Nonlinear Relationship Research Articles

Unlocking code to livability: indemnificatory housing supply, fiscal decentralization, and housing prices in China

ABSTRACT By developing a multi-sector general equilibrium model, this article investigates the nonlinear relationship between indemnificatory housing supply (IHS) and housing prices in China. Utilizing panel data from 31 provinces from 2009 to 2019, we find that the overall effect of IHS on housing prices is negative at the current stage. However, as the IHS expands, this effect transitions from negative to positive, exhibiting a U-shaped relationship driven by a stable demand-shifting effect and an increasingly dominant supply-crowding-out effect. Furthermore, fiscal decentralization exhibits a nonlinear moderating effect, first strengthening and then weakening the effect of IHS on lowering housing prices. Our findings demonstrate the relationship between IHS and housing prices from a nonlinear perspective, highlight the critical role of fiscal decentralization, and provide policy insights for promoting economic growth and improving social welfare.

The correlation between hemoglobin concentration and blood pressure during pregnancy in different trimesters

BackgroundMaternal hemoglobin (Hb) is related to nutritional status, and it widely fluctuates during pregnancy. However, the relationship between Hb and blood pressure (BP) during pregnancy is unclear. This study aimed to estimate the associations between maternal Hb in various trimesters and its changes with BP during pregnancy.MethodsWe conducted this study by using data from the Electronic Medical Record System (EMRS) database of Zhoushan Maternal and Child Care Hospital in Zhejiang Province, China. The pregnancy duration was divided into seven periods: before 14th, 14 to 17th, 18 to 22nd, 23 to 27th, 28 to 31st, 32 to 35th, and 36 to delivery; generalized estimating equations (GEE) and multiple linear regressions were used for exploring their associations.Results8168 pregnant women in the first trimester were included in this study. Hb level decreased significantly from the first to late-second trimester and then increased but did not return to the level of the first trimester. After adjustment for the potential confounders, compared with women with Hb 110 ~ 119 g/L, those with Hb < 110 g/L had low systolic BP (SBP) and diastolic BP (DBP) in the second and third trimesters and those with Hb ≥ 120 g/L had high SBP and DBP in various trimesters. Furthermore, the dose-response effect was observed between them; except that those with Hb < 100 g/L had high SBP in T1 (β = 2.85, P = 0.012). Hb concentration changes during pregnancy were also positively associated with BP level. Furthermore, Hb had a higher effect on DBP than SBP. It is worth noting that the Hb concentration in > = 140 g/L groups had significantly higher SBP and DBP than the other groups. A significant nonlinear relationship was found between hemoglobin changes with both SBP and DBP in different gestational weeks (P non-linear < 0.05).ConclusionsThis study found that maternal Hb was positively associated with BP during pregnancy and maternal Hb > 120 g/L during pregnancy and increased maternal Hb from the first to third trimesters should especially be given more attention.

U-shaped association of uric acid to HDL cholesterol ratio (UHR) with ALL-cause and cardiovascular mortality in diabetic patients: NHANES 1999–2018

ObjectiveTo investigate the relationship between the uric acid to high-density lipoprotein cholesterol ratio (UHR) and ALL-cause and cardiovascular mortality among diabetic patients.MethodsThis study utilized health data from diabetic patients included in the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018. The Kaplan-Meier curves was employed to preliminarily explore the association between UHR, its components, and all-cause and cardiovascular mortality in diabetic patients, as well as to analyze UHR levels and mortality across different genders. Subsequently, the Cox proportional hazards model was used to further investigate the relationship between UHR, its components, and mortality in diabetic patients. Restricted cubic spline (RCS) curves were applied to examine the nonlinear relationship between UHR, its components, and mortality, with a particular focus on the association between UHR and mortality across different genders.ResultsThis longitudinal cohort study included a total of 6,370 participants, comprising 3,268 males and 3,102 females. Kaplan-Meier analysis revealed a positive correlation between UHR, UA, and mortality in diabetic patients, while the association between HDL and mortality was negligible. The Cox proportional hazards model demonstrated a positive association between UHR and mortality in the diabetic population, while the statistical effects of UA and HDL on mortality were less pronounced compared to UHR. When analyzed by gender, no significant linear relationship was observed between UHR and mortality in either males or females. Subsequently, RCS analysis indicated a U-shaped nonlinear relationship between UHR and mortality in the overall diabetic population and among female patients, with a similar trend observed in males. Furthermore, stratified RCS analysis confirmed the persistence of the U-shaped relationship between UHR and prognosis across most subgroups.ConclusionThis study found a U-shaped relationship between UHR and both ALL-cause and cardiovascular mortality in diabetic population. This suggests that clinicians should control UHR around 9–10 to improve the long-term prognosis of diabetic patients.

Prediction and parametric assessment of soil one-dimensional vertical free swelling potential using ensemble machine learning models

Investigating soil swelling potential is indeed a critical research area in geotechnical engineering, given its significant influence on the stability and longevity of civil structures. This study aims to predict and assess the one-dimensional vertical free swelling potential of soils using ensemble machine learning models. Within the study context, a large dataset encompassing a wide array of soil parameters from 210 soil samples, including moisture content, unit weight, plasticity, and clay content, will be used. These parameters are critical in understanding the swelling behavior of soils under varying environmental and load conditions. The novel approach of this research lies in the application of ensemble machine learning techniques, which offer a robust framework to analyze complex, nonlinear relationships within soil properties. Another key aspect of this research is the parametric assessment, where the influence of individual soil properties on swelling potential is investigated using feature importance and partial dependence analyses. These analyses provide valuable insights into the relative importance of different soil parameters on soil behavior. The outcomes of this study contribute to soil mechanics and machine learning applications in geotechnical engineering and offer practical implications for engineers and practitioners. Besides, the predictive models developed in this study aid in more informed decision-making in the design and construction of civil structures, particularly in swelling-prone areas.

The Mediating Role of Social and Institutional Trust in the Relationship Between Internet Use and Subjective Well-Being

The nonlinear relationship between internet use and subjective well-being deserves further explanation with reference to the underlying mechanism of how Internet use impacts subjective well-being. We explored the underlying mechanism of the relationship between internet use and subjective well-being and argued that social and institutional trust mediate that relationship. We have tested our hypotheses with the European Social Survey (ESS) data of 14,037 individuals from 22 European countries. We employed the partial least squared-based structural equation modeling (PLS-SEM) using WarpPLS 8.0 software. Our data analysis results reveal that the relationship between internet use and subjective well-being is partially mediated by social and institutional trust, such that internet use boosts individuals’ social and institutional trust and subsequently increases subjective well-being.

Association between cardiometabolic index and all-cause and cause-specific mortality among the general population: NHANES 1999–2018

BackgroundCardiometabolic index (CMI) is a comprehensive clinical parameter which integrates overweight and abnormal lipid metabolism. However, its relationship with all-cause, cardiovascular disease (CVD), and cancer mortality is still obscure. Thus, a large-scale cohort study was conducted to illustrate the causal relation between CMI and CVD, cancer, and all-cause mortality among the common American population.MethodsOur research was performed on the basis of National Health and Nutrition Examination Survey (NHANES) database, involving 40,275 participants ranging from 1999 to 2018. The formula of CMI is [waist circumference (cm) / height (cm)] × [triglyceride (mg/dL) / high-density lipoprotein cholesterol (mg/dL)]. Outcome variables consisted of CVD, cancer, and all-cause mortality, which were identified by the International Classification of Diseases (ICD)-10. The correlation between CMI and mortality outcomes was analyzed utilizing the Kaplan–Meier survival modeling, univariate/multivariate Cox regression analysis, smooth curve fitting analysis, threshold effect analysis, and subgroup analysis. Stratification factors for subgroups included age, race/ethnicity, sex, smoking behavior, drinking behavior, BMI, hypertension, and diabetes.ResultsThe baseline characteristics table includes 4,569 all-cause-induced death cases, 1,113 CVD-induced death cases, and 1,066 cancer-induced death cases. Without adjustment for potential covariates, significantly positive causal correlation existed between CMI and all-cause mortality (HR = 1.03, 95% CI 1.02,1.04, P-value<0.05), CVD mortality (HR = 1.04, 95% CI 1.03, 1.05, P-value<0.05) and cancer mortality(HR = 1.03, 95% CI 1.02, 1.05, P-value<0.05); whereas, after confounding factors were completely adjusted, the relationship lost statistical significance in CMI subgroups (P for trend>0.05). Subgroup analysis found no specific subgroups. Under a fully adjusted model, a threshold effect analysis was performed combined with smooth curve fitting, and the findings suggested an L-shaped nonlinear association within CMI and all-cause mortality (the Inflection point was 0.98); in particular, when the baseline CMI was below 0.98, there existed a negative correlation with all-cause mortality with significance (HR 0.59, 95% CI 0.43, 0.82, P-value<0.05). A nonlinear relation was observed between CMI and CVD mortality. Whereas, the correlation between CMI and cancer mortality was linear.ConclusionsAmong the general American population, baseline CMI levels exhibited an L-shaped nonlinear relationship with all-cause mortality, and the threshold value was 0.98. What’s more, CMI may become an effective indicator for CVD, cancer, and all-cause mortality prediction. Further investigation is essential to confirm our findings.

Dietary Iron Intake and Mental and Behavioral Disorders Due to Use of Tobacco: A UK Biobank Study

Background: Over 1 billion smokers worldwide, one-third of whom have mental and behavioral disorders, exist. However, factors influencing mental and behavioral disorders due to the use of tobacco remain largely unexplored. This study aims to investigate the relationship between dietary iron intake and mental and behavioral disorders due to the use of tobacco. Methods: Using large population cohort data from the UK Biobank (500,000 participants at 22 assessment centers between 2006 and 2010), we employed logistic and Cox regression analyses to explore both cross-sectional and longitudinal associations between dietary iron intake and mental and behavioral disorders due to the use of tobacco. Additionally, we assessed the nonlinear relationship between dietary iron intake and these disorders using restricted cubic spline plots. Results: Logistic regression analysis indicated that dietary iron intake was negatively associated with mental and behavioral disorders due to the use of tobacco. The Cox regression results supported a protective effect of increased dietary iron intake against these disorders. Stratified and sensitivity analyses were consistent with the primary findings. Restricted cubic spline plots revealed a nonlinear relationship between dietary iron intake and mental and behavioral disorders due to the use of tobacco. In the total sample, as well as in both age groups and the male subgroup, the risk reduction rate initially accelerated before slowing down. In contrast, the risk reduction rate in the female group declined rapidly at first and then leveled off. Conclusions: This study demonstrates that dietary iron intake has a protective effect against mental and behavioral disorders due to the use of tobacco, revealing a nonlinear association between these two traits. These findings provide important insights for the profilaxy and treatment of mental and behavioral disorders due to the use of tobacco in the future.

Bending test and numerical analysis of inflatable tubes

In this paper, bending test of an inflatable, polyvinylchloride (PVC)-coated fabric tube is carried out to study the deformation, bearing capacity, and wrinkling characteristics. In order to illustrate the effect of hoop stress, two nonlinear stress-strain relationships are measured through the uniaxial tension and the biaxial tension tests. Meanwhile, a computing method based the fiber beam model is developed for the inflatable tube. This method can consider nonlinearity of membrane materials and wrinkling effect easily. The numerical results using the stress-strain relationship considering hoop stress effect are shown to more accurately predict the load-displacement response in bending test. As the test results shown, the cross-section remains basically circular and the strain distribution in no wrinkling region approximately satisfies the plane section assumption before and after wrinkling. Furthermore, the wrinkling load of the inflatable tube in bending test is compared with the numerical results. Lastly, wrinkling expansion process and wrinkling angle at ultimate load are detailed.

Optimized Air Quality Index and Meteorological Predictions with Machine Learning and IoT

Air Quality Index (AQI) prediction and forecasting play pivotal roles in assessing and managing air pollution, contributing to public health and environmental sustainability. This paper provides a comprehensive review of recent advancements, methodologies, challenges, and future directions in AQI prediction and forecasting. Recent research has seen a surge in the development of machine learning, statistical, and hybrid models for AQI prediction. These models leverage various input data sources such as meteorological data, satellite imagery, and pollutant emissions data to enhance prediction accuracy. Furthermore, the integration of advanced techniques like deep learning and ensemble modeling has shown promising results in capturing complex nonlinear relationships and improving forecast precision. Challenges persist, including the need for real-time data integration, model interpretability, and addressing spatial and temporal variations in air quality. Additionally, the impact of emerging factors such as climate change and urbanization on AQI prediction requires further investigation. Future research directions focus on the development of hybrid models that integrate multiple data sources, including sensor networks and IoT devices, to improve spatial and temporal resolution. Moreover, there is a growing emphasis on the incorporation of uncertainty quantification techniques to provide probabilistic forecasts and enhance decision-making under uncertainty. In conclusion, this paper underscores the importance of AQI prediction and forecasting in addressing air pollution challenges and promoting public health. By advancing methodologies, addressing challenges, and exploring emerging research avenues, we can strive towards more accurate, reliable, and actionable AQI predictions for sustainable urban development and environmental stewardship.

Nonlinear relationship between serum Klotho and chronic kidney disease in US adults with metabolic syndrome.

Current evidence regarding the effects of serum Klotho among patients with metabolic syndrome (MetS) is scarce. This study explored the relationship between serum Klotho levels and the odds of chronic kidney disease (CKD) in middle-aged and older populations with MetS. This cross-sectional study analyzed data from 4870 adults aged 40-79 years who participated in the National Health and Nutrition Survey (NHANES) from 2007 to 2016. CKD was identified at urinary albumin to creatinine ratio (UACR) of 30 mg/g or higher and/or an estimated glomerular filtration rate (eGFR) below 60 mL/min/1.73 m2. Measurement of serum Klotho concentration was determined via enzyme-linked immunosorbent assay (ELISA) and subsequently divided into four quartiles (Q1-Q4). The NHANES criteria were followed in calculating the sampling weights. Multivariable logistic regression models were employed to assess the correlation between Klotho and CKD, while generalized linear models with cubic spline functions and smooth curve fitting were utilized to detect any nonlinear relationship. Additionally, subgroup analysis and a range of sensitivity analyzes were conducted. Results showed that a nonlinear L-shaped relationship existed between serum Klotho levels and CKD risk, with the lowest prevalence observed at 9.63-9.94 pg/mL Klotho concentrations. With a two-segment linear regression model, an inflection point of 9.88 pg/mL was noted. Hypertension status was identified as an interaction mediator (P interaction = 0.006). Sensitivity analysis showed stable results. A nonlinear L-shaped relationship exists between serum Klotho levels and risks of CKD among middle-aged and older adults with MetS, with the lowest prevalence observed at 9.63 to 9.94 pg/mL Klotho concentrations. Our findings, if replicated, underscore the need to estimate the optimal serum Klotho concentrations and the consequential inverse relationship, thus implying the potential of Klotho as both a serum biomarker and a possible preventive or therapeutic intervention.

An Application of Logistic Regression in Bank Lending Prediction: A Machine Learning Perspective

Logistic regression has been widely applied in bank lending prediction due to its simplicity, efficiency, and interpretability. This paper provides a review of the theoretical foundations, practical applications, and recent advancements of logistic regression in this domain. Through case studies and literature analysis, the author focuses on the challenges faced by logistic regression in the modeling process, such as data imbalance and nonlinear relationships, as well as optimization strategies like regularization and ensemble learning. Despite the proliferation of machine learning methods, logistic regression remains the preferred choice and benchmark for credit risk modeling, thanks to its robust predictive performance and regulatory friendliness. Looking forward, the author argues that the integration of logistic regression with other machine learning techniques, coupled with domain knowledge, will further enhance its applicability and predictive power. However, striking a balance between model complexity and interpretability, while improving model robustness and extrapolation, remains an important area for future research. This paper offers new perspectives and insights for credit risk management practices in banks and other financial institutions.

Does digital transformation matter for international diversification? The role of product market competition

Purpose This study aims to investigate the impact of product market competition on the relationship between firm digital transformation and international diversification. It aims to uncover how competition moderates this relationship and to reveal the nonlinear dynamics between digital transformation and international diversification in strategic decision-making processes. Design/methodology/approach Using a panel logistic regression analysis, this study examines data from 235 Malaysian nonfinancial listed companies from 2012 to 2019. The analysis focuses on the manufacturing and technology industries due to the availability of digital transformation data, leading to a data set of 1,180 year-firm observations. Findings The results reveal a nonlinear relationship between digital transformation and international diversification, intensified by product market competition. Initially, digital transformation positively affects international diversification, but this effect turns negative as competition increases. Robustness checks validate these findings, indicating that competition’s impact varies with the level of digital transformation. Research limitations/implications This study’s findings are based on text analysis as a proxy for digital transformation, which may not fully capture organizational changes. Future research could use reported transformation costs or mandatory disclosures. In addition, this study focuses solely on international diversification, excluding other forms of diversification and financial constraints. Practical implications Policymakers should recognize that high product market competition can negate the benefits of digital transformation on internationalization. They need to balance promoting digital transformation with addressing competitive challenges. Managers should analyze the competitive landscape before pursuing international expansion, as high competition can diminish the advantages of digital transformation. Originality/value This research enriches agency and resource-based view theories by revealing the complex dynamics between digital transformation, competition and international diversification. It introduces a parabolic relationship between competition and diversification, challenging traditional assumptions and providing a comprehensive framework for understanding strategic decisions in competitive environments.

Coupling of state space modules and attention mechanisms: An input-aware multi-contrast MRI synthesis method.

Medical imaging plays a pivotal role in the real-time monitoring of patients during the diagnostic and therapeutic processes. However, in clinical scenarios, the acquisition of multi-modal imaging protocols is often impeded by a number of factors, including time and economic costs, the cooperation willingness of patients, imaging quality, and even safety concerns. We proposed a learning-based medical image synthesis method to simplify the acquisition of multi-contrast MRI. We redesigned the basic structure of the Mamba block and explored different integration patterns between Mamba layers and Transformer layers to make it more suitable for medical image synthesis tasks. Experiments were conducted on the IXI (a total of 575 samples, training set: 450 samples; validation set: 25 samples; test set: 100 samples) and BRATS (a total of 494 samples, training set: 350 samples; validation set: 44 samples; test set: 100 samples) datasets to assess the synthesis performance of our proposed method in comparison to some state-of-the-art models on the task of multi-contrast MRI synthesis. Our proposed model outperformed other state-of-the-art models in some multi-contrast MRI synthesis tasks. In the synthesis task from T1 to PD, our proposed method achieved the peak signal-to-noise ratio (PSNR) of 33.70 dB (95% CI, 33.61, 33.79) and the structural similarity index (SSIM) of 0.966 (95% CI, 0.964, 0.968). In the synthesis task from T2 to PD, the model achieved a PSNR of 33.90 dB (95% CI, 33.82, 33.98) and SSMI of 0.971 (95% CI, 0.969, 0.973). In the synthesis task from FLAIR to T2, the model achieved PSNR of 30.43 dB (95% CI, 30.29, 30.57) and SSIM of 0.938 (95% CI, 0.935, 0.941). Our proposed method could effectively model not only the high-dimensional, nonlinear mapping relationships between the magnetic signals of the hydrogen nucleus in tissues and the proton density signals in tissues, but also of the recovery process of suppressed liquid signals in FLAIR. The model proposed in our work employed distinct mechanisms in the synthesis of images belonging to normal and lesion samples, which demonstrated that our model had a profound comprehension of the input data. We also proved that in a hierarchical network, only the deeper self-attention layers were responsible for directing more attention on lesion areas.

Machine Learning-Based Ground Peak Acceleration Attenuation Prediction Model

ABSTRACT For earthquake mitigation and disaster management, it is important to rapidly predict modified Mercalli intensity (MMI) attenuation patterns across the entire earthquake-affected area. The quick prediction of hazardous areas after a destructive earthquake can effectively reduce the number of casualties and extent of property damage. Peak ground acceleration (PGA) is closely related to earthquake MMI. Traditional PGA predictions are constructed based on empirical formulas, which struggle to explain the nonlinear relation between PGA and epicentral distance. To address this issue, we propose a machine learning-based PGA attenuation model. We utilized seismic data from the Japan K-NET and KiK-net networks collected during 2000–2023 to construct a dataset and applied three different models to fit the data. The best-fit model was selected based on the performance results of these models. Using this model, three earthquake events with MJMA >6 in 2023 were predicted to evaluate potentially hazardous areas resulting from earthquakes. The results indicated that the model could effectively predict potentially hazardous areas and rapidly generate seismic MMI maps after an earthquake. Additionally, the machine learning model successfully addressed the nonlinear relation between PGA and epicentral distance.

Russian multicenter observational HDQ study on the validation of the questionnaire for hemorrhoidal disease

AIM: to develop and validate hemorrhoidal disease (HD) specific national questionnaire.MATERIALS AND METHODS: the Russian questionnaire, which included questions on the most typical HD symptoms, was developed by leading national specialists-proctologists. The regression analysis was used to evaluate linear and nonlinear relationships between the fact of the HD presence, objectively confirmed by a doctor, and the patients’ answers to the questions from the questionnaire. The final version of the questionnaire included only questions regarding clinical characteristics typical for hemorrhoids that were significantly associated with the presence of the disease. Validation of the questionnaire was performed on both training and test samples of patients to determine its sensitivity (Se) and specificity (Sp) in identifying patients with HD.RESULTS: initially, 7 main domains reflecting the presence and severity of symptoms typical for HD were included in the questionnaire. A training sample of patients was used to search for linear and nonlinear relationships between objectively confirmed hemorrhoids and responses to questions in the initial questionnaire. The final version of the questionnaire, consisting only of questions regarding symptoms that have demonstrated to be significant predictors of hemorrhoids, was validated on a training sample (Se = 86.5%, Sp = 73.4%, and the probability of identifying a patient with HD, according to Bayes’ theorem = 66.6%). After the developed version of the questionnaire was validated on a test sample, the final screening questionnaire for HD demonstrated Se 83.7%, Sp 74.1%, and the probability of identifying patients with HD with a positive questionnaire result, calculated according to Bayes’ theorem, was 67%.CONCLUSION: the first national questionnaire for screening HD has diagnostic characteristics indicating its high accuracy in identifying patients with HD and allowing it to be recommended for use in the general population.

On the Properties of New Polyurethane Fast-Curing Polymer Materials.

A sequences of unconfined compressive strength tests and flexural tests were conducted in this study to evaluate the curing performance of a new type of polyurethane sand fast-curing polymer material. The mechanical properties of the material were investigated under different curing temperatures (-10 °C to 60 °C), particle sizes (10-15 mesh, 60-80 mesh, 100-120 mesh, and 325 mesh), and material proportions (20% to 60%). Additionally, SEM analysis was employed to further reveal the reinforcement mechanism. The results demonstrated that the developed polyurethane polymer material exhibited superior curing properties and applicability across a wide temperature range of -10 °C to 60 °C. Both the compressive strength and flexural strength of the solidified sand increased with the increase in solidification temperature, resulting in improved curing effects. This material exhibited the best curing properties when using sand within the 100-120 mesh range. As the particle size decreased under the remaining specifications, there was a reduction in specimen strain and an increase in strength, while still maintaining favorable ductility. The optimal proportion for polyurethane material was 40%. Moreover, the nonlinear mathematical relationships between the strength and multiple influencing factors were established through multivariate regression analysis. The sand consolidation specimens exhibited X-shaped conjugate shear failure, which tended to occur at the weak interface between the sand and material. Lastly, Pearson's correlation analysis revealed a strong positive correlation between temperature and material content with strength.

Acoustic Waves in Axial Crystals and Quasicrystals with Preliminary Finite Strains

The dynamic problem of acoustic wave propagation in elastic bodies with preliminary finite deformations is formulated. Several types of anisotropic elastic materials are considered: hexagonal crystals of class [Formula: see text], transversally isotropic materials and axial quasicrystals with an arbitrary order of the rotary axis. A variant of nonlinear hypoelasticity relations is constructed for these materials. In the constitutive relations, the tensor connecting the rate of change of the stress tensor and the strain rate tensor is a function of deformations. The propagation of acoustic waves in a hypoelastic material with preliminary finite deformations and stresses is described as a process of weak perturbations superimposed on finite strains. The characteristic features of elastic wave propagation in hexagonal crystals and axial quasicrystals are revealed. Within the framework of the constructed models, the most significant differences are found in the phase velocities of a transverse wave with a polarization vector located in the crystallographic plane (110). Unlike hexagonal crystals, in axial quasicrystals, there are such directions of acoustic wave propagation for which the phase velocities of transverse waves are insensitive to the degree of preliminary deformations. Based on the solution of the problem of elastic wave propagation, a program of dynamic experiments with macrosamples, which allows to distinguish axial quasicrystals from hexagonal crystals by the peculiarities of acoustic wave propagation, is proposed.

Neuromechanical Circuits of the Spinal Motor Apparatus.

The evolution of mechanisms for terrestrial locomotion has resulted in multi-segmented limbs that allow navigation on irregular terrains, changing of direction, manipulation of external objects, and control over the mechanical properties of limbs important for interaction with the environment, with corresponding changes in neural pathways in the spinal cord. This article is focused on the organization of these pathways, their interactions with the musculoskeletal system, and the integration of these neuromechanical circuits with supraspinal mechanisms to control limb impedance. It is argued that neural pathways from muscle spindles and Golgi tendon organs form a distributive impedance controller in the spinal cord that controls limb impedance and coordination during responses to external disturbances. These pathways include both monosynaptic and polysynaptic components. Autogenic, monosynaptic pathways serve to control the spring-like properties of muscles preserving the nonlinear relationship between stiffness and force. Intermuscular monosynaptic pathways compensate for inertial disparities between the inertial properties of limb segments and help to control inertial coupling between joints and axes of rotation. Reciprocal inhibition controls joint stiffness in conjunction with feedforward cocontraction commands. Excitatory force feedback becomes operational during locomotion and increases muscular stiffness to accommodate the higher inertial loads. Inhibitory force feedback is widely distributed among muscles. It is integrated with excitatory pathways from muscle spindles and Golgi tendon organs to determine limb stiffness and interjoint coordination during interactions with the environment. The intermuscular distribution of force feedback is variable and serves to modulate limb stiffness to meet the physical demands of different motor tasks. © 2024 American Physiological Society. Compr Physiol 14:5789-5838, 2024.

A Study of the Response Surface Methodology and Finite Element Method to Model Aircraft Engine Body Deformations During Hole Drilling.

The aviation industry is still looking for effective manufacturing methods. Currently, the challenge is still the machining of shapes in thin-walled materials. This work focuses on the analysis of the influence of these parameters on deformations during the drilling process of holes in the adapters of aircraft engine body accessories. The drilling process was carried out in the thin-walled superalloy Inconel 718, which is classified as a difficult-to-machine material. During the analyses, experimental studies based on the Response Surface Method (RSM) and finite element method (FEM) calculations were carried out simultaneously. The aim of this work was to analyze the developed mathematical models describing nonlinear relationships between cutting parameters, cutting forces, and deformations of the aircraft engine body characterized by a complex, thin-walled geometric structure. By using the proposed solutions, it is possible to achieve integration between the techniques of conducting research, performing calculations using FEM, and designing the machining. The advantage of this comprehensive approach used in our work is the development of mathematical models that strongly fit the results of the research. The results of analyses and calculations presented in the article and the research methodology used can be applied to industrial conditions.

Hedonic adaptation in cultural consumption: evidence from China Family Panel Studies

Understanding the impact of cultural consumption on well-being has both economic and policy implications, offering valuable insights for theory and practice. Most researchers have identified linear relationships between these concepts, but few have explored the nonlinear relationship. This study utilizes household economic data and draws on the China Family Panel Studies (CFPS) database from 2010, 2014, and 2018 to collect a valid sample of 47,949 individuals from 31 provinces. Our findings support the idea that cultural consumption has a significantly positive effect on subjective well-being and that internet use helps strengthen this relationship. Moreover, there is a “satiation point” where excessive cultural consumption may lead to a decrease in subjective well-being. In the case of excessive cultural consumption, cultural consumption leads to a more noticeable decrease in well-being. Our results hold after various robustness checks and after endogeneity concerns are addressed through instrumental variable methodology. Furthermore, heterogeneity analysis revealed that this effect is more pronounced in the elderly and female populations.