Nonlinear Relationship Research Articles

Anthropometric indicators and cardiovascular diseases risk in pre-diabetic and diabetic adults: NHANES 1999–2018 cross-sectional analysis

BackgroundSince cardiovascular disease (CVD) stands as the primary cause of death in those with diabetes, and given the substantial influence of obesity as a common risk factor for both diabetes and atherosclerotic conditions, this investigation sought to find the relationship between anthropometric indicators and CVD risk within these populations. MethodsOur study examined 36,329 adults, including those with diagnosed diabetes, pre-diabetes, and without diabetes from National Health and Nutrition Examination Survey (NHANES) data spanning 1999 to 2018. Various anthropometric indicators such as body mass index (BMI), waist circumference, weight-adjusted waist index (WWI), waist-to-height ratio (WHtR), weight, and height were assessed. Baseline characteristics were compared among the three groups after weighting. Participants were then grouped based on anthropometric indicators, and logistic regression models were used to analyze the association between these indicators and CVD risk in the total diabetes group (including diabetic and pre-diabetic individuals). Threshold effect analysis was conducted to explore nonlinear relationships, and mediation analyses assessed whether serum parameters influenced these relationships. ResultsThis cross-sectional study involved 36,329 participants, weighted to a count of approximately 160.9 million, including over 45.9 million pre-diabetic individuals and around 16.6 million diabetic individuals. Baseline analysis showed significant associations between all six anthropometric indicators and CVD risk across patients with different diabetes statuses. Weighted restricted cubic spline (RCS) curve analysis highlighted increased CVD risk among the total diabetes group for each anthropometric indicator compared to the non-diabetic group. Anthropometric indicators were then divided into quartiles, and after adjusting for confounders, Model 3 revealed that the highest BMI group had a heightened risk of CVD compared to the lowest BMI group. Similar trends were observed in the WWI and WHtR subgroups. Threshold effect analysis of anthropometric indicators unveiled nonlinear associations between waist circumference, height, WWI and CVD risk. Mediation analysis suggested that lipid parameters, especially HDL, significantly mediated these relationships. ConclusionIn individuals with diabetes and pre-diabetes, BMI, weight, and WHtR displayed a consistent, linear increase correlation with CVD risk. Conversely, the link between waist circumference, height, and WWI and CVD risk showcased a more complex, nonlinear pattern. Moreover, HDL level emerged as notable mediator in the association between anthropometric indicators and the risk of CVD.

Impact of China’s Digital Economy on Integrated Urban–Rural Development

This paper measures the comprehensive level of the digital economy and integrated urban–rural development across Chinese provinces from 2013 to 2022. Using a two-way fixed effects model, it explores the nonlinear relationship, regional heterogeneity, and underlying causes of how the digital economy empowers integrated urban–rural development. The study finds that: (1) The digital economy promotes integrated urban–rural development, with a quadratic polynomial regression model and threshold regressive model revealing an inverted U-shaped relationship. The turning point has not yet been reached, and the promoting relationship shows a diminishing marginal trend. (2) In terms of sub-dimensions, the strengths of the promoting effect are ranked as follows: digital infrastructure construction, industrial digitization, and digital industrialization. (3) Regionally, the digital economy’s promoting effect on integrated urban–rural development is strongest in Central China, followed by Eastern China, and weakest in Western China. The innovation and contribution of this paper lie in discovering the nonlinear impact of the digital economy on China’s integrated urban–rural development, analyzing its intrinsic driving forces and regional differences, and providing valuable references for implementing regionally differentiated development policies for the digital economy and integrated urban–rural development in various regions of China.

Organizational responses to transparency determinants

PurposeDespite considerable evidence of the benefits of organizational transparency, policies to enhance transparency often fail or are met with resistance and unexpected results. In part, this is due to a lack of knowledge about the drivers of organizational transparency and their interrelationships. This study examines the interplay among the forces that influence organizational transparency, and thus answers numerous calls for developing a deeper theoretical understanding of the determinants of organizational transparency. We propose three forces that influence organizational transparency and theorize how they combine in nonlinear ways to form five archetypical transparency regimes that organizations operate within. We then discuss contingencies to organizational transparency within each regime.Design/methodology/approachWe employ configurational theorizing to capture the complexity of transparency and the nonlinear relationships among the forces of transparency.FindingsWe propose three forces that influence organizational transparency: institutional, societal, and leadership. We identify configurations of the three forces that yield five archetypical transparency regimes. We then discuss contingencies for cultivating organizational transparency within each regime. Vanguard transparency and pioneering transparency represent the desired regimes for fostering organizational transparency. In contrast, hollow transparency and deceptive transparency reveal a combination of determinants that cultivate less desirable forms of organizational transparency. Paradoxical transparency represents a regime in which socially desirable outcomes are associated with undesirable consequences for an organization.Research limitations/implicationsThis paper is among the first to theorize the drivers of organizational transparency and to discuss the limits and boundaries of organizational responses to transparency determinants.Practical implicationsDespite the many benefits of transparency, we explain why efforts to enhance organizational transparency often fail or are met with mixed results. By considering the three forces, managers and policymakers can avoid unexpected and undesired organizational responses to transparency regimes.Social implicationsWe propose five transparency regimes that place a spotlight on social contingencies to enhance transparency.Originality/valueThis study offers an integrative theory of organizational responses to transparency determinants and develops its theoretical foundations. The model integrates the fragmented empirical findings from previous studies on the determinants of transparency and draws attention to overlooked institutional, societal, and leadership forces that influence organizational transparency.

The threshold effect and driving path of the rapid transit system on tourism eco-efficiency in China

Rapid transit is important for the green development of China’s tourism industry because it profoundly affects the optimal structural layout and rational allocation of elements that impact regional tourism. Based on provincial panel data from 2009 to 2020, the entropy weight method, tourism carbon emission measurement model, Super efficiency slacks-based measure model and threshold regression model were used to determine the impact of the rapid transit system on tourism eco-efficiency in China. The findings showed that rapid transit system has increased steadily, while tourism eco-efficiency has fluctuated significantly, and the relative development characteristics of rapid transit system and tourism eco-efficiency gradually changed from “lagging rapid transit system” to “lagging tourism eco-efficiency.” In addition, the rapid transit system and the highway and civil aviation have a nonlinear relationship with tourism eco-efficiency, while the high-speed railway has not passed the threshold effect test, and at the regional level, the impacts of rapid transit system have heterogeneous characteristics. As the deepening construction of rapid transit system, its threshold effect on tourism eco-efficiency will change from positive “V” to positive “U” type, promoting the more efficient development of environmentally friendly tourism.

Relationship between dietary inflammation index and frailty in patients with osteoarthritis

Background Osteoarthritis (OA) is a common chronic joint disease that significantly affects an individual’s quality-of-life and frailty has become one of the common complications in OA patients as the disease progresses. The relationship between dietary patterns is not clear. Methods All participants are from the National Health and Nutrition Examination Survey (NHANES) and have been diagnosed with OA. The dietary inflammation index (DII) is calculated based on the dietary intake reported by the participants. Logistic regression analysis is used to investigate the relationship between DII and frailty. Restricted cubic splines are utilised to explore their nonlinear relationship. Mediation analysis is conducted to explore the role of inflammation in this relationship. Results A total of 2,530 OA patients were included in the study, with an average age of 64.46 (12.67) years. After adjusting for covariates, for each one standard deviation increase in DII, the risk of frailty increased by 15% (OR = 1.15, 95% CI = 1.03–1.28). Compared to patients with DII < –1, patients with DII > 1 had a significantly higher risk of frailty (OR = 1.50, 95% CI = 1.05-2.14). Conclusions The findings of this study indicate a positive association between DII and the risk of frailty in OA patients. These results underscore the potential impact of dietary interventions in improving the quality-of-life for OA patients.

Association Between Maternal Copper/Zinc Intake and the Risk of Preterm Birth

Background: The relationship between maternal copper and zinc intake and the risk of preterm birth is unclear Objectives: This study was designed to investigate the effects of daily copper and zinc intake before and during pregnancy on the risk of preterm birth and to assess whether there is an interaction between copper and zinc intake and the risk of preterm birth Methods: A nested case - control study was conducted in Lanzhou, involving 880 cases and 8017 controls. Eligible participants were interviewed about their diet and characteristics during pregnancy. Unconditional logistic regression was used to determine the association between dietary copper and zinc intake and the risk of preterm birth, including its clinical subtypes. A multivariate adjusted restricted cubic spline (RCS) model was used to investigate the nonlinear relationship between dietary copper and zinc intake and the risk of preterm birth. Results: The study revealed that compared to pregnant women with the highest dietary copper intake before and during pregnancy, those with copper intake in the lower three quartiles (quartiles 1, 2, and 3) had increased risks of preterm birth, with adjusted odds ratios (ORs) of 1.05 (0.96 - 1.14), 1.05 (0.93 - 1.18), and 1.04 (0.82 - 1.32), respectively. The trend test yielded significant results (P = 0.013), particularly in late pregnancy, indicating an association between lower copper intake and increased risks of preterm and spontaneous preterm birth. Maternal dietary zinc intake during pregnancy was positively associated with the risk of preterm birth. The adjusted ORs for quartile 2 compared to quartile 4 were 1.29 (1.09 - 1.52), 1.55 (1.13 - 2.12), and 1.20 (1.00 - 1.46) throughout pregnancy, indicating significantly increased risks in the mid to late stages of pregnancy. No significant association was found between zinc intake and medically induced preterm birth. Zinc intake below the nutritional reference value in late pregnancy was significantly associated with an increased risk of preterm birth (P &lt; 0.05). A nonlinear relationship was observed between copper/zinc intake and the risk of preterm birth (P Nonlinear &lt; 0.05). A synergistic effect of low copper and zinc intake on the risk of preterm birth was found (OR: 2.23, 95% CI: 1.64 - 3.04, P &lt; 0.001). Conclusions: Efforts to promote adequate copper and zinc intake before and during pregnancy need to be intensified to reduce the incidence of preterm birth.

An open-source membrane stretcher for simultaneous mechanical and structural characterizations of soft materials and biological tissues

The ability to simultaneously measure material mechanics and structure is central for understanding their nonlinear relationship that underlies the mechanical properties of materials, such as hysteresis, strain-stiffening and -softening, and plasticity. This experimental capability is also critical in biomechanics and mechanobiology research, as it enables direct characterizations of the intricate interplay between cellular responses and tissue mechanics. Stretching devices developed over the past few decades, however, do not often allow simultaneous measurements of the structural and mechanical responses of the sample. In this work, we introduce an open-source stretching system that can apply uniaxial strain at a submicron resolution, report the tensile force response of the sample, and be mounted on an inverted microscope for real-time imaging. Our system consists of a pair of stepper-based linear motors that stretch the sample symmetrically, a force transducer that records the sample tensile force, and an optically clear sample holder that allows for high-magnification microscopy. Using polymer samples and cellular specimens, we characterized the motion control accuracy, force measurement robustness, and microscopy compatibility of our stretching system. We envision that this uniaxial stretching system will be a valuable tool for characterizing soft and living materials.

Regge trajectory relation for the universal description of the heavy-heavy systems: Diquarks, mesons, baryons and tetraquarks

By employing the nonlinear Regge trajectory relation M=mR+βx(x+c0x)2/3(x=l,nr), we investigate the heavy-heavy systems, such as the doubly heavy diquarks, the doubly heavy mesons, the heavy-heavy baryons, and the heavy-heavy tetraquarks. The fitted Regge trajectories illustrate that these heavy-heavy systems satisfy the above formula and show the existence of an universal description of the heavy-heavy systems. The universality embodies not only the universal behavior M∼x2/3 but also the universal parameters. The values of cfnr and cfl vary with different heavy-heavy systems, but they are close to one. There is an inequality βnr>βl, and it holds for all the discussed heavy-heavy systems. Moreover, the expression of βx [Eq. (11)] explains its variation with the change of the constituents' masses.

The nonlinear impact of climate change on inland waterway transportation in the Upper Mississippi—Illinois River Region

The U.S. inland waterway system has played a critical role in promoting economic growth by efficiently transporting agricultural and manufacturing commodities along major riverine systems, such as the Mississippi River. However, the system faces escalating challenges due to the growing disruptions caused by climate change. For instance, the drought of the Mississippi River during the fall of 2022 resulted in an unprecedented increase in barge shipping rates. While the industry generally recognizes that climate change can significantly impact agricultural supply chains, it remains unclear how various environmental factors, including changes in water levels and temperature, affect inland waterway operations. This study aims to fill this research gap by conducting an empirical assessment of the impact of climate change-related factors on the operations of the inland waterway system in the Upper Mississippi—Illinois River Region. Unlike previous studies using conventional regression analysis, this study investigates the nonlinear relationship between changes in barge shipping rates and various environmental factors using the Gradient Boosting Decision Trees machine-learning method. Our analysis identifies the 29 ft gage height as a pivotal threshold influencing barge rates; rates tend to rise as the gage height decreases, and surpassing this threshold can also lead to increased shipping rates. This new approach enhances the model’s predictive capacity, allowing for a better understanding of the nonlinear effects of climate change on barge rates and productivity. It also enables planners and operational agencies to better understand the uncertainty of environmental conditions on the variations in barge rates and productivity performance. These findings provide valuable insights for decision-makers to understand the threshold effect of environmental conditions on inland waterway operations and facilitate the creation of effective adaptive strategies to mitigate future risks and consequences of climate change-induced disruptions.

A new microscopic traffic-flow model based on the spatiotemporal continuous system concept considering nonlinear human response

A brand-new traffic model is introduced to precisely simulate the individual movement of vehicles, departing from the traditional macroscopic perspective (Eulerian scope or fluid dynamics concept) to adopt a microscopic approach (Lagrangian scope). This model is built on the first principle of Newton’s kinetic equation, explicitly incorporating nonlinear relations to represent human responses to a physical stimulus (observed information), such as the velocity gap between one’s own vehicle and a certain targeted one or the gap to a preceding vehicle, unlike the conventional car-following concept. The model posits that a driver’s recognition occurs intermittently rather than continuously, following specific probabilistic distributions of which the formulation is physically justified. Overall, the model can be described as a spatiotemporally continuous formulation unlike cellular automaton (CA) traffic models, crucial for capturing traffic dynamics at the micro and macro levels. The model is validated using a real traffic-flow dataset from a highway, and the results of the conventional CA traffic model are compared with those of the present one” along with a note for authorial verification. Animated simulations confirm that the present model can reproduce realistic flow dynamics characterized by smoother acceleration and deceleration compared to the conventional CA traffic model. The complete simulation source code for replicating the model is made publicly available.

Nonlocal strain gradient-based nonlinear vibration analysis of nonlinear FG three-phase HJCNT/MWCNT/epoxy composite microbeam resonators using a modified micromechanical model

This paper aims to study nonlinear dynamic behavior of functionally graded (FG) three-phase composite microbeam resonators made of an epoxy matrix and two reinforcements namely multi-walled carbon nanotubes (MWCNTs) and hetero-junction carbon nanotubes (HJCNTs). The effective mechanical properties of the composite microbeam are obtained using the modified Halpin-Tsai micromechanical model. The microbeam surrounding medium is simulated using a two-parameter elastic foundation. The von-Karman’s geometric nonlinearity relations are incorporated and the equations of motion are derived based on the nonlocal strain gradient Euler–Bernoulli beam model. A new closed-form analytical solution is obtained using the homotopy perturbation method. The effects of vibration amplitude, nanofiber volume fraction, nanofiber distribution pattern, small-scale parameters and the foundation parameters on the nonlinear frequency and deflection of the FG three-phase composite microbeams are studied in detail. The findings of the paper are valuable for researchers in the field of microbeam resonators.

Nonlinear Relationship of Multi-Source Land Use Features with Temporal Travel Distances at Subway Station Level: Empirical Study from Xi’an City

The operation of the subway system necessitates a comprehensive understanding of passenger flow characteristics at station locations, as well as a keen awareness of the average travel distances at these stations. Moreover, the travel distances at the station level bear a direct relationship with the built environment composed of land use characteristics within the station’s catchment area. To this end, we selected the land use features within an 800 m radius of the station (land use area, distribution of points of interest, and the surrounding living environment) as the influencing factors, with the travel distances at peak hours on the subway network in Xi’an as the research subject. An improved SSA-XGBOOST-SHAP interpretable machine learning framework was established. The research findings demonstrate that the proposed enhanced model outperforms traditional machine learning or linear regression methods in terms of R-squared, MAE, and RMSE. Furthermore, the distance from the city center, road network density, the number of public transit routes, and the land use mix have a pronounced influence on travel distances, reflecting the significant impact that mature built environments can have on passenger attraction. Additionally, the analysis reveals a notable nonlinear relationship and threshold effect between the built environment variables comprising land use and the travel distances during peak hours. The research results provide data-driven support for operational strategy management and line capacity optimization, as well as theoretical underpinnings for enhancing the efficiency and sustainability of the entire subway system.

Analysis of human capital effects introducing Bayesian quantile regression in the process of industrial structural upgrading.

In recent years, with the continuous evolution of the global economy and the adjustment of industrial structures, the understanding of the role played by human capital in the process of economic development has become particularly important. However, existing research on the impact of human capital on economic growth often adopts traditional regression methods, failing to comprehensively consider the heterogeneity and nonlinear relationships in the data. Therefore, to more accurately understand the influence of human capital on economic growth at different stages, this study employs Bayesian quantile regression method (BQRM). By incorporating BQRM, a better capture of the dynamic effects of human capital in the process of industrial structure upgrading is achieved, offering policymakers more targeted and effective policy recommendations to drive the economy towards a more sustainable direction. Additionally, the experiment also examines the impact of other key factors such as technological progress, capital investment, and labor market conditions on economic growth. These factors, combined with human capital, collectively promote the upgrading of industrial structure and the sustainable development of the economy. This study, by introducing BQRM, aims to fill the research gap regarding the impact of human capital on economic development during the industrial structural upgrading process. In the backdrop of the ongoing evolution of the global economy and adjustments in industrial structure, understanding the role of human capital in economic development becomes particularly crucial. To better comprehend the direct impact of human capital, the experiment collected macroeconomic data, including GDP, industrial structure, labor skills, and human capital, from different regions over the past 20 years. By establishing a dynamic panel data model, this study delves into the trends in the impact of human capital at various stages of industrial structure upgrading. The research findings indicate that during the high-speed growth phase, the contribution of human capital to GDP growth is 15.2% ± 2.1%, rising to 23.8% ± 3.4% during the period of industrial structure adjustment. Technological progress, capital investment, and labor market conditions also significantly influence economic growth at different stages. In terms of innovation improvement, this study pioneers the use of BQRM to gain a deeper understanding of the role of human capital in economic development, providing more targeted and effective policy recommendations. Ultimately, to promote sustainable economic development, the experiment proposes concrete and targeted policy recommendations, emphasizing government support in training and skill development. This study not only fills a research gap in the relevant field but also provides substantive references for decision-makers, driving the economy towards a more sustainable direction.

Analysis of Characteristics and Sensitivity of Photochemical Pollution During High Ozone Season in Shanghai from 2016 to 2020

The sensitivity analysis of ozone generation in key ozone-polluted regions and cities is an important basis for the prevention and control of near-surface ozone （O3） pollution. Based on the five-year data of ozone, VOCs, and NOx from three typical stations in Shanghai, namely Dianshan Lake Station （suburban area）, Pudong Station （urban area）, and Xinlian Station （industrial area） from 2016 to 2020, the nonlinear relationship between ozone and precursors （VOCs and NOx） during the high-ozone season in the five years was quantitatively analyzed using an observation model. The results showed that the peak months of near-surface ozone in Shanghai were from April to September during 2016 to 2020, with the highest values appearing from June to August. The volume fraction of VOCs and NO2 concentration had a strong indicative significance for the O3 concentration at Pudong Station. The O3 concentration at Dianshan Lake Station was mainly influenced by regional environment, meteorological factors, and cross-regional transmission. The ozone concentration at Xinlian Station was a combination of environmental background concentration and industrial area photochemical pollution. Pudong Station and Dianshan Lake Station were in the VOCs control zone. Xinlian Station was gradually closer to the NOx control zone from 2016 to 2019, transitioning to the VOCs control zone since 2020. The L·OH of Pudong Station, Dianshan Lake Station, and Xinlian Station were： NOx control area&gt;collaborative control area&gt;VOCs control area.

Well logging restoration based on spatial-temporal information mining from muti-scale correlation graph representation

Geological reservoir characterization using well-loggingg data often faces challenges, such as data distortion or even missing values. To tackle this, traditional signal restoration theories and machine learning techniques, such as neural networks, have been commonly employed. However, they struggle to fully explore correlation information among logging curves within the same well and lack adaptability across different wells. In this paper, a novel Logging Curve Restoration method based on Spatial-Temporal Information Mining (LCR-STIM) using multi-scale correlation graph representation is proposed. Specially, a novel multi-scale graph representation technique is employed to build spatial (i.e., horizontal) nonlinear relationships among logging curves. Subsequently, deep forest model is introduced to generate multi-scale predictions to uncover the rich spatial information hidden in the multi-scale graph representations. Finally, to fully utilize temporal information in the obtained different multi-scale predictions, Long Short-Term Memory (LSTM) network is employed to combine the multi-scale restoration results to predict target logging data accurately. Taking the well logging data of 41 wells from Daqing oil field as training data and the target logging curves from 3 other wells as the test data, experimental results shown that the restoration performance can be improved by at least 20% compared with the state-of-the-art comparison methods.

Dynamic Analysis of the Nonlinear Relationship Between Key Metal Prices and New Energy Vehicle Market

Up against the global energy transformation, the new energy vehicle industry has been in a stage of rapid development. Key metals represented by nickel are indispensable raw materials supporting the new energy vehicle industry, with their price fluctuations influencing its development, thereby affecting the global energy transformation. Studying the dynamic nonlinear relationship between the price fluctuation of the nickel as the key metal and the new energy vehicle market will provide a new perspective for understanding the dynamic development of the new energy vehicle market. Based on nickel price data, new energy vehicle market index, combined with Markov vector autoregressive model and cumulative impulse response function, this paper conducts research on the impact of nickel price fluctuations on the new energy vehicle market. According to the empirical results, the impact of nickel price fluctuations on the new energy vehicle market is different under various regimes. During the low-speed and high-speed development periods, nickel price fluctuations have a negative impact on the new energy vehicle market. During the stable development period, nickel price fluctuations mainly have a positive impact on the new energy vehicle market. Based on the above conclusions, this paper proposes policy suggestions for the relevant subjects.

Research on CO concentration detection based on deep learning and TDLAS technology

In order to improve the accuracy of detecting CO and other gases in the industrial field, a CO concentration detection scheme based on tunable diode laser absorption spectroscopy (TDLAS) and the CO absorption spectral line at 1566 nm was proposed. At the outset, after removing the outliers from the original data, the harmonic signal is filtered using a moving average filtering method to enhance the signal-to-noise ratio. Subsequently, a one-dimensional convolutional neural network (1D-CNN) is employed to extract the peak and valley values, as well as the mean values of each segment of harmonic data. In the end, the Long short term memory network (LSTM) model establishes a nonlinear fitting relationship between gas characteristics and concentration, and the attention layer is added to further optimize the network weight parameters. The experimental results indicate that the R2 for the test set is 0.9884, and the RMSE is 156.869. Through Allan deviation analysis, the detection limit reaches 1.9605 ppm at an integration time of 60 s. Compared to the traditional regression model, the accuracy and stability are significantly improved, making it highly suitable for gas detection in the industrial circle.

Kernel Bayesian logistic tensor decomposition with automatic rank determination for predicting multiple types of miRNA-disease associations.

Identifying the association and corresponding types of miRNAs and diseases is crucial for studying the molecular mechanisms of disease-related miRNAs. Compared to traditional biological experiments, computational models can not only save time and reduce costs, but also discover potential associations on a large scale. Although some computational models based on tensor decomposition have been proposed, these models usually require manual specification of numerous hyperparameters, leading to a decrease in computational efficiency and generalization ability. Additionally, these linear models struggle to analyze complex, higher-order nonlinear relationships. Based on this, we propose a novel framework, KBLTDARD, to identify potential multiple types of miRNA-disease associations. Firstly, KBLTDARD extracts information from biological networks and high-order association network, and then fuses them to obtain more precise similarities of miRNAs (diseases). Secondly, we combine logistic tensor decomposition and Bayesian methods to achieve automatic hyperparameter search by introducing sparse-induced priors of multiple latent variables, and incorporate auxiliary information to improve prediction capabilities. Finally, an efficient deterministic Bayesian inference algorithm is developed to ensure computational efficiency. Experimental results on two benchmark datasets show that KBLTDARD has better Top-1 precision, Top-1 recall, and Top-1 F1 for new type predictions, and higher AUPR, AUC, and F1 values for new triplet predictions, compared to other state-of-the-art methods. Furthermore, case studies demonstrate the efficiency of KBLTDARD in predicting multiple types of miRNA-disease associations.

Perirenal fat thickness contributes to the estimated 10-year risk of cardiovascular disease and atherosclerotic cardiovascular disease in type 2 diabetes mellitus.

Perirenal adipose tissue (PAT) has emerged as a potential therapeutic target for cardiovascular disease (CVD). However, the relationship between increased perirenal fat thickness (PrFT) and CVD risks in individuals with type 2 diabetes mellitus (T2DM) remains uncertain. This study aimed to evaluate the association between PrFT and the estimated 10-year risk of CVD and atherosclerotic cardiovascular disease (ASCVD) in T2DM. The final analysis included 704 participants. PrFT was quantified using non-enhanced computed tomography scans, while the estimated 10-year CVD and ASCVD risk assessments were based on the Framingham and China-PAR equation risk scores, respectively. Multiple regression analysis was employed to analyze the correlation between PrFT and these risk scores. Higher quartiles of PrFT displayed elevated Framingham and China-PAR equation risk scores (P<0.001). After adjusting for cardiometabolic risk factors and visceral fat area, PrFT remained significantly correlated with Framingham equation risk scores in men (β=0.098, P=0.036) and women (β=0.099, P=0.032). Similar correlations were observed between PrFT and China-PAR equation risk scores in men (β=0.106, P=0.009) and women (β=0.108, P=0.007). Moreover, PrFT emerged as an independent variable associated with a high estimated 10-year risk of CVD and ASCVD, with odds ratios (ORs) of 1.14 (95% CI: 1.04-1.25, P=0.016) in men and 1.20 (95% CI: 1.11-1.31, P<0.001) in women for high estimated CVD risk, and ORs of 1.22 (95% CI: 1.08-1.41, P=0.009) in men and 1.34 (95% CI: 1.12-1.60, P<0.001) in women for high estimated 10-year ASCVD risk. Furthermore, restricted cubic spline analyses confirmed a nonlinear relationship between PrFT and high estimated CVD and ASCVD risk in both genders (P for nonlinearity and overall < 0.05). PrFT contributed as an independent variable to the estimated 10-year risk of CVD and ASCVD in T2DM.

Intelligent RCS Extrapolation Technology of Target Inspired by Physical Mechanism Based on Scattering Center Model

In this paper, a technology named SCM−ANN combining physical scattering mechanisms and artificial intelligence is proposed to realize radar cross-section (RCS) extrapolation of non-cooperative conductor targets with higher efficiency. Firstly, an adaptive scattering center (SC) extraction algorithm is used to construct the scattering center model (SCM) for non-cooperative targets from radar echoes in the low-frequency band (LFB). Secondly, an artificial neural network (ANN) is constructed to capture the nonlinear relationship between the real LFB echoes and those reconstructed from the SCM. Finally, the SCM is used to reconstruct echoes in the high-frequency band (HFB), and these reconstructions, together with the trained ANN, optimize the extrapolated HFB RCS. For the SCM−ANN technology, physical mechanistic modes are used for trend prediction, and artificial intelligence is used for regression optimization based on trend prediction. Simulation results show that the proposed method can achieve a 50% frequency extrapolation range, with an average prediction error reduction of up to 40% compared with the traditional scheme. By incorporating physical mechanisms, this proposed approach offers improved accuracy and an extended extrapolation range compared with the RCS extrapolation techniques relying solely on numerical prediction.