Multivariate Nonlinear Regression Research Articles

Numerical simulation of composite swirl/film double-wall cooling structures and chamber designs for enhanced overall cooling effectiveness

A novel double-wall with composite swirl/film cooling structure is proposed in this work to enhance the cooling effectiveness of contemporary gas turbine blades. The influence of different shaped chambers and Biot numbers on the overall cooling effectiveness (OCE) are investigated by Reynolds time-averaged Navier-Stokes simulations and conjugate heat transfer analysis. The results suggest that the proposed composite cooling configuration exhibits outstanding OCE in the simulated range. When the Rm is 1.00 and the Biot number is 0.11, the OCE of the composite structure is improved by 8.92 % over the conventional double-wall structure, and the distribution is more uniform. In addition, the optimal flow and cooling performance of the composite swirl/film cooling structure at high Rm conditions are obtained. The OCE is very sensitive to the variation of the Biot number. Finally, the empirical correlations obtained by the multivariate nonlinear regression fitting method are in good agreement with the simulations, with errors within 10 %. The results also provide important guidance for the development of advanced gas turbine blades double-wall cooling structures.

Performance of double-arch tunnels under internal BLEVE

Double-arch tunnels, as one of the popular forms of tunnels, might be exposed to boiling liquid expanding vapour explosions (BLEVEs) associated with transported liquified petroleum gas (LPG), which could cause damage to the tunnel and even catastrophic collapse of the tunnel in extreme cases. However, very limited study has investigated the performance of double-arch tunnels when exposed to internal BLEVEs and in most analyses of tunnel responses to accidental explosions. The TNT-equivalence method was used to approximate the explosion load, which may lead to inaccurate tunnel response predictions. This study numerically investigates the response of typical double-arch tunnels to an internal BLEVE resulting from the instantaneous rupture of a 20 m3 LPG tank. Effects of various factors, including in-situ stresses, BLEVE locations, and lining configurations on tunnel responses are examined. The results show that the double-arch tunnels at their early-operation ages are more vulnerable to severe damage when exposed to the BLEVE due to the low action of in-situ stress of rock mass on the response of early-age tunnels. It is also found that directing the LPG tank to different driving lanes inside tunnels can affect the BLEVE-induced tunnel response more significantly than varying the configurations of tunnel lining. Moreover, installing section-steel arches in the mid-wall can effectively improve the blast resistance of the double-arch tunnels against the internal BLEVE. In addition, the prediction models based on multi-variate nonlinear regressions and machine learning methods are developed to predict the BLEVE-induced damage levels of the double-arch tunnels without and with section-steel arches.

Numerical study of wave run-up on sea dikes with vegetated foreshores

Integrating coastal vegetation into sea dikes is a nature-based approach aimed at combining disaster prevention with ecological sustainability in coastal areas. This study investigates the impact of vegetation on the wave run-up on dikes through numerical analysis. The numerical model used in the study solves the Reynolds-averaged Navier–Stokes equations by adding a vegetation resistance force to account for momentum loss. A stabilized k–ω shear stress transport model considering the vegetation effect was adopted for turbulence closure. A series of numerical simulations was carried out on the wave run-up (Ru) on dikes, focusing on the effects of different vegetation heights, densities, zone lengths, and dike slopes under various wave conditions. The results indicate that vegetation can significantly decrease Ru and may cause the wave to change from breaking to nonbreaking on dikes. The Ru behaviors depend on whether waves break and can be well characterized by the Iribarren number and dimensionless wave momentum flux parameter under breaking and nonbreaking conditions, respectively. Finally, the multivariate non-linear regression (MNLR) and artificial neural network (ANN) methods were adopted to explore a prediction model for evaluating Ru. Comparisons showed that the prediction performance of the ANN model is superior to that of the MNLR model. The ANN model has the potential as a promising predictive tool for obtaining wave run-up on dikes with vegetated foreshores under breaking and nonbreaking conditions.

Numerical Simulation Study on Blasting-induced Damage Characteristics of Frozen Rock

Rock masses in cold regions are exposed to a negative temperature environment for extended periods, causing significant changes in their mechanical properties. This inevitably results in substantial differences in the blasting characteristics compared to rocks at normal temperatures. To reveal the influence of different temperatures on the blasting characteristics of frozen rocks, this paper utilized a finite element method-based LS-DYNA software for numerical simulation of single borehole blasting at freezing temperatures of -5°C, -10°C, -20°C, and -30°C. The results showed that under negative temperature conditions, the degree of rock blasting-induced damage gradually decreased as the rock temperature decreased. As the temperature decreased from -5°C to -30°C, the volume of rock blasting-induced damage decreased by approximately 14%. Additionally, this paper has further investigated the effects of ignition locations, free surface, and borehole burdens on the blasting-induced damage characteristics of frozen rock. It was found that the volume of the blast cavity is the largest in reverse detonation. While forward detonation is more favorable for damaging the top rock and helps to improve the rock fragmentation at the orifice. Increasing the free surface can significantly improve the effect of blasting-induced damage. There is an optimal borehole burden distance, which can fully utilize the explosive energy and break the rock efficiently. Finally, based on multivariate nonlinear regression analysis, an empirical formula of the reduction coefficient of the specific charge for frozen rock mass is developed, which can provide a theoretical basis for designing and optimizing rock engineering blasting in alpine and cold regions.

Study of nanofluid flow in a stationary cone–disk system with temperature-dependent viscosity and thermal conductivity

The substantial temperature gradient experienced by systems operating at relatively high temperatures significantly impacts the transport characteristics of fluids. Hence, considering temperature-dependent fluid properties is critical for obtaining realistic prediction of fluid behavior and optimizing system performance. The current study focuses on the flow of nanofluids in a stationary cone–disk system (SCDS), taking into account temperature-dependent thermal conductivity and viscosity. The influence of Brownian motion, thermophoresis, and Rosseland radiative flux on the heat transport features are also examined. The Reynolds model for viscosity and Chiam's model for thermal conductivity are employed. The Navier–Stokes equation, the energy equation, the incompressibility condition, and the continuity equation for nanoparticles constitute the governing system. The Lie-group transformations lead the self-similar ordinary differential equations, which are then solved numerically. Multi-variate non-linear regression models for the rate of heat and mass transfers on the disk surface were developed. Our study reveals a notable decrease in the rate of heat and mass transfer when pre-swirl exists in the flow. The significant influence of nanofluid slip mechanisms on the effective temperature and nanofluid volume fraction (NVF) within the system is highlighted. Furthermore, the variable viscosity property enhances the temperature and NVF of the SCDS.

Experimental study and multi-objective optimization of the shear mechanical properties of recycled aggregate concrete with hybrid fibers and nano SiO2

Structural imperfections (microcracks, micropores, and weak interfacial transition zones) impact the shear mechanical properties of recycled aggregate concrete (RAC). Fibers and nanoparticles can mitigate these issues, enhancing the overall quality of recycled aggregates (RAs). Various hybrid systems, including basalt fiber (BF), polypropylene fiber (PPF), and nano-SiO2 (NS), were added to the RAC for double shear testing. Regression models were developed using the response surface method to optimize the hybrid systems. The study showed that the NS-BF-PPF hybrid system achieved the optimal shear strength, shear strain, shear toughness index, and shear fracture energy (6.99MPa, 9.58×10-4, 0.716, and 34.603J, respectively). These values were superior to those of the BF-PPF hybrid system and single system. The three-dimensional reticulated supporting structure devised by the BF-PPF effectively remedied the structural defects in the RA at various levels. Furthermore, the fibers integrated into the hybrid system act as bridges, absorbing shear energy during the pullout process. The incorporation of nanoscale NS stimulated the hydration response of the cement, resulting in a more compact pore structure within the RAC. Nonlinear multivariate regression models were fabricated utilizing the response surface method to maximize strength. The optimal contents of BF and PFF in the BF-PPF hybrid system were 0.344% and 0.3%, respectively. The optimal contents of NS, BF, and PPF in the NS-BF-PPF hybrid system were determined to be 3.5%, 0.5%, and 0.1%, respectively. The combination of fibers and nanoparticles elicits a synergistic effect, significantly augmenting the shear performance of RAC.

Advancements in predicting scour depth induced by turbulent wall jets: A comparative analysis of mathematical formulations and machine learning models

This study examines the scour depth induced by turbulent wall jets and proposes novel mathematical formulations to predict the depth of scouring. Through a comprehensive gamma test, key parameters influencing the scour depth are identified, including the apron length, densimetric Froude number, median sediment size, tailwater level, Reynolds number, and Froude number of the jet. Regression analysis is subsequently conducted to establish relationships between the dependent parameter and the aforementioned independent variables. A comparative analysis is then undertaken between the measured scour depths and those predicted by existing equations from previous studies. Furthermore, predictive models leveraging the support vector machine, artificial neural network with particle swarm optimization, M5 tree algorithm, gene expression programming, and adaptive neuro-fuzzy inference system (ANFIS) are developed using the collected data. Statistical metrics are employed to evaluate the performance of each model and the regression equation. The effectiveness of each model in predicting scour depth is demonstrated. Notably, ANFIS yields a coefficient of determination of 0.809 and a root mean square error (RMSE) of 1.585. Multi-nonlinear regression analysis exhibits a coefficient of determination of 0.752 and an RMSE of 0.421, while the M5 tree achieves a coefficient of determination of 0.739 and an RMSE of 1.874, demonstrating superior performance compared to other machine learning techniques and regression equations employed in this study.

Understanding Plugging Agent Emplacement Depth with Polymer Shear Thinning: Insights from Experiments and Numerical Modeling

Polymer-plugging agents are widely employed in profile control and water-plugging measures, serving as a crucial component for efficient reservoir development. However, quantitatively monitoring the emplacement depth of polymer-plugging agents in low-permeability and high-permeability layers remains a challenging bottleneck. Presently, insufficient attention on shear thinning, a critical rheological property for water shut-off and profile control, has limited our understanding of polymer distribution laws. In this study, polymer shear-thinning experiments are firstly conducted to explore polymer variations with flow rate. The novelty of the research is that varying polymer viscosity is implemented instead of the fixed-fluid viscosity that is conventionally used. The fitted correlation is then integrated into the 2D and 3D heterogeneous numerical models for simulations, and a multivariate nonlinear regression analysis is performed based on the simulation results. The results show that lower polymer emplacement depth ratios corresponded to higher viscosity loss rates under the same flow rate. An increase in the initial permeability ratio corresponds to a decrease in the emplacement ratio, along with a reduction in the fraction of the plugging agent penetrating the low permeability formations. The model was applied to the Kunan Oilfield and demonstrated a polymer reduction of approximately 3000 tons compared to traditional methods. Despite the slightly complex nature of the multivariate nonlinear mathematical model, it presents clear advantages in controlling plugging agent distribution and estimating dosage, laying good theoretical ground for the effective and efficient recovery of subsurface resources.

Study on the Improvement of Droplet Penetration Effect by Nozzle Tilt Angle under the Influence of Orthogonal Side Wind.

This study investigates the impact of varying side wind velocities and nozzle inclination angles on droplet penetration during plant protection spraying operations, focusing on citrus trees. Experiments were conducted across four wind speed levels (0, 1, 2, 3 m/s) perpendicular to the nozzle direction and seven nozzle inclination levels (0°, 8°, 15°, 23°, 30°, 38°, 45°) to evaluate droplet distribution under different spraying parameters. A baseline condition with 0 m/s wind speed and a 0° nozzle angle served as the control. Utilizing Computational Fluid Dynamics (CFD) and regression analysis techniques in conjunction with field trials, the droplet penetration was analyzed. Results indicate that at constant wind speeds, adjusting the nozzle inclination angle against the direction of the side wind can significantly enhance droplet deposition in the canopy, with a 23° inclination providing the optimal increase in deposition volume, averaging a change of +16.705 μL/cm2. Multivariate nonlinear regression analysis revealed that both wind speed and nozzle inclination angle significantly affect the droplet penetration ratio, demonstrating a correlation between these factors, with wind speed exerting a greater impact than nozzle angle. Increasing the nozzle inclination angle at higher wind speeds improves the penetration ratio, with the optimal parameters being a 23° angle and 3 m/s wind speed, showing a 12.6% improvement over the control. The model fitted for the impact of nozzle angle and wind speed on droplet penetration was validated through field experiments, identifying optimal angles for enhancing penetration at wind speeds of 1, 2, and 3 m/s as 8°, 17°, and 25°, respectively. This research provides insights for improving droplet penetration techniques in plant protection operations.

Research on the Prediction of Infiltration Depth of Xiashu Loess Slopes Based on Particle Swarm Optimized Back Propagation (PSO-BP) Neural Network

The Xiashu loess exhibits expansion when in contact with water and contraction when water is lost, making it highly susceptible to the influence of rainfall. Therefore, it is essential to investigate the infiltration behavior of rainwater in Xiashu loess slopes under various conditions. The depth of infiltration in slopes directly affects the depth of landslide failure and serves as an important indicator for studying slope infiltration characteristics; only a handful of academics have delved into its study. This article is based on on-site rainfall experiments on Xiashu loess slopes, using three main factors, rainfall intensity, rainfall duration, and slope angle, as discrimination indicators for the infiltration depth of Xiashu loess slopes. The particle swarm optimization algorithm is employed to optimize the BP neural network and establish a PSO-BP neural network prediction model. The experimental data are accurately predicted and compared with the multivariate nonlinear regression model and traditional BP neural network models. The results demonstrate that the PSO-BP neural network model exhibits a better fit and higher prediction accuracy than the other two models. This model provides a novel approach for rapidly determining the infiltration depth of Xiashu loess slopes under different rainfall conditions. The results of this study lay the foundation for the prediction of the landslide damage depth and infiltration of Xiashu loess slopes.

The association between prealbumin concentration at admission and mortality in elderly patients with hip fractures: a cohort study.

To evaluate the association between prealbumin concentration at admission and mortality in elderly patients with hip fractures. Elderly patients with hip fractures were screened between Jan 2015 and Sep 2019. Demographic and clinical characteristics of the patients were collected. Linear and nonlinear multivariate Cox regression models were used to identify the association between prealbumin concentration at admission and mortality. All analyses were performed using EmpowerStats and the R software. This cohort study included 2387 patients who met the study criteria. The mean follow-up was 37.64months. The prealbumin concentration was 162.67 ± 43.2mg/L. Multivariate Cox regression showed that prealbumin concentration was associated with mortality in geriatric patients with hip fracture (hazard ratio [HR] = 0.95, 95% confidence intervals [CI]: 0.93-0.97, P < 0.0001). In addition, an inflection point effect was observed in the nonlinear association. The inflection point was 162.2mg/L. If it is less than this inflection point, then every 10mg/L increase in prealbumin was associated with a 7% reduction in the risk of death (HR = 0.93, 95%CI: 0.90-0.96, P < 0.0001). Whengreater than the inflection point,there was nodifference in the risk of death (HR = 0.99, 95%CI: 0.95-1.03, P = 0.5127). The prealbumin concentrations at admission were nonlinearly associated with long-term mortality in geriatric hip fractures, and 162.2mg/L could be considered a prognostic factor of mortality risk.

Plasma SMOC2 Predicts Prognosis in Patients with Heart Failure: A Prospective Cohort.

Heart failure (HF) is a chronic disease with a poor prognosis, making it extremely important to assess the prognosis of patients with HF for accurate treatment. Secreted modular calcium-binding protein 2 (SMOC2) is a cysteine-rich acidic secreted protein that plays a pathophysiological role in many diseases, including regulation of vascular growth factor activity. It has previously been found that SMOC2 plays an essential role in cardiac fibrosis in our previous preclinical study, but whether it can be used as a clinical marker in heart failure patients remains unclear. The purpose of this research was to evaluate the correlation between plasma levels of SMOC2 and the prognosis for individuals with HF. HF patients diagnosed with ischemic cardiomyopathy were enrolled from January to December 2021. Baseline plasma levels of SMOC2 were measured after demographic and clinical features were collected. Linear and nonlinear multivariate Cox regression models were used to determine the association between plasma SMOC2 and patient outcomes during follow-up. All analysis was performed using SPSS, EmpowerStats, and R software. The study included 188 patients, and the average follow-up time was 489.5±88.3 days. The plasma SMOC2 concentrations were positively correlated with N-terminal pro-B-type Natriuretic Peptide (NT-proBNP), left ventricular end-diastolic diameter (LVEDd), and length of hospital stay and were negatively correlated with left ventricular ejection fraction (LVEF) at baseline. A total of 53 patients (28.2%) were rehospitalized due to cardiac deterioration, 14 (7.4%) died, and 37 (19.7%) developed malignant arrhythmias. A fully adjusted multivariate COX regression model showed that SMOC2 is associated with readmission (HR = 1.02, 95% CI:1.012-1.655). A significant increase in rehospitalization risk was observed in group Q2 (HR =1.064, 95% CI: 1.037, 3.662, p=0.005) and group Q3 (HR =1.085, 95% CI:1.086, 3.792, p=0.009) in comparison with group Q1. The p for trend also shows a linear correlation across the three models (P < 0.001). SMOC2 was associated with the severity of HF in patients, but not with all-cause deaths and arrhythmias during follow-up. Plasma SMOC2 is associated with the severity of HF and readmission rate, and is a good predictor of the risk of readmission in patients.

Mathematical Model of Graphene Yield in Ultrasonic Preparation

Based on the Box–Behnken design (BBD) methodology, an experimental study of the preparation of graphene using ultrasonication was conducted. The yield of graphene served as the response variable, with ultrasonication process time, ultrasonic power, the graphite initial weight, and their interactive effects acting as the independent variables influencing the yield. A multivariate nonlinear regression model was established to describe the ultrasonic production of graphene. Verification of the experiments suggests that the developed multivariate nonlinear regression model is highly significant and provides a good fit, enabling an effective prediction of the graphene yield. The yield of graphene was found to increase with higher ultrasonic power but decrease with longer ultrasonication times and the initial weight of the graphite. The optimal process parameters according to the regression model were determined to be 30 min of ultrasonication time, an ultrasonic power of 1500 W, and a graphite initial weight of 0.5 g. Under these conditions, the yield of graphene reached 31.6%, with a prediction error of 2.8% relative to the actual value. Furthermore, the results were corroborated with the aid of scanning electron microscopy (SEM), Raman spectroscopy, and transmission electron microscopy (TEM). It was observed that under constant ultrasonic power and graphite initial weight, a reduction in the ultrasonication processing time led to an increase in the thickness of the graphene. Continuing to increase the ultrasonication time beyond 30 min did not decrease the thickness of the graphene but rather reduced its lateral size. Decreasing the ultrasonic power resulted in thicker graphene, and even with an extended ultrasonication time, the quality of the graphene was inferior compared to that produced under the optimal processing parameters.

Association between weight-adjusted-waist index and depression in US adults: A cross-sectional study

BackgroundCurrent evidence implicates a significant association between depression and obesity and related metabolic dysfunction. The weight-adjusted-waist index (WWI) was recently identified as an ideal index that integrates total body fat, muscle mass, and bone mass. This study investigated the relationship between WWI and depressive symptoms in adults. MethodsParticipants from the National Health and Nutrition Examination Survey (2005–2018) were enrolled. Depressive symptom severity was measured with the Patient Health Questionnaire-9 (PHQ-9). Survey-weighted multivariable logistic regression, subgroup analysis, and generalized additive models were used to determine the relationship between WWI and depressive symptoms. ResultsA total of 34,575 participants were included, with a mean WWI of 11.01; 2,979 participants were suspected of having depressive symptoms (PHQ-9 score ≥ 10). A significant positive association was identified between WWI and depressive symptoms (odds ratio = 1.416, 95 % confidence interval: 1.303–1.539, P < 0.0001). Subgroup analyses suggested that the association between WWI and depressive symptoms was stronger in individuals who were female, overweight, divorced, middle-aged or older (over 40 years old), and had diabetes. Furthermore, the non-linear multivariable regression revealed an inflection point for the WWI at 11.438, and the association was only significant when the WWI was higher than this point. LimitationsThis study was retrospective and only included participants from the United States; therefore, further validation is needed from studies in other countries, especially middle-to-low-income countries, using longitudinal cohorts. ConclusionsThis study identified a significant positive association between WWI and depressive symptoms.

Establishing and modeling the causality relationship of hydro-climatic and land cover change variables with water quality over Lake Tana, Ethiopia

Lakes, the most widespread inland water bodies in the globe, are highly susceptible to change in trophic state due to external factors. Changing hydro-climatic conditions and land cover changes (LCC) can cause lake water quality deterioration. This study establishes the quantitative relationship between variability in the water quality index and changes in hydro-climatic and LCC variables. Water quality is represented by the Forel-ule index (FUI) whereas the hydro-climatic variables considered in this study are lake bottom layer temperature (lblt), lake total layer temperature (ltlt), precipitation, runoff, evaporation, lake skin temperature (lskt), surface wind speed and air temperature. The LCC is quantified by lower and higher level leaf area index (Lv-lai and Hv-lai). FUI has a positive relationship with surface wind speed, precipitation, runoff, ltlt, lblt, and LCC and a negative relationship with evaporation, lskt, and air temperature with 95% confidence level over most parts of the Lake. The temporal correlation is also apparent from the long-term trend pattern. A significant decreasing trend is observed in FUI and lake bottom layer temperature (lblt). In contrast, an insignificant increasing trend is observed in air temperature and lake skin temperature (lskt). The changes in LCC, runoff, precipitation, and surface wind speed is insignificant between 2000 and 2020. Moreover, the phase composites of FUI and hydro-climatic and LCC variables derived from multichannel singular spectrum analysis (MSSA) show strong seasonal modulation of water quality by hydro-climatic and LCC variables. The annual cycle represented by the first two eigenmodes (except wind speed which is represented by the second and third eigenmodes) accounts for between 27.41% (wind speed) to 52.32% (precipitation) of the total joint spatiotemporal variability of FUI and the driving variables. The convergent cross-mapping (CCM) analysis shows that cross-map skill (ρ2) is increased with increasing library length (L) and time delay (τ), which suggests significant causal effects of hydro-climatic and LCC variables on FUI and the lagged causation is consistent with maximum values of ρ2. The significant feedback of FUI to changes in hydro-climatic and LCC variables shows the possibility of hindcast/forecast of the historical/future status of water quality from hydro-climatic and LCC variables. As a result, a multivariate nonlinear regression model (MNWQFM) is developed to forecast the lake water quality index from the hydro-climatic and LCC variables. The model has high performance with R2 of 83.6% and root means square error (RMSE) of 0.15 in FUI.

Proposing a novel mathematical model for hospital pneumatic system

Hospital Pneumatic Systems, specializing in pneumatic systems, are among the most essential components for hospitals. It offers efficient and cost-effective solutions to problems related to the transportation of various materials in hospitals. However, in existing systems, the need for compressed air is met without worrying about cost control and without depending on the sample transported, and this not only makes the system inefficient but also may cause sample degradation. The main purpose of this study is to provide speed/pressure control according to the type of material transported to eliminate the disadvantages of existing systems such as energy use and sample degradation. In this study, a new mathematical model is presented that can be used to make more energy-efficient hospital pneumatic systems. Although there are many studies on various pneumatic systems in the literature, there is not enough for the control of hospital pneumatic systems. According to the results obtained in this study, the system parameters were determined and the mathematical model of the system was obtained by using the Multivariate nonlinear regression method. A genetic algorithm was used to test the validity of the obtained mathematical model and to optimize the coefficient of the input parameters of the model. It is expected that this proposed model will contribute to the use of hospital pneumatic systems and provide a scientific and practical solution to the proposed mathematical model. The proposed mathematical model provides up to %43 more efficient transportation over the currently used system that has been tested.

Crystallinity and mechanical properties of polypropylene products foamed by microcellular injection molding process

AbstractMicrocellular injection molding (MIM) is a pivotal technique for lightweight molding. Polypropylene (PP) is widely used in MIM due to light weight and easy processing. The crystallinity of foamed PP products influences the load‐bearing structures and consequently mechanical properties. To address the deterioration of mechanical properties caused by internal porous structure, a novel method is proposed to regulate the crystallinity by optimizing process parameters, thereby improving mechanical properties. Wide‐angle x‐ray diffraction, mechanical testing, and melt flow simulation are applied to investigate the influence of process parameters on crystallinity and mechanical properties. Parameters are further optimized by establishing a multivariate nonlinear regression model for process parameters and crystallinity. The results show that increasing melt temperature, injection rate, mold temperature, and cooling time stimulate crystallization, enhancing strength and decreasing elongation at break. Conversely, high injection ratio of supercritical fluid obstructs crystallization, reducing strength, and increasing elongation at break. The crystallinity increases by 20.81%; the corresponding tensile, flexural, and shear strength and corresponding modulus increases by 5.39% and 10.28%, 7.60% and 18.50%, 5.00% and 11.37%, and elongation at break decreased by 5.19% through the optimization of regression model. The proposed method is feasible to upgrade the mechanical properties of foamed PP products.

Improving wheelchair user sitting posture to alleviate lumbar fatigue: a study utilizing sEMG and pressure sensors.

The wheelchair is a widely used rehabilitation device, which is indispensable for people with limited mobility. In the process of using a wheelchair, they often face the situation of sitting for a long time, which is easy to cause fatigue of the waist muscles of the user. Therefore, this paper hopes to provide more scientific guidance and suggestions for the daily use of wheelchairs by studying the relationship between the development of muscle fatigue and sitting posture. First, we collected surface Electromyography (sEMG) of human vertical spine muscle and analyzed it in the frequency domain. The obtained Mean Power Frequency (MPF) was used as the dependent variable. Then, the pose information of the human body, including the percentage of pressure points, span, and center of mass as independent variables, was collected by the array of thin film pressure sensors, and analyzed by a multivariate nonlinear regression model. When the centroid row coordinate of the cushion pressure point is about 16(range, 7.7-16.9), the cushion pressure area percentage is about 80%(range, 70.8%-89.7%), and the cushion pressure span range is about 27(range, 25-31), the backrest pressure point centroid row coordinate is about 15(range, 9.1-18.2), the backrest pressure area percentage is about 35%(range, 11.8%-38.7%), and the backrest pressure span range is about 16(range, 9-22). At this time, the MPF value of the subjects decreased by a small percentage, and the fatigue development of the muscles was slower. In addition, the pressure area percentage at the seat cushion is a more sensitive independent variable, too large or too small pressure area percentage will easily cause lumbar muscle fatigue. The results show that people should sit in the middle and back of the seat cushion when riding the wheelchair, so that the Angle of the hip joint can be in a natural state, and the thigh should fully contact the seat cushion to avoid the weight of the body concentrated on the buttocks; The back should be fully in contact with the back of the wheelchair to reduce the burden on the waist, and the spine posture can be adjusted appropriately according to personal habits, but it is necessary to avoid maintaining a chest sitting position for a long time, which will cause the lumbar spine to be in an unnatural physiological Angle and easily lead to fatigue of the waist muscles.

Multi-objective prediction and optimization of performance of three-layer latent heat storage unit based on intermittent charging and discharging strategies

An intermittent heat charging and discharging strategy is proposed for on-demand thermal utilization in a three-layer latent heat storage unit filled with nanoparticle-enhanced phase change materials. To optimize the utilization ratio of phase change materials, and the stored and released thermal exergy amounts, a multi-objective prediction and optimization methodology combining orthogonal experimental design, range and variance analyses, multi-nonlinear regression models, and non-dominated sorting genetic algorithm-II is introduced while considering the variables of nanoparticle concentration, heat transfer fluid velocity, and intermittent time interval. Results show that the time interval presents the most significant influence. Multi-nonlinear regression models for the above three variables are established with determination factors of 0.9871, 0.9625, and 0.9253, respectively. The ultimate optimal results are 0.8, 57094.03 J, and 43066.73 J, achieved at the three variables of 44.37 min, 0.38 m s−1 and 8.99%, respectively. The maximum verification error of 5.11% indicates the reliability of this methodology. The methodology aims to enhance the overall performance of the three-layer latent heat storage system by mitigating the constraints associated with single-performance optimization.

Differences in spine growth potential for Sanders maturation stages 7A and 7B have implications for treatment of idiopathic scoliosis.

This study aimed to clarify the differences in spine and total body height growth and curve progression between Sanders maturation stage (SMS) 7A and 7B in patients with adolescent idiopathic scoliosis (AIS). This retrospective case-control study involving patients with AIS at SMS 7 evaluated the differential gains in the spine (T1-S1) and total body height and curve progression between SMS 7A and 7B. A validated formula was used to calculate the corrected height, accounting for height loss due to scoliosis. A multivariable non-linear and logistic regression model was applied to assess the distinct growth and curve progression patterns between the SMS 7 subtypes, adjusting for potential confounders. A total of 231 AIS patients (83% girls, mean age 13.9 ± 1.2years) were included, with follow-up averaging 3.0years. Patients at SMS 7A exhibited larger gains in spine height (9.9mm vs. 6.3mm) and total body height (19.8mm vs. 13.4mm) compared with SMS 7B. These findings remained consistent even after adjustments for curve magnitude. Non-linear regression models showed continued spine and total body height increases plateauing after 2years, significantly greater in SMS 7A. More SMS 7A patients had curve progression over 10°, with an adjusted odds ratio of 3.31. This study revealed that patients staged SMS 7A exhibited more spine and total body growth and a greater incidence of substantial curve progression than those at 7B. These findings imply that delaying brace discontinuation until reaching 7B could be beneficial, particularly for those with larger curves. Level III (Case-control study).