cross entropy (MCE) is proposed in this paper. The proposal is known as GOA–jDE, and it merges GOA with self-adaptive differential evolution (jDE) to improve the search efficiency, preserving the population diversity especially in the later iterations. A series of experiments is conducted on various satellite images for evaluating the performance of the algorithm. Both low and high levels of the segmentation are taken into account, increasing the dimensionality of the problem. The proposed approach is compared with the standard color image thresholding methods, as well as the advanced satellite image thresholding techniques based on different criteria. Friedman test and Wilcoxon’s rank sum test are performed to assess the significant difference between the algorithms. The superiority of the proposed method is illustrated from different aspects, such as average fitness function value, peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), standard deviation (STD), convergence performance, and computation time. Furthermore, natural images from the Berkeley segmentation dataset are also used to validate the strong robustness of the proposed method.

A novel multilevel threshold segmentation method for color satellite images based on Masi entropy is proposed in this paper. Lévy multiverse optimization algorithm (LMVO) has a strong advantage over the traditional multiverse optimization algorithm (MVO) in finding the optimal solution for the segmentation in the three channels of an RGB image. As the work advancement introduces a Lévy multiverse optimization algorithm which uses tournament selection instead of roulette wheel selection, and updates some formulas in the algorithm with mutation factor. Then, the proposal is called TLMVO, and another advantage is that the population diversity of the algorithm in the latest iterations is maintained. The Masi entropy is used as an application and combined with the improved TLMVO algorithm for satellite color image segmentation. Masi entropy combines the additivity of Renyi entropy and the non-extensibility of Tsallis entropy. By increasing the number of thesholds, the quality of segmenttion becomes better, then the dimensionality of the problem also increases. Fitness function value, average CPU running time, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Feature Similarity Index (FSIM) were used to evaluate the segmentation results. Further statistical evaluation was given by Wilcoxon’s rank sum test and Friedman test. The experimental results show that the TLMVO algorithm has wide adaptability to high-dimensional optimization problems, and has obvious advantages in objective function value, image quality detection, convergence performance and robustness.

Diffusion tensor imaging (DTI) is known to suffer from long acquisition time, which greatly limits its practical and clinical use. Undersampling of k-space data provides an effective way to reduce the amount of data to acquire while maintaining image quality. Radial undersampling is one of the most popular non-Cartesian k-space sampling schemes, since it has relatively lower sensitivity to motion than Cartesian trajectories, and artifacts from linear reconstruction are more noise-like. Therefore, radial imaging is a promising strategy of undersampling to accelerate acquisitions. The purpose of this study is to investigate various radial sampling schemes as well as reconstructions using compressed sensing (CS). In particular, we propose two randomly perturbed radial undersampling schemes: golden-angle and random angle. The proposed methods are compared with existing radial undersampling methods, including uniformity-angle, randomly perturbed uniformity-angle, golden-angle, and random angle. The results on both simulated and real human cardiac diffusion weighted (DW) images show that, for the same amount of k-space data, randomly sampling around a random radial line results in better reconstruction quality for DTI indices, such as fractional anisotropy (FA), mean diffusivities (MD), and that the randomly perturbed golden-angle undersampling yields the best results for cardiac CS-DTI image reconstruction.

The average switching model of modular multilevel converter (MMC) is built in this paper when the hot reserved strategy is adopted as a fault-tolerant control. When the MMC SM faults, the rest of the SMs cannot support the DC-link voltage, which results in interruption of the inverter. To tackle this issue, a novel fault-tolerant control strategy is proposed to bypass the SM under fault and re-regulate the SM capacitor voltage and carrier phase-shift angle to maintain the main components of circulating current, and reduce the Total Harmonic Distortion (THD) of grid connected current to enable the stable operation of the photovoltaic inverter. The maximum power tracking control is improved to solve the problem of long restoration time when faults occur, based on a constant voltage startup method combined with a fix-step incremental conductance method. Simulations in MATLAB/Simulink and experimental results have verified the feasibility and effectiveness of the proposed control strategy.

CYP2E1 promoter polymorphisms can lead to significant interindividual differences in expression of CYP2E1. Using a database of CYP2E1 gene polymorphisms established in 2010, our study aimed to functionally characterize the single nucleotide polymorphisms (SNPs) of the promoter region and corresponding haplotypes in the Chinese Han population. Six novel SNPs and seven haplotypes with a frequency equal to or greater than 0.01 were constructed on a luciferase reporter system on the basis of site-directed mutagenesis. Dual luciferase reporter systems were used to analyze regulatory activity. The constructs including single novel SNP mutations exhibited insignificant change in luciferase activity, whereas, the activity produced by Haplo1(GTTGCTATAT), Haplo2 (CTTGCTATAT) and Haplo7 (GAGCTCACAT), containing a -333T>A polymorphism was significantly greater than for the wild type in Hep G2 cells (p

Chinese Xin Wen Ke construction innovation is the integration of philosophy and social science, scientific technology, and industry, with the combination of diverse academic subjects and concentrations. Through academic orientation, resources integration, and behavioral modeling, academic leadership of university teachers can be effectively promoted, and the applicative solutions for the emerging obstacles in the process of the combination can be provided. It also can boost the quality of academic leadership of university teachers in relation to orientating values and philosophies, locating the goals, promoting cooperation, and refining achievements. According to recent research, to analyze the contents and structures can foster the quality development in academic leadership of university teachers. This article will draw an overall assessment on different components of academic leadership of university teachers under Chinese Xin Wen Ke construction innovation context, including research reviews and definitions, functions, structures analysis, and cultivation methods, aiming to provide evidence to demonstrate its significance.

Newly developed flexible plant wearable devices have recently been regarded as a promising smart strategy for monitoring plant physiological signals in forestry genetics and breeding. However, the interfacial contact between the plant and the device can interfere with the normal physiological functions of the plant and easily induce abiotic stress effects, affecting plant growth and detection results. Here, we develop a simple, scalable, and respiratory plant-friendly wearable (PW) sensor that can effectively mitigate abiotic stresses and significantly reduce potential damage to plants. Notably, the frog foot inspired structure has a transparent, stretchable hexagonal prism array structure that provides four azimuthal escape routes for water vapor and high transparency (92%) with little obstruction to light. The results show that the PW sensor significantly ameliorates the effects of compound abiotic stress caused by flooding and light exposure and achieves stress levels comparable to those of the conventional hydrogel wearable device within a delay of about 2 weeks. Meanwhile, by utilizing the dual network structure formed by cross-linking between the materials, the PW sensor has excellent sensitivity to a wide range of ambient temperature (30-100 °C) and humidity (30-100% RH) variations. In addition, we have designed a wireless monitoring system to facilitate real-time remote monitoring of plant growth microenvironments. This work is expected to establish an innovative perspective for plant-friendly wearable interfaces, thereby expanding the application scope of flexible plant-friendly wearables. [ABSTRACT FROM AUTHOR]

Will there be sustained engagement in live fitness broadcasts after the COVID-19 pandemic? Despite being viewed as a passing trend, fitness live streaming is believed to have a limited impact on long-term public interest in sports. This study seeks to explore the attitudes, participation rates, and underlying factors related to fitness live streaming, focusing on observations from China. A cross-sectional survey was conducted involving 408 respondents, utilizing a custom-designed questionnaire to evaluate participation in fitness live streaming. Participation in live fitness broadcasts was found to be shaped by a combination of objective circumstances and personal preferences. Furthermore, individuals displayed varied levels of involvement influenced by age, education, and occupation, affecting their cognitive engagement, preferred activities, workout durations, and overall outcomes. Notably, women exhibited greater emotional engagement and activity levels than men. Factors like exercise effectiveness, flow experience, and cognitive engagement significantly influenced individuals' intentions to exercise, leading to increased behavioral participation. This iterative process of enhancing exercise intentions and actual participation underscored the positive impact of live fitness broadcasts on public engagement in physical activities. Flow state and peer effects were not significant factors influencing participation in fitness live broadcasts. This absence of influence may be attributed to internet connectivity limitations and exercise preference variations, rendering the peer effect negligible in online fitness contexts. Additionally, willingness to engage in fitness activities does not always translate into actual participation, highlighting a potential gap between intention and action in fitness engagement. Participation in live fitness activities positively correlated with individuals' perceived proficiency and effectiveness in exercise. Emotional engagement, flow experience, and a readiness to engage in physical activities positively affected sports participation. Post-epidemic, the internet remains a significant factor in shaping the fitness industry, giving rise to innovative models. Integrating online and offline exercise methods is increasingly poised to be embraced as a mainstream approach. [ABSTRACT FROM AUTHOR]