Nonlinear Relationship Research Articles

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.

How to protect those who protect: psychosocial risks in child welfare workers during the pandemic

ABSTRACT Child welfare professionals face elevated psychosocial risks, especially during the COVID-19 pandemic, yet they are often overlooked in specialized literature, especially for Spanish workers. Most studies use linear models, neglecting nonlinear relationships. This study examined the role of job demands, resources, and COVID-19 impact on burnout, psychosomatic problems, and work engagement in 196 Spanish child welfare professionals. Demands predicted burnout and psychosomatic problems in both Hierarchical Regression Modelling (HRM) and Qualitative Comparative Analysis (QCA). Resources positively predicted engagement in both analyses. COVID-19 impact was significantly related to psychosomatic problems in QCA, highlighting nuances not captured by HRM.

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, 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.

H[formula omitted] adsorption on Li and Ca functionalized few layers graphene: Insights from grand canonical Monte Carlo studies

Functionalized graphene with alkali or alkaline metals is a potential candidate for H2 adsorption and storage at pressures equal to or below 100 bar. A rigorous study was conducted to understand the effect of functionalizing Few Layers Graphene (FLG) with Li and Ca atoms on the hydrogen uptake and its interaction with pressure, temperature, and pore width. The results show a complex interplay between the added weight of the functionalized atoms and the increased solid–fluid interactions finding a nonlinear relationship between the degree of functionalization and the adsorption uptake. It was found that Li-FLG and Ca-FLG can match and exceed 65 mg H2/g at low temperatures and high pressure.

Is face and information availability important in green purchasing among young consumers?

With the growing awareness of environmental issues available across various media platforms, consumers, particularly the younger generation, are more conscious of their consumption and its impact on the environment. This trend can be observed in the surging demand for environmentally friendly and animal-test-free products on the market. However, despite the young consumer group’s critical role in the marketplace, existing research in this area remains limited, demanding further investigation. Recognising the significance of this trend, this study employs a two-stage partial least squares structural equation modelling-artificial neural network (PLS-SEM-ANN) approach to analyse the antecedents influencing green consumption among young Chinese consumers. This study proposes a conceptual research model that extends the norm activation model (NAM) by analysing 366 self-reported questionnaires. The first-stage PLS-SEM results reveal significant positive correlations between personal norms (PN), environmental knowledge (EK), information availability (IA), social norms (SN) and green consumption intention (GCI). However, face consciousness (FC) was found to have no significant effect on GCI. The second-stage ANN sensitivity analysis shows that PN emerged as the most influential factor on GCI, followed by IA, SN, and EK. This ranking diverges from the PLS-SEM results, suggesting potential hidden nonlinear relationships between IA, SN, EK and GCI. Among the significant predictors of PN, the ascription of responsibility (AR) ranks first, followed by an awareness of consequence (AC) and SN. With its unique two-stage PLS-SEM-ANN approach to green consumption among young consumers, this study offers valuable insights for both marketers and researchers. Marketers gain a new tool to predict GCI more effectively, while researchers can explore the intricate interplay of factors shaping sustainable consumption choices. Methodologically, the present study is one of the few that applies extended NAM using two-stage PLS-SEM-ANN in the context of green consumption.

Investigating urban mobility through multi-source public transportation data: A multiplex network perspective

The integration of multi-source and diverse spatio-temporal travel data provides a comprehensive insight into urban mobility. Using data from Shenzhen's public transportation system, this study presents an analytical framework based on multiplex networks to examine variations in multi-mode public transportation usage (metro, bus, taxi, and shared bike) and their correlation with the built environment. This framework encompasses the analysis of network topological characteristics, centrality, and communities. The examination of network topological characteristics reveals that the multiplex transportation network exhibits high global accessibility and local connectivity. Network centrality analysis, focusing on weighted outdegree centrality, captures the patterns of public transportation ridership. Centrality modeling, employing the light gradient boosting machine, demonstrates a nonlinear relationship between ridership and the built environment. Factors including population density, residential land use percentage, entertainment service density, restaurant density, and metro station density consistently exhibit positive correlations with ridership across different times of the day. The community structure analysis, using consensus community detection, indicates that distinct urban areas exhibit clustering behavior based on public transportation demand patterns, forming distinct communities that closely align with the functional zoning of urban planning. These findings could provide valuable insights for the strategic planning of transportation services and the built environment.

Using Human Resources Data to Predict Turnover of Community Mental Health Employees: Prediction and Interpretation of Machine Learning Methods.

This study used machine learning (ML) to predict mental health employees' turnover in the following 12 months using human resources data in a community mental health centre. The data contain 621 employees' information (e.g., demographics, job information and client information served by employees) hired between 2011 and 2021 (56.5% turned over during the study period). Six ML methods (i.e., logistic regression, elastic net, random forest [RF], gradient boosting machine [GBM], neural network and support vector machine) were used to predict turnover, along with graphical and statistical tools to interpret predictive relationship patterns and potential interactions. The result suggests that RF and GBM led to better prediction according to specificity, sensitivity and area under the curve (>0.8). The turnover predictors (e.g., past work years, work hours, wage, age, exempt status, educational degree, marital status and employee type) were identified, including those that may be unique to the mental health employee population (e.g., training hours and the proportion of clients with schizophrenia diagnosis). It also revealed nonlinear and nonmonotonic predictive relationships (e.g., wage and employee age), as well as interaction effects, such that past work years interact with other variables in turnover prediction. The study indicates that ML methods showed the predictability of mental health employee turnover using human resources data. The identified predictors and the nonlinear and interactive relationships shed light on developing new predictive models for turnover that warrant further investigations.

Inversion Study on Landslide Seepage Field Based on Swarm Intelligence Optimization Least-Square Support Vector Machine Algorithm

The permeability coefficient of landslide mass, a key parameter in the study of reservoir landslides, is commonly obtained through in situ and laboratory tests; however, the tests are costly and subject to high variability, leading to potential biases. In this paper, a new method was proposed to inversely estimate the permeability coefficient of landslide layers using monitoring data of groundwater level (GWL). First, the landslide transient seepage simulation was conducted to generate sample data for permeability coefficients and GWL during a reservoir operation cycle. Second, using GWL data as input and permeability coefficient data as output, the least-square support vector machine (LSSVM) was trained with two optimization algorithms, the particle swarm optimization (PSO) algorithm and the whale optimization algorithm (WOA), to construct the nonlinear mapping relationship between simulated GWL and permeability coefficients. Third, the accurate permeability coefficients for landslide seepage simulation were inverted or predicted based on the monitored GWL. Finally, using the inverted permeability coefficients for landslide seepage simulation, we compared simulation results with actual monitored GWL and achieved good consistency. In addition, this paper compared the inversion effects of three different algorithms: the standard LSSVM, PSO-LSSVM, and WOA-LSSVM. This study showed that these three algorithms had good nonlinear fitting effects in studying landslide seepage fields. Among them, using the inversion values from PSO-LSSVM for landslide seepage simulation resulted in the smallest relative error compared to actual monitoring data. Within a single reservoir operation cycle, the simulated water level changes were also largely consistent with the monitored water level changes. The results could provide a reference to determine landslide permeability coefficients and seepage.

Early Postoperative Prediction of Complications and Readmission After Colorectal Cancer Surgery Using an Artificial Neural Network.

Early predictors of postoperative complications can risk-stratify patients undergoing colorectal cancer surgery. However, conventional regression models have limited power to identify complex nonlinear relationships among a large set of variables. We developed artificial neural network models to optimize the prediction of major postoperative complications and risk of readmission in patients undergoing colorectal cancer surgery. The aim of this study was to develop an artificial neural network model to predict postoperative complications using postoperative laboratory values, and compare these models' accuracy to standard regression methods. This retrospective study included patients who underwent elective colorectal cancer resection between January 1, 2016, and July 31, 2021. Clinical data, cancer stage, and laboratory data from postoperative day 1 to 3 were collected. Models of complications and readmission risk were created using multivariable logistic regression and single-layer neural networks. National Cancer Institute-Designated Comprehensive Cancer Center. Adult colorectal cancer patients. Accuracy of predicting postoperative major complication, readmission and anastomotic leak using the area under the receiver-operating characteristic curve. Neural networks had larger areas under the curve for predicting major complications compared to regression models (neural network 0.811; regression model 0.724, p < 0.001). Neural networks also showed an advantage in predicting anastomotic leak (p = 0.036) and readmission using postoperative day 1-2 values (p = 0.014). Single-center, retrospective design limited to cancer operations. In this study, we generated a set of models for early prediction of complications after colorectal surgery. The neural network models provided greater discrimination than the models based on traditional logistic regression. These models may allow for early detection of postoperative complications as soon as postoperative day 2. See Video Abstract.

Female top managers, female owners and corporate financial sustainability: new evidence from a panel smooth transition regression model

This study investigates the nonlinear link between gender diversity at various levels and business financial sustainability for a large sample of 54759 enterprises in 80 developing countries from 2015 to 2022. Previous studies have shown that female participation may bring both advantages and costs to businesses. The empirical evidence on whether it improves or degrades company results is inconclusive. This research attempts to improve our knowledge of the complicated link between gender diversity and company financial sustainability. It explains how female managers’ and female owners’ effects on business sustainability is dependent on their different levels of engagement in the firm. Results of the Panel Smooth Transition Regression model (PSTR) demonstrate a nonlinear relationship between gender diversity and firm sustainability. Depending on the measure of female participation, the model contains one threshold at 39% of female senior managers and 34% of female owners, as well as two extreme regimes. The results reveal that below these thresholds the relationship is negative. Misunderstandings and a lack of coherence between groups of different genders may affect decision-making and the capacity to come up with the best responses. However, increasing gender diversity has a favourable influence on firm sustainability above the projected thresholds. This research supports the theory that a critical mass condition is required before a minority group begins to alter organisational functioning. Our findings demonstrate that diversity has an influence on collective decision making and, hence, sustainability only when a critical mass is reached. These findings offer numerous managerial insights and policy recommendations for developing-country enterprises looking to improve their financial sustainability.

How does working time impact perceived mental disorders? New insights into the U-shaped relationship

Based on a large-scale nationally representative survey in China, this paper uses the exogenous impact of automation on working hours as the instrumental variable to examine working time’s impact on perceived mental disorders, on the basis of dealing with endogeneity. Different from existing literature, it is found that the impact of working time on perceived mental disorders is U-shaped, rather than linear. Mental disorders firstly decrease with working hours. After working more than 48.688 h per week, further increases in working time carry notable mental health costs, leading to a positive relationship between working hours and depression. The turning point of this U-shaped relationship is almost in line with the International Labor Organization’s 48 working hours/week standard, justifying it from a mental health perspective. In addition, we further exclude the possibility of more complex nonlinear relationships between working time and perceived mental disorders. Furthermore, heterogeneities are found in the effects of working hours on mental disorders across different subgroups. Males are more depressed when working overtime. Older workers have a lower tolerance for overwork stress. The turning point is smaller for the highly educated group and they are more sensitive to working longer. Those with higher socioeconomic status are less depressed after exceeding the optimal hours of work. The increase in depression among rural workers faced with overwork is not prominent. Perceived mental disorders are lower among immigrants and those with higher health status. In addition, labor protection and social security help to weaken mental disorders caused by overtime work. In conclusion, this paper demonstrates that working time has a U-shaped impact on perceived mental disorders and highlights the vulnerability of certain groups, providing a reference for setting optimal working hours from a mental health perspective.

Association between frailty and acute kidney injury after cardiac surgery: unraveling the moderation effect of body fat through an international, retrospective, multi-cohort study.

Acute kidney injury (AKI) is a common and serious complication after cardiac surgery that significantly affects patient outcomes. Given the limited treatment options available, identifying modifiable risk factors is critical. Frailty and obesity, two heterogeneous physiological states, have significant implications for identifying and preventing AKI. Our study investigated the interplay among frailty, body composition, and AKI risk after cardiac surgery to inform patient management strategies. This retrospective cohort study included three international cohorts. Primary analysis was conducted in adult patients who underwent cardiac surgery between 2014 and 2019 at Wuhan XX Hospital, China. We tested the generalizability of our findings with data from two independent international cohorts, the Medical Information Mart for Intensive Care IV (MIMIC-IV) and the eICU Collaborative Research Database. Frailty was assessed using a clinical lab-based frailty index (FI-LAB), while total body fat percentage (BF%) was calculated based on a formula accounting for BMI, sex, and age. Logistic regression models were used to analyze the associations between frailty, body fat, and AKI, adjusting for pertinent covariates. A total of 8785 patients across three international cohorts were included in the study. In the primary analysis of 3,569 patients from Wuhan XX Hospital, moderate and severe frailty were associated with an increased AKI risk after cardiac surgery. Moreover, a nonlinear relationship was observed between body fat percentage and AKI risk. When stratified by the degree of frailty, lower body fat correlated with a decreased incidence of AKI. Extended analyses using the MIMIC-IV and eICU cohorts (n=3,951 and n=1,265, respectively) validated these findings and demonstrated that a lower total BF% was associated with decreased AKI incidence. Moderation analysis revealed that the effect of frailty on AKI risk was moderated by the body fat percentage. Sensitivity analyses demonstrated results consistent with the main analyses. Higher degrees of frailty were associated with an elevated risk of AKI following cardiac surgery, and total BF% moderated this relationship. This research underscores the significance of integrating frailty and body fat assessments into routine cardiovascular care to identify high-risk patients for AKI and implement personalized interventions to improve patient outcomes.

Scaling Laws Behind Penetrative Turbulence: History and Perspectives

AbstractAn unstably stratified flow entering into a stably stratified flow is referred to as penetrative convection, which is crucial to many physical processes and has been thought of as a key factor for extreme weather conditions. Past theoretical, numerical, and experimental studies on penetrative convection are reviewed, along with field studies providing insights into turbulence modeling. The physical factors that initiate penetrative convection, including internal heat sources, nonlinear constitutive relationships, centrifugal forces and other complicated factors are summarized. Cutting-edge methods for understanding transport mechanisms and statistical properties of penetrative turbulence are also documented, e.g., the variational approach and quasilinear approach, which derive scaling laws embedded in penetrative turbulence. Exploring these scaling laws in penetrative convection can improve our understanding of large-scale geophysical and astrophysical motions. To better the model of penetrative turbulence towards a practical situation, new directions, e.g., penetrative convection in spheres, and radiation-forced convection, are proposed.

Identifying Factors Influencing Surface Deformations from Underground Mining Using SAR Data, Machine Learning, and the SHAP Method

The article presents the results of significance analyses of selected mining and geological variables for an area of underground mining activity. The study area was a region of an underground copper ore mine located in southwest Poland. The input data consisted of satellite radar data from the Sentinel 1 mission as well as mining and geological data. The line-of-sight subsidence, calculated with the use of the small baseline subset method and arranged in time series, was decomposed to extract the vertical component. The significance analysis of individual variables for the observed surface subsidence was performed using the SHapley Additive exPlanations method for the XGBoost machine learning model. The results of the analysis showed that the observed ground surface subsidence velocities were most influenced by the thickness of the PZ3 layer, which is located approximately 200 m above the roof of the mined seam, the thickness of the seam, and the timing of mining. It was also found that the proposed model was able to detect a nonlinear relationship between the analyzed excavations. The most significant influence on ground subsidence over mine excavations are mining parameters such as the spatially averaged thickness of the deposit and the time since liquidation of the deposit. The proposed approach can be successfully employed in planning both mining operations and mine closure in such a manner that the environmental impact is minimized.

Association of the systemic immune-inflammation index with all-cause and cardiovascular mortality in individuals with rheumatoid arthritis

The systemic immune-inflammation index (SII), a metric reflecting systemic inflammatory response and immune activation, remains underexplored concerning its correlation with mortality among rheumatoid arthritis (RA) patients. This study aimed to delineate the association between SII and both all-cause and cardiovascular mortality within the cohort of American adults diagnosed with RA, utilizing data from the National Health and Nutrition Examination Survey (NHANES) spanning 1999 to 2018. The investigation extracted data from NHANES cycles between 1999 and 2018, identifying RA patients through questionnaire responses. The SII was computed based on complete blood counts, employing the formula: (platelets × neutrophils) / lymphocytes. The optimal SII cutoff value for significant survival outcomes was determined using maximally selected rank statistics. Multivariable Cox proportional hazards models assessed the relationship between SII levels and mortality (all-cause and cardiovascular) among RA patients, with subgroup analyses examining potential modifications by clinical confounders. Additionally, restricted cubic spline (RCS) analyses were conducted to explore the linearity of the SII-mortality association. The study encompassed 2070 American adults with RA, among whom 287 exhibited a higher SII (≥ 919.75) and 1783 a lower SII (< 919.75). Over a median follow-up duration of 108 months, 602 participants died. After adjustments for demographic, socioeconomic, and lifestyle variables, a higher SII was associated with a 1.48-fold increased risk of all-cause mortality (hazard ratio [HR] = 1.48, 95% confidence interval [CI] 1.21–1.81, P < 0.001) and a 1.51-fold increased risk of cardiovascular mortality (HR = 1.51, 95% CI 1.04–2.18, P = 0.030) compared to a lower SII. Kaplan–Meier analyses corroborated significantly reduced survival rates within the higher SII cohort for both all-cause and cardiovascular mortality (Pall-cause mortality < 0.0001 and Pcardiovascular mortality = 0.0004). RCS analyses confirmed a positive nonlinear relationship between SII and mortality rates. In conclusion, the SII offers a straightforward indicator of the equilibrium between detrimental innate inflammation and beneficial adaptive immunity. Our investigation, utilizing a comprehensive and nationally representative sample, reveals that elevated SII levels independently forecast a greater risk of mortality from all causes, as well as cardiovascular-specific mortality, in individuals suffering from RA. These insights underscore the clinical relevance of the SII as an affordable and readily accessible biomarker. Its incorporation into regular clinical practice could significantly enhance the precision of risk assessment and forecasting for patients with RA, facilitating more tailored and effective management strategies. Specifically, patients with high SII levels could be identified for more stringent cardiovascular risk management, including closer monitoring, lifestyle interventions, and aggressive pharmacological treatments to mitigate their increased risk of mortality.

Dissipative Quantum Hopfield Network: A numerical analysis

Abstract We present extensive simulations of a quantum version of the Hopfield Neural Network to explore its emergent behavior. The system is a network of $N$ qubits oscillating at a given $\Omega$ frequency and which are coupled via Lindblad jump operators built with local fields $h_i$ depending on some given stored patterns. Our simulations show the emergence of pattern-antipattern oscillations of the overlaps with the stored patterns similar (for large $\Omega$ and small temperature) to those reported within a recent mean-field description of such a system, and which are originated deterministically by the quantum term including $s_x^i$ qubit operators. However, in simulations we observe that such oscillations are stochastic due to the interplay between noise and the inherent metastability of the pattern attractors induced by quantum oscillations, and then are damped in finite systems when one averages over many quantum trajectories. In addition, we report the system behavior for large number of stored patterns at the lowest temperature we can reach in simulations (namely $T=0.005\, T_C$). Our study reveals that for small-size systems the quantum term of the Hamiltonian has a negative effect on storage capacity, decreasing the overlap with the starting memory pattern for increased values of $\Omega$ and number of stored patterns. However, it also impedes the system to be trapped for long time in mixtures and spin-glass states. Interestingly, the system also presents a range of $\Omega$ values for which, although the initial pattern is destabilized due to quantum oscillations, other patterns can be retrieved and remain stable even for many stored patterns, implying a quantum-dependent nonlinear relationship between the recall process and the number of stored patterns.

Association Between Myopia and Pupil Diameter in Preschoolers: Evidence from a Machine Learning Approach Based on a Real-World Large-Scale Dataset.

Previous studies have explored the connections between various ocular biological parameters with myopia. Our previous study also found that pupil data can predict the myopic progression during the interventions for myopia. However, studies exploring the association between pupil diameter and myopia in preschoolers with myopia were lacking. Hence this study was aimed to investigate the association between pupil diameter and myopia in preschoolers with myopia based on a real-world, large-scale dataset. Data containing 650,671 preschoolers were collected from a total of 1943 kindergartens in Shenzhen, China. Refraction and pupil parameters were collected. After data filtering, the occurrence of myopia and its association with age, gender, pupil diameter, and other variables, were analyzed. Random forest (RF) and eXtreme gradient boosting (XGBoost) were selected from seven machine learning algorithms to build the model. The mean decrease accuracy (MDA), mean decrease Gini (MDG), and gain feature importance (GFI) techniques were employed to quantify the importance of pupil diameter and other features. After the assessments, 51,325 valid records with complete pupil data were included, and 3468 (6.76%) were identified as myopia based on the calculated cycloplegic refraction. Preschoolers with myopia presented reduced pupil diameter and greater variation (5.00 ± 0.99mm) compared to non-myopic preschoolers (6.22 ± 0.67mm). A nonlinear relationship was found according to the scatterplots between pupil diameter and refraction (R2 = 0.14). Especially preschoolers with myopia had reduced pupil diameter compared to emmetropic preschoolers, but hyperope did not experience additional pupil enlargement. After adjusting for other covariates, this relationship is still consistent (P < 0.001). XGBoost and RF algorithms presented the highest performance and validated the importance of pupil diameter in myopia. Based on a real-world large-scale dataset, the current study illuminated that preschoolers with myopia had a reduced pupil diameter compared to emmetropic preschoolers with a nonlinear pattern. Machine learning algorithms visualized and validated the pivotal role of pupil diameter in myopia. chictr.org Identifier: ChiCTR2200057391.

Nonlinear and abnormal relationship between turbidity and suspended solids concentration in mountainous rivers: A case study of the Lai Chi Wo river in Hong Kong, China

Suspended solids concentration (SSC) in a river is closely relevant to river water turbidity. Investigation of their relationship in this study is accompanied by observed turbidity and SSC values, which were obtained from the testing results of water samples and monitored conditions in streamflow. The water samples were collected from two observation stations with a broad range of sediment concentrations in the Lai Chi Wo catchment in Hong Kong, China. We classified the target rainfall events into single-peak event type and dual-peak event type for a distinguished discussion of the relationship between SSC and turbidity in this study. At a finer classification, each event is separated into defined processes for the analysis, where two main processes refer to the periods that SSC rises from a normal state to a peak state first and the followed periods that SSC recesses to ordinary status gradually. It is advised by the analysis results that the estimation of SSC through turbidity values should be based on the same rainfall types for the upstream station. However, the results show that the classification of rainfall types does not need to take downstream areas into consideration. Furthermore, current research implies that the individual established connections between SSC and turbidity value at different stages (particularly referring to the rising period and recessing period) could be applied to estimate SSC at the same station via continuous turbidity values for both this and other ungauged stations with similar topographical features in the future. Meanwhile, this research approach provides new insight exploring various behaviors of sediments at different stages during an integral rainfall event. A comparison of distinguished performances of sediment during corresponding stages in a rainfall event makes contributions to diverse relationship between SSC and turbidity in the mountainous river.

Back-propagation ANN to Predict Cleanliness and Quality of Cotton Spinning Preparatory Output: A Comprehensive Research

Background: Modern spinning preparatory has undergone drastic technological changes, but still, individual’s expertise-based decisions govern the complex and non-linear multivariant relationships prevailing amongst raw material (cotton) variables, machine variables, process variables (waste), and product (card sliver) quality. The scientifically precise prediction regarding the cleanliness and quality of card sliver and waste control for the given inputted cotton variables processed on the state-of-the-art machinery setup without waiting for the production and testing of card sliver is still impossible. Methods: The present work describes the use of Aritificial Neural Networks (ANN) for ruling out these limitations on scientific grounds. A complex system targeted at ANN was developed using the "newff" function on the mill's five-year database. Single-group ANN was initially designed to determine the influence of inherent variations in raw cotton fibre properties and trash content on blow room and card performance. A multi-group approach of ANN was developed at a later stage to define the influence of complex interactions amongst various fibre properties on three main quality measures considered at blow room and carding, viz., i) influence of blow room and card on fibre length properties, ii) fibre damage at blow room or improvement at card, and iii) degree of cleanliness of the output material. Results: Reverse modelling for both groups was also successfully designed to demarcate feed cotton quality and cleanliness requirements for targeted blow room or card cleaning performance. Conclusion: A high level of positive endurance was observed for all ANNs. Multigroup networking has proven to be more precise than single group networking.

J-Shaped Relationship Between Weight-Adjusted-Waist Index and Cardiovascular Disease Risk in Hypertensive Patients with Obstructive Sleep Apnea: A Cohort Study.

A newly introduced obesity-related index, the weight-adjusted-waist index (WWI), emerges as a promising predictor of cardiovascular disease (CVD). Given the known synergistic effects of hypertension and obstructive sleep apnea (OSA) on cardiovascular risk, we aimed to explore the relationship between the WWI and CVD risk specifically within this high-risk cohort. A total of 2265 participants with hypertension and OSA were included in the study. Multivariate Cox regression analysis was used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for CVD events. The restricted cubic spline (RCS) was used to further evaluate the nonlinear dose-response relationship. During a median follow-up period of 6.8 years, 324 participants experienced a CVD event. Multivariate Cox regression analysis revealed that compared to the reference group, the HRs for the second, third, and fourth groups were 1.12 (95% CI, 0.79-1.59), 1.35 (95% CI, 0.96-1.89), and 1.58 (95% CI, 1.13-2.22), respectively. Moreover, RCS analysis illustrated a clear J-shaped relationship between the WWI and CVD risk, particularly notable when WWI exceeded 11.5 cm/√kg, signifying a significant increase in CVD risk. There was a J-shaped relationship between WWI and CVD in hypertensive patients with OSA, especially when the WWI was greater than 11.5 cm/√kg, the risk of CVD was significantly increased.