Additive Model Research Articles

Associations between dietary macronutrient composition and cardiometabolic health: data from NHANES 1999–2014

PurposeDietary macronutrients significantly impact cardiometabolic health, yet research often focuses on individual macronutrient relationships. This study aimed to explore the associations between dietary macronutrient composition and cardiometabolic health.MethodsThis study included 33,681 US adults (49.7 ± 18.3 years; 52.5% female) from the National Health and Nutrition Examination Survey during 1999–2014. Dietary data was derived from 1 to 2 separate 24-hour recalls and cardiometabolic health included lipid profile, glycemic control, blood pressure, and adiposity collected in a mobile examination center. Associations between dietary macronutrient composition and cardiometabolic health were examined using generalized additive models adjusted for age, socio-demographics, lifestyle, and diet quality.ResultsIn females, triglycerides (P < 0.01) and HDL cholesterol (P < 0.01) were the least optimal in diets containing lower fat (10%) and higher carbohydrate (75%). In males, HDL cholesterol was positively associated with fat (P < 0.01) and no association with triglycerides was detected. Total-C associations were male specific (P = 0.01) and highest in diets composed of 25% protein, 30% carbohydrate, and 45% fat. In both sexes, systolic blood pressure (P ≤ 0.02) was highest in diets containing lower fat (10%) coupled with moderate protein (25%). Diastolic blood pressure associations were female specific (P < 0.01) with higher values in those consuming the upper range of fat (55%). There were no associations of macronutrient composition with glycemic control or adiposity.ConclusionThis study revealed sex-specific relationships between macronutrient composition and cardiometabolic health. Future research is needed to explore these relationships across age groups.

A Non-Linear Exploration of the Digital Economy’s Impact on Agricultural Carbon Emission Efficiency in China

As the global climate crisis intensifies, improving agricultural carbon emission efficiency has become crucial for achieving the sustainable development goals (SDGs). This study investigates the complex, non-linear relationship between China’s digital economy and agricultural carbon emission efficiency, utilizing panel data from Chinese provinces spanning 2012–2022. We employ a multi-method approach, including the Super-SBM model for efficiency measurement, two-way fixed effects models, quantile regression, and Generalized Additive Models (GAMs) for empirical analysis. Our findings reveal: (1) The digital economy significantly enhances agricultural carbon emission efficiency, but with distinct non-linear characteristics across different dimensions. (2) The impact varies among digital economy aspects: the digital economy foundation shows the most substantial influence, followed by the rural digital industry level, while rural digital infrastructure has a relatively minor effect. (3) A threshold effect is observed, with the digital economy’s impact more pronounced in regions with higher agricultural carbon emission efficiency. (4) GAM analysis unveils complex non-linear patterns: the rural digital industry’s impact initially decreases before increasing, the digital economy foundation shows an overall increasing trend with plateaus, and rural digital infrastructure exhibits a near-linear relationship. (5) Sensitivity analysis indicates that agricultural carbon emission efficiency is most responsive to changes in the digital economy foundation, followed by the rural digital industry level. These findings provide nuanced insights into the digital economy’s role in enhancing agricultural sustainability. We propose targeted policy recommendations, including accelerating rural digital infrastructure development, optimizing the rural digital industry structure, and implementing context-specific digital facility construction. These strategies aim to fully leverage the digital economy’s potential in improving agricultural carbon emission efficiency, contributing to China’s “dual carbon” goals and sustainable agricultural development.

Neurodegeneration in the cortical sulcus is a feature of chronic traumatic encephalopathy and associated with repetitive head impacts

Neurodegeneration is a seminal feature of many neurological disorders. Chronic traumatic encephalopathy (CTE) is caused by repetitive head impacts (RHI) and is characterized by sulcal tau pathology. However, quantitative assessments of regional neurodegeneration in CTE have not been described. In this study, we quantified three key neurodegenerative measures, including cortical thickness, neuronal density, and synaptic proteins, in contact sport athletes (n = 185) and non-athlete controls (n = 52) within the sulcal depth, middle, and gyral crest of the dorsolateral frontal cortex. Cortical thickness and neuronal density were decreased within the sulcus in CTE compared to controls (p’s < 0.05). Measurements of synaptic proteins within the gyral crest showed a reduction of α-synuclein with CTE stage (p = 0.002) and variable changes in PSD-95 density. After adjusting for age, multiple linear regression models demonstrated a strong association between the duration of contact sports play and cortical thinning (p = 0.001) and neuronal loss (p = 0.032) within the sulcus. Additional regression models, adjusted for tau pathology, suggest that within the sulcus, the duration of play was associated with neuronal loss predominantly through tau pathology. In contrast, the association of duration of play with cortical thinning was minimally impacted by tau pathology. Overall, CTE is associated with cortical atrophy and a predominant sulcal neurodegeneration. Furthermore, the duration of contact sports play is associated with measures of neurodegeneration that are more severe in the cortical sulcus and may occur through tau-dependent and independent mechanisms.

Dimensions of Audit Committee Quality and CEO Compensation: Examining the Linear Relationship

This study examined the linear relationship between audit committee attributes, proxied with audit committee female diversity, audit committee diligent meeting, audit committee independence and audit committee size and Chief Executive Officer’s compensation, with firm size as a control variable. The study utilised data from 53 non-financial firms between 2013 and 2022 listed in the Nigerian Exchange Group. Using, STAT 14, the study also presented the descriptive statistics information, the correlation matrix and results of Random-effects GLS regression of the study. The results indicate that approximately 6.88% of the variation in Chief Executive Officer’s compensation can be explained by the combined effect of the independent variables, as evidenced by an R-squared value of 0.0688 and an adjusted R-squared of 0.0599. The model’s overall significance is confirmed by an F-statistic of 7.75 and a very low P-value (0.0000), we therefore reject the null hypothesis. The findings reveal that audit committee female diversity, audit committee diligent meeting, and audit committee independence do not have significant linear relationships with CEO compensation, as indicated by their respective P-values (0.542, 0.814, and 0.916). However, audit committee size displays a statistically significant negative linear relationship with CEO compensation (P = 0.000), suggesting that larger audit committees may be associated with lower CEO compensation due to increased oversight or stronger corporate governance structures. Consequently, further investigation with additional variables or alternative modelling approaches is recommended to gain deeper insights into other determinants of CEO compensation.

Resilience or Collapse? Reconstructing the Water Quality Time Series of a Tropical River Impacted by a Mine Tailings Dam Failure

The 2015 Fundão tailings dam collapse in Mariana, Brazil, was a major environmental catastrophe. Assessing its long-term effects on water quality is critical for environmental restoration and policy development. In this study, we reconstructed a 15-year time series of five water quality parameters to assess whether the collapse caused permanent changes. Using public data from the Minas Gerais Water Institute (IGAM), we fitted generalized additive models for location, scale, and shape to model long-term trends in turbidity, total solids, conductivity, pH, and dissolved oxygen. Predictor variables included daily precipitation and smooth functions for time and longitudinal distance along the river. As expected, turbidity and total solids increased sharply after the collapse; however, the mean values returned to pre-collapse levels within four years. Conductivity, which was already elevated pre-collapse, remained high following the passage of the tailings plume. Although we observed a tendency toward pre-collapse values, the long-term conductivity mean did not fully stabilize to previous levels. No clear patterns were observed for pH or dissolved oxygen. This study highlights the acute impact of the dam collapse on five water quality parameters in the Doce River and illustrates the river’s subsequent stabilization process, although other important and chronic impacts are still persistent. Long-term studies such as this provide valuable insights into the dynamics of fluvial systems.

Kernel density regression in the additive model: a B-spline approach

Kernel density regression in the additive model: a B-spline approach

Dissection of X chromosome dosage compensation for quantitative traits in sheep using different statistical models

Since males and females have different number of X chromosome, different mechanisms have evolved to equalize dosage of gene products from the X chromosome between XX females and XY males. The aim of this study was to study X chromosome dosage compensation for growth rate (GR), Kleiber ratio (KR), efficiency of growth (EF) and relative growth rate (RGR) in Zandi sheep. A two steps procedure was adopted to analysis data. In the first step, each trait was analyzed with a series of 6 animal models including different combinations of direct and maternal effects. Using Akanke’s Information Criterion (AIC) the best model (Model I) was selected for each trait. In the second step, five additional models were fitted by adding X chromosome effects to the Model I, considering 5 strategies for modeling X chromosome dosage compensation: (1) no global dosage compensation (ngdc), (2) random inactivation in the homogametic sex (hori), (3) doubling of the single shared sex chromosome in the heterogametic sex (hedo), (4) halving expression of both sex chromosomes in the homogametic sex (hoha) or (5) inactivation of the paternal sex chromosome in the homogametic sex (hopi). Predictive ability of models was measured using the mean squared error of prediction (MSE) and Pearson’s correlation coefficient between the real and predicted values of records Correlations between traits due to autosomal- and X-linked genetic effects were estimated by bi-variate analyses. For GR and KR, models including X-linked effects lead to a much better fit of data, expressed by the strong decrease in the AIC criterion. Models including X-linked effects had also better predictive ability as they provided smaller MSE and higher For GR and KR, although all strategies for modeling X chromosome dosage compensation improved general properties of the model, the model “ngdc” fitted the data significantly better than other models. Including X-linked genetic effects in the model led to 10% (GR, KR) decrease in the autosomal additive variance, and 7% (KR) to 19% (GR) decrease in the residual variance. Estimates of autosomal heritability (), were 0.15 ± 0.03, 0.13 ± 0.03, 0.9 ± 0.03 and 0.13 ± 0.03 for GR, KR, EF and RGR, respectively. X-linked heritability () was 0.08 ± 0.03 for GR and 0.04 ± 0.03 for KR, respectively. Maternal heritability () were 0.02 ± 0.01, 0.01 ± 0.01, 0.03 ± 0.02 and 0.03 ± 0.02 for GR, KR, EF and RGR, respectively. For GR and KR, the Spearman’s correlation between breeding values obtained from the best model and model I deviated from unity, indicating re-ranking of top animals across models. The X-linked additive genetic correlation and autosomal additive genetic correlation were similar in terms of sign and magnitude in a way that they were all positive and high. As considering X-linked genetic effects resulted to an improvement in the general properties of the model and possibility of re-ranking of top animals, including these effects in the model, considering dosage compensation on the X chromosome was recommended.

Pacific nurse sharks Ginglymostoma unami exhibit yearly patterns of site fidelity to a tropical embayment subjected to seasonal upwelling

Seasonal upwelling in tropical coastal regions can create dynamic oceanographic conditions similar to temperate systems, which are known to influence the distribution and movement of marine species. Mobile marine species may seek warmer environments (i.e. exhibit behavioral thermoregulation) in response to stronger winds during the upwelling season, which cause cold, nutrient-rich waters to rise and cool shallower environments. Pacific nurse sharks Ginglymostoma unami are present in the upwelling regions of the Eastern Tropical Pacific coastline, serving as an ideal model species to study the effects of changing water temperatures on movement behavior. In this study, we analyzed 4 yr of passive acoustic telemetry data to quantify the movement, residency, and habitat use of G. unami in Santa Elena Bay, north Pacific coast of Costa Rica, located within the Papagayo upwelling system. A total of 28 sharks (15 males and 13 females) ranging from 85 to 256 cm total length were monitored inside the bay. The mean (±SD) residency index (i.e. number of days detected relative to the number of days monitored) of individual sharks was 37 ± 28%. Generalized additive mixed models revealed seasonal and diel patterns of G. unami use of Santa Elena Bay that were associated with water temperature and wind speed changes during the upwelling season. G. unami likely uses the warmer waters of Santa Elena Bay as a thermal refuge during the upwelling season to avoid colder adjacent waters. This research will serve as a baseline to understand the movement behavior of this species as climate change alters upwelling patterns.

The relationship of serum klotho levels and triglyceride glucose index-related indicators

BackgroundKlotho, an anti-aging protein, is linked to energy metabolism. There is limited research on the association of serum klotho and triglyceride glucose (TyG) index-related indicators. Our research aims to investigate the relationship of serum klotho with TyG-BMI (body mass index), TyG-WC (waist circumference), and TyG-WHtR (waist-to-height ratio).MethodsFrom 2007 to 2016, we examined 6,370 participants in the National Health and Nutrition Examination Survey (NHANES). The enzyme-linked immunosorbent assay (ELISA) was utilized to measure serum klotho. We calculated the TyG-BMI, TyG-WC, and TyG-WHtR based on fasting triglycerides, fasting glucose, BMI, WC, and WHtR. Multiple linear regression analysis was used to evaluate the association of serum klotho with TyG-BMI, TyG-WC, and TyG-WHtR. Additionally, generalized additive model (GAM) and smoothing curves were used to evaluate the linear and nonlinear relationships. A piecewise regression model was also utilized to test for threshold effects and determine the breakpoints. Finally, the potential independent associations of serum klotho with TyG-BMI, TyG-WC, and TyG-WHtR were further explored using subgroup analysis.ResultsWe observed a statistically significant difference in serum klotho levels across different quartiles of the population. Based on the multiple linear regression analysis, serum klotho levels were negatively associated with TyG-related indicators. There was a nonlinear relationship between the serum klotho and TyG-BMI, TyG-WC, and TyG-WHtR. The segmented regression analysis revealed that the breakpoints of TyG-BMI, TyG-WC, and TyG-WHtR were 5.42, 6.67, and 1.89, respectively. Subgroup analysis showed that TyG-related indicators interacted with gender and diabetes.ConclusionsIn this study, a negative and nonlinear relationship was identified between serum klotho and TyG-related indicators. Further research is needed to clarify the potential mechanisms that may link serum klotho to TyG-BMI, TyG-WC, and TyG-WHtR.

NONPARAMETRIC IDENTIFICATION AND ESTIMATION OF A GENERALIZED ADDITIVE MODEL WITH A FLEXIBLE ADDITIVE STRUCTURE AND UNKNOWN LINK

This paper proposes a nonparametric approach to identify and estimate the generalized additive model with a flexible additive structure and with possibly discrete variables when the link function is unknown. Our approach allows for a flexible additive structure which provides applied researchers the flexibility to specify their model according to economic theory or practical experience. Motivated by the concerns from empirical research, our method also allows for multiple discrete variables in the covariates. By transforming our model into a generalized additive model with univariate component functions, our identification and estimation thereby follows a procedure adapted from the case with univariate components. The estimators converge to normal distributions in large sample with a one-dimensional convergence rate for the link function and a $d_k$ -dimensional convergence rate for the component function $f_k(\cdot )$ defined on ${\mathbb R}^{d_k}$ for all k.

Nonlinear relationship between diabetes mellitus duration and diabetic retinopathy

To investigate the non-linear relationship between diabetes mellitus (DM) duration and the development of diabetic retinopathy (DR). By investigating the association between these variables, our goal is to contribute to the existing knowledge regarding the impact of DM duration on the development and severity of DR. A retrospective cross-sectional study was conducted on 420 patients in the Department of Endocrinology at Guangdong Provincial People’s Hospital, who had undergone ophthalmic consultations from December 2017 to November 2018. The analysis of DM duration and DR utilized a generalized additive model to identify both linear and non-linear connections. The threshold effect was determined using a two-piece regression model. The study included a total of 420 patients, with a mean age of 58.7 years. Of these, 56.9% (239/420) were male. The prevalence of DR was 38.33% (161/420). After adjusting for confounding factors, a nonlinear relationship between DM duration and DR was observed, with a turning point at 8 years. On the left side of the turning point, the prevalence increased by 24% per 1-year increase in DM duration (OR: 1.24; 95% CI: 1.11–1.38; P<0.0001). However, no statistically significant differences were found on the right side of the turning point (OR: 1.02; 95% CI: 0.97–1.08; P = 0.4987). Our study identified a non-linear relationship between DM duration and DR in patients. When the DM duration is less than 8 years, a positive correlation exists between DM duration and DR. However, once the DM duration exceeds 8 years, the effect reaches saturation, and no significant correlation is observed.

Assessing Performance of Estimation Techniques in Time Series Analysis when Trend-cycle Component is Linear

Abstract: Two decomposition techniques are Buys-Ballot and least square techniques are presented in this study. The two important patterns that may be discussed are trend and seasonality and two competing models are additive and multiplicative models. The trend-cycle component is linear. The emphasis is to assess the performance of Buys-Ballot estimates and least square estimates using accuracy measures (Mean Error (ME), Mean Square Error (MSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Results show that the two estimation techniques are very good in estimating the linear trend parameters and seasonal effects when the model for decomposition is additive. It differs for multiplicative model.

Implementation and evaluation of digital twin framework for Internet of Things based healthcare systems

AbstractThe integration of digital twins (DTs) in healthcare is critical but remains limited in real‐time patient monitoring due to challenges in achieving low‐latency telemetry transmission and efficient resource management. This paper addresses these limitations by presenting a novel cloud‐based DT framework that optimises real‐time healthcare monitoring, providing a timely solution for critical healthcare needs. The framework incorporates a Pyomo‐based dynamic optimisation model, which reduces telemetry latency by 32% and improves response time by 52%, surpassing existing systems. Leveraging low‐cost, low‐latency multimodal sensors, the system continuously monitors critical physiological parameters, including SpO2, heart rate, and body temperature, enabling proactive health interventions. A DT definition language (Digital Twin Definition Language)‐based time series analysis and twin graph platform further enhance sensor connectivity and scalability. Additionally, the integration of machine learning (ML) strengthens predictive accuracy, achieving 98% real‐time accuracy and 99.58% under cross‐validation (cv = 20) using the XGBoost algorithm. Empirical results demonstrate substantial improvements in processing time, system stability, and learning capacity, with real‐time predictions completed in 17 ms. This framework represents a significant advancement in healthcare monitoring, offering a responsive and scalable solution to latency and resource constraints in real‐time applications. Future research could explore incorporating additional sensors and advanced ML models to further expand its impact in healthcare applications.

The Impact of Macroeconomic Factors on Gold Prices

This study analyzes the influence of significant macroeconomic variables, like interest rates, inflation, GDP growth rates, and currency rates, on gold prices. Analysis of a 20-year dataset from 2003 to 2023, employing descriptive statistics and regression techniques, demonstrates substantial connections between these variables and changes in gold prices. The results show that inflation and exchange rates have a positive relationship with gold prices, while GDP growth rate has a modest inverse relationship. Interest rates exhibit a weak inverse relationship, although it is not statistically significant. The model explains approximately 93% of the variation in gold prices, underscoring the complex interplay between macroeconomic factors and gold price movements. The results are significant for investors aiming to mitigate inflation or economic instability, as well as for policymakers evaluating the effects of monetary policy on gold demand. This research contributes to the understanding of gold's role in the global financial system and highlights areas for future investigation, including the incorporation of additional factors and advanced modelling techniques. Keywords: Gold prices, Macroeconomic factors, GDP growth rate, Interest rates, Inflation, Exchange rates, Regression analysis.

Identification of the Driving Factors to Algal Biomass in Lake Dianchi: Implications for Eutrophication Control

Accurate analysis of spatiotemporal variations in algal biomass and their underlying causes is crucial for controlling algal blooms and enhancing aquatic ecological quality. The present study, spanning 2011 to 2020, was conducted across 10 sites in Lake Dianchi, where peak algal biomass levels occur from May to September, with higher concentrations in the lake’s northern zones compared to other areas. Employing Spearman’s correlation analysis, generalized additive models (GAMs), and random forest (RF) techniques, the relationships between algal biomass and water quality indicators were investigated. Spearman’s correlation analysis revealed a positive relationship between chlorophyll a (Chla) and total phosphorus (TP) across various spatial scales. RF analysis identified TP as the most influential factor on a lake-wide scale; while in localized RF models, organic pollution-related indicators (COD, CODMn, and BOD5) and TP consistently emerged as the primary predictors of Chla at most sites. GAM results indicated spatially variable and nonlinear responses of algal biomass to predictors, reinforcing TP’s significance lake-wide and at many localized scales. This comprehensive analytical approach provides valuable insights into the role of water quality factors and nonlinear dynamics, thereby advancing our understanding of the relationships between algal biomass and environmental conditions. These findings are pivotal for the development of scientifically informed strategies for lake management and conservation.

Treatment of blunt splenic injury in children in China.

Nonoperative management (NOM) is recognized as a viable treatment for pediatric closed splenic trauma. However, clinical guidelines are applied inconsistently, resulting in different treatment strategies in different regions. This study aimed to investigate the independent risk factors influencing the length of stay in pediatric closed splenic injuries and to analyze the key determinants in the choice of surgical treatment to optimize inpatient management and patient care and improve outcomes. A retrospective evaluation of medical records of pediatric patients with blunt splenic injury (BSI) admitted to Wuhan Children's Hospital from 2020 to 2024 was conducted. The dataset included demographics, mechanism of injury, injury grade, associated injuries, therapeutic measures, and outcomes, which were subjected to statistical analysis. Factors influencing length of hospital stay and treatment regimen were also analyzed. A total of 88.5% of patients underwent NOM, with 11% requiring splenic embolization due to hemodynamic instability or arterial hemorrhage. Surgery was required in 11.5% of patients, primarily for combined gastrointestinal perforation, or peritonitis. One patient died due to brain injury. Trauma scores and transfusion requirements were higher in the surgical group (37.7 ± 16.1 vs. 17.2 ± 13.1, p < 0.001; 21.7% vs. 100%, p < 0.001). Multivariate logistic regression showed that gastrointestinal complications significantly influenced the decision to operate (p = 0.0087). A generalized additive model showed a corresponding increase in length of stay with increasing injury severity, with the curve flattening in the mid to high ISS range (40-60). NOM remains an effective and preferred treatment strategy for pediatric BSI, particularly in the setting of stable hemodynamic parameters. This approach reduces the need for surgical intervention and associated complications while preserving splenic function. The study highlights that gastrointestinal complications are important determinants of surgical management. Further research into long-term outcomes and advancements in conservative management are needed.

The association of thyroid stimulating hormone and body fat in adults.

Thyroid stimulating hormone (TSH) has been proven to be closely associated with metabolic abnormalities, including obesity. The objective of this investigation was to scrutinize the intricate association between TSH concentration and obesity, within the adult population. The study focused on a comprehensive examination of the relationship, delving into specific adiposity parameters such as total percent fat (TPF), android percent fat (APF), and gynoid percent fat (GPF). This study included 809 participants aged 20 and above with normal TSH values from the National Health and Nutrition Examination Survey (NHANES) 2007-2012. Multivariable linear regression models examined the associations of TSH concentration with TPF, APF, and GPF. Subgroup analyses, stratified by sex, were performed using multivariable linear regression. Fitted smoothing curves and generalized additive models addressed non-linear relationships between TSH and TPF, APF, and GPF. In fully adjusted models, a significant positive association was observed between TPF and TSH (β = 0.01, 95% CI: 0.00-0.02, p<0.05), while no such association was evident in APF and GPF. Upon sex stratification, females exhibited significant positive correlations between TSH and TPF, APF, and GPF (all p < 0.001), contrasting with males where no such correlations were found. Notably, a non-linear association was identified in males, specifically a U-shaped curve (inflection point: 32.6%) for TSH and APF. The study unveiled a statistically significant positive association between TSH and TPF in adults. Upon sex stratification, similar statistically significant relationships were observed between TSH and adiposity (TPF, APF, GPF) in females, while males exhibited a U-shaped non-linear relationship between TSH and APF.

Higher patient-to-physician ratios associated with worse outcomes in the emergency department

ObjectiveTo investigate the association between patient-to-physician ratios, a measure of physician workload, and various patient outcomes in the emergency department (ED). MethodsThis retrospective observational study analyzed 406,220 ED visits at a tertiary care center in Taipei, Taiwan, between January 2015 and December 2019. The dynamic patient-to-physician ratio was calculated using minute-by-minute data to reflect real-time physician workload. Multivariable regression models, adjusted for potential confounders, assessed the association between this ratio and 7-day mortality (primary outcome), ED length of stay, waiting time, and medical expenses (secondary outcomes). Generalized additive models were used to explore non-linear relationships. Sensitivity analyses evaluated alternative mortality timeframes, missing data handling, and a simplified patient-to-physician ratio. External validation was performed using data from two additional hospitals. ResultsHigher patient-to-physician ratios were significantly associated with increased odds of 7-day mortality. Compared to ratios of less than 10, the adjusted odds ratios were 1.46 (95% CI 1.16, 1.83) for ratios between 10 and 19, 1.79 (95% CI 1.43, 2.25) for ratios between 20 and 29, and 1.95 (95% CI 1.53, 2.49) for ratios of 30 and above. Similar trends of increased risk were observed for longer ED length of stay, prolonged waiting times, and higher medical expenses. Sensitivity analyses using alternative mortality timeframes, missing data handling methods, and the simplified patient-to-physician ratio yielded consistent results, supporting the main findings. ConclusionsHigher patient-to-physician ratios are associated with worse outcomes for ED patients. Our findings suggest that maintaining ratios below 10 may be ideal for optimizing care quality, while ratios exceeding 20 pose significant risks to patients.

Stability of Early Maturing Soybean Genotypes in Poland

Poland is one of the northernmost countries in the world where soybean breeding is actively conducted. A large collection of soybean genotypes was analyzed in three locations in Poland during the 2022–2023 growing seasons. The objective of this research was to determine the stability of yield, as well as the protein and fat content in the seeds of 280 genotypes. The phenotypic correlations across genotypes and environments were assessed using the Spearman coefficient. The statistical analysis was provided using the additive main effects and multiplicative interaction model. The stability of genotypes was assessed using the weighted average of absolute scores. The main factor differentiating the results between locations was the variation in weather conditions. The variety Karok, which ranks among the top 10% in genotype selection indices for yield and thousand-seed weight, is recommended based on studies conducted within the Post-Registration Variety Testing System in three voivodeships in Poland. Among the most stable genotypes, in terms of the traits analyzed in this paper, are many registered and currently grown soybean varieties such as Obelix Pompei, Coraline, Kapral, ES Mentor, NS Mercury, Pepita, Pollux, Silesia, and Heihe 45. The yield stability of soybean genotypes during the development of new varieties should be one of the factors analyzed by breeders.

A High-Precision Real-Time Temperature Acquisition Method Based on Magnetic Nanoparticles

The unique magnetothermal properties of magnetic nanoparticles enable the development of a high-precision, real-time, noninvasive temperature measurement method with significant potential in the biomedical field. Based on a low-frequency alternating magnetic field excitation model, we construct two additional magnetic field excitation models—alternating current–direct current superposition and dual-frequency superposition—to extract harmonic amplitude components from the magnetization response. To increase the accuracy of harmonic information acquisition, the effects of the truncation error, excitation magnetic field frequency, and amplitude are thoroughly analyzed, and optimal parameter values are selected to minimize the error. A single algorithm is designed for temperature inversion, and a joint algorithm is proposed to optimize the performance of the single algorithm. Under low-frequency alternating-current magnetic field excitation, the autonomous group particle swarm optimization method achieves superior real-time performance in terms of temperature inversion and running time. Compared with the opposition learning gray wolf optimizer and particle swarm optimization–gray wolf optimization, the proposed method achieves reductions of 52% and 68%, respectively. Additionally, under dual-frequency superimposed magnetic field excitation, a higher temperature inversion accuracy is achieved compared with that of the particle swarm optimization–gray wolf optimization algorithm, reducing the error from 0.237 K to 0.094 K.