Search Results (353)

This study investigates the dynamic behaviors of the discrete epidemic model influenced by media coverage through integrated analytical and numerical approaches. The primary objective is to quantitatively assess the impact of media coverage on disease outbreak patterns using mathematical modeling. Firstly, the Euler method is used to discretize the model (2), and the periodic solution is strictly analyzed. Secondly, the coefficients and conditions of restricted flip and Neimark–Sacker bifurcation are studied by using the center manifold theorem and bifurcation theory. By calculating the largest Lyapunov exponent near the critical bifurcation point, the occurrence of chaos and limit cycles is proved. On this basis, the chaotic control of the system is carried out by using state feedback and hybrid control. Under certain conditions, the chaos and bifurcation of the system can be stabilized by control strategies. Numerical simulations further reveal bifurcation dynamics, chaotic behaviors, and control technologies. Our results show that media coverage is a key factor in regulating the intensity of disease transmission and chaos. The control technology can effectively prevent the large-scale outbreak of epidemic diseases. Importantly, enhanced media coverage can effectively promote public awareness and defensive behaviors, thereby contributing to the mitigation of disease transmission. Full article

Chaotic systems, characterized by extreme sensitivity to initial conditions and complex dynamical behaviors, exhibit significant potential for applications in various fields. Effective control of chaotic system synchronization is particularly crucial in sensor-related applications. This paper proposes a preset-time fuzzy zeroing neural network (PTCFZNN) model based on Takagi–Sugeno fuzzy control to achieve chaotic synchronization in aperiodic parameter exciting chaotic systems. The designed PTCFZNN model accurately handles the complex dynamic variations inherent in chaotic systems, overcoming the challenges posed by aperiodic parameter excitation to achieve synchronization. Additionally, field-programmable gate array (FPGA) verification experiments successfully implemented the PTCFZNN-based chaotic system synchronization control on hardware platforms, confirming its feasibility for practical engineering applications. Furthermore, experimental studies on chaos-masking communication applications of the PTCFZNN-based chaotic system synchronization further validate its effectiveness in enhancing communication confidentiality and anti-jamming capability, highlighting its important application value for securing sensor data transmission. Full article

The rapid development of marine recirculating aquaculture systems (RASs) worldwide offers an efficient and sustainable approach to aquaculture. However, the slow start-up of the nitrification process under low-temperature conditions remains a significant challenge. This study evaluated multiple start-up strategies for moving bed biofilm reactors (MBBRs) operating at 13–15 °C. Among them, the salinity-gradient (SG) strategy exhibited the best performance, reducing the start-up time by 38 days compared to the control, with microbial richness (Chao1 index) reaching 396 and diversity (Shannon index) of 4.89. Inoculation with mature biofilm (MBI) also showed excellent results, shortening the start-up period by 26 days and achieving a stable total ammonia nitrogen (TAN) effluent concentration below 0.5 mg/L within 132 days. MBI exhibited the highest microbial richness (Chao1 index = 808) and diversity (Shannon index = 5.55), significantly higher than those of the control (Chao1 index = 279, Shannon index = 3.90) and other treatments. The hydraulic retention time-gradient (HRT) strategy contributed to performance improvement as well, with a 24-day reduction in start-up time and a Chao1 index of 663 and a Shannon index is 4.69. In contrast, nitrifying bacteria addition (NBA) and carrier adhesion layer modification (CALM) had limited effects on start-up efficiency or microbial diversity, with Chao1 indices of only 255 and 228, and Shannon indices were both 3.24, respectively. Overall, the results indicate that salinity acclimation, mature biofilm inoculation, and extended HRT are effective approaches for promoting microbial community adaptation and enhancing MBBR start-up under low-temperature marine conditions. Full article

Mine water hazards represent one of the principal threats to safe coal mine operations; therefore, accurately predicting mine water inflow is critical for drainage system design and water hazard mitigation. Because mine water inflow is governed by the combined influence of multiple hydrogeological factors and thus exhibits pronounced non-linear characteristics, conventional approaches are inadequate in terms of forecasting accuracy and medium- to long-term predictive capability. To address this issue, this study proposes a Chaos-Autoformer-based method for predicting mine water inflow. First, the univariate inflow series is mapped into an m-dimensional phase space by means of phase-space reconstruction from chaos theory, thereby fully preserving its non-linear features; the reconstructed vectors are then used to train and forecast inflow with an improved Chaos-Autoformer model. On top of the original Autoformer architecture, the proposed model incorporates a Chaos-Attention mechanism and a Lyap-Dropout scheme, which enhance sensitivity to small perturbations in initial conditions and complex non-linear propagation paths while improving stability in long-horizon forecasting. In addition, the loss function integrates the maximum Lyapunov exponent error and earth mode decomposition (EMD) indices so as to jointly evaluate dynamical consistency and predictive performance. An empirical analysis based on monitoring data from the Liangshuijing Coal Mine for 2022–2025 demonstrates that the trained model delivers high accuracy and stable performance. Ablation experiments further confirm the significant contribution of the chaos-aware components: when these modules are removed, forecasting accuracy declines to only 76.5%. Using the trained model to predict mine water inflow for the period from June 2024 to June 2025 yields a root mean square error (RMSE) of 30.73 m³/h and a coefficient of determination (R²) of 0.895 against observed data, indicating excellent fitting and predictive capability for medium- to long-term tasks. Extending the forecast to July 2025–November 2027 reveals a pronounced annual cyclical pattern in future mine water inflow, with markedly higher inflow in summer than in winter and an overall slowly declining trend. These findings show that the Chaos-Autoformer can achieve high-precision medium- and long-term predictions of mine water inflow, thereby providing technical support for proactive deployment and refined management of mine water hazard prevention. Full article

With the upgrading of consumption driving the transformation of the home furnishing industry towards personalized customization, panel furniture enterprises are confronted with a core contradiction between large-scale production and individualized demands: The traditional production management model is unable to cope with the chaos in production scheduling, resource waste, and low collaborative efficiency caused by small-batch and multi-variety orders. This paper proposes an intelligent production scheduling system that integrates Enterprise Resource Planning (ERP), Manufacturing Execution System (MES), Advanced Planning and Scheduling (APS), and Warehouse Management System (WMS), and elaborates on its data processing methods and specific application processes in each production stage. Compared with the traditional model, it effectively overcomes limitations such as coarse-grained planning, delayed execution, and information islands in middle-level systems, achieving deep collaboration between planning, workshop execution, and warehouse logistics. Empirical studies show that this system not only can effectively reduce the production costs of customized panel furniture manufacturers, enhance their market competitiveness, but also provides a digital transformation framework for the entire customized panel furniture manufacturing industry, with significant theoretical and practical value. Full article

To investigate the effects of intercropping green manure on the tea plantation ecosystem, this study was conducted using 40-year-old Camellia sinensis cv. “Fuding Dabai” tea plants at the Tea Experimental Base of the Jiangxi Institute of Cash Crops. Four treatments were established: clean tillage (CK), tea intercropped with ryegrass (Lolium perenne, TRG), tea intercropped with rapeseed (Brassica napus, TRP), and tea intercropped with alfalfa (Medicago sativa, TAL). The study systematically evaluated the effects of green manure on tea yield, soil nutrient content, enzyme activity, and microbial community structure. The results showed that intercropping with green manure significantly increased the bud density, hundred-bud weight, and yield of tea in spring, summer, and autumn, with the TAL treatment showing the best overall performance. In terms of soil physicochemical properties, green manure treatments significantly improved soil organic matter, total nitrogen, available nitrogen, available phosphorus, and available potassium contents, with TRP and TAL showing the most pronounced improvements. Enzyme activity analysis indicated that the TRP treatment significantly enhanced the activities of amylase, urease, and invertase. High-throughput sequencing results revealed that green manure treatments significantly increased both the number of bacterial and fungal OTUs (Operational Taxonomic Units) and alpha diversity indices. The TAL and TRP treatments showed superior performance in terms of Shannon, Chao, and ACE indices compared to CK. Principal coordinate analysis (PCoA) indicated that green manure had a greater influence on fungal community structure than on bacterial structure. Correlation analysis demonstrated that dominant microbial taxa were significantly associated with soil nitrogen, phosphorus, and potassium levels, suggesting that green manure modulates microbial community composition by improving soil nutrient status. Intercropping green manure significantly increased tea yield and soil quality compared with clean tillage. Alfalfa intercropping (TAL) increased tea yield by 49.61%, 40.88%, and 43.79% in spring, summer, and autumn, respectively, compared with the control. Soil organic matter and total nitrogen under TAL were 29.02% and 15.67% higher than the control, while rapeseed intercropping (TRP) increased available phosphorus by 186%. TAL and TRP also enhanced microbial diversity, with bacterial Shannon index values 14.11% and 11.25% higher than the control. These results indicate that alfalfa intercropping is the most effective green manure practice for improving tea plantation productivity and soil ecology. Full article

This paper addresses the challenge of determining optimal parameters in chaotic systems used for image encryption algorithms based on chaos theory. A baseline algorithm employing a third-order Lorenz chaotic system is examined, incorporating core procedures such as permutation (shuffling) and diffusion. Graphical results are presented to illustrate the variation of image entropy in relation to changes in system parameters. The analysis reveals a distinct region in the parameter space where entropy reaches its highest values. Based on these observations, an optimality criterion is formulated, defining an objective function that captures the entropy’s sensitivity to two key system parameters, including the bifurcation parameter. A complex objective function is derived, and the optimization problem is solved using a modified version of the Price algorithm enhanced with artificial intelligence techniques. The proposed modification demonstrates superior performance in locating the global extremum of the objective function, resulting in enhanced security of the encrypted image. Numerical and graphical results for various images are provided, along with a comparative analysis between the standard and the modified Price method. Full article

This study aimed to investigate the effect of storage time on the fermentation quality, bacterial community structure, and metabolic profiles of Jinmu grain grass silage. It was ensiled in vacuum bags for 60 days. Samples were collected after 0, 3, 7, 15, 30, and 60 days of ensiling. Nutritional analysis revealed no significant differences in dry matter (DM), ether extract (EE), crude protein (CP), neutral detergent fiber (NDF), or acid detergent fiber (ADF) across storage periods (p > 0.05), but relative feeding value (RFV) significantly increased at 30 and 60 days (p < 0.05). Fermentation quality improved with prolonged storage, pH values declined to 4.01 at 60 days, while lactic acid (LA) and acetic acid (AA) increased significantly (p < 0.05). Butyric acid(BA) was undetected. 16S rDNA sequencing showed bacterial diversity (Chao1, Simpson, and Shannon indices) increased significantly at 30 and 60 days (p < 0.01); The relative abundance of Lacticaseibacillus, and Amylolactobacillus at 30 days were significantly higher than 0 and 60 days (p < 0.05); The relative abundance of Stenotrophomonas, Serratia, Comamonas, GKS98_freshwater_group, and Sphingobium at 60 days were significantly higher than 0 and 30 days (p < 0.05). Comprehensive targeted metabolomics identified 2958 metabolites. There were 256 differential metabolites shared by the comparison groups at 0, 30, and 60 days. The pathways for enrichment of differential metabolites mainly include plant hormone signal transduction, Histidine metabolism, arginine biosynthesis, etc. In conclusion, the storage time of Jinmu grain grass silage can enhance its fermentation quality by influencing microbial communities and metabolic pathways. Full article

Carnosic acid (CA), a natural phenolic terpenoid compound, is widely distributed in plants such as sage and rosemary, and exhibits a strong antioxidant capacity. The aim of this study was to investigate the effects of different levels of CA on growth performance, antioxidant capacity, and intestinal health of yellow-feathered broilers, and then to determine the optimal dose of CA to promote sustainable broiler production. A total of 384 1-day-old yellow-feathered broilers were randomly allocated into six treatment groups with eight replicates per group and eight birds per replicate pen. The control group (CON) was fed a basal diet and the CA treated groups (CA5, CA10, CA20, CA40, and CA80) were fed diets given different doses of CA (5, 10, 20, 40, and 80 mg/kg), respectively, for 53 days (1~21 d and 22~53 d). The results showed that, in the later stages of the experiment, supplementation with 10, 20, and 40 mg/kg of CA increased (p < 0.05) the final body weight and average daily gain. Morphometric analyses of the jejunum showed that supplementation of CA increased (p < 0.05) the ratio of villus height to crypt depth (V/C). Antioxidant indices revealed that CA significantly reduced MDA levels in plasma, liver, and jejunum, while enhancing activities of GSH-Px, T-SOD, and T-AOC (p < 0.05). Moreover, CA upregulated hepatic Nrf2, HO-1, GSH-Px, and GSR expression via downregulated Keap1. The analysis of intestinal microbiota showed that CA increased (p < 0.05) microbial α diversity (Ace, Chao, and Sobs indices) and increased (p < 0.05) beneficial bacteria, such as Streptococcus, Enterococcus, and Phascolarctobacterium. In conclusion, CA improves growth performance, antioxidant capacity, intestinal health, and gut microbial diversity in broilers. Under the conditions of this experiment, quadratic regressions for different variables showed that the optimal range for supplemental CA in chicken’s diet was 19.11~76.85 mg/kg. Combined with experimental observation and regression analysis, the optimal level of supplementation was 40 mg/kg. Full article

Teaching in Early Childhood Education and Care (ECEC) is a highly demanding profession, particularly in challenging educational contexts, such as Greece. This study aims to explore teacher resilience and examine its associations with self-efficacy and environmental chaos. Specifically, the study explores the mediating role of self-efficacy in the relationship between teacher resilience and environmental chaos. Two hundred and six ECEC teachers from Greece were recruited via convenience sampling to participate in this study. The measures used were the Connor–Davidson Resilience Scale (CD-RISC), the Efficacy subscale of Teacher Subjective Wellbeing Questionnaire (TSWQ), and the Confusion, Hubbub, and Order Scale (CHAOS-D). The results of descriptive statistics showed that teachers’ levels of resilience were moderate to high. Correlational analyses revealed significant positive relationships between teacher resilience and self-efficacy, as well as significant negative relationships between both resilience and self-efficacy and a chaotic classroom atmosphere. The mediation analysis demonstrated that self-efficacy partially mediated the negative relationship between a chaotic atmosphere and teacher resilience. Taken together, the findings highlight the importance of mitigating environmental stressors and fostering robust self-efficacy beliefs in order to enhance the resilience and overall well-being of ECEC professionals. Full article

Boolean chaotic systems solely composed of logic devices have been successfully applied in fields such as random number generation, reservoir computing, and radar detection because of their simple structure and amenability to integration. However, noise in a circuit makes Boolean chaotic systems less robust, which means noise transforms the outputs from chaotic to periodic. In this paper, the characteristics of the process through which logic devices respond to input signals are called device response characteristics. A device’s response characteristic parameters can adjust its response speed and the results it yields to the same input signal. The relationship between logical device response characteristic parameters and the time delay parameter was studied. The results indicate that the distribution range and continuity of chaos in the time delay parameter space can be enhanced by reducing the logical device response characteristic parameters, thereby improving the robustness of a Boolean chaotic system. This research is significant for the hardware design of Boolean chaotic system, as it details the selection of appropriate devices for enhancing chaotic time delay parameter space and robustness. Full article

(1) Background: Affected by multiple factors, the decline in fish species diversity in some aquatic ecosystems has become increasingly pronounced. At a broad spatial scale, economic development has been widely recognized as one of the key factors influencing the fish distribution pattern. However, at a small scale, within a single river basin, the effects of economic development on the freshwater fish distribution and communities remain largely uninvestigated. (2) Methods: environmental DNA (eDNA) samples were collected from 26 sampling sites of the Lixian River in both the summer (June) and winter (November). Economic data from the Lixian River basin were collected, and analyses, including multivariate regression tree analysis and generalized linear model fitting, were performed using R 4.3.2. (3) Results: A total of 65 fish species was characterized, and the Chao1 diversity indices in the upstream (13.42) and downstream (13.00) were significantly higher than those in the middle reaches (8.55, p < 0.01) of this river. The species communities exhibited an obvious gradient changing pattern from the upstream to the downstream reaches, with parameters of water quality, including transparency, pH, dissolved oxygen and temperature, and climatic factors functioning as the key variables. Furthermore, the generalized linear model analysis revealed significant positive correlations between agricultural population (p = 0.00106), total grain production (p = 0.00476), total population (p = 0.00192) and the Chao1 index. (4) Conclusions: Climatic factors are the key factors affecting the fish diversity in the Lixian River. In less economically developed areas, the development of local economic activities may enhance fish diversity. Full article

The impact of burdock tea (BT) made from burdock (Arctium lappa) roots in normal individuals and animal models remains largely unknown, particularly on lung protection. This study examined responses of oxidative stress, inflammation, and the microbiota within the cecum and the lung to BT treatment in healthy Wistar rats. A middle-dose BT reduced the Chao1 and Shannon indices, and both low and middle doses induced structural alterations in the cecal microbiota. Additionally, low doses increased the abundances of Phascolarctobacterium, Alloprevotella, Desulfovibrio, and the NK4A214 group. In the lung, middle and high doses increased Corynebacterium, with high doses also boosting Megasphaera and Lactobacillus. Functionally, low doses downregulated the biosynthesis of antibiotics in the cecal microbiota, while middle doses reduced the Epstein–Barr virus and Escherichia coli pathogenic infection pathways; additionally, middle and high doses modulated chromosomal proteins and bile acid biosynthesis in the pulmonary microbiota. BT treatment enhanced the content of short-chain fatty acids (SCFAs), upregulated the expression of GPR43, and suppressed NLRP3 expression in both the colon and lung tissues, while concurrently promoting the expression of ZO-1 and Occludin. Furthermore, serum levels of IL-1β and IL-6, as well as tissue levels of MDA, were significantly reduced. Notably, propionate exhibited an inverse correlation with MDA, IL-6, and NLRP3, while showing a positive correlation with ZO-1. Similarly, acetate was negatively correlated with MDA and NLRP3 and positively correlated with ZO-1. Overall, BT exhibits a nontoxic profile and may protect lung tissue through its antioxidant nature and gut–lung axis mediated by SCFAs. Full article

Natural dynamical systems can often display various long-term behaviours, ranging from entirely predictable decaying states to unpredictable, chaotic regimes or, more interestingly, highly correlated and intricate states featuring emergent phenomena. That, of course, imposes a level of generality on the models we use to study them. Among those models, coupled oscillators and cellular automata (CA) present a unique opportunity to advance the understanding of complex temporal behaviours because of their conceptual simplicity but very rich dynamics. In this contribution, we review the work completed by our research team over the last few years in the development and application of an alternative information-based characterization scheme to study the emergent behaviour and information handling of nonlinear systems, specifically Adler-type oscillators under different types of coupling: local phase-dependent (LAP) coupling and Kuramoto-like local (LAK) coupling. We thoroughly studied the long-term dynamics of these systems, identifying several distinct dynamic regimes, ranging from periodic to chaotic and complex. The systems were analysed qualitatively and quantitatively, drawing on entropic measures and information theory. Measures such as entropy density (Shannon entropy rate), effective complexity measure, and Lempel–Ziv complexity/information distance were employed. Our analysis revealed similar patterns and behaviours between these systems and CA, which are computationally capable systems, for some specific rules and regimes. These findings further reinforce the argument around computational capabilities in dynamical systems, as understood by information transmission, storage, and generation measures. Furthermore, the edge of chaos hypothesis (EOC) was verified in coupled oscillators systems for specific regions of parameter space, where a sudden increase in effective complexity measure was observed, indicating enhanced information processing capabilities. Given the potential for exploiting this non-anthropocentric computational power, we propose this alternative information-based characterization scheme as a general framework to identify a dynamical system’s proximity to computationally enhanced states. Furthermore, this study advances the understanding of emergent behaviour in nonlinear systems. It explores the potential for leveraging the features of dynamical systems operating at the edge of chaos by coupling them with computationally capable settings within machine learning frameworks, specifically by using them as reservoirs in Echo State Networks (ESNs) for time series forecasting and predictive modeling. This approach aims to enhance the predictive capacity, particularly that of chaotic systems, by utilising EOC systems’ complex, sensitive dynamics as the ESN reservoir. Full article