Residue and dietary intake risk assessment of lufenuron and chlorfenapyr and its corresponding metabolite in cabbage under field conditions.

A simple, low-cost, and highly sensitive method using a modified QuECHERS procedure based on a liquid chromatography-tandem mass spectrometer (LC-MS/MS) was established to simultaneously quantify lufenuron and chlorfenapyr and the corresponding metabolite tralopyril in cabbage for the first time. On the basis of this method, terminal residue and dietary risk of lufenuron and chlorfenapyr in cabbage were investigated. The recoveries of lufenuron, chlorfenapyr, and tralopyril ranged from 88 to 110%, with relative standard deviation of less than 12.4%. The field trial results showed that at the pre-harvest interval (PHI) of 21 days, the terminal residues of lufenuron, chlorfenapyr, and tralopyril in the supervised trials were not higher than 0.02 mg/kg, and the highest detected residue levels of lufenuron, chlorfenapyr, and tralopyril were 0.047, 0.055, and <0.02 mg·kg-1 at 14-day pre-harvest respectively, which were lower than the maximum residue limits (MRLs) for cabbage established in China. For the dietary risk assessment, the national estimated daily intakes (NEDIs) as proportion of acceptable daily intakes (ADIs) were 80.4% and 29.9% for chlorfenapyr and lufenuron respectively indicating an acceptable dietary risk to Chinese population.

[Quantitative Assessment of the Impact of Climate Change on the Growing Season of Vegetation Gross Primary Productivity in the Middle and Lower Reaches of the Yangtze River].

Quantitatively determining the direct, indirect, and comprehensive effects of climatic factors on the growing season of the vegetation GPP (GPPGS) in the middle and lower reaches of the Yangtze River at the regional and vegetation type scales can provide a scientific basis for the management and restoration of regional vegetation resources under the background of global climate change. Using MODIS GPP data, meteorological data, and vegetation type data, combined with Theil-Sen Median trend analysis and the Mann-Kendall significance test, the spatiotemporal characteristics of the GPPGS in the middle and lower reaches of the Yangtze River were investigated at different temporal and spatial scales. Path analysis was used to further reveal the direct, indirect, and comprehensive effects of climate factors on GPPGS variation in different vegetation types. The results showed that:① from 2000 to 2021, the vegetation GPPGS in the middle and lower reaches of the Yangtze River showed a fluctuating upward trend, with a rising rate (in terms of C, same below) of 2.70 g·(m2·a)-1 (P<0.01). The GPPGS of different vegetation types all showed a significant upward trend (P<0.01), with shrubs having the highest upward rate of 3.31 g·(m2·a)-1 and cultivated vegetation having the lowest upward rate of 2.54 g·(m2·a)-1. ② The proportion of the area with an upward trend in GPPGS in the middle and lower reaches of the Yangtze River was 88.11%. The proportion of the area with an upward trend in GPPGS was greater than 84% for all different vegetation types, with shrubs (49.76%) and cultivated vegetation (44.36%) having significantly higher proportions of the area with an upward trend than that in other vegetation types. ③ The path analysis results showed that precipitation and the maximum temperature had a significant positive direct effect on vegetation GPPGS (P<0.05), whereas solar radiation had a non-significant positive effect (P ≥ 0.05). The indirect effects of maximum temperature, precipitation, and solar radiation on vegetation GPPGS were all non-significantly negative (P ≥ 0.05). Under the combined effects of direct and indirect influences, precipitation and maximum temperature had a non-significant positive effect on vegetation GPPGS (P ≥ 0.05), whereas solar radiation had a non-significant negative effect on vegetation GPPGS (P ≥ 0.05). Among different vegetation types, precipitation was the main climate factor affecting the changes in GPPGS of cultivated vegetation, whereas the maximum temperature was the main climate factor affecting the changes in GPPGS of coniferous forests, broad-leaved forests, shrubs, and grasslands. ④ The changes in vegetation GPPGS in the middle and lower reaches of the Yangtze River were mainly influenced by the direct effects of maximum temperature, precipitation, and solar radiation, with the direct effect of precipitation dominating 56.72% of the changes in GPPGS. The research results can provide a reference for quantifying the carbon sequestration potential of vegetation in the middle and lower reaches of the Yangtze River and formulating ecological restoration governance policies tailored to local conditions under the background of global climate change.

Dynamic Event-Triggered Synchronization of Markov Jump Neural Networks via Sliding Mode Control.

This article proposes an asynchronous and dynamic event-based sliding mode control strategy to efficiently address the synchronization problem of Markov jump neural networks. By designing an adaptive law, and a triggered threshold in the form of a diagonal matrix, a special dynamic event-triggered scheme is applied to send the control signals only at triggered moments. An asynchronous sliding mode controller with gain uncertainty is designed by constructing a specified sliding manifold. Then, linear matrix inequalities are used to represent sufficient conditions for guaranteeing system synchronization. The error system trajectories are pushed onto the sliding surface by the controller. Eventually, the availability of the presented control strategy is demonstrated by an illustrative example.

Parthenocarpy in Cucurbitaceae: Advances for Economic and Environmental Sustainability.

Parthenocarpy is an important agricultural trait that not only produces seedless fruits, but also increases the rate of the fruit set under adverse environmental conditions. The study of parthenocarpy in Cucurbitaceae crops has considerable implications for cultivar improvement. This article provides a comprehensive review of relevant studies on the parthenocarpic traits of several major Cucurbitaceae crops and offers a perspective on future developments and research directions.

Synchronization of Markov Jump Neural Networks With Communication Constraints via Asynchronous Output Feedback Control.

This article is concerned with the synchronization issue of discrete Markov jump neural networks (MJNNs). First, to save communication resources, a universal communication model, including event-triggered transmission, logarithmic quantization, and asynchronous phenomenon, is proposed, which is close to the actual situation. Here, to further reduce conservatism, a more general event-triggered protocol is constructed by developing the threshold parameter as a diagonal matrix. To cope with mode mismatch between the nodes and controllers due to potentially occurring time lag and packet dropouts, a hidden Markov model (HMM) method is adopted. Second, considering that state information of nodes may not be available, the asynchronous output feedback controllers are devised by a novel decoupling strategy. Then, sufficient conditions based on linear matrix inequalities (LMIs) for dissipative synchronization of MJNNs are proposed with the virtue of Lyapunov techniques. Third, by eliminating asynchronous terms, a corollary with less computational cost is devised. Finally, two numerical examples verify the effectiveness of the above results.

Environmental management through examining the technical factors of carbon emissions in South Asian economies.

COP27 has provided a new direction in overcoming the issue of climate change. In the world of growing environmental degradation and climate change issues, the South Asian economies are playing a vital role in tackling these issues. Still, the literature focuses on industrialized economies while ignoring the newly emerging economies. This study evaluates the technological factors' influence on carbon emission in the four small South Asian economies (Sri Lanka, Bangladesh, Pakistan, and India) from 1989 to 2021. This study found the long-run equilibrium relationship between the variables using second-generation estimating tools. Employing the non-parametric and robust parametric approach, this study found that economic performance and development are substantial emissions drivers. In contrast, energy technology and tech innovation are the region's key environmental sustainability factors. Further, the study found that trade positively yet insignificantly impacts pollution. This study suggests further investment in energy technology and technological innovation to improve the production of energy-efficient products and services in these emerging economies.

[Spatio-temporal Variation in PM2.5 Concentration and Its Relationship with Vegetation Landscape Patterns in Typical Economic Zones in China from 2000 to 2020].

This study explored the temporal and spatial variation in PM2.5 concentration and its relationship with the vegetation landscape pattern in three typical economic zones in China, which is of great significance for regional PM2.5pollution control and atmospheric environmental protection. In this study, the pixel binary model, Getis-Ord Gi* analysis, Theil-Sen Median analysis, Mann-Kendall significance test, Pearson correlation analysis, and multiple correlation analysis were used to explore the spatial cluster and spatio-temporal variation in PM2.5 and its correlation with the vegetation landscape index in the three economic zones of China on the basis of PM2.5 concentration data and MODIS NDVI data set. The results showed that PM2.5 in the Bohai Economic Rim was mainly dominated by the expansion of hot spots and the reduction in cold spots from 2000 to 2020. The proportion of cold spots and hot spots in the Yangtze River Delta showed insignificant changes. Both cold and hot spots in the Pearl River Delta had expanded. PM2.5 showed a downward trend in the three major economic zones from 2000 to 2020, and the magnitudes of increasing rates were higher in the Pearl River Delta, followed by those in the Yangtze River Delta and Bohai Economic Rim. From 2000 to 2020, PM2.5 exhibited a downward trend in the context of all vegetation coverage grades, and PM2.5 had most significantly improved within extremely low vegetation coverage in the three economic zones. On the landscape scale, PM2.5 values were mostly correlated with aggregation index in the Bohai Economic Rim, with the largest patch index in the Yangtze River Delta and Shannon's diversity in the Pearl River Delta, respectively. Under the context of different vegetation coverage levels, PM2.5showed the highest correlation with aggregation index in the Bohai Economic Rim, landscape shape index in the Yangtze River Delta, and percent of landscape in the Pearl River Delta, respectively. PM2.5 showed significant differences with vegetation landscape indices in the three economic zones. The combined effect of multiple vegetation landscape pattern indices on PM2.5 was stronger than that of the single vegetation landscape pattern index. The above results indicated that the spatial cluster of PM2.5 in the three major economic zones had changed, and PM2.5 showed a decreasing trend in the three economic zones during the study period. The relationship between PM2.5 and vegetation landscape indices exhibited obvious spatial heterogeneity in the three economic zones.

Event-Triggered Resilient Filtering With the Interval Type Uncertainty for Markov Jump Systems.

The problem of event-triggered resilient filtering for Markov jump systems is investigated in this article. The hidden Markov model is used to characterize asynchronous constraints between the filters and the systems. Gain uncertainties of the resilient filter are the interval type in this article, which is more accurate than the norm-bounded type to model the uncertain phenomenon. The number of linear matrix inequalities constraints can be decreased significantly by separating the vertices of the uncertain interval, so that the difficulty of calculation and calculation time can be reduced. Moreover, the event-triggered scheme is applied to depress the consumption of network resources. In order to find a balance between reducing bandwidth consumed and improving system performance, the threshold parameter is designed as a diagonal matrix in the event-triggered scheme. Utilizing the convex optimization method, the sufficient conditions are derived to guarantee that the filtering error systems are stochastically stable and satisfy the extended dissipation performance. Finally, a single-link robot arm system is delivered to certify the effectiveness and advantages of the proposed method.

How does environmental policy stringency influence green innovation for environmental managements?

In the struggle to limit global climate change and rising temperatures, the United Nations Climate Change Conference (COP27) was held in Egypt last November. Bringing together nations to recognize climate change as a global concern and to create new "building blocks" to enhance the implementation of the Paris Agreement through actions that can move the world toward a greener, and carbon free future. This study examines a panel of high-income economies from the Organization for Economic Cooperation and Development (OECD) to investigate the empirical linkage between Green Innovations (GI), Disaggregated trade (exports and imports), Environmental policy stringency (EPS), and Consumption-based carbon dioxide emissions from 1990 to 2020. We proceed with the panel cointegration check based on the results of the diagnostic tests. The method of moment quantile regressions (MMQR) is used to investigate the relationships between CCO2 and various variables in different quantiles. The data show that GI, export, imports, and EPS are major contributors in explaining the substantial variance in CCO2 emissions in the chosen panel. Specifically, severe environmental rules boost the benefits of green technologies through the use of environmentally friendly technology. Imports, on the other hand, have been determined to be harmful to environmental quality. As a result, member economies should reform their environmental policies to include consumption-based emissions objectives and discourage people' desire for carbon-intensive items from developing countries. This will eventually result in a decrease in consumption-based carbon emissions, assisting in the achievement of true emissions reduction goals and COP27 targets.

Modifying the Power and Performance of 2-Dimensional MoS2 Field Effect Transistors.

Over the past 60 years, the semiconductor industry has been the core driver for the development of information technology, contributing to the birth of integrated circuits, Internet, artificial intelligence, and Internet of Things. Semiconductor technology has been evolving in structure and material with co-optimization of performance-power-area-cost until the state-of-the-art sub-5-nm node. Two-dimensional (2D) semiconductors are recognized by the industry and academia as a hopeful solution to break through the quantum confinement for the future technology nodes. In the recent 10 years, the key issues on 2D semiconductors regarding material, processing, and integration have been overcome in sequence, making 2D semiconductors already on the verge of application. In this paper, the evolution of transistors is reviewed by outlining the potential of 2D semiconductors as a technological option beyond the scaled metal oxide semiconductor field-effect transistors. We mainly focus on the optimization strategies of mobility (µ), equivalent oxide thickness (EOT), and contact resistance (RC ), which enables high ON current (Ion ) with reduced driving voltage (Vdd ). Finally, we prospect the semiconductor technology roadmap by summarizing the technological development of 2D semiconductors over the past decade.

Energy scheduling for DoS attack over multi-hop networks: Deep reinforcement learning approach.

This paper studies the energy scheduling for Denial-of-Service (DoS) attack against remote state estimation over multi-hop networks. A smart sensor observes a dynamic system, and transmits its local state estimate to a remote estimator. Due to the limited communication range of the sensor, some relay nodes are employed to deliver data packets from the sensor to the remote estimator, which constitutes a multi-hop network. To maximize the estimation error covariance with energy constraint, a DoS attacker needs to determine the energy level implemented on each channel. This problem is formulated as an associated Markov decision process (MDP), and the existence of an optimal deterministic and stationary policy (DSP) is proved for the attacker. Besides, a simple threshold structure of the optimal policy is obtained, which significantly reduces the computational complexity. Furthermore, an up-to-date deep reinforcement learning (DRL) algorithm, dueling double Q-network (D3QN), is introduced to approximate the optimal policy. Finally, a simulation example illustrates the developed results and verifies the effectiveness of D3QN for optimal DoS attack energy scheduling.

[Accumulation Characteristics and Health Risk Assessment of Heavy Metals in Soil-Crop System Based on Soil Parent Material Zoning].

This study used both the element occurrence form analysis and the chronic health risk assessment method to investigate the accumulation characteristics of heavy metals in the soil-crop system and the health risk assessment of agricultural products in northeastern Yunnan, which is a typical area of Southwest China where heavy metals are enriched in soil. Based on the study of 1137 groups of agricultural products and corresponding root soils, the results showed that the soil cadmium (Cd) and lead (Pb) in the lead-zinc ore concentration area were higher than the risk-intervention values of the "Soil Environmental Quality Risk control standard for soil contamination of agricultural land" (GB 15618-2018), whereas the soil Cd in the other parent material areas was within the screening-intervention buffer zone, and Pb was below the minimum risk-screening value. According to the National Food Safety Standard of China (GB 2762-2017), the heavy metal Cd in potatoes and soybeans in the area seriously exceeded the standard, the heavy metal Pb in tartary buckwheat and walnut exceeded the limit value, and the exceeding rate of heavy metal Cd in crops from these parent material areas showed: clastic rock>basalt>lead-zinc ore>carbonate ≈ Quaternary sedimentary>sand (mud) rock. According to the U.S. Environmental Protection Agency's assessment method for the chronic health risk of heavy metal intake by humans, the grains and potatoes, staple foods, and fruits had low chronic health risks of heavy metal intake. Agricultural products from the parent material area of clastic rock, sand (mud) rock, Quaternary sedimentary, and lead-zinc ore concentration showed health risks; with the change in soil physical and chemical properties and the increase in the types of edible crops, the risk will gradually increase. Based on this research, it is urgent to carry out real-time monitoring of agricultural products in the area.

Security-Based Passivity Analysis of Markov Jump Systems via Asynchronous Triggering Control.

This article considers the security-based passivity problem for a class of discrete-time Markov jump systems in the presence of deception attacks, where the deception attacks aim to change the transmitted signal. Considering the impact of deception attacks on network disruption, it causes the existence of time-varying delays in signal transmission inevitably, which makes the controlled system and the controller work asynchronously. The asynchronous control method is employed to overcome the nonsynchronous phenomenon between the system mode and controller mode. On the other hand, to reduce the frequency of data transmission, a resilient asynchronous event-triggered control scheme taking deception attacks into account is designed to save communication resources, and the proposed controller can cover some existing ones as special examples. Moreover, different triggering conditions corresponding to different jumping modes are developed to decide whether state signals should be transferred. A new stability criterion is derived to ensure the passivity of the resultant system although there exist deception attacks. Finally, a simulation example is given to verify the theoretical analysis.

Co-fermentation Approach of Fructose and Glucose to Ethanol from Chinese Elaeagnus angustifolia Fruit (EAF).

The soluble and fermentable carbohydrate contents was detected over 47% of glucose and fructose in Chinese Elaeagnus angustifolia fruit powder (EAF), being over 47 wt% sugar content more than that of grape. Ethanol was therefore fermented directly from EAF, and different submerged fermentation modes were comparatively employed to optimize ethanol harvest. The results indicated that glucose has certain competitive inhibition on fructose bio-utilization, as well as the EAF solid residue involved fermentation mode also hindered the fermented-ethanol titer. Pectinase addition and in situ hydrolysis seemed to assist somewhat the fermentation. The water-solute fermentation mode is preferable, and glucose and fructose components were completely consumed and converted to 80.96 g/L ethanol at 87.6% ethanol yield even under tannin and pectin inhibition. The fermentation result could provide some experimental data and an approach to not only new biomass resource explores of bioethanol and alcohol beverage production, but also the technological development on valorization commercials of EAF in global draught areas.

Cost-effective and sensitive indicator-displacement array (IDA) assay for quality monitoring of black tea fermentation.

Herein, a new indicator-displacement array (IDA) sensor was developed for the quality evaluation of black tea fermentation. On the principle of the reversible covalent binding of phenylboronic acid and catechol, phenylboronic acids were selected as acceptors for targeted binding to polyphenols. Pyrocatechol violet and alizarin red S were used as indicators of the reaction. The IDA sensors have sensitive differential responses to fermented tea samples, achieving an assessment of the fermentation degree with accuracies of 80.39-88.00% by support vector machine (SVM). In addition, the key polyphenol components of the fermentation process were accurately predicted by the IDA and SVM regression with ratio of prediction to deviation values of 1.55-1.72, 2.03-2.21, and 2.03-2.08 for total polyphenols, total catechins, and epigallocatechin-3-O-gallate, respectively. In conclusion, the developed IDA sensor is capable of the in-situ quality monitoring of black tea fermentation, with the advantages being cost-effectiveness, sensitivity, and rapidity.

Family socioeconomic status and mental health in Chinese adolescents: the multiple mediating role of social relationships.

BACKGROUND: The purpose of this study is to explore whether social relationships of family and school contexts mediate the influence of socioeconomic status (SES) on Chinese adolescents' mental health. METHODS: A school-based study was conducted among a sample aged 13-18 in East China (n = 6902). We used scales for measuring social relationships and self-rated mental health. Family SES was computed from subjective socioeconomic status, education and occupation of parents.The mediation model was tested by using Path Analysis in IBM SPSS-Amos. RESULTS: The results showed that SES can significantly influence adolescent mental health through parent-child relationship, student-teacher relationship and student-student relationship. The total effect, direct effect and total indirect effect were -0.209 (95% CI = -0.299, -0.136), -0.090 (95% CI = -0.174, -0.007), -0.119 (95% CI = -0.187, -0.078) for boys, and -0.337 (95% CI = -0.478, -0.230), -0.132 (95% CI = -0.283, 0.010), -0.205 (95% CI = -0.351, -0.085) for girls. CONCLUSION: The link between SES and adolescent mental health can be explained by social relationships. Focusing on the parent-child, student-student and student-teacher relationship interventions may contribute to improving the mental health of Chinese adolescents, especially in low socioeconomic groups, as well as female students.

First-line immunotherapy of neuronal surface antibody-mediated autoimmune encephalitis: Assessment of therapeutic effectiveness and cost-efficiency.

OBJECTIVE: To evaluate the therapeutic effectiveness and cost-efficiency of first-line immunotherapies on neuronal surface antibody-mediated autoimmune encephalitis (AE) based on a real-world observational study in China. METHODS: Our study retrospectively collected the clinical and paraclinical data of patients with definite neuronal surface antibody-mediated AE between July 2014 and July 2020. Regular follow-up was performed after administering standard regimens of first-line immunotherapies, including intravenous methylprednisolone (IVMP) and / or intravenous immunoglobulin (IVIG). Therapeutic effectiveness was reflected by modified Rankin Scale scores. The health resource utilization and direct medical costs were extracted to analyze the cost-efficiency. RESULTS: Among the 78 eligible patients, 48 (61.5%) were males with a median age of 40 years. More than half (56, 71.8%) were treated with combination therapy, with the rest receiving IVMP and IVIG monotherapy (both of 11, 14.1%). Related objective variables, i.e., sex, onset age, disease course, onset symptoms, antibody types, abnormal paraclinical results, disease severity, and the health insurance, showed insignificant differences on the selection of therapy. Each therapy showed similar short-term (4-week) and long-term (1-year) therapeutic effects. Yet the single or combination of IVIG had a slightly better effectiveness but higher cost than the monotherapy of IVMP. CONCLUSION: The combination of IVMP and IVIG was used more frequently than either alone, which may be associated with neurologist's personal experience and patient's wishes. Though with similar therapeutic effectiveness, the use of IVMP alone might be a better choice with a better cost-efficiency.

Assessment of hypokalemia and clinical prognosis in Patients with COVID-19 in Yangzhou, China.

BACKGROUND: Hypokalemia is a frequent electrolyte imbalance in patients with COVID-19. The aim of this study was to estimate the association between hypokalemia and clinical prognosis in patients with moderate COVID-19. METHODS: A single-center, retrospective, observational study was conducted on 81 non-ICU admitted patients with moderate COVID-19 according to the criteria issued by the Chinese Health Bureau in the Third People's Hospital of Yangzhou (Northern Jiangsu People's Hospital New District Branch) from 4th to 25th August 2021. The demographic, clinical, and laboratory data were reviewed and collected, then the correlation between hypokalemia and prognosis was determined. RESULTS: The level of serum potassium of patients ranged from 2.80 mmol/L to 4.70 mmol/L. Hypokalemia was detected in 39 out of the 81 included patients (48.15%) during hospitalization. Patients with hypokalemia had prolonged days of negative nucleic acid conversion and hospital stay. Correlation analysis showed that the level of serum potassium was negatively correlated with days of negative nucleic acid conversion and length of hospital stay. Bivariate logistic regression analysis proved that hypokalemia was a risk factor for prolonged hospital stay in patients with moderate COVID-19. CONCLUSION: Hypokalemia was prevalent in patients with moderate COVID-19 in Yangzhou, China. Hypokalemia was associated with the prolonged hospital stay in patients with moderate COVID-19.

Quantitative assessment of the relative contributions of climate change and human activities to NPP changes in the Southwest Karst area of China.

Net primary production (NPP) is an essential component of the terrestrial carbon cycle and an essential factor of ecological processes. In global change research, it was the core content to study the driving forces of NPP change. In this paper, we focused on the Southwest Karst area of China and analyzed the response mechanisms of NPP to topography, land-use types, climatic change, and human activities. Our results showed that (1) changes in elevation and slope lead to significant differences in the spatial distribution of NPP. With the increase of elevation and slope, NPP first increased and then decreased, their critical values were 2000 m and 15°, respectively. (2) NPP varied significantly among different land-use types. The average NPP of the forest was the highest, and the average NPP of cultivated land increased fastest. (3) Temperature and precipitation had the most substantial influence on NPP, both of them promoted the increase of NPP, and the effect of temperature was more obvious in the Southwest Karst area. (4) Ecological engineering significantly promoted the change of NPP, while animal husbandry significantly inhibited the change of NPP. (5) There were significant spatial differences in the driving effects and corresponding contributions of climatic change and human activities; both of them promoted the increase of NPP in the Southwest Karst area of China. Under climatic change and human activities, NPP increased by 1.24 gC·m-2·year-1 and 2.29 gC·m-2·year-1, respectively. The contributions rates of climatic change and human activities separately accounted for 35% and 65%. The contribution of human activities on NPP was much higher than that of climatic change in the Southwest Karst area, and the results suggested that we should focus on the role of human activities on NPP change.

Resilient Asynchronous State Estimation for Markovian Jump Neural Networks Subject to Stochastic Nonlinearities and Sensor Saturations.

This article studies the problem of dissipativity-based asynchronous state estimation for a class of discrete-time Markov jump neural networks subject to randomly occurring nonlinearities, sensor saturations, and stochastic parameter uncertainties. First, two stochastic nonlinearities occurring in the system are described by statistical means and obey two Bernoulli processes independently. Then, the hidden Markov model is used to characterize the real communication environment closely between the designed estimator and the system model due to the networked-induced phenomenons that also lead to randomly occurring parametric uncertainties of the estimator considered modeled by two Bernoulli processes. A new criterion is established to guarantee that the resulting error system is stochastically stable with predefined dissipativity performance. Finally, we provide a simulation example to validate the theoretical analysis.