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Highlights Additive-assisted layer-by-layer (LBL) deposition enables organic solar cells to achieve an unprecedented power conversion efficiency of 20.8%, the highest efficiency to date. The gradient fibrillar morphology enabled by additive-assisted LBL processing promotes the formation of bulk p-i-n structure, improving exciton and carrier diffusion, and reducing recombination losses. The wrinkle pattern morphology achieved by additive-assisted LBL processing is constructed to enhance the light capture capability.

Highlights A unique approach is proposed: constructing a polymer fiber rigid network with high glass transition temperature. Frozen bulk heterojunction morphology impeded deterioration of exciton quenching, charge transport, and charge extraction properties during thermal aging. The strategy is universal and can be further optimized for enhanced thermal stability and improved mechanical resilience.

Abstract All-small-molecule organic solar cells with good batch-to-batch reproducibility combined with non-halogen solvent processing show great potential for commercialization. However, non-halogen solvent processing of all-small-molecule organic solar cells are rarely reported and its power conversion efficiencies are very difficult to improve. Herein, we designed and synthesized a small molecule donor BM-ClEH that can take advantage of strong aggregation property induced by intramolecular chlorine-sulfur non-covalent interaction to improve molecular pre-aggregation in tetrahydrofuran and corresponding micromorphology after film formation. Tetrahydrofuran-fabricated all-small-molecule organic solar cells based on BM-ClEH:BO-4Cl achieved high power conversion efficiencies of 15.0% in binary device and 16.1% in ternary device under thermal annealing treatment. In contrast, weakly aggregated BM-HEH without chlorine-sulfur non-covalent bond is almost inefficient under same processing conditions due to poor pre-aggregation induced disordered π-π stacking, indistinct phase separation and exciton dissociation. This work promotes the development of non-halogen solvent processing of all-small-molecule organic solar cells and provides further guidance.

Abstract Organic solar cells (OSCs) have demonstrated over 19% power conversion efficiency (PCE) with the help of material innovation and device optimization. Co‐working with newly designed materials, traditional solvent additives, 1‐chloronaphthalene (CN), and 1,8‐diodooctane (DIO) are still powerful in morphology modulation towards satisfying efficiencies. Here, we chose recently reported high‐performance polymer donors (PM6 & D18‐Fu) and small molecular acceptors (Y6 & L8‐BO) as active layer materials and processed them by different conditions (CN or DIO or none). Based on corresponding 12 groups of device results, and their film morphology characterizations (both ex‐situ and in‐situ ones), the property‐performance relationships are revealed case by case. It is thereby supposed to be taken as a successful attempt to demonstrate the importance and complexity of donor‐acceptor‐additive interaction, since the device performance and physics analyses are also tightly combined with morphology variation. Furthermore, ternary blend construction for PCE improvement provides an approaching 19% level and showcases the potential of understanding‐guided‐optimization (UGO) in the future of OSCs.

Currently, the knowledge of long noncoding RNA (lncRNA)‐encoded peptides is quite lacking in esophageal squamous cell carcinoma (ESCC). In this study, we simultaneously identified six lncRNA open reading frames (ORFs) with peptide‐coding abilities including lysine‐specific demethylase 4A antisense RNA 1 (KDM4A‐AS1) ORF by combining weighted gene co‐expression network analysis (WGCNA) for ESCC clinical samples, ribosome footprints, ORF prediction, mass spectrometry (MS) identification, and western blotting. KDM4A‐AS1 ORF‐encoded peptide reduced ESCC cell viability and migratory ability. Co‐immunoprecipitation and MS analysis revealed that KDM4A‐AS1‐encoded peptide specifically bound with 103 proteins in ESCC cells, and enrichment analysis suggested that peptide‐bound proteins were related to fatty acid metabolism and redox process. Cell and molecular experiments demonstrated that KDM4A‐AS1‐encoded peptide inhibited stearoyl‐CoA desaturase and fatty acid synthase expression, increased reactive oxygen species level, and reduced mitochondrial membrane potential in ESCC cells. In summary, multiple lncRNAs with translation potential were simultaneously identified by combining multiple approaches in ESCC, providing novel identification strategies for lncRNA‐encoded peptides. Moreover, lncRNA KDM4A‐AS1‐encoded peptide weakened ESCC cell viability and migratory capacity and functioned in fatty acid metabolism and redox process.

Although much promising synthetic progress in conjugated polymer-based organic solar cells (OSCs) has resulted in significant improvement in power conversion efficiencies (PCEs) of from over 15 to >19.0% in the last five years, the sophisticated and complex reactions from at least two families’ monomers with remarkably different electron push–pull effects could still pose an unavoidable material burden for the commercialization of OSCs in the coming future. Therefore, the method of preparing a homopolymer from a sole monomer would significantly reduce the synthetic steps and costs in order to pave the way for the large-scale production of OSC materials. Therefore, alkylthio-thiophenyl-substituted benzo[1,2-b;4,5-b′]dithiophene (BDTTS) as the sole and key structural moiety with dihalogen and distannyl functional groups was designed and synthesized, respectively, in this study, for facile monomer syntheses and polymerizations to achieve three wide-bandgap homopolymer donors of BDTTS-alt-BDTT-Cl (P13), BDTTS-alt-BDTT (P15), and BDTTS (P14), respectively. The structural symmetry dependency on their physical, electrochemical, and optical properties, thin-film morphologies, and photovoltaic (PV) performance was investigated in detail. As a result, OSCs based on the asymmetric polymer P15, paired with BTP-eC9 as the electron acceptor, presented the best PV performance, with a PCE of 11.5%, a fill factor (FF) of 65.87%, and a short-circuit current (JSC) of 22.04 mA·cm−2, respectively. This PCE value is among the highest ones reported for BDT-type homopolymer donor-based OPVs, providing us with knowledge for obtaining promising PV performance from devices made of P15-like materials.

This paper investigates the exponential stability (ES) of nonlinear discrete-time (DT) systems with stochastic impulses and Markovian jump. Employing the Lyapunov function method and the subsequence technique, the sufficient conditions for exponential stability of the $pth$ moments (ES- $pth$ ) of the system are established. Generally, even if all the Markovian jump subsystems (MJSSs) are not ES- $pth$ in the absence of impulses, impulses can still be used to achieve the ES- $pth$ of the system in a specially designed interval, that is, when the impulses and Markovian jump signals meet the corresponding conditions. On the contrary, when all MJSSs are stable without impulses, as long as the impulses parameters and Markovian jump signals are relatively balanced, the system can still maintain the property of ES- $pth$ . Finally, the effectiveness of results is further verified with three examples.

AbstractTo evaluate the effect of hysteroscopy and suture on uterine scar diverticulum (CSD) after caesarean section and its influence on blood loss, operation time and antibiotic time. Eighty-nine patients with CSD were divided into observation group (n = 41 cases) and control group (n = 48 cases). Control group received laparoscopic scar diverticulum resection and suture, while the observation group received hysteroscopic resection of scar diverticulum. The operation time of the observation group was longer, while the bleeding volume and antibiotic administration time were significantly lower. The surgical treatment efficiency of the observation group (97.56%) was significantly higher. After treatment, the observation group’s diverticulum repair indexes (width, depth and lower uterine muscular layer thickness) were better than those of the control group (p

SLAM (Simultaneous Localization and Mapping), as one of the basic functions of mobile robots, has become a hot topic in the field of robotics this year. The majority of SLAM systems in use today, however, disregard the impact of dynamic objects on the system by defining the external environment as static. A SLAM system suitable for dynamic scenes is proposed, aiming at the issue that dynamic objects in real scenes can affect the localization accuracy and map effect of traditional visual SLAM systems. Initially, the enhanced lightweight YOLOv5s target detection algorithm is employed to detect dynamic objects in each frame of the image. Simultaneously, an assessment is conducted on the feature points present on dynamic objects to determine their potential impact on system accuracy, subsequently guiding the decision to retain or exclude these feature points. The preserved static feature points are then utilized for pose estimation and map construction. Experiments on the publicly available TUM dataset and the KITTI dataset are conducted to compare the system in this paper with ORB-SLAM 3, DS-SLAM, and DynaSLAM, and the algorithm is verified to have better performance.

A partial shading condition (PSC) is one of the most common problems in the photovoltaic (PV) system. It causes the output power of a PV system drastically decrease. Meta-heuristic algorithms (MHA) can track the maximum power point in a power-voltage (P-V) curve with multiple peaks. Grey wolf optimization (GWO) algorithm is a new optimization algorithm based on MHA. It has been used to solve optimization problems in many applications including MPPT for a PV system. However, the accuracy and tracking time in the original GWO (OGWO) can still be further improved for various PSCs. Therefore, there have been some modified grey wolf optimization (MGWO) algorithms proposed to improve the GWO. Nevertheless, only incremental improvement has been made. Therefore, a modified GWO, named enhanced grey wolf optimization (EGWO) is proposed in this paper. The proposed method adds the weighting average, the pouncing behavior and nonlinear convergence factor in the OGWO. In particular, since real wolves may engage in pouncing action when they are hunting, inclusion of pouncing completes the GWO algorithm and yields great improvements. As will be shown via simulation and experiment, the EGWO can drastically reduce the tracking time (up to 45.5% of the OGWO) and the dynamic tracking efficiency can be improved by more than 2%, compared to the OGWO. Moreover, the EGWO achieves the highest maximum power point compared to some of the existing GWO and other swarm based algorithms.

BackgroundGut microbiota has been reported to play an important role in diabetic kidney disease (DKD), however, the alterations of gut bacteria have not been determined.MethodsStudies comparing the differences of gut microbiome between patients with DKD and non-DKD individuals using high-throughput sequencing technology, were systematically searched and reviewed. Outcomes were set as gut bacterial diversity, microbial composition, and correlation with clinical parameters of DKD. Qualitative data were summarized and compared through a funnel R script, and quantitative data were estimated by meta-analysis.ResultsA total of 15 studies and 1640 participants were included, the comparisons were conducted between DKD, diabetes mellitus (DM), non-diabetic kidney disease (NDKD), and healthy controls. There were no significant differences of α-diversity between DKD and DM, and between DKD and NDKD, however, significant lower microbial richness was found in DKD compared to healthy controls. Different bacterial compositions were found between DKD and non-DKD subjects. The phylum Actinobacteria were found to be enriched in DKD compared to healthy controls. At the genus level, we found the enrichment of Hungatella, Bilophila, and Escherichia in DKD compared to DM, patients with DKD showed lower abundances of Faecalibacterium compared to those with NDKD. The genera Butyricicoccus, Faecalibacterium, and Lachnospira were depleted in DKD compared to healthy controls, whereas Hungatella, Escherichia, and lactobacillus were significantly enriched. The genus Ruminococcus torques group was demonstrated to be inversely correlated with estimated glomerular filtration rate of DKD.ConclusionsGut bacterial alterations was demonstrated in DKD, characterized by the enrichment of the genera Hungatella and Escherichia, and the depletion of butyrate-producing bacteria, which might be associated with the occurrence and development of DKD. Further studies are still needed to validate these findings, due to substantial heterogeneity.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42022340870.

This paper focuses on the problem of the pth moment and almost sure exponential stability of impulsive neutral stochastic functional differential equations (INSFDEs). Based on the Lyapunov function and average dwell time (ADT), two sufficient criteria for the exponential stability of INSFDEs are derived, which manifest that the result obtained in this paper is more convenient to be used than those Razumikhin conditions in former literature. Finally, two numerical examples and simulations are given to verify the validity of our result.

Background: The coronavirus disease 2019 (COVID-19) pandemic has been reported to have negative psychological impact on mental health. Nonetheless, there are few studies investigating the impacts on pregnant women. This study investigated the psychological impact of COVID-19 pandemic on pregnant women, and the associated risk factors that moderated this impact.Methods and Materials: A total of 2,798 pregnant participants were recruited from the Guangzhou Women and Children's Medical Center. The Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7) and Insomnia Severity Index (ISI) were used to assess depression, generalized anxiety disorder and insomnia, respectively, during and before the COVID-19 pandemic. The Impact of Event Scale-Revised (IES-R) was used to assess psychological stress during the COVID-19 pandemic.Results: During the COVID-19 pandemic, over one third of pregnant participants reported mild depression, around 20% experienced mild generalized anxiety, about one third reported problems with sleeping, and more than 15% felt mild psychological stress. The occurrence of psychological problems was significantly higher during the COVID-19 pandemic when compared to before the outbreak. The previously described pattern that pregnant women in the first trimester are more likely to report depression, and those in the third trimester are more likely to report insomnia and psychological stress, was also recognized in our study population. Mental health issues existing before the outbreak were risk factors, while family support was a protective factor in the occurrence of the measured mental health problems during the COVID-19 pandemic.Conclusion: Our data suggest pregnant women's mental health is inevitably affected during the COVID-19 pandemic. Pregnant women in the first and third trimester and those who experienced mental issues before the outbreak may be particularly affected.

Improving the power conversion efficiency is the main target of the organic solar cell research. Here Yuan et al. develop unconventional electron-deficient-core-based non-fullerene acceptors to achieve both low voltage loss and high current density, leading to a certified high efficiency of 12.6%.

Stripe rust caused by Puccinia striiformis f. sp. tritici is an important wheat disease worldwide that is greatly influenced by environmental conditions. Ultraviolet B (UV-B) radiation is one important environmental factor affecting the occurrence and epidemiology of wheat stripe rust. Investigating UV-B radiation effects on the epidemiology of stripe rust may be conducive to monitoring and predicting this disease. In this study, wheat seedlings were exposed to UV-B radiation during different periods under laboratory conditions and radiation effects on epidemiological components of wheat stripe rust were investigated. Results showed that incubation period was shortened, and the infection efficiency, sporulation quantity and disease index increased when UV-B radiation was performed only pre-inoculation. When the UV-B radiation was performed only post-inoculation or both pre- and post-inoculation, the incubation period was prolonged, and the infection efficiency, sporulation quantity and disease index were reduced. When healthy wheat seedlings were inoculated using urediospores collected from wheat leaves irradiated by UV-B only post-inoculation or both pre- and post-inoculation, infection efficiency, sporulation quantity and disease index were also reduced. However, in the latter, the disease incubation period did not differ under varying UV-B radiation intensities compared to that when wheat leaves were not treated with UV-B radiation. Overall, the effects of direct exposure of wheat plants to UV-B radiation with different intensities in different periods on epidemiological components of wheat stripe rust were systematically explored, and the results suggest that the effects of UV-B radiation increased gradually with the increase of UV-B radiation intensity. This information provides a basis for monitoring and predicting this disease as well as for conducting further studies on pathogen virulence variation.

The emergence of new physiological races of Puccinia striiformis f. sp. tritici (Pst) causing wheat stripe rust can lead to the loss of resistance of wheat cultivars to stripe rust, thus resulting in severe losses in wheat yield. In this study, after the germination of urediospores of three Pst strains including the original strain (CYR32, a dominant physiological race of Pst in China) and two virulence-mutant strains (CYR32-5 and CYR32-61) acquired from CYR32 via UV-B radiation, proteomic analysis based on isobaric tags for relative and absolute quantification (iTRAQ) technology was performed on the strains. A total of 2,271 proteins were identified, and 59, 74, and 64 differentially expressed proteins (DEPs) were acquired in CYR32-5 vs. CYR32, CYR32-61 vs. CYR32, and CYR32-61 vs. CYR32-5, respectively. The acquired DEPs were mainly involved in energy metabolism, carbon metabolism, and cellular substance synthesis. Furthermore, quantitative reverse transcription PCR assays were used to determine the relative expression of the 6, 7, and 1 DEPs of CYR32-5 vs. CYR32, CYR32-61 vs. CYR32, and CYR32-61 vs. CYR32-5, respectively, at the transcriptional level. The relative expression levels of one, five, and one gene, respectively, encoding the DEPs, were consistent with the corresponding protein abundance determined by iTRAQ technology. Compared with CYR32, the DEPs associated with energy metabolism and stress—including E3JWK6, F4S0Z3, and A8N2Q4—were up-regulated in the mutant strains. The results indicated that the virulence-mutant strains CYR32-5 and CYR32-61 had more tolerance to stress than the original strain CYR32. The results obtained in this study are of great significance for exploring the virulence variation mechanisms of Pst, monitoring the changes in Pst populations, breeding new disease-resistant wheat cultivars, and managing wheat stripe rust sustainably.

A series of polyimide (PI)/SiO _2 aerogel microspheres were prepared by using polyamide acid salt and hydrolyzed tetraethyl orthosilicate based on the reverse-phase emulsion method. Then, PI/SiO _2 aerogel microspheres were carbonized and etched to obtain carbon aerogel microspheres (CAMs). Scanning electron microscope, transmission electron microscope and nitrogen isothermal adsorption were used to characterize the micro-morphology and pore structure of the microspheres; and electrochemical workstation was used to test the electrochemical performance of the CAMs. The results showed that CAMs with different pore structures and specific surface area were obtained by adjusting the content of SiO _2 . Highest specific surface area of 1166.9 m ^2 g ^−1 and a total pore volume of 1.2369 cm ^3 g ^−1 were achieved at a SiO _2 content of 50%. When used as the electrode materials for supercapacitors, these CAMs demonstrated a maximum specific capacitance of 125.1 F g ^−1 in a three-electrode system and a maximum capacitance of 53.3% at 30 A g ^−1 . This article provides a new strategy for the preparation of CAMs with high specific surface area by using linear PI precursor and SiO _2 support skeleton.

Abstract Planting resistant cultivars is the most economical and effective measure to control wheat stripe rust caused by Puccinia striiformis f. sp. tritici (Pst), but the cultivars often lose their resistance due to the emergence of new physiological races. The UV‐B‐irradiated urediospores of the Pst physiological race CYR32 in China were inoculated on wheat cultivar Guinong 22 for screening virulence‐mutant strains. CYR32 and mutant strains (CYR32‐5 and CYR32‐61) before and after UV‐B radiation were used to conduct urediospore morphological and histopathological observations and an investigation of epidemiological components. The results showed that UV‐B radiation affected the urediospore morphology of each strain. UV‐B radiation inhibited urediospore invasion and hyphal elongation, which mainly manifested as decreases in germination rate, quantities of hyphal branches, haustorial mother cells and haustoria and hyphal length. After wheat cultivar Mingxian 169 was inoculated with the UV‐B‐irradiated urediospores, the incubation period was prolonged, and the infection efficiency, lesion expansion rate, total sporulation quantity and area under the disease progress curve were reduced. The results demonstrated that CYR32‐5 and CYR32‐61 may have more tolerance to UV‐B radiation than CYR32. The results are significant for understanding mechanisms of Pst virulence variations and implementing sustainable management of wheat stripe rust.

This paper is concerned with stability analysis of discrete-time stochastic delay systems with impulses. By using the sums average value of the time-varying coefficients and the average impulsive interval, two sufficient criteria for exponential stability of discrete-time impulsive stochastic delay systems are derived, which are more convenient to be applied than those Razumikhin-type conditions in previous literature. Both pth moment asymptotic stability and pth moment exponential stability are considered. Finally, two numerical examples to illustrate the effectiveness.

Stripe rust caused by Puccinia striiformis f. sp. tritici (Pst) is a devastating wheat disease worldwide. Potential application of near-infrared spectroscopy (NIRS) in detection of pathogen amounts in latently Pst-infected wheat leaves was investigated for disease prediction and control. A total of 300 near-infrared spectra were acquired from the Pst-infected leaf samples in an incubation period, and relative contents of Pst DNA in the samples were obtained using duplex TaqMan real-time PCR arrays. Determination models of the relative contents of Pst DNA in the samples were built using quantitative partial least squares (QPLS), support vector regression (SVR), and a method integrated with QPLS and SVR. The results showed that the kQPLS-SVR model built with a ratio of training set to testing set equal to 3 : 1 based on the original spectra, when the number of the randomly selected wavelength points was 700, the number of principal components was 8, and the number of the built QPLS models was 5, was the best. The results indicated that quantitative detection of Pst DNA in leaves in the incubation period could be implemented using NIRS. A novel method for determination of latent infection levels of Pst and early detection of stripe rust was provided.

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It is important to implement detection and assessment of plant diseases based on remotely sensed data for disease monitoring and control. Hyperspectral data of healthy leaves, leaves in incubation period and leaves in diseased period of wheat stripe rust and wheat leaf rust were collected under in-field conditions using a black-paper-based measuring method developed in this study. After data preprocessing, the models to identify the diseases were built using distinguished partial least squares (DPLS) and support vector machine (SVM), and the disease severity inversion models of stripe rust and the disease severity inversion models of leaf rust were built using quantitative partial least squares (QPLS) and support vector regression (SVR). All the models were validated by using leave-one-out cross validation and external validation. The diseases could be discriminated using both distinguished partial least squares and support vector machine with the accuracies of more than 99%. For each wheat rust, disease severity levels were accurately retrieved using both the optimal QPLS models and the optimal SVR models with the coefficients of determination (R2) of more than 0.90 and the root mean square errors (RMSE) of less than 0.15. The results demonstrated that identification and severity evaluation of stripe rust and leaf rust at the leaf level could be implemented based on the hyperspectral data acquired using the developed method. A scientific basis was provided for implementing disease monitoring by using aerial and space remote sensing technologies.

Maximum Power Point Tracking (MPPT) enables photovoltaic (PV) systems to extract as much solar energy as possible. Depending on which type of controller is used, PV systems can be classified as centralized MPPT (CMPPT) or decentralized MPPT (DMPPT). In substring-level systems, it is known that the energy yield of DMPPT can outweigh the power electronics cost. At the substring level, it is usually assumed that the PV curve exhibits a single peak, even under partial shading. Thus, the control algorithms for DMPPT are usually less complicated than those employed in CMPPT systems. This paper provides a comprehensive review of four simple DMPPT algorithms, which are perturb and observe (P&O), incremental conductance (INC), golden section search (GSS), and Newton’s quadratic interpolation (NQI). The comparison of these algorithms are done from the perspective of numerical analysis. Guidelines on how to set initial conditions and convergence criteria are thoroughly explained. This is of great interest to PV engineers when selecting algorithms for use in MPPT implementations. In addition, various problems that have never previously been identified before are highlighted and discussed. For instance, the problems of NQI trap is identified and methods on how to mitigate it are also discussed. All the algorithms are tested under various conditions including static, dynamic, and rapid changes of irradiance. Both simulation and experimental results indicate that P&O and INC are the best algorithms for DMPPT.

Stripe rust caused by Puccinia striiformis f. sp. tritici (Pst) is an important disease on wheat. In this study, quantitative determination of germinability of Pst urediospores was investigated by using near infrared reflectance spectroscopy (NIRS) combined with quantitative partial least squares (QPLS) and support vector regression (SVR). The near infrared spectra of the urediospore samples were acquired using FT-NIR MPA spectrometer and the germination rate of each sample was measured using traditional spore germination method. The best QPLS model was obtained with vector correction as the preprocessing method of the original spectra and 4000–12000 cm−1 as the modeling spectral region while the modeling ratio of the training set to the testing set was 4 : 1. The best SVR model was built when vector normalization was used as the preprocessing method, the modeling ratio was 5 : 1 and the modeling spectral region was 8000–11000 cm−1. The results showed that the effect of the best model built using QPLS or SVR was satisfactory. This indicated that quantitative determination of germinability of Pst urediospores using near infrared spectroscopy technology is feasible. A new method based on NIRS was provided for rapid, automatic, and nondestructive determination of germinability of Pst urediospores.

Stripe rust and leaf rust with similar symptoms are two important wheat diseases. In this study, to investigate a method to identify and assess the two diseases, the canopy hyperspectral data of healthy wheat, wheat in incubation period, and wheat in diseased period of the diseases were collected, respectively. After data preprocessing, three support vector machine (SVM) models for disease identification and six support vector regression (SVR) models for disease index (DI) inversion were built. The results showed that the SVM model based on wavelet packet decomposition coefficients with the overall identification accuracy of the training set equal to 99.67% and that of the testing set equal to 82.00% was better than the other two models. To improve the identification accuracy, it was suggested that a combination model could be constructed with one SVM model and two models built using K-nearest neighbors (KNN) method. Using the DI inversion SVR models, the satisfactory results were obtained for the two diseases. The results demonstrated that identification and DI inversion of stripe rust and leaf rust can be implemented based on hyperspectral data at the canopy level.

This paper is concerned with pth moment input-to-state stability (p-ISS) and stochastic input-to-state stability (SISS) of impulsive stochastic systems with time delays. Razumikhin-type theorems ensuring p-ISS/SISS are established for the mentioned systems with external input affecting both the continuous and the discrete dynamics. It is shown that when the impulse-free delayed stochastic dynamics are p-ISS/SISS but the impulses are destabilizing, the p-ISS/SISS property of the impulsive stochastic systems can be preserved if the length of the impulsive interval is large enough. In particular, if the impulse-free delayed stochastic dynamics are p-ISS/SISS and the discrete dynamics are marginally stable for the zero input, the impulsive stochastic system is p-ISS/SISS regardless of how often or how seldom the impulses occur. To overcome the difficulties caused by the coexistence of time delays, impulses, and stochastic effects, Razumikhin techniques and piecewise continuous Lyapunov functions as well as stochastic analysis techniques are involved together. An example is provided to illustrate the effectiveness and advantages of our results.

The problem of stability for nonlinear impulsive stochastic functional differential equations with delayed impulses is addressed in this paper. Based on the comparison principle and an impulsive delay differential inequality, some exponential stability and asymptotical stability criteria are derived, which show that the system will be stable if the impulses’ frequency and amplitude are suitably related to the increase or decrease of the continuous stochastic flows. The obtained results complement ones from some recent works. Two examples are discussed to illustrate the effectiveness and advantages of our results.

A weighted average finite difference method for solving the two-sided space-fractional convection-diffusion equation is given, which is an extension of the weighted average method for ordinary convection-diffusion equations. Stability, consistency, and convergence of the new method are analyzed. A simple and accurate stability criterion valid for this method, arbitrary weighted factor, and arbitrary fractional derivative is given. Some numerical examples with known exact solutions are provided.

Fractional-order diffusion equations are viewed as generalizations of classical diffusion equations, treating super-diffusive flow processes. In this paper, in order to solve the fractional advection-diffusion equation, the fractional characteristic finite difference method is presented, which is based on the method of characteristics (MOC) and fractional finite difference (FD) procedures. The stability, consistency, convergence, and error estimate of the method are obtained. An example is also given to illustrate the applicability of theoretical results.

A class of generalized impulsive stochastic functional differential systems with delayed impulses is considered. By employing piecewise continuous Lyapunov functions and the Razumikhin techniques, several criteria on the exponential stability and uniform stability in terms of two measures for the mentioned systems are obtained, which show that unstable stochastic functional differential systems may be stabilized by appropriate delayed impulses. Based on the stability results, delayed impulsive controllers which mean square exponentially stabilize linear stochastic delay systems are proposed. Finally, numerical examples are given to verify the effectiveness and advantages of our results.

The robust filtering problem for a class of uncertain discrete-time fuzzy stochastic systems with sensor nonlinearities and time-varying delay is investigated. The parameter uncertainties are assumed to be time varying norm bounded in both the state and measurement equations. By using the Lyapunov stability theory and some new relaxed techniques, sufficient conditions are proposed to guarantee the robustly stochastic stability with a prescribed H∞ performance level of the filtering error system for all admissible uncertainties, sensor nonlinearities, and time-varying delays. These conditions are dependent on the lower and upper bounds of the time-varying delays and are obtained in terms of a linear matrix inequality (LMI). Finally, two simulation examples are provided to illustrate the effectiveness of the proposed methods.

By using the Lyapunov functions and the Razumikhin techniques, the exponential stability of impulsive functional differential systems with delayed impulses is investigated. The obtained results have shown that the system will stable if the impulses’ frequency and amplitude are suitably related to the increase or decrease of the continuous flows, and they improve and complement ones from some recent works. An example is provided to illustrate the effectiveness and the advantages of the results obtained.