Search Results (386)

WSN coverage optimization faces two key challenges: firstly, traditional algorithms are prone to getting stuck in local optima, leading to ‘coverage holes’ in node deployment; Secondly, in dynamic scenarios (such as imbalanced energy consumption of nodes), the convergence speed of the algorithm is slow, making it difficult to maintain high coverage in real time. This study focuses on the coverage optimization problem of wireless sensor networks (WSNs) and proposes improvements to the Flamingo Search Optimization Algorithm (FSA). Specifically, the algorithm is enhanced by integrating the elite opposition-based learning strategy and the stagewise step-size control strategy, which significantly improves its overall performance. Additionally, the introduction of a cosine variation factor combined with the stagewise step-size control strategy enables the algorithm to effectively break free from local optima constraints in the later stages of iteration. The improved Flamingo Algorithm is applied to optimize the deployment strategy of sensing nodes, thereby enhancing the coverage rate of the sensor network. First, an appropriate number of sensing nodes is selected according to the target area, and the population is initialized using a chaotic sequence. Subsequently, the improved Flamingo Algorithm is adopted to optimize and solve the coverage model, with the coverage rate as the fitness function and the coordinates of all randomly distributed sensing nodes as the initial foraging positions. Next, a search for candidate foraging sources is performed to obtain the coordinates of sensing nodes with higher fitness; the coordinate components of these candidate foraging sources are further optimized through chaos theory to derive the foraging source with the highest fitness. Finally, the coordinates of the optimal foraging source are output, which correspond to the coordinate values of all sensing nodes in the target area. Experimental results show that after 100 and 200 iterations, the coverage rate of the improved Flamingo Search Optimization Algorithm is 7.48% and 5.68% higher than that of the original FSA, respectively. Furthermore, the findings indicate that, by properly configuring the Flamingo population size and the number of iterations, the improved algorithm achieves a higher coverage rate compared to other benchmark algorithms. Full article

Soil microorganisms play a crucial role in maintaining soil functionality and ecological balance by participating in key processes such as organic matter decomposition, nutrient cycling, soil structure formation, and plant health support. High-throughput sequencing was utilized in this study to systematically investigate the influence of different crop types, maize (Zea mays), soybean (Glycine max), and Eleutherococcus senticosus, on the communities and assembly mechanisms of soil microorganisms in a cold-temperate agroecosystem. The results reveal that cultivation practices led to significant differences in soil chemical properties compared to fallow land (CK). Total carbon (TC), total nitrogen (TN), and available nitrogen (AN) were significantly lower in CK than in cultivated soils, with the highest values observed in maize treatments among all crop types (p < 0.05). Furthermore, the alpha diversity of bacteria in the maize and soybean treatments was significantly higher than that in CK, while there was no significant difference between the Eleutherococcus senticosus treatment and CK. However, no significant differences were observed in the ACE and Chao1 indices of the soil fungal communities across the four crop types. Beta diversity of bacterial and fungal communities exhibited significant variations under different crop cultivation practices. Specifically, compared with CK, the relative abundance of Sphingomonas, which contributes to the degradation of complex organic compounds, and Gemmatimonas, which plays a role in nitrogen cycling, significantly increased, whereas the relative abundance of Clavaria, a genus capable of decomposing recalcitrant lignin and cellulose, decreased. Analysis of community assemblies revealed that both bacterial and fungal communities were predominantly influenced by deterministic processes across all crop types. This finding provides a scientific basis for maintaining soil fertility in a targeted manner, precisely protecting crop health and optimizing agricultural management efficiently, thereby supporting sustainable agricultural practices. In conclusion, by examining microbial diversity and community dynamics across different crops, along with the underlying environmental factors, this study aims to enhance our understanding of plant–microbe interactions and provide insights for sustainable agricultural practices in cold-temperate regions. Full article

Environmental physicochemical factors and microorganisms play critical roles in the health of oysters. However, the impact of high-density oyster farming—a highly efficient filter-feeding bivalve system—on environmental conditions and microbial community structure and function remains poorly understood. This study conducted four-season monitoring of the water and sediment parameters in a semi-enclosed bay commercial oyster aquaculture (OA) system and a control area (CT), coupled with 16S rRNA amplicon sequencing of the environmental microbiota. Oyster aquaculture caused negligible disruption to water column parameters but significantly increased the concentrations of total organic carbon (TOC, annual mean OA vs. CT:1.15% vs. 0.56%), sulfides (annual mean OA vs. CT:67.72 vs. 24.99 mg·kg⁻¹), and heavy metals (Cd, Pb, Cu, Zn, and Cr) in the sediment. α-diversity (Shannon and Chao indices) exhibited minimal overall perturbation, with significant inter-regional differences observed only in winter for both water and sediment. The bacterial community structure of the water column was significantly altered only in winter, whereas sediment communities showed structural shifts in spring, summer, and autumn. Water microbiota were primarily influenced by turbidity, dissolved oxygen, salinity, the Si/N ratio, and silicates. Sediment microbiota were correlated with Pb, Cu, Zn, TOC, Cr, and sediment particle size. Water bacterial functions displayed only four significantly divergent biogeochemical processes annually (sulfur compound respiration; OA vs. CT). In contrast, sediment bacteria exhibited 29 significantly disrupted functions annually, with the greatest seasonal divergence in winter (11/67 functions). Spring, summer, and autumn sediment functions showed distinct patterns. Understanding these environmental–microbial interactions is essential for sustainable oyster aquaculture and ecological optimization. Full article

This research explored the effects of supplementing taurine in a low-fishmeal diet on the growth, hepatic antioxidant capacity, muscle quality, intestinal health, and alleviation of inflammatory response of golden pompano (Trachinotus ovatus). Over an eight-week period, 300 juvenile fish (initial weight 9.4 ± 0.47 g) were randomly allocated into 12 net enclosures (1.0 × 1.0 × 1.5 m), with each treatment group comprising three replicate cages containing 25 specimens. The results demonstrated that an optimal taurine inclusion level of 1.0–1.5% significantly promoted growth, as evidenced by the increased weight gain rate (WGR) and specific growth rate (SGR). It also protected hepatic health by reducing alanine aminotransferase (ALT) activity and enhancing antioxidant capacity. Activation of the hepatic Nrf2/Keap-1/HO-1 signaling pathway increased the level of antioxidant gene expression, including catalase (CAT) and superoxide dismutase (SOD). In addition, the appropriate supplementation of taurine significantly down-regulated muscle hardness-related genes (cathepsin B (CatB) and cathepsin L (CatL)) and promoted the growth and differentiation of myoblasts, thus improving muscle quality. The chymotrypsin of fish fed the A25T10 diet was significantly higher than those in other groups (p < 0.05). The amylase (AMY) of fish fed the A25T15 diet was significantly higher than those in other groups (p < 0.05). The Chao1, Shannon, and Simpson of fish fed the A25T15 diet were significantly higher than those in other groups (p < 0.05). Proteobacteria were the most abundant in group A25T10. The relative abundance of Photobacterium rose in the A25RT10 group. In this study, taurine supplementation can down-regulate the expression of intestinal pro-inflammatory factors (interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), interleukin-8 (IL-8)) and up-regulate the expression of anti-inflammatory factor interleukin-10 (IL-10), enhance intestinal immunity, and improve intestinal digestion and absorption. Therefore, the addition of 1–1.5% taurine to low-fishmeal feeds can improve the growth performance of golden pompano. Full article

This study proposes the Chaos Fusion Mutation-Based Weighted Mean of Vectors Algorithm, an advanced optimization technique within the weighted mean of vectors (INFO) framework for synthesizing unequally spaced linear arrays. The proposed algorithm incorporates three complementary mechanisms: a good-point-set initialization to enhance early population coverage, a sine–tent–cosine (STC) chaos–based adaptive parameterization to balance exploration and exploitation, and a normal-cloud mutation to preserve diversity and prevent premature convergence. Array-factor (AF) optimization is posed as a constrained problem, simultaneously minimizing sidelobe level (SLL) and achieving deep-null steering, with penalties applied to enforce geometric and engineering constraints. Across diverse array-synthesis tasks, the proposed algorithm consistently attains lower peak SLLs and more accurate nulls, with faster and more stable convergence than benchmark metaheuristics. Across five simulation scenarios, it demonstrates robust superiority, notably surpassing an enhanced IWO in the combined objectives of deep-null suppression and maximum SLL reduction. In a representative engineering example, we obtain an SLL and a deep null of approximately −32.30 and −125.1 dB, respectively, at 104°. Evaluation of the CEC2020 real-world constrained problems confirms robust convergence and competitive statistical ranking. For reproducibility, all data and code are publicly accessible, as detailed in the Data Availability section. Full article

Reservoir computing is an approach to machine learning that leverages the dynamics of a complex system alongside a simple, often linear, machine learning model for a designated task. While many efforts have previously focused their attention on integrating neurons, which produce an output in response to large, sustained inputs, we focus on using differentiating neurons, which produce an output in response to large changes in input. Here, we introduce a small-world graph built from rings of differentiating neurons as a Reservoir Computing substrate. We find the coupling strength and network topology that enable these small-world networks to function as an effective reservoir. The dynamics of differentiating neurons naturally give rise to oscillatory dynamics when arranged in rings, where we study their computational use in the Reservoir Computing setting. We demonstrate the efficacy of these networks in the MNIST digit recognition task, achieving comparable performance of 90.65% to existing Reservoir Computing approaches. Beyond accuracy, we conduct systematic analysis of our reservoir’s internal dynamics using three complementary complexity measures that quantify neuronal activity balance, input dependence, and effective dimensionality. Our analysis reveals that optimal performance emerges when the reservoir operates with intermediate levels of neural entropy and input sensitivity, consistent with the edge-of-chaos hypothesis, where the system balances stability and responsiveness. The findings suggest that differentiating neurons can be a potential alternative to integrating neurons and can provide a sustainable future alternative for power-hungry AI applications. Full article

The rapid development of the Internet of Things (IoT) poses significant problems in securing heterogeneous, massive, and high-volume network traffic against cyber threats. Traditional intrusion detection systems (IDSs) are often found to be poorly scalable, or are ineffective computationally, because of the presence of redundant or irrelevant features, and they suffer from high false positive rates. Addressing these limitations, this study proposes a hybrid intelligent model that combines quantum computing, chaos theory, and deep learning to achieve efficient feature selection and effective intrusion classification. The proposed system offers four novel modules for feature optimization: chaotic swarm intelligence, quantum diffusion modeling, transformer-guided ranking, and multi-agent reinforcement learning, all of which work with a graph-based classifier enhanced with quantum attention mechanisms. This architecture allows as much as 75% feature reduction, while achieving 4% better classification accuracy and reducing computational overhead by 40% compared to the best-performing models. When evaluated on benchmark datasets (NSL-KDD, CICIDS2017, and UNSW-NB15), it shows superior performance in intrusion detection tasks, thereby marking it as a viable candidate for scalable and real-time IoT security analytics. Full article

This article presents an innovative chaotic communication scheme that integrates the multiuser access technique known as Wideband Code Division Multiple Access (W-CDMA) with the chaos-based selective strategy Chaos-Based Selective Symbol (CSS) and the unconventional modulation Chaos Parameter Shift Keying (CPSK). The system is designed to operate in the 2.45 GHz band and provides a robust and efficient alternative to conventional schemes such as Quadrature Amplitude Modulation (QAM). The proposed CPSK modulation enables the encoding of information for multiple users by regulating the 36 parameters of a Reconfigurable Chaotic Oscillator (RCO), theoretically allowing the simultaneous transmission of up to $2^{24}$ independent users over the same channel. The CSS technique encodes each user’s information using a unique chaotic segment configuration generated by the RCO; this serves as a reference for binary symbol encoding. W-CDMA further supports the concurrent transmission of data from multiple users through orthogonal sequences, minimizing inter-user interference. The system was digitally implemented on the Artix-7 AC701 FPGA (XC7A200TFBG676-2) to evaluate logic-resource requirements, while RF validation was carried out using a ZedBoard FPGA equipped with an AD9361 transceiver. Experimental results demonstrate optimal performance in the 2.45 GHz band, confirming the effectiveness of the chaos-based W-CDMA approach as a multiuser access technique for high-spectral-density environments and its potential for use in 5G applications. Full article

Research on unmanned aerial vehicle (UAV) path planning technology in urban operation scenarios faces the challenge of multi-objective collaborative optimization. Currently, mainstream path planning algorithms, including the multi-objective particle swarm optimization (MOPSO) algorithm, generally suffer from premature convergence to local optima and insufficient stability. This paper proposes a Zaslavskii chaotic multi-objective particle swarm optimization (ZAMOPSO) algorithm to address these issues. First, three-dimensional urban environment models with asymmetric layouts, symmetric layouts, and no-fly zones were constructed, and a multi-objective model was established with path length, flight altitude variation, and safety margin as optimization objectives. Second, the Zaslavskii chaotic sequence perturbation mechanism is introduced to improve the algorithm’s global search capability, convergence speed, and solution diversity. Third, nonlinear decreasing inertia weights and asymmetric learning factors are employed to balance global and local search abilities, preventing the algorithm from being trapped in local optima. Additionally, a guidance particle selection strategy based on congestion distance is introduced to enhance the diversity of the solution set. Experimental results demonstrate that ZAMOPSO significantly outperforms other multi-objective optimization algorithms in terms of convergence, diversity, and stability, generating Pareto solution sets with broader coverage and more uniform distribution. Finally, ablation experiments verified the effectiveness of the proposed algorithmic mechanisms. This study provides a promising solution for urban UAV path planning problems, while also providing theoretical support for the application of swarm intelligence algorithms in complex environments. Full article

The restoration of “Heitutan” degraded grasslands on the Qinghai-Tibetan Plateau was hindered by suboptimal grass species mixtures, leading to low vegetation productivity, impaired soil nutrient cycling, and microbial functional degradation. Based on a 22-year controlled field experiment, this study systematically elucidated the regulatory mechanisms of different artificial grass mixtures on vegetation community characteristics, soil physicochemical properties, and bacterial community structure and function. The results demonstrated that mixed-sowing treatments significantly improved soil conditions and enhanced aboveground biomass. The HC treatment (Elymus nutans Griseb. + Poa crymophila Keng ex L. Liu cv. ‘Qinghai’ + Festuca sinensis Keng ex S. L. Lu cv. ‘Qinghai’) achieved aboveground biomass of 1580.0 and 1645.0 g·m⁻², representing 66.14% and 60.91% increases, respectively, compared to the HA monoculture (E. nutans). Concurrently, this treatment increased soil organic matter content by 52.3% and 48.4%, total nitrogen by 59.4% and 69.2%, while reducing electrical conductivity by 48.99% and 51.72%, with optimal pH stabilization (7.34–7.38). These findings confirmed that optimized grass mixtures effectively enhance soil physicochemical properties and carbon–nitrogen retention. Microbiome analysis revealed that the HE treatment (E. nutans + P. crymophila + F. sinensis + Poa poophagorum Bor. + Festuca kryloviana Reverd. cv. ‘Huanhu’) exhibited superior α-diversity indices (OTU, Shannon, Ace, Chao1, Pielou) with increases of 9.36%, 4.20%, 15.0%, 1.76%, and 13.4%, respectively, over HA, accompanied by optimal community evenness (lowest Simpson index). Core bacterial phyla included Pseudomonadota (22.7–29.9%), Acidobacteriota (21.5–23.6%), and Actinomycetota (13.6–16.0%), with significant suppression of pathogenic bacteria. Co-occurrence network analysis identified specialized functional modules, with HC and HD treatments (E. nutans + P. crymophila + F. sinensis + P. poophagorum) forming a “nitrogen transformation–antibiotic secretion” network (57.3% positive connections). Structural equation modeling (SEM) revealed that mixed sowing had the strongest direct effect on bacterial diversity (β = 0.76), surpassing indirect effects via soil (β = 0.37) and vegetation (β = 0.11). Redundancy analysis (RDA) identified vegetation cover (24.7% explained variance) and soil pH (20.0%) as key drivers of bacterial community assembly. Principal component analysis (PCA) confirmed HC and HD treatments as the most effective restoration strategies. This study elucidated a tripartite “vegetation–soil–microorganism” restoration mechanism, demonstrating that intermediate-diversity mixtures (3–4 species) optimize ecosystem recovery through niche complementarity, pathogen suppression, and enhanced nutrient cycling. These findings provided a scientific basis for species selection in alpine grassland restoration. Full article

Online customer reviews contain rich sentimental expressions of customer preferences on products, which is valuable information for analyzing customer preferences in product design. The adaptive neuro fuzzy inference system (ANFIS) was applied to the establishment of customer preference models based on online reviews, which can address the fuzziness of customers’ emotional responses in comments and the nonlinearity of modeling. However, due to the black box problem in ANFIS, the nonlinearity of the modeling cannot be shown explicitly. To solve the above problems, a chaos-driven ANFIS approach is proposed to develop customer preference models using online comments. The model’s nonlinear relationships are represented transparently through the fuzzy rules obtained, which provide human-readable equations. In the proposed approach, online reviews are analyzed using sentiment analysis to extract the information that will be used as the data sets for modeling. After that, the chaos optimization algorithm (COA) is applied to determine the polynomial structure of the fuzzy rules in ANFIS to model the customer preferences. Using laptop products as a case study, several approaches are evaluated for validation, including fuzzy regression, fuzzy least-squares regression, ANFIS, ANFIS with subtractive cluster, and ANFIS with K-means. Compared to the other five approaches, the values of mean relative error, variance of error, and confidence interval of validation error are improved based on the proposed approach. Full article

The power system’s dynamic frequency stability is affected by common-mode ultra-low-frequency oscillation and differential-mode low-frequency oscillation. Traditional frequency control based on generators is facing the problem of capacity reduction. It is urgent to explore new regulation resources such as photovoltaics. To address this issue, this paper proposes a distributed active support method based on photovoltaic systems via state–disturbance observation and dynamic surface consensus control. A three-layer distributed control framework is constructed to suppress low-frequency oscillations and ultra-low-frequency oscillations. To solve the high-order problem of the regional grid model and to obtain its unmeasurable variables, a regional observer estimating both system states and external disturbances is designed. Furthermore, a distributed dynamic frequency stability control method is proposed for wide-area photovoltaic clusters based on the dynamic surface control theory. In addition, the stability of the proposed distributed active support method has been proven. Moreover, a parameter tuning algorithm is proposed based on improved chaos game theory. Finally, simulation results demonstrate that, even under a 0–2.5 s time-varying communication delay, the proposed method can restrict the frequency deviation and the inter-area frequency difference index to 0.17 Hz and 0.014, respectively. Moreover, under weak communication conditions, the controller can also maintain dynamic frequency stability. Compared with centralized control and decentralized control, the proposed method reduces the frequency deviation by 26.1% and 17.1%, respectively, and shortens the settling time by 76.3% and 42.9%, respectively. The proposed method can effectively maintain dynamic frequency stability using photovoltaics, demonstrating excellent application potential in renewable-rich power systems. Full article

Background: In the dynamic world of commercial aviation, the efficient management of ground handling (GH) operations in aircraft turnarounds is an increasingly complex challenge, often perceived as operational chaos. Methods: This paper introduces the “Critical Minute Theorem” (CMT), a novel framework that integrates mathematical architecture principles into the optimization of GH processes. CMT identifies singular temporal thresholds, $t_k^{\mathrm{*}}$ at which small local disturbances generate nonlinear, system-wide disruptions. Results: By formulating the turnaround as a set of algebraic dependencies and nonlinear differential relations, the case studies demonstrate that delays are not random but structurally determined. The practical contribution of this study lies in showing that early recognition and intervention at these critical minutes significantly reduces propagated delays. Three case analyses are presented: (i) a fueling delay initially causing 9 min of disruption, reduced to 3.7 min after applying CMT-based reordering; (ii) baggage mismatch scenarios where CMT-guided list restructuring eliminates systemic deadlock; and (iii) PRM assistance delays mitigated by up to 12–15 min through anticipatory task reorganization. Conclusions: These results highlight that CMT enables predictive, non-technological control in turnaround operations, repositioning the human analyst as an architect of time capable of restoring structure where the system tends to collapse. Full article

Driven by the concept of sustainable aquaculture, natural feed additives with growth-promoting, antioxidant, and immune-enhancing properties have become a key research focus. This study assessed the effects of dietary red pepper powder (Capsicum annuum) supplementation at 0%, 0.5%, 1.0%, and 2.0% over 50 days on the growth, digestive function, immune and antioxidant capacities, intestinal microbiota, and gene expression in Strongylocentrotus intermedius (S. intermedius). The results indicated that red pepper powder significantly promoted growth and decreased the feed conversion ratio (FCR) (p < 0.05), with the 1.0% group showing the highest growth rate. Additionally, supplementation improved gonadal coloration and increased crude protein and lipid contents in the gonads, particularly in the 1.0% and 2.0% groups (p < 0.05). Supplementation with 1.0% and 2.0% red pepper powder enhanced digestive, immune, and antioxidant enzyme activities, while reducing malondialdehyde (MDA) levels, indicating lower lipid peroxidation. α-diversity analysis revealed the highest ACE, Chao, and Shannon indices and the lowest Simpson index in the 1.0% group, indicating greater microbial diversity. Community analysis revealed that in the red pepper powder treatment groups, beneficial bacteria, such as Firmicutes and Unclassified_f__Rhodobacteraceae, increased in relative abundance, while potential pathogens like Arcobacter, and Epsilonbacteraeota were less abundant. Red pepper powder supplementation upregulated key immune- and antioxidant-related genes while downregulating pro-inflammatory and stress-associated genes. Overall, optimal dietary supplementation of red pepper powder, particularly at 1.0%, enhanced antioxidant and immune functions, optimized intestinal microbiota, mitigated oxidative stress, and consequently promoted growth, improved gonadal quality, and strengthened overall health in S. intermedius. Full article

In the pursuit of sustainable development, optimizing water resources management while maintaining ecological balance is crucial. This study introduces a Chaos-enhanced Harris Hawks Optimizer (CEHHO) aimed at optimizing natural flow patterns in cascade reservoirs. First, an ecological scheduling model considering ensuring guaranteed output is established based on the similarity of ecological flows. Subsequently, the CEHHO algorithm is proposed, which uses tilted skew chaos mapping for population initialization, improving the quality of the initial population. In the exploration phase, an adaptive strategy enhances the efficiency of group search algorithms, enabling effective navigation of the complex solution space. A random difference mutation strategy, combined with the Q-learning algorithm, mitigates premature convergence and maintains algorithmic diversity. Comparative analysis with the existing technology under different typical hydrological frequency shows that the search accuracy and convergence efficiency of the proposed method are significantly improved. Under the guaranteed output limit of 1000 MW, the proposed method enhances the optimal, median, mean, and worst values by 293.92, 493.23, 422.14, and 381.15, respectively, compared to the HHO. Furthermore, the results of the multi-purpose guaranteed output scenario highlight the superior detection and exploitation capabilities of this algorithm. These findings highlight the great potential of the proposed method for practical engineering applications, providing a reliable tool for optimizing water resources management while maintaining ecological balance. Full article