Nonlinear Patterns Research Articles

Association between non-insulin-based insulin resistance indicators and frailty progression: a national cohort study and mendelian randomization analysis

BackgroundInsulin resistance (IR) is linked to an increased risk of frailty, yet it remains unclear whether the non-insulin-based IR indicators are associated with frailty trajectories and physical function decline. It aimed to examine the associations of triglyceride-glucose (TyG) index, metabolic score for insulin resistance (METS-IR), estimated glucose disposal rate (eGDR) and with long-term deficit-accumulation frailty trajectories and physical function decline.MethodsData from 6722 participants in the China Health and Retirement Longitudinal Study (CHARLS) were analyzed. Baseline TyG index, METS-IR, eGDR, along with the frailty index (FI) over nine years, were calculated. FI trajectories were assessed using group-based trajectory model (GBTM). Logistic regression models were used to analyze the associations between IR indicators with FI trajectory and frailty risk. Restricted cubic splines (RCS) models were utilized to detect potential dose-response associations. Linear mixed-effects model was used to evaluate associations with FI development speed. Age, gender, educational level, marital status, smoking status, drinking status, life satisfaction, social activity and sleep duration were adjusted. Additionally, a two-sample Mendelian randomization (MR) was performed to assess the causality of observed associations.ResultsThree FI trajectories including low-stable frailty, moderate-increasing frailty, and accelerated rising frailty were identified. Regarding the frail risk, each SD increment in TyG index was associated with a 16.1% increase in the risk of frailty (OR = 1.161; 95%CI: 1.092, 1.235). An inverse association was observed for eGDR with the OR (95%CI) being 0.741 (0.696, 0.788). A linear relationship was observed between baseline TyG index and frailty risk (P nonlinear = 0.696), but nonlinear association patterns for eGDR (P nonlinearity < 0.010) and METS-IR (P nonlinearity < 0.010). Each SD increment of TyG index was associated with greater FI increase (β = 0.005 SD/y; 95%CI = 0.002, 0.008 SD/y; P < 0.001). A similar association pattern was observed for METS-IR, and participants in the highest quartile of METS-IR showed significantly greater FI progression, with β value of 0.013 (95% CI = 0.004, 0.022). Each SD increment of eGDR was associated with a slower increase in FI (β=-0.006 SD/y, 95% CI=-0.009, -0.003 SD/y; P < 0.001). Participants in the highest quartile of eGDR presented a lower annual change in FI compared with participants in quartile 1 group during follow-up (β=-0.013 SD/y, 95% CI=-0.022, -0.005 SD/y; P for trend = 0.001). Similar findings were observed for physical function decline. Findings from MR analysis showed a causal relationship between higher TyG index and increased risk of frailty (β = 0.214, 95% CI = 0.079, 0.349; P = 0.002).ConclusionsThe non-insulin-based IR indicators, including TyG index, METS-IR and eGDR, were independently associated with the frailty progression and physical function decline. Monitoring and managing abnormal glucose metabolism should be recommended as a part of comprehensive strategies to prevent or delay the progression of frailty.

Oxidative stress and retinopathy: evidence from epidemiological studies

BackgroundPrevious studies have suggested oxidative stress may play a key role in the pathogenesis of retinopathy, while evidence from observational studies directly linking oxidative biomarkers to clinically relevant outcomes has been limited. This study aims to investigate the association between oxidative balance score (OBS) and prevalence of retinopathy in a nationally representative sample of U.S. adults, including both those with and without diabetes.MethodsData were obtained from the NHANES 2005–2008, including 3,287 participants. OBS was calculated from 16 dietary and 4 lifestyle components and categorized into tertiles. Weighted logistic regression was used to assess the association between OBS and retinopathy in the overall, diabetic, and non-diabetic populations, and Cox proportional hazards models evaluated the link between OBS and all-cause mortality in those with retinopathy. Restricted cubic spline (RCS) analysis explored the dose-response relationship between OBS and retinopathy. Subgroup analyses and interaction tests were conducted based on age, sex, race, BMI, education level, and diabetes status.ResultsParticipants in the highest OBS tertile had a 28% lower risk of retinopathy compared to those in the lowest tertile (OR 0.72, 95% CI 0.54–0.95, P = 0.023). RCS analysis showed a significant overall association between higher OBS and reduced retinopathy risk, without a nonlinear pattern. In participants with retinopathy, higher OBS was linked to a 60% reduction in all-cause mortality (HR 0.40, 95% CI 0.24–0.66, P < 0.001). Subgroup analysis revealed stronger inverse associations between OBS and retinopathy in younger individuals and those with higher education, with a significant interaction between OBS and age (P for interaction < 0.05).ConclusionOur study provides evidence that higher cumulative antioxidant exposure assessed by OBS is associated with a reduced risk and severity of retinopathy and lower all-cause mortality in U.S. adults with retinopathy, highlighting the importance of maintaining a favorable oxidative balance in retinal health.

Rupiah Exchange Rate Prediction with Long Short-Term Memory Algorithm

The fluctuation of the Rupiah exchange rate against foreign currencies in Asia presents a significant challenge in maintaining Indonesia’s economic stability. This study aims to forecast Rupiah exchange rates using the Long Short-Term Memory (LSTM) algorithm. Weekly exchange rate data from 2020 to 2024 were analyzed using a machine learning approach. The process involved data normalization, model training, and evaluation using Mean Absolute Per- centage Error (MAPE) and R-Squared. The results indicate that the LSTM model effectively captures non-linear patterns in time series data with high accuracy. This model implementation provides valuable insights for financial decision-makers, regulators, and academics in understanding the dynamics of foreign exchange markets.

A Deep-Learning Method for Remaining Useful Life Prediction of Power Machinery via Dual-Attention Mechanism.

Remaining useful life (RUL) prediction is a cornerstone of Prognostic and Health Management (PHM) for power machinery, playing a crucial role in ensuring the reliability and safety of these critical systems. In recent years, deep learning techniques have shown great promise in RUL prediction, providing more reliable and accurate outcomes. However, existing models often struggle with comprehensive feature extraction, especially in capturing the complex behavior of power machinery, where non-linear degradation patterns arise under varying operational conditions. To tackle this limitation, we propose a multi-feature fusion model leveraging a dual-attention mechanism. Initially, convolutional neural networks (CNNs) and channel attention mechanisms are employed to preliminarily extract spatial features. Subsequently, a layer combining a Gate Recurrent Unit (GRU) and self-attention mechanisms is used to further extract and integrate temporal features. Finally, RUL values are predicted via regression. The effectiveness of the proposed method was validated on C-MAPSS datasets, and its superior performance in RUL prediction was demonstrated through comparative analysis with other methods.

Comparison of the asymmetric multifractal behavior of green and U.S. bonds against benchmark financial assets

Presently, financial portfolio managers lack a solid basis for building a reliable risk management strategy for green debt instrument investments due to the lack of compelling growth and resilience data. Therefore, this study assesses the role of green bonds in financial markets by assessing and correlating their complex scaling behaviors across multiple periods with those of key benchmark assets (e.g., conventional bonds, high-yield bonds, Euro-Dollar exchange, Dow Jones Industrial Index, Bitcoin, and Gold). Specifically, we explore linear and nonlinear correlation patterns using cross-correlation tests and the dynamic conditional correlation model, focusing on bond interactions under various degrees of freedom. Our analysis reveals that although most assets exhibit nonlinear correlations, Bitcoin uniquely aligns linearly with U.S. bonds under certain conditions. Green bonds, however, display nonlinear correlations with Bitcoin and stand out for their distinct upward financial persistence. We find also that green bonds are primary drivers in the financial domain, highlighted by their pronounced interactions and the consistent cross-correlation with the Euro-Dollar exchange rate. Moreover, green bonds have the lowest multifractality, showing persistent upward trends and antipersistent downward trends, rendering them quite resilient during periods of high volatility. These results imply that green bonds may be advantageous to portfolio risk management strategies, especially during crises when diversification and hedging tactics are needed.

Age-dependent interaction between serum zinc and triglyceride-glucose index among American adults: National Health and Nutrition Examination Survey

IntroductionZinc plays a crucial role in glucose metabolism. The association between serum zinc and insulin resistance has recently been investigated as well, but the findings are inconsistent. The triglyceride-glucose index (TyG) is frequently utilized in epidemiological research to assess insulin resistance. The association between serum zinc levels and TyG has not yet been explored. Therefore, we designed this cross-sectional study to assess the relationship between serum zinc and TyG in adults using data from the National Health and Nutrition Examination Survey (NHANES).MethodsA cross-sectional analysis was performed on 1,610 adults aged ≥20 years who participated in the National Health and Nutrition Examination Survey (NHANES) 2011–2016. The participants were stratified by age, and the differences in log-transformed serum zinc quartiles and TyG were further evaluated in age groups &amp;lt;60 years and ≥60 years using multivariable linear regression with an interaction test. Additionally, a restricted cubic spline (RCS) model was employed to examine the dose-response relationships between log-transformed serum zinc and TyG.ResultsIn this cross-sectional study, a significant interaction was observed between log-transformed serum zinc and TyG in individuals aged &amp;lt;60 years and those aged ≥60 years when log-transformed serum zinc was transformed into a categorical variable (P-value for the likelihood ratio test for the interaction was P = 0.017). Additionally, in the fully adjusted analyses, the association between log-transformed serum zinc and TyG in the age &amp;lt;60 years group demonstrated a J-shaped nonlinear pattern (P for nonlinearity = 0.014), with an inflection point at ~1.94 μg/dL. While in the age ≥60 years group, it exhibited an inverted-L shaped nonlinear pattern (P for nonlinearity &amp;lt; 0.001***).ConclusionThere is a significant relationship between log-transformed serum zinc and TyG in adults in the United States, with age potentially influencing this association. Further prospective studies are needed to offer additional evidence and insights into these findings.

A constitutive model for coal gangue coarse-grained subgrade filler incorporating particle breakage

The accumulation and discharge amount of coal gangue are substantial, occupying significant land resources over time. Utilizing coal gangue as subgrade filler can generate notable economic and social benefits. Coal gangue coarse-grained soil (CGSF) was used to conduct a series of large-scale vibration compaction tests and large-scale triaxial tests. The results indicate that the maximum dry density of CGSF initially increases and then decreases with the increase in fractal dimension. The stress–strain curves of the samples exhibit a distinct nonlinear growth pattern. Analysis of the compaction effect suggests that the compaction degree of CGSF should not be lower than 93%. As the confining pressure increases, the extent of failure strength improvement due to increased compaction decreases. Additionally, the failure strength of samples initially increases and then decreases with the increase in coarse particle content. A modified quadratic polynomial fractal model gradation equation was proposed to describe the gradation of samples after particle breakage. Based on this, a new quantitative index for particle breakage was established. Analysis of particle breakage in samples revealed that higher confining pressure and greater coarse particle content lead to increased particle breakage. The breakage exhibited a significant size effect, and the impact of particle gradation on sample breakage was greater than that of confining pressure. The stress–strain relationship of CGSF was analyzed by using a logarithmic constitutive model, and the correlation between model parameters and the newly derived particle breakage index was generated. A constitutive model incorporating particle breakage for CGSF was established, and its accuracy was validated.

ENHANCING 〖PM〗_(2.5) PREDICTION IN KEMAYORAN DISTRICT, DKI JAKARTA USING DEEP BILSTM METHOD

Worldwide air pollution is a concern, and this is especially true in Indonesia, where most people breathe air that is more contaminated than recommended by the WHO. The concentration of presents notable health hazards. The respiratory system is the primary route of absorption for , allowing it to enter the lung alveoli and enter the bloodstream. Given the significant health risks associated with exposure, accurate forecasting methods are crucial to anticipate and mitigate its effects. Traditional forecasting methods like ARIMA have limitations in handling non-linear and complex patterns. Therefore, an accurate machine learning method is needed to improve forecasting performance. This research employs Deep Bidirectional Long-Short Term Memory (BiLSTM), a deep learning model particularly suited for time series forecasting due to its ability to capture both past and future dependencies in sequential data. To achieve accurate and precise forecasts for predicting concentration levels in Kemayoran District in November , 2023 (24 hours), this research utilized hourly concentration data from May until October , 2023, using Deep BiLSTM. The outcomes demonstrated the efficiency of the model, attaining a Mean Absolute Percentage Error (MAPE) of 17.1540% (training) and 14.2862% (testing) with an 80:20 data split. The optimal parameters, which comprised 24 timesteps, Adam optimizers with a learning rate of 0.001, 16 batch sizes, 1000 epochs, and ReLU activation functions across multiple BiLSTM layers, showcased the model’s effectiveness in forecasting the concentration in Kemayoran District, DKI Jakarta, on November , 2023.

Nonlinear response of soil organic carbon sequestration to deadwood decomposition in a subtropical–temperate ecotonal forest

Abstract The decomposition of deadwood is a crucial process for the accumulation and sequestration of soil organic carbon (SOC) in forest ecosystems. Yet, the response of SOC to various classes of deadwood decay and the underlying mechanisms remain unclear. Here, we investigated the dynamics of SOC, soil properties, extracellular enzyme activities, and phospholipid fatty acid biomarkers across five decay classes (defined from 1 to 5) of Masson pine (Pinus massoniana Lamb.) downed deadwood in a subtropical-temperate ecotone forest in Central China. Results showed a nonlinear response pattern of SOC along the deadwood decomposition gradient, with the maximum value at the decay class 4. Soil available nitrogen content, bacterial biomass, fungal biomass, the ratio of fungal to bacterial biomass, cellulase, and ligninase all increased, whereas soil pH decreased along with the intensification of deadwood decay. Increases in SOC content were associated with a direct positive effect of bacteria, and direct and indirect positive effects of fungi via cellulase, but ligninase showed no significant relationship with SOC content. These findings suggest that cellulose and microbial biomass determine soil C formation and sequestration during deadwood decomposition. This study highlights the importance of the nonlinear response of SOC to deadwood decay intensification, thus helping to predict future C–climate feedbacks.

Multifractal Analysis on Ozone Depletion and Climate Change: The Time Series Approaches

The ozone layer has acted as the planet’s natural sunscreen, protecting people, plants, and animals from harmful ultraviolet rays. The Antarctic ozone hole was first announced in a paper by the British Antarctic Survey’s Joe Farman, Brian Gardiner, and Jonathan Shanklin in 1985. Many investigations are still conducting to determine the connection between ozone depletion and climate change. This research study investigates the impact of the ozone layer’s depletion at the Antarctic pole on global climate change data such as temperature and precipitation, after the year 1985 through a fractal dimension, Multifractal Detrended Fluctuation Analysis (MFDFA), and standard correlation coefficient. For this, the research work has analyzed 45 years of climate change variables such as global monthly temperature anomaly, global monthly precipitation anomaly, and Southern Hemisphere minimum ozone time series data from 1979 to 2023. The fractal dimension of the time series is obtained by rescaled range analysis, which is used to identify the fractality of the time series and long-range correlations and persistence. To study the multifractality of these fractal time series, MFDFA procedure has been applied. By applying MFDFA to these time series data, this research has identified significant multifractal characteristics, indicating complex dynamics and long-range correlations, and identified potential nonlinear patterns. This research provides valuable insights into the complex dynamics of time series data, as revealed by the calculated exponent values and MFDFA spectrum. The strong correlation observed between the exponent values of temperature anomalies, and precipitation anomalies, with ozone depletion time series provides compelling evidence for the significant impact of ozone depletion on climate change. These results highlight the potential of multifractality for understanding the intricate mechanisms underlying climate change.

Unexpected effects of pandemic-related changes on mental health: Evidence from a nationwide survey in South Korea.

Substantial evidence indicates that the COVID-19 pandemic has adversely affected population mental health globally. However, most studies assumed a linear relationship where only negative pandemic-induced changes led to worse mental health outcomes, overlooking the complex relationship between COVID-19-related changes and mental health. This study examined how various types and magnitudes of pandemic-related changes relate to depression and suicidal thoughts in a large, nationwide adult population sample. We analyzed data from the 2021 Korean Community Health Survey, a cross-sectional survey of 229,213 adults. The study examined the association between mental health outcomes and three types of pandemic-related changes: daily life impact (scored 0-100), economic activities (employment and income changes), and health behaviors (physical activity, instant food consumption, alcohol consumption, and smoking). Complex sample multiple logistic regression analysis was used to assess these associations, adjusting for sociodemographic factors. The relationship between pandemic-related changes and mental health showed non-linear patterns. Compared to those reporting moderate changes, individuals reporting either no change (depression: aOR 1.253, 95% CI 1.135-1.384; suicidal thoughts: aOR 1.355, 95% CI 1.236-1.486) or complete disruption (depression: aOR 1.895, 95% CI 1.667-2.155; suicidal thoughts: aOR 1.788, 95% CI 1.558-2.052) in daily life showed higher risks of poor mental health. Unexpectedly, positive changes such as improved working conditions (suicidal thoughts: aOR 1.419, 95% CI 1.200-1.677) and increased income (depression: aOR 1.304, 95% CI 1.139-1.493; suicidal thoughts: aOR 1.244, 95% CI 1.079-1.435) were also associated with adverse mental health outcomes. This study reveals that both minimal and substantial changes in daily life, as well as both positive and negative changes in economic conditions and health behaviors, were associated with poor mental health outcomes during the pandemic. These findings suggest the need for comprehensive mental health interventions that consider various types and magnitudes of life changes during crisis situations.

Enhancing Intrusion Detection Systems with Adaptive Neuro-Fuzzy Inference Systems

Network security has become increasingly critical in recent years. Among the various aspects of network security and considering several approaches to network security, intrusion detection systems (IDSs) have gained considerable attention. The prominence of this factor, among other factors of network security, is due to its ability to address the complex and uncertain nature of security breaches. Whenever data flow over the network, precise categorization of normal and malicious data is necessary. Past IDS systems lack precise categorization. Thus, the present study focuses on the use of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier to categorize network instances into malicious types and normal behavior. Using the KDD99 dataset, the performance of ANFIS is evaluated and compared with that of traditional machine learning models such as decision trees and multilayer perceptrons. Through experimentation with different membership functions, such as Gaussian, triangular, bell-shaped, and sigmoidal functions, Gaussian functions are identified as optimal for this specific task. The results underscore the effectiveness of ANFIS, leveraging the strengths of both artificial neural networks (ANNs) and fuzzy reasoning systems. ANFIS demonstrates superior capabilities in understanding nonlinear interaction patterns, adapting to evolving threats, and facilitating rapid learning in intrusion detection applications.

Modeling soil respiration in summer maize cropland based on hyperspectral imagery and machine learning

IntroductionSoil respiration (SR), the release of carbon dioxide (CO2) from soil due to the decomposition of organic matter and root respiration, is an important indicator for understanding agricultural carbon cycling and assessing anthropogenic impacts on the environment. Hyperspectral remote sensing offers a potential rapid, non-destructive approach for monitoring in agriculture. However, it remains uncertain whether hyperspectral remote sensing can provide an accurate and efficient method for estimating SR rate in croplands, particularly across different maize growth stages of under varying drought conditions.MethodsIn the study, we investigated the potential of combining hyperspectral remote sensing data with machine learning model (ML) to quantify SR rate in croplands. A drought field experiment was conducted, and SR and hyperspectral imagery were collected during four maize growth stages: Jointing Stage (JS), Tasseling Stage (TS), Flowering Stage (FS), and Grain Filling Stage (GFS). We compared the performance of traditional multiple linear regression (MLR) with that of an ML model (extreme gradient boosting, XGBoost), in simulating SR rate across these four growth stages.ResultsOur findings demonstrated that the simulation of the XGBoost model, utilizing soil temperature (Ts) and hyperspectral data, outperformed the MLR model. Across different growth stages, the SR simulated by the XGBoost model (R2 = 0.8103) was more reliable than that of the MLR model (R2 = 0.7451). The XGBoost model can also effectively capture the impact of drought treatments on SR.DiscussionThe XGBoost model’s tree-based structure allows it to effectively capture complex interactions and nonlinear patterns within variables, while its high sensitivity to changes in SR rates under drought conditions makes it more reliable for modeling SR across different growth stages compared to the linear-based MLR model. This study highlights the great promise of ML combined with hyperspectral imaging in predicting SR rate in croplands, which will help guide future agricultural management and environmental informatics.

Plastic harmful ingredients reduce the level of neurofilament light chain protein.

Population exposure to plastics is increasing, and plasticizers are frequently detected in humans as important ingredients of plastic products. However, patterns of exposure to harmful ingredients of plastics and their effects on neurofilament light chain (NFL), a marker of active brain pathology, are currently inconclusive. Herein, we employed a range of statistical methods to thoroughly investigate the impact of 24 plastic hazardous ingredients and their varying exposure patterns on NFL concentrations in the blood of the general population in 533 participants. Generalized linear model revealed a positive correlation between Mono-isononyl phthalate and Mono (2-Ethyl- 5-Hydroxyhexyl) Phthalate (MEHHP) with NFL. Furthermore, a significant dose-response relationship was observed between MEHHP and NFL, while Butyl paraben and Mono (Hydroxy-Isononyl) Ester exhibited a distinct "inverted U-shaped" nonlinear pattern with NFL. Additionally, Weighted Quantile Sum model allowed us to identify the mixed effects of all 24 plastic hazardous ingredients, with Mono(2-Ethyl-5-Oxohexyl) Phthalate, Mono-isobutyl phthalate, Mon butyl Phthalate, Propyl paraben and Triclosan occupying prominent positions. Finally, the latent profile analysis categorized exposures into high, medium, and low patterns, confirming that higher exposure to plastic hazardous ingredients posed a significant risk factor for elevated NFL levels in the blood. Exposure to plastic hazardous ingredients significantly increases the risk of NFL, the present contributes to early detection and intervention to reduce the incidence of neurodevelopmental disorder.

Spatiotemporal Nonlinear Characteristics and Threshold Effects of China's Water Resources

China faces shortage of water resources, particularly in the context of rapid population growth and accelerating urbanization, making the changes in its water resources among the most pronounced on Earth. Additionally, the complex interplay between climate change and human activities leads to nonlinear and non-stationary patterns in China's water resources. This study utilizes high-resolution water storage monitoring data to comprehensively analyze the nonlinear changes in water storage and its relationships with human footprint, precipitation, and temperature, revealing the complex dynamics of water storage changes across China. We used advanced data analysis techniques to identify critical points where water storage patterns shifted significantly, with categories of nonlinear changes that first decrease then increase and those that first increase then decrease together accounting for 55.62% of the observations. The northeastern and western border areas of China are hotspots for these nonlinear changes, and the analysis identifies 2019 as a year with a high frequency of turning points. The piecewise linear regression analysis found that when the human footprint exceeds a specific threshold, its negative impact on water storage significantly intensifies; when precipitation and temperature exceed certain thresholds, their impact on water storage shifts from negative to positive. These findings not only reveal the complex spatiotemporal distribution characteristics of water storage change turning points across China but also emphasize that water resource management strategies in China and globally need to adopt more comprehensive and dynamic approaches in the context of global climate change. To meet future challenges, it is essential to integrate multiple variables and develop flexible, adaptive management strategies to ensure the sustainable use and effective management of water resources.

Long-term exposure to PM2.5 and its constituents and visual impairment in schoolchildren: A population-based survey in Guangdong province, China.

Exposure to fine particulate matter (PM2.5) has been linked to visual impairment. Nevertheless, evidence associating PM2.5 constituents with visual impairment in schoolchildren is sparse. To explore the effects of long-term exposure to PM2.5 and its constituents on visual impairment. We conducted a large cross-sectional population-based study to recruit 59,054 schoolchildren from four cities in Guangdong province, China, and used the ChinaHighAirPollutants (CHAP) dataset to estimate 3-year average concentrations of PM2.5 and its constituents (black carbon (BC), organic matter (OM), nitrate (NO3-), sulfate (SO42-) and ammonium (NH4+)) based on individuals' home addresses. Visual acuity was measured with a standardized logarithmic chart, and visual impairment was defined as visual acuity below 4.9 (Snellen 5/6 equivalent) in at least one eye. We utilized generalized linear mixed models and weighted quantile sum regression to assess the association between PM2.5 constituents and visual impairment. The observed associations typically displayed a nonlinear pattern. Compared to the lowest quartile of PM2.5 and its constituents, the fourth quartile was associated with higher odds of visual impairment in schoolchildren (e.g., the adjusted odds ratio (OR) was 1.23 (95% CI: 1.13, 1.33) for PM2.5, 1.53 (95% CI: 1.40, 1.67) for OM, and 1.35 (95% CI: 1.27, 1.44) for BC), respectively. Similarly, joint exposure to PM2.5 constituents was associated with visual impairment (OR = 1.17, 95% CI: 1.13, 1.22), while BC and OM contributed more to the observed associations. Long-term exposure to PM2.5 and its constituents was significantly associated with higher rates of visual impairment in schoolchildren, with combustion-related BC and OM potentially driving the observed associations.

Extreme soil salinity reduces N and P metabolism and related microbial network complexity and community immigration rate.

Soil microbiomes are well known to suffer from the effects of rising salinity. There are, however, no current understandings regarding its specific effects on microbial metabolic functions associated with nitrogen (N) and phosphorus (P) cycling, particularly in the Yellow River Delta (YRD), one of the largest estuaries in the world. This research examined soil microbiomes at 50 sites in the YRD region to analyze their co-occurrence networks and their relationship with N (nitrification, denitrification, dissimilatory, assimilatory, fixation, and mineralization) and P (solubilization, mineralization, transportation, and regulation) metabolism processes. Our findings indicate a notable reduction in soil multifunctionality as salinity levels increase, with Halofilum-ochraceum playing a significant role in nitrification, whereas Bacteroidetes-SB0662-bin-6 helps solubilize inorganic P in highly saline areas. High soil salinity negatively affected the amoA gene involved in nitrification and increased the nosZ gene involved in denitrification in extreme salinity soil with 8.2g/kg salt content. Extreme salinity significantly reduced the expression of genes involved in inorganic P solubilization, such as ppa and ppx. Additionally, the alkaline P gene phoD exhibited significant decreases in extremely saline soils, thereby impeding the mineralization of organic P. The neutral community models indicated that microbial community immigration rate showed a linear negative relationship with soil EC in the six N and four P processes. Salinization, however, displayed a nonlinear pattern with clearly defined thresholds on the community of microbes involved in N and P cycling. Reduced microbial diversity and interactions are causing a decline in soil multifunctionality, and the soil multifunctionality and network edges jointly limited the microbial community immigration rate involved in N and P cycling. It is crucial to preserve soil microbial functions to support nutrient cycling and predict the ecological effects of soil salinization.

Research on short-term precipitation forecasting method based on CEEMDAN-GRU algorithm

Precipitation forecasting is vital for managing disasters, urban traffic, and agriculture. This study develops an improved model for short-term precipitation forecasting by combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Gated Recurrent Unit (GRU). Using precipitation data from January 1, 2019, to December 31, 2022, as a sample, the model capitalizes on CEEMDAN’s superior signal decomposition capabilities and GRU’s ability to capture nonlinear dynamic patterns in time series. To assess the model’s effectiveness, comparisons were conducted with 12 benchmark models, including CEEMDAN-LSTM, EMD-GRU, EMD-LSTM, BI-LSTM, GRU, LSTM, and TCN. The results demonstrate that the CEEMDAN-GRU model achieves higher accuracy and stability in short-term precipitation forecasting. Leveraging an Adam optimizer with adaptive learning rate reduction enhances convergence and ensures reliable predictions, achieving an R²of 0.7915, MAE of 0.05382, and MSE of 0.09081.

Factors Affecting Listening Comprehension of English-Majored Vietnamese First-Year Students

Listening skills are critically important for effective communication, but many students struggle to develop the skills. This study investigates factors affecting listening ability of first-year English-majored Vietnamese students at a university in Ho Chi Minh City. The study used a mixed-methods approach, combining a 20-questionnaire survey of first-year English-majored Vietnamese students (n=55), and a qualitative method through interviewing students on the issue (n=6). Using the conventional research tools and Random Forest Regressor, an explainable AI, to capture the non-linear patterns in the data to understand the factors affecting. The findings revealed a range of elements, including prior language proficiency, learning strategies, classroom environment, and access to authentic listening materials and technology. Issues with pronunciation and intonation tend to hinder the comprehension of spoken English, while personal barriers like anxiety and lack of confidence further impede listening ability. By implementing targeted interventions and support, the study seeks to improve listening practices among first-year students, aiding them in navigating the complexities of language acquisition more effectively.

High-Impedance Fault Detection in DC Microgrid Lines Using Open-Set Recognition

Detection of high-impedance faults in direct current microgrid lines presents a challenge for most conventional protection schemes because the magnitude of the fault current is similar to other transients that occur during normal operation. However, the waveform of high-impedance faults differs from that of other transients as it is characterized by a repetitive and nonlinear pattern caused by current reignition. Various methods have been proposed to exploit fault response waveforms for detecting high-impedance faults, including those based on deep discriminative intelligent classification. Different from previous works that focus on closed-set classification, this study frames fault detection as an open-set recognition problem, employing a neural network as the classifier. The resulting approach enables the detection of high-impedance faults as outliers from the normal operating states of microgrid lines with passive constant impedance loads and requires only the Fourier transform of the current signal as input to the neural network. Remarkably, the proposed solution eliminates the need for hard-to-model high-impedance faults in the training dataset and hence is more generally applicable. The proposed method consistently outperforms commercially available high-impedance fault detection systems, achieving high accuracy in fault detection.