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The hydroxyl-terminated polyether (HTPE) solid propellants are widely recognized as promising alternatives for hydroxyl-terminated polybutadiene (HTPB) propellants due to their compliance with insensitive munition characteristics of composite solid propellant. This study reports thermal stability investigation of HTPE solid propellants in different scales with the help of differential scanning calorimetry (DSC, 0.9 mg), accelerating rate calorimetry (ARC, 0.219 g), and slow cook-off (112 g) tests by taking HTPE/A3/Al/AP/PSAN composite solid propellant as an example. More importantly, for the first time, thermal decomposition kinetic parameters of HTPE solid propellant were calculated by employing iso-conversional Friedman method and using DSC experimental data at different heating rates. The simulation results of adiabatic combustion and slow cook-off of the HTPE propellant were obtained by applying the kinetic parameters in Advanced Kinetics and Technology Solutions software, which exhibited satisfied consistency with the experimental results, with less than 2% average deviation. Finally, the validated kinetic parameters were further employed to describe the large-scale thermal hazards of HTPE propellant and the predicted self-accelerating decomposition temperature of HTPE propellant in 50 kg-scale was 94.2 °C. Our simulative study provided a cogent and convenient strategy to understand thermal hazard of HTPE solid propellant in different scales during usage, storage, and transportation.

The photooxidation process of aniline-containing simulated wastewater under visible light irradiation over BiOBr microcrystal grains with different shapes was studied. The distinctive surface microstructures of the BiOBr microcrystals, like clustered flower petals and quadrate lamellas, were produced by using imidazole ionic liquid and inorganic bromide as Br sources and by solvothermal and hydrothermal route, respectively. The ionic liquid not only can impact the products’ morphology, but also can largely improve the photocatalytic activity of the BiOBr microcrystals due to the soft templating effect. The top degradation ratio of the aniline wastewater photocatalyzed by lamellar BiOBr crystals is 23.71%, but the equivalent value photocatalyzed by flower petal-like BiOBr crystals is almost twice as much; that is, it reaches 46.51%. The photodegradation effect and mechanism over differently shaped BiOBr microcrystals have been compared.

Abstract Aiming to improve the estimation and prediction accuracy of a target's position, this paper proposes a state estimation method for photoelectric tracking systems, based on the evaluation of the tracked target's motion intention. Traditional photoelectric tracking systems utilize external physical quantities such as the position, velocity, and acceleration of the target as the estimated states. While this method can output good results for pre‐modelled target positions, it struggles to maintain the accuracy when facing manoeuvering targets or complex motion patterns targets. Here, the relevant parameters of the tracked target's motion intention are directly estimated innovatively, like estimating the circling point position rather than the circular flying target's position and velocity. This approach enables recognizing the target's motion intention and leads to precise estimation, which specifically consists of an interacting multiple model approach, multiple unscented Kalman estimators, and a robust estimator. The effectiveness and stability of this estimator are validated through software simulations and experiments on a dual‐reflection mirror platform.

This paper proposes a short-term electric load forecasting method combining convolutional neural networks and gated recurrent units with an attention mechanism. By integrating CNNs and GRUs, the method can fully leverage the strengths of CNNs in feature extraction and the advantages of GRUs in sequence modeling, enabling the model to comprehend signal data more comprehensively and effectively extract features from sequential data. The introduction of the attention mechanism allows the traditional model to dynamically focus on important parts of the input data while ignoring the unimportant parts. This capability enables the model to utilize input information more efficiently, thereby enhancing model performance. This paper applies the proposed model to a dataset comprising regional electric load and meteorological data for experimentation. The results show that compared with other common models, the proposed model effectively reduces the mean absolute error and root mean square error (121.51 and 263.43, respectively) and accurately predicts the short-term change in regional power load.

Aiming at the surrounding rock control problem of double basic roof cutting and retaining roadway in inclined soft and hard rock strata, this paper expounds on the comprehensive roadway protection mechanism of goaf side-roof-solid coal side under this geological condition, and puts forward the comprehensive control technology of goaf side “U-shaped steel roof cutting and bottom cutting + high strength anchor cable + metal mesh gangue retaining”, retaining roof “groove steel truss anchor cable + roof cutting anchor cable + single pillar roof”, and solid coal side “groove steel truss anchor cable strong anchoring”. On this basis, the blasting roof cutting parameters such as hole layout form, hole spacing and angle, hole layout depth, charge parameters and sealing mud length are designed. The field results show that the roof cutting hole of about 9.5 m is beneficial to the surrounding rock control of roof cutting and retaining roadway in inclined soft and hard rock strata. The final deformation of the roof and solid coal side of the cutting seam is about 192 mm and 119 mm, which ensures the safe mining of the lower working face.

Abstract In this study, to achieve accurate tracking of the desired trajectory during passive control of the lower limb rehabilitation robot, an adaptive sliding mode controller based on disturbance observer and radial basis function neural network (RBFNN) is proposed for the lower limb rehabilitative robot in the presence of uncertain parameters and external bounded disturbances. First, the Euler–Lagrange dynamic model of the lower limb rehabilitative robot is described. Second, a sliding mode controller is designed to stabilize the system with an improved sliding mode reach rate under the assumption that all parameters of the dynamics model are known. To achieve a sliding mode controller without the above assumptions, the proposed adaptive RBFNN and the disturbance observers are employed to compensate for disturbances and the uncertainties in the robot's dynamic mode via feedforward loops. The Lyapunov stability theory is used to prove that the proposed controller has accomplished a significant control effect with excellent performance and the output tracking error can be converted to a very small neighborhood through reasonable design parameters. Finally, the performance of the controller based on the state feedback and state observer are demonstrated by numerical simulations, respectively.

In this paper, we investigate the finite-time boundedness problems for large-scale continuous-time networked dynamical systems constituted by lots of subsystems. The interactions among these subsystems are arbitrary, and every subsystem has different dynamics. The linear time-varying and time-invariant cases are discussed respectively. Sufficient conditions for the design of finite-time boundedness state feedback controller are derived, which efficiently utilize the characteristic of system structure with the block-diagonal structure of system parameter matrices and the sparseness of the system topology. Sufficient conditions depending only on parameter matrices of the individual subsystem are also provided. Furthermore, sufficient conditions are provided for the design of the distributed output feedback controller with finite-time boundedness. Several numerical simulations have been used to show the validity of the derived conditions in the analysis of a large-scale networked system.

The robust stability problem of spatially interconnected systems with signal saturation among the many composed subsystems is considered. The system structure is usually sparse, and each subsystem has different dynamics. Firstly, a robust stability condition is established based on integral quadratic constraint (IQC) theory, which makes full use of the sparseness of the subsystem connection topology. Secondly, a decoupling robust condition that only depends on the subsystem parameters is proved. Finally, it is shown through numerical simulations that the obtained conditions are computationally valid in analyzing spatially interconnected systems with signal saturation constraints among the subsystems.

Abstract In this study, the state estimation problems for linear discrete systems with uncertain parameters, deterministic input signals and d‐step measurement delay are investigated. A robust state estimator with a similar iterative form and comparable computational complexity to the Kalman filter is derived based on the state augmentation method and the sensitivity penalisation of the innovation process. It is discussed that the steady‐state properties such as boundedness and convergence of the robust state estimator under the assumptions that the system parameters are time invariant. Numerical simulation results show that compared with the Kalman filter, the obtained state estimator is more robust to modelling errors and has nice estimation accuracy.

ObjectiveTrigeminal neuralgia (TN) is one of the leading causes of facial pain and seriously affects patients' quality of life. Foramen ovale (FO) radiofrequency thermocoagulation is a classic approach for the treatment of TN that has failed pharmacological therapy. This study summarized the safety and efficacy of transforaminal radiofrequency thermocoagulation for TN by comparing puncture approaches or guidance techniques, thereby providing higher-quality clinical evidence.MethodsDatabases including PubMed, Embase, Cochrane Library, CNKI, and Wanfang were searched for relevant studies published before May 2022. Relevant data were extracted for analysis to compare methodological variables and clinical outcomes.ResultsThis meta-analysis included 27 studies with a total of 1,897 patients. In terms of puncture approaches, FO had a significant advantage in reducing VAS at 12 months postoperatively (P = 0.019) and efficacy (P = 0.043). However, FO performed poorly on complications (P

Abstract In this study, a class of linear system, which is with no exogenous input and suffered from constant measurement delay and uncertain model‐parameter errors, is under consideration. To combat both the parametric uncertainties and constant measurement delay, a novel robust state estimator is proposed. Accounting for the constant measurement delay, a clever approach is utilised to expand the state vector and the system model is converted into an augmented delay‐free model. Considering the deterioration of estimation performance caused by stochastic model‐parameter errors, the sensitivity penalisation function of model‐parameter errors is defined and introduced into the objective function of the regularised least‐squares (RLS) problem, whose solution is the standard Kalman filter. Furthermore, by restricting the range of introduced parameter, the objective function of the modified RLS problem is converted into a strict convex function. Then, the recursive procedure of the proposed estimator is derived. The asymptotic stability conditions of the proposed estimator and the conditions for boundness of the estimation error matrix are given. Numerical simulations show the effectiveness of the estimator proposed in this paper.

Enhancing electrode capacitance without compromising one other metrics for solid-state supercapacitors is of high interest yet difficult to achieve. Here the authors demonstrate a strategy of using nanofluidic electrode with very low porosity to increase the electrochemical capacitance of gel-based solid state supercapacitor.

Abstract This paper discusses the state estimation problems of an uncertain linear discrete time‐varying state space model with d‐step state delay. Based on the principle of minimising the expectation of estimation errors and the method of state augmentation, a robust state estimation algorithm is proposed. Specially, this estimator retains the form of Kalman‐like filter and the characteristics of fast recursive calculation. Moreover, the conditions of bounded estimation error covariance and the proof of asymptotic unbiasedness of the filter are given. Finally, numerical examples are used to verify the effectiveness and the wide applicability of this estimator.

This paper considers the robust H∞ performance problem of continuous-time uncertain large-scale networked systems (LSNSs). The systems consist of numerous arbitrarily connected subsystems, each of which has different dynamics. Currently it is computationally difficult to manage systems with the existing lumped analysis method; therefore, exploiting the structural properties of the systems, sufficient conditions are derived for robust H∞ performance. Based on these results, an analysis condition that depends only on the parameters of each single subsystem is further obtained. Some numerical simulations are proposed to verify the validity and superiority of the developed conditions.

In this paper, we focus on the event-triggered robust state estimation problems for nonlinear networked systems with constant measurement delays against denial-of-service (DoS) attacks. The computation of the extended Kalman filter (EKF) generates errors of linearization approximations, which can result in increased state estimation errors, and subsequently amplifies the linearization errors. DoS attacks interfere with the transmission of measurements sent to the remote robust state estimator by overloading the communication networks, while the communication rate of the communication channel is constrained. Therefore, an event-triggered robust state estimation algorithm based on sensitivity penalization with an explicit packet arrival parameter is derived to defend against DoS attacks and linearization errors. Meanwhile, the presence of measurement delays precludes the direct use of conventional state estimation algorithms, prompting us to devise an innovative state augmentation method. The results of the numerical simulations show that the proposed robust state estimator can appreciably improve the accuracy of state estimation.

This paper investigates the robust fusion estimation problem for multi-sensor systems with communication constraints, parameter uncertainty, d-step state delays, and deterministic control inputs. The multi-sensor system consists of a fusion center and some sensor nodes with computational capabilities, between which there are random packet drops. The state augmentation method is utilized to transform a time-delay system into a non-time-delay one. The robust state estimation algorithm is derived based on the sensitivity penalty for each sensor node to reduce the impact of modelling errors, and modelling errors here are not limited to a unique form, which implies that the fusion estimator applies to a wide range of situations. An event-triggered transmission strategy has been adopted to effectively alleviate the communication burden from the sensor node to the fusion center. Moreover, the fusion estimator handles packet drops arising from unreliable channels, and the corresponding pseudo-cross-covariance matrix is provided. Some conditions are given to ensure that the estimation error of the robust fusion estimator is uniformly bounded. Two sets of numerical simulations are provided to illustrate the effectiveness of the derived fusion estimator.

Ascorbic acid (AsA) is an important antioxidant for scavenging reactive oxygen species and it is essential for human health. Strawberry (Fragaria × ananassa) fruits are rich in AsA. In recent years, strawberry has been regarded as a model for non-climacteric fruit ripening. However, in contrast to climacteric fruits, such as tomato, the regulatory mechanism of AsA accumulation in strawberry fruits remains largely unknown. In this study, we first identified 125 AsA metabolism-related genes from the cultivated strawberry “Camarosa” genome. The expression pattern analysis using an available RNA-seq data showed that the AsA biosynthetic-related genes in the D-mannose/L-galactose pathway were downregulated remarkably during fruit ripening which was opposite to the increasing AsA content in fruits. The D-galacturonate reductase gene (GalUR) in the D-Galacturonic acid pathway was extremely upregulated in strawberry receptacles during fruit ripening. The FaGalUR gene above belongs to the aldo-keto reductases (AKR) superfamily and has been proposed to participate in AsA biosynthesis in strawberry fruits. To explore whether there are other genes in the AKR superfamily involved in regulating AsA accumulation during strawberry fruit ripening, we further implemented a genome-wide analysis of the AKR superfamily using the octoploid strawberry genome. A total of 80 FaAKR genes were identified from the genome and divided into 20 subgroups based on phylogenetic analysis. These FaAKR genes were unevenly distributed on 23 chromosomes. Among them, nine genes showed increased expression in receptacles as the fruit ripened, and notably, FaAKR23 was the most dramatically upregulated FaAKR gene in receptacles. Compared with fruits at green stage, its expression level increased by 142-fold at red stage. The qRT-PCR results supported that the expression of FaAKR23 was increased significantly during fruit ripening. In particular, the FaAKR23 was the only FaAKR gene that was significantly upregulated by abscisic acid (ABA) and suppressed by nordihydroguaiaretic acid (NDGA, an ABA biosynthesis blocker), indicating FaAKR23 might play important roles in ABA-mediated strawberry fruit ripening. In a word, our study provides useful information on the AsA metabolism during strawberry fruit ripening and will help understand the mechanism of AsA accumulation in strawberry fruits.

Solid granules have good lubrication performance and load-bearing capacity, which has been concerned by researchers. However, the correlation mechanism between lubrication performance and granular medium dynamics is hardly focused on. In this study, the characteristics of two parallel friction pairs lubricated by the granular medium are experimentally investigated to study the nonlinear friction behavior during this process. The experimental results showed that the fluctuation behavior of friction force and friction coefficient with time shows a more flat nonlinear trend with the increase of the shear velocity and the pressure load. The discrete element simulation results corresponding to the experiment revealed that the change of nonlinear behavior of measured friction and friction coefficient is closely related to the evolution of force chains and dynamic characteristics of this granular flow lubrication system. The shear dilatancy amplitude of this granular flow lubrication system decreases obviously with the increase of the shear velocity and the pressure load. The flow states of granular medium is the main reason influencing the nonlinearity of shear gap. This study reveals the key factors affecting the friction nonlinearity and provides an evidence for the formation of granular shear dilatancy between two friction pairs lubricated by granular medium.

Short-term load forecasting (STLF) with excellent precision and prominent efficiency plays a significant role in the stable operation of power grid and the improvement of economic benefits. In this paper, a novel model based on data mining and deep learning is proposed. Firstly, the preprocessing of data includes normalization of historical load, and fuzzification of influencing factors (meteorological factors, date types and economy) based on Pearson correlation coefficient (PCC). Secondly, kernel fuzzy c-means (KFCM) modified by particle swarm optimization (PSO-KFCM) algorithm clusters the daily load curve. In the clustering experiments, the within-cluster sum of squared error (SSE) index is presented to determine the number of clusters and the clustering validity has a 31.9% enhancement compared with the traditional FCM algorithm. Thirdly, the cosine similarity establishes the resemblance between the prediction date and each cluster, and the similar cluster is determined according to the principle of maximum similarity. Finally, a multivariate and multi-step hybrid model MMCNN-LSTM based on convolution neural network (CNN) and long short-term memory (LSTM) neural network is proposed to forecast the load in following 24 hours, in which similar cluster data is applied to training set. To demonstrate the effectiveness of proposed integrated technique, the accuracy has been verified in three predictive experiments. The fruitful results indicated that the average mean absolute percent error (MAPE) in the entire test set was only 1.34%, a 3.02% reduction compared to a single LSTM.

Robust state estimation problem is investigated for discrete-time linear state space models with uncertain parameters, deterministic control input and d-step state delay. Firstly, the original system is transformed into a non-time-delay system based on the method of state augmentation. Then a robust state estimation algorithm is proposed based on the sensitivity penalty method and the derivation is given. Moreover, compared with the standard Kalman filter, this algorithm has similar iterative form and considerable computational complexity. Finally, numerical simulations are utilized to show the effectiveness of this algorithm.

Abstract Two‐dimensional (2D) materials are being increasingly exploited for ion transport and storage under nanoconfinement. Here we demonstrate distinct ion transport behavior upon potential‐induced redox reaction in 2D tungstate anion cross‐linked polyaniline (TALP) electrode. It is found that, in the neutral electrolyte, SO42− ion serves as the main charge carrier in 2D polyaniline backbone when the electrical double layer charging dominates. While in an acidic electrolyte, proton transport in TALP turns to be the dominant ion behavior that is associated with the proton‐promoting surface redox processes. Moreover, higher capacitance along with better capacitive retention at a high rate is also demonstrated for TALP electrode in acidic electrolyte compared with that in the neutral environment. The results show that 2D nanoionics can be manipulated by applying redox‐active materials to build the nanochannels that allow the regulation of surface charge and chemistry with potential‐specific redox reactions.

This paper investigates the dissipativity analysis of large-scale networked systems with linear time-invariant dynamics. The networked system is composed of a large number of subsystems whose connections are arbitrary, and each subsystem can have different dynamics. A sufficient and necessary condition for the strict dissipativity analysis of the networked system is derived, which takes advantage of the block-diagonal structure of the system parameter matrix and the sparseness characteristics of the subsystem interconnections. Then, a necessary condition and a sufficient condition that depend only on a single subsystem parameter are given separately. Numerical simulations illustrate that compared with the existing results, the conditions suggested in this paper have higher computational efficiency in the dissipative analysis of large-scale networked systems.

The problem of robust state estimation for a class of uncertain nonlinear systems with Markov jump is investigated. The uncertain nonlinear system under consideration is represented by the Takagi–Sugeno (T–S) fuzzy model because it is difficult to describe. Firstly, different from the traditional T–S fuzzy modeling method, the deviation of the linear system approaching a nonlinear system is considered, which is represented as a model error in system modeling. Secondly, through a robust state estimation method based on the sensitivity penalty, we develop a robust state estimator for linear subsystems, and the fuzzy robust state estimator is obtained by fuzzy rules. Thirdly, the stability and boundedness of the fuzzy robust state estimator are proved under the assumption conditions to ensure the reliability of the obtained estimator. Finally, some numerical examples are given to verify the effectiveness of the fuzzy robust state estimator.

This article explores the problems of robust information fusion for wireless sensor networks with state delay, parameter uncertainty, and communication constraints. Based on a data-driven transmission strategy, the robust fusion estimator proposed in this article can greatly reduce the possibility of network congestion, while ensuring the accuracy of the estimation fusion. The uncertainty of random parameters in the model is not limited to special forms, which means that this estimator is applicable to a wide range of situations. The pseudo cross covariance matrix used for estimation fusion is derived by local robust state estimation algorithm, which is based on state augmentation and expectation minimization of estimation errors. Furthermore, we prove that the error variance of this fusion estimator is uniformly bounded, and the corresponding condition is given. According to this condition, the selection of key matrices and vectors in the transmission strategy is determined. Finally, some numerical simulations are utilized to verify the performance of this algorithm.

The vibration signals of rolling bearing are often non-stationary and non-linear, and consequently it is much more difficult to extract the deep characteristics in the time domain. In this paper, a new fault diagnosis method is proposed to identify the fault types of rolling bearings combined the benefits of the modified ensemble empirical mode decomposition (MEEMD), quantum particle swarm optimization (QPSO) and least squares support vector machine (LSSVM) algorithms. In this method, the vibration signals are decomposed by the MEEMD algorithm to obtain the intrinsic mode function (IMF) components. After normalizing the energy moment characteristics of each IMF component, the feature vectors can be obtained and conveniently input into the LSSVM model optimized by the QPSO algorithm to perform training and identification. It can effectively improve the performance on decomposition and extraction of vibration signals, and further improve the accuracy of the fault diagnosis. The proposed method is verified by the results of the experiments. It shows that this technique can extract the characteristics of the vibration signals effectively and identify them accurately.

The vibration signals of rolling bearing are often highly nonstationary and nonlinear, and consequently it is not accurate to extract and identify the characteristics of these signals by the traditional methods. In order to improve the performance on the feature extraction from bearing signals and the accuracy of the diagnosis, it requires effective signal processing and diagnose algorithms. In this paper, a new fault diagnosis algorithm which combines complementary ensemble empirical mode decomposition (CEEMD), probabilistic neural network (PNN) and particle swarm optimization (PSO) algorithm optimized by improved linear decreasing weight (LDW) algorithm is proposed. In this method, firstly the vibration signals are decomposed into a number of Intrinsic Mode Functions (IMFs) by the CEEMD algorithm since it has good adaptive ability to nonstable signals and can effectively extract fault features. Then the improved LDWPSO algorithm is introduced to solve the problem that the selection of smoothing factor in PNN model is arbitrary and uncertain. Finally, train and diagnose the fault types of rolling bearing using the LDWPSO-PNN model. The proposed method is verified by the experimental datasets. The results indicate that the method can extract the feature vectors of the vibration signals and distinguish them effectively.

In this paper, a robust filter is derived to estimate the state of a nonlinear system which is described by T-S fuzzy model. Firstly, this paper studies a robust state estimation algorithm based on the relationship between Kalman filter and regularized least square, and the algorithm considers the influence of model error caused by system parameter uncertainty of the system in any way. The form of the algorithm is similar to Kalman filter, and the computational complexity is similar to that of Kalman filter. Secondly, the robust filter is combined with the fuzzy model, and the fuzzy rule is used to approximate the nonlinear system. The new algorithm is used to estimate the state of the system. Finally, the new algorithm is compared with the fuzzy Kalman filter based on actual and nominal parameters by simulation experiment, and the simulation experimental results are given to illustrate the effectiveness of the proposed method, which proves that the proposed robust state estimator is better than the fuzzy Kalman filter based on nominal parameters.

This article proposes a robust state estimator with adaptively adjusted observation noise covariance for uncertain linear systems. Since the residuals between observation data and estimation data satisfying the normal distribution, the Mahalanobis distance are known to be Chi-square distributed. We use Chi-square tests to timely distinguish outliers that beyond the confidence interval and adjust the estimated observation noise covariance to a more likely value. Combined with the robust estimation method, an improved algorithm is derived to deal with uncertainties in both system parameters and observation noise covariance. Based on the proposed prediction form, we test the obtained robust state estimator with different deterioration conditions of observation noise covariance and compare it with the estimators without adaptive factor. The simulation results show that the derived state estimator may reduce the accumulation of estimation errors, smooth the estimated state curve, and the performance of the proposed estimator can be significantly improved as the deterioration enlarging.

Using the discrete element method, we investigate the force chain characteristics of dense particles in a parallel-plate particle flow friction system, and considering the influence of pressure load as well as shear velocity. Simulation results show that the shear dilatancy process of this particle friction system can be divided into three typical stages: plastic expansion stage, whole flow stage, and plastic compression stage. The distribution of force chain generally decreases with the increase of dimensionless contact force f. The distribution and bearing rates of force chains depend on the pressure load as well as the shear velocity. The direction of force chain gradually changes closer to the y-axis with the increase of the pressure load, and shifts from y-axis to x-axis with the increase of shear velocity. The magnitude of the total force chain decreases with shear velocity. Moreover, the total force chain fluctuates significantly at lower shear velocity and start-up stage.

Strawberry (Fragaria × ananassa) fruits are rich in ascorbic acid (AsA) and anthocyanin, which are essential antioxidants for human health. However, the underlying regulatory mechanism of these antioxidant accumulation, especially AsA accumulation in strawberry fruits, remains largely unknown. In this study, we identified FaAKR23 was a regulator of AsA and anthocyanin accumulation. We transiently expressed FaAKR23 in strawberry fruits and conducted metabolic and molecular analyses to explore the role of FaAKR23 in AsA and anthocyanin accumulation. Transient silencing of FaAKR23 (FaAKR23-RNAi) in strawberry fruits significantly decreased the AsA and anthocyanin contents compared with control (empty vector-RNAi, EV-RNAi). Correspondingly, expression of some structural genes and regulatory factors involved in these two antioxidants’ accumulation was dramatically repressed. In addition, transcriptome analysis of EV-RNAi and FaAKR23-RNAi fruits suggested that FaAKR23 was also involved in starch and sucrose metabolism as well as plant–pathogen interaction. Overall, these results not only provide the coordinated regulatory function of FaAKR23 on AsA and anthocyanin accumulation but also offer a promising candidate gene for strawberry breeding with high antioxidants.

The paper presents the use of micro-X-ray computed tomography (CT) system and associated automatic loading device in visualizing and analyzing the propagation of penny-shaped flaw in gypsum-like 3D printing specimen. During the loading process, a micro-X-ray computed tomography (CT) system was used to scan the specimen with a resolution of 30 × 30 μm2. The volumetric images of specimen were reconstructed based on two-dimensional images. Thus, the propagation of penny-shaped flaw in gypsum-like 3D printing specimen in spatial was observed. The device can record the evolution of the internal penny-shaped flaw by X-ray CT scanning and the evolution of the surface crack by digital radiography at the same time. Fractal analysis was employed to quantify the cracking process. Two- and three-dimensional box-counting methods were applied to analyze slice images and volumetric images, respectively. Comparison between fractal dimensions calculated from two- and three-dimensional box-counting method was carried out. The results show that the fractal dimension increases with the propagation of cracks. Moreover, the common approach to obtain the 3D fractal dimension of a self-similar fractal object by adding one to its corresponding 2D fractal dimension is found to be inappropriate.

Chinese jujube (Ziziphus jujuba Mill, 2n = 2× = 24, Rhamnaceae) is an economically important Chinese native species. It has high nutritional value, and its medicinal properties have led to extensive use in traditional oriental medicine. The characterization of genotypes using molecular markers is important for genetic studies and plant breeding. However, few simple sequence repeat (SSR) markers are available for this species. In this study, 1,488 unique SSR clones were isolated from Z. jujuba 'Dongzao' using enriched genomic libraries coupled with a three-primer colony PCR screening strategy, yielding a high enrichment rate of 73.3%. Finally, 1,188 (80.87%) primer pairs were amplified successfully in the size expected for 'Dongzao'. A total of 350 primer pairs were further selected and evaluated for their ability to detect polymorphisms across a panel of six diverse cultivars; among these, 301 primer pairs detected polymorphisms, and the polymorphism information content (PIC) value across all loci ranged from 0.15 to 0.82, with an average of 0.52. An analysis of 76 major cultivars employed in Chinese jujube production using 31 primer pairs revealed comparatively high genetic diversity among these cultivars. Within-population differences among individuals accounted for 98.2% of the observed genetic variation. Neighbor-joining clustering divided the cultivars into three main groups, none of which correspond to major geographic regions, suggesting that the genetics and geographical origin of modern Chinese jujube cultivars might not be linked. The current work firstly reports the large-scale development of Chinese jujube SSR markers. The development of these markers and their polymorphic information represent a significant improvement in the available Chinese jujube genomic resources and will facilitate both genetic and breeding applications, further accelerating the development of new cultivars.

Siberian apricot (Prunus sibirica L.), an ecologically and economically important tree species with a high degree of tolerance to a variety of extreme environmental conditions, is widely distributed across the mountains of northeastern and northern China, eastern and southeastern regions of Mongolia, Eastern Siberia, and the Maritime Territory of Russia. However, few studies have examined the genetic diversity and population structure of this species. Using 31 nuclear microsatellites, we investigated the level of genetic diversity and population structure of Siberian apricot sampled from 22 populations across China. The number of alleles per locus ranged from 5 to 33, with an average of 19.323 alleles. The observed heterozygosity and expected heterozygosity ranged from 0.037 to 0.874 and 0.040 to 0.924 with average values of 0.639 and 0.774, respectively. A STRUCTURE-based analysis clustered all of the populations into four genetic clusters. Significant genetic differentiation was observed between all population pairs. A hierarchical analysis of molecular variance attributed about 94% of the variation to within populations. No significant difference was detected between the wild and semi-wild groups, indicating that recent cultivation practices have had little impact on the genetic diversity of Siberian apricot. The Mantel test showed that the genetic distance among the populations was not significantly correlated with geographic distance (r = 0.4651, p = 0.9940). Our study represents the most comprehensive investigation of the genetic diversity and population structure of Siberian apricot in China to date, and it provides valuable information for the collection of genetic resources for the breeding of Siberian apricot and related species.