Search Results (211)

Background/Objectives: Selective serotonin reuptake inhibitors (SSRIs), widely prescribed for anxiety disorders, may negatively impact the gut microbiota, contributing to dysbiosis. Considering the gut–brain axis’s importance in mental health, probiotics could represent an effective adjunctive strategy. This study evaluated the effects of Lactobacillus helveticus R0052 and Bifidobacterium longum R0175 on microbiota composition, metabolic activity, and immune markers in fecal samples from patients with anxiety on SSRIs, using the SHIME^® (Simulator of the Human Intestinal Microbial Ecosystem) model. Methods: The fecal microbiotas of four patients using sertraline or escitalopram were inoculated in SHIME^® reactors simulating the ascending colon. After stabilization, a 14-day probiotic intervention was performed. Microbial composition was assessed by 16S rRNA sequencing. Short-chain fatty acids (SCFAs), ammonia, and GABA were measured, along with the prebiotic index (PI). Intestinal barrier integrity was evaluated via transepithelial electrical resistance (TEER), and cytokine levels (IL-6, IL-8, IL-10, TNF-α) were analyzed using a Caco-2/THP-1 co-culture system. The statistical design employed in this study for the analysis of prebiotic index, metabolites, intestinal barrier integrity and cytokines levels was a repeated measures ANOVA, complemented by post hoc Tukey’s tests to assess differences across treatment groups. For the 16S rRNA sequencing data, alpha diversity was assessed using multiple metrics, including the Shannon, Simpson, and Fisher indices to evaluate species diversity, and the Chao1 and ACE indices to estimate species richness. Beta diversity, which measures microbiota similarity across groups, was analyzed using weighted and unweighted UniFrac distances. To assess significant differences in beta diversity between groups, a permutational multivariate analysis of variance (PERMANOVA) was performed using the Adonis test. Results: Probiotic supplementation increased Bifidobacterium and Lactobacillus, and decreased Klebsiella and Bacteroides. Beta diversity was significantly altered, while alpha diversity remained unchanged. SCFA levels increased after 7 days. Ammonia levels dropped, and PI values rose. TEER values indicated enhanced barrier integrity. IL-8 and TNF-α decreased, while IL-6 increased. GABA levels remained unchanged. Conclusions: The probiotic combination of Lactobacillus helveticus R0052 and Bifidobacterium longum R0175 modulated gut microbiota composition, metabolic activity, and inflammatory responses in samples from individuals with anxiety on SSRIs, supporting its potential as an adjunctive strategy to mitigate antidepressant-associated dysbiosis. However, limitations—including the small pooled-donor sample, the absence of a healthy control group, and a lack of significant GABA modulation—should be considered when interpreting the findings. Although the SHIME^® model is considered a gold standard for microbiota studies, further clinical trials are necessary to confirm these promising results. Full article

This paper presents a novel wireless control framework for electric drive systems by employing a fuzzy brain emotional learning neural network (FBELNN) controller in conjunction with a Disturbance Observer (DO). The communication scheme uses chaotic system dynamics to ensure data confidentiality and robustness against disturbance in wireless environments. To be applied to embedded microprocessors, the continuous-time chaotic system is discretized using the Grunwald–Letnikov approximation. To avoid the loss of generality of chaotic behavior, Lyapunov exponents are computed to validate the preservation of chaos in the discrete-time domain. The FBELNN controller is then developed to synchronize two non-identical chaotic systems under different initial conditions, enabling secure data encryption and decryption. Additionally, the DOB is introduced to estimate and mitigate the effects of bounded uncertainties and external disturbances, enhancing the system’s resilience to stealthy attacks. The proposed control structure is experimentally implemented on a wireless communication system utilizing ESP32 microcontrollers (Espressif Systems, Shanghai, China) based on the ESP-NOW protocol. Both control and feedback signals of the electric drive system are encrypted using chaotic states, and real-time decryption at the receiver confirms system integrity. Experimental results verify the effectiveness of the proposed method in achieving robust synchronization, accurate signal recovery, and a reliable wireless control system. The combination of FBELNN and DOB demonstrates significant potential for real-time, low-cost, and secure applications in smart electric drive systems and industrial automation. Full article

To address the conflicting demands of the energy crisis, environmental pollution, and economic growth, the electric vehicle (EV) industry has expanded rapidly, facilitating the widespread adoption of power batteries. This paper investigates the use of chaos theory and machine learning for predicting the remaining useful life (RUL) of lithium-ion batteries. Firstly, the mutual information method determines the time delay of the monitoring sequence, while the improved false nearest neighbor method (Cao algorithm) establishes the embedding dimension, yielding the phase space reconstruction parameters. Secondly, the maximum Lyapunov exponent identifies the chaotic properties of the capacity decay time series, and a prediction dataset is constructed based on phase space reconstruction theory. Finally, leveraging the chaotic time-series features, a support vector machine (SVM) model is developed for lithium-ion battery RUL prediction. The algorithm is subsequently validated through simulation using the NASA battery dataset. The results demonstrate that the proposed method achieves high predictive accuracy and stability, providing reliable RUL estimates for the battery management system (BMS). Full article

Constructed wetlands (CWs) are widely implemented as nature-based solutions for delivering essential ecosystem services such as water purification, carbon sequestration, and habitat provision. However, biodiversity monitoring within CWs remains limited and unevenly integrated into performance evaluations. This scoping review analyzed 76 peer-reviewed studies to assess current methods for biodiversity monitoring, explore linkages to ecosystem functions, and examine the diversity indices most frequently applied. Results revealed a predominant focus on microbial communities, primarily assessed through high-throughput sequencing and general ecological indices such as the Shannon–Wiener Diversity Index and Chao1 Richness Estimator, with limited taxonomic depth or functional specificity. Plant and animal biodiversity were addressed less frequently and were rarely linked to treatment outcomes or ecosystem services beyond regulation. Vertical subsurface flow systems were the most studied configuration, particularly in lab-scale studies, while free water surface systems exhibited greater microbial phylum richness. These findings highlight a critical need for CW-specific biodiversity monitoring frameworks that integrate microbial, plant, and faunal assessments using functionally relevant phylogenetic indices such as Rao’s Quadratic Entropy and Faith’s Phylogenetic Diversity. Emphasis on standardization, trait-based analyses, and mechanistic approaches is essential for enhancing ecological interpretation and ensuring biodiversity is recognized as a central component of CW design, performance, and resilience. Full article

Dynamic criticality—the balance between order and chaos—is fundamental to genome regulation and cellular transitions. In this study, we investigate the distinct behaviors of gene expression dynamics in MCF-7 breast cancer cells under two stimuli: heregulin (HRG), which promotes cell fate transitions, and epidermal growth factor (EGF), which binds to the same receptor but fails to induce cell-fate changes. We model the system as an open, nonequilibrium thermodynamic system and introduce a convergence-based approach for the robust estimation of information-thermodynamic metrics. Our analysis reveals that the Shannon entropy of the critical point (CP) dynamically synchronizes with the entropy of the rest of the whole expression system (WES), reflecting coordinated transitions between ordered and disordered phases. This phase synchronization is driven by net mutual information scaling with CP entropy dynamics, demonstrating how the CP governs genome-wide coherence. Furthermore, higher-order mutual information emerges as a defining feature of the nonlinear gene expression network, capturing collective effects beyond simple pairwise interactions. By achieving thermodynamic phase synchronization, the CP orchestrates the entire expression system. Under HRG stimulation, the CP becomes active, functioning as a Maxwell’s demon with dynamic, rewritable chromatin memory to guide a critical transition in cell fate. In contrast, under EGF stimulation, the CP remains inactive in this strategic role, passively facilitating a non-critical transition. These findings establish a biophysical framework for cell fate determination, paving the way for innovative approaches in cancer research and stem cell therapy. Full article

There are serious ecological and environmental risks associated with groundwater level decline, particularly in areas with little in situ monitoring. In order to monitor and assess the resilience and dependability of groundwater storage, this paper proposes a solid methodology that combines data from land surface models and satellite gravimetry. In particular, the GRACE Groundwater Drought Index (GGDI) is used to analyze the estimated groundwater storage anomalies (GWSA) from the Gravity Recovery and Climate Experiment (GRACE) and the Global Land Data Assimilation System (GLDAS). Aquifer resilience, or the likelihood of recovery after stress, and aquifer reliability, or the long-term probability of remaining in a satisfactory state, are calculated using the core method. The two main components of the methodology are (a) calculating GWSA by subtracting the surface and soil moisture components from GLDAS, total water storage from GRACE, and comparing the results to in situ groundwater level data; and (b) standardizing GWSA time series to calculate GGDI and then estimating aquifer resilience and reliability based on predetermined threshold criteria. Using this framework, we validate GRACE-derived GWSA with in situ observations in eight sub-basins of the Chao Phraya River (CPR) basin, obtaining Pearson correlation coefficients greater than 0.82. With all sub-basins displaying values below 35%, the results raise significant questions about resilience and dependability. This method offers a framework that can be applied to assessments of groundwater sustainability worldwide. Full article

Water use efficiency (WUE) and water stress (WS) are keys indicators of water sustainability, particularly in regions with rising demand and limited supply. In Thailand, increasing water use across sectors and climate variability have raised concerns about long-term availability. This study applied Sustainable Development Goal (SDG) indicators 6.4.1 (WUE) and 6.4.2 (WS) at the river basin level, covering 22 basins from 2015 to 2022, to provide a more localized perspective than national assessments. A modified version of the FAO’s monitoring framework was applied, using standardized formulas based on sectoral water withdrawals and economic productivity. Supplementary data were gathered through estimation techniques, field surveys, and stakeholder consultations. The results showed a 21.0% decline in WUE and a rise in WS from 9.68% to 13.8%, indicating increased pressure on water resources. A very strong negative correlation was found between WUE and WS (r = −0.97, p < 0.001), although causation could not be inferred. Regional differences were evident: basins such as Tha Chin and Chao Phraya showed worsening conditions, while the Peninsula–West Coast remained relatively stable. These findings suggest the need for targeted policies to improve water use efficiency, especially in agriculture, and to enhance monitoring systems. Increasing wastewater reuse and implementing efficiency measures could help to reduce stress in vulnerable basins and support Thailand’s progress to achieving SDG 6.4. Full article

This study explored how potted treatments (with and without Scleroderma bovista inoculation) shape rhizosphere microbial diversity in hazel across five soils using split-root cultivation. Three treatments (control, split-root, split-root with S. bovista) were analyzed for root growth and microbial dynamics. S. bovista inoculation consistently enhanced root parameters (number, tips) in all soils. Potted treatments (with and without S. bovista inoculation) altered microbial features (OTU/ASV), with only 0.9–3.3% of features remaining unchanged. At the class level, potting increased Agaricomycetes abundance while reducing Sordariomycetes, a trend amplified by S. bovista. Potting decreased species richness estimates (ACE and Chao1), while both treatments lowered diversity index (Shannon index). Potted treatments without S. bovista inoculation drove stronger shifts in species composition than inoculation. Findings reveal potting and S. bovista synergistically recruit symbiotic fungi via root exudates, establishing disease-suppressive communities that selectively inhibit pathotrophic fungi (particularly plant pathogen Coniothyrium and fungal parasite Cladobotryum) while roughly maintaining non-pathogenic saprotrophic microbes essential for organic matter decomposition. This work provides insights for optimizing hazel orchard management and ectomycorrhizal agent development. Full article

Standard correlation analysis is one of the frequently used methods in financial markets. However, this matrix can give erroneous results in the conditions of chaos, fractional systems, entropy, and complexity for the variables. In this study, we employed the time-dependent correlation matrix based on isospectral flow using the Lie group method to assess the price of Bitcoin and gold from 19 July 2010 to 31 December 2024. Firstly, we showed that the variables have a chaotic and fractional structure. Lo’s rescaled range (R/S) and the Mandelbrot–Wallis method were used to determine fractionality and long-term dependence. We estimated and tested the d parameter using GPH and Phillips’ estimators. Renyi, Shannon, Tsallis, and HCT tests determined entropy. The KSC determined the evidence of the complexity of the variables. Hurst exponents determined mean reversion, chaos, and Brownian motion. Largest Lyapunov and Hurst exponents and entropy methods and KSC found evidence of chaos, mean reversion, Brownian motion, entropy, and complexity. The BDS test determined nonlinearity, and later, the time-dependent correlation matrix was obtained by using the stochastic SO(2) Lie group. Finally, we obtained robustness check results. Our results showed that the time-dependent correlation matrix obtained by using the stochastic SO(2) Lie group method yielded more successful results than the ordinary correlation and covariance matrix and the Spearman correlation and covariance matrix. If policymakers, financial managers, risk managers, etc., use the standard correlation method for economy or financial policies, risk management, and financial decisions, the effects of nonlinearity, fractionality, entropy, and chaotic structures may not be fully evaluated or measured. In such cases, this can lead to erroneous investment decisions, bad portfolio decisions, and wrong policy recommendations. Full article

The use of environmental DNA (eDNA) metabarcoding to analyze fish species diversity across different aquatic ecosystems is well documented. Nonetheless, there is a gap in validating eDNA metabarcoding studies on the diversity and structure of fish communities in coastal ecosystems, particularly in comparing these findings with bottom trawl catch data. In this study, we employed eDNA metabarcoding to explore species composition and relative abundance in fish communities, taxonomic-level diversity variations, and the interplay between community structures and environmental factors in the Yellow Sea and compared these results with those obtained from bottom trawl catches. In addition, we compared the various methods used to estimate the distributions of taxonomic, phylogenetic, and functional diversity factors. We found that eDNA metabarcoding detected a greater number of species (86 vs. 41), genera (73 vs. 37), and families (42 vs. 25) than bottom trawl results at each sampling station. eDNA metabarcoding provided higher Shannon, Simpson, and Chao1 alpha diversity indices than the bottom trawl results. The PCoA results showed that eDNA metabarcoding samples could be more clearly separated at the sampling sites in the Zhuanghe (ZH) and Lianyungang (LYG) areas than bottom trawling samples. The RDA analysis indicated that temperature, along with NO3- and NH₄⁺ concentrations, were pivotal in shaping the geographical patterns of fish communities, as identified through eDNA metabarcoding, echoing findings from bottom trawling studies. Furthermore, our findings suggest that eDNA barcoding surpasses bottom trawling in detecting taxonomic and phylogenetic diversity, as well as in uncovering greater functional diversity at the local level. Conclusively, eDNA metabarcoding emerges as a valuable complement to bottom trawling, offering a multifaceted approach to biodiversity monitoring that not only boosts efficiency but also reduces environmental impact on coastal ecosystems. Full article

Small, forested catchments are prototypes of terrestrial ecosystems and have been studied in several disciplines of environmental science over several decades. Time series of water and matter fluxes and nutrient concentrations from these systems exhibit a bewildering diversity of spatiotemporal patterns, indicating the intricate nature of processes acting on a large range of time scales. Nonlinear dynamics is an obvious framework to investigate catchment time series. We analyzed selected long-term data from three headwater catchments in the Bramke valley, Harz mountains, Lower Saxony in Germany at common biweekly resolution for the period 1991 to 2023. For every time series, we performed gap filling, detrending, and removal of the annual cycle using singular system analysis (SSA), and then calculated metrics based on ordinal pattern statistics: the permutation entropy, permutation complexity, and Fisher information, as well as their generalized versions (q-entropy and α-entropy). Further, the position of each variable in Tarnopolski diagrams is displayed and compared to reference stochastic processes, like fractional Brownian motion, fractional Gaussian noise, and β noise. Still another way of distinguishing deterministic chaos and structured noise, and quantifying the latter, is provided by the complexity from ordinal pattern positioned slopes (COPPS). We also constructed horizontal visibility graphs and estimated the exponent of the decay of the degree distribution. Taken together, the analyses create a characterization of the dynamics of these systems which can be scrutinized for universality, either across variables or between the three geographically very close catchments. Full article

Photovoltaic systems are affected by light intensity, temperature, and radiation angle, which influence their efficiency. Accurate estimation of PV module parameters is essential for improving performance. This paper presents an improved optimization technique based on the White Shark Optimizer (WSO) algorithm to optimize key characteristics of the PV module, including current, voltage, series resistance, shunt resistance, and ideality factor. The proposed method incorporates opposition-based learning (OBL) and chaos theory to improve search efficiency. A critical aspect of PV module modeling is inherent symmetry in electrical and thermal characteristics, where balanced parameter estimation ensures uniform energy conversion efficiency. With the application of symmetrical search techniques during the process of optimization, the proposed method enhances convergence robustness and stability, ensuring consistent and precise results across different PV models. Experimental evaluations conducted on three PV models—Single Diode Model (SDM), Double Diode Model (DDM), and general photovoltaic modules—demonstrate that the proposed method outperforms existing metaheuristic techniques such as Jumping Spider Optimization (JSO), Harris Hawks Optimization (HHO), WOA, Gray Wolf Optimizer (GWO), and basic WSO. Key results show improvements in the Friedman rating by 8.1%, 10.79%, and 9.6% for the SDM, DDM, and PV modules, respectively. Additionally, the proposed method achieves superior parameter estimation accuracy, as evidenced by reduced RMSE values compared to the competing algorithms. This work highlights the importance of advanced optimization techniques in maximizing PV output power while maintaining symmetry in parameter estimation. By ensuring a balanced and systematic optimization approach, this study assists in the development of robust and efficient solutions for PV system modeling. Full article

Leaf area index (LAI) serves as a crucial indicator for assessing vegetation growth status, and unmanned aerial vehicle (UAV) optical remote sensing technology provides an effective approach for forest pest-related research. This study investigated the feasibility of LAI estimation in Moso bamboo (Phyllostachys pubescens) forests with different damage levels using UAV data while simultaneously exploring the scale effects of various spatial resolutions. Through image resampling using 10 distinct spatial resolutions and field data classification based on Pantana phyllostachysae Chao pest severity (healthy and mild damaged as Scheme 1, moderate damaged and severe damaged as Scheme 2, and all as Scheme 3), three machine learning algorithms (SVM, RF, and XGBoost) were employed to establish LAI estimation models for both single and mixed damage levels. Comparative analysis was conducted across different schemes, algorithms, and spatial resolutions to identify optimal estimation models. The results showed that (1) XGBoost-based regression models achieved superior performance across all schemes, with optimal model accuracy consistently observed at 3 m spatial resolutions; (2) minimal scale effects occurred at a 3 m resolution for Schemes 1 and 2, while Scheme 3 showed lowest scale effects at 1.5 m followed by 3 m resolutions; (3) Scheme 3 exhibited significant advantages in mixed damaged bamboo forest inversion with robust performance across all damage levels, whereas Schemes 1 and 2 demonstrated higher accuracy for single damaged scenarios compared to mixed damaged. This research validates the feasibility of incorporating pest stress factors into LAI estimation through different pest damage models, offering novel perspectives and technical support for parameter inversion in Moso bamboo forests. Full article

Gamasid mites (Acari: Mesostigmata) are ecologically diverse arthropods, many of which act as vectors for zoonotic diseases such as rickettsial pox and hemorrhagic fever with renal syndrome. This study investigates the faunal and ecological patterns of gamasid mites across five zoogeographic microregions in Yunnan Province, China, a biodiversity hotspot with complex topography. From 1990 to 2022, 18,063 small mammal hosts (primarily rodents) were surveyed, yielding 167 mite species (141,501 specimens). The key findings include the following: (1) Low host specificity: most mite species parasitized >10 host species, with Laelaps nuttalli, L. echidninus, Dipolaelaps anourosorecis, L. guizhouensis, L. turkestanicus, and L. chini dominating (>76.59% abundance). (2) Environmental heterogeneity: mountainous and outdoor habitats exhibited higher mite diversity than flatland/indoor environments. (3) Zoonotic risks: thirteen vector species with low host specificity were identified, potentially amplifying disease transmission. (4) Ecological niche dynamics: high niche overlaps (e.g., Laelaps guizhouensis vs. L. xingyiensis: O_ik = 0.997) and positive interspecific correlations (e.g., L. echidninus vs. L. nuttalli: R = 0.97, p < 0.01) suggest co-occurrence trends on shared hosts. (5) Biogeographic patterns: mite communities were clustered distinctly by microregion, with the highest similarity being obtained between western/southern plateaus (IV and V) and unique diversity in the Hengduan Mountains (I). (6) Chao 1 estimation predicted 203 total mite species in Yunnan, 36 of which were undetected in the current sampling. These results highlight the interplay of biogeography, host ecology, and environmental factors in shaping mite distributions, with implications for zoonotic disease surveillance in biodiverse regions. Full article

This article is devoted to the investigation of synchronization noise immunity in quadrature chaos shift keying (QCSK) communication systems and its profound impact on system performance. The study focuses on Colpitts and Vilnius chaos oscillators in different synchronization configurations, and the reliability of the system in the particular configuration is assessed using the bit error rate (BER) estimation. The research considers synchronization imbalances and demonstrates their effect on the accuracy of data detection and overall transmission stability. The article proposes an approach for optimal bit detection in the case of imbalanced synchronization and correlated chaotic signals in data transmission. The study practically shows the importance of the proposed decision-making technique, revealing that certain adjustments can significantly enhance system noise resilience. Full article